Repository: verl-project/verl Branch: main Commit: 8e24127f4234 Files: 1128 Total size: 8.0 MB Directory structure: gitextract_5e2u4bw9/ ├── .gemini/ │ └── config.yaml ├── .git-blame-ignore-revs ├── .github/ │ ├── CODEOWNERS │ ├── ISSUE_TEMPLATE/ │ │ ├── bug-report.yml │ │ ├── config.yml │ │ └── feature-request.yml │ ├── PULL_REQUEST_TEMPLATE.md │ ├── dependabot.yml │ └── workflows/ │ ├── README.md │ ├── check-pr-title.yml │ ├── cpu_unit_tests.yml │ ├── doc.yml │ ├── docker-build-ascend-a2.yml │ ├── docker-build-ascend-a3.yml │ ├── e2e_ascend.yml │ ├── e2e_fully_async_policy.yml │ ├── e2e_fully_async_policy_ascend.yml │ ├── e2e_one_step_off_policy.yml │ ├── e2e_one_step_off_policy_ascend.yml │ ├── e2e_ppo_grpo_trainer_trtllm.yml │ ├── e2e_ppo_trainer.yml │ ├── e2e_ppo_trainer_megatron_sglang.yml │ ├── e2e_ppo_trainer_megatron_sglang_2.yml │ ├── e2e_ppo_trainer_megatron_vllm.yml │ ├── e2e_ppo_trainer_megatron_vllm_2.yml │ ├── e2e_ppo_trainer_megatron_vllm_2_ascend.yml │ ├── e2e_ppo_trainer_veomni_vllm.yml │ ├── e2e_sft_llm.yml │ ├── e2e_sft_llm_ascend.yml │ ├── e2e_sft_vlm.yml │ ├── gpu_unit_tests.yml │ ├── model.yml │ ├── model_ascend.yml │ ├── nightly_ascend.yml │ ├── npu_unit_tests.yml │ ├── pre-commit.yml │ ├── precommit-autofix.yml │ ├── reward_model_sglang.yml │ ├── reward_model_vllm.yml │ ├── reward_model_vllm_ascend.yml │ ├── sanity.yml │ ├── scorecard.yml │ ├── secrets_scan.yml │ ├── sgl.yml │ ├── type-coverage-check.yml │ └── vllm.yml ├── .gitignore ├── .gitmodules ├── .pre-commit-config.yaml ├── .readthedocs.yaml ├── CONTRIBUTING.md ├── LICENSE ├── Notice.txt ├── README.md ├── docker/ │ ├── Dockerfile.isaaclab230 │ ├── Dockerfile.stable.sglang │ ├── Dockerfile.stable.trtllm │ ├── Dockerfile.stable.vllm │ ├── README.md │ ├── ascend/ │ │ ├── Dockerfile.ascend.sglang_8.3.rc1_a2 │ │ ├── Dockerfile.ascend.sglang_8.3.rc1_a3 │ │ ├── Dockerfile.ascend_8.2.rc1_a2 │ │ ├── Dockerfile.ascend_8.2.rc1_a3 │ │ ├── Dockerfile.ascend_8.3.rc1_a2 │ │ ├── Dockerfile.ascend_8.3.rc1_a3 │ │ ├── Dockerfile.ascend_8.5.0_a2 │ │ └── Dockerfile.ascend_8.5.0_a3 │ ├── aws/ │ │ ├── Dockerfile.extention.awsefa │ │ └── Dockerfile.ngc.vllm0.8.sagemaker │ ├── rocm/ │ │ ├── Apptainerfile.rocm │ │ ├── Dockerfile.rocm │ │ ├── Dockerfile.rocm7 │ │ ├── Dockerfile.rocm_verl-0.3.0.post1 │ │ └── Dockerfile.rocm_verl-0.4.1 │ ├── verl0.4-cu124-torch2.6-fa2.7.4/ │ │ ├── Dockerfile.app.sglang.vllm.mcore0.12 │ │ ├── Dockerfile.app.sglang.vllm.mcore0.12.deepep │ │ ├── Dockerfile.app.sglang.vllm.mcore0.13.preview │ │ ├── Dockerfile.app.vllm.mcore0.12 │ │ ├── Dockerfile.app.vllm.mcore0.12.deepep │ │ ├── Dockerfile.app.vllm.mcore0.13.preview │ │ ├── Dockerfile.base │ │ └── README.md │ ├── verl0.5-cu126-torch2.7-fa2.7.4/ │ │ ├── Dockerfile.app.sglang0.4.10.post2.mcore0.13 │ │ ├── Dockerfile.app.sglang0.4.9.post6.mcore0.13 │ │ ├── Dockerfile.app.vllm.mcore0.13 │ │ ├── Dockerfile.app.vllm.mcore0.15 │ │ ├── Dockerfile.base.torch2.7.1 │ │ └── README.md │ ├── verl0.5-cu126-torch2.7.1-fa2.8.0/ │ │ ├── Dockerfile.app.sglang.mcore0.12 │ │ ├── Dockerfile.app.sglang.mcore0.13.preview │ │ ├── Dockerfile.base │ │ └── README.md │ ├── verl0.5-preview-cu128-torch2.7.1-fa2.8.0/ │ │ ├── Dockerfile.app.sglang.megatron │ │ ├── Dockerfile.base │ │ └── README.md │ ├── verl0.6-cu128-torch2.8.0-fa2.7.4/ │ │ ├── Dockerfile.app.sglang │ │ ├── Dockerfile.base │ │ └── Dockerfile.vllm011.mcore_gpt-oss │ └── verl0.6.1-experimental/ │ ├── Dockerfile.sglang056exp │ └── Dockerfile.vllm012exp ├── docs/ │ ├── Makefile │ ├── README.md │ ├── README_vllm0.7.md │ ├── README_vllm0.8.md │ ├── _static/ │ │ ├── custom.css │ │ └── js/ │ │ ├── resizable-sidebar.js │ │ └── runllm-widget.js │ ├── advance/ │ │ ├── agent_loop.rst │ │ ├── async-on-policy-distill.md │ │ ├── attention_implementation.rst │ │ ├── checkpoint.rst │ │ ├── dpo_extension.rst │ │ ├── fp8.md │ │ ├── fsdp_extension.rst │ │ ├── fully_async.md │ │ ├── grafana_prometheus.md │ │ ├── megatron_extension.rst │ │ ├── mtp.md │ │ ├── one_step_off.md │ │ ├── placement.rst │ │ ├── ppo_lora.rst │ │ ├── reward_loop.rst │ │ ├── rollout_skip.rst │ │ ├── rollout_trace.rst │ │ └── rope.rst │ ├── algo/ │ │ ├── baseline.md │ │ ├── collabllm.md │ │ ├── dapo.md │ │ ├── dppo.md │ │ ├── entropy.md │ │ ├── gpg.md │ │ ├── grpo.md │ │ ├── opo.md │ │ ├── otb.md │ │ ├── ppo.md │ │ ├── rollout_corr.md │ │ ├── rollout_corr_math.md │ │ ├── spin.md │ │ └── sppo.md │ ├── amd_tutorial/ │ │ ├── amd_build_dockerfile_page.rst │ │ └── amd_vllm_page.rst │ ├── api/ │ │ ├── data.rst │ │ ├── single_controller.rst │ │ ├── trainer.rst │ │ └── utils.rst │ ├── ascend_tutorial/ │ │ ├── contribution_guide/ │ │ │ └── ascend_ci_guide_zh.rst │ │ ├── examples/ │ │ │ ├── ascend_performance_analysis_guide.md │ │ │ ├── ascend_retool_best_pratice.rst │ │ │ ├── ascend_sglang_best_practices.rst │ │ │ ├── dapo_multi_model_optimization_practice.md │ │ │ ├── gspo_optimization_practice.md │ │ │ └── run_qwen3_32B_megatron_1k_256k_npu.md │ │ ├── faq/ │ │ │ └── faq.rst │ │ ├── features/ │ │ │ ├── ascend_backend_features.md │ │ │ └── ascend_consistency.rst │ │ ├── profiling/ │ │ │ ├── ascend_profiling_en.rst │ │ │ └── ascend_profiling_zh.rst │ │ └── quick_start/ │ │ ├── ascend_quick_start.rst │ │ ├── ascend_sglang_quick_start.rst │ │ └── dockerfile_build_guidance.rst │ ├── blog/ │ │ └── v0.7.md │ ├── conf.py │ ├── data/ │ │ └── transfer_queue.md │ ├── examples/ │ │ ├── config.rst │ │ ├── gsm8k_example.rst │ │ ├── multi_modal_example.rst │ │ ├── ppo_code_architecture.rst │ │ ├── sandbox_fusion_example.rst │ │ └── skypilot_examples.rst │ ├── faq/ │ │ └── faq.rst │ ├── hybrid_flow.rst │ ├── index.rst │ ├── perf/ │ │ ├── best_practices.rst │ │ ├── device_tuning.rst │ │ ├── dpsk.md │ │ ├── nsight_profiling.md │ │ ├── perf_tuning.rst │ │ ├── perf_tuning_on_ascend.rst │ │ ├── torch_profiling.md │ │ └── verl_profiler_system.md │ ├── preparation/ │ │ ├── prepare_data.rst │ │ └── reward_function.rst │ ├── requirements-docs.txt │ ├── sglang_multiturn/ │ │ ├── interaction_system.rst │ │ ├── multiturn.rst │ │ ├── sandbox_fusion.rst │ │ └── search_tool_example.rst │ ├── single_controller.rst │ ├── start/ │ │ ├── agentic_rl.rst │ │ ├── install.rst │ │ ├── more_resources.rst │ │ ├── multinode.rst │ │ ├── quickstart.rst │ │ └── ray_debug_tutorial.rst │ └── workers/ │ ├── automodel_workers.rst │ ├── fsdp_workers.rst │ ├── megatron_workers.rst │ ├── model_engine.rst │ ├── ray_trainer.rst │ ├── sglang_worker.rst │ └── trtllm_worker.rst ├── examples/ │ ├── cispo_trainer/ │ │ └── run_cispo_qwen2_5_0_5b_gsm8k.sh │ ├── data_preprocess/ │ │ ├── aime2024_multiturn_w_tool.py │ │ ├── dapo_multiturn_w_tool.py │ │ ├── full_hh_rlhf.py │ │ ├── geo3k.py │ │ ├── geo3k_multiturn_w_tool.py │ │ ├── gsm8k.py │ │ ├── gsm8k_multiturn_sft.py │ │ ├── gsm8k_multiturn_w_interaction.py │ │ ├── gsm8k_multiturn_w_tool.py │ │ ├── gsm8k_tool_agent_loop.py │ │ ├── hellaswag.py │ │ ├── math_dataset.py │ │ ├── multiturn.py │ │ ├── pokemon.py │ │ └── preprocess_search_r1_dataset.py │ ├── dppo_trainer/ │ │ ├── dppo.md │ │ └── run_qwen30b_dppo.sh │ ├── fapo_trainer/ │ │ ├── README.md │ │ ├── prepare_data.py │ │ ├── reward_fn.py │ │ ├── run_qwen_7b_rm_colocate.sh │ │ └── run_qwen_7b_rm_standalone.sh │ ├── gdpo_trainer/ │ │ └── run_qwen1_5b_gdpo.sh │ ├── generation/ │ │ ├── run_deepseek7b_mutli_node.sh │ │ └── run_deepseek_v2_lite_math.sh │ ├── gmpo_trainer/ │ │ ├── README.md │ │ ├── run_qwen2_5-7b_math.sh │ │ ├── test_dapo_7b_math.sh │ │ └── test_dapo_qwen3_30b_math.sh │ ├── gpg_trainer/ │ │ ├── gpg.md │ │ ├── run_qwen2-7b_math.sh │ │ └── run_qwen2-7b_math_megatron.sh │ ├── grpo_trainer/ │ │ ├── README.md │ │ ├── run_deepseek671b_math_megatron_80gb.sh │ │ ├── run_deepseek671b_math_megatron_96gb.sh │ │ ├── run_deepseek7b_llm.sh │ │ ├── run_deepseek7b_llm_math.sh │ │ ├── run_deepseek7b_llm_math_megatron.sh │ │ ├── run_deepseek7b_llm_seq_balance.sh │ │ ├── run_glm41v_9b.sh │ │ ├── run_gptoss_20b.sh │ │ ├── run_minicpmo2_6.sh │ │ ├── run_mistral13b_skyworkrm_hhrlhf.sh │ │ ├── run_moonlight16b_math_megatron.sh │ │ ├── run_nemotron_nano_v3_megatron.sh │ │ ├── run_qwen2-32b_sglang_fsdp_npu.sh │ │ ├── run_qwen2-7b.sh │ │ ├── run_qwen2-7b_math.sh │ │ ├── run_qwen2-7b_math_megatron.sh │ │ ├── run_qwen2-7b_math_megatron_lora.sh │ │ ├── run_qwen2-7b_math_megatron_trtllm.sh │ │ ├── run_qwen2-7b_math_trtllm.sh │ │ ├── run_qwen2-7b_seq_balance.sh │ │ ├── run_qwen2-7b_seq_balance_math_megatron.sh │ │ ├── run_qwen2-7b_sgl_megatron.sh │ │ ├── run_qwen2_5-32b_grpo_megatron_vllm_npu.sh │ │ ├── run_qwen2_5-3b_gsm8k_grpo_lora.sh │ │ ├── run_qwen2_5-3b_gsm8k_grpo_lora_from_adapter.sh │ │ ├── run_qwen2_5-7b_math_megatron_diff_tp.sh │ │ ├── run_qwen2_5_32b_grpo_npu.sh │ │ ├── run_qwen2_5_7b_grpo_discrete_prof_npu.sh │ │ ├── run_qwen2_5_7b_grpo_e2e_prof_npu.sh │ │ ├── run_qwen2_5_7b_grpo_npu.sh │ │ ├── run_qwen2_5_vl-7b-megatron.sh │ │ ├── run_qwen2_5_vl-7b-sglang.sh │ │ ├── run_qwen2_5_vl-7b-trtllm.sh │ │ ├── run_qwen2_5_vl-7b.sh │ │ ├── run_qwen2_5_vl-7b_freeze_vision.sh │ │ ├── run_qwen2_5_vl-7b_lora.sh │ │ ├── run_qwen2_5_vl-7b_seq_balance.sh │ │ ├── run_qwen2_5_vl_32b_npu.sh │ │ ├── run_qwen2_5_vl_3b_npu.sh │ │ ├── run_qwen2_5_vl_3b_trtllm.sh │ │ ├── run_qwen2_5_vl_7b_npu.sh │ │ ├── run_qwen3-235b_megatron_96gb.sh │ │ ├── run_qwen3-30b_dapo_megatron_fp8_trtllm.sh │ │ ├── run_qwen3-32b_npu.sh │ │ ├── run_qwen3-4b_gsm8k_grpo_lora_merge.sh │ │ ├── run_qwen3-8b.sh │ │ ├── run_qwen3-8b_npu.sh │ │ ├── run_qwen3_235b_megatron_npu.sh │ │ ├── run_qwen3_4b_grpo_vllm_1k_npu.sh │ │ ├── run_qwen3_5-35b-megatron.sh │ │ ├── run_qwen3_8b_grpo_sglang_1k_spmd_npu.sh │ │ ├── run_qwen3_8b_grpo_sglang_32k_spmd_npu.sh │ │ ├── run_qwen3_vl-235b-megatron.sh │ │ ├── run_qwen3_vl-30b-megatron.sh │ │ ├── run_qwen3_vl-8b-megatron.sh │ │ ├── run_qwen3_vl-8b_npu.sh │ │ ├── run_qwen3_vl_30b_vllm_fsdp_npu.sh │ │ ├── run_qwen3moe-30b_grpo_megatron_vllm_npu.sh │ │ ├── run_qwen3moe-30b_megatron_96gb.sh │ │ ├── run_qwen3moe-30b_megatron_lora.sh │ │ ├── run_qwen3moe-30b_megatron_lora_fp16.sh │ │ ├── run_qwen3moe-30b_sglang_megatron_npu.sh │ │ ├── run_qwen3next_80b_fsdp_npu.sh │ │ └── run_seed_oss_36b.sh │ ├── gspo_trainer/ │ │ ├── run_qwen30b_gspo.sh │ │ ├── run_qwen3_32b_gspo_npu.sh │ │ ├── test_gspo_3b_math.sh │ │ ├── test_gspo_3b_math_slurm.sh │ │ └── test_gspo_qwen30b_a3b_ep.sh │ ├── mtp_trainer/ │ │ ├── runtime_env.yaml │ │ ├── test_dapo_mimo_7b_with_mtp_math_megatron.sh │ │ └── test_dapo_mimo_7b_with_mtp_math_megatron_4_4.sh │ ├── otb_trainer/ │ │ └── run_qwen2_5-7b.sh │ ├── ppo_trainer/ │ │ ├── README.md │ │ ├── run_deepseek7b_llm.sh │ │ ├── run_deepseek7b_llm_modelscope.sh │ │ ├── run_deepseek7b_llm_pfppo.sh │ │ ├── run_deepseek7b_llm_sandbox_fusion.sh │ │ ├── run_deepseek7b_llm_sp2.sh │ │ ├── run_deepseek_full_hh_rlhf.sh │ │ ├── run_deepseek_math_gsm8k_megatron.sh │ │ ├── run_deepseek_math_gsm8k_megatron_nsys.sh │ │ ├── run_gemma.sh │ │ ├── run_moonlight16b_a3b_gsm8k_megatron.sh │ │ ├── run_qwen1.5_moe_a2.7b-gsm8k_megatron.sh │ │ ├── run_qwen2-7b_math_gsm8k_megatron.sh │ │ ├── run_qwen2-7b_rm.sh │ │ ├── run_qwen2-7b_rm_reward_loop_colocate.sh │ │ ├── run_qwen2-7b_rm_seq_balance.sh │ │ ├── run_qwen2-7b_rm_seq_balance_fused_kernels.sh │ │ ├── run_qwen2-7b_rm_seq_balance_nsys.sh │ │ ├── run_qwen2-7b_seq_balance.sh │ │ ├── run_qwen2-7b_sglang_seq_balance.sh │ │ ├── run_qwen2.5-32b.sh │ │ ├── run_qwen2.5-3b_rm_reward_loop_colocate.sh │ │ └── run_qwen3-8b_npu.sh │ ├── prefix_grouper/ │ │ ├── README.md │ │ └── run_qwen3_prefix_grouper.sh │ ├── ray/ │ │ └── tutorial.ipynb │ ├── reinforce_plus_plus_trainer/ │ │ ├── run_qwen2-7b_math_rf.sh │ │ └── run_qwen2-7b_math_rf_baseline.sh │ ├── remax_trainer/ │ │ ├── run_qwen2.5-3b_seq_balance.sh │ │ └── run_qwen2.5-7b_seq_balance.sh │ ├── rloo_trainer/ │ │ └── run_qwen2-7b.sh │ ├── rollout_correction/ │ │ ├── run_with_rollout_corr.sh │ │ └── run_with_rollout_corr_multi_rs.sh │ ├── router_replay/ │ │ ├── README.md │ │ ├── run_qwen30_a3b_megatron_sglang.sh │ │ └── run_qwen30_a3b_megatron_vllm.sh │ ├── sapo_trainer/ │ │ ├── run_qwen30b_sapo.sh │ │ └── run_qwen3_8b_sapo_npu.sh │ ├── sft/ │ │ ├── gsm8k/ │ │ │ ├── run_deepseek_6b7.sh │ │ │ ├── run_gemma_2b.sh │ │ │ ├── run_gemma_7b.sh │ │ │ ├── run_mimo_megatron_mtp.sh │ │ │ ├── run_nemotron_nano_v3.sh │ │ │ ├── run_qwen3_30b_automodel.sh │ │ │ ├── run_qwen3_5_megatron.sh │ │ │ ├── run_qwen3_8b_sft_peft_sp2_npu.sh │ │ │ ├── run_qwen_05_automodel.sh │ │ │ ├── run_qwen_05_peft.sh │ │ │ ├── run_qwen_05_sp2.sh │ │ │ ├── run_qwen_05_sp2_liger.sh │ │ │ └── run_seed_oss_36b_sft.sh │ │ ├── multiturn/ │ │ │ └── run_qwen_05_sp2.sh │ │ └── vlm/ │ │ └── run_qwen3_vl_2b.sh │ ├── sglang_multiturn/ │ │ ├── README.md │ │ ├── config/ │ │ │ ├── geo3k_multiturn_grpo.yaml │ │ │ ├── geo3k_multiturn_megatron_grpo.yaml │ │ │ ├── gsm8k_multiturn_grpo.yaml │ │ │ ├── gsm8k_multiturn_grpo_server.yaml │ │ │ ├── gsm8k_multiturn_grpo_w_interaction.yaml │ │ │ ├── gsm8k_multiturn_megatron_grpo.yaml │ │ │ ├── interaction_config/ │ │ │ │ └── gsm8k_interaction_config.yaml │ │ │ ├── retool_multiturn_grpo.yaml │ │ │ ├── search_multiturn_grpo.yaml │ │ │ ├── search_multiturn_grpo_one_step_off.yaml │ │ │ └── tool_config/ │ │ │ ├── geo3k_tool_config.yaml │ │ │ ├── gsm8k_tool_config.yaml │ │ │ ├── mcp_server.json │ │ │ ├── mcp_tool_config.yaml │ │ │ ├── sandbox_fusion_tool_config.yaml │ │ │ └── search_tool_config.yaml │ │ ├── geo3k/ │ │ │ ├── run_qwen2.5-3b_geo3k_multiturn.sh │ │ │ ├── run_qwen2.5-3b_geo3k_multiturn_4xgpu.sh │ │ │ └── run_qwen2.5-3b_megatron_geo3k_multiturn.sh │ │ ├── gsm8k_toolcall_shaping/ │ │ │ ├── gsm8k_toolcall_shaping.py │ │ │ └── run_gsm8k_grpo_toolcall_shaping.sh │ │ ├── run_qwen0.5b_gsm8k_multiturn_curriculum.sh │ │ ├── run_qwen2.5-0.5b_gsm8k_multiturn_w_interaction.sh │ │ ├── run_qwen2.5-3b_gsm8k_multiturn.sh │ │ ├── run_qwen2.5-3b_gsm8k_multiturn_4xgpu.sh │ │ ├── run_qwen2.5-3b_gsm8k_multiturn_4xgpu_server.sh │ │ ├── run_qwen2.5-3b_gsm8k_multiturn_server.sh │ │ ├── run_qwen2.5-3b_gsm8k_multiturn_vllm_fsdp.sh │ │ ├── run_qwen2.5-3b_gsm8k_tool_agent_mlflow.sh │ │ ├── run_qwen2.5-3b_megatron_gsm8k_multiturn.sh │ │ ├── run_qwen3-4b_gsm8k_multiturn.sh │ │ ├── run_qwen3_4b_dapo_multiturn.sh │ │ └── search_r1_like/ │ │ ├── local_dense_retriever/ │ │ │ ├── download.py │ │ │ └── retrieval_server.py │ │ └── run_qwen2.5-3b_instruct_search_multiturn.sh │ ├── skypilot/ │ │ ├── README.md │ │ ├── verl-grpo.yaml │ │ ├── verl-multiturn-tools.yaml │ │ └── verl-ppo.yaml │ ├── slurm/ │ │ └── ray_on_slurm.slurm │ ├── split_placement/ │ │ ├── README.md │ │ ├── config/ │ │ │ └── ppo_trainer_split.yaml │ │ ├── main_ppo_split.py │ │ ├── run_deepseek7b_llm.sh │ │ └── split_monkey_patch.py │ ├── tuning/ │ │ ├── 0.5b/ │ │ │ └── qwen2-0.5b_grpo-lora_1_h100_fsdp_vllm.sh │ │ ├── 1.5b/ │ │ │ └── qwen2-1.5b_grpo-lora_1_h100_fsdp_vllm.sh │ │ ├── 14b/ │ │ │ ├── qwen2-14b_grpo-lora_2_h100_fsdp_vllm.sh │ │ │ └── qwen2_14b_grpo_4_h800_fsdp_vllm.sh │ │ ├── 32b/ │ │ │ ├── qwen2-32b_grpo-lora_4_h100_fsdp_vllm.sh │ │ │ └── qwen2_32B_grpo_8_h20_megatron_vllm.sh │ │ ├── 3b/ │ │ │ └── qwen2-3b_grpo-lora_1_h100_fsdp_vllm.sh │ │ ├── 70b/ │ │ │ ├── qwen2-70b_grpo_32_h20_fsdp_vllm.sh │ │ │ ├── qwen2-70b_grpo_32_h800_fsdp_vllm.sh │ │ │ └── qwen2-72b_grpo-lora_8_h100_fsdp_vllm.sh │ │ └── 7b/ │ │ ├── qwen2-7b_grpo-lora_1_h100_fsdp_vllm.sh │ │ └── qwen2-7b_grpo_2_h800_fsdp_vllm.sh │ └── tutorial/ │ └── agent_loop_get_started/ │ ├── agent_loop_tutorial.ipynb │ └── sandbox.py ├── pyproject.toml ├── requirements-cuda.txt ├── requirements-npu.txt ├── requirements-test.txt ├── requirements.txt ├── requirements_sglang.txt ├── scripts/ │ ├── __init__.py │ ├── converter_hf_to_mcore.py │ ├── diagnose.py │ ├── generate_trainer_config.sh │ ├── init_random_model.py │ ├── install_sglang_mcore_npu.sh │ ├── install_vllm_sglang_mcore.sh │ ├── legacy_model_merger.py │ ├── megatron_merge_lora.py │ ├── print_cfg.py │ ├── rollout_viewer.py │ └── veomni/ │ ├── moe_merge.py │ └── moe_split.py ├── setup.py ├── tests/ │ ├── README.md │ ├── __init__.py │ ├── checkpoint_engine/ │ │ ├── __init__.py │ │ ├── test_correctness_on_gpu.py │ │ ├── test_correctness_on_npu.py │ │ ├── test_special_server_adapter.py │ │ └── test_utils.py │ ├── experimental/ │ │ ├── agent_loop/ │ │ │ ├── agent_utils.py │ │ │ ├── qwen_vl_tool_chat_template.jinja2 │ │ │ ├── test_agent_loop_extra_fields_schema_on_cpu.py │ │ │ ├── test_basic_agent_loop.py │ │ │ ├── test_gpt_oss_tool_parser.py │ │ │ ├── test_multi_modal.py │ │ │ └── test_standalone_rollout.py │ │ ├── reward_loop/ │ │ │ ├── reward_fn.py │ │ │ ├── test_agent_reward_loop_colocate.py │ │ │ ├── test_agent_reward_loop_standalone.py │ │ │ ├── test_async_token_bucket_on_cpu.py │ │ │ ├── test_math_verify.py │ │ │ ├── test_rate_limited_reward_manager_on_cpu.py │ │ │ ├── test_reward_model_disrm.py │ │ │ └── test_reward_model_genrm.py │ │ └── vla/ │ │ └── test_sim_envs.py │ ├── interactions/ │ │ ├── __init__.py │ │ ├── test_gsm8k_interaction.py │ │ └── test_interaction_registry.py │ ├── kill_github_tests.sh │ ├── models/ │ │ ├── test_engine.py │ │ ├── test_tiled_mlp_accuracy.py │ │ ├── test_transformer.py │ │ └── test_transformers_ulysses.py │ ├── single_controller/ │ │ ├── __init__.py │ │ ├── base/ │ │ │ └── test_decorator.py │ │ ├── check_worker_alive/ │ │ │ └── main.py │ │ ├── detached_worker/ │ │ │ ├── README.md │ │ │ ├── client.py │ │ │ ├── run.sh │ │ │ └── server.py │ │ ├── test_auto_padding_on_cpu.py │ │ ├── test_colocated_workers.py │ │ ├── test_colocated_workers_fused.py │ │ ├── test_data_transfer.py │ │ ├── test_decorator_on_cpu.py │ │ ├── test_device_mesh_register.py │ │ ├── test_driverfunc_to_worker.py │ │ ├── test_fused_workers_on_cpu.py │ │ ├── test_get_set_dispatch_collect_cpu.py │ │ ├── test_high_level_scheduling_api.py │ │ ├── test_nested_worker.py │ │ ├── test_ray_collectives.py │ │ ├── test_ray_local_envs_on_cpu.py │ │ ├── test_ray_utils_on_cpu.py │ │ ├── test_rvdz.py │ │ ├── test_split_resource_pool.py │ │ ├── test_worker_group_basics.py │ │ └── test_worker_group_torch.py │ ├── special_distributed/ │ │ ├── README.md │ │ ├── run_all.sh │ │ ├── test_fsdp_ckpt.py │ │ ├── test_mcore_config_converter.py │ │ ├── test_tensor_dict.py │ │ └── test_torch_functional.py │ ├── special_e2e/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── check_custom_rwd_fn.py │ │ ├── check_results.py │ │ ├── envs/ │ │ │ ├── __init__.py │ │ │ └── digit_completion/ │ │ │ ├── __init__.py │ │ │ ├── task.py │ │ │ └── tokenizer.py │ │ ├── generation/ │ │ │ ├── run_gen_qwen05.sh │ │ │ └── run_gen_qwen05_server.sh │ │ ├── ppo_trainer/ │ │ │ ├── expert_parallel/ │ │ │ │ ├── qwen2moe_minimal.json │ │ │ │ └── qwen3moe_minimal.json │ │ │ ├── run_function_reward.sh │ │ │ ├── run_model_reward.sh │ │ │ ├── run_single_gpu.sh │ │ │ └── run_single_gpu_with_engine.sh │ │ ├── run_dapo.sh │ │ ├── run_fully_async_policy.sh │ │ ├── run_geo3k_fsdp_sgl_multiturn_w_tool.sh │ │ ├── run_grpo_lora_with_merge.sh │ │ ├── run_gsm8k_fsdp_sgl_multiturn_sf_tool.sh │ │ ├── run_gsm8k_fsdp_sgl_multiturn_w_tool.sh │ │ ├── run_one_step_off_policy.sh │ │ ├── run_ppo_trainer_megatron.sh │ │ ├── run_ppo_trainer_torchtitan.sh │ │ ├── run_ppo_trainer_veomni.sh │ │ ├── run_test.sh │ │ └── sft/ │ │ ├── compare_sft_engine_results.py │ │ ├── run_sft.sh │ │ ├── run_sft_engine.sh │ │ └── test_sft_engine_all.sh │ ├── special_npu/ │ │ ├── nightly_ci_ascend/ │ │ │ ├── run_grpo_qwen25-7b-instruct_fsdp_npu.sh │ │ │ ├── run_grpo_qwen25-vl-3b-instruct_fsdp_npu.sh │ │ │ └── run_ppo_qwen3-8b_fsdp_npu.sh │ │ ├── run_qwen2_5_05b_grpo.sh │ │ ├── run_qwen2_5_05b_grpo_mindspeed.sh │ │ ├── run_qwen2_5_05b_sft_peft_sp2.sh │ │ ├── run_qwen2_5_vl_3b_npu.sh │ │ ├── run_qwen3_06b_ppo.sh │ │ └── run_qwen3_30b_grpo_mindspeed.sh │ ├── special_sanity/ │ │ ├── check_api_docs.py │ │ ├── check_dataproto_usage.py │ │ ├── check_device_api_usage.py │ │ ├── check_docs_time_info.py │ │ ├── check_docstrings.py │ │ ├── check_license.py │ │ ├── check_pr_description.py │ │ ├── check_pr_title.py │ │ ├── test_config_docs.py │ │ ├── test_import.py │ │ ├── type_coverage_check.py │ │ ├── validate_imported_docs.py │ │ └── validate_structure.py │ ├── special_standalone/ │ │ ├── README.md │ │ └── test_memory_buffers.py │ ├── test_base_config_on_cpu.py │ ├── test_protocol_on_cpu.py │ ├── test_protocol_v2_on_cpu.py │ ├── trainer/ │ │ ├── __init__.py │ │ ├── config/ │ │ │ ├── __init__.py │ │ │ ├── legacy_ppo_megatron_trainer.yaml │ │ │ ├── legacy_ppo_trainer.yaml │ │ │ ├── test_algo_config_on_cpu.py │ │ │ └── test_legacy_config_on_cpu.py │ │ └── ppo/ │ │ ├── __init__.py │ │ ├── test_core_algos_on_cpu.py │ │ ├── test_metric_utils_on_cpu.py │ │ ├── test_rollout_corr.py │ │ └── test_rollout_corr_integration.py │ ├── utils/ │ │ ├── _test_module.py │ │ ├── ckpt/ │ │ │ ├── test_checkpoint_cleanup_on_cpu.py │ │ │ └── test_esi_save_ckpt_on_cpu.py │ │ ├── dataset/ │ │ │ ├── test_create_rl_sampler_on_cpu.py │ │ │ ├── test_multiturn_sft_dataset_on_cpu.py │ │ │ ├── test_rl_collate_fn_on_cpu.py │ │ │ └── test_rl_dataset_on_cpu.py │ │ ├── debug/ │ │ │ └── test_metrics.py │ │ ├── megatron/ │ │ │ └── test_pipeline_parallel.py │ │ ├── reward_score/ │ │ │ ├── reward_score/ │ │ │ │ └── test_sandbox_fusion_on_cpu.py │ │ │ └── test_sandbox_on_cpu.py │ │ ├── test_activation_offload.py │ │ ├── test_bucketed_weight_transfer.py │ │ ├── test_check_ipc_version_support_on_npu.py │ │ ├── test_check_profiler_output.py │ │ ├── test_config_on_cpu.py │ │ ├── test_flops_counter.py │ │ ├── test_fs_on_cpu.py │ │ ├── test_fsdp2_peft_wrapping.py │ │ ├── test_fsdp_lora_merge.py │ │ ├── test_groupwise.py │ │ ├── test_import_utils_on_cpu.py │ │ ├── test_linear_cross_entropy.py │ │ ├── test_mlflow_key_sanitization.py │ │ ├── test_model_on_cpu.py │ │ ├── test_normalize_peft_param_name.py │ │ ├── test_normalize_peft_param_name_on_cpu.py │ │ ├── test_nvtx_profile.py │ │ ├── test_padding_on_cpu.py │ │ ├── test_prepare_micro_batches_with_group_size.py │ │ ├── test_rollout_skip_on_cpu.py │ │ ├── test_rollout_trace_on_cpu.py │ │ ├── test_seqlen_balancing.py │ │ ├── test_server_profiler.py │ │ ├── test_shared_memory.py │ │ ├── test_special_linear_cross_entropy_tp.py │ │ ├── test_special_mstx_profile.py │ │ ├── test_temp_env_on_cpu.py │ │ ├── test_timeout_decorator_cpu.py │ │ ├── test_tokenizer_normalize_on_cpu.py │ │ ├── test_torch_functional.py │ │ └── test_torch_profile.py │ └── workers/ │ ├── actor/ │ │ └── test_special_dp_actor.py │ ├── config/ │ │ ├── test_actor_config_on_cpu.py │ │ ├── test_critic_config_on_cpu.py │ │ ├── test_engine_config_on_cpu.py │ │ ├── test_model_config_on_cpu.py │ │ └── test_optim_config_on_cpu.py │ ├── critic/ │ │ └── test_special_dp_critic.py │ ├── reward_manager/ │ │ └── test_registry_on_cpu.py │ ├── rollout/ │ │ ├── perf/ │ │ │ └── vllm_async_rollout.py │ │ ├── resource/ │ │ │ └── tool_configs/ │ │ │ ├── mcp_server.json │ │ │ ├── mcp_tool_config │ │ │ ├── sandbox_fusion_tool_config │ │ │ └── search_tool_config │ │ ├── rollout_sglang/ │ │ │ └── test_http_server_engine.py │ │ ├── rollout_trtllm/ │ │ │ ├── __init__.py │ │ │ ├── test_adapter.py │ │ │ ├── test_async_server.py │ │ │ └── test_trtllm_rollout_utils.py │ │ ├── rollout_vllm/ │ │ │ ├── run_fsdp_vllm.py │ │ │ └── test_vllm_abort.py │ │ ├── test_hf_rollout.py │ │ ├── test_sglang_async_rollout_multimodal_delta.py │ │ ├── test_sglang_rollout_sharding_manager.py │ │ └── test_vllm_cli_args_on_cpu.py │ ├── test_fsdp_attn_implementation.py │ └── test_fsdp_workers.py └── verl/ ├── __init__.py ├── base_config.py ├── checkpoint_engine/ │ ├── README.md │ ├── __init__.py │ ├── base.py │ ├── hccl_checkpoint_engine.py │ ├── kimi_checkpoint_engine.py │ ├── mooncake_checkpoint_engine.py │ ├── nccl_checkpoint_engine.py │ └── nixl_checkpoint_engine.py ├── experimental/ │ ├── __init__.py │ ├── agent_loop/ │ │ ├── __init__.py │ │ ├── agent_loop.py │ │ ├── prometheus_utils.py │ │ ├── single_turn_agent_loop.py │ │ ├── tool_agent_loop.py │ │ ├── tool_parser.py │ │ └── utils.py │ ├── dataset/ │ │ ├── __init__.py │ │ └── sampler.py │ ├── dynamic_dataset/ │ │ ├── __init__.py │ │ └── dynamicgen_dataset.py │ ├── fully_async_policy/ │ │ ├── README.md │ │ ├── README_zh.md │ │ ├── agent_loop/ │ │ │ ├── __init__.py │ │ │ └── agent_loop.py │ │ ├── config/ │ │ │ ├── fully_async_ppo_megatron_trainer.yaml │ │ │ └── fully_async_ppo_trainer.yaml │ │ ├── detach_utils.py │ │ ├── fully_async_main.py │ │ ├── fully_async_rollouter.py │ │ ├── fully_async_trainer.py │ │ ├── message_queue.py │ │ ├── shell/ │ │ │ ├── dapo_30b_a3b_base_math_fsdp.sh │ │ │ ├── dapo_7b_async_retool.sh │ │ │ ├── dapo_7b_math_fsdp2_16_16.sh │ │ │ ├── dapo_7b_math_fsdp2_32_32.sh │ │ │ ├── dapo_7b_math_fsdp2_4_12.sh │ │ │ ├── dapo_7b_math_fsdp2_4_4.sh │ │ │ ├── dapo_7b_math_fsdp2_64_64.sh │ │ │ ├── dapo_7b_math_fsdp2_64_64_mis.sh │ │ │ ├── dapo_7b_math_fsdp2_8_8.sh │ │ │ ├── geo3k_qwen25vl_7b_megatron_4_4.sh │ │ │ ├── grpo_30b_a3b_base_math_megatron_96_32.sh │ │ │ ├── grpo_30b_a3b_base_math_megatron_96_32_mis.sh │ │ │ └── runtime_env.yaml │ │ └── unittest/ │ │ └── simple_streaming_demo.py │ ├── one_step_off_policy/ │ │ ├── README.md │ │ ├── config/ │ │ │ ├── one_step_off_ppo_megatron_trainer.yaml │ │ │ └── one_step_off_ppo_trainer.yaml │ │ ├── main_ppo.py │ │ ├── ray_trainer.py │ │ └── shell/ │ │ ├── dapo_7b_math_fsdp2_4_12.sh │ │ ├── dapo_7b_math_fsdp2_64_64.sh │ │ ├── dapo_7b_math_fsdp2_64_64_ris.sh │ │ ├── dapo_7b_math_fsdp2_colocate.sh │ │ ├── dapo_7b_math_fsdp2_sglang_4_12.sh │ │ ├── dapo_7b_math_fsdp2_sglang_colocate.sh │ │ ├── dapo_7b_math_megatron_4_12.sh │ │ ├── dapo_7b_math_megatron_colocate.sh │ │ ├── grpo_0.6b_gsm8k_fsdp2_2_6.sh │ │ ├── grpo_0.6b_gsm8k_fsdp2_sglang_2_6.sh │ │ ├── grpo_3b_gsm8k_fsdp2_2_6.sh │ │ └── grpo_qwen3_8b_gsm8k_fsdp2_8_8_npu.sh │ ├── reward_loop/ │ │ ├── __init__.py │ │ ├── reward_loop.py │ │ ├── reward_manager/ │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── dapo.py │ │ │ ├── gdpo.py │ │ │ ├── limited.py │ │ │ ├── naive.py │ │ │ ├── registry.py │ │ │ └── remote.py │ │ ├── reward_model.py │ │ └── router/ │ │ ├── inner_sglang_router.py │ │ └── naive_router.py │ ├── separation/ │ │ ├── __init__.py │ │ ├── engine_workers.py │ │ ├── ray_trainer.py │ │ └── utils.py │ └── vla/ │ ├── README.md │ ├── config/ │ │ ├── rob_ppo_trainer.yaml │ │ └── rob_sac_trainer.yaml │ ├── dp_rob.py │ ├── env_loop.py │ ├── envs/ │ │ ├── __init__.py │ │ ├── action_utils.py │ │ ├── isaac_env/ │ │ │ ├── __init__.py │ │ │ └── isaac_env.py │ │ └── libero_env/ │ │ ├── __init__.py │ │ ├── libero_env.py │ │ ├── utils.py │ │ └── venv.py │ ├── fsdp_workers.py │ ├── main_ppo.py │ ├── main_sac.py │ ├── models/ │ │ ├── __init__.py │ │ ├── modules/ │ │ │ └── mlp.py │ │ ├── openvla_oft/ │ │ │ ├── __init__.py │ │ │ ├── configuration_prismatic.py │ │ │ ├── constants.py │ │ │ ├── modeling_prismatic.py │ │ │ ├── processing_prismatic.py │ │ │ └── train_utils.py │ │ ├── pi0_torch/ │ │ │ ├── __init__.py │ │ │ ├── configuration_pi0_torch.py │ │ │ ├── model/ │ │ │ │ ├── modeling_pi0.py │ │ │ │ └── paligemma_with_expert.py │ │ │ ├── modeling_pi0_torch.py │ │ │ ├── pi0_utils.py │ │ │ └── policy/ │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ └── libero_policy.py │ │ └── register_vla_models.py │ ├── naive_rollout_rob.py │ ├── prepare_libero_dataset.py │ ├── requirements_vla.txt │ ├── rob_ray_trainer.py │ ├── run_pi05_libero_sac.sh │ ├── run_pi05_libero_sac_disagg.sh │ ├── run_simpleVLA_isaac_disagg.sh │ ├── run_simpleVLA_libero_grpo.sh │ ├── sac/ │ │ ├── base.py │ │ ├── naive_rollout_pi05.py │ │ ├── replay_pool.py │ │ ├── sac_actor.py │ │ └── sac_ray_trainer.py │ └── workers/ │ └── env/ │ ├── env_loop_wg_test.py │ ├── env_manager.py │ └── env_worker.py ├── interactions/ │ ├── __init__.py │ ├── base.py │ ├── gsm8k_interaction.py │ ├── utils/ │ │ ├── __init__.py │ │ └── interaction_registry.py │ └── weather_interaction.py ├── model_merger/ │ ├── __init__.py │ ├── __main__.py │ ├── base_model_merger.py │ ├── fsdp_model_merger.py │ └── megatron_model_merger.py ├── models/ │ ├── README.md │ ├── __init__.py │ ├── llama/ │ │ ├── __init__.py │ │ └── megatron/ │ │ ├── __init__.py │ │ ├── checkpoint_utils/ │ │ │ ├── __init__.py │ │ │ ├── llama_loader.py │ │ │ ├── llama_loader_depracated.py │ │ │ └── llama_saver.py │ │ ├── layers/ │ │ │ ├── __init__.py │ │ │ ├── parallel_attention.py │ │ │ ├── parallel_decoder.py │ │ │ ├── parallel_linear.py │ │ │ ├── parallel_mlp.py │ │ │ └── parallel_rmsnorm.py │ │ └── modeling_llama_megatron.py │ ├── mcore/ │ │ ├── __init__.py │ │ ├── bridge.py │ │ ├── config_converter.py │ │ ├── loader.py │ │ ├── mbridge.py │ │ ├── model_forward.py │ │ ├── model_forward_1f1b_overlap.py │ │ ├── model_forward_fused.py │ │ ├── model_initializer.py │ │ ├── mtp_patch.py │ │ ├── patch.py │ │ ├── qwen2_5_vl/ │ │ │ ├── __init__.py │ │ │ ├── attention.py │ │ │ ├── model.py │ │ │ ├── rope_utils.py │ │ │ ├── vision_config.py │ │ │ ├── vision_model.py │ │ │ └── vision_transformer_block.py │ │ ├── readme.md │ │ ├── registry.py │ │ ├── saver.py │ │ ├── util.py │ │ └── weight_converter.py │ ├── qwen2/ │ │ ├── __init__.py │ │ └── megatron/ │ │ ├── __init__.py │ │ ├── checkpoint_utils/ │ │ │ ├── __init__.py │ │ │ ├── qwen2_loader.py │ │ │ ├── qwen2_loader_depracated.py │ │ │ └── qwen2_saver.py │ │ ├── layers/ │ │ │ ├── __init__.py │ │ │ ├── parallel_attention.py │ │ │ ├── parallel_decoder.py │ │ │ ├── parallel_linear.py │ │ │ ├── parallel_mlp.py │ │ │ └── parallel_rmsnorm.py │ │ └── modeling_qwen2_megatron.py │ ├── registry.py │ ├── transformers/ │ │ ├── __init__.py │ │ ├── apertus.py │ │ ├── dense_common.py │ │ ├── glm4v.py │ │ ├── kimi_vl.py │ │ ├── llama.py │ │ ├── monkey_patch.py │ │ ├── npu_patch.py │ │ ├── qwen2.py │ │ ├── qwen2_vl.py │ │ ├── qwen3_vl.py │ │ └── tiled_mlp.py │ └── weight_loader_registry.py ├── protocol.py ├── py.typed ├── single_controller/ │ ├── __init__.py │ ├── base/ │ │ ├── __init__.py │ │ ├── decorator.py │ │ ├── worker.py │ │ └── worker_group.py │ └── ray/ │ ├── __init__.py │ └── base.py ├── third_party/ │ ├── __init__.py │ ├── torch/ │ │ ├── __init__.py │ │ └── distributed/ │ │ ├── __init__.py │ │ ├── _state_dict_utils.py │ │ └── checkpoint/ │ │ ├── __init__.py │ │ └── state_dict.py │ └── vllm/ │ └── __init__.py ├── tools/ │ ├── __init__.py │ ├── base_tool.py │ ├── geo3k_tool.py │ ├── gsm8k_tool.py │ ├── image_zoom_in_tool.py │ ├── mcp_base_tool.py │ ├── mcp_search_tool.py │ ├── sandbox_fusion_tools.py │ ├── schemas.py │ ├── search_tool.py │ └── utils/ │ ├── __init__.py │ ├── mcp_clients/ │ │ ├── McpClientManager.py │ │ └── utils.py │ ├── search_r1_like_utils.py │ └── tool_registry.py ├── trainer/ │ ├── README.md │ ├── __init__.py │ ├── config/ │ │ ├── __init__.py │ │ ├── _generated_ppo_megatron_trainer.yaml │ │ ├── _generated_ppo_torchtitan_trainer.yaml │ │ ├── _generated_ppo_trainer.yaml │ │ ├── _generated_ppo_veomni_trainer.yaml │ │ ├── actor/ │ │ │ ├── actor.yaml │ │ │ ├── dp_actor.yaml │ │ │ ├── megatron_actor.yaml │ │ │ ├── torchtitan_actor.yaml │ │ │ └── veomni_actor.yaml │ │ ├── algorithm/ │ │ │ └── rollout_correction.yaml │ │ ├── algorithm.py │ │ ├── config.py │ │ ├── critic/ │ │ │ ├── critic.yaml │ │ │ ├── dp_critic.yaml │ │ │ ├── megatron_critic.yaml │ │ │ ├── torchtitan_critic.yaml │ │ │ └── veomni_critic.yaml │ │ ├── data/ │ │ │ └── legacy_data.yaml │ │ ├── engine/ │ │ │ ├── automodel.yaml │ │ │ ├── fsdp.yaml │ │ │ ├── megatron.yaml │ │ │ ├── torchtitan.yaml │ │ │ └── veomni.yaml │ │ ├── evaluation.yaml │ │ ├── legacy_reward_impl.yaml │ │ ├── model/ │ │ │ └── hf_model.yaml │ │ ├── model_engine/ │ │ │ ├── dp.yaml │ │ │ ├── torchtitan.yaml │ │ │ └── veomni.yaml │ │ ├── npu_profile/ │ │ │ └── npu_profile.yaml │ │ ├── optim/ │ │ │ ├── automodel.yaml │ │ │ ├── fsdp.yaml │ │ │ ├── megatron.yaml │ │ │ ├── torchtitan.yaml │ │ │ └── veomni.yaml │ │ ├── ppo_megatron_trainer.yaml │ │ ├── ppo_trainer.yaml │ │ ├── profiler/ │ │ │ └── profiler.yaml │ │ ├── ref/ │ │ │ ├── dp_ref.yaml │ │ │ ├── megatron_ref.yaml │ │ │ ├── ref.yaml │ │ │ ├── torchtitan_ref.yaml │ │ │ └── veomni_ref.yaml │ │ ├── reward/ │ │ │ └── reward.yaml │ │ ├── rollout/ │ │ │ └── rollout.yaml │ │ └── sft_trainer_engine.yaml │ ├── constants_ppo.py │ ├── main_eval.py │ ├── main_generation_server.py │ ├── main_ppo.py │ ├── ppo/ │ │ ├── __init__.py │ │ ├── core_algos.py │ │ ├── metric_utils.py │ │ ├── prefix_grouper_utils.py │ │ ├── ray_trainer.py │ │ ├── reward.py │ │ ├── rollout_corr_helper.py │ │ └── utils.py │ ├── runtime_env.yaml │ ├── sft_trainer.py │ └── sft_trainer_ray.py ├── utils/ │ ├── __init__.py │ ├── activation_offload.py │ ├── attention_utils.py │ ├── chat_template.py │ ├── checkpoint/ │ │ ├── __init__.py │ │ ├── checkpoint_handler.py │ │ ├── checkpoint_manager.py │ │ ├── fsdp_checkpoint_manager.py │ │ └── megatron_checkpoint_manager.py │ ├── config.py │ ├── dataset/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── dataset_utils.py │ │ ├── multiturn_sft_dataset.py │ │ ├── rl_dataset.py │ │ ├── rm_dataset.py │ │ └── vision_utils.py │ ├── debug/ │ │ ├── __init__.py │ │ ├── metrics.py │ │ ├── performance.py │ │ └── trajectory_tracker.py │ ├── device.py │ ├── distributed.py │ ├── experimental/ │ │ ├── __init__.py │ │ └── torch_functional.py │ ├── flops_counter.py │ ├── fp8_utils.py │ ├── fs.py │ ├── fsdp_utils.py │ ├── groupwise.py │ ├── hdfs_io.py │ ├── import_utils.py │ ├── kernel/ │ │ ├── __init__.py │ │ ├── fp8_kernel.py │ │ ├── kernels.py │ │ └── linear_cross_entropy.py │ ├── logger/ │ │ ├── __init__.py │ │ └── aggregate_logger.py │ ├── logging_utils.py │ ├── megatron/ │ │ ├── __init__.py │ │ ├── dist_checkpointing.py │ │ ├── memory.py │ │ ├── optimizer.py │ │ ├── pipeline_parallel.py │ │ ├── router_replay_patch.py │ │ ├── router_replay_utils.py │ │ ├── sequence_parallel.py │ │ └── tensor_parallel.py │ ├── megatron_peft_utils.py │ ├── megatron_utils.py │ ├── memory_utils.py │ ├── metric/ │ │ ├── __init__.py │ │ └── utils.py │ ├── model.py │ ├── net_utils.py │ ├── npu_flash_attn_utils.py │ ├── profiler/ │ │ ├── __init__.py │ │ ├── config.py │ │ ├── empty_annotations.py │ │ ├── mstx_profile.py │ │ ├── nvtx_profile.py │ │ ├── performance.py │ │ ├── profile.py │ │ └── torch_profile.py │ ├── py_functional.py │ ├── qat/ │ │ ├── __init__.py │ │ ├── core.py │ │ ├── linear.py │ │ ├── quantizer.py │ │ └── vllm_patch.py │ ├── ray_utils.py │ ├── rendezvous/ │ │ ├── __init__.py │ │ └── ray_backend.py │ ├── reward_score/ │ │ ├── __init__.py │ │ ├── geo3k.py │ │ ├── gsm8k.py │ │ ├── math_batch.py │ │ ├── math_dapo.py │ │ ├── math_reward.py │ │ ├── math_verify.py │ │ ├── prime_code/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── testing_util.py │ │ │ └── utils.py │ │ ├── prime_math/ │ │ │ ├── __init__.py │ │ │ ├── grader.py │ │ │ └── math_normalize.py │ │ ├── rlla.py │ │ ├── sandbox_fusion/ │ │ │ ├── __init__.py │ │ │ └── utils.py │ │ └── search_r1_like_qa_em.py │ ├── rollout_skip.py │ ├── rollout_trace.py │ ├── seqlen_balancing.py │ ├── sglang/ │ │ └── sglang_fp8_utils.py │ ├── tensordict_utils.py │ ├── tokenizer.py │ ├── torch_dtypes.py │ ├── torch_functional.py │ ├── tracking.py │ ├── transformers_compat.py │ ├── trtllm/ │ │ └── trtllm_fp8_utils.py │ ├── ulysses.py │ └── vllm/ │ ├── __init__.py │ ├── npu_vllm_patch.py │ ├── patch.py │ ├── utils.py │ └── vllm_fp8_utils.py ├── version/ │ └── version └── workers/ ├── __init__.py ├── actor/ │ ├── __init__.py │ ├── base.py │ ├── dp_actor.py │ └── megatron_actor.py ├── config/ │ ├── __init__.py │ ├── actor.py │ ├── critic.py │ ├── engine.py │ ├── megatron_peft.py │ ├── model.py │ ├── optimizer.py │ ├── reward.py │ └── rollout.py ├── critic/ │ ├── __init__.py │ ├── base.py │ ├── dp_critic.py │ └── megatron_critic.py ├── engine/ │ ├── __init__.py │ ├── automodel/ │ │ ├── __init__.py │ │ ├── transformer_impl.py │ │ └── utils.py │ ├── base.py │ ├── fsdp/ │ │ ├── __init__.py │ │ ├── transformer_impl.py │ │ └── utils.py │ ├── megatron/ │ │ ├── __init__.py │ │ ├── transformer_impl.py │ │ └── utils.py │ ├── mindspeed/ │ │ ├── __init__.py │ │ └── transformer_impl.py │ ├── torchtitan/ │ │ ├── __init__.py │ │ ├── transformer_impl.py │ │ └── utils.py │ ├── utils.py │ └── veomni/ │ ├── __init__.py │ ├── transformer_impl.py │ └── utils.py ├── engine_workers.py ├── fsdp_workers.py ├── megatron_workers.py ├── reward_manager/ │ ├── __init__.py │ ├── abstract.py │ ├── batch.py │ ├── dapo.py │ ├── naive.py │ ├── prime.py │ └── registry.py ├── rollout/ │ ├── __init__.py │ ├── base.py │ ├── hf_rollout.py │ ├── naive/ │ │ ├── __init__.py │ │ └── naive_rollout.py │ ├── replica.py │ ├── schemas.py │ ├── sglang_rollout/ │ │ ├── __init__.py │ │ ├── async_sglang_server.py │ │ ├── http_server_engine.py │ │ ├── sglang_rollout.py │ │ └── utils.py │ ├── tokenizer.py │ ├── trtllm_rollout/ │ │ ├── trtllm_async_rollout.md │ │ ├── trtllm_async_server.py │ │ ├── trtllm_rollout.py │ │ └── trtllm_worker_extension.py │ ├── utils.py │ └── vllm_rollout/ │ ├── __init__.py │ ├── bucketed_weight_transfer.py │ ├── utils.py │ ├── vllm_async_server.py │ └── vllm_rollout.py ├── sharding_manager/ │ ├── __init__.py │ ├── base.py │ └── fsdp_ulysses.py └── utils/ ├── __init__.py ├── losses.py └── padding.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gemini/config.yaml ================================================ have_fun: false code_review: disable: false comment_severity_threshold: HIGH max_review_comments: -1 pull_request_opened: help: false summary: false code_review: true ignore_patterns: [] ================================================ FILE: .git-blame-ignore-revs ================================================ # Local uasge: git config blame.ignoreRevsFile .git-blame-ignore-revs # [dev] feat: immigrate from yapf & pylint to ruff based on pre-commit # Changed 268 files, +10k/-9k lines. This is the biggest formatter change. b00f77d8559b48d57a33c0132a5ba1c81891a536 # [ci] refactor: reduce ruff line-length from 300 to 120 # Changed 238 files, +6k/-1k lines. Global formatting change. 00a10a8ef389556f957a2f36132b2358fd6a109f # [Lint] fix: linting errors in all files # Changed 179 files, +1k/-3k lines. Global lint fix. 8e5ad4688a13de81727c014a3c2e2fb26324bc20 ================================================ FILE: .github/CODEOWNERS ================================================ /docs @eric-haibin-lin @zhaochenyang20 @hongpeng-guo /docs/amd_tutorial @yushengsu-thu /docs/slang_multiturn @zhaochenyang20 @SwordFaith /docs/ascend_tutorial @FightingZhen /third_party/sglang @zhaochenyang20 @SwordFaith /third_party/vllm @PeterSH6 @wuxibin89 /examples/grpo_trainer @vermouth1992 @PeterSH6 @tardis-key @FightingZhen @ji-huazhong /verl/single_controller @zw0610 @wuxibin89 @hongpeng-guo /verl/trainer @eric-haibin-lin @vermouth1992 @tongyx361 @PeterSH6 /verl/models/mcore @ISEEKYAN @vermouth1992 /verl/models/transformers @vermouth1992 @PeterSH6 @tardis-key @FightingZhen @ji-huazhong /verl/workers/engine @eric-haibin-lin @vermouth1992 @ZihengJiang /verl/workers/roles @eric-haibin-lin @vermouth1992 @ZihengJiang /verl/workers/engine/fsdp @eric-haibin-lin @vermouth1992 @ZihengJiang /verl/workers/rollout/vllm_rollout @wuxibin89 @PeterSH6 @chenhaiq /verl/workers/rollout/sglang_rollout @zhaochenyang20 @SwordFaith @chenhaiq /verl/workers/actor/megatron_actor.py @ISEEKYAN @vermouth1992 /verl/workers/critic/megatron_critic.py @ISEEKYAN @vermouth1992 /verl/workers/megatron_workers.py @ISEEKYAN @vermouth1992 /verl/experimental @wuxibin89 @ArronHZG /tests/single_controller @zw0610 @wuxibin89 /tests/trainer @eric-haibin-lin @vermouth1992 @tongyx361 @PeterSH6 /tests/workers/rollout/vllm_rollout @wuxibin89 @PeterSH6 @chenhaiq ================================================ FILE: .github/ISSUE_TEMPLATE/bug-report.yml ================================================ # modified from https://github.com/huggingface/transformers/blob/main/.github/ISSUE_TEMPLATE/bug-report.yml?plain=1 name: "\U0001F41B Bug Report" description: Submit a bug report to help us improve verl labels: [ "bug" ] body: - type: markdown attributes: value: | Thanks for taking the time to fill out this bug report! 🤗 - type: textarea id: system-info attributes: label: System Info description: Please share your system info with us. 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Paste at least one query link here: ... - [ ] Format the PR title as `[{modules}] {type}: {description}` (This will be checked by the CI) - `{modules}` include `fsdp`, `megatron`, `veomni`, `sglang`, `vllm`, `rollout`, `trainer`, `ci`, `training_utils`, `recipe`, `hardware`, `deployment`, `ray`, `worker`, `single_controller`, `misc`, `perf`, `model`, `algo`, `env`, `tool`, `ckpt`, `doc`, `data`, `cfg`, `reward`, `fully_async`, `one_step_off` - If this PR involves multiple modules, separate them with `,` like `[megatron, fsdp, doc]` - `{type}` is in `feat`, `fix`, `refactor`, `chore`, `test` - If this PR breaks any API (CLI arguments, config, function signature, etc.), add `[BREAKING]` to the beginning of the title. - Example: `[BREAKING][fsdp, megatron] feat: dynamic batching` ### Test > For changes that can not be tested by CI (e.g., algorithm implementation, new model support), validate by experiment(s) and show results like training curve plots, evaluation results, etc. ### API and Usage Example > Demonstrate how the API changes if any, and provide usage example(s) if possible. ```python # Add code snippet or script demonstrating how to use this ``` ### Design & Code Changes > Demonstrate the high-level design if this PR is complex, and list the specific changes. ### Checklist Before Submitting > [!IMPORTANT] > Please check all the following items before requesting a review, otherwise the reviewer might deprioritize this PR for review. - [ ] Read the [Contribute Guide](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md). - [ ] Apply [pre-commit checks](https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md#code-linting-and-formatting): `pre-commit install && pre-commit run --all-files --show-diff-on-failure --color=always` - [ ] Add / Update [the documentation](https://github.com/volcengine/verl/tree/main/docs). - [ ] Add unit or end-to-end test(s) to [the CI workflow](https://github.com/volcengine/verl/tree/main/.github/workflows) to cover all the code. 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Here is a template to assist you. This template is designed for using Vemlp machines. Currently, for each workflow, you need to create a `setup` and a `cleanup` job. When using this template, the main parts you need to modify are the `IMAGE` environment variable and the specific `job steps`. ```yaml name: Your Default Workflow on: push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" - ".github/workflows/template.yml" concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} permissions: contents: read env: IMAGE: "your vemlp image" # e.g. "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_URL: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" # public veFaas api jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} task-id: ${{ steps.create-runner.outputs.task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_URL }}" image: "${{ env.DEFAULT_IMAGE }}" your_job: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'default-runner' }}"] steps: xxxx # your jobs cleanup: runs-on: ubuntu-latest needs: [setup, your_job] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_URL }}" task-id: "${{ needs.setup.outputs.task-id }}" ``` ### Model and Dataset To avoid CI relies on network, we pre-download dataset on a NFS on the CI machine. The path for models are \${HOME}/models and the path for dataset is \${HOME}/models/hf_data. ================================================ FILE: .github/workflows/check-pr-title.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. on: pull_request: types: [opened, edited, synchronize] jobs: check-title: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: '3.11' - name: Run PR title checker run: python3 tests/special_sanity/check_pr_title.py env: PR_TITLE: ${{ github.event.pull_request.title }} - name: Run PR description checker run: python3 tests/special_sanity/check_pr_description.py env: PR_TITLE: ${{ github.event.pull_request.title }} GITHUB_EVENT_PATH: ${{ github.event_path }} ================================================ FILE: .github/workflows/cpu_unit_tests.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: cpu_unit_tests on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" - .github/workflows/cpu_unit_tests.yml # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" cpu_unit_tests: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 20 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable TORCH_COMPILE_DISABLE: 1 TORCHINDUCTOR_DISABLE: 1 steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install --upgrade "transformers>=5.0.0" - name: Download datasets run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k - name: Running CPU unit tests run: | echo '[pytest]' > pytest.ini echo 'python_files = *_on_cpu.py' >> pytest.ini pytest -s -x --asyncio-mode=auto tests/ cleanup: runs-on: ubuntu-latest needs: [setup, cpu_unit_tests] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/doc.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: doc_test on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" - "docs/**" - .github/workflows/doc.yml # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read # for checkout pages: write # for deploy-pages id-token: write # for deploy-pages jobs: doc_test: runs-on: ubuntu-latest timeout-minutes: 5 # Increase this timeout value as needed strategy: matrix: python-version: ["3.10"] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 with: python-version: ${{ matrix.python-version }} - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip install -r docs/requirements-docs.txt - name: Run doc make html run: | cd docs make clean make html SPHINXOPTS="--keep-going -w _build/sphinx.log" if grep -q ": ERROR:" _build/sphinx.log; then echo "🚨 Sphinx doc build contained ERRORs - see _build/sphinx.log" exit 1 fi if grep -q "WARNING: document isn't included in any toctree" _build/sphinx.log; then echo "🚨 Sphinx doc build contained WARNING. Please include newly added docs in index.rst. See _build/sphinx.log for details" exit 1 fi if grep -q "WARNING: Inline emphasis" _build/sphinx.log; then echo "🚨 Sphinx doc build contained WARNING. Please check inline emphasis is correct. See _build/sphinx.log for details" exit 1 fi if grep -q "WARNING: Definition list ends without a blank line" _build/sphinx.log; then echo "🚨 Sphinx doc build contained WARNING. Please check if the indentation is correct. See _build/sphinx.log for details" exit 1 fi ================================================ FILE: .github/workflows/docker-build-ascend-a2.yml ================================================ name: docker-build-ascend-a2 on: workflow_dispatch: push: branches: ["main"] paths: - "docker/ascend/Dockerfile.ascend_8.5.0_a2" - ".github/workflows/docker-build-ascend-a2.yml" release: types: [published] schedule: - cron: "0 16 * * *" jobs: build-ascend-image-a2: if: ${{ github.event_name != 'pull_request' && github.repository_owner == 'verl-project' }} runs-on: ubuntu-latest concurrency: group: ${{ github.workflow }}-${{ github.ref }}-build-ascend-image-a2 cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} steps: - name: Remove unnecessary parts in github actions runners to free up disk space uses: jlumbroso/free-disk-space@v1.3.1 with: tool-cache: true - name: Checkout code uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" - name: Get base image name and tag id: base_image run: | BASE_IMAGE_FULL=$(grep '^FROM' ./docker/ascend/Dockerfile.ascend_8.5.0_a2 | head -1 | cut -d' ' -f2) echo "Base image full: $BASE_IMAGE_FULL" BASE_IMAGE_TAG=$(echo "$BASE_IMAGE_FULL" | cut -d':' -f2) echo "Base image tag: $BASE_IMAGE_TAG" NEW_IMAGE_NAME="verl-$BASE_IMAGE_TAG" echo "New image name: $NEW_IMAGE_NAME" echo "base_image_tag=$BASE_IMAGE_TAG" >> "$GITHUB_OUTPUT" echo "new_image_name=$NEW_IMAGE_NAME" >> "$GITHUB_OUTPUT" - name: Get image tag id: version run: | BRANCH_NAME=$(echo "${{ github.ref }}" | sed 's/refs\/heads\///g' | sed 's/[^a-zA-Z0-9._-]/_/g') if [ "${{ github.event_name }}" = "release" ]; then echo "tag=${{ steps.base_image.outputs.new_image_name }}-${{ github.event.release.tag_name }}" >> "$GITHUB_OUTPUT" elif [ "$BRANCH_NAME" = "main" ]; then echo "tag=${{ steps.base_image.outputs.new_image_name }}-latest" >> "$GITHUB_OUTPUT" fi - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - name: Login to Quay.io uses: docker/login-action@v3 with: registry: quay.io username: ${{ secrets.QUAY_USERNAME }} password: ${{ secrets.QUAY_PASSWORD }} - name: Clean Docker cache before build run: | docker system prune -a -f --volumes || true - name: Build and push images Quay uses: docker/build-push-action@v6 with: context: . platforms: linux/amd64,linux/arm64 file: ./docker/ascend/Dockerfile.ascend_8.5.0_a2 push: true tags: | quay.io/ascend/verl:${{ steps.version.outputs.tag }} cache-from: type=gha cache-to: type=gha,mode=max build-args: | BUILDKIT_INLINE_CACHE=1 ================================================ FILE: .github/workflows/docker-build-ascend-a3.yml ================================================ name: docker-build-ascend-a3 on: workflow_dispatch: push: branches: ["main"] paths: - "docker/ascend/Dockerfile.ascend_8.5.0_a3" - ".github/workflows/docker-build-ascend-a3.yml" release: types: [published] schedule: - cron: "0 19 * * *" jobs: build-ascend-image-a3: if: ${{ github.event_name != 'pull_request' && github.repository_owner == 'verl-project' }} runs-on: ubuntu-latest concurrency: group: ${{ github.workflow }}-${{ github.ref }}-build-ascend-image-a3 cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} steps: - name: Remove unnecessary parts in github actions runners to free up disk space uses: jlumbroso/free-disk-space@v1.3.1 with: tool-cache: true - name: Checkout code uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" - name: Get base image name and tag id: base_image run: | BASE_IMAGE_FULL=$(grep '^FROM' ./docker/ascend/Dockerfile.ascend_8.5.0_a3 | head -1 | cut -d' ' -f2) echo "Base image full: $BASE_IMAGE_FULL" BASE_IMAGE_TAG=$(echo "$BASE_IMAGE_FULL" | cut -d':' -f2) echo "Base image tag: $BASE_IMAGE_TAG" NEW_IMAGE_NAME="verl-$BASE_IMAGE_TAG" echo "New image name: $NEW_IMAGE_NAME" echo "base_image_tag=$BASE_IMAGE_TAG" >> "$GITHUB_OUTPUT" echo "new_image_name=$NEW_IMAGE_NAME" >> "$GITHUB_OUTPUT" - name: Get image tag id: version run: | BRANCH_NAME=$(echo "${{ github.ref }}" | sed 's/refs\/heads\///g' | sed 's/[^a-zA-Z0-9._-]/_/g') if [ "${{ github.event_name }}" = "release" ]; then echo "tag=${{ steps.base_image.outputs.new_image_name }}-${{ github.event.release.tag_name }}" >> "$GITHUB_OUTPUT" elif [ "$BRANCH_NAME" = "main" ]; then echo "tag=${{ steps.base_image.outputs.new_image_name }}-latest" >> "$GITHUB_OUTPUT" fi - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - name: Login to Quay.io uses: docker/login-action@v3 with: registry: quay.io username: ${{ secrets.QUAY_USERNAME }} password: ${{ secrets.QUAY_PASSWORD }} - name: Clean Docker cache before build run: | docker system prune -a -f --volumes || true - name: Build and push images Quay uses: docker/build-push-action@v6 with: context: . platforms: linux/amd64,linux/arm64 file: ./docker/ascend/Dockerfile.ascend_8.5.0_a3 push: true tags: | quay.io/ascend/verl:${{ steps.version.outputs.tag }} cache-from: type=gha cache-to: type=gha,mode=max build-args: | BUILDKIT_INLINE_CACHE=1 ================================================ FILE: .github/workflows/e2e_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main paths: - ".github/workflows/e2e_ascend.yml" - "examples/data_preprocess/**" - "examples/grpo_trainer/**" - "examples/ppo_trainer/**" - "examples/sft/**" - "verl/experimental/one_step_off_policy/**" - "tests/special_npu/**" - "tests/special_sanity/check_device_api_usage.py" - "verl/**" - "pyproject.toml" - "requirements-npu.txt" - "setup.py" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} permissions: contents: read jobs: llm_rl_job: if: github.repository_owner == 'verl-project' name: E2E Ascend testing for RL training scenarios of LLM models runs-on: linux-aarch64-a2b3-8 timeout-minutes: 120 container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout volcengine/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install -e . - name: Check final pip list run: | pip list - name: Preprocess gsm8k dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running gsm8k e2e training tests with PPO on ASCEND NPU (FSDP backend) run: | ray stop --force bash tests/special_npu/run_qwen3_06b_ppo.sh rm -rf $HOME/ckpts - name: Running gsm8k e2e training tests with GRPO on ASCEND NPU (FSDP backend) run: | ray stop --force bash tests/special_npu/run_qwen2_5_05b_grpo.sh rm -rf $HOME/ckpts - name: Running gsm8k e2e training tests with GRPO on ASCEND NPU (MindSpeed backend) run: | ray stop --force USE_DIST_CKPT=True bash tests/special_npu/run_qwen2_5_05b_grpo_mindspeed.sh rm -rf $HOME/dist_ckpt/qwen2_5_05b_grpo_mindspeed rm -rf $HOME/ckpts - name: Running gsm8k e2e training tests with GRPO on ASCEND NPU (MindSpeed backend, MoE Model) run: | ray stop --force USE_DIST_CKPT=True USE_DUMMY_MODEL=True DUMMY_MODEL_CONFIG_PATH=tests/special_e2e/ppo_trainer/expert_parallel/qwen3moe_minimal.json DUMMY_MODEL_PATH=$HOME/dist_ckpt/qwen3_30b_grpo_mindspeed bash tests/special_npu/run_qwen3_30b_grpo_mindspeed.sh vlm_rl_job: if: github.repository_owner == 'verl-project' name: E2E Ascend testing for RL training scenarios of VLM models runs-on: linux-aarch64-a2b3-8 timeout-minutes: 120 container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout volcengine/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install -e . - name: Check final pip list run: | pip list - name: Preprocess geo3k dataset run: | python examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/.cache/datasets/hiyouga/geometry3k - name: Running geo3k e2e training tests with GRPO on ASCEND NPU run: | ray stop --force bash tests/special_npu/run_qwen2_5_vl_3b_npu.sh rm -rf $HOME/ckpts ================================================ FILE: .github/workflows/e2e_fully_async_policy.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_fully_async_policy on: # Trigger the workflow on push or pull request, # but only for the main branch # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/*trainer*" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" - "verl/experimental/fully_async_policy" pull_request: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Home - "verl/experimental/fully_async_policy" # Entrypoints - ".github/workflows/e2e_fully_async_policy.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/run_fully_async_policy.sh" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" # Test FSDP2 strategy e2e_fully_async_policy_fsdp2: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 10 # Increase timeout for async training env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "fsdp2" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install cupy-cuda12x==13.6.0 - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running the E2E test with fully_async_policy algorithm (FSDP2) run: | ray stop --force bash tests/special_e2e/run_fully_async_policy.sh # Test Megatron strategy e2e_fully_async_policy_megatron: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 10 # Increase timeout for async training env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "megatron" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install cupy-cuda12x==13.6.0 - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running the E2E test with fully_async_policy algorithm (Megatron) run: | ray stop --force bash tests/special_e2e/run_fully_async_policy.sh cleanup: runs-on: ubuntu-latest needs: [setup, e2e_fully_async_policy_fsdp2] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_fully_async_policy_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_fully_async_policy_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/*trainer*" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" - "verl/experimental/fully_async_policy" pull_request: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Home - "verl/experimental/fully_async_policy" # Entrypoints - ".github/workflows/e2e_fully_async_policy_ascend.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/run_fully_async_policy.sh" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: # Test FSDP2 strategy e2e_fully_async_policy_fsdp2_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "fsdp2" device_name: "npu" steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running the E2E test with fully_async_policy algorithm (FSDP2) run: | ray stop --force bash tests/special_e2e/run_fully_async_policy.sh # Test Megatron strategy e2e_fully_async_policy_megatron_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "megatron" device_name: "npu" steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running the E2E test with fully_async_policy algorithm (Megatron) run: | ray stop --force bash tests/special_e2e/run_fully_async_policy.sh ================================================ FILE: .github/workflows/e2e_one_step_off_policy.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_one_step_off_policy on: # Trigger the workflow on push or pull request, # but only for the main branch # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/*trainer*" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" - "verl/experimental/one_step_off_policy" pull_request: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Home - "verl/experimental/one_step_off_policy" # Entrypoints - ".github/workflows/e2e_one_step_off_policy.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/run_one_step_off_policy.sh" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" # Test FSDP2 strategy e2e_one_step_off_policy_fsdp2: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 10 # Increase timeout for async training env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "fsdp2" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install cupy-cuda12x==13.6.0 - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running the E2E test with one_step_off_policy algorithm (FSDP2) run: | ray stop --force bash tests/special_e2e/run_one_step_off_policy.sh # Test Megatron strategy e2e_one_step_off_policy_megatron: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 10 # Increase timeout for async training env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "megatron" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install cupy-cuda12x==13.6.0 - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running the E2E test with one_step_off_policy algorithm (Megatron) run: | ray stop --force bash tests/special_e2e/run_one_step_off_policy.sh cleanup: runs-on: ubuntu-latest needs: [setup, e2e_one_step_off_policy_fsdp2, e2e_one_step_off_policy_megatron] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_one_step_off_policy_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_one_step_off_policy_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/*trainer*" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" - "verl/experimental/one_step_off_policy" pull_request: branches: - main - v0.* paths: - "**/*.py" - "!**/*.md" - "!**/*.sh" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Home - "verl/experimental/one_step_off_policy" # Entrypoints - ".github/workflows/e2e_one_step_off_policy_ascend.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/run_one_step_off_policy.sh" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: # Test FSDP2 strategy e2e_one_step_off_policy_fsdp2_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "fsdp2" device_name: "npu" steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running the E2E test with one_step_off_policy algorithm (FSDP2) run: | ray stop --force bash tests/special_e2e/run_one_step_off_policy.sh # Test Megatron strategy e2e_one_step_off_policy_megatron_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ACTOR_STRATEGY: "megatron" device_name: "npu" steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running the E2E test with one_step_off_policy algorithm (Megatron) run: | ray stop --force export PYTHONPATH=$PYTHONPATH:/Megatron-LM bash tests/special_e2e/run_one_step_off_policy.sh ================================================ FILE: .github/workflows/e2e_ppo_grpo_trainer_trtllm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_megatron_trtllm on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # Recipes - "!recipe/**" # FSDP - "!verl/workers/**/*dp_*.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Recipes - "!recipe/**" # FSDP - "!verl/workers/**/*dp_*.py" # Entrypoints - "verl/workers/rollout/trtllm_rollout/**" - "tests/workers/rollout/rollout_trtllm/**" - ".github/workflows/e2e_ppo_grpo_trainer_trtllm.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "examples/data_preprocess/dapo_multiturn_w_tool.py" - "examples/data_preprocess/aime2024_multiturn_w_tool.py" - "examples/grpo_trainer/run_qwen2-7b_math_trtllm.sh" - "examples/grpo_trainer/run_qwen2-7b_math_megatron_trtllm.sh" - "examples/grpo_trainer/run_qwen3-30b_dapo_megatron_fp8_trtllm.sh" # add back when ppo flow is ready # - "tests/special_e2e/run_ppo_trainer_megatron.sh" # - "verl/trainer/main_ppo.py" # - "verl/trainer/config/ppo_megatron_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:trtllm1.3.0rc4" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" trtllm_unit_tests: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install pytest-asyncio pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Run TRTLLM unit tests run: | export TRTLLM_TEST_MODEL_PATH_ROOT="${HOME}/models" ray stop --force pytest -v -s \ tests/workers/rollout/rollout_trtllm/test_adapter.py \ tests/workers/rollout/rollout_trtllm/test_async_server.py \ tests/workers/rollout/rollout_trtllm/test_trtllm_rollout_utils.py e2e_grpo_trainer_fsdp-qwen2: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k --local_save_dir ${PWD}/data/gsm8k - name: Running GSM8K E2E training tests with FSDP on 8 L20 GPUs (Qwen) run: | ray stop --force DATADIR=${HOME}/data \ bash examples/grpo_trainer/run_qwen2-7b_math_trtllm.sh 2 \ trainer.total_training_steps=1 \ data.train_files="['${PWD}/data/gsm8k/train.parquet']" \ data.val_files="['${PWD}/data/gsm8k/test.parquet']" \ trainer.logger='["console"]' \ actor_rollout_ref.model.path="${HOME}/models/Qwen/Qwen2.5-0.5B-Instruct" - name: clean up run: | rm -rf checkpoints e2e_grpo_trainer_megatron-qwen2: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k --local_save_dir ${PWD}/data/gsm8k - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen) run: | ray stop --force DATADIR=${HOME}/data \ ACTOR_TP=2 \ bash examples/grpo_trainer/run_qwen2-7b_math_megatron_trtllm.sh 2 \ trainer.total_training_steps=1 \ data.train_files="['${PWD}/data/gsm8k/train.parquet']" \ data.val_files="['${PWD}/data/gsm8k/test.parquet']" \ trainer.logger='["console"]' \ actor_rollout_ref.model.path="${HOME}/models/Qwen/Qwen2.5-0.5B-Instruct" - name: clean up run: | rm -rf checkpoints e2e_grpo_trainer_fsdp-vlm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install qwen_vl_utils pip3 install mathruler - name: Prepare GEO3K dataset run: | python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k --local_save_dir ${PWD}/data/geo3k - name: Running GEO3K E2E training tests with FSDP on 8 L20 GPUs (VLM) run: | ray stop --force DATADIR=${HOME}/data \ bash examples/grpo_trainer/run_qwen2_5_vl_3b_trtllm.sh 2 \ trainer.total_training_steps=1 \ data.train_files="['${PWD}/data/geo3k/train.parquet']" \ data.val_files="['${PWD}/data/geo3k/test.parquet']" \ trainer.logger='["console"]' \ actor_rollout_ref.model.path="${HOME}/models/Qwen/Qwen3-VL-2B-Instruct" - name: clean up run: | rm -rf checkpoints - name: Prepare DAPO-Math-17k and AIME-2024 datasets (data_preprocess) run: | python3 examples/data_preprocess/dapo_multiturn_w_tool.py --local_save_dir ${PWD}/data/dapo-math-17k python3 examples/data_preprocess/aime2024_multiturn_w_tool.py --local_save_dir ${PWD}/data/aime-2024 - name: Running DAPO E2E with FP8 TRT-LLM rollout (Qwen3-0.6B) run: | ray stop --force export INFER_TP=2 ACTOR_TP=2 ACTOR_PP=2 ACTOR_VPP=2 ACTOR_EP=1 ACTOR_CP=2 REF_TP=2 REF_PP=2 REF_VPP=2 REF_EP=1 REF_CP=2 GEN_MOE_TP=null GEN_MOE_EP=null export NNODES=1 GPUS_PER_NODE=8 TRTLLM_MOE_BACKEND=CUTLASS export DATA_DIR=${PWD} DAPO_MATH_TRAIN=${PWD}/data/dapo-math-17k/train.parquet AIME_VAL=${PWD}/data/aime-2024/train.parquet MODEL_PATH=${HOME}/models/Qwen/Qwen3-0.6B bash examples/grpo_trainer/run_qwen3-30b_dapo_megatron_fp8_trtllm.sh \ reward_model.reward_kwargs.overlong_buffer_cfg.len=258 \ reward_model.reward_kwargs.max_resp_len=512 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.train_batch_size=32 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.rollout.max_num_seqs=16 \ actor_rollout_ref.rollout.max_num_batched_tokens=1024 \ actor_rollout_ref.rollout.max_model_len=1024 \ actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=False \ actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=False \ trainer.total_training_steps=1 \ trainer.logger='["console"]' - name: clean up run: | rm -rf checkpoints cleanup: runs-on: ubuntu-latest needs: [setup, trtllm_unit_tests, e2e_grpo_trainer_fsdp-qwen2, e2e_grpo_trainer_megatron-qwen2, e2e_grpo_trainer_fsdp-vlm] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_ppo_trainer.yml ================================================ name: e2e_ppo_trainer on: # Trigger the workflow on push or pull request, # but only for the main branch # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # Megatron - "!verl/workers/**/megatron_*.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!**/*.md" - "!docker/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Docs - "!docs/**" # Megatron - "!verl/workers/**/megatron_*.py" # Entrypoints - ".github/workflows/e2e_ppo_trainer.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/ppo_trainer" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: pre_commit_for_ppo: runs-on: ubuntu-latest strategy: matrix: python-version: ["3.12"] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 with: python-version: ${{ matrix.python-version }} - name: Install the current repository run: | pip install pre-commit hydra-core pip3 install --no-deps -e . - name: Set ruff --output-format=github run: | sed -i 's/--output-format=full/--output-format=github/' .pre-commit-config.yaml git add .pre-commit-config.yaml - uses: pre-commit/action@v3.0.1 with: extra_args: "" # Overriding default "--all-files" ================================================ FILE: .github/workflows/e2e_ppo_trainer_megatron_sglang.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_megatron_sglang on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" # Entrypoints - "verl/worksers/rollout/sglang_rollout/*" - ".github/workflows/e2e_ppo_trainer_megatron_sglang.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/run_ppo_trainer_megatron.sh" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_megatron_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" e2e_ppo_trainer_megatron-deepseek: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ENGINE: sglang steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install git+https://github.com/ISEEKYAN/mbridge.git@main --no-deps --no-build-isolation pip3 install --no-deps -e . - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (DeepSeek) run: | ray stop --force OPTIM_MEMORY_EFFICIENT=True ENGINE=sglang SAVE_FREQ=1 MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (DeepSeek) run: | ray stop --force export VLLM_USE_V1=1 ray start --head ENGINE=sglang MODE=async RESUME_MODE=auto MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct TOTAL_TRAIN_STEPS=2 bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Profiling GRPO GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Deepseek) run: | ray stop --force PROFILE_ENABLE=True ENGINE=sglang ADV_ESTIMATOR=grpo USE_DYNAMIC_BSZ=False MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct bash tests/special_e2e/run_ppo_trainer_megatron.sh if [ -z "$( ls -A '/tmp/ray/session_latest/logs/nsight/' )" ]; then echo "[ERROR] not found any profiling files" exit 1 else echo "[SUCCESS] profile success" fi - name: clean up run: | rm -rf checkpoints # Qwen3-0.6B: dense, tie_word_embeddings=True e2e_ppo_trainer_megatron-qwen3: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable ENGINE: sglang steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen3) testing learning rate scheduler run: | ray stop --force ALL_OFFLOAD=True VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 LR_WARMUP_STEPS=1 TOTAL_TRAIN_STEPS=2 MODEL_ID=Qwen/Qwen3-0.6B bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with FP8 rollout run: | ray stop --force export VLLM_USE_V1=1 ROLLOUT_QUANTIZATION=fp8 TOTAL_TRAIN_STEPS=2 MODEL_ID=Qwen/Qwen3-0.6B bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: clean up run: | rm -rf checkpoints cleanup: runs-on: ubuntu-latest needs: [setup, e2e_ppo_trainer_megatron-deepseek, e2e_ppo_trainer_megatron-qwen3] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_ppo_trainer_megatron_sglang_2.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_megatron_sglang_2 on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" # Entrypoints - "verl/worksers/rollout/sglang_rollout/*" - ".github/workflows/e2e_ppo_trainer_megatron_sglang.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/run_ppo_trainer_megatron.sh" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_megatron_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" e2e_ppo_trainer_fsdp_sglang: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 40 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm and save ckpt run: | ray stop --force ENGINE=sglang bash tests/special_e2e/ppo_trainer/run_function_reward.sh e2e_ppo_trainer_fsdp-qwen2_5vl-3b: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . # Geo3k - name: Prepare GEO3K dataset run: | ray stop --force python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k/ - name: Running GEO3K VLM E2E training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ ENGINE=sglang ROLLOUT_MODE=async GPU_MEMORY_UTILIZATION=0.6 ACTOR_FSDP_PARAM_OFFLOAD=True \ ACTOR_FSDP_OPTIMIZER_OFFLOAD=True REF_FSDP_PARAM_OFFLOAD=True \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GEO3K VLM E2E with rmpad using torch fused kernel (Qwen2.5-VL) run: | ray stop --force FUSED_KERNELS=True TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ ENGINE=sglang ROLLOUT_MODE=async GPU_MEMORY_UTILIZATION=0.6 ACTOR_FSDP_PARAM_OFFLOAD=True \ ACTOR_FSDP_OPTIMIZER_OFFLOAD=True REF_FSDP_PARAM_OFFLOAD=True \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GEO3K VLM E2E with rmpad using triton fused kernel (Qwen2.5-VL) run: | ray stop --force FUSED_KERNELS=True FUSED_KERNEL_BACKEND=triton \ TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ ENGINE=sglang ROLLOUT_MODE=async GPU_MEMORY_UTILIZATION=0.6 ACTOR_FSDP_PARAM_OFFLOAD=True \ ACTOR_FSDP_OPTIMIZER_OFFLOAD=True REF_FSDP_PARAM_OFFLOAD=True \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh cleanup: runs-on: ubuntu-latest needs: [setup, e2e_ppo_trainer_fsdp-qwen2_5vl-3b, e2e_ppo_trainer_fsdp_sglang] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_ppo_trainer_megatron_vllm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_megatron_vllm on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" # Entrypoints - ".github/workflows/e2e_ppo_trainer_megatron_vllm.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/run_ppo_trainer_megatron.sh" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_megatron_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" # deepseek-ai/deepseek-coder-1.3b-instruct: dense, tie_word_embeddings=False e2e_ppo_trainer_megatron-deepseek: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps --force-reinstall . pip3 install git+https://github.com/ISEEKYAN/mbridge.git@main --no-deps --no-build-isolation pip3 install math-verify - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k # Full training save&load - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron, use mbridge e2e to pre-load and save (Deepseek) run: | ray stop --force ALL_OFFLOAD=True SAVE_FREQ=1 MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct COMMON_PP=4 COMMON_VPP=null COMMON_CP=1 USE_MBRIDGE=True USE_DIST_CKPT=False \ bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron, use mbridge e2e to pre-load and save (Deepseek) run: | ray stop --force RESUME_MODE=auto MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct TOTAL_TRAIN_STEPS=2 SAVE_FREQ=1 COMMON_PP=4 COMMON_VPP=null COMMON_CP=1 USE_MBRIDGE=True USE_DIST_CKPT=False \ bash tests/special_e2e/run_ppo_trainer_megatron.sh # LoRA training save&load - name: clean up and install Megatron-Bridge run: | rm -rf checkpoints pip3 install git+https://github.com/NVIDIA-NeMo/Megatron-Bridge.git@83a7c11 --no-deps --no-build-isolation pip3 install git+https://github.com/NVIDIA/Megatron-LM.git@5455f0a --no-deps --no-build-isolation pip3 install "nvidia-modelopt[torch]>=0.37.0" transformers==4.57.1 - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron, use Megatron-Bridge LoRA e2e to pre-load and save (Deepseek) run: | ray stop --force ALL_OFFLOAD=True SAVE_FREQ=1 MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct COMMON_PP=4 LORA_RANK=8 COMMON_VPP=null COMMON_CP=1 USE_MBRIDGE=True VANILLA_MBRIDGE=False VALUE_VANILLA_MBRIDGE=False USE_DIST_CKPT=False \ bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron, use Megatron-Bridge LoRA e2e to pre-load and save (Deepseek) run: | ray stop --force RESUME_MODE=auto MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct TOTAL_TRAIN_STEPS=2 SAVE_FREQ=1 COMMON_PP=4 LORA_RANK=8 COMMON_VPP=null COMMON_CP=1 USE_MBRIDGE=True VANILLA_MBRIDGE=False VALUE_VANILLA_MBRIDGE=False USE_DIST_CKPT=False \ bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: clean up run: | rm -rf checkpoints # Qwen3-0.6B: dense, tie_word_embeddings=True e2e_ppo_trainer_megatron-qwen3: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install math-verify - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen3) testing learning rate scheduler run: | ray stop --force ALL_OFFLOAD=True VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 LR_WARMUP_STEPS=1 TOTAL_TRAIN_STEPS=2 MODEL_ID=Qwen/Qwen3-0.6B bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with FP8 rollout run: | ray stop --force export VLLM_USE_V1=1 ROLLOUT_QUANTIZATION=fp8 TOTAL_TRAIN_STEPS=2 MODEL_ID=Qwen/Qwen3-0.6B bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: clean up run: | rm -rf checkpoints cleanup: runs-on: ubuntu-latest needs: [setup, e2e_ppo_trainer_megatron-deepseek, e2e_ppo_trainer_megatron-qwen3] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_ppo_trainer_megatron_vllm_2.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_megatron_vllm_2 on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" # Entrypoints - ".github/workflows/e2e_ppo_trainer_megatron_vllm_2.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/run_ppo_trainer_megatron.sh" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_megatron_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" e2e_ppo_trainer_megatron-moe-expert-parallel: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps --force-reinstall . pip3 install git+https://github.com/NVIDIA-NeMo/Megatron-Bridge.git@83a7c11 --no-deps --no-build-isolation pip3 install git+https://github.com/NVIDIA/Megatron-LM.git@5455f0a --no-deps --no-build-isolation pip3 install "nvidia-modelopt[torch]>=0.37.0" transformers==4.57.1 - name: Prepare GSM8K dataset run: | python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron-Bridge (Qwen3-30B-A3B-Instruct-2507) run: | ray stop --force ADV_ESTIMATOR=grpo USE_DUMMY_MODEL=True DUMMY_MODEL_CONFIG_PATH=tests/special_e2e/ppo_trainer/expert_parallel/qwen2moe_minimal.json \ PPO_MAX_TOKEN_LEN=1024 FWD_MAX_TOKEN_LEN=1024 \ MAX_PROMPT_LENGTH=512 MAX_RESPONSE_LENGTH=512 \ MODEL_ID=Qwen/Qwen3-30B-A3B-Instruct-2507 USE_MBRIDGE=True VANILLA_MBRIDGE=False VALUE_VANILLA_MBRIDGE=False \ COMMON_PP=2 COMMON_VPP=null COMMON_CP=1 COMMON_TP=4 COMMON_EP=4 COMMON_ETP=1 INFER_TP=8 \ USE_DIST_CKPT=True ALL_OFFLOAD=True SKIP_SAVE_HF_MODEL=1 bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: Running GSM8K E2E training tests with 3D parallelism with FP8 rollout on 8 L20 GPUs with Megatron-Bridge (Qwen3-30B-A3B-Instruct-2507) run: | ray stop --force ADV_ESTIMATOR=grpo USE_DUMMY_MODEL=True DUMMY_MODEL_CONFIG_PATH=tests/special_e2e/ppo_trainer/expert_parallel/qwen2moe_minimal.json \ PPO_MAX_TOKEN_LEN=1024 FWD_MAX_TOKEN_LEN=1024 \ MAX_PROMPT_LENGTH=512 MAX_RESPONSE_LENGTH=512 \ MODEL_ID=Qwen/Qwen3-30B-A3B-Instruct-2507 USE_MBRIDGE=True VANILLA_MBRIDGE=False VALUE_VANILLA_MBRIDGE=False \ COMMON_PP=2 COMMON_VPP=null COMMON_CP=1 COMMON_TP=4 COMMON_EP=4 COMMON_ETP=1 INFER_TP=2 \ USE_DIST_CKPT=True ALL_OFFLOAD=True SKIP_SAVE_HF_MODEL=1 ROLLOUT_QUANTIZATION=fp8 bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: clean up run: | rm -rf checkpoints - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron-Bridge LoRA (Qwen3-30B-A3B-Instruct-2507) run: | ray stop --force ADV_ESTIMATOR=grpo USE_DUMMY_MODEL=True DUMMY_MODEL_CONFIG_PATH=tests/special_e2e/ppo_trainer/expert_parallel/qwen2moe_minimal.json \ PPO_MAX_TOKEN_LEN=1024 FWD_MAX_TOKEN_LEN=1024 \ MAX_PROMPT_LENGTH=512 MAX_RESPONSE_LENGTH=512 LORA_RANK=8 CRITIC_LORA_RANK=8 \ MODEL_ID=Qwen/Qwen3-30B-A3B-Instruct-2507 USE_MBRIDGE=True VANILLA_MBRIDGE=False VALUE_VANILLA_MBRIDGE=False \ COMMON_PP=2 COMMON_VPP=null COMMON_CP=1 COMMON_TP=4 COMMON_EP=2 COMMON_ETP=1 INFER_TP=8 \ USE_DIST_CKPT=False LORA_MERGE=True ALL_OFFLOAD=True SKIP_SAVE_HF_MODEL=1 bash tests/special_e2e/run_ppo_trainer_megatron.sh - name: clean up run: | rm -rf checkpoints e2e_ppo_trainer_fsdp_vllm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare GSM8K dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k # Function RM - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (FSDP_SIZE=8) run: | ray stop --force VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-fsdp-size8" bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm after resuming run: | ray stop --force RESUME_MODE=auto VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-fsdp-size8" bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test merging FSDP checkpoints (Qwen Actor) run: | exp_name="qwen2.5-0.5b-function-reward-minimal-fsdp-size8" python -m verl.model_merger test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (DDP_SIZE=2, FSDP_SIZE=4) run: | ray stop --force VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True FSDP_SIZE=4 USE_KL=True VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-ddp-size2-fsdp-size4" bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test merging DDP+FSDP checkpoints (Qwen Actor) run: | exp_name="qwen2.5-0.5b-function-reward-minimal-ddp-size2-fsdp-size4" python -m verl.model_merger test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (FSDP2) run: | ray stop --force VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-fsdp2-size8" STRATEGY=fsdp2 bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test merging FSDP2 checkpoints (Qwen Actor) run: | exp_name="qwen2.5-0.5b-function-reward-minimal-fsdp2-size8" python -m verl.model_merger test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface - name: Running GSM8K E2E without rmpad using function rm run: | ray stop --force RM_PAD=False bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm (GRPO) run: | ray stop --force CUSTOM_REWARD_FN=True ADV_ESTIMATOR=grpo USE_KL=True bash tests/special_e2e/ppo_trainer/run_function_reward.sh # - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm (ReMax) # run: | # ray stop --force # ADV_ESTIMATOR=remax USE_KL=True bash tests/special_e2e/ppo_trainer/run_function_reward.sh # LoRA tests - name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm run: | ray stop --force ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm and layered_summon run: | ray stop --force ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors LAYERED_SUMMON=True TOTAL_TRAIN_STEPS=1 SAVE_FREQ=1 FSDP_SIZE=4 VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal" bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test GRPO LoRA checkpoints merging function run: | export EXP_NAME="qwen2.5-0.5b-function-reward-minimal" ls checkpoints/verl-test/${EXP_NAME}/global_step_1/actor cat checkpoints/verl-test/${EXP_NAME}/global_step_1/actor/huggingface/config.json python3 -m verl.model_merger merge --backend fsdp --local_dir checkpoints/verl-test/${EXP_NAME}/global_step_1/actor/ --target_dir checkpoints/verl-test/${EXP_NAME}/global_step_1/actor/huggingface - name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm and layered_summon with fsdp2 run: | ray stop --force ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors LAYERED_SUMMON=True STRATEGY=fsdp2 bash tests/special_e2e/ppo_trainer/run_function_reward.sh e2e_ppo_trainer_fsdp-qwen2_5vl-3b: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 40 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . # Geo3k - name: Prepare GEO3K dataset run: | python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k/ - name: Running GEO3K VLM GRPO E2E training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ SP_SIZE=2 \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GEO3K VLM PPO E2E training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=gae RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ SP_SIZE=2 \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GEO3K VLM GRPO E2E lora training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ SP_SIZE=2 \ LORA_RANK=32 LORA_EXCLUDE=".*visual.*" \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh cleanup: runs-on: ubuntu-latest needs: [ setup, e2e_ppo_trainer_megatron-moe-expert-parallel, e2e_ppo_trainer_fsdp-qwen2_5vl-3b, e2e_ppo_trainer_fsdp_vllm, ] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_ppo_trainer_megatron_vllm_2_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_megatron_vllm_2_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" - "!verl/utils/fsdp_utils.py" - "!verl/utils/checkpoint/fsdp_checkpoint_manager.py" - "!verl/model_merger/fsdp_model_merger.py" # Entrypoints - ".github/workflows/e2e_ppo_trainer_megatron_vllm_2_ascend.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/run_ppo_trainer_megatron.sh" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_megatron_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: e2e_ppo_trainer_fsdp_vllm_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 90 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k # Function RM - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (DDP_SIZE=2, FSDP_SIZE=4) run: | ray stop --force VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True FSDP_SIZE=4 USE_KL=True VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-ddp-size2-fsdp-size4" bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test merging DDP+FSDP checkpoints (Qwen Actor) run: | exp_name="qwen2.5-0.5b-function-reward-minimal-ddp-size2-fsdp-size4" python -m verl.model_merger test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (FSDP2) run: | ray stop --force VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-fsdp2-size8" STRATEGY=fsdp2 bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test merging FSDP2 checkpoints (Qwen Actor) run: | exp_name="qwen2.5-0.5b-function-reward-minimal-fsdp2-size8" python -m verl.model_merger test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface - name: Running GSM8K E2E without rmpad using function rm run: | ray stop --force RM_PAD=False bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm (GRPO) run: | ray stop --force CUSTOM_REWARD_FN=True ADV_ESTIMATOR=grpo USE_KL=True bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm and layered_summon run: | ray stop --force ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors LAYERED_SUMMON=True TOTAL_TRAIN_STEPS=1 SAVE_FREQ=1 FSDP_SIZE=4 VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal" bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Test GRPO LoRA checkpoints merging function run: | export EXP_NAME="qwen2.5-0.5b-function-reward-minimal" ls checkpoints/verl-test/${EXP_NAME}/global_step_1/actor cat checkpoints/verl-test/${EXP_NAME}/global_step_1/actor/huggingface/config.json python3 -m verl.model_merger merge --backend fsdp --local_dir checkpoints/verl-test/${EXP_NAME}/global_step_1/actor/ --target_dir checkpoints/verl-test/${EXP_NAME}/global_step_1/actor/huggingface - name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm and layered_summon with fsdp2 run: | ray stop --force ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors LAYERED_SUMMON=True STRATEGY=fsdp2 bash tests/special_e2e/ppo_trainer/run_function_reward.sh e2e_ppo_trainer_fsdp-qwen2_5vl-3b_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . pip install trl==0.26.0 - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models # Geo3k - name: Prepare GEO3K dataset run: | python examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/.cache/datasets/hiyouga/geometry3k - name: Running GEO3K VLM GRPO E2E training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ SP_SIZE=2 \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GEO3K VLM PPO E2E training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=gae RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ SP_SIZE=2 \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh - name: Running GEO3K VLM GRPO E2E lora training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \ MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \ MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \ ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \ SP_SIZE=2 \ LORA_RANK=32 LORA_EXCLUDE=".*visual.*" \ bash tests/special_e2e/ppo_trainer/run_function_reward.sh ================================================ FILE: .github/workflows/e2e_ppo_trainer_veomni_vllm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_ppo_trainer_veomni_vllm on: # Trigger the workflow on push or pull request, # but only for the main branch. # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. push: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!verl/trainer/fsdp_sft_trainer.py" # Megatron - "!verl/workers/**/megatron_*.py" pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!docker/**" # Docs - "!**/*.md" - "!docs/**" - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Megatron - "!verl/workers/**/megatron_*.py" # Entrypoints - ".github/workflows/e2e_ppo_trainer_veomni_vllm.yml" - "examples/data_preprocess/gsm8k.py" - "examples/data_preprocess/geo3k.py" - "tests/special_e2e/run_ppo_trainer_veomni.sh" - "verl/trainer/main_ppo.py" - "verl/trainer/config/ppo_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" e2e_ppo_trainer_veomni_vllm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install git+https://github.com/ByteDance-Seed/VeOmni.git@v0.1.4 - name: Prepare GSM8K dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Prepare GEO3K dataset run: | ray stop --force python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k/ - name: Running GSM8K E2E training tests on 8 L20 GPUs with veomni engine (FSDP_SIZE=4, USP=2) run: | ray stop --force FSDP_SIZE=4 SP_SIZE=2 bash tests/special_e2e/run_ppo_trainer_veomni.sh - name: Running GEO3K E2E training tests on 8 L20 GPUs with veomni engine (FSDP_SIZE=8, USP=1) run: | ray stop --force MODEL_ID=Qwen/Qwen3-VL-2B-Instruct TRAIN_FILES=${HOME}/data/geo3k/train.parquet VAL_FILES=${HOME}/data/gsm8k/test.parquet FSDP_SIZE=8 SP_SIZE=1 bash tests/special_e2e/run_ppo_trainer_veomni.sh cleanup: runs-on: ubuntu-latest needs: [ setup, e2e_ppo_trainer_veomni_vllm, ] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_sft_llm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_sft_llm on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Megatron - "!verl/workers/**/megatron_*.py" # Entrypoints - ".github/workflows/e2e_sft_llm.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/sft" - "verl/trainer/fsdp_sft_trainer.py" - "verl/trainer/config/sft_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" e2e_sft_llm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install peft pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install git+https://github.com/ByteDance-Seed/VeOmni.git@v0.1.4 - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k_multiturn_sft.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm run: | ray stop --force bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs w/o rmpad using function rm run: | ray stop --force RM_PAD=False bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with sequence parallism run: | ray stop --force SP_SIZE=2 bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests on 8 L20 GPUs with sequence parallism and liger run: | ray stop --force SP_SIZE=2 LIGER=True bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests with LoRA run: | ray stop --force LORA_RANK=32 bash tests/special_e2e/sft/run_sft.sh - name: Run GSM8K E2E training and resume tests resuming from the checkpoint manager run: | ray stop --force LORA_RANK=32 RESUME_MODE=auto TOTAL_TRAIN_STEP=2 bash tests/special_e2e/sft/run_sft.sh # TODO: multiturn - name: Running GSM8K E2E training tests with multiturn and various configs and compare results run: | bash tests/special_e2e/sft/test_sft_engine_all.sh cleanup: runs-on: ubuntu-latest needs: [setup, e2e_sft_llm] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/e2e_sft_llm_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_sft_llm_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Megatron - "!verl/workers/**/megatron_*.py" # Entrypoints - ".github/workflows/e2e_sft_llm_ascend.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/sft" - "verl/trainer/fsdp_sft_trainer.py" - "verl/trainer/config/sft_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: e2e_sft_llm_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 90 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install -e . pip install git+https://github.com/ByteDance-Seed/VeOmni.git@v0.1.4 pip install pandas==2.3.3 pip uninstall -y mbridge pip install git+https://github.com/ISEEKYAN/mbridge.git@89eb10 - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare gsm8k dataset run: | python3 examples/data_preprocess/gsm8k_multiturn_sft.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running GSM8K E2E training tests on 8 NPUs with rmpad using function rm run: | ray stop --force bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests on 8 NPUs w/o rmpad using function rm run: | ray stop --force RM_PAD=False bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests on 8 NPUs with sequence parallism run: | ray stop --force SP_SIZE=2 bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests with LoRA run: | ray stop --force LORA_RANK=32 bash tests/special_e2e/sft/run_sft.sh - name: Run GSM8K E2E training and resume tests resuming from the checkpoint manager run: | ray stop --force LORA_RANK=32 RESUME_MODE=auto TOTAL_TRAIN_STEP=2 bash tests/special_e2e/sft/run_sft.sh - name: Running GSM8K E2E training tests with multiturn and various configs and compare results run: | ray stop --force rm -rf ~/verl/test/log mkdir -p ~/verl/test/log export VERL_FILE_LOGGER_ROOT=~/verl/test/log # test with single gpu as golden echo "run with single gpu as golden" BACKEND=fsdp SP_SIZE=1 FSDP_SIZE=1 NUM_GPUS=1 FSDP_STRATEGY=fsdp VERL_FILE_LOGGER_PATH=~/verl/test/log/golden.jsonl bash tests/special_e2e/sft/run_sft_engine.sh # test with fsdp 1 echo "run with sp2 fsdp_size2 num_gpus8 fsdp_strategy fsdp pad_mode no_padding" BACKEND=fsdp SP_SIZE=2 FSDP_SIZE=2 NUM_GPUS=8 FSDP_STRATEGY=fsdp PAD_MODE=no_padding bash tests/special_e2e/sft/run_sft_engine.sh # test with fsdp 1 use_remove_padding and pad_mode no_padding echo "run with sp4 fsdp_size4 num_gpus8 fsdp_strategy fsdp pad_mode no_padding use_remove_padding False" BACKEND=fsdp SP_SIZE=1 FSDP_SIZE=-1 NUM_GPUS=8 FSDP_STRATEGY=fsdp PAD_MODE=no_padding USE_REMOVE_PADDING=False bash tests/special_e2e/sft/run_sft_engine.sh # test with fsdp 2 echo "run with sp2 fsdp_size2 num_gpus8 fsdp_strategy fsdp2" BACKEND=fsdp SP_SIZE=2 FSDP_SIZE=2 NUM_GPUS=8 FSDP_STRATEGY=fsdp2 bash tests/special_e2e/sft/run_sft_engine.sh # test with veomni echo "run with sp2 fsdp_size4 num_gpus8 fsdp_strategy fsdp2" BACKEND=veomni SP_SIZE=2 FSDP_SIZE=4 NUM_GPUS=8 FSDP_STRATEGY=fsdp2 bash tests/special_e2e/sft/run_sft_engine.sh # test with megatron echo "run with tp2 pp2 vpp2 cp2 num_gpus8" BACKEND=megatron TP_SIZE=2 PP_SIZE=2 VPP_SIZE=NULL CP_SIZE=2 NUM_GPUS=8 bash tests/special_e2e/sft/run_sft_engine.sh # test with cp in ray echo "run with tp2 pp2 vpp2 cp2 num_gpus8 mode=ray" BACKEND=megatron TP_SIZE=2 PP_SIZE=2 VPP_SIZE=NULL CP_SIZE=2 NUM_GPUS=8 mode=ray bash tests/special_e2e/sft/run_sft_engine.sh rm -rf ~/verl/test/log ================================================ FILE: .github/workflows/e2e_sft_vlm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: e2e_sft_vlm on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Megatron - "!verl/workers/**/megatron_*.py" # Entrypoints - ".github/workflows/e2e_sft_vlm.yml" - "examples/data_preprocess/gsm8k.py" - "tests/special_e2e/sft" - "verl/trainer/fsdp_sft_trainer.py" - "verl/trainer/config/sft_trainer.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" e2e_sft_vlm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install peft pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install git+https://github.com/ByteDance-Seed/VeOmni.git@v0.1.4 - name: Prepare pokemon-gpt4o-captions dataset run: | ray stop --force python3 examples/data_preprocess/pokemon.py --local_dataset_path ${HOME}/models/hf_data/pokemon-gpt4o-captions - name: Running Pokemon E2E training tests with multiturn and various configs and compare results run: | MODEL_ID=Qwen/Qwen3-VL-2B-Instruct DATASET_DIR=~/data/pokemon-gpt4o-captions VPP_SIZE=null bash tests/special_e2e/sft/test_sft_engine_all.sh cleanup: runs-on: ubuntu-latest needs: [setup, e2e_sft_vlm] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/gpu_unit_tests.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: GPU unit tests on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.4.x paths: - "**/*.py" - .github/workflows/gpu_unit_tests.yml pull_request: branches: - main - v0.4.x paths: # The order that you define paths patterns matters: # A matching negative pattern (prefixed with !) after a positive match will exclude the path. # A matching positive pattern after a negative match will include the path again. - "**/*.py" # Other entrypoints - "!examples/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # Entrypoints - .github/workflows/gpu_unit_tests.yml - "tests/**test_*.py" # Ignore CPU tests - "!tests/*_on_cpu.py" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" gpu_unit_tests: if: github.repository_owner == 'verl-project' needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 60 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1" HF_HUB_ENABLE_HF_TRANSFER: 1 steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install hf_transfer pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install cupy-cuda12x==13.6.0 pytest-asyncio pip3 install --ignore-installed blinker pip3 install --ignore-installed mlflow "numpy<2.0" - name: Run all GPU unit tests run: | pytest -s -x --ignore-glob="*on_npu.py" --ignore-glob="*test_special_*.py" --ignore-glob='*on_cpu.py' --ignore-glob="*test_vllm*" --ignore-glob="*_sglang*" --ignore-glob="*_hf_rollout*" --ignore-glob="tests/models/" --ignore-glob='tests/special*' --ignore-glob="tests/experimental" --ignore-glob="tests/workers/reward_model" --ignore-glob="*test_shared_memory*" --ignore-glob="tests/workers/rollout/rollout_trtllm" --ignore-glob="*test_bucketed_weight_transfer*" tests/ - name: Testing LinearCrossEntropyTP Correctness, Computation Time and Memory Consumption run: | LOW_MEMORY=True torchrun --standalone --nnodes=1 --nproc-per-node=8 tests/utils/test_special_linear_cross_entropy_tp.py - name: Testing FSDP2 actor functionality run: | torchrun --standalone --nnodes=1 --nproc-per-node=2 tests/workers/actor/test_special_dp_actor.py - name: Testing FSDP2 critic functionality run: | torchrun --standalone --nnodes=1 --nproc-per-node=2 tests/workers/critic/test_special_dp_critic.py cleanup: runs-on: ubuntu-latest needs: [setup, gpu_unit_tests] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/model.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. # name: Check PR Title name: model on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "verl/**/*.py" # Entrypoints - ".github/workflows/model.yml" - "tests/special_distributed/test_fsdp_ckpt.py" - "tests/special_distributed/test_tensor_dict.py" - "tests/models/**" - "tests/special_distributed/run_all.sh" # Declare permissions just read content. permissions: contents: read # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" model_rmpad: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 20 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository and upgrade to latest transformers(4.54.0)/flash_attn, transformers 4.55.0 has strange behavior with model backward run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install --upgrade "transformers<5.0.0" - name: Running rmpad model tests on 8 L20 GPUs + flash_attn 2.5.8 run: | pytest -s tests/models/test_transformer.py - name: Running rmpad model tests on 8 L20 GPUs + latest flash_attn run: | pytest -s tests/models/test_transformer.py - name: Running FSDP rmpad model tests on 8 L20 GPUs + latest flash_attn run: | STRATEGY=fsdp torchrun --nproc_per_node=8 tests/special_distributed/test_fsdp_ckpt.py - name: Running transformers ulysses tests on 8 L20 GPUs + latest transformers run: | torchrun --nproc_per_node=8 -m pytest tests/models/test_transformers_ulysses.py - name: Running transformers ulysses tests on 8 L20 GPUs + transformers 4.54.1 run: | pip3 install transformers==4.54.1 torchrun --nproc_per_node=8 -m pytest tests/models/test_transformers_ulysses.py - name: Run distributed test run: | bash tests/special_distributed/run_all.sh # TODO: Move this back to model_rmpad once FSDP2 is stable. # NOTE: List as an independent job to make rerun easier. model_rmpad_fsdp2_unstable: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 20 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository and upgrade to latest transformers/flash_attn run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Running FSDP2 rmpad model tests on 8 L20 GPUs + latest flash_attn run: | STRATEGY=fsdp2 torchrun --nproc_per_node=8 tests/special_distributed/test_fsdp_ckpt.py model_engine: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 20 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Download model config files run: | hf download Qwen/Qwen2.5-0.5B-Instruct --local-dir $HOME/models/Qwen/Qwen2.5-0.5B-Instruct - name: Running mcore engine tests on 8 L20 GPUs run: | ray stop --force pytest -s -x tests/models/test_engine.py cleanup: runs-on: ubuntu-latest needs: [setup, model_rmpad, model_rmpad_fsdp2_unstable, model_engine] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/model_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. # name: Check PR Title name: model_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "verl/**/*.py" # Entrypoints - ".github/workflows/model_ascend.yml" - "tests/special_distributed/test_fsdp_ckpt.py" - "tests/special_distributed/test_tensor_dict.py" - "tests/models/**" - "tests/special_distributed/run_all.sh" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} permissions: contents: read jobs: model_rmpad_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e .[test] - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Running rmpad model tests on 8 NPUs run: | pytest -s tests/models/test_transformer.py - name: Running FSDP rmpad model tests on 8 NPUs run: | STRATEGY=fsdp torchrun --nproc_per_node=8 tests/special_distributed/test_fsdp_ckpt.py - name: Running transformers ulysses tests on 8 NPUs run: | torchrun --nproc_per_node=8 -m pytest tests/models/test_transformers_ulysses.py - name: Run distributed test run: | bash tests/special_distributed/run_all.sh # TODO: Move this back to model_rmpad once FSDP2 is stable. # NOTE: List as an independent job to make rerun easier. model_rmpad_fsdp2_unstable_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e .[test] - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Running FSDP2 rmpad model tests on 8 NPUs run: | STRATEGY=fsdp2 torchrun --nproc_per_node=8 tests/special_distributed/test_fsdp_ckpt.py ================================================ FILE: .github/workflows/nightly_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: nightly_ci_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch # For push, for now only anti-patterns are specified so it is more conservative # and achieves higher coverage. schedule: - cron: "0 17 * * *" # Declare permissions just read content. permissions: contents: read jobs: # Test ppo qwen3-8b fsdp+vllm nightlyCI_ppo-qwen3-8b-fsdp-vllm_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 180 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running nightlyCI_ppo-qwen3-8b-fsdp-vllm_ascend run: | ray stop --force bash tests/special_npu/nightly_ci_ascend/run_ppo_qwen3-8b_fsdp_npu.sh # Test grpo qwen25-7b-Instruct fsdp+vllm nightlyCI_grpo-qwen25-7b-Instruct-fsdp-vllm_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 180 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare GSM8K dataset run: | python examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k - name: Running nightlyCI_grpo-qwen25-7b-Instruct-fsdp-vllm_ascend run: | ray stop --force bash tests/special_npu/nightly_ci_ascend/run_grpo_qwen25-7b-instruct_fsdp_npu.sh # Test grpo qwen25-vl-3b-Instruct fsdp+vllm nightlyCI_grpo-qwen25-vl-3b-Instruct-fsdp-vllm_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 180 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e . - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Preprocess geo3k dataset run: | python examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/.cache/datasets/hiyouga/geometry3k - name: Running nightlyCI_grpo-qwen25-vl-3b-Instruct-fsdp-vllm_ascend run: | ray stop --force bash tests/special_npu/nightly_ci_ascend/run_grpo_qwen25-vl-3b-instruct_fsdp_npu.sh ================================================ FILE: .github/workflows/npu_unit_tests.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - `npu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix on ascend device. # - Since cpu/gpu/npu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: NPU unit tests on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* paths: - "**/*.py" - .github/workflows/npu_unit_tests.yml pull_request: branches: - main paths: # The order that you define paths patterns matters: # A matching negative pattern (prefixed with !) after a positive match will exclude the path. # A matching positive pattern after a negative match will include the path again. - "**/*.py" # Other entrypoints - "!examples/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" - "!recipe/**" # Entrypoints - .github/workflows/npu_unit_tests.yml - "tests/**test_*.py" # Ignore CPU tests - "!tests/*_on_cpu.py" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: npu_unit_tests: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout volcengine/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e .[test] pip install mlflow pytest-asyncio - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Run all NPU unit tests run: | pytest -s -x --ignore-glob="*test_special_*.py" --ignore-glob="*on_cpu.py" --ignore-glob="*test_vllm*" --ignore-glob="*_sglang*" --ignore-glob="*_hf_rollout*" --ignore-glob="tests/models/" --ignore-glob="tests/special*" --ignore-glob="tests/experimental" --ignore-glob="tests/workers/reward_model" --ignore-glob="*test_rvdz*" --ignore-glob="*test_ray_collectives*" --ignore-glob="*test_nvtx_profile*" --ignore-glob="tests/checkpoint_engine" --ignore-glob="*test_shared_memory*" --ignore-glob="tests/workers/rollout/rollout_trtllm" --ignore-glob="*test_fsdp_lora_merge*" --ignore-glob="*test_activation_offload*" --ignore-glob="*test_normalize_peft_param_name.py*" tests/ - name: Testing activation offload run: | pytest -s -x tests/utils/test_activation_offload.py - name: Testing normalize peft param name run: | pytest -s -x tests/utils/test_normalize_peft_param_name.py - name: Testing FSDP2 actor functionality run: | torchrun --standalone --nnodes=1 --nproc-per-node=2 tests/workers/actor/test_special_dp_actor.py - name: Testing FSDP2 critic functionality run: | torchrun --standalone --nnodes=1 --nproc-per-node=2 tests/workers/critic/test_special_dp_critic.py - name: Running NPU profiling unit tests run: | pytest -s -x tests/utils/test_special_mstx_profile.py ================================================ FILE: .github/workflows/pre-commit.yml ================================================ # c.f. https://github.com/pre-commit/action?tab=readme-ov-file#using-this-action name: pre-commit # No need to avoid / cancel lightweight pre-commit jobs on: schedule: - cron: "0 0 * * 0" pull_request: push: branches: - main - v0.* # Allow manual triggering workflow_dispatch: # Declare permissions just read content. permissions: contents: read jobs: pre-commit: runs-on: ubuntu-latest strategy: matrix: python-version: ["3.12"] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 with: python-version: ${{ matrix.python-version }} - name: Install the current repository run: | pip install pre-commit hydra-core pip install --no-deps -e . - name: Set ruff --output-format=github run: | sed -i 's/--output-format=full/--output-format=github/' .pre-commit-config.yaml git add .pre-commit-config.yaml # Check "--all-files" by default - uses: pre-commit/action@v3.0.1 ================================================ FILE: .github/workflows/precommit-autofix.yml ================================================ name: scheduled pre-commit autofix on: schedule: # Every hour - cron: "0 * * * *" workflow_dispatch: permissions: contents: write pull-requests: write jobs: precommit: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v4 with: fetch-depth: 0 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install pre-commit run: | python -m pip install --upgrade pip pip install pre-commit hydra-core - name: Run pre-commit run: | pre-commit run --all-files || true - name: Create or update PR uses: peter-evans/create-pull-request@v6 with: branch: bot/precommit-autofix delete-branch: true title: "[ci] chore: scheduled pre-commit autofix" commit-message: "chore: auto-fix pre-commit issues" body: | This PR was created automatically by a scheduled GitHub Action. - Runs `pre-commit run --all-files` - Triggered hourly labels: | automated pre-commit ================================================ FILE: .github/workflows/reward_model_sglang.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. # name: Check PR Title name: reward_model_sglang on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "verl/**/*.py" # Entrypoints - ".github/workflows/reward_model_sglang.yml" - "tests/experimental/reward_loop/**" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" reward_model_sglang: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK: "True" NCCL_SHM_DISABLE: "1" NCCL_P2P_DISABLE: "1" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install sglang-router==0.2.2 - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k --local_dir ${HOME}/data/gsm8k - name: Running sglang generative reward model tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=sglang pytest -s -x tests/experimental/reward_loop/test_reward_model_genrm.py - name: Running sglang discriminative reward model tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=sglang pytest -s -x tests/experimental/reward_loop/test_reward_model_disrm.py - name: Running sglang agent loop with reward manager tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=sglang pytest -s -x tests/experimental/reward_loop/test_agent_reward_loop_standalone.py - name: Running sglang agent loop with reward model colocate tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=sglang pytest -s -x tests/experimental/reward_loop/test_agent_reward_loop_colocate.py cleanup: runs-on: ubuntu-latest needs: [setup, reward_model_sglang] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/reward_model_vllm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. # name: Check PR Title name: reward_model_vllm on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "verl/**/*.py" # Entrypoints - ".github/workflows/reward_model_vllm.yml" - "tests/experimental/reward_loop/**" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" reward_model_vllm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 30 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK: "True" NCCL_SHM_DISABLE: "1" NCCL_P2P_DISABLE: "1" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k --local_dir ${HOME}/data/gsm8k - name: Running vllm generative reward model tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_reward_model_genrm.py - name: Running vllm discriminative reward model tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_reward_model_disrm.py - name: Running vllm agent loop with reward manager tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_agent_reward_loop_standalone.py - name: Running vllm agent loop with reward model colocate tests on 8 L20 GPUs run: | unset http_proxy https_proxy HTTP_PROXY HTTPS_PROXY ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_agent_reward_loop_colocate.py cleanup: runs-on: ubuntu-latest needs: [setup, reward_model_vllm] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/reward_model_vllm_ascend.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. # name: Check PR Title name: reward_model_vllm_ascend on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "verl/**/*.py" # Entrypoints - ".github/workflows/reward_model_vllm_ascend.yml" - "tests/experimental/reward_loop/**" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: reward_model_vllm_ascend: if: github.repository_owner == 'verl-project' runs-on: linux-aarch64-a2b3-8 timeout-minutes: 60 # Increase this timeout value as needed container: image: swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: | pip list - name: Checkout verl-project/verl repo uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install the current repository run: | pip install -r requirements-npu.txt pip install --no-deps -e .[test] - name: Check final pip list run: | pip list - name: Prepare weights run: | ln -s /root/.cache/models ~/models - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k --local_dir ${HOME}/data/gsm8k - name: Running vllm generative reward model tests on 8 NPUs run: | ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_reward_model_genrm.py - name: Running vllm discriminative reward model tests on 8 NPUs run: | ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_reward_model_disrm.py - name: Running vllm agent loop with reward manager tests on 8 NPUs run: | ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_agent_reward_loop_standalone.py - name: Running vllm agent loop with reward model colocate tests on 8 NPUs run: | export HCCL_HOST_SOCKET_PORT_RANGE=auto export HCCL_NPU_SOCKET_PORT_RANGE=auto ROLLOUT_NAME=vllm pytest -s -x tests/experimental/reward_loop/test_agent_reward_loop_colocate.py ================================================ FILE: .github/workflows/sanity.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. # name: Check PR Title name: sanity on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" - .github/workflows/sanity.yml - "tests/special_sanity/**" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read jobs: sanity: runs-on: ubuntu-latest timeout-minutes: 5 # Increase this timeout value as needed strategy: matrix: python-version: ["3.10"] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 with: python-version: ${{ matrix.python-version }} - name: Install the current repository run: | pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu pip3 install -r requirements.txt pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Run sanity test run: | pytest -s -x tests/special_sanity - name: Run license test run: | python3 tests/special_sanity/check_license.py --directories . - name: Assert naming convention run: | if grep -rIn --exclude-dir=.git --exclude-dir=.github --exclude-dir=venv --exclude-dir=__pycache__ 'veRL' .; then echo "Please use verl instead of veRL in the codebase" exit 1 fi - name: Assert SGLang naming convention run: | if grep -rIn --exclude-dir=.git --exclude-dir=.github --exclude-dir=venv --exclude-dir=__pycache__ --exclude=ascend_sglang_best_practices.rst -E 'Sglang|sgLang|sglAng|sglaNg|sglanG' .; then echo "Please use SGLang or sglang as the formal name of SGLang rollout engine" exit 1 fi - name: Validate test folder structure run: python3 tests/special_sanity/validate_structure.py - name: Assert documentation requirement for functions run: python3 tests/special_sanity/validate_imported_docs.py - name: Assert device api usage in verl/verl run: python3 tests/special_sanity/check_device_api_usage.py --directory ./verl - name: Assert documentation time info run: python3 tests/special_sanity/check_docs_time_info.py - name: Check docstrings for specified files run: python3 tests/special_sanity/check_docstrings.py - name: Check DataProto for specified folders run: python3 tests/special_sanity/check_dataproto_usage.py -d ./verl/workers/engine ================================================ FILE: .github/workflows/scorecard.yml ================================================ # This workflow uses actions that are not certified by GitHub. They are provided # by a third-party and are governed by separate terms of service, privacy # policy, and support documentation. name: Scorecard supply-chain security on: # For Branch-Protection check. Only the default branch is supported. See # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection branch_protection_rule: # To guarantee Maintained check is occasionally updated. See # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained schedule: - cron: "27 7 * * 1" push: branches: - main - v0.* # Declare default permissions as read only. permissions: read-all jobs: analysis: name: Scorecard analysis runs-on: ubuntu-latest permissions: # Needed to upload the results to code-scanning dashboard. security-events: write # Needed to publish results and get a badge (see publish_results below). id-token: write # Uncomment the permissions below if installing in a private repository. # contents: read # actions: read steps: - name: "Checkout code" uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1 with: persist-credentials: false - name: "Run analysis" uses: ossf/scorecard-action@0864cf19026789058feabb7e87baa5f140aac736 # v2.3.1 with: results_file: results.sarif results_format: sarif # (Optional) "write" PAT token. Uncomment the `repo_token` line below if: # - you want to enable the Branch-Protection check on a *public* repository, or # - you are installing Scorecard on a *private* repository # To create the PAT, follow the steps in https://github.com/ossf/scorecard-action?tab=readme-ov-file#authentication-with-fine-grained-pat-optional. # repo_token: ${{ secrets.SCORECARD_TOKEN }} # Public repositories: # - Publish results to OpenSSF REST API for easy access by consumers # - Allows the repository to include the Scorecard badge. # - See https://github.com/ossf/scorecard-action#publishing-results. # For private repositories: # - `publish_results` will always be set to `false`, regardless # of the value entered here. publish_results: true # Upload the results to GitHub's code scanning dashboard (optional). # Commenting out will disable upload of results to your repo's Code Scanning dashboard - name: "Upload to code-scanning" uses: github/codeql-action/upload-sarif@9e8d0789d4a0fa9ceb6b1738f7e269594bdd67f0 #v3.28.9 with: sarif_file: results.sarif ================================================ FILE: .github/workflows/secrets_scan.yml ================================================ on: push: branches: - main - v0.* pull_request: permissions: contents: read jobs: test: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1 with: fetch-depth: 0 - name: Secret Scanning uses: trufflesecurity/trufflehog@7dc056a193116ba8d82154bf0549381c8fb8545c # v3.88.14 with: extra_args: --results=verified,unknown ================================================ FILE: .github/workflows/sgl.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: sgl on: # workflow_dispatch: # Manual # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* paths: - "**/*.py" - .github/workflows/sgl.yml pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" # Megatron - "!verl/workers/**/megatron_*.py" # vLLM - "!**/*vllm*" # Entrypoints - ".github/workflows/sgl.yml" - "tests/rollout/*sglang*" - "tests/rollout/async_rollout_utils.py" - "tests/workers/rollout/*interaction*" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:sgl059.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" sgl: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 35 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: 1 SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK: "True" NCCL_SHM_DISABLE: "1" NCCL_P2P_DISABLE: "1" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install cupy-cuda12x==13.6.0 pytest-asyncio pip3 install hf_transfer fastmcp pytest-asyncio pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Test the latest SGLang Rollout async with agent loop run: | ROLLOUT_NAME=sglang pytest -svvv tests/experimental/agent_loop sgl_checkpoint_engine: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 35 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: 1 SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK: "True" NCCL_SHM_DISABLE: "1" NCCL_P2P_DISABLE: "1" steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install cupy-cuda12x==13.6.0 pytest-asyncio pip3 install hf_transfer fastmcp pytest-asyncio pip3 install -r requirements-test.txt pip3 install --no-deps -e . - name: Test SGLang ServerAdapter with Checkpoint Engine (NCCL) run: | ROLLOUT_NAME=sglang pytest -svvv tests/checkpoint_engine/test_special_server_adapter.py cleanup: runs-on: ubuntu-latest needs: [setup, sgl, sgl_checkpoint_engine] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .github/workflows/type-coverage-check.yml ================================================ name: Type Annotation and Docstring Coverage on: pull_request: paths: - '**/*.py' - '.github/workflows/type-coverage-check.yml' jobs: type-coverage-check: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 # 🚨 Important: fetch full history so `origin/main` is available - name: Set up Python uses: actions/setup-python@v5 with: python-version: '3.10' - name: Install dependencies run: | pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu pip3 install -r requirements.txt pip3 install --no-deps -e . - name: Run type annotation coverage check run: | python3 tests/special_sanity/type_coverage_check.py - name: Run docstring coverage check run: | python3 tests/special_sanity/check_api_docs.py verl ================================================ FILE: .github/workflows/vllm.yml ================================================ # # Tests layout # Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: # - `tests/trainer` for testing functionality related to `verl/trainer` # - `tests/models` for testing functionality related to `verl/models` # - ... # There are a few folders with `special_` prefix, created for special purposes: # - `special_distributed`: unit tests that must run with multiple GPUs # - `special_e2e`: end-to-end tests with training/generation scripts # - `special_npu`: tests for NPUs # - `special_sanity`: a suite of quick sanity tests # - `special_standalone`: a set of test that are designed to run in dedicated environments # Accelerators for tests # - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. # - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # # Workflow layout # All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: # 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` # 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` # 3. End-to-end tests: `e2e_*.yml` # 4. Unit tests # - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` # - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. # - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when # - new workflow yaml is added to `.github/workflows` # - new tests are added to workflow mentioned in 2. name: vllm on: # Trigger the workflow on push or pull request, # but only for the main branch push: branches: - main - v0.* pull_request: branches: - main - v0.* paths: - "**/*.py" # Other entrypoints - "!examples/**" - "!tests/**" - "!verl/trainer/main_*.py" - "!verl/trainer/fsdp_sft_trainer.py" # FSDP - "!verl/workers/**/*dp_*.py" # Megatron - "!verl/workers/**/megatron_*.py" # SGLang - "!**/*sglang*" # Entrypoints - ".github/workflows/vllm.yml" - "tests/special_e2e/generation" - "tests/workers/rollout" - "verl/trainer/main_generation.py" - "verl/trainer/config/generation.yaml" # Cancel jobs on the same ref if a new one is triggered concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # Declare permissions just read content. permissions: contents: read env: IMAGE: "verl-ci-cn-beijing.cr.volces.com/verlai/verl:vllm017.dev2" DYNAMIC_RUNNER_ENDPOINT: "https://sd10g3clalm04ug7alq90.apigateway-cn-beijing.volceapi.com/runner" jobs: setup: if: github.repository_owner == 'verl-project' runs-on: ubuntu-latest outputs: runner-label: ${{ steps.create-runner.outputs.runner-label }} mlp-task-id: ${{ steps.create-runner.outputs.mlp-task-id }} steps: - uses: actions/checkout@v4 - id: create-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "create" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-image: "${{ env.IMAGE }}" vllm: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 35 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install --upgrade "transformers<5.0" # - name: Download Model to Use # run: | # hf download Qwen/Qwen2.5-0.5B-Instruct --local-dir ${HOME}/models/Qwen/Qwen2.5-0.5B-Instruct # hf download Qwen/Qwen2.5-1.5B-Instruct --local-dir ${HOME}/models/Qwen/Qwen2.5-1.5B-Instruct # hf download Qwen/Qwen2.5-VL-3B-Instruct --local-dir ${HOME}/models/Qwen/Qwen2.5-VL-3B-Instruct # hf download OldKingMeister/Qwen2.5-1.5B-Instruct-YaRN --local-dir ${HOME}/models/OldKingMeister/Qwen2.5-1.5B-Instruct-YaRN # export HF_HUB_OFFLINE=1 - name: Prepare gsm8k dataset run: | ray stop --force python3 examples/data_preprocess/gsm8k.py --local_dataset_path ${HOME}/models/hf_data/gsm8k - name: Test the latest vLLM Rollout async with agent loop run: | ROLLOUT_NAME=vllm pytest -svvv tests/experimental/agent_loop - name: Test vllm server abort functionality run: | pytest tests/workers/rollout/rollout_vllm/test_vllm_abort.py -v -s vllm_checkpoint_engine: needs: setup runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"] timeout-minutes: 35 # Increase this timeout value as needed env: HTTP_PROXY: ${{ secrets.PROXY_HTTP }} HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }} NO_PROXY: "localhost,127.0.0.1,hf-mirror.com" HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: Install the current repository run: | pip3 install pytest-asyncio pip3 install -r requirements-test.txt pip3 install --no-deps -e . pip3 install --upgrade "transformers<5.0" pip3 install cupy-cuda12x==13.6.0 - name: Test vLLM ServerAdapter with Checkpoint Engine (NCCL) run: | ROLLOUT_NAME=vllm pytest -svvv tests/checkpoint_engine/test_special_server_adapter.py - name: Test bucketed weight transfer run: | pytest -svvv tests/utils/test_bucketed_weight_transfer.py cleanup: runs-on: ubuntu-latest needs: [setup, vllm, vllm_checkpoint_engine] if: always() steps: - id: destroy-runner uses: volcengine/vemlp-github-runner@v1 with: mode: "destroy" faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}" mlp-task-id: "${{ needs.setup.outputs.mlp-task-id }}" ================================================ FILE: .gitignore ================================================ **/*.pt **/checkpoints **/wget-log **/_build/ **/*.ckpt **/outputs **/*.tar.gz **/playground **/wandb /pyrightconfig.json # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class dataset/* tensorflow/my_graph/* .idea/ # C extensions *.so # Distribution / packaging .Python # env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ tmp/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ pytest.ini output.txt # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # IPython Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv venv/ .venv/ ENV/ # Spyder project settings .spyderproject # Rope project settings .ropeproject # vscode .vscode # Mac .DS_Store # vim *.swp # emacs *~ # ckpt *.lock # data *.parquet # local logs logs log outputs .history ================================================ FILE: .gitmodules ================================================ [submodule "recipe"] path = recipe url = https://github.com/verl-project/verl-recipe.git ================================================ FILE: .pre-commit-config.yaml ================================================ repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: "v0.12.2" hooks: - id: ruff args: ["--fix", "--show-fixes", "--output-format=full"] exclude: ^.*\.(ipynb)$ - id: ruff-format - repo: https://github.com/pre-commit/mirrors-mypy rev: "v1.17.0" hooks: - id: mypy - repo: local hooks: - id: autogen-trainer-cfg name: Generate and verify verl/trainer/config/_generated_*.yaml entry: scripts/generate_trainer_config.sh language: script pass_filenames: false - repo: local hooks: - id: check-docstrings name: Check doc string coverage entry: python3 tests/special_sanity/check_docstrings.py language: python pass_filenames: false - repo: local hooks: - id: check-license name: Check license entry: python3 tests/special_sanity/check_license.py --directories examples scripts tests verl setup.py language: python pass_filenames: false - repo: local hooks: - id: compileall name: Compile all python files entry: sh -c 'PYTHONWARNINGS=error python3 -m compileall -q . -x "(^|[\\/])(\.venv|venv|\.git)([\\/]|$)"' language: python pass_filenames: false ================================================ FILE: .readthedocs.yaml ================================================ # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details version: 2 build: os: ubuntu-22.04 tools: python: "3.11" rust: "1.70" sphinx: configuration: docs/conf.py python: install: - requirements: docs/requirements-docs.txt - method: pip path: . ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to verl Thank you for considering a contribution to verl! We welcome contributions of any kind - bug fixes, enhancements, documentation improvements, or even just feedback. Whether you're an experienced developer or this is your first open-source project, your help is invaluable. Your support can take many forms: - Report issues or unexpected behaviors. - Suggest or implement new features. - Improve or expand documentation. - Review pull requests and assist other contributors. - Spread the word: share verl in blog posts, social media, or give the repo a ⭐. ## Finding Issues to Contribute Looking for ways to dive in? Check out these issues: - [Good first issues](https://github.com/volcengine/verl/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22) - [Call for contribution](https://github.com/volcengine/verl/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22call%20for%20contribution%22) Furthermore, you can learn the development plan and roadmap via [RFC](https://github.com/volcengine/verl/issues?q=is%3Aissue%20state%3Aopen%20label%3ARFC) and [Roadmap](https://github.com/volcengine/verl/issues?q=state%3Aopen%20label%3A%22roadmap%22). ## Developing - **Python-only**: install verl via `pip install -e .[test,vllm]` or `pip install -e .[test,sglang]` and iterate quickly. For full dependency setup, check out the verl [installation doc](https://verl.readthedocs.io/en/latest/start/install.html). ## Code Linting and Formatting We rely on pre-commit to keep our code consistent. To set it up: ```bash pip install pre-commit pre-commit install # for staged changes pre-commit run # for all files in the repo pre-commit run --all-files # run a specific hook with pre-commit # pre-commit run --all-files --show-diff-on-failure --color=always pre-commit run --all-files --show-diff-on-failure --color=always ruff pre-commit run --all-files --show-diff-on-failure --color=always autogen-trainer-cfg ``` ## Testing Our test suites run on GitHub Actions. Check these workflows for details: - [GPU unit tests](https://github.com/volcengine/verl/blob/main/.github/workflows/gpu_unit_tests.yml) - [CPU unit tests](https://github.com/volcengine/verl/blob/main/.github/workflows/cpu_unit_tests.yml) - [vLLM tests](https://github.com/volcengine/verl/blob/main/.github/workflows/vllm.yml) - [SGLang tests](https://github.com/volcengine/verl/blob/main/.github/workflows/sgl.yml) ### Adding CI tests If possible, please add CI test(s) for your new feature: 1. Find the most relevant workflow yml file, which usually corresponds to a `hydra` default config (e.g. `ppo_trainer`, `ppo_megatron_trainer`, `sft_trainer`, etc). 2. Add related path patterns to the `paths` section if not already included. 3. Minimize the workload of the test script(s) (see existing scripts for examples). ## Building the Docs ``` # Ensure verl is on your PYTHONPATH, e.g.: pip install -e .[test] # Install documentation dependencies cd docs pip install -r requirements-docs.txt # Generate HTML docs make clean make html # Preview locally python -m http.server -d _build/html/ ``` Open your browser at http://localhost:8000 to explore the docs. ## Pull Requests & Code Reviews Thanks for submitting a PR! To streamline reviews: - Follow our Pull Request Template for title format and checklist. - Adhere to our pre-commit lint rules and ensure all checks pass. - Update docs for any user-facing changes. - Add or update tests in the CI workflows, or explain why tests aren't applicable. ## License See the [LICENSE](https://github.com/volcengine/verl/blob/main/LICENSE) file for full details. ## Thank You We appreciate your contributions to verl. Your efforts help make the project stronger and more user-friendly. Happy coding! ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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👋 Hi, everyone! verl is a RL training library initiated by ByteDance Seed team and maintained by the verl community.

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verl: Volcano Engine Reinforcement Learning for LLMs

verl is a flexible, efficient and production-ready RL training library for large language models (LLMs). verl is the open-source version of **[HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2)** paper. verl is flexible and easy to use with: - **Easy extension of diverse RL algorithms**: The hybrid-controller programming model enables flexible representation and efficient execution of complex post-training dataflows. Build RL dataflows such as GRPO, PPO in a few lines of code. - **Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as FSDP, Megatron-LM, vLLM, SGLang, etc - **Flexible device mapping**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes. - Ready integration with popular HuggingFace models verl is fast with: - **State-of-the-art throughput**: SOTA LLM training and inference engine integrations and SOTA RL throughput. - **Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases.
verl-arch.png

## News - [2026/01] verl has been migrated to the [verl-project](https://github.com/verl-project) - [2026/01] verl first meetup was successfully held in Shanghai on 01/10, hosted by Volcengine and NVIDIA, the slides has been uploaded to [verl-data](https://github.com/verl-project/verl-data). - [2026/01] The `recipe` directory has been migrated to a dedicated repository: [verl-recipe](https://github.com/verl-project/verl-recipe) and added as a submodule. See https://github.com/volcengine/verl/pull/4795. It can be used as it was after `git submodule update --init --recursive recipe`. Note that [`transfer_queue`](verl/experimental/transfer_queue), [`fully_async_policy`](verl/experimental/fully_async_policy), [`one_step_off_policy`](verl/experimental/one_step_off_policy) and [`vla`](verl/experimental/vla) are kept under [`verl/experimental`](verl/experimental) since they are planned to be merged into the main library. Use them through `verl.experimental.{module}`. - [2025/12] [Mind Lab](https://macaron.im/mindlab) successfully used [verl](https://github.com/volcengine/verl) and [Megatron-bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) to train GRPO Lora for Trillion-parameter model on 64 H800 - See their [techblog](https://macaron.im/mindlab/research/building-trillion-parameter-reasoning-rl-with-10-gpus). - [2025/10] verl is presented in the [PyTorch Conference 2025](https://pytorch.org/event/pytorch-conference-2025/). - [2025/08] verl is presented in the [PyTorch Expert Exchange Webinar](https://www.youtube.com/watch?v=Vd79NmmqY3Q&t=2s). [Slides](https://github.com/eric-haibin-lin/verl-community/blob/main/slides/verl_talk_pytorch_2025_08.pdf) available. - [2025/07] The [ReTool](https://arxiv.org/pdf/2504.11536) recipe is fully open sourced. [Blog](https://www.notion.so/verl-reTool-recipe-Using-multi-round-conversations-and-code-sandboxing-to-improve-the-math-of-large-23a8b5b7feba80b386b2e5b5e3c1cde0) - [2025/07] The first verl meetup will be held at ICML Vancouver on July 16th! Please [join us](https://lu.ma/0ek2nyao) if you are at ICML! (onsite only) - [2025/06] verl with Megatron backend enables large MoE models such as [DeepSeek-671B and Qwen3-235B](https://verl.readthedocs.io/en/latest/perf/dpsk.html). - [2025/03] [DAPO](https://dapo-sia.github.io/) is the open-sourced SOTA RL algorithm that achieves 50 points on AIME 2024 based on the Qwen2.5-32B pre-trained model, surpassing the previous SOTA achieved by DeepSeek's GRPO (DeepSeek-R1-Zero-Qwen-32B). DAPO's training is fully powered by verl and the reproduction code is available in `recipe/dapo` now.
more...
## Key Features - **FSDP**, **FSDP2** and **Megatron-LM** for training. - **vLLM**, **SGLang** and **HF Transformers** for rollout generation. - Compatible with Hugging Face Transformers and Modelscope Hub: [Qwen-3](https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_qwen3-8b.sh), Qwen-2.5, Llama3.1, Gemma2, DeepSeek-LLM, etc - Supervised fine-tuning. - Reinforcement learning with [PPO](examples/ppo_trainer/), [GRPO](examples/grpo_trainer/), [GSPO](https://github.com/verl-project/verl-recipe/tree/main/gspo/), [ReMax](examples/remax_trainer/), [REINFORCE++](https://verl.readthedocs.io/en/latest/examples/config.html#algorithm), [RLOO](examples/rloo_trainer/), [PRIME](https://github.com/verl-project/verl-recipe/tree/main/prime/), [DAPO](https://github.com/verl-project/verl-recipe/tree/main/dapo/), [DrGRPO](https://github.com/verl-project/verl-recipe/tree/main/drgrpo), [KL_Cov & Clip_Cov](https://github.com/verl-project/verl-recipe/tree/main/entropy) etc. - Support model-based reward and function-based reward (verifiable reward) for math, [coding](https://github.com/verl-project/verl-recipe/tree/main/dapo), etc - Support vision-language models (VLMs) and [multi-modal RL](examples/grpo_trainer/run_qwen2_5_vl-7b.sh) with Qwen2.5-vl, Kimi-VL - [Multi-turn with tool calling](https://github.com/volcengine/verl/tree/main/examples/sglang_multiturn) - LLM alignment recipes such as [Self-play preference optimization (SPPO)](https://github.com/verl-project/verl-recipe/tree/main/sppo) - Flash attention 2, [sequence packing](examples/ppo_trainer/run_qwen2-7b_seq_balance.sh), [sequence parallelism](examples/ppo_trainer/run_deepseek7b_llm_sp2.sh) support via DeepSpeed Ulysses, [LoRA](examples/sft/gsm8k/run_qwen_05_peft.sh), [Liger-kernel](examples/sft/gsm8k/run_qwen_05_sp2_liger.sh). - Scales up to 671B models and hundreds of GPUs with [expert parallelism](https://github.com/volcengine/verl/pull/1467) - Multi-gpu [LoRA RL](https://verl.readthedocs.io/en/latest/advance/ppo_lora.html) support to save memory. - Experiment tracking with wandb, swanlab, mlflow and tensorboard. - Hardware Support: Supports NVIDIA, AMD, [Ascend](https://github.com/volcengine/verl/blob/main/docs/ascend_tutorial/quick_start/ascend_quick_start.rst) ## Upcoming Features and Changes - Q3 Roadmap https://github.com/volcengine/verl/issues/2388 - DeepSeek 671b optimizations with Megatron https://github.com/volcengine/verl/issues/1033 - Multi-turn rollout and tools using optimizations https://github.com/volcengine/verl/issues/1882 - [Agent integration](https://github.com/volcengine/verl/tree/main/verl/experimental/agent_loop) - Async and off-policy architecture https://github.com/volcengine/verl/pull/2231 - List of breaking changes since v0.4 https://github.com/volcengine/verl/discussions/2270 ## Getting Started Documentation **Quickstart:** - [Installation](https://verl.readthedocs.io/en/latest/start/install.html) - [Quickstart](https://verl.readthedocs.io/en/latest/start/quickstart.html) - [Programming Guide](https://verl.readthedocs.io/en/latest/hybrid_flow.html) & [Tech Talk](https://hcqnc.xetlk.com/sl/3vACOK) (in Chinese) - [PPO in verl](https://verl.readthedocs.io/en/latest/algo/ppo.html) - [GRPO in verl](https://verl.readthedocs.io/en/latest/algo/grpo.html) **Running a PPO example step-by-step:** - [Prepare Data for Post-Training](https://verl.readthedocs.io/en/latest/preparation/prepare_data.html) - [Implement Reward Function for Dataset](https://verl.readthedocs.io/en/latest/preparation/reward_function.html) - [PPO Example Architecture](https://verl.readthedocs.io/en/latest/examples/ppo_code_architecture.html) - [Config Explanation](https://verl.readthedocs.io/en/latest/examples/config.html) **Reproducible algorithm baselines:** - [RL performance on coding, math](https://verl.readthedocs.io/en/latest/algo/baseline.html) **For code explanation and advance usage (extension):** - PPO Trainer and Workers - [PPO Ray Trainer](https://verl.readthedocs.io/en/latest/workers/ray_trainer.html) - [PyTorch FSDP Backend](https://verl.readthedocs.io/en/latest/workers/fsdp_workers.html) - [Megatron-LM Backend](https://verl.readthedocs.io/en/latest/index.html) - Advanced Usage and Extension - [Add Models with the FSDP Backend](https://verl.readthedocs.io/en/latest/advance/fsdp_extension.html) - [Add Models with the Megatron-LM Backend](https://verl.readthedocs.io/en/latest/advance/megatron_extension.html) - [Multi-turn Rollout Support](https://verl.readthedocs.io/en/latest/sglang_multiturn/multiturn.html) - [Search Tool Integration](https://verl.readthedocs.io/en/latest/sglang_multiturn/search_tool_example.html) - [Sandbox Fusion Integration](https://verl.readthedocs.io/en/latest/examples/sandbox_fusion_example.html) - [Deployment using Separate GPU Resources](https://github.com/volcengine/verl/tree/main/examples/split_placement) - [Extend to Other RL(HF) algorithms](https://verl.readthedocs.io/en/latest/advance/dpo_extension.html) - [Ray API design tutorial](https://verl.readthedocs.io/en/latest/advance/placement.html) **Blogs from the community** - [When Reasoning Models Break Tokenization: The Hidden Complexity of Multiturn Training](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/multi-turn/fast_tokenization/multiturn_tokenization_and_masking.md) - [verl deployment on AWS SageMaker](https://medium.com/@kaige.yang0110/run-verl-on-sagemaker-using-4x8-l40s-gpus-8e6d5c3c61d3) - [verl x SGLang Multi-turn Code Walkthrough](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/multi-turn/code-walk-through/readme_EN.md) - [Optimizing SGLang Memory Usage in verl](https://hebiao064.github.io/rl-memory-management) - [SGLang, verl, OpenBMB and Tsinghua University: Pioneering End-to-End Multi-Turn RLHF](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/multi-turn/verl-multiturn-rollout-Release.md) - [Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration](https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html) - [veMLP x verl ：玩转强化学习训练](https://mp.weixin.qq.com/s/7nbqxk4knMGd-hQE9ls2tA) - [使用 verl 进行 GRPO 分布式强化学习训练最佳实践](https://www.volcengine.com/docs/6459/1463942) - [HybridFlow verl 原文浅析](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/readme.md) - [最高提升 20 倍吞吐量！豆包大模型团队发布全新 RLHF 框架，现已开源！](https://team.doubao.com/en/blog/%E6%9C%80%E9%AB%98%E6%8F%90%E5%8D%8720%E5%80%8D%E5%90%9E%E5%90%90%E9%87%8F-%E8%B1%86%E5%8C%85%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%9B%A2%E9%98%9F%E5%8F%91%E5%B8%83%E5%85%A8%E6%96%B0-rlhf-%E6%A1%86%E6%9E%B6-%E7%8E%B0%E5%B7%B2%E5%BC%80%E6%BA%90) ## Performance Tuning Guide The performance is essential for on-policy RL algorithm. We have written a detailed [performance tuning guide](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html) to help you optimize performance. ## Upgrade to vLLM >= v0.8.2 verl now supports vLLM>=0.8.2 when using FSDP as the training backend. Please refer to [this document](https://github.com/volcengine/verl/blob/main/docs/README_vllm0.8.md) for the installation guide and more information. Please avoid vllm 0.7.x, which contains bugs that may lead to OOMs and unexpected errors. ## Use Latest SGLang SGLang is fully supported with verl, and SGLang RL Group is working extensively on building unique features, including multi-turn agentic RL, VLM RLHF, server-based RL, and partial rollout. Please refer to [this document](https://verl.readthedocs.io/en/latest/workers/sglang_worker.html) for the installation guide and more information. ## Upgrade to FSDP2 verl is fully embracing FSDP2! FSDP2 is recommended by torch distributed team, providing better throughput and memory usage, and is composible with other features (e.g. torch.compile). To enable FSDP2, simply use verl main and set the following options: ``` actor_rollout_ref.ref.strategy=fsdp2 actor_rollout_ref.actor.strategy=fsdp2 critic.strategy=fsdp2 ``` Furthermore, FSDP2 cpu offloading is compatible with gradient accumulation. You can turn it on to save memory with `actor_rollout_ref.actor.fsdp_config.offload_policy=True`. For more details, see https://github.com/volcengine/verl/pull/1026 ## AMD Support (ROCm Kernel) verl now supports FSDP as the training engine (Megatron support coming soon) and both integrates with vLLM and SGLang as inference engines. Please refer to [this document](https://github.com/volcengine/verl/blob/main/docs/amd_tutorial/amd_build_dockerfile_page.rst) for the installation guide and more information, and [this document](https://github.com/volcengine/verl/blob/main/docs/amd_tutorial/amd_vllm_page.rst) for the vLLM performance tuning for ROCm. ## Citation and acknowledgement If you find the project helpful, please cite: - [HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2) - [A Framework for Training Large Language Models for Code Generation via Proximal Policy Optimization](https://i.cs.hku.hk/~cwu/papers/gmsheng-NL2Code24.pdf) ```bibtex @article{sheng2024hybridflow, title = {HybridFlow: A Flexible and Efficient RLHF Framework}, author = {Guangming Sheng and Chi Zhang and Zilingfeng Ye and Xibin Wu and Wang Zhang and Ru Zhang and Yanghua Peng and Haibin Lin and Chuan Wu}, year = {2024}, journal = {arXiv preprint arXiv: 2409.19256} } ``` verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The project is adopted and contributed by Bytedance, Anyscale, LMSys.org, [Alibaba Qwen team](https://github.com/QwenLM/), Shanghai AI Lab, Tsinghua University, UC Berkeley, UCLA, UIUC, University of Hong Kong, ke.com, [All Hands AI](https://www.all-hands.dev/), [ModelBest](http://modelbest.cn/), JD AI Lab, Microsoft Research, [StepFun](https://www.stepfun.com/), Amazon, LinkedIn, Meituan, [Camel-AI](https://www.camel-ai.org/), [OpenManus](https://github.com/OpenManus), Xiaomi, NVIDIA research, [Baichuan](https://www.baichuan-ai.com/home), [RedNote](https://www.xiaohongshu.com/), [SwissAI](https://www.swiss-ai.org/), [Moonshot AI (Kimi)](https://www.moonshot-ai.com/), Baidu, Snowflake, Skywork.ai, JetBrains, [IceSword Lab](https://www.iceswordlab.com), and many more. ## Awesome Projects Built with `verl` Welcome to register your awesome project build with `verl` for other developers' reference! - [TinyZero](https://github.com/Jiayi-Pan/TinyZero): a reproduction of **DeepSeek R1 Zero** recipe for reasoning tasks ![GitHub Repo stars](https://img.shields.io/github/stars/Jiayi-Pan/TinyZero) - [SkyThought](https://github.com/NovaSky-AI/SkyThought): RL training for Sky-T1-7B by NovaSky AI team. ![GitHub Repo stars](https://img.shields.io/github/stars/NovaSky-AI/SkyThought) - [simpleRL-reason](https://github.com/hkust-nlp/simpleRL-reason): SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild ![GitHub Repo stars](https://img.shields.io/github/stars/hkust-nlp/simpleRL-reason) - [Easy-R1](https://github.com/hiyouga/EasyR1): **Multi-modal** RL training framework ![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/EasyR1) - [OpenManus-RL](https://github.com/OpenManus/OpenManus-RL): LLM Agents RL tuning framework for multiple agent environments. ![GitHub Repo stars](https://img.shields.io/github/stars/OpenManus/OpenManus-RL) - [rllm](https://github.com/agentica-project/rllm): async RL training with [verl-pipeline](https://github.com/agentica-project/verl-pipeline) ![GitHub Repo stars](https://img.shields.io/github/stars/agentica-project/rllm) - [RAGEN](https://github.com/ZihanWang314/ragen): a general-purpose reasoning **agent** training framework ![GitHub Repo stars](https://img.shields.io/github/stars/ZihanWang314/ragen) - [Search-R1](https://github.com/PeterGriffinJin/Search-R1): RL with reasoning and **searching (tool-call)** interleaved LLMs ![GitHub Repo stars](https://img.shields.io/github/stars/PeterGriffinJin/Search-R1) - [ReSearch](https://github.com/Agent-RL/ReSearch): Learning to **Re**ason with **Search** for LLMs via Reinforcement Learning ![GitHub Repo stars](https://img.shields.io/github/stars/Agent-RL/ReSearch) - [Skywork-OR1](https://github.com/SkyworkAI/Skywork-OR1): Skywork open reaonser series ![GitHub Repo stars](https://img.shields.io/github/stars/SkyworkAI/Skywork-OR1) - [ToRL](https://github.com/GAIR-NLP/ToRL): Scaling tool-integrated RL ![GitHub Repo stars](https://img.shields.io/github/stars/GAIR-NLP/ToRL) - [Absolute Zero Reasoner](https://github.com/LeapLabTHU/Absolute-Zero-Reasoner): [A no human curated data self-play framework for reasoning](https://arxiv.org/abs/2505.03335) ![GitHub Repo stars](https://img.shields.io/github/stars/LeapLabTHU/Absolute-Zero-Reasoner) - [verl-agent](https://github.com/langfengQ/verl-agent): A scalable training framework for **long-horizon LLM/VLM agents**, along with a new algorithm **GiGPO** ![GitHub Repo stars](https://img.shields.io/github/stars/langfengQ/verl-agent) - [RL-Factory](https://github.com/Simple-Efficient/RL-Factory): An easy and efficient RL post-training framework for Agentic Learning ![GitHub Repo stars](https://img.shields.io/github/stars/Simple-Efficient/RL-Factory) - [ReTool](https://retool-rl.github.io/): ReTool: reinforcement learning for strategic tool use in LLMs. Code release is in progress... - [verl-tool](https://github.com/TIGER-AI-Lab/verl-tool): An unified and easy-to-extend tool-agent training framework based on verl![GitHub Repo stars](https://img.shields.io/github/stars/TIGER-AI-Lab/verl-tool) - [PRIME](https://github.com/PRIME-RL/PRIME): Process reinforcement through implicit rewards ![GitHub Repo stars](https://img.shields.io/github/stars/PRIME-RL/PRIME) - [MemAgent](https://github.com/BytedTsinghua-SIA/MemAgent): MemAgent: Reshaping Long-Context LLM with Multi-Conv RL based Memory Agent ![GitHub Repo stars](https://img.shields.io/github/stars/BytedTsinghua-SIA/MemAgent) - [POLARIS](https://github.com/ChenxinAn-fdu/POLARIS): A Post-training recipe for scaling RL on Advanced Reasoning models ![GitHub Repo stars](https://img.shields.io/github/stars/ChenxinAn-fdu/POLARIS) - [GUI-R1](https://github.com/ritzz-ai/GUI-R1): **GUI-R1**: A Generalist R1-style Vision-Language Action Model For **GUI Agents** ![GitHub Repo stars](https://img.shields.io/github/stars/ritzz-ai/GUI-R1) - [DeepRetrieval](https://github.com/pat-jj/DeepRetrieval): RL Training of **Search Agent** with **Search/Retrieval Outcome** ![GitHub Repo stars](https://img.shields.io/github/stars/pat-jj/DeepRetrieval) - [Code-R1](https://github.com/ganler/code-r1): Reproducing R1 for **Code** with Reliable Rewards ![GitHub Repo stars](https://img.shields.io/github/stars/ganler/code-r1) - [DeepResearcher](https://github.com/GAIR-NLP/DeepResearcher): Scaling deep research via reinforcement learning in real-world environments ![GitHub Repo stars](https://img.shields.io/github/stars/GAIR-NLP/DeepResearcher) - [VAGEN](https://github.com/RAGEN-AI/VAGEN): Training VLM agents with multi-turn reinforcement learning ![GitHub Repo stars](https://img.shields.io/github/stars/RAGEN-AI/VAGEN) - [RM-R1](https://arxiv.org/abs/2505.02387): RL training of reasoning reward models ![GitHub Repo stars](https://img.shields.io/github/stars/RM-R1-UIUC/RM-R1) - [Dr. MAS](https://arxiv.org/pdf/2602.08847): Stable **end-to-end RL** post-training for **multi-agent LLM systems** ![GitHub Repo stars](https://img.shields.io/github/stars/langfengQ/DrMAS) - [LUFFY](https://arxiv.org/pdf/2504.14945): Learning to Reason under Off-Policy Guidance![GitHub Repo stars](https://img.shields.io/github/stars/ElliottYan/LUFFY) - [DeepMath](https://github.com/zwhe99/DeepMath): DeepMath-103K data and series models for math reasoning![GitHub Repo stars](https://img.shields.io/github/stars/zwhe99/DeepMath) - [PACS](https://github.com/ritzz-ai/PACS): Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR ![GitHub Repo stars](https://img.shields.io/github/stars/ritzz-ai/PACS) - [Entropy Mechanism of RL](https://github.com/PRIME-RL/Entropy-Mechanism-of-RL): The Entropy Mechanism of Reinforcement Learning for Large Language Model Reasoning![GitHub Repo stars](https://img.shields.io/github/stars/PRIME-RL/Entropy-Mechanism-of-RL) - [LLaSA-TTS-GRPO](https://github.com/channel-io/ch-tts-llasa-rl-grpo): TTS fine-tuning with GRPO optimization based on LLASA models ![GitHub Repo stars](https://img.shields.io/github/stars/channel-io/ch-tts-llasa-rl-grpo) - [PF-PPO](https://arxiv.org/abs/2409.06957): Policy Filtration for PPO based on the reliability of reward signals for more efficient and robust RLHF. - [RACRO](https://github.com/gyhdog99/RACRO2): Build multi-modal reasoning models via decoupling it into query-conditioned captioning and text-only reasoning ![GitHub Repo stars](https://img.shields.io/github/stars/gyhdog99/RACRO2) - [Agent Lightning](https://github.com/microsoft/agent-lightning): A flexible and extensible framework that enables seamless agent optimization for any existing agent framework. ![GitHub Repo stars](https://img.shields.io/github/stars/microsoft/agent-lightning) - [VTool-R1](https://github.com/VTOOL-R1/vtool-r1): VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use. ![GitHub Repo stars](https://img.shields.io/github/stars/VTOOL-R1/vtool-r1) - [Kimina-Prover-RL](https://github.com/project-numina/kimina-prover-rl/tree/main/recipe/kimina_prover_rl): Training pipeline for formal theorem proving, based on a paradigm inspired by DeepSeek-R1. - [RL-PLUS](https://github.com/YihongDong/RL-PLUS): Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization. - [rStar2-Agent](https://github.com/microsoft/rStar): Using reinforcement learning with multi-step tool-calling for math tasks, rStar2-Agent-14B reaches frontier-level math reasoning in just 510 RL training steps ![GitHub Repo stars](https://img.shields.io/github/stars/microsoft/rStar) - [Vision-SR1](https://github.com/zli12321/Vision-SR1): Self-Rewarding Vision-Language Model via Reasoning Decomposition ![GitHub Repo stars](https://img.shields.io/github/stars/zli12321/Vision-SR1) - [SimpleVLA-RL](https://github.com/PRIME-RL/SimpleVLA-RL): SimpleVLA-RL: A Simple yet Effective Vision-Language Action Model for Reinforcement Learning ![GitHub Repo stars](https://img.shields.io/github/stars/PRIME-RL/SimpleVLA-RL) - [Table-R1](https://github.com/Table-R1/Table-R1): Table-R1: Inference-Time Scaling for Table Reasoning ![GitHub Repo stars](https://img.shields.io/github/stars/Table-R1/Table-R1) - [Revisual-R1](https://github.com/CSfufu/Revisual-R1): Revisual-R1: Advancing Multimodal Reasoning From Optimized Cold Start to Staged Reinforcement Learning ![GitHub Repo stars](https://img.shields.io/github/stars/CSfufu/Revisual-R1) - [ARES](https://github.com/shawn0728/ARES): ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping ![GitHub Repo stars](https://img.shields.io/github/stars/shawn0728/ARES) - [Meta-Bandit-LLM](https://github.com/sanxing-chen/meta-bandit-llm): Meta-Bandit-LLM: Long-horizon multiturn interactive training for meta-bandit agents ![GitHub Repo stars](https://img.shields.io/github/stars/sanxing-chen/meta-bandit-llm) - [PokeeResearch](https://github.com/Pokee-AI/PokeeResearchOSS): PokeeResearch: State-of-the-art 7B DeepResearch Agent that leverages web search and content reading capabilities to answer complex questions using the most up-to-date information available online. ![Github Repo Stars](https://img.shields.io/github/stars/Pokee-AI/PokeeResearchOSS) - [Search Self-play](https://github.com/Alibaba-Quark/SSP): Pushing the Frontier of Agent Capability without Supervision ![GitHub Repo stars](https://img.shields.io/github/stars/Alibaba-Quark/SSP) - [OneThinker](https://github.com/tulerfeng/OneThinker): All-in-one Reasoning Model for Image and Video ![GitHub Repo stars](https://img.shields.io/github/stars/tulerfeng/OneThinker) - [OpenTinker](https://github.com/open-tinker/OpenTinker): Democratizing Agentic Reinforcement Learning as a Service ![GitHub Repo stars](https://img.shields.io/github/stars/open-tinker/OpenTinker) - [FlowRL](https://github.com/Xuekai-Zhu/FlowRL): Matching reward distributions via **flow balance** for diverse exploration and generalizable reasoning ![GitHub Repo stars](https://img.shields.io/github/stars/Xuekai-Zhu/FlowRL) - [Logic-RL](https://github.com/Unakar/Logic-RL): a reproduction of DeepSeek R1 Zero on 2K Tiny Logic Puzzle Dataset. ![GitHub Repo stars](https://img.shields.io/github/stars/Unakar/Logic-RL) - [Seed-Coder](https://github.com/ByteDance-Seed/Seed-Coder): RL training of Seed-Coder boosts performance on competitive programming ![GitHub Repo stars](https://img.shields.io/github/stars/ByteDance-Seed/Seed-Coder) - [all-hands/openhands-lm-32b-v0.1](https://www.all-hands.dev/blog/introducing-openhands-lm-32b----a-strong-open-coding-agent-model): A strong, open coding agent model, trained with [multi-turn fine-tuning](https://github.com/volcengine/verl/pull/195) - [s3](https://github.com/pat-jj/s3) **Efficient Yet Effective** Search Agent Training via RL ![GitHub Repo stars](https://img.shields.io/github/stars/pat-jj/s3) - [Rec-R1](https://arxiv.org/pdf/2503.24289): Bridging Generative Large Language Models and Recommendation Systems via Reinforcement Learning - [Explore RL Data Scaling](https://arxiv.org/abs/2503.22230): Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback - [FIRE](https://arxiv.org/abs/2410.21236): Flaming-hot initiation with regular execution sampling for large language models - [DQO](https://arxiv.org/abs/2410.09302): Enhancing multi-Step reasoning abilities of language models through direct Q-function optimization - [ProRL](https://arxiv.org/abs/2505.24864): Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models - [cognition-engineering](https://github.com/gair-nlp/cognition-engineering): Test time scaling drives cognition engineering. ![GitHub Repo stars](https://img.shields.io/github/stars/gair-nlp/cognition-engineering) - [Trust Region Preference Approximation](https://github.com/XueruiSu/Trust-Region-Preference-Approximation): A simple and stable **reinforcement learning algorithm** for LLM reasoning. ![GitHub Repo stars](https://img.shields.io/github/stars/XueruiSu/Trust-Region-Preference-Approximation) - [AdaRFT](https://github.com/uscnlp-lime/verl): Efficient Reinforcement Finetuning via **Adaptive Curriculum Learning** ![GitHub Repo stars](https://img.shields.io/github/stars/uscnlp-lime/verl) - [critic-rl](https://github.com/HKUNLP/critic-rl): LLM critics for code generation ![GitHub Repo stars](https://img.shields.io/github/stars/HKUNLP/critic-rl) - [self-rewarding-reasoning-LLM](https://arxiv.org/pdf/2502.19613): self-rewarding and correction with **generative reward models** ![GitHub Repo stars](https://img.shields.io/github/stars/RLHFlow/Self-rewarding-reasoning-LLM) - [DeepEnlighten](https://github.com/DolbyUUU/DeepEnlighten): Reproduce R1 with **social reasoning** tasks and analyze key findings ![GitHub Repo stars](https://img.shields.io/github/stars/DolbyUUU/DeepEnlighten) - [MetaSpatial](https://github.com/PzySeere/MetaSpatial): Reinforcing **3D Spatial Reasoning** in **VLMs** for the **Metaverse** ![GitHub Repo stars](https://img.shields.io/github/stars/PzySeere/MetaSpatial) - [PURE](https://github.com/CJReinforce/PURE): **Credit assignment** is the key to successful reinforcement fine-tuning using **process reward model** ![GitHub Repo stars](https://img.shields.io/github/stars/CJReinforce/PURE) - [cognitive-behaviors](https://github.com/kanishkg/cognitive-behaviors): Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs ![GitHub Repo stars](https://img.shields.io/github/stars/kanishkg/cognitive-behaviors) - [deepscaler](https://github.com/agentica-project/rllm/tree/deepscaler): iterative context scaling with GRPO ![GitHub Repo stars](https://img.shields.io/github/stars/agentica-project/deepscaler) - [DAPO](https://dapo-sia.github.io/): the fully open source SOTA RL algorithm that beats DeepSeek-R1-zero-32B ![GitHub Repo stars](https://img.shields.io/github/stars/volcengine/verl) - [NoisyRollout](https://github.com/NUS-TRAIL/NoisyRollout): Reinforcing Visual Reasoning with Data Augmentation ![GitHub Repo stars](https://img.shields.io/github/stars/NUS-TRAIL/NoisyRollout) - [SPEAR](https://github.com/TencentYoutuResearch/SPEAR): **Self-imitation** with **Progressive Exploration** for Agentic Reinforcement Learning (ICLR 2026) ![GitHub Repo stars](https://img.shields.io/github/stars/TencentYoutuResearch/SPEAR) - [RuleReasoner](https://github.com/bigai-nlco/RuleReasoner): **RuleReasoner:** Reinforced Rule-based Reasoning via **Domain-aware Dynamic Sampling** (ICLR 2026) ![GitHub Repo stars](https://img.shields.io/github/stars/bigai-nlco/RuleReasoner) - [MetaphorStar](https://metaphorstar.github.io/): **Image Metaphor** Understanding and Reasoning with End-to-End **Visual Reinforcement Learning** ![GitHub Repo stars](https://img.shields.io/github/stars/MING-ZCH/MetaphorStar) ## Contribution Guide See [contributions guide](CONTRIBUTING.md) ## About [ByteDance Seed Team](https://team.doubao.com/) Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society. You can get to know Bytedance Seed better through the following channels👇
We are HIRING! Send us an [email](mailto:the.verl.project@gmail.com) if you are interested in internship/FTE opportunities in RL for agents. ================================================ FILE: docker/Dockerfile.isaaclab230 ================================================ #FROM nvcr.nju.edu.cn/nvidia/isaac-lab:2.3.0 FROM isaac-lab-base:latest ENV ACCEPT_EULA=Y ENTRYPOINT [] # desktop RUN --mount=type=cache,target=/var/cache/apt \ sed -i 's/archive.ubuntu.com/mirrors.ivolces.com/g' /etc/apt/sources.list && \ sed -i 's/security.ubuntu.com/mirrors.ivolces.com/g' /etc/apt/sources.list && \ apt-get update && \ DEBIAN_FRONTEND=noninteractive apt-get install -y locales && \ locale-gen en_US.UTF-8 && \ update-locale LANG=en_US.UTF-8 LC_CTYPE=en_US.UTF-8 && \ apt-get install -y wget curl \ xfce4 \ xfce4-goodies \ xorg \ dbus-x11 \ x11-xserver-utils \ tigervnc-standalone-server \ tigervnc-common \ tigervnc-tools \ fonts-dejavu \ fonts-liberation # cuda 12.2 RUN --mount=type=cache,target=/var/cache/apt \ cd /tmp && \ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub && \ apt-key add 3bf863cc.pub && \ echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \ apt-get update && \ apt-get install -y libcusparselt0 libnccl2=2.27.3-1+cuda12.2 libglfw3 libgl1-mesa-glx libosmesa6 && \ rm -f 3bf863cc.pub # libero RUN --mount=type=cache,target=/root/.cache/pip \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install easydict==1.9 robosuite==1.4.0 bddl==1.0.1 future==0.18.2 cloudpickle==2.1.0 RUN --mount=type=cache,target=/root/.cache/pip \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install transformers[hf_xet] RUN --mount=type=cache,target=/root/.cache/pip \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install --upgrade numpy==1.26.4 ray[default] \ accelerate codetiming datasets dill hydra-core pandas peft pyarrow>=19.0.0 pybind11 pylatexenc # openvla-oft RUN --mount=type=cache,target=/root/.cache/pip \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install pre-commit torchdata packaging>=20.0 uvicorn fastapi latex2sympy2_extended math_verify tensorboard # flash_attn RUN cd /tmp && \ wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.0.post2/flash_attn-2.8.0.post2+cu12torch2.7cxx11abiFALSE-cp311-cp311-linux_x86_64.whl && \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install /tmp/flash_attn-2.8.0.post2+cu12torch2.7cxx11abiFALSE-cp311-cp311-linux_x86_64.whl && \ rm -f /tmp/flash_attn-2.8.0.post2+cu12torch2.7cxx11abiFALSE-cp311-cp311-linux_x86_64.whl RUN --mount=type=cache,target=/root/.cache/pip \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install --upgrade protobuf==3.20.3 timm==0.9.16 RUN --mount=type=cache,target=/root/.cache/pip \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install orjson==3.11.3 pyvers==0.1.0 tensordict==0.10.0 --force --no-deps RUN mkdir -p /root/.vnc && \ cat <<'EOP' > /root/.vnc/xstartup #!/bin/sh unset SESSION_MANAGER unset DBUS_SESSION_BUS_ADDRESS [ -r \$HOME/.Xresources ] && xrdb \$HOME/.Xresources xsetroot -solid grey exec startxfce4 EOP RUN cat <<'EOP' > /root/.vnc/config geometry=1920x1080 depth=24 desktop=Isaac-Sim-Desktop dpi=96 localhost=no EOP RUN cat <<'EOP' > /root/start_isaac_vnc.sh #!/bin/bash # 设置显示变量 export DISPLAY=:1 # 检查VNC是否运行 if ! pgrep -f "Xvnc.*:1" > /dev/null; then echo "Starting VNC server..." vncserver :1 -localhost no -geometry 1920x1080 -depth 24 -desktop "Isaac-Sim-Desktop" sleep 3 fi # 启动Isaac Sim echo "Starting Isaac Sim..." /workspace/isaaclab/_isaac_sim/isaac-sim.sh --allow-root EOP RUN chmod +x /root/.vnc/xstartup && \ chmod +x /root/start_isaac_vnc.sh RUN /workspace/isaaclab/_isaac_sim/isaac-sim.sh --allow-root --ext-precache-mode RUN cd /root && \ git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git && \ cd LIBERO && \ git apply <<'EOP' diff --git a/setup.py b/setup.py index 59d4900..dbe9811 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,8 @@ long_description = "".join(lines) setup( name="libero", - packages=[package for package in find_packages() if package.startswith("libero")], + #packages=[package for package in find_packages() if package.startswith("libero")], + packages=["libero"], install_requires=[], eager_resources=["*"], include_package_data=True, EOP RUN cd /root/LIBERO && \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install -e . # libero config RUN mkdir -p /root/.libero && \ cat <<'EOP' > /root/.libero/config.yaml assets: /root/LIBERO/libero/libero/./assets bddl_files: /root/LIBERO/libero/libero/./bddl_files benchmark_root: /root/LIBERO/libero/libero datasets: /root/LIBERO/libero/libero/../datasets init_states: /root/LIBERO/libero/libero/./init_files EOP # from https://github.com/nvidia-china-sae/RobotLearningLab COPY RobotLearningLab/ /root/RobotLearningLab/ RUN cd /workspace/isaaclab/ && \ rm -rf source && \ ln -s /root/RobotLearningLab/source source && \ /workspace/isaaclab/_isaac_sim/python.sh -m pip install -e ./source/isaaclab # Ray cmd RUN /workspace/isaaclab/_isaac_sim/python.sh -m pip install colorama && \ cat <<'EOP' >> /root/.bashrc alias ray='/workspace/isaaclab/_isaac_sim/python.sh /workspace/isaaclab/_isaac_sim/kit/python/lib/python3.11/site-packages/ray/scripts/scripts.py' EOP ================================================ FILE: docker/Dockerfile.stable.sglang ================================================ # sgl059 FROM lmsysorg/sglang:v0.5.9 ARG PIP_NO_CACHE_DIR=1 RUN pip install pybind11 RUN pip install nvidia-mathdx RUN MAX_JOBS=128 pip install -v --disable-pip-version-check --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git RUN export NVTE_FRAMEWORK=pytorch && MAX_JOBS=128 NVTE_BUILD_THREADS_PER_JOB=4 pip3 install --resume-retries 999 --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.12 # RUN pip install --upgrade transformers tokenizers RUN pip install codetiming mathruler pylatexenc qwen_vl_utils cachetools pytest-asyncio RUN pip install --no-build-isolation flash_attn==2.8.3 RUN NSIGHT_VERSION=2025.6.1_2025.6.1.190-1_$(if [ "$(uname -m)" = "aarch64" ]; then echo "arm64"; else echo "amd64"; fi) && \ wget https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_6/nsight-systems-${NSIGHT_VERSION}.deb && \ apt-get update && apt-get install -y libxcb-cursor0 && \ apt-get install -y ./nsight-systems-${NSIGHT_VERSION}.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-${NSIGHT_VERSION}.deb # sglang image has already installed DeepEP RUN pip3 install --no-deps trl==0.27.0 RUN pip3 install nvtx matplotlib liger_kernel RUN pip install -U git+https://github.com/ISEEKYAN/mbridge.git RUN pip install --no-deps git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.16.0 RUN pip install git+https://github.com/volcengine/verl.git@v0.7.0 && \ pip uninstall -y verl RUN sed -i '/nvidia-cudnn-cu12/d' /usr/local/lib/python3.12/dist-packages/torch-2.9.1+cu129.dist-info/METADATA && \ pip install --no-deps --force-reinstall nvidia-cudnn-cu12==9.16.0.29 # for packages compiled from source code RUN apt-get update && \ apt-get install -y --allow-downgrades --allow-change-held-packages \ libcudnn9-cuda-12=9.16.0.29-1 \ libcudnn9-dev-cuda-12=9.16.0.29-1 \ libcudnn9-headers-cuda-12=9.16.0.29-1 && \ rm -rf /var/lib/apt/lists/* ================================================ FILE: docker/Dockerfile.stable.trtllm ================================================ # Base image from NGC TensorRT-LLM, which includes a pre-installed TensorRT-LLM. # For available images, visit: https://nvidia.github.io/TensorRT-LLM/installation/containers.html # Use TRTLLM_BASE_IMAGE to specify the base image (default: release:1.2.0rc6) ARG TRTLLM_BASE_IMAGE=nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc4 FROM ${TRTLLM_BASE_IMAGE} # ============================================================================== # Install Megatron dependencies # ============================================================================== # DeepEP is required for IBGDA support. # Clone and build gdrcopy and deepep-nvshmem dependencies. WORKDIR /home/dpsk_a2a RUN git clone -b v2.5.1 https://github.com/NVIDIA/gdrcopy.git && \ pushd gdrcopy && \ make prefix=/usr/local lib_install && \ popd && rm -rf gdrcopy && \ pip install nvidia-nvshmem-cu13==3.3.20 && \ export NVSHMEM_DIR=/usr/local/lib/python3.12/dist-packages/nvidia/nvshmem && \ export LD_LIBRARY_PATH="${NVSHMEM_DIR}/lib:$LD_LIBRARY_PATH" && \ export PATH="${NVSHMEM_DIR}/bin:$PATH" && \ pushd ${NVSHMEM_DIR}/lib && \ ln -s libnvshmem_host.so.3 libnvshmem_host.so && \ popd && \ git clone -b v1.2.1 https://github.com/deepseek-ai/DeepEP.git && \ pushd DeepEP && \ wget https://raw.githubusercontent.com/NVIDIA/Megatron-LM/refs/tags/core_v0.15.0/docker/patches/deepep.patch && \ patch -p1 < deepep.patch && \ TORCH_CUDA_ARCH_LIST="9.0 10.0 12.0" python setup.py install && \ popd && rm -rf deepep # Install Python dependencies RUN pip3 install --no-cache-dir --no-deps trl && \ pip3 install --no-cache-dir nvtx matplotlib liger_kernel cachetools && \ pip install --no-cache-dir -U git+https://github.com/ISEEKYAN/mbridge.git && \ pip install --no-deps --no-cache-dir git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.15.0 # ============================================================================== # Install verl dependencies # ============================================================================== RUN pip install git+https://github.com/volcengine/verl.git@v0.7.0 RUN pip uninstall -y verl RUN pip install "verl[mcore] @ git+https://github.com/volcengine/verl.git@v0.7.0" RUN pip uninstall -y verl # ============================================================================== # Install a specific TensorRT-LLM on demand # ============================================================================== # Note: The NGC image already includes a pre-installed TensorRT-LLM, but you can install a specific version if needed. # Refer to https://nvidia.github.io/TensorRT-LLM/installation/index.html for more details. ================================================ FILE: docker/Dockerfile.stable.vllm ================================================ # vllm017 FROM nvidia/cuda:12.9.1-devel-ubuntu22.04 ARG DEBIAN_FRONTEND=noninteractive ARG PIP_NO_CACHE_DIR=1 RUN apt-get update && apt-get install -y \ git \ wget \ cmake \ build-essential \ libibverbs-dev \ libnuma-dev \ librdmacm-dev \ numactl \ software-properties-common \ vim && \ add-apt-repository ppa:deadsnakes/ppa -y && \ apt-get update && \ apt-get install -y \ python3.12 \ python3.12-dev \ && rm -rf /var/lib/apt/lists/* RUN wget https://bootstrap.pypa.io/get-pip.py && \ python3.12 get-pip.py && \ rm get-pip.py RUN ln -sf /usr/bin/python3.12 /usr/bin/python3 && \ ln -sf /usr/bin/python3.12 /usr/bin/python RUN pip install torch==2.10.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu129 RUN pip install vllm==0.17.0 RUN pip install pybind11 RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb && \ dpkg -i cuda-keyring_1.1-1_all.deb && \ apt-get update && \ apt-get -y install cudnn && \ rm -rf /var/lib/apt/lists/* RUN pip install nvidia-mathdx RUN MAX_JOBS=128 pip install -v --disable-pip-version-check --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git RUN export NVTE_FRAMEWORK=pytorch && \ MAX_JOBS=128 \ NVTE_BUILD_THREADS_PER_JOB=4 \ pip3 install --resume-retries 999 --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.12 # RUN pip install --upgrade transformers tokenizers RUN pip install codetiming mathruler pylatexenc qwen_vl_utils cachetools pytest-asyncio RUN export FLASH_ATTENTION_FORCE_BUILD="TRUE" && MAX_JOBS=16 pip install --no-build-isolation flash_attn==2.8.3 RUN NSIGHT_VERSION=2025.6.1_2025.6.1.190-1_$(if [ "$(uname -m)" = "aarch64" ]; then echo "arm64"; else echo "amd64"; fi) && \ wget https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_6/nsight-systems-${NSIGHT_VERSION}.deb && \ apt-get update && apt-get install -y libxcb-cursor0 && \ apt-get install -y ./nsight-systems-${NSIGHT_VERSION}.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-${NSIGHT_VERSION}.deb && \ rm -rf /var/lib/apt/lists/* # ========================= # Install DeepEP # ========================= # Clone and build deepep and deepep-nvshmem WORKDIR /home/dpsk_a2a RUN git clone -b v2.5.1 https://github.com/NVIDIA/gdrcopy.git && \ cd gdrcopy && \ make prefix=/usr/local lib_install && \ cd .. && rm -rf gdrcopy ENV GDRCOPY_HOME=/usr/local RUN git clone -b hybrid-ep https://github.com/deepseek-ai/DeepEP.git && \ export NVSHMEM_DIR=/usr/local/lib/python3.12/dist-packages/nvidia/nvshmem && \ export LD_LIBRARY_PATH="${NVSHMEM_DIR}/lib:$LD_LIBRARY_PATH" && \ export PATH="${NVSHMEM_DIR}/bin:$PATH" && \ cd ${NVSHMEM_DIR}/lib && \ ln -sf libnvshmem_host.so.3 libnvshmem_host.so && \ cd /home/dpsk_a2a/DeepEP && \ export CPATH=/usr/local/cuda/targets/x86_64-linux/include/cccl:$CPATH && \ python setup.py install RUN pip3 install --no-deps trl==0.27.0 RUN pip3 install nvtx matplotlib liger_kernel RUN pip install -U git+https://github.com/ISEEKYAN/mbridge.git RUN pip install --no-deps git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.16.0 RUN pip install git+https://github.com/volcengine/verl.git@v0.7.0 && \ pip uninstall -y verl RUN apt-get update && apt-get install -y curl \ && rm -rf /var/lib/apt/lists/* RUN apt-get update && \ apt-get install -y --allow-downgrades --allow-change-held-packages \ libcudnn9-cuda-12=9.16.0.29-1 \ libcudnn9-dev-cuda-12=9.16.0.29-1 \ libcudnn9-headers-cuda-12=9.16.0.29-1 && \ rm -rf /var/lib/apt/lists/* ================================================ FILE: docker/README.md ================================================ # Dockerfiles of verl We provide pre-built Docker images for quick setup. And from this version, we utilize a new image release hierarchy for productivity and stability. Start from v0.6.0, we use vllm and sglang release image as our base image. Start from v0.7.0, since vllm/vllm-openai:v0.12.0 is a minimal image without some essential libraries, we use nvidia/cuda:12.9.1-devel-ubuntu22.04 as our base image for vllm. ## Base Image - vLLM: https://hub.docker.com/r/nvidia/cuda - SGLang: https://hub.docker.com/r/lmsysorg/sglang ## Application Image Upon base image, the following packages are added: - flash_attn - Megatron-LM - Apex - TransformerEngine - DeepEP Latest docker file: - [Dockerfile.stable.vllm](https://github.com/volcengine/verl/blob/main/docker/Dockerfile.stable.vllm) - [Dockerfile.stable.sglang](https://github.com/volcengine/verl/blob/main/docker/Dockerfile.stable.sglang) All pre-built images are available in dockerhub: https://hub.docker.com/r/verlai/verl. For example, `verlai/verl:sgl059.latest`, `verlai/verl:vllm017.latest`. You can find the latest images used for development and ci in our github workflows: - [.github/workflows/vllm.yml](https://github.com/volcengine/verl/blob/main/.github/workflows/vllm.yml) - [.github/workflows/sgl.yml](https://github.com/volcengine/verl/blob/main/.github/workflows/sgl.yml) ## Installation from Docker After pulling the desired Docker image and installing desired inference and training frameworks, you can run it with the following steps: 1. Launch the desired Docker image and attach into it: ```sh docker create --runtime=nvidia --gpus all --net=host --shm-size="10g" --cap-add=SYS_ADMIN -v .:/workspace/verl --name verl sleep infinity docker start verl docker exec -it verl bash ``` 2. If you use the images provided, you only need to install verl itself without dependencies: ```sh # install the nightly version (recommended) git clone https://github.com/volcengine/verl && cd verl pip3 install --no-deps -e . ``` [Optional] If you hope to switch between different frameworks, you can install verl with the following command: ```sh # install the nightly version (recommended) git clone https://github.com/volcengine/verl && cd verl pip3 install -e .[vllm] pip3 install -e .[sglang] ``` ## Release History - 2026/03/10: update vllm stable image to vllm==0.17.0; update sglang stable image to sglang==0.5.9 - 2026/01/17: update vllm stable image to torch==2.9.1, cudnn==9.16, deepep==1.2.1 - 2025/12/23: update vllm stable image to vllm==0.12.0; update sglang stable image to sglang==0.5.6 - 2025/11/18: update vllm stable image to vllm==0.11.1; update sglang stable image to sglang==0.5.5 ================================================ FILE: docker/ascend/Dockerfile.ascend.sglang_8.3.rc1_a2 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-910b-ubuntu22.04-py3.11 ARG ASCEND_CANN_PATH="/usr/local/Ascend" ARG PIP_INDEX_URL="https://mirrors.aliyun.com/pypi/simple" ARG PTA_BASE_VERSION="torch_npu-2.7.1.post2-cp311-cp311-manylinux_2_28" ARG PTA_URL="https://gitcode.com/Ascend/pytorch/releases/download/v7.3.0-pytorch2.7.1" # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential net-tools iputils-ping && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip config set global.index-url ${PIP_INDEX_URL} && \ pip config set install.trusted-host mirrors.aliyun.com && \ pip install --upgrade pip setuptools packaging && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ echo "[LOG INFO] Detected architecture: $ARCH" && \ # Set extra pip index for x86_64 platform if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.5.8 https://github.com/sgl-project/sglang.git && \ git clone https://github.com/sgl-project/sgl-kernel-npu.git && cd sgl-kernel-npu && git checkout 46b73de && cd .. && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout f2b0977e && cd .. # Install repositories with low update frequency RUN cd sglang && \ # Install sglang mv python/pyproject.toml python/pyproject.toml.backup && \ mv python/pyproject_other.toml python/pyproject.toml && \ pip install -e "python[srt_npu]" && \ pip install torch==2.7.1 torchvision==0.22.1 && \ # Install torch_npu ARCH=$(uname -m) && wget ${PTA_URL}/${PTA_BASE_VERSION}_${ARCH}.whl && pip install ${PTA_BASE_VERSION}_${ARCH}.whl && \ echo "[LOG INFO] Torch_npu version is: ${PTA_BASE_VERSION}_${ARCH}.whl" && \ cd .. # Install sgl-kernel-npu RUN ARCH=$(uname -m) && \ # Export and source env export LD_LIBRARY_PATH=${ASCEND_CANN_PATH}/ascend-toolkit/8.3.RC1/${ARCH}-linux/devlib/linux/${ARCH}:$LD_LIBRARY_PATH && \ source ${ASCEND_CANN_PATH}/ascend-toolkit/set_env.sh && \ source ${ASCEND_CANN_PATH}/nnal/atb/set_env.sh && \ pip install pybind11 && \ cd sgl-kernel-npu && \ bash build.sh && \ pip install output/torch_memory_saver*.whl && \ pip install output/sgl_kernel_npu*.whl && \ # Deep_ep package is compiled for A3 by default; Recompile in deepep2 mode for A2, following https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/deep_ep/README.md. bash build.sh -a deepep2 && \ pip install output/deep_ep*.whl && \ cd "$(pip show deep-ep | grep -E '^Location:' | awk '{print $2}')" && ln -s deep_ep/deep_ep_cpp*.so && cd - && \ cd .. # Install MindSpeed & Megatron RUN pip install -e MindSpeed && \ pip install git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.1 && \ # Remove existing triton or triton-ascend installed by some third-party packages pip uninstall -y triton timm && \ # Install mbridge pip install mbridge && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Prepare and install verl (update frequently) RUN git clone --recursive https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ pip install ray==2.46.0 click==8.2.1 cachetools && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend.sglang_8.3.rc1_a3 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-a3-ubuntu22.04-py3.11 ARG ASCEND_CANN_PATH="/usr/local/Ascend" ARG PIP_INDEX_URL="https://mirrors.aliyun.com/pypi/simple" ARG PTA_BASE_VERSION="torch_npu-2.7.1.post2-cp311-cp311-manylinux_2_28" ARG PTA_URL="https://gitcode.com/Ascend/pytorch/releases/download/v7.3.0-pytorch2.7.1" # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential net-tools iputils-ping && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip config set global.index-url ${PIP_INDEX_URL} && \ pip config set install.trusted-host mirrors.aliyun.com && \ pip install --upgrade pip setuptools packaging && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ echo "[LOG INFO] Detected architecture: $ARCH" && \ # Set extra pip index for x86_64 platform if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.5.8 https://github.com/sgl-project/sglang.git && \ git clone https://github.com/sgl-project/sgl-kernel-npu.git && cd sgl-kernel-npu && git checkout 46b73de && cd .. && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout f2b0977e && cd .. # Install repositories with low update frequency RUN cd sglang && \ # Install sglang mv python/pyproject.toml python/pyproject.toml.backup && \ mv python/pyproject_other.toml python/pyproject.toml && \ pip install -e "python[srt_npu]" && \ pip install torch==2.7.1 torchvision==0.22.1 && \ # Install torch_npu ARCH=$(uname -m) && wget ${PTA_URL}/${PTA_BASE_VERSION}_${ARCH}.whl && pip install ${PTA_BASE_VERSION}_${ARCH}.whl && \ echo "[LOG INFO] Torch_npu version is: ${PTA_BASE_VERSION}_${ARCH}.whl" && \ cd .. # Install sgl-kernel-npu RUN ARCH=$(uname -m) && \ # Export and source env export LD_LIBRARY_PATH=${ASCEND_CANN_PATH}/ascend-toolkit/8.3.RC1/${ARCH}-linux/devlib/linux/${ARCH}:$LD_LIBRARY_PATH && \ source ${ASCEND_CANN_PATH}/ascend-toolkit/set_env.sh && \ source ${ASCEND_CANN_PATH}/nnal/atb/set_env.sh && \ pip install pybind11 && \ cd sgl-kernel-npu && \ bash build.sh && \ pip install output/torch_memory_saver*.whl && \ pip install output/sgl_kernel_npu*.whl && \ pip install output/deep_ep*.whl && \ cd "$(pip show deep-ep | grep -E '^Location:' | awk '{print $2}')" && ln -s deep_ep/deep_ep_cpp*.so && cd - && \ cd .. # Install MindSpeed & Megatron RUN pip install -e MindSpeed && \ pip install git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.1 && \ # Remove existing triton or triton-ascend installed by some third-party packages pip uninstall -y triton timm && \ # Install mbridge pip install mbridge && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Prepare and install verl (update frequently) RUN git clone --recursive https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ pip install ray==2.46.0 click==8.2.1 cachetools && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend_8.2.rc1_a2 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.2.rc1-910b-ubuntu22.04-py3.11 # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip install --upgrade pip setuptools packaging && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ # Set extra pip index for x86_64 platform echo "[LOG INFO] Detected architecture: $ARCH" && \ if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.9.1 https://github.com/vllm-project/vllm && \ git clone --depth 1 --branch v0.9.1 https://github.com/vllm-project/vllm-ascend.git && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout f2b0977e && cd .. && \ git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # Install repositories with low update frequency RUN ARCH=$(uname -m) && \ # Export and source env if [ "$ARCH" = "aarch64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.2.RC1/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \ elif [ "$ARCH" = "x86_64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.2.RC1/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \ fi && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ source /usr/local/Ascend/nnal/atb/set_env.sh && \ # Install torch & torch_npu & torchvision pip install torch==2.5.1 torch_npu==2.5.1 torchvision==0.20.1 && \ # Install vllm cd vllm && VLLM_TARGET_DEVICE=empty pip install -v -e . && cd .. && \ # Install vllm-ascend cd vllm-ascend && pip install -v -e . && cd .. && \ # Install MindSpeed & Megatron pip install -e MindSpeed && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge ENV PYTHONPATH="/Megatron-LM${PYTHONPATH:+:${PYTHONPATH}}" # Prepare and install verl (update frequently) RUN git clone --depth 1 https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend_8.2.rc1_a3 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.2.rc1-a3-ubuntu22.04-py3.11 # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip install --upgrade pip setuptools packaging && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ # Set extra pip index for x86_64 platform echo "[LOG INFO] Detected architecture: $ARCH" && \ if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.9.1 https://github.com/vllm-project/vllm && \ git clone --depth 1 --branch v0.9.1 https://github.com/vllm-project/vllm-ascend.git && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout f2b0977e && cd .. && \ git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # Install repositories with low update frequency RUN ARCH=$(uname -m) && \ # Export and source env if [ "$ARCH" = "aarch64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.2.RC1/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \ elif [ "$ARCH" = "x86_64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.2.RC1/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \ fi && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ source /usr/local/Ascend/nnal/atb/set_env.sh && \ # Install torch & torch_npu & torchvision pip install torch==2.5.1 torch_npu==2.5.1 torchvision==0.20.1 && \ # Install vllm cd vllm && VLLM_TARGET_DEVICE=empty pip install -v -e . && cd .. && \ # Install vllm-ascend cd vllm-ascend && pip install -v -e . && cd .. && \ # Install MindSpeed & Megatron pip install -e MindSpeed && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge ENV PYTHONPATH="/Megatron-LM${PYTHONPATH:+:${PYTHONPATH}}" # Prepare and install verl (update frequently) RUN git clone --depth 1 https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend_8.3.rc1_a2 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-910b-ubuntu22.04-py3.11 # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip install --upgrade pip packaging setuptools==80.10.2 && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ # Set extra pip index for x86_64 platform echo "[LOG INFO] Detected architecture: $ARCH" && \ if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.11.0 https://github.com/vllm-project/vllm.git && \ git clone --depth 1 --branch v0.11.0 https://github.com/vllm-project/vllm-ascend.git && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout f2b0977e && cd .. && \ git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # Install repositories with low update frequency RUN ARCH=$(uname -m) && \ # Export and source env if [ "$ARCH" = "aarch64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.3.RC1/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \ elif [ "$ARCH" = "x86_64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.3.RC1/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \ fi && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ source /usr/local/Ascend/nnal/atb/set_env.sh && \ # Install torch & torch_npu & torchvision pip install torch==2.7.1 torch_npu==2.7.1 torchvision==0.22.1 transformers==4.57.6 && \ # Install vllm cd vllm && VLLM_TARGET_DEVICE=empty pip install -v -e . && cd .. && \ # Install vllm-ascend cd vllm-ascend && pip install -v -e . && cd .. && \ # Install MindSpeed & Megatron pip install -e MindSpeed && \ # Remove existing triton or triton-ascend installed by some third-party packages pip uninstall -y triton triton-ascend && \ # Install mbridge pip install mbridge && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge ENV PYTHONPATH="/Megatron-LM${PYTHONPATH:+:${PYTHONPATH}}" # Prepare and install verl (update frequently) RUN git clone --depth 1 https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend_8.3.rc1_a3 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-a3-ubuntu22.04-py3.11 # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip install --upgrade pip packaging setuptools==80.10.2 && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ # Set extra pip index for x86_64 platform echo "[LOG INFO] Detected architecture: $ARCH" && \ if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.11.0 https://github.com/vllm-project/vllm.git && \ git clone --depth 1 --branch v0.11.0 https://github.com/vllm-project/vllm-ascend.git && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout f2b0977e && cd .. && \ git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # Install repositories with low update frequency RUN ARCH=$(uname -m) && \ # Export and source env if [ "$ARCH" = "aarch64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.3.RC1/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \ elif [ "$ARCH" = "x86_64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.3.RC1/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \ fi && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ source /usr/local/Ascend/nnal/atb/set_env.sh && \ # Install torch & torch_npu & torchvision pip install torch==2.7.1 torch_npu==2.7.1 torchvision==0.22.1 transformers==4.57.6 && \ # Install vllm cd vllm && VLLM_TARGET_DEVICE=empty pip install -v -e . && cd .. && \ # Install vllm-ascend cd vllm-ascend && pip install -v -e . && cd .. && \ # Install MindSpeed & Megatron pip install -e MindSpeed && \ # Remove existing triton or triton-ascend installed by some third-party packages pip uninstall -y triton triton-ascend && \ # Install mbridge pip install mbridge && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge ENV PYTHONPATH="/Megatron-LM${PYTHONPATH:+:${PYTHONPATH}}" # Prepare and install verl (update frequently) RUN git clone --depth 1 https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend_8.5.0_a2 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.5.0-910b-ubuntu22.04-py3.11 ARG SOC_VERSION="ascend910b1" # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip install --upgrade pip packaging setuptools==80.10.2 && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ # Set extra pip index for x86_64 platform echo "[LOG INFO] Detected architecture: $ARCH" && \ if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.13.0 https://github.com/vllm-project/vllm.git && \ git clone -b releases/v0.13.0 https://github.com/vllm-project/vllm-ascend.git && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout 2.3.0_core_r0.12.1 && cd .. && \ git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # Install repositories with low update frequency RUN ARCH=$(uname -m) && \ # Export and source env if [ "$ARCH" = "aarch64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/cann-8.5.0/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \ elif [ "$ARCH" = "x86_64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/cann-8.5.0/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \ fi && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ source /usr/local/Ascend/nnal/atb/set_env.sh && \ # Install transformers pip install transformers==4.57.6 && \ # Install vllm cd vllm && pip install -r requirements/build.txt && \ VLLM_TARGET_DEVICE=empty pip install -v -e. && cd .. && \ # Install vllm-ascend cd vllm-ascend && pip install -r requirements.txt && \ export COMPILE_CUSTOM_KERNELS=1 && pip install -v -e . && cd .. && \ # Install MindSpeed & Megatron pip install -e MindSpeed && \ pip install -e Megatron-LM && \ # Remove existing triton installed by some third-party packages pip uninstall -y triton && \ # Install mbridge pip install mbridge torch_npu==2.8.0 && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Prepare and install verl (update frequently) RUN git clone --recursive https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/ascend/Dockerfile.ascend_8.5.0_a3 ================================================ # Pull base image FROM swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.5.0-a3-ubuntu22.04-py3.11 ARG SOC_VERSION="ascend910_9392" # Prepare required system dependencies RUN apt-get update -y && \ apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \ pip install --upgrade pip packaging setuptools==80.10.2 && \ pip cache purge # Prepare repositories with low update frequency RUN ARCH=$(uname -m) && \ # Set extra pip index for x86_64 platform echo "[LOG INFO] Detected architecture: $ARCH" && \ if [ "$ARCH" = "x86_64" ]; then \ pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \ fi && \ # Clone libs git clone --depth 1 --branch v0.13.0 https://github.com/vllm-project/vllm.git && \ git clone -b releases/v0.13.0 https://github.com/vllm-project/vllm-ascend.git && \ git clone https://gitcode.com/Ascend/MindSpeed.git && \ cd MindSpeed && git checkout 2.3.0_core_r0.12.1 && cd .. && \ git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # Install repositories with low update frequency RUN ARCH=$(uname -m) && \ # Export and source env if [ "$ARCH" = "aarch64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/cann-8.5.0/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \ elif [ "$ARCH" = "x86_64" ]; then \ export LD_LIBRARY_PATH=/usr/local/Ascend/cann-8.5.0/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \ fi && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ source /usr/local/Ascend/nnal/atb/set_env.sh && \ # Install transformers pip install transformers==4.57.6 && \ # Install vllm cd vllm && pip install -r requirements/build.txt && \ VLLM_TARGET_DEVICE=empty pip install -v -e. && cd .. && \ # Install vllm-ascend cd vllm-ascend && pip install -r requirements.txt && \ export COMPILE_CUSTOM_KERNELS=1 && pip install -v -e . && cd .. && \ # Install MindSpeed & Megatron pip install -e MindSpeed && \ pip install -e Megatron-LM && \ # Remove existing triton installed by some third-party packages pip uninstall -y triton && \ # Install mbridge pip install mbridge torch_npu==2.8.0 && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Prepare and install verl (update frequently) RUN git clone --recursive https://github.com/volcengine/verl.git && \ cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. && \ # Clear extra files rm -rf /tmp/* /var/tmp/* && \ pip cache purge # Show install results RUN pip list # Setting Default Commands CMD ["/bin/bash"] ================================================ FILE: docker/aws/Dockerfile.extention.awsefa ================================================ # Base Image support aws EFA # Build Image with frameworks based on this FROM verlai/verl:app-verl0.6-transformers4.56.1-sglang0.5.2-mcore0.13.0-te2.2 # For aws instances with EFA net interface (Sagemaker AI Pod) # install EFA driver: ######## AWS EFA ############ ENV NCCL_VERSION=2.25.1-1 ENV DEBIAN_FRONTEND=noninteractive ENV EFA_INSTALLER_VERSION=1.40.0 ENV AWS_OFI_NCCL_VERSION=1.14.2 ENV FI_EFA_SET_CUDA_SYNC_MEMOPS=0 ENV FI_PROVIDER=efa RUN apt update && apt install -y linux-image-generic libhwloc-dev RUN cd /tmp && \ curl -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz && \ tar -xf aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz && \ cd aws-efa-installer && \ ./efa_installer.sh -y -g --skip-kmod --skip-limit-conf --no-verify && \ ldconfig && \ rm -rf /tmp/aws-efa-installer /var/lib/apt/lists/* # NCCL EFA Plugin RUN cd /tmp && \ curl -LO https://github.com/aws/aws-ofi-nccl/archive/refs/tags/v${AWS_OFI_NCCL_VERSION}.tar.gz && \ tar -xzf /tmp/v${AWS_OFI_NCCL_VERSION}.tar.gz && \ rm /tmp/v${AWS_OFI_NCCL_VERSION}.tar.gz && \ mv aws-ofi-nccl-${AWS_OFI_NCCL_VERSION} aws-ofi-nccl && \ cd /tmp/aws-ofi-nccl && \ ./autogen.sh && \ ./configure --prefix=/opt/amazon/efa \ --with-libfabric=/opt/amazon/efa \ --with-cuda=/usr/local/cuda \ --enable-platform-aws \ --with-mpi=/opt/amazon/openmpi && \ make -j$(nproc) install && \ rm -rf /tmp/aws-ofi/nccl # NCCL RUN echo "/usr/local/lib" >> /etc/ld.so.conf.d/local.conf && \ echo "/opt/amazon/openmpi/lib" >> /etc/ld.so.conf.d/efa.conf && \ ldconfig ENV OMPI_MCA_pml=^cm,ucx \ OMPI_MCA_btl=tcp,self \ OMPI_MCA_btl_tcp_if_exclude=lo,docker0,veth_def_agent \ OPAL_PREFIX=/opt/amazon/openmpi \ NCCL_SOCKET_IFNAME=^docker,lo,veth_def_agent \ FI_EFA_USE_HUGE_PAGE=0 # docker build -t verl:awsefa --label "commit=$(git rev-parse --short HEAD)" . # on aws: # docker run --ipc=host --privileged --name verldev --gpus all --network=host --shm-size=1800gb -itd verl:awsefa ================================================ FILE: docker/aws/Dockerfile.ngc.vllm0.8.sagemaker ================================================ # Using a pre-built image from AWS DLC which contains the current version of python (3.10) and supported cuda version (12.1) FROM 763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:2.1.0-transformers4.36.0-gpu-py310-cu121-ubuntu20.04 # uninstall nv-pytorch fork RUN pip3 uninstall -y pytorch-quantization \ pytorch-triton torch torch-tensorrt torchvision \ xgboost transformer_engine flash_attn apex megatron-core # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install systemctl RUN apt-get update && \ apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \ apt-get clean # Install tini RUN apt-get update && \ apt-get install -y tini && \ apt-get clean # Install torch-2.6.0 + vllm-0.8.2 RUN pip install --no-cache-dir vllm==0.8.2 torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 tensordict torchdata==0.11.0 \ transformers>=4.49.0 accelerate datasets peft hf-transfer \ ray[default] codetiming hydra-core pandas pyarrow>=15.0.0 pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler \ pytest pre-commit py-spy pyext ruff tensorboard # Install flash_attn-2.7.4.post1 RUN pip uninstall -y transformer-engine flash-attn && \ pip install flash-attn==2.7.4.post1 --no-build-isolation # Fix cv2 RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --no-cache-dir nvidia-ml-py>=12.560.30 opencv-python-headless==4.8.0.74 fastapi==0.115.6 && \ pip install --no-cache-dir --upgrade optree>=0.13.0 # Install verl RUN pip install --no-cache-dir verl[vllm] -U # Reset pip config RUN pip config unset global.index-url && \ pip config unset global.extra-index-url ================================================ FILE: docker/rocm/Apptainerfile.rocm ================================================ Bootstrap: docker # Support - Traing: fsdp; Inference: vllm # FROM: rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 # Support - Traing: fsdp; Inference: vllm, sglang FROM lmsysorg/sglang:v0.4.5-rocm630 %environment export PYTORCH_ROCM_ARCH="gfx90a;gfx942" export HIPCC_COMPILE_FLAGS_APPEND="--amdgpu-target=gfx90a;gfx942 -D__HIP_PLATFORM_AMD__" export CFLAGS="-D__HIP_PLATFORM_AMD__" export CXXFLAGS="-D__HIP_PLATFORM_AMD__" %post # Create source directory mkdir -p /opt/src # Uninstall and reinstall vllm pip uninstall -y vllm cd /opt/src git clone -b v0.6.3 https://github.com/vllm-project/vllm.git cd vllm MAX_JOBS=$(nproc) python3 setup.py install cd /opt rm -rf /opt/src/vllm # Install dependencies pip install "tensordict<0.6" --no-deps pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ "ray[data,train,tune,serve]" \ torchdata \ transformers \ wandb \ orjson \ pybind11 # Clone and install verl from GitHub cd /opt git clone https://github.com/volcengine/verl.git cd verl # Uncomment to use a specific version # git checkout v0.3.0.post0 pip install -e . --no-deps # Install torch_memory_saver pip install git+https://github.com/ExtremeViscent/torch_memory_saver.git --no-deps ================================================ FILE: docker/rocm/Dockerfile.rocm ================================================ # FROM "compute-artifactory.amd.com:5000/rocm-plus-docker/framework/compute-rocm-rel-6.4:94_ubuntu22.04_py3.10_pytorch_release-2.7_575e247" # FROM "rlfoundation.azurecr.io/rocm6.3.4:vllm-0.8.5-numa-patch-ubuntu-22.04" FROM "rlsys/rocm-6.3.4-patch:rocm6.3.4-numa-patch_ubuntu-22.04" SHELL ["/bin/bash", "-ceuxo", "pipefail"] ENV MAX_JOBS=512 ENV PATH="/usr/local/python3.12/bin:$PATH" RUN ln -sf /usr/bin/python3.12 /usr/bin/python && \ ln -sf /usr/bin/pip3.12 /usr/bin/pip ############################################ ############################################ RUN apt-get update RUN apt-get install -y pkg-config liblzma-dev ############################################ ############################################ ########################################### ##########Install TransformerEngine######## ########################################### WORKDIR /workspace/ # transformer-engine install # https://github.com/ROCm/TransformerEngine RUN rm -rf TransformerEngine RUN git clone --recursive https://github.com/ROCm/TransformerEngine.git WORKDIR /workspace/TransformerEngine RUN git checkout 236178e5 # git checkout bb061ade # git checkout 864405c ENV NVTE_FRAMEWORK=pytorch ENV NVTE_ROCM_ARCH=gfx942 ENV NVTE_USE_HIPBLASLT=1 ENV NVTE_USE_ROCM=1 # export CMAKE_PREFIX_PATH="/opt/rocm:/opt/rocm/hip:/usr/local:/usr:${CMAKE_PREFIX_PATH:-}" ENV CMAKE_PREFIX_PATH="/opt/rocm:/opt/rocm/hip:/usr/local:/usr" # ENV NVTE_BUILD_MAX_JOBS=$(MAX_JOBS) RUN MAX_JOBS=$(MAX_JOBS) pip install . -vvv WORKDIR /workspace/ ########################################### ########################################### ########################################### #################################################################################### ################Install vllm - sglang require vllm 0.6.7 dependency################# #################################################################################### #### Require vllm 0.6.7 - checkout 113274a0 WORKDIR /workspace/ RUN rm -rf vllm RUN pip uninstall -y vllm # Refer to here (down-grade vllm to 0.6.3): https://docs.vllm.ai/en/v0.6.3/getting_started/amd-installation.html RUN git clone https://github.com/ROCm/vllm.git # git clone https://github.com/vllm-project/vllm.git WORKDIR /workspace/vllm RUN git checkout 113274a0 ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" #ENV MAX_JOBS=512 ENV MAX_JOBS=${MAX_JOBS} RUN pip install "boto3>=1.26.0" RUN pip install setuptools_scm # will add src into py. You can delete the repo RUN python3 setup.py install WORKDIR /workspace/ #################################################################################### #################################################################################### #################################################################################### ########################################### ############For hack docker################ ########################################### RUN pip install setuptools==75.8.0 ########################################### ########################################### ########################################### ########################################### ############build sgalng################### ########################################### # Set environment variables ENV BASE_DIR=/sgl-workspace ENV BUILD_TYPE=all ENV SGL_REPO=https://github.com/sgl-project/sglang ENV SGL_BRANCH=v0.4.6.post5 ENV TRITON_REPO=https://github.com/ROCm/triton.git ENV TRITON_COMMIT=improve_fa_decode_3.0.0 ENV AITER_REPO=https://github.com/ROCm/aiter.git ENV AITER_COMMIT=v0.1.2 # v0.1.2 version - commit id: 9d11f47 # ENV AITER_COMMIT=9d11f47 ENV HIP_FORCE_DEV_KERNARG=1 ENV HSA_NO_SCRATCH_RECLAIM=1 ENV SGLANG_SET_CPU_AFFINITY=1 ENV SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 ENV NCCL_MIN_NCHANNELS=112 ENV MOE_PADDING=1 ENV VLLM_FP8_PADDING=1 ENV VLLM_FP8_ACT_PADDING=1 ENV VLLM_FP8_WEIGHT_PADDING=1 ENV VLLM_FP8_REDUCE_CONV=1 ENV TORCHINDUCTOR_MAX_AUTOTUNE=1 ENV TORCHINDUCTOR_MAX_AUTOTUNE_POINTWISE=1 ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942" ENV AMDGPU_TARGETS=gfx942 ENV ROCM_ARCH=gfx942 ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" # Switch to working directory WORKDIR /sgl-workspace # Clean and create directory RUN rm -rf /sgl-workspace && mkdir -p /sgl-workspace # Clone and build sglang RUN git clone ${SGL_REPO} \ && cd sglang \ && git checkout ${SGL_BRANCH} || echo "Using default branch" \ && cd sgl-kernel \ && rm -f pyproject.toml \ && mv pyproject_rocm.toml pyproject.toml \ && python setup_rocm.py install \ && cd .. \ && if [ "$BUILD_TYPE" = "srt" ]; then \ python -m pip --no-cache-dir install -e "python[srt_hip]"; \ else \ python -m pip --no-cache-dir install -e "python[all_hip]"; \ fi \ && cd /sgl-workspace \ && cp -r /sgl-workspace/sglang /sglang \ && python -m pip cache purge # Install common Python packages RUN pip install IPython orjson python-multipart torchao pybind11 # Rebuild Triton RUN pip uninstall -y triton || true \ && git clone ${TRITON_REPO} \ && cd triton \ && git checkout ${TRITON_COMMIT} \ && cd python \ && python3 setup.py install \ && cd /sgl-workspace # ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942 --amdgpu-lower-module-lds-strategy=1" # ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942" # Build aiter #version: Commit 9d11f47 # && git checkout ${AITER_COMMIT} \ RUN pip uninstall -y aiter || true RUN git clone ${AITER_REPO} \ && cd aiter \ && git checkout ${AITER_COMMIT} \ && git submodule sync \ && git submodule update --init --recursive \ && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py install \ && cd /sgl-workspace # && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py develop \ # && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py develop \ # Copy MI300X config RUN find /sgl-workspace/sglang/python/sglang/srt/layers/quantization/configs/ \ /sgl-workspace/sglang/python/sglang/srt/layers/moe/fused_moe_triton/configs/ \ -type f -name '*MI300X*' | \ xargs -I {} sh -c 'vf_config=$(echo "$1" | sed "s/MI300X/MI300X_VF/"); cp "$1" "$vf_config"' -- {} # Environment setup complete. RUN echo "Environment setup complete." WORKDIR /workspace/ ########################################### ########################################### ########################################### ########################################### ###############vllm v0.8.5################# ########################################### # ENV GITHUB_USERNAME=yushengsu-thu # ENV GITHUB_MAIL=yushengsu@gmail.com # RUN git config --global user.name "${GITHUB_USERNAME}" \ # && git config --global user.email "${GITHUB_MAIL}" WORKDIR /workspace/ ENV VLLM_TARGET_DEVICE=rocm ENV ROCM_PATH=/opt/rocm ENV SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev # Find the repo path in: DockerFile/Dockerfile.rocm_yang # RUN git clone https://github.com/RLFoundation/vllm-patch.git RUN pip uninstall -y vllm || true RUN rm -rf vllm-patch RUN git clone https://github.com/RLFoundation/vllm-patch.git \ && cd vllm-patch \ && git checkout v0.8.5-sleep-numa \ && rm -rf build/ dist/ *.egg-info \ && ln -sf /opt/rocm/lib/libamdhip64.so /usr/lib/libamdhip64.so \ && SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev PYTORCH_ROCM_ARCH="gfx90a;gfx942" MAX_JOBS=${MAX_JOBS} python3 setup.py install # RUN SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev PYTORCH_ROCM_ARCH="gfx90a;gfx942" MAX_JOBS=${MAX_JOBS} python3 setup.py develop WORKDIR /workspace/ ########################################### ########################################### ########################################### ######################################### #### Install megatron-core############### ######################################### RUN pip uninstall -y megatron-core && \ git clone https://github.com/yushengsu-thu/Megatron-LM-amd_version.git && \ cd Megatron-LM-amd_version && \ pip install -vvv -e . && \ cd /workspace/ ######################################### ######################################### ######################################### ####################################### ################apex################### ####################################### WORKDIR /workspace/ RUN pip uninstall -y apex && \ git clone https://github.com/ROCm/apex.git && \ cd apex && \ python setup.py install && \ cd /workspace/ ####################################### ####################################### ####################################### ################################################################################ ###########################Add torch_memory_saver############################### ################################################################################ # Set environment variables ENV HIPCC_COMPILE_FLAGS_APPEND="--amdgpu-target=gfx90a;gfx942 -D__HIP_PLATFORM_AMD__" ENV CFLAGS="-D__HIP_PLATFORM_AMD__" ENV CXXFLAGS="-D__HIP_PLATFORM_AMD__" RUN pip install "git+https://github.com/YangWang92/torch_memory_saver_numa.git@numa" ################################################################################ ################################################################################ ################################################################################ ######################################## ######Install ray####################### ######################################## # need to add this patch: https://github.com/ray-project/ray/pull/53531/files RUN pip uninstall ray -y RUN pip install "ray[data,train,tune,serve]>=2.47.0" ######################################## ######################################## ######################################## ########################################## #######Install other dependencies######### ########################################## RUN pip install "tensordict==0.6.2" --no-deps && \ pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ torchdata \ wandb \ orjson \ pybind11 WORKDIR /workspace/ RUN git clone https://github.com/volcengine/verl.git && \ cd verl && \ pip install -e . ########################################## ########################################## ########################################## WORKDIR /workspace/ CMD ["/usr/bin/bash"] ================================================ FILE: docker/rocm/Dockerfile.rocm7 ================================================ # default base image ARG REMOTE_VLLM="1" ARG COMMON_WORKDIR=/app ARG BASE_IMAGE=rocm/vllm-dev:base FROM ${BASE_IMAGE} AS base ARG ARG_PYTORCH_ROCM_ARCH ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}} # Install some basic utilities RUN apt-get update -q -y && apt-get install -q -y \ sqlite3 libsqlite3-dev libfmt-dev libmsgpack-dev libsuitesparse-dev \ apt-transport-https ca-certificates wget curl # Remove sccache RUN python3 -m pip install --upgrade pip RUN apt-get purge -y sccache; python3 -m pip uninstall -y sccache; rm -f "$(which sccache)" ARG COMMON_WORKDIR WORKDIR ${COMMON_WORKDIR} # ----------------------- # vLLM fetch stages FROM base AS fetch_vllm_0 ONBUILD COPY ./ vllm/ FROM base AS fetch_vllm_1 #ARG VLLM_REPO="https://github.com/ROCm/vllm.git" #ARG VLLM_BRANCH="main" ARG VLLM_REPO=https://github.com/HollowMan6/vllm.git ARG VLLM_BRANCH="sleep_amd" ONBUILD RUN git clone ${VLLM_REPO} \ && cd vllm \ && git checkout ${VLLM_BRANCH} FROM fetch_vllm_${REMOTE_VLLM} AS fetch_vllm # ----------------------- # vLLM build stages FROM fetch_vllm AS build_vllm # Build vLLM RUN cd vllm \ && python3 -m pip install -r requirements/rocm.txt \ && python3 setup.py clean --all \ && ln -sf /opt/rocm/lib/libamdhip64.so /usr/lib/libamdhip64.so \ && VLLM_TARGET_DEVICE=rocm ROCM_PATH=/opt/rocm/ VLLM_GPU_LANG=HIP SETUPTOOLS_SCM_PRETEND_VERSION=0.11.0.dev python3 setup.py bdist_wheel --dist-dir=dist #&& python3 setup.py bdist_wheel --dist-dir=dist FROM scratch AS export_vllm ARG COMMON_WORKDIR COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/dist/*.whl / COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/requirements /requirements COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/benchmarks /benchmarks COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/tests /tests COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/examples /examples COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/.buildkite /.buildkite # ----------------------- # Test vLLM image FROM base AS test RUN python3 -m pip install --upgrade pip && rm -rf /var/lib/apt/lists/* # Install vLLM #RUN --mount=type=bind,from=export_vllm,src=/,target=/install \ COPY --from=export_vllm /*.whl /install COPY --from=export_vllm /requirements /install/requirements COPY --from=export_vllm /benchmarks /install/benchmarks COPY --from=export_vllm /tests /install/tests COPY --from=export_vllm /examples /install/examples COPY --from=export_vllm /.buildkite /install/.buildkite RUN cd /install \ && pip install -U -r requirements/rocm.txt \ && pip install -U -r requirements/rocm-test.txt \ && pip uninstall -y vllm \ && pip install *.whl WORKDIR /vllm-workspace ARG COMMON_WORKDIR COPY --from=build_vllm ${COMMON_WORKDIR}/vllm /vllm-workspace # install development dependencies (for testing) RUN cd /vllm-workspace \ && rm -rf vllm \ && python3 -m pip install -e tests/vllm_test_utils \ && python3 -m pip install lm-eval[api]==0.4.4 \ && python3 -m pip install pytest-shard # ----------------------- # Final vLLM image FROM base AS final RUN python3 -m pip install --upgrade pip && rm -rf /var/lib/apt/lists/* # Error related to odd state for numpy 1.20.3 where there is no METADATA etc, but an extra LICENSES_bundled.txt. # Manually remove it so that later steps of numpy upgrade can continue RUN case "$(which python3)" in \ *"/opt/conda/envs/py_3.9"*) \ rm -rf /opt/conda/envs/py_3.9/lib/python3.9/site-packages/numpy-1.20.3.dist-info/;; \ *) ;; esac RUN python3 -m pip install --upgrade huggingface-hub[cli] # Install vLLM RUN --mount=type=bind,from=export_vllm,src=/,target=/install \ cd /install \ && pip install -U -r requirements/rocm.txt \ && pip uninstall -y vllm \ && pip install *.whl ARG COMMON_WORKDIR # Copy over the benchmark scripts as well COPY --from=export_vllm /benchmarks ${COMMON_WORKDIR}/vllm/benchmarks COPY --from=export_vllm /examples ${COMMON_WORKDIR}/vllm/examples ENV RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES=1 ENV TOKENIZERS_PARALLELISM=false # ENV that can improve safe tensor loading, and end-to-end time ENV SAFETENSORS_FAST_GPU=1 # Performance environment variable. ENV HIP_FORCE_DEV_KERNARG=1 # ----------------------- # Install verl ARG VERL_REPO=https://github.com/volcengine/verl.git ARG VERL_BRANCH=main RUN pip install "tensordict==0.6.2" --no-deps && \ pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ torchdata \ wandb \ orjson \ pybind11 WORKDIR /workspace/ RUN git clone ${VERL_REPO} && \ cd verl && \ git checkout ${VERL_BRANCH} && \ pip install -e . CMD ["/bin/bash"] ================================================ FILE: docker/rocm/Dockerfile.rocm_verl-0.3.0.post1 ================================================ # Build the docker in the repo dir: # docker build -f docker/Dockerfile.rocm -t verl-rocm:03.04.2015 . # docker images # you can find your built docker # Support - Traing: fsdp; Inference: vllm # FROM rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 # Support - Traing: fsdp; Inference: vllm, sglang FROM lmsysorg/sglang:v0.4.6.post5-rocm630 # Set working directory # WORKDIR $PWD/app # Set environment variables ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" ENV HIPCC_COMPILE_FLAGS_APPEND="--amdgpu-target=gfx90a;gfx942 -D__HIP_PLATFORM_AMD__" ENV CFLAGS="-D__HIP_PLATFORM_AMD__" ENV CXXFLAGS="-D__HIP_PLATFORM_AMD__" # Install vllm RUN pip uninstall -y vllm && \ rm -rf vllm && \ git clone -b v0.6.3 https://github.com/vllm-project/vllm.git && \ cd vllm && \ MAX_JOBS=$(nproc) python3 setup.py install && \ cd .. && \ rm -rf vllm # Copy the entire project directory COPY . . # Install dependencies RUN pip install "tensordict==0.6.2" --no-deps && \ pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ "ray[data,train,tune,serve]<2.45.0" \ torchdata \ transformers \ wandb \ orjson \ pybind11 RUN git clone https://github.com/volcengine/verl.git && \ cd verl && \ pip install -e . # Install torch_memory_saver RUN pip install git+https://github.com/ExtremeViscent/torch_memory_saver.git --no-deps ================================================ FILE: docker/rocm/Dockerfile.rocm_verl-0.4.1 ================================================ # FROM "compute-artifactory.amd.com:5000/rocm-plus-docker/framework/compute-rocm-rel-6.4:94_ubuntu22.04_py3.10_pytorch_release-2.7_575e247" # FROM "rlfoundation.azurecr.io/rocm6.3.4:vllm-0.8.5-numa-patch-ubuntu-22.04" FROM "rlsys/rocm-6.3.4-patch:rocm6.3.4-numa-patch_ubuntu-22.04" SHELL ["/bin/bash", "-ceuxo", "pipefail"] ENV MAX_JOBS=512 ENV PATH="/usr/local/python3.12/bin:$PATH" RUN ln -sf /usr/bin/python3.12 /usr/bin/python && \ ln -sf /usr/bin/pip3.12 /usr/bin/pip ############################################ ############################################ RUN apt-get update RUN apt-get install -y pkg-config liblzma-dev ############################################ ############################################ ########################################### ##########Install TransformerEngine######## ########################################### WORKDIR /workspace/ # transformer-engine install # https://github.com/ROCm/TransformerEngine RUN rm -rf TransformerEngine RUN git clone --recursive https://github.com/ROCm/TransformerEngine.git WORKDIR /workspace/TransformerEngine RUN git checkout 236178e5 # git checkout bb061ade # git checkout 864405c ENV NVTE_FRAMEWORK=pytorch ENV NVTE_ROCM_ARCH=gfx942 ENV NVTE_USE_HIPBLASLT=1 ENV NVTE_USE_ROCM=1 # export CMAKE_PREFIX_PATH="/opt/rocm:/opt/rocm/hip:/usr/local:/usr:${CMAKE_PREFIX_PATH:-}" ENV CMAKE_PREFIX_PATH="/opt/rocm:/opt/rocm/hip:/usr/local:/usr" # ENV NVTE_BUILD_MAX_JOBS=$(MAX_JOBS) RUN MAX_JOBS=$(MAX_JOBS) pip install . -vvv WORKDIR /workspace/ ########################################### ########################################### ########################################### #################################################################################### ################Install vllm - sglang require vllm 0.6.7 dependency################# #################################################################################### #### Require vllm 0.6.7 - checkout 113274a0 WORKDIR /workspace/ RUN rm -rf vllm RUN pip uninstall -y vllm # Refer to here (down-grade vllm to 0.6.3): https://docs.vllm.ai/en/v0.6.3/getting_started/amd-installation.html RUN git clone https://github.com/ROCm/vllm.git # git clone https://github.com/vllm-project/vllm.git WORKDIR /workspace/vllm RUN git checkout 113274a0 ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" #ENV MAX_JOBS=512 ENV MAX_JOBS=${MAX_JOBS} RUN pip install "boto3>=1.26.0" RUN pip install setuptools_scm # will add src into py. You can delete the repo RUN python3 setup.py install WORKDIR /workspace/ #################################################################################### #################################################################################### #################################################################################### ########################################### ############For hack docker################ ########################################### RUN pip install setuptools==75.8.0 ########################################### ########################################### ########################################### ########################################### ############build sgalng################### ########################################### # Set environment variables ENV BASE_DIR=/sgl-workspace ENV BUILD_TYPE=all ENV SGL_REPO=https://github.com/sgl-project/sglang ENV SGL_BRANCH=v0.4.6.post5 ENV TRITON_REPO=https://github.com/ROCm/triton.git ENV TRITON_COMMIT=improve_fa_decode_3.0.0 ENV AITER_REPO=https://github.com/ROCm/aiter.git ENV AITER_COMMIT=v0.1.2 # v0.1.2 version - commit id: 9d11f47 # ENV AITER_COMMIT=9d11f47 ENV HIP_FORCE_DEV_KERNARG=1 ENV HSA_NO_SCRATCH_RECLAIM=1 ENV SGLANG_SET_CPU_AFFINITY=1 ENV SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 ENV NCCL_MIN_NCHANNELS=112 ENV MOE_PADDING=1 ENV VLLM_FP8_PADDING=1 ENV VLLM_FP8_ACT_PADDING=1 ENV VLLM_FP8_WEIGHT_PADDING=1 ENV VLLM_FP8_REDUCE_CONV=1 ENV TORCHINDUCTOR_MAX_AUTOTUNE=1 ENV TORCHINDUCTOR_MAX_AUTOTUNE_POINTWISE=1 ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942" ENV AMDGPU_TARGETS=gfx942 ENV ROCM_ARCH=gfx942 ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" # Switch to working directory WORKDIR /sgl-workspace # Clean and create directory RUN rm -rf /sgl-workspace && mkdir -p /sgl-workspace # Clone and build sglang RUN git clone ${SGL_REPO} \ && cd sglang \ && git checkout ${SGL_BRANCH} || echo "Using default branch" \ && cd sgl-kernel \ && rm -f pyproject.toml \ && mv pyproject_rocm.toml pyproject.toml \ && python setup_rocm.py install \ && cd .. \ && if [ "$BUILD_TYPE" = "srt" ]; then \ python -m pip --no-cache-dir install -e "python[srt_hip]"; \ else \ python -m pip --no-cache-dir install -e "python[all_hip]"; \ fi \ && cd /sgl-workspace \ && cp -r /sgl-workspace/sglang /sglang \ && python -m pip cache purge # Install common Python packages RUN pip install IPython orjson python-multipart torchao pybind11 # Rebuild Triton RUN pip uninstall -y triton || true \ && git clone ${TRITON_REPO} \ && cd triton \ && git checkout ${TRITON_COMMIT} \ && cd python \ && python3 setup.py install \ && cd /sgl-workspace # ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942 --amdgpu-lower-module-lds-strategy=1" # ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942" # Build aiter #version: Commit 9d11f47 # && git checkout ${AITER_COMMIT} \ RUN pip uninstall -y aiter || true RUN git clone ${AITER_REPO} \ && cd aiter \ && git checkout ${AITER_COMMIT} \ && git submodule sync \ && git submodule update --init --recursive \ && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py install \ && cd /sgl-workspace # && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py develop \ # && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py develop \ # Copy MI300X config RUN find /sgl-workspace/sglang/python/sglang/srt/layers/quantization/configs/ \ /sgl-workspace/sglang/python/sglang/srt/layers/moe/fused_moe_triton/configs/ \ -type f -name '*MI300X*' | \ xargs -I {} sh -c 'vf_config=$(echo "$1" | sed "s/MI300X/MI300X_VF/"); cp "$1" "$vf_config"' -- {} # Environment setup complete. RUN echo "Environment setup complete." WORKDIR /workspace/ ########################################### ########################################### ########################################### ########################################### ###############vllm v0.8.5################# ########################################### # ENV GITHUB_USERNAME=yushengsu-thu # ENV GITHUB_MAIL=yushengsu@gmail.com # RUN git config --global user.name "${GITHUB_USERNAME}" \ # && git config --global user.email "${GITHUB_MAIL}" WORKDIR /workspace/ ENV VLLM_TARGET_DEVICE=rocm ENV ROCM_PATH=/opt/rocm ENV SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev # Find the repo path in: DockerFile/Dockerfile.rocm_yang # RUN git clone https://github.com/RLFoundation/vllm-patch.git RUN pip uninstall -y vllm || true RUN rm -rf vllm-patch RUN git clone https://github.com/RLFoundation/vllm-patch.git \ && cd vllm-patch \ && git checkout v0.8.5-sleep-numa \ && rm -rf build/ dist/ *.egg-info \ && ln -sf /opt/rocm/lib/libamdhip64.so /usr/lib/libamdhip64.so \ && SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev PYTORCH_ROCM_ARCH="gfx90a;gfx942" MAX_JOBS=${MAX_JOBS} python3 setup.py install # RUN SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev PYTORCH_ROCM_ARCH="gfx90a;gfx942" MAX_JOBS=${MAX_JOBS} python3 setup.py develop WORKDIR /workspace/ ########################################### ########################################### ########################################### ######################################### #### Install megatron-core############### ######################################### RUN pip uninstall -y megatron-core && \ git clone https://github.com/yushengsu-thu/Megatron-LM-amd_version.git && \ cd Megatron-LM-amd_version && \ pip install -vvv -e . && \ cd /workspace/ ######################################### ######################################### ######################################### ####################################### ################apex################### ####################################### WORKDIR /workspace/ RUN pip uninstall -y apex && \ git clone https://github.com/ROCm/apex.git && \ cd apex && \ python setup.py install && \ cd /workspace/ ####################################### ####################################### ####################################### ################################################################################ ###########################Add torch_memory_saver############################### ################################################################################ # Set environment variables ENV HIPCC_COMPILE_FLAGS_APPEND="--amdgpu-target=gfx90a;gfx942 -D__HIP_PLATFORM_AMD__" ENV CFLAGS="-D__HIP_PLATFORM_AMD__" ENV CXXFLAGS="-D__HIP_PLATFORM_AMD__" RUN pip install "git+https://github.com/YangWang92/torch_memory_saver_numa.git@numa" ################################################################################ ################################################################################ ################################################################################ ######################################## ######Install ray####################### ######################################## # need to add this patch: https://github.com/ray-project/ray/pull/53531/files RUN pip uninstall ray -y RUN pip install "ray[data,train,tune,serve]>=2.47.0" ######################################## ######################################## ######################################## ########################################## #######Install other dependencies######### ########################################## RUN pip install "tensordict==0.6.2" --no-deps && \ pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ torchdata \ wandb \ orjson \ pybind11 WORKDIR /workspace/ RUN git clone https://github.com/volcengine/verl.git && \ cd verl && \ pip install -e . ########################################## ########################################## ########################################## WORKDIR /workspace/ CMD ["/usr/bin/bash"] CMD ["/usr/bin/bash"] ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.sglang.vllm.mcore0.12 ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.6.post5 and torch-memory-saver RUN pip install --resume-retries 999 "sglang[all]==0.4.6.post5" --no-cache-dir --find-links https://flashinfer.ai/whl/cu124/torch2.6/flashinfer-python && pip install torch-memory-saver --no-cache-dir # Some sglang operations in 0.4.6.post5 require vllm # [Warning] vllm can have some packages not compatible with sglang, for example, flashinfer RUN pip install --resume-retries 999 --no-cache-dir vllm==0.8.5.post1 # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Fix for transformers 4.53.0 RUN pip3 install --no-cache-dir "transformers[hf_xet]<4.52.0" # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.sglang.vllm.mcore0.12.deepep ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.6.post5 and torch-memory-saver RUN pip install --resume-retries 999 "sglang[all]==0.4.6.post5" --no-cache-dir --find-links https://flashinfer.ai/whl/cu124/torch2.6/flashinfer-python && pip install torch-memory-saver --no-cache-dir # Some sglang operations in 0.4.6.post5 require vllm # [Warning] vllm can have some packages not compatible with sglang, for example, flashinfer RUN pip install --resume-retries 999 --no-cache-dir vllm==0.8.5.post1 # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Fix for transformers 4.53.0 RUN pip3 install --no-cache-dir "transformers[hf_xet]<4.52.0" # Install mbridge RUN pip3 install --no-cache-dir mbridge # Install DeepEP ## the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so ## Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ## Build deepep-nvshmem RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.sglang.vllm.mcore0.13.preview ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.6.post5 and torch-memory-saver RUN pip install --resume-retries 999 "sglang[all]==0.4.6.post5" --no-cache-dir --find-links https://flashinfer.ai/whl/cu124/torch2.6/flashinfer-python && pip install torch-memory-saver --no-cache-dir # Some sglang operations in 0.4.6.post5 require vllm # [Warning] vllm can have some packages not compatible with sglang, for example, flashinfer RUN pip install --resume-retries 999 --no-cache-dir vllm==0.8.5.post1 # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.5 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_r0.13.0 # Fix for transformers 4.53.0 RUN pip3 install --no-cache-dir "transformers[hf_xet]<4.52.0" # Install mbridge RUN pip3 install --no-cache-dir mbridge # Install DeepEP ## the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so ## Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ## Build deepep-nvshmem RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.vllm.mcore0.12 ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install torch-2.6.0+cu124 + vllm-0.8.5.post1 # torch-2.6.0+cu124: cxx11abi=False # torch-2.6.0+cu126: cxx11abi=True # see https://github.com/flashinfer-ai/flashinfer/issues/911 RUN pip install --resume-retries 999 --no-cache-dir vllm==0.8.5.post1 # Install flashinfer-0.2.2.post1+cu126 (cxx11abi=True) # vllm-0.8.3 does not support flashinfer>=0.2.3 # see https://github.com/vllm-project/vllm/pull/15777 RUN aria2c --max-tries=9999 https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.2.post1/flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \ pip install --no-cache-dir flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \ rm flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Fix for transformers 4.53.0 RUN pip3 install --no-cache-dir "transformers[hf_xet]<4.52.0" # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.vllm.mcore0.12.deepep ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install torch-2.6.0+cu124 + vllm-0.8.5.post1 # torch-2.6.0+cu124: cxx11abi=False # torch-2.6.0+cu126: cxx11abi=True # see https://github.com/flashinfer-ai/flashinfer/issues/911 RUN pip install --resume-retries 999 --no-cache-dir vllm==0.8.5.post1 # Install flashinfer-0.2.2.post1+cu126 (cxx11abi=True) # vllm-0.8.3 does not support flashinfer>=0.2.3 # see https://github.com/vllm-project/vllm/pull/15777 RUN aria2c --max-tries=9999 https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.2.post1/flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \ pip install --no-cache-dir flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \ rm flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Fix for transformers 4.53.0 RUN pip3 install --no-cache-dir "transformers[hf_xet]<4.52.0" # Install mbridge RUN pip3 install --no-cache-dir mbridge # Install DeepEP ## the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so ## Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ## Build deepep-nvshmem RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.vllm.mcore0.13.preview ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install torch-2.6.0+cu124 + vllm-0.8.5.post1 # torch-2.6.0+cu124: cxx11abi=False # torch-2.6.0+cu126: cxx11abi=True # see https://github.com/flashinfer-ai/flashinfer/issues/911 RUN pip install --resume-retries 999 --no-cache-dir vllm==0.8.5.post1 # Install flashinfer-0.2.2.post1+cu126 (cxx11abi=True) # vllm-0.8.3 does not support flashinfer>=0.2.3 # see https://github.com/vllm-project/vllm/pull/15777 RUN aria2c --max-tries=9999 https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.2.post1/flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \ pip install --no-cache-dir flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl && \ rm flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.5 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Install mbridge RUN pip3 install --no-cache-dir mbridge # Install DeepEP ## the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so ## Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ## Build deepep-nvshmem RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.base ================================================ # Base Docker Image of verl, with CUDA/Torch/FlashAttn/Apex/TransformerEngine, without other frameworks # Target: verlai/verl:base-v2-cu124-cudnn9.8-torch2.6-fa2.8.0-te2.3 # Start from the NVIDIA official image (ubuntu-22.04 + cuda-12.6 + python-3.10) # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html FROM nvcr.io/nvidia/pytorch:24.08-py3 # Define environments ENV MAX_JOBS=16 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Define installation arguments ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple # Set apt source RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \ { \ echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \ } > /etc/apt/sources.list # Install systemctl RUN apt-get update && \ apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \ apt-get clean # Install tini RUN apt-get update && \ apt-get install -y tini aria2 && \ apt-get clean # Change pip source RUN pip config set global.index-url "${PIP_INDEX}" && \ pip config set global.extra-index-url "${PIP_INDEX}" && \ python -m pip install --upgrade pip # Uninstall nv-pytorch fork RUN pip uninstall -y torch torchvision torchaudio \ pytorch-quantization pytorch-triton torch-tensorrt \ xgboost transformer_engine flash_attn apex megatron-core grpcio # Reinstall CUDA 12.4 RUN aria2c https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin && \ mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 RUN aria2c --always-resume=true --max-tries=99999 https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb && \ dpkg -i cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb && \ cp /var/cuda-repo-ubuntu2204-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \ apt-get update && \ apt-get -y install cuda-toolkit-12-4 && \ rm cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb && \ update-alternatives --set cuda /usr/local/cuda-12.4 && \ rm -rf /usr/local/cuda-12.6 RUN pip install --resume-retries 999 --no-cache-dir torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 RUN pip install --resume-retries 999 --no-cache-dir "tensordict==0.6.2" torchdata "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff # Install flash-attn-2.7.4.post1 (cxx11abi=False) RUN wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl && \ pip install --no-cache-dir flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl # Fix packages RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" # Install cudnn RUN aria2c --max-tries=9999 https://developer.download.nvidia.com/compute/cudnn/9.8.0/local_installers/cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ dpkg -i cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ cp /var/cudnn-local-repo-ubuntu2204-9.8.0/cudnn-*-keyring.gpg /usr/share/keyrings/ && \ apt-get update && \ apt-get -y install cudnn-cuda-12 && \ rm cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb # Install Apex RUN git clone https://github.com/NVIDIA/apex.git && \ cd apex && \ pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ # Profiling tools RUN aria2c --always-resume=true --max-tries=99999 https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_3/nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ apt-get update && apt-get install -y libxcb-cursor0 && \ dpkg -i ./nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb # Fix opencv RUN pip install --resume-retries 999 --no-cache-dir opencv-python RUN pip install --resume-retries 999 --no-cache-dir opencv-fixer && \ python -c "from opencv_fixer import AutoFix; AutoFix()" RUN pip install --resume-retries 999 --no-cache-dir cuda-bindings # Reset pip config RUN pip config unset global.index-url && \ pip config unset global.extra-index-url RUN apt-get update && \ apt-get install -y libfreeimage3 libfreeimage-dev zlib1g htop ================================================ FILE: docker/verl0.4-cu124-torch2.6-fa2.7.4/README.md ================================================ # verl image with verl v0.4.x ## Important packages version ```txt cuda==12.4 cudnn==9.8.0 torch==2.6.0 flash_attn=2.7.4 sglang==0.4.6.post5 vllm==0.8.5.post1 nvidia-cudnn-cu12==9.8.0.87 transformer_engine==2.3 megatron.core==core_v0.12.2 # Preview transformer_engine==2.5 megatron.core==core_r0.13.0 ``` ## Target - Base image: - `verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4` - App image: - `verlai/verl:app-verl0.4-sglang0.4.6.post5-vllm0.8.5-mcore0.12.2-te2.2`: SGLang requires vLLM in 0.4.6.post5 version, vLLM can have some package conflicts with SGLang - `verlai/verl:app-verl0.4-sglang0.4.6.post5-vllm0.8.5-mcore0.12.2-te2.2-deepep`: Built with deepep - `verlai/verl:app-verl0.4-vllm0.8.5-mcore0.12.2-te2.2` - `verlai/verl:app-verl0.4-vllm0.8.5-mcore0.12.2-te2.2-deepep`: Built with deepep - Preview image: - `verlai/verl:app-verl0.4-sglang0.4.6.post5-vllm0.8.5-mcore0.13.0-te2.2-preview` - `verlai/verl:app-verl0.4-vllm0.8.5-mcore0.13.0-te2.2-preview` ================================================ FILE: docker/verl0.5-cu126-torch2.7-fa2.7.4/Dockerfile.app.sglang0.4.10.post2.mcore0.13 ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.7.4 # Define environments ENV MAX_JOBS=8 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.10 # Install FlashInfer Python package RUN pip install --upgrade pip setuptools packaging RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation flashinfer-python==0.2.9rc1 RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation "sglang[all]==0.4.10.post2" # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]==4.55.4" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.13.0 # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.5-cu126-torch2.7-fa2.7.4/Dockerfile.app.sglang0.4.9.post6.mcore0.13 ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.7.4 # Define environments ENV MAX_JOBS=8 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.10 # Install FlashInfer Python package RUN pip install --upgrade pip setuptools packaging RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation flashinfer-python==0.2.9rc1 RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation "sglang[all]==0.4.9.post6" # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]==4.55.4" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.13.0 # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.5-cu126-torch2.7-fa2.7.4/Dockerfile.app.vllm.mcore0.13 ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.7.4 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install torch-2.7.1+cu126 + vllm-0.10.0 RUN pip install --resume-retries 999 --no-cache-dir vllm==0.10.0 # Fix packages # transformers 4.54.0 still not support RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.55.4" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.13.0 # Install mbridge RUN pip3 install --no-cache-dir mbridge # Fix qwen vl RUN pip3 install --no-cache-dir --no-deps trl ================================================ FILE: docker/verl0.5-cu126-torch2.7-fa2.7.4/Dockerfile.app.vllm.mcore0.15 ================================================ # Start from the verl base image # Dockerfile.base FROM iseekyan/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.7.4-h100 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install torch-2.7.1+cu126 + vllm-0.10.0 RUN pip install --resume-retries 999 --no-cache-dir vllm==0.10.0 # Fix packages # transformers 4.54.0 still not support RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.55.4" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.7 RUN pip install onnxscript # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.15.0rc4 # Install mbridge RUN pip3 install --no-cache-dir mbridge==v0.15.0 # Fix qwen vl RUN pip3 install --no-cache-dir --no-deps trl ================================================ FILE: docker/verl0.5-cu126-torch2.7-fa2.7.4/Dockerfile.base.torch2.7.1 ================================================ # Base Docker Image of verl, with CUDA/Torch/FlashAttn/Apex/TransformerEngine, without other frameworks # Target: verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0-fi0.2.6 # Start from the NVIDIA official image (ubuntu-22.04 + cuda-12.6 + python-3.10) # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html FROM nvcr.io/nvidia/pytorch:24.08-py3 # Define environments ENV MAX_JOBS=16 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Define installation arguments ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple # Set apt source RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \ { \ echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \ } > /etc/apt/sources.list # Install systemctl RUN apt-get update && \ apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \ apt-get clean # Install tini RUN apt-get update && \ apt-get install -y tini aria2 libfreeimage3 libfreeimage-dev zlib1g htop && \ apt-get clean # Change pip source RUN pip config set global.index-url "${PIP_INDEX}" && \ pip config set global.extra-index-url "${PIP_INDEX}" && \ python -m pip install --upgrade pip # Uninstall nv-pytorch fork RUN pip uninstall -y torch torchvision torchaudio \ pytorch-quantization pytorch-triton torch-tensorrt \ xgboost transformer_engine flash_attn apex megatron-core grpcio RUN pip install --resume-retries 999 --no-cache-dir torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 # Install flash-attn-2.7.4.post1, although built with torch2.6, it is compatible with torch2.7 # https://github.com/Dao-AILab/flash-attention/issues/1644#issuecomment-2899396361 RUN ABI_FLAG=$(python -c "import torch; print('TRUE' if torch._C._GLIBCXX_USE_CXX11_ABI else 'FALSE')") && \ URL="https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abi${ABI_FLAG}-cp310-cp310-linux_x86_64.whl" && \ FILE="flash_attn-2.7.4.post1+cu12torch2.6cxx11abi${ABI_FLAG}-cp310-cp310-linux_x86_64.whl" && \ wget -nv "${URL}" && \ pip install --no-cache-dir "${FILE}" # Fix packages RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" # Install cudnn RUN aria2c --max-tries=9999 https://developer.download.nvidia.com/compute/cudnn/9.8.0/local_installers/cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ dpkg -i cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ cp /var/cudnn-local-repo-ubuntu2204-9.8.0/cudnn-*-keyring.gpg /usr/share/keyrings/ && \ apt-get update && \ apt-get -y install cudnn-cuda-12 && \ rm cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb # Install Apex RUN pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" --resume-retries 999 git+https://github.com/NVIDIA/apex.git # Profiling tools RUN aria2c --always-resume=true --max-tries=99999 https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_3/nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ apt-get update && apt-get install -y libxcb-cursor0 RUN apt-get install -y ./nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb RUN pip install --resume-retries 999 --no-cache-dir "tensordict==0.6.2" torchdata "transformers[hf_xet]>=4.52.3" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas cuda-bindings \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff # Install DeepEP ## the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so ## Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ## Build deepep-nvshmem RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install # Reset pip config RUN pip config unset global.index-url && \ pip config unset global.extra-index-url ================================================ FILE: docker/verl0.5-cu126-torch2.7-fa2.7.4/README.md ================================================ # verl image with verl v0.5 ## Important packages version ```txt cuda==12.6 cudnn==9.8.0 torch==2.7.1 flash_attn=2.7.4.post1 sglang==0.4.9.post6 vllm==0.8.5.post1 nvidia-cudnn-cu12==9.8.0.87 transformer_engine==2.3 megatron.core==core_v0.12.2 # Preview transformer_engine==2.5 megatron.core==core_r0.13.0 ``` ## Target - Base image: - `verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.7.4`: We offer a base image with deep ep built in, for vllm/sglang - App image: - `verlai/verl:app-verl0.5-transformers4.55.4-vllm0.10.0-mcore0.13.0-te2.2` - `verlai/verl:app-verl0.5-transformers4.55.4-sglang0.4.10.post2-mcore0.13.0-te2.2` - `iseekyan/verl:app-verl0.5-transformers4.55.4-vllm0.10.0-mcore0.15.0-te2.7` ================================================ FILE: docker/verl0.5-cu126-torch2.7.1-fa2.8.0/Dockerfile.app.sglang.mcore0.12 ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0 # Define environments ENV MAX_JOBS=8 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.8 and torch-memory-saver # Install FlashInfer Python package RUN pip install --upgrade pip setuptools packaging RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation flashinfer-python==0.2.6.post1 RUN pip install --resume-retries 999 --no-cache-dir "sglang[all]==0.4.8" && pip install torch-memory-saver --no-cache-dir # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.3 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.5-cu126-torch2.7.1-fa2.8.0/Dockerfile.app.sglang.mcore0.13.preview ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0 # Define environments ENV MAX_JOBS=8 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.8 and torch-memory-saver # Install FlashInfer Python package RUN pip install --upgrade pip setuptools packaging RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation flashinfer-python==0.2.6.post1 RUN pip install --resume-retries 999 --no-cache-dir "sglang[all]==0.4.8" && pip install torch-memory-saver --no-cache-dir # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.5 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.2 # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.5-cu126-torch2.7.1-fa2.8.0/Dockerfile.base ================================================ # Base Docker Image of verl, with CUDA/Torch/FlashAttn/Apex/TransformerEngine, without other frameworks # Target: verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0-fi0.2.6 # Start from the NVIDIA official image (ubuntu-22.04 + cuda-12.6 + python-3.10) # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html FROM nvcr.io/nvidia/pytorch:24.08-py3 # Define environments ENV MAX_JOBS=16 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Define installation arguments ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple # Set apt source RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \ { \ echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \ } > /etc/apt/sources.list # Install systemctl RUN apt-get update && \ apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \ apt-get clean # Install tini RUN apt-get update && \ apt-get install -y tini aria2 libfreeimage3 libfreeimage-dev zlib1g htop && \ apt-get clean # Change pip source RUN pip config set global.index-url "${PIP_INDEX}" && \ pip config set global.extra-index-url "${PIP_INDEX}" && \ python -m pip install --upgrade pip # Uninstall nv-pytorch fork RUN pip uninstall -y torch torchvision torchaudio \ pytorch-quantization pytorch-triton torch-tensorrt \ xgboost transformer_engine flash_attn apex megatron-core grpcio RUN pip install --resume-retries 999 --no-cache-dir torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 # Install flash-attn-2.8.0.post2 (cxx11abi=True) RUN ABI_FLAG=$(python -c "import torch; print('TRUE' if torch._C._GLIBCXX_USE_CXX11_ABI else 'FALSE')") && \ URL="https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.0.post2/flash_attn-2.8.0.post2+cu12torch2.7cxx11abi${ABI_FLAG}-cp310-cp310-linux_x86_64.whl" && \ FILE="flash_attn-2.8.0.post2+cu12torch2.7cxx11abi${ABI_FLAG}-cp310-cp310-linux_x86_64.whl" && \ wget -nv "${URL}" && \ pip install --no-cache-dir "${FILE}" # Fix packages RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" # Install cudnn RUN aria2c --max-tries=9999 https://developer.download.nvidia.com/compute/cudnn/9.8.0/local_installers/cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ dpkg -i cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ cp /var/cudnn-local-repo-ubuntu2204-9.8.0/cudnn-*-keyring.gpg /usr/share/keyrings/ && \ apt-get update && \ apt-get -y install cudnn-cuda-12 && \ rm cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb # Install Apex RUN pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" --resume-retries 999 git+https://github.com/NVIDIA/apex.git # Profiling tools RUN aria2c --always-resume=true --max-tries=99999 https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_3/nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ apt-get update && apt-get install -y libxcb-cursor0 RUN apt-get install -y ./nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb RUN pip install --resume-retries 999 --no-cache-dir "tensordict==0.6.2" torchdata "transformers[hf_xet]>=4.53" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas cuda-bindings \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pyext pre-commit ruff # Install DeepEP ## the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so ## Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ## Build deepep-nvshmem RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install # Reset pip config RUN pip config unset global.index-url && \ pip config unset global.extra-index-url ================================================ FILE: docker/verl0.5-cu126-torch2.7.1-fa2.8.0/README.md ================================================ # verl image with verl v0.5 ## Important packages version ```txt cuda==12.6 cudnn==9.8.0 torch==2.7.1 flash_attn=2.8.0 ## sglang==0.4.8 vllm==0.8.5.post1 nvidia-cudnn-cu12==9.8.0.87 transformer_engine==2.3 megatron.core==core_v0.12.2 # Preview transformer_engine==2.5 megatron.core==core_r0.13.0 ``` ## Target - Base image: - `verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0`: We offer a base image with deep ep built in - App image: - `verlai/verl:app-verl0.5-sglang0.4.9-mcore0.12.2` - `verlai/verl:app-verl0.5-sglang0.4.9-mcore0.13.0-preview` - vllm temporarily not support latest version ================================================ FILE: docker/verl0.5-preview-cu128-torch2.7.1-fa2.8.0/Dockerfile.app.sglang.megatron ================================================ # Start from the verl base image # Dockerfile.base FROM verlai/verl:base-verl0.5-preview-cu128-cudnn9.8-torch2.7.1-fa2.8.0-fi0.2.6 # Define environments ENV MAX_JOBS=8 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Install sglang-0.4.8 and torch-memory-saver # Install FlashInfer Python package RUN pip install --resume-retries 999 --no-cache-dir --no-build-isolation flashinfer-python==0.2.6.post1 RUN pip install --resume-retries 999 --no-cache-dir "sglang[all]==0.4.8" && pip install torch-memory-saver --no-cache-dir # Fix packages RUN pip install --no-cache-dir "tensordict==0.6.2" "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pre-commit ruff RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --resume-retries 999 --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" RUN pip install --resume-retries 999 --no-cache-dir nvidia-cudnn-cu12==9.8.0.87 # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --resume-retries 999 --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.5 # Install Megatron-LM RUN pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/Megatron-LM.git@core_r0.13.0 # Install mbridge RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.5-preview-cu128-torch2.7.1-fa2.8.0/Dockerfile.base ================================================ # Base Docker Image of verl, with CUDA/Torch/FlashAttn/Apex/TransformerEngine, without other frameworks # Target: verlai/verl:base-verl0.5-preview-cu128-cudnn9.8-torch2.7.1-fa2.8.0-fi0.2.6 # Start from the NVIDIA official image (ubuntu-22.04 + cuda-12.6 + python-3.10) # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html FROM nvcr.io/nvidia/pytorch:25.02-py3 # Define environments ENV MAX_JOBS=16 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" # Define installation arguments ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple # Set apt source RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \ { \ echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \ echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \ } > /etc/apt/sources.list # Install systemctl RUN apt-get update && \ apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \ apt-get clean # Install tini RUN apt-get update && \ apt-get install -y tini aria2 libfreeimage3 libfreeimage-dev zlib1g htop && \ apt-get clean # Change pip source RUN pip config set global.index-url "${PIP_INDEX}" && \ pip config set global.extra-index-url "${PIP_INDEX}" && \ python -m pip install --upgrade pip # Uninstall nv-pytorch fork RUN pip uninstall -y torch torchvision torchaudio \ pytorch-quantization pytorch-triton torch-tensorrt \ xgboost transformer_engine flash_attn apex megatron-core grpcio RUN pip install --resume-retries 999 --no-cache-dir torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu128 # Install flash-attn-2.8.0.post2 (cxx11abi=True) RUN ABI_FLAG=$(python -c "import torch; print('TRUE' if torch._C._GLIBCXX_USE_CXX11_ABI else 'FALSE')") && \ URL="https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.0.post2/flash_attn-2.8.0.post2+cu12torch2.7cxx11abi${ABI_FLAG}-cp312-cp312-linux_x86_64.whl" && \ FILE="flash_attn-2.8.0.post2+cu12torch2.7cxx11abi${ABI_FLAG}-cp312-cp312-linux_x86_64.whl" && \ wget -nv "${URL}" && \ pip install --no-cache-dir "${FILE}" # Fix packages RUN pip uninstall -y pynvml nvidia-ml-py && \ pip install --no-cache-dir --upgrade "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" # Install cudnn RUN aria2c --max-tries=9999 https://developer.download.nvidia.com/compute/cudnn/9.8.0/local_installers/cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ dpkg -i cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb && \ cp /var/cudnn-local-repo-ubuntu2204-9.8.0/cudnn-*-keyring.gpg /usr/share/keyrings/ && \ apt-get update && \ apt-get -y install cudnn-cuda-12 && \ rm cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb # Install Apex RUN pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" --resume-retries 999 git+https://github.com/NVIDIA/apex.git # Profiling tools RUN aria2c --always-resume=true --max-tries=99999 https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_3/nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ apt-get update && apt-get install -y libxcb-cursor0 RUN apt-get install -y ./nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.3.1/target-linux-x64/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-2025.3.1_2025.3.1.90-1_amd64.deb RUN pip install --resume-retries 999 --no-cache-dir "tensordict==0.6.2" torchdata "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas cuda-bindings \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pre-commit ruff # Reset pip config RUN pip config unset global.index-url && \ pip config unset global.extra-index-url ================================================ FILE: docker/verl0.5-preview-cu128-torch2.7.1-fa2.8.0/README.md ================================================ # verl image with verl v0.5 ## Important packages version ```txt cuda==12.8 cudnn==9.8.0 torch==2.7.1 flash_attn=2.8.0 ## sglang==0.4.8 transformer_engine==2.5 megatron.core==core_r0.13.0 nvidia-cudnn-cu12==9.8.0.87 ``` ## Target - Base image: - `verlai/verl:base-verl0.5-preview-cu128-cudnn9.8-torch2.7.1-fa2.8.0`: We offer a base image with flash infer 0.2.6.post1 built in - App image: - `verlai/verl:app-verl0.5-preview-sglang0.4.8-mcore0.13.0-preview` - vllm temporarily not support latest version ## !!!Notice!!! - pyext is lack of maintainace and cannot work with python 3.12, consider using replacement and deprecating this package. ================================================ FILE: docker/verl0.6-cu128-torch2.8.0-fa2.7.4/Dockerfile.app.sglang ================================================ FROM verlai/verl:base-verl0.6-cu128-cudnn9.8-torch2.8.0-fa2.7.4 RUN pip install --no-cache-dir "sglang[all]==0.5.2" RUN pip install --no-cache-dir "torch-memory-saver==0.0.9rc1" ================================================ FILE: docker/verl0.6-cu128-torch2.8.0-fa2.7.4/Dockerfile.base ================================================ # Start from the NVIDIA official image (ubuntu-24.04 + cuda-12.8 + python-3.12) # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-03.html FROM nvcr.io/nvidia/pytorch:25.03-py3 # Define environments ENV MAX_JOBS=32 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn ENV DEBIAN_FRONTEND=noninteractive ENV NODE_OPTIONS="" ENV PIP_ROOT_USER_ACTION=ignore ENV HF_HUB_ENABLE_HF_TRANSFER="1" ENV PIP_CONSTRAINT="" ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple # Change pip source RUN pip config set global.index-url "${PIP_INDEX}" && \ pip config set global.extra-index-url "${PIP_INDEX}" && \ pip config set global.no-cache-dir "true" && \ python -m pip install --upgrade pip # Install systemctl RUN apt-get update && \ apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \ apt-get clean # Install libxml2 RUN apt-get update && \ apt-get install -y libxml2 aria2 && \ apt-get clean # Uninstall nv-pytorch fork RUN pip uninstall -y torch torchvision torchaudio \ pytorch-quantization pytorch-triton torch-tensorrt \ transformer_engine flash_attn apex megatron-core \ xgboost opencv grpcio # Fix packages RUN pip install --no-cache-dir tensordict torchdata "transformers[hf_xet]==4.55.4" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=19.0.1" pandas \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler blobfile xgrammar \ pytest py-spy pre-commit ruff # Fix cv2 RUN rm -rf /usr/local/lib/python3.11/dist-packages/cv2 # Install torch RUN pip install --no-cache-dir torch==2.8.0 --index-url https://download.pytorch.org/whl/cu128 # Install flash-attn RUN pip install --no-cache-dir --no-build-isolation flash_attn==2.7.4.post1 # Install DeepEP # the dependency of IBGDA RUN ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so # Clone and build deepep and deepep-nvshmem RUN git clone -b v2.3.1 https://github.com/NVIDIA/gdrcopy.git && \ git clone https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout a84a248 # Prepare nvshmem RUN wget https://developer.nvidia.com/downloads/assets/secure/nvshmem/nvshmem_src_3.2.5-1.txz && \ tar -xvf nvshmem_src_3.2.5-1.txz && mv nvshmem_src deepep-nvshmem && \ cd deepep-nvshmem && git apply ../DeepEP/third-party/nvshmem.patch ## Build deepep-nvshmem RUN apt-get install -y ninja-build cmake ENV CUDA_HOME=/usr/local/cuda ### Set MPI environment variables. Having errors when not set. ENV CPATH=/usr/local/mpi/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH ENV LD_LIBRARY_PATH=/usr/local/x86_64-linux-gnu:$LD_LIBRARY_PATH ENV GDRCOPY_HOME=/workspace/gdrcopy ENV GDRCOPY_INCLUDE=/workspace/gdrcopy/include RUN cd deepep-nvshmem && \ NVSHMEM_SHMEM_SUPPORT=0 \ NVSHMEM_UCX_SUPPORT=0 \ NVSHMEM_USE_NCCL=0 \ NVSHMEM_MPI_SUPPORT=0 \ NVSHMEM_IBGDA_SUPPORT=1 \ NVSHMEM_PMIX_SUPPORT=0 \ NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \ NVSHMEM_USE_GDRCOPY=1 \ cmake -G Ninja -S . -B build/ -DCMAKE_INSTALL_PREFIX=/workspace/deepep-nvshmem/install && cmake --build build/ --target install ENV NVSHMEM_DIR=/workspace/deepep-nvshmem/install ENV LD_LIBRARY_PATH=$NVSHMEM_DIR/lib:$LD_LIBRARY_PATH ENV PATH=$NVSHMEM_DIR/bin:$PATH ## Build deepep RUN cd DeepEP && \ python setup.py install # Install Apex RUN pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git # Install TransformerEngine RUN export NVTE_FRAMEWORK=pytorch && pip3 install --no-deps --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@v2.2.1 # Install Megatron-LM RUN git clone -b core_v0.13.0 https://github.com/NVIDIA/Megatron-LM.git && \ cd Megatron-LM && pip3 install --no-deps -e . # Install mbridge RUN pip3 install --no-cache-dir git+https://github.com/ISEEKYAN/mbridge.git ================================================ FILE: docker/verl0.6-cu128-torch2.8.0-fa2.7.4/Dockerfile.vllm011.mcore_gpt-oss ================================================ FROM nvcr.io/nvidia/nemo:25.07.gpt_oss RUN git clone -b v0.11.0 --depth 1 https://github.com/vllm-project/vllm.git /opt/vllm RUN pip install setuptools_scm RUN cd /opt/vllm && pip install --no-deps --no-build-isolation --no-cache-dir -e . RUN pip install cbor2 setproctitle blake3 openai_harmony pybase64 msgspec partial_json_parser py-cpuinfo diskcache gguf RUN pip install --upgrade transformers tokenizers RUN pip install codetiming tensordict mathruler pylatexenc RUN pip3 install --no-cache-dir mbridge ================================================ FILE: docker/verl0.6.1-experimental/Dockerfile.sglang056exp ================================================ # Dockerfile for verlai/verl:sgl056.exp FROM lmsysorg/sglang:v0.5.6.post1 RUN pip install pybind11 RUN pip install nvidia-mathdx RUN pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git RUN export NVTE_FRAMEWORK=pytorch && MAX_JOBS=128 NVTE_BUILD_THREADS_PER_JOB=4 pip3 install --resume-retries 999 --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.11 RUN pip install --upgrade --no-cache-dir transformers tokenizers RUN pip install codetiming tensordict mathruler pylatexenc qwen_vl_utils RUN pip install --no-cache-dir --no-build-isolation flash_attn==2.8.1 RUN NSIGHT_VERSION=2025.6.1_2025.6.1.190-1_$(if [ "$(uname -m)" = "aarch64" ]; then echo "arm64"; else echo "amd64"; fi) && \ wget https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_6/nsight-systems-${NSIGHT_VERSION}.deb && \ apt-get update && apt-get install -y libxcb-cursor0 && \ apt-get install -y ./nsight-systems-${NSIGHT_VERSION}.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-${NSIGHT_VERSION}.deb # ========================= # Install HybridEP # ========================= WORKDIR /home/ RUN git clone --branch hybrid-ep https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout 3f601f7ac1c062c46502646ff04c535013bfca00 && \ TORCH_CUDA_ARCH_LIST="9.0;10.0" pip install --no-build-isolation . # ========================= # Install Qwen3-Next dependencies # ========================= WORKDIR /home/ # Install causal-conv1d and flash-linear-attention RUN cd /tmp && \ git clone https://github.com/Dao-AILab/causal-conv1d.git && \ cd causal-conv1d && \ unset PIP_CONSTRAINT && \ CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install --no-build-isolation . && \ cd .. && \ rm -rf causal-conv1d && \ pip install flash-linear-attention RUN pip install --no-cache-dir torch-memory-saver RUN pip3 install --no-cache-dir --no-deps trl RUN pip3 install nvtx matplotlib liger_kernel RUN pip install -U git+https://github.com/ISEEKYAN/mbridge.git RUN pip install --no-deps --no-cache-dir git+https://github.com/NVIDIA/Megatron-LM.git@1d462bd37dac21cfa14177405d4921eedb987052 # latest dev branch on 20251209 RUN pip install git+https://github.com/volcengine/verl.git@v0.6.1 RUN pip uninstall -y verl ================================================ FILE: docker/verl0.6.1-experimental/Dockerfile.vllm012exp ================================================ # dockerfile for verlai/verl:vll012.exp FROM nvcr.io/nvidia/pytorch:25.11-py3 RUN git clone -b v0.12.0 --depth 1 https://github.com/vllm-project/vllm.git /opt/vllm RUN pip install setuptools_scm RUN cd /opt/vllm && pip install --no-deps --no-build-isolation --no-cache-dir -e . RUN pip install -r /opt/vllm/requirements/common.txt RUN pip install pybind11 RUN export NVTE_FRAMEWORK=pytorch && MAX_JOBS=128 NVTE_BUILD_THREADS_PER_JOB=4 pip3 install --resume-retries 999 --no-cache-dir --no-build-isolation git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.11 RUN pip install --upgrade --no-cache-dir transformers tokenizers RUN pip install codetiming tensordict mathruler pylatexenc qwen_vl_utils RUN pip install flash_attn #==2.8.1 RUN apt update && apt install numactl RUN NSIGHT_VERSION=2025.6.1_2025.6.1.190-1_$(if [ "$(uname -m)" = "aarch64" ]; then echo "arm64"; else echo "amd64"; fi) && \ wget https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_6/nsight-systems-${NSIGHT_VERSION}.deb && \ apt-get update && apt-get install -y libxcb-cursor0 && \ apt-get install -y ./nsight-systems-${NSIGHT_VERSION}.deb && \ rm -rf /usr/local/cuda/bin/nsys && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys /usr/local/cuda/bin/nsys && \ rm -rf /usr/local/cuda/bin/nsys-ui && \ ln -s /opt/nvidia/nsight-systems/2025.6.1/nsys-ui /usr/local/cuda/bin/nsys-ui && \ rm nsight-systems-${NSIGHT_VERSION}.deb # ========================= # Install HybridEP # ========================= WORKDIR /home/ RUN git clone --branch hybrid-ep https://github.com/deepseek-ai/DeepEP.git && \ cd DeepEP && git checkout 3f601f7ac1c062c46502646ff04c535013bfca00 && \ TORCH_CUDA_ARCH_LIST="9.0;10.0" pip install --no-build-isolation . # ========================= # Install Qwen3-Next dependencies # ========================= WORKDIR /home/ # Install causal-conv1d and flash-linear-attention RUN cd /tmp && \ git clone https://github.com/Dao-AILab/causal-conv1d.git && \ cd causal-conv1d && \ unset PIP_CONSTRAINT && \ CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install --no-build-isolation . && \ cd .. && \ rm -rf causal-conv1d && \ pip install flash-linear-attention RUN pip3 install --no-cache-dir --no-deps trl RUN pip3 install nvtx matplotlib liger_kernel RUN pip install -U git+https://github.com/ISEEKYAN/mbridge.git RUN pip install --no-deps --no-cache-dir git+https://github.com/NVIDIA/Megatron-LM.git@1d462bd37dac21cfa14177405d4921eedb987052 # latest dev branch on 20251209 RUN pip install git+https://github.com/volcengine/verl.git@v0.6.1 RUN pip uninstall -y verl ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = verl SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/README.md ================================================ # verl documentations ## Build the docs ```bash # If you want to view auto-generated API docstring, please make sure verl is available in python path. For instance, install verl via: # pip install .. -e[test] # Install dependencies needed for building docs. pip install -r requirements-docs.txt # Build the docs. make clean make html ``` ## Open the docs with your browser ```bash python -m http.server -d _build/html/ ``` Launch your browser and navigate to http://localhost:8000 to view the documentation. Alternatively you could drag the file `_build/html/index.html` to your local browser and view directly. ================================================ FILE: docs/README_vllm0.7.md ================================================ # Upgrading to vllm >= 0.7 Note: verl+vllm 0.8.3 is now stable. Please see ``docs/README_vllm0.8.md`` for upgrade guide. ## Installation Note: At time of writing, verl+vllm 0.7.x supports **FSDP** for training and **vLLM** for rollout. ``` # Create the conda environment conda create -n verl python==3.10 conda activate verl # Install verl git clone https://github.com/volcengine/verl.git cd verl pip3 install -e . # Install the latest stable version of vLLM pip3 install vllm==0.7.3 # Install flash-attn pip3 install flash-attn --no-build-isolation ``` Note that if you are installing lower versions of vLLM (0.7.0, 0.7.1, 0.7.2), you need to make some tiny patches manually on vllm (/path/to/site-packages/vllm after installation) after the above steps: - vllm/distributed/parallel_state.py: Remove the assertion below: ``` if (world_size != tensor_model_parallel_size * pipeline_model_parallel_size): raise RuntimeError( f"world_size ({world_size}) is not equal to " f"tensor_model_parallel_size ({tensor_model_parallel_size}) x " f"pipeline_model_parallel_size ({pipeline_model_parallel_size})") ``` - vllm/executor/uniproc_executor.py: change `local_rank = rank` to `local_rank = int(os.environ["LOCAL_RANK"])` - vllm/model_executor/model_loader/weight_utils.py: remove the `torch.cuda.empty_cache()` in `pt_weights_iterator` ## Features ### Use cuda graph After installation, examples using FSDP as training backends can be used. By default, the `enforce_eager` is set to True, which disables the cuda graph. To enjoy cuda graphs and the sleep mode of vLLM>=0.7, add the following lines to the bash script: ``` actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ ``` For a typical job like examples/ppo_trainer/run_qwen2-7b_seq_balance.sh, the rollout generation time is 85 seconds with vLLM0.7.0. By enabling the cudagraph, the generation duration is further reduced to 62 seconds. **Note:** Currently, if the `n` is greater than 1 in `SamplingParams` in vLLM>=0.7, there is a potential performance issue on the stability of rollout generation time (Some iterations would see generation time bursts) using vLLM's V0 Engine. ### Use vLLM V1 Engine Using the vLLM V1 engine can avoid instability issues and achieve additional performance improvements. To use the V1 engine, you can first uninstall the previously installed vLLM and then follow the steps below to install the newer version. ``` git clone https://github.com/vllm-project/vllm.git cd vllm git checkout 2275784 sed -i "903a\ data_parallel_size = world_size // pipeline_model_parallel_size // tensor_model_parallel_size" ./vllm/distributed/parallel_state.py VLLM_USE_PRECOMPILED=1 pip install --editable . ``` Then you can enable the V1 engine by setting `export VLLM_USE_V1=1`. In some benchmark tests, the V1 engine demonstrates a 1.5x speed improvement over the vLLM V0 engine. The stable support of the vLLM V1 engine is available on verl main. ================================================ FILE: docs/README_vllm0.8.md ================================================ # Upgrading to vLLM >= 0.8 Last updated: 05/04/2025. ## Installation Note: This version of verl+vLLM 0.8+ supports **FSDP** for training and **vLLM** for rollout. ```bash # Create the conda environment conda create -n verl python==3.10 conda activate verl # Install verl git clone https://github.com/volcengine/verl.git cd verl pip3 install -e . # Install the latest stable version of vLLM pip3 install vllm==0.8.3 # Install flash-attn pip3 install flash-attn --no-build-isolation ``` We have a pre-built docker image for verl+vLLM 0.8.3. You can direct import it with the following command: ```bash docker pull hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0 ``` ## Features vLLM 0.8+ supports cuda graph and V1 engine by default in verl. To enable these features, remember to add the following lines to the bash script: ```bash actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ ``` and also **remove** the environment variable if it exists: ## Notes When you just directly upgrade vllm>=0.8, some dependency packages may undergo version changes. If you encounter the following problems: ```bash in from torch.multiprocessing.reductions import ForkingPickler ImportError: cannot import name 'ForkingPickler' from 'torch.multiprocessing.reductions' (/opt/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py) ``` You need to upgrade `tensordict` to version 0.6.2 using the command `pip install tensordict==0.6.2`. ================================================ FILE: docs/_static/custom.css ================================================ /* Make the documentation use full screen width */ .wy-nav-content { max-width: none !important; width: 100% !important; padding: 1.618em 3.236em !important; } /* Adjust the content wrapper - will be set by JavaScript */ .wy-nav-content-wrap { margin-left: 300px; transition: margin-left 0.2s ease; width: auto !important; position: relative !important; background: white !important; min-height: 100vh !important; } /* Make the main content area responsive */ .rst-content { max-width: none !important; 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} /* Make logo/title area responsive */ .wy-side-nav-search > div.version { width: 100% !important; } .wy-side-nav-search > a { width: 100% !important; display: block !important; white-space: nowrap !important; overflow: hidden !important; text-overflow: ellipsis !important; } /* Responsive adjustments for narrow sidebar */ @media (max-width: 300px) { .wy-side-nav-search > a { font-size: 0.9em !important; } .wy-side-nav-search input[type="text"] { font-size: 0.8em !important; } } /* Ensure search input doesn't overflow */ .wy-side-nav-search form { width: 100% !important; margin: 0 !important; } /* Make search icon responsive */ .wy-side-nav-search .wy-dropdown { width: 100% !important; } /* Adjust search results dropdown width */ .wy-side-nav-search .wy-dropdown-menu { width: 100% !important; max-width: none !important; left: 0 !important; right: 0 !important; } /* Resize handle is created by JavaScript */ /* Make sure the sidebar content doesn't overflow */ .wy-side-scroll { width: 100% !important; flex: 1 !important; overflow-y: auto !important; overflow-x: hidden !important; padding-right: 10px !important; box-sizing: border-box !important; scroll-behavior: auto !important; /* Prevent smooth scrolling on sidebar itself */ } /* Ensure proper scroll behavior for main content area */ html { scroll-behavior: smooth !important; } /* Ensure anchor links work properly in main content */ .wy-nav-content-wrap { scroll-behavior: smooth !important; } /* Fix scroll to target for anchor links */ .rst-content { scroll-behavior: smooth !important; } /* Fix anchor scroll offset to account for fixed header */ .rst-content .section { scroll-margin-top: 60px; } /* Fix anchor scroll offset for headers */ .rst-content h1, .rst-content h2, .rst-content h3, .rst-content h4, .rst-content h5, .rst-content h6 { scroll-margin-top: 60px; } /* Fix anchor scroll offset for specific scroll targets */ .rst-content .headerlink { scroll-margin-top: 60px; } /* Fix sidebar navigation styling */ .wy-menu-vertical { width: 100% !important; 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const content = document.querySelector('.wy-nav-content-wrap'); if (!sidebar || !content) return; // Create resize handle const resizeHandle = document.createElement('div'); resizeHandle.className = 'resize-handle'; sidebar.appendChild(resizeHandle); let isResizing = false; let startX = 0; let startWidth = 0; // Get initial width const getInitialWidth = () => { return 300; // Default width }; // Save width to localStorage const saveWidth = (width) => { localStorage.setItem('sidebar-width', width); }; // Load width from localStorage const loadWidth = () => { const savedWidth = localStorage.getItem('sidebar-width'); if (savedWidth) { const width = parseInt(savedWidth, 10); if (width >= 200 && width <= 600) { return width; } } return getInitialWidth(); }; // Apply width to sidebar and content const applyWidth = (width) => { // Update sidebar width sidebar.style.width = width + 'px'; // Update content margin with !important to override any CSS content.style.setProperty('margin-left', width + 'px', 'important'); 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document.body.appendChild(overlay); e.preventDefault(); }); // Mouse move document.addEventListener('mousemove', (e) => { if (!isResizing) return; const width = startWidth + e.clientX - startX; const clampedWidth = Math.max(200, Math.min(600, width)); applyWidth(clampedWidth); }); // Mouse up document.addEventListener('mouseup', () => { if (!isResizing) return; isResizing = false; sidebar.classList.remove('resizing'); document.body.style.cursor = ''; document.body.style.userSelect = ''; // Remove overlay const overlay = document.getElementById('resize-overlay'); if (overlay) { overlay.remove(); } // Save the current width const currentWidth = parseInt(window.getComputedStyle(sidebar).width, 10); saveWidth(currentWidth); }); // Handle window resize - removed to prevent infinite loop // The sidebar width is fixed and managed by drag functionality, no need to recalculate on window resize // Double-click to reset to default width resizeHandle.addEventListener('dblclick', () => { const defaultWidth = 300; 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if (targetElement) { // Calculate offset for fixed header const headerHeight = 60; const elementPosition = targetElement.getBoundingClientRect().top; const offsetPosition = elementPosition + window.pageYOffset - headerHeight; window.scrollTo({ top: offsetPosition, behavior: 'smooth' }); // Update URL hash if (history.pushState) { history.pushState(null, null, '#' + targetId); } else { location.hash = '#' + targetId; } } } // For external links, navigate normally else if (href && !href.startsWith('#') && !href.startsWith('javascript:')) { console.log('Navigating to external link:', href); window.location.href = href; } }); // Replace the old link with the new one link.parentNode.replaceChild(newLink, link); }); navigationFixed = true; // Handle initial page load with hash if (window.location.hash) { // Use requestAnimationFrame for better timing requestAnimationFrame(() => { const targetId = window.location.hash.substring(1); const targetElement = document.getElementById(targetId); if (targetElement) { const headerHeight = 60; const elementPosition = targetElement.getBoundingClientRect().top; const offsetPosition = elementPosition + window.pageYOffset - headerHeight; window.scrollTo({ top: offsetPosition, behavior: 'smooth' }); } }); } } // Try to set up navigation fix immediately setupNavigationFix(); // If it didn't work, use MutationObserver to watch for when sidebar links are added if (!navigationFixed) { const observer = new MutationObserver(function(mutations) { mutations.forEach(function(mutation) { if (mutation.type === 'childList' && mutation.addedNodes.length > 0) { // Check if sidebar links were added const sidebarLinks = document.querySelectorAll('.wy-menu-vertical a'); if (sidebarLinks.length > 0) { setupNavigationFix(); if (navigationFixed) { observer.disconnect(); } } } }); }); // Start observing the document for changes observer.observe(document.body, { childList: true, subtree: true }); // Fallback timeout in case MutationObserver doesn't work setTimeout(function() { if (!navigationFixed) { setupNavigationFix(); } observer.disconnect(); }, 5000); } }); ================================================ FILE: docs/_static/js/runllm-widget.js ================================================ document.addEventListener("DOMContentLoaded", function () { var script = document.createElement("script"); script.type = "module"; script.id = "runllm-widget-script"; script.src = "https://widget.runllm.com"; script.setAttribute("version", "stable"); script.setAttribute("crossorigin", "true"); script.setAttribute("runllm-keyboard-shortcut", "Mod+j"); script.setAttribute("runllm-name", "verl Chatbot"); script.setAttribute("runllm-position", "TOP_RIGHT"); script.setAttribute("runllm-assistant-id", "679"); script.async = true; document.head.appendChild(script); }); ================================================ FILE: docs/advance/agent_loop.rst ================================================ Agent Loop ========== Last updated: 07/17/2025. .. versionadded:: 0.4.2 [status: alpha] .. warning:: Agent Loop is ready for use, but the API may change in future releaes. Agent Loop is designed as general interface for multi-turn rollout and agentic reinforcement learning. **Design goal**: - Plugable user defined agent loop - Provide standard request generate api with different inference frameworks - Provide request level load balance between multiple inference servers **Non-goal**: - How tool is defined and how to call tool In high level overview, agent loop is given a prompt, run user defined loop: call LLM generate api, call tools, ... and return the final output. The final output is then calculated reward and used as trajectory for RL training. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/agent_loop_overview.svg?raw=true API Design ---------- ``AgentLoopBase`` class is the abstraction of agent loop, and ``run`` method is the only interface that user need to implement. The run method, given prompt messages in format: [{"role": "user"}, {"content": "..."}], and additional sampling params, could do whatever user wants, such as - call LLM generate api - call tools: web search, database query, code sandbox, ... - environment interaction - reflection - ... .. code:: python class AgentLoopBase(ABC): @abstractmethod async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput: """Run agent loop to interact with LLM server and environment. Args: sampling_params (Dict[str, Any]): LLM sampling params. **kwargs: dataset fields from `verl.utils.dataset.RLHFDataset`. Returns: AgentLoopOutput: Agent loop output. """ raise NotImplementedError After running user defined loop, run method should return ``AgentLoopOutput``, including prompt token ids, response token ids, and response mask. .. code:: python class AgentLoopOutput(BaseModel): """Agent loop output.""" prompt_ids: list[int] """Prompt token ids.""" response_ids: list[int] """Response token ids including LLM generated token, tool response token.""" response_mask: list[int] """Response mask, 1 for LLM generated token, 0 for tool response token.""" .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/agent_loop_output.svg?raw=true .. note:: AgentLoopOutput only output one trajectory for a given prompt, multiple trajectories output is still under discussion. Architecture Design ------------------- .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/agent_loop_architecture.png?raw=true A single PPO step contain two phase: rollout and train. In rollout phase: 1. PPOTrainer sample a batch from dataset and call ``AgentLoopManager.generate_sequences``. 2. AgentLoopManager ``wake_up`` all async LLM server instances, which will sync weights between inference engine(vLLM/SGLang) and training engine(FSDP/Megatron-LM). 3. AgentLoopManager split batch into chunks and send each chunk to ``AgentLoopWorker``. 4. AgentLoopWorker receive chunk and for each prompt, spawn a user defined ``AgentLoopBase`` instance, run ``run`` coroutine until end and get ``AgentLoopOutput``. .. tip:: AgentLoopWorker schedules multiple coroutines concurrently. If number of AgentLoopWorker equals batch_size, then each worker is response for one prompt. In agent loop, when user need LLM generate response: 5. Call ``AsyncLLMServerManager.generate`` with prompt_ids. 6. AsyncLLMServerManager select a server instance with least request in first turn and send request to it. (In following turns, the request will be sent to the same server instance). 7. AsyncLLMServer receive a request, issue ipc/rpc with model_runner, and generate response. (There's slight differences between vLLM and SGLang, see below). When all prompts in all AgentLoopWorker finish, AgentLoopManager gather results and return to PPOTrainer. 8. AgentLoopManager ``sleep`` all server instances, which will free kv cache and offload weights to CPU memory. AsyncLLMServer ~~~~~~~~~~~~~~ AsyncLLMServer is the abstraction of LLM server with two types of generation api: - `OpenAI chat completion `_: generate response for the given chat conversation. - Token in token out: generate response ids for the given token ids. We have officially supported vLLM and SGLang AsyncLLMServer, both of them implement the two api and are well tested. Other inference engine should be easy to plug-in by implement the ``AsyncServerBase`` class. .. code:: python class AsyncServerBase(ABC): @abstractmethod async def chat_completion(self, raw_request: Request) -> JSONResponse: """OpenAI chat completion API. Args: raw_request (Request): raw json request Returns: JSONResponse: json response API reference: https://platform.openai.com/docs/api-reference/chat/create """ raise NotImplementedError @abstractmethod async def generate(self, prompt_ids: list[int], sampling_params: dict[str, Any], request_id: str) -> list[int]: """Generate response ids given prompt ids. Args: prompt_ids (List[int]): prompt ids sampling_params (Dict[str, Any]): sampling params request_id (str): request id Returns: List[int]: response ids """ raise NotImplementedError Chat completion vs Token in token out ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. warning:: The following conclusion is based on our recent experience and is still open to investigation and discussion. Almost all agent frameworks (LangGraph, CrewAI, LlamaIndex, etc) call LLM with OpenAI chat completion api, and keep chat history as messages. So user may expect that we should use the chat completion api in multi-turn rollout. But based on our recent experience on single-turn training on DAPO and multi-turn training on `retool `_, we found the token_ids from apply the final messages may not equal to the token_ids by concat prompt_ids and response_ids in each turn. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/multi_turn.png?raw=true **Where does this inconsistency happened?** First, the tool parser may alter the content. For example .. code:: json {"role": "assistant", "content": "Let me call a ... and get the result"} After tool_calls extraction, the messages is like this: .. code:: json {"role": "assistant", "content": "Let me call a and get the result", "tool_calls": [{"name": "foo", "arguments": "{}"}]} Encode the extracted message back is not equal to the original LLM generated response_ids. Second, the `decode-encode` may also lead to inconsistency: `Agent-R1 issue#30 `_. **What is the impact of this inconsistency?** This inconsistency is not a big problem for serving/agent system, but is critical to RL training. It causes the trajectory deviate from the policy model distribution. We have observed that apply_chat_template to the final chat history messages make PPO training not even converged in single-turn. vLLM ^^^^ .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/async_vllm.png?raw=true For vLLM, the Async LLM Engine is running in same process as the server, and ModelRunner is running in same process as FSDP/Megatron-LM workers. Async LLM Engine communicate with ModelRunner through ZeroMQ. When server receive a request, it directly call engine to generate response_ids. SGLang ^^^^^^ .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/async_sglang.png?raw=true For SGLang, the Async LLM Engine is running in same process as FSDP/Megatron-LM worker-0, and it spawn multiple subprocesses as ModelRunner. Also, Async LLM Engine communicate with ModelRunner through ZeroMQ. When server receive a request, it remote call the worker-0 and get response_ids. AsyncLLMServerManager ~~~~~~~~~~~~~~~~~~~~~ AsyncLLMServerManager serve as proxy to multiple AsyncLLMServer instances, provides: - load balance: select a server instance with least request in first turn and send request to it. - sticky session: bind request_id to server instance, so that the same request_id will be sent to the same server instance in following turns. AsyncLLMServerManager is passed to ``AgentLoopBase.__init__``, whenever user want to interact with LLM in agent loop, they can call ``AsyncLLMServerManager.generate`` to generate response_ids. .. code:: python class AsyncLLMServerManager: async def generate( self, request_id, *, prompt_ids: list[int], sampling_params: dict[str, Any], ) -> list[int]: """Generate tokens from prompt ids. Args: request_id (str): request id for sticky session. prompt_ids (List[int]): List of prompt token ids. sampling_params (Dict[str, Any]): Sampling parameters for the chat completion. Returns: List[int]: List of generated token ids. """ ... Next ---- - :doc:`Agentic RL Training<../start/agentic_rl>`: Quick start agentic RL training with gsm8k dataset. - `LangGraph MathExpression `_: Demonstrate how to use LangGraph to build agent loop. - `Retool `_: End-to-end retool paper reproduction using tool agent. ================================================ FILE: docs/advance/async-on-policy-distill.md ================================================ # Recipe: Async On-Policy Knowledge Distillation Trainer **Authors:** Brilliant Hanabi, furunding **Last updated:** 2025-11-08 ## 1. Background On-policy knowledge distillation (KD) trains a student policy to imitate a stronger teacher using samples drawn from the student's current policy. For each on-policy rollout the teacher returns soft, top-k token distributions and the student is optimized with a token-wise sparse KL objective that focuses learning on the teacher's high-probability modes. Because training examples come from the student's own state distribution, KD reduces distributional mismatch relative to off-policy distillation or supervised fine-tuning (SFT), improving stability and sample efficiency. Compared with reinforcement learning, KD avoids high-variance reward-based optimization and complex reward design by providing dense, informative per-token targets, which typically yields faster convergence and simpler scaling. Recent empirical and implementation-focused writeups (e.g., [ThinkingMachines' blog on on-policy distillation](https://thinkingmachines.ai/blog/on-policy-distillation/)) also demonstrate that on-policy distillation can deliver high-quality behavior with substantially lower compute and data requirements than many alternative approaches. Built on verl’s Ray-based single-controller components, we initially assembled a strictly on-policy KD pipeline where rollout generation, teacher knowledge acquisition, and policy optimization ran in lockstep. In practice, this synchronous design proved highly inefficient: the three stages had to wait for one another, creating pipeline bubbles and underutilized GPUs. To address this, we extend the asynchronous schedulers introduced by the One-Step-Off Policy pipeline to overlap these phases. This overlap preserves the same distillation objective while trading some strict on-policy guarantees for substantial gains in end-to-end throughput and hardware utilization. ## 2. Distillation Overview and Objective This recipe centers on on-policy knowledge distillation: the student policy learns from a stronger teacher on samples generated by the current policy (on-policy). For each input prompt, the student (actor) generates responses; the teacher provides top-k token distributions, and the student is trained to match them token-wise. Core components: 1. Teacher signal: top-k log-probabilities and token indices per valid token position. 2. Student objective: sparse, token-level KL divergence between student logits and teacher top-k distribution. Objective: encourage student probabilities $Q$ to cover teacher modes $P$ using token-wise $\mathrm{KL}(P\,\|\,Q)$ computed on the teacher's top-k support. ## 3. Efficient System Design ### 3.1 Schedulers (One-Step / Two-Step Off-Policy) The native (serial) on-policy distillation process is shown in the figure below. ![Zero-Step-Off Scheduler](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/zero-step-off-distill.png) This recipe supports optional schedulers that overlap generation, teacher querying, and updates to improve throughput without changing the distillation objective. #### 3.1.1 One-Step-Off-Policy ![One-Step-Off Scheduler](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/one-step-off-distill.png) - Warm-up: 2 steps. - Overlap pattern: rollout while actor update; weight sync while teacher retrieving. - Timing keys: `sync_rollout_weights`, `wait_prev_gen`, `wait_prev_teacher`. #### 3.1.2 Two-Step-Off-Policy ![Two-Step-Off Scheduler](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/two-step-off-distill.png) - Warm-up: 3 steps. - Overlap pattern: rollout, actor update while teacher retrieving; interleave weight sync. - Timing keys: `sync_rollout_weights`, `max(wait_prev_gen, wait_prev_prev_teacher)`. Tip: Use `two_step_off` when teacher takes much more time than sync; `one_step_off` for simpler overlapping. Practical details: - Inputs per batch: `teacher_topk_logps`, `teacher_topk_indices`, `attention_mask` (to select valid token positions). - Loss injection: last pipeline stage computes KL via a logits processor; earlier stages remain unchanged. - Optional dynamic micro-batching groups sequences by density to reduce padding overhead. The pipeline: 1. Actor parameters are synchronized to a rollout worker group (nccl broadcast) with a little bit latency. 2. Rollout workers (vLLM-backed) generate sequences asynchronously (`async_generate_sequences`). 3. Teacher client service (ZeroMQ based) returns top-k log-probabilities + token indices for each sequence (batched micro-requests), enabling KL-based guidance. 4. Megatron actor performs a KL divergence computation between student logits and teacher top-k distributions (custom TP-aware kernel in `megatron_kl_loss.py`). 5. Scheduling strategies (`one_step_off_scheduler`, `two_step_off_scheduler`) can overlap phases (optional for throughput): ### 3.2 Weights sync between actor and rollout We initially followed the weight synchronization path from the One-Step-Off-Policy recipe (Ray collective broadcast across all actor and rollout ranks, plus Megatron-side allgather of parameter shards). In practice this became the dominant bottleneck, so we made three changes: 1. Batch-and-bulk load on the rollout side: instead of streaming tensors one-by-one (in one-step-off-policy recipe), we stage a bundle of parameter tensors and issue a single batched load into the rollout engine. In our setup this reduced the weight-loading time by roughly 3×. 2. Batch-and-bulk broadcast between the actor and rollout: instead of streaming tensors one-by-one (in one-step-off-policy recipe), we stage a bundle of parameter tensors and issue a single batched broadcast between the actor and rollout workers. 3. Replace allgather with gather-to-root in Megatron: parameter shards are gathered to actor rank 0 (rather than allgathered to everyone), and that root then serves as the single source for broadcasting to rollout ranks. On top of the previous change, 2 and 3 changes delivered an additional ~4× speedup in the synchronization phase. ## 4. High-Level Data & Control Flow ``` Driver (TaskRunner) ├─ Initialize Ray, tokenizer, datasets, worker groups ├─ Build ResourcePoolManager (actor vs rollout GPU layouts) ├─ Trainer.fit() ├─ init_workers(): build actor + rollout groups, broadcast weight metadata, create nccl collective group ├─ continuous_iterator(): epochs → batches ├─ scheduler (see Section 6) • _async_gen_next_batch(): optional weight sync + non-blocking rollout • _async_get_teacher_knowledge(): submit teacher requests, store future ├─ For each step: • Sync rollout weights • Retrieve (batch, gen_output, teacher_output) from futures • Merge gen + teacher outputs → DataProto • Compute metrics (response length stats, timing, throughput) • Update actor (forward_backward_batch + KL loss + optimizer step) • (Optional) save checkpoint ``` > Note: Schedulers are optional and explained later; the distillation objective is independent of how phases are overlapped. ## 5. Key Components ### 5.1 `OnPolicyDistillTrainer` (`ray_trainer.py`) - Creates `GenerationBatchFuture` objects holding rollout and (later) teacher futures. - Adds scheduling + teacher integration + modified metric emission (KL, timing, MFU). ### 5.2 Actor Worker (Megatron) - `OnPolicyDistillActor.update_policy()` orchestrates micro-batch forward/backward. - KL Loss injection via `logits_processor` during forward on pipeline last stage. ### 5.3 Rollout Worker (vLLM / SGLang) - Pure inference mode (`init_model` builds model; no optimizer). - `async_generate_sequences` returns a Ray future for overlapping. ### 5.4 Teacher Service (`teacher/`) - Proxy + worker architecture (ZMQ REQ/REP) for batched top-k retrieval. - `TeacherClient.submit()` returns a `Future`; aggregator composes micro-batches. - Configurable temperature, max tokens, only-response mode. ### 5.5 KL Loss (`megatron_kl_loss.py`) - Performs normalization & stable per-token probability construction across TP shards. - Gradient is (student_probs - teacher_sparse_probs) scaled by upstream grad. ## 6. Configuration Highlights (`on_policy_distill_trainer.yaml`) | Section | Purpose | Notable Keys | |---------|---------|-------------| | actor_rollout_ref.teacher | Teacher server | server_ip, server_port, n_server_workers | | trainer | Global training control | total_epochs, save_freq, scheduler (one_step_off | two_step_off), n_gpus_per_node, nnodes | | rollout | Resource split for rollout | n_gpus_per_node, nnodes | **Remember to set `trainer.n_gpus_per_node`, `trainer.nnodes`, `rollout.n_gpus_per_node` and `rollout.nnodes` to allocate GPU resources.** ### Dynamic Batch Size Enable by: ``` actor_rollout_ref.actor.use_dynamic_bsz=True actor_rollout_ref.actor.max_token_len=6000 # cap post-group token length ``` Improves utilization under variable sequence lengths. ### Resource Guidelines - Actor pool: `trainer.nnodes * trainer.n_gpus_per_node` GPUs. - Rollout pool: `rollout.nnodes * rollout.n_gpus_per_node` GPUs. - Ensure teacher server capacity ≈ `n_server_workers` to avoid stalls (monitor `wait_prev_teacher`). ## 7. Usage Examples ### 7.1 Launch Teacher Server Before training process, you should have a teacher server to provide logp information. We provide a toy teacher server example with vLLM. It needs `telnet` to check proxy status, and `python` command to run. So if you have not installed `telnet`, you can just delete these code in `start_server.sh`. And some OS use `python3` rather than `python`, so you also need to modify it. Also you can change the port of teacher if you meet port conflict. There are 3 arguments can be set for vllm backend `--tp-size`, `--n-logprobs` and `--ckpt-path` in `start_server.sh` / `worker.py`. You should set before you start server. We also provide a toy multi-node teacher server. You can start the main node using `start_server.sh` and start the slave nodes using `join_server.sh`. Still remember to set args in `join_server.sh`, especially the `$PROXY_IP` and `$PROXY_BACKEND_PORT` of main node. When training, student will automatically use the teacher's topk (n-logprobs) to set its own topk argument at line 83 of `recipe/gkd/megatron_kl_loss.py`, so you don't need to set student's topk argument. ```bash cd recipe/gkd/teacher bash start_server.sh # Exports ports and launches proxy + worker (default vLLM backend) ``` Verify with: ```bash telnet localhost 15555 ``` ### 7.2 Minimal Local (Megatron + vLLM) Run ```bash python3 -m recipe.gkd.main_gkd \ --config-path=recipe/gkd/config \ --config-name=on_policy_distill_trainer \ actor_rollout_ref.model.path=/path/to/MODEL \ data.train_files=/path/to/train.parquet \ trainer.total_epochs=2 \ trainer.n_gpus_per_node=4 rollout.n_gpus_per_node=2 \ actor_rollout_ref.teacher.server_ip=127.0.0.1 \ actor_rollout_ref.teacher.server_port=15555 \ trainer.scheduler=one_step_off ``` (Requires a running teacher server). ### 7.3 Ray Job Submission (Distilled 16B Example) See `run_moonlight_dsv3_training.sh` for a full script including: - Dist ckpt path setup (`dist_checkpointing_path`) - Expert parallel sizing (EP / ETP) - Dynamic batch sizing - Two-step-off scheduling for deeper overlap. Submit (after adjusting paths): ```bash bash recipe/gkd/run_moonlight_dsv3_training.sh ``` ## 8. Metrics & Monitoring Emitted metrics include (prefixes may vary): - Timing: `timing/wait_prev_gen`, `timing/sync_rollout_weights`, `timing/get_teacher_knowledge`, `timing/update_actor`. - Sequence stats: `response_seq_len/*` (avg, max, min, counts). - Performance: `perf/mfu/actor`, `perf/max_memory_allocated_gb`, `perf/cpu_memory_used_gb`. - Distillation: `actor/kl_loss`, `actor/grad_norm`, `actor/lr`. Interpretation Tips: - High `wait_prev_teacher` → scale `n_server_workers` and allocate more teacher GPUs or reduce per-request batch size, or just use `two_step_off`. - High `wait_prev_gen` with uniform lengths → allocate more rollout GPUs. - High `sync_rollout_weights` → check NCCL env / network congestion and try to modify `actor_rollout_ref.rollout.update_weights_bucket_megabytes`. ## 9. Extensibility Notes - Add new schedulers by following interface returning `(epoch, batch, gen_output, teacher_output, timing_dict)`. - Integrate different distillation signals (e.g., hidden states, intermediate reasoning tokens) by extending `teacher_utils.get_teacher_knowledge` and modifying `logits_processor`. ## 10. Functional Support Summary | Category | Supported | |----------|-----------| | Train engine | Megatron | | Rollout engine | vLLM | | Distillation signal | Teacher top-k logprobs & indices | | Scheduling | one_step_off, two_step_off | ## 11. Quick Checklist Before Running - Teacher server reachable (`telnet `). - `actor_rollout_ref.model.path` contains the correct Megatron/HF config artifacts. - `train_files` points to a parquet dataset compatible with this recipe's dataset loader. - NCCL environment vars set (see `config/runtime_env.yaml`). --- Feel free to open issues or PRs to extend scheduler variants, add new distillation objectives, or broaden engine support, and more improvement. ================================================ FILE: docs/advance/attention_implementation.rst ================================================ .. _attention-implementation-override: Attention Implementation Override ================================== Last updated: 10/31/2025. By default, VERL's FSDP workers use ``flash_attention_2`` as the attention implementation for improved performance. However, you can now override this setting to use different attention implementations based on your needs. Supported Attention Implementations ----------------------------------- The following attention implementations are supported (subject to model and hardware compatibility): - ``flash_attention_2``: High-performance attention implementation (default) - ``eager``: Standard PyTorch attention implementation - ``sdpa``: Scaled Dot-Product Attention (PyTorch native) When to Override ---------------- You might want to override the attention implementation in the following scenarios: - **Debugging**: Use ``eager`` for easier debugging and better error messages - **Compatibility**: Some models or hardware configurations may not support ``flash_attention_2`` - **Memory constraints**: Different implementations have different memory characteristics - **Performance tuning**: Testing different implementations for optimal performance Configuration Examples ----------------------- PPO Training with Eager Attention ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To override the attention implementation for the actor, rollout, and reference models: .. code:: bash python3 ppo_trainer.py \ +actor_rollout_ref.model.override_config.attn_implementation=eager \ [other parameters...] PPO Training with SDPA Attention ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: bash python3 ppo_trainer.py \ +actor_rollout_ref.model.override_config.attn_implementation=sdpa \ [other parameters...] Critic Model Override ~~~~~~~~~~~~~~~~~~~~~ For training configurations that include a critic model, you can also override its attention implementation: .. code:: bash python3 ppo_trainer.py \ +actor_rollout_ref.model.override_config.attn_implementation=eager \ +critic.model.override_config.attn_implementation=eager \ [other parameters...] YAML Configuration ~~~~~~~~~~~~~~~~~~ You can also specify the attention implementation in your YAML configuration file: .. code:: yaml actor_rollout_ref: model: override_config: attn_implementation: eager # other overrides... critic: # if using a critic model model: override_config: attn_implementation: eager # other overrides... Important Notes --------------- **Backward Compatibility**: If you don't specify ``attn_implementation`` in the override config, VERL will continue to use ``flash_attention_2`` by default, ensuring backward compatibility with existing configurations. **Model Support**: Not all models support all attention implementations. Ensure your model is compatible with the chosen attention implementation before training. **Performance Impact**: Different attention implementations have varying performance characteristics. ``flash_attention_2`` typically offers the best performance, while ``eager`` provides better debugging capabilities. **Hardware Dependencies**: Some attention implementations (like ``flash_attention_2``) may require specific hardware or CUDA versions. If you encounter compatibility issues, try using ``eager`` or ``sdpa``. Troubleshooting --------------- If you encounter errors when using a specific attention implementation: 1. **Check model compatibility**: Verify that your model supports the chosen attention implementation 2. **Try eager attention**: Use ``attn_implementation=eager`` as a fallback for debugging 3. **Check hardware requirements**: Ensure your hardware supports the attention implementation 4. **Review error messages**: Attention implementation errors often provide clear guidance on supported options Example Error Resolution ~~~~~~~~~~~~~~~~~~~~~~~~ If you see an error like "flash_attention_2 is not supported", you can resolve it by switching to eager attention: .. code:: bash # Instead of the default flash_attention_2 python3 ppo_trainer.py +actor_rollout_ref.model.override_config.attn_implementation=eager This override ensures your training can proceed while you investigate the flash attention compatibility issue. ================================================ FILE: docs/advance/checkpoint.rst ================================================ .. _checkpoint-page: Using Checkpoints to Support Fault Tolerance Training ===================================================== Last updated: 06/25/2025. There could be training errors or machine failure during the whole RLHF training process, so it is recommended to enable checkpoints to minimize your loss. The API Interface has already been listed in :ref:`config-explain-page`, and we will not repeat them. But there are still some technique details we hope to clarify. .. note:: Notice that the ``checkpoint.contents`` field has no effect to FSDP checkpoint except ``hf_model``, the other 3 fields are binded together to save and load. We recommend to include ``model``, ``optimizer`` and ``extra`` all. Checkpoint Saving Directory Structure ------------------------------------- Commonly, we use the ``default_local_dir`` declared in ``ppo_trainer.yaml`` or ``ppo_megatron_trainer.yml`` to work as preffix when saving checkpoints, which is ``checkpoints/${trainer.project_name}/${trainer.experiment_name}``. So the inner checkpoint structure of **FSDP** is like: .. code:: checkpoints/${trainer.project_name}/${trainer.experiment_name} ├── global_steps_${i} │ ├── actor │ │ ├── huggingface # default save config and tokenizer, save huggingface model if include ``hf_model`` in checkpoint.contents │ │ └── fsdp_config.json # FSDP config file, including world_size and fsdp version │ │ ├── model_world_size_{self.world_size}_rank_{self.rank}.pt │ │ ├── optim_world_size_{self.world_size}_rank_{self.rank}.pt │ │ └── extra_state_world_size_{self.world_size}_rank_{self.rank}.pt │ ├── critic │ │ ├── huggingface │ │ └── fsdp_config.json │ │ ├── model_world_size_{self.world_size}_rank_{self.rank}.pt │ │ ├── optim_world_size_{self.world_size}_rank_{self.rank}.pt │ │ └── extra_state_world_size_{self.world_size}_rank_{self.rank}.pt └── latest_checkpointed_iteration.txt All model shards, optimizers and extra states are stored together, in a sharded and distributed way. While **Megatron** current checkpoint structure is: .. code:: checkpoints/${trainer.project_name}/${trainer.experiment_name} ├── global_steps_${i} │ ├── actor │ │ ├── huggingface # default save config and tokenizer, save huggingface model if include ``hf_mode`` in checkpoint.contents │ │ └── dist_ckpt # save sharded model/optimizer/rng_states, naming the same as Megatron │ └── critic │ │ ├── huggingface │ │ └── dist_ckpt └── latest_checkpointed_iteration.txt Convert FSDP and Megatron Checkpoints to HuggingFace Format Model ----------------------------------------------------------------- We provide a tool to convert the FSDP and Megatron checkpoints to HuggingFace format model. The tool is located in ``verl/model_merger``. For older versions of verl that don't include fsdp_config.json in checkpoints, you can use the legacy model merger located at ``verl/scripts/legacy_model_merger.py``. The script supports two main sub-commands: `merge` (to convert and save checkpoints) and `test` (to validate merged checkpoints against a reference model). The arguments for the `merge` sub-command are as follows: .. code:: bash usage: python -m verl.model_merger merge [-h] --backend {fsdp,megatron} [--local_dir LOCAL_DIR] [--tie-word-embedding] [--is-value-model] [--use_cpu_initialization] [--target_dir TARGET_DIR] [--hf_upload_path HF_UPLOAD_PATH] [--private] options: -h, --help show this help message and exit --backend {fsdp,megatron} The backend of the model --local_dir LOCAL_DIR Path to the saved model checkpoints --tie-word-embedding Whether to tie word embedding weights (currently only Megatron supported) --is-value-model Whether the model is a value model (currently only Megatron supported) --use_cpu_initialization Whether to use CPU initialization for the model. This is useful for large models that cannot fit into GPU memory during initialization. --target_dir TARGET_DIR Directory to save the merged huggingface model --hf_upload_path HF_UPLOAD_PATH Hugging Face repository ID to upload the model --private Whether to upload the model to a private Hugging Face repository Example usage for merging Megatron checkpoints: .. code:: bash python -m verl.model_merger merge \ --backend megatron \ --tie-word-embedding \ --local_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model Example usage for distributed merging Megatron checkpoints: .. code:: bash torchrun --nproc_per_node 1 --nnodes 8 --node_rank ${RANK} -m verl.model_merger merge \ --backend megatron \ --tie-word-embedding \ --local_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model Example usage for merging FSDP checkpoints: .. code:: bash python -m verl.model_merger merge \ --backend fsdp \ --local_dir checkpoints/verl_fsdp_gsm8k_examples/qwen2_5_0b5_fsdp_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model Megatron Merger details ----------------------- Current implement of decoder layers uses ``nn.ModuleList`` to store the layers, and thus the model layers on every PP rank and VPP rank starts their index from 0. There are 3 ways to correct this behavior: 1. Modify the decoder layer's state_dict, add ``offset`` to each layer's index, thus rewrite ``nn.ModuleList`` implementation. 2. Modify the layer index when saving checkpoint and recover them when loading checkpoint. 3. The Checkpoint merger do this work, calculate the actual ``offset`` from ``state_dict`` only, a little complex. Current implementation use solution 2. HuggingFace to Megatron DistCheckpoint details ---------------------------------------------- Through ``mbridge``, we can directly save the mcore model to huggingface format during training. No need to convert the model to Megatron dist-checkpoint format. Original Checkpoint Utils ------------------------- Original Checkpoint Utils refer to original checkpoint implementation in ``verl/models/[model]/megatron/checkpoint_utils``. We only need ``[model]_loader.py`` in original checkpoint utils now, since we get rid of storing ``hf_model`` every time (which is not recommended for large model training, try only saving sharded models if you can). .. note:: Note that ``[model]_loader`` only support environments where **storage clusters are able to connect with every calculation nodes**. Because it utilizes **sharded load way to minimize the loading checkpoint overhead**. Every rank loads its own data from ``state_dict`` which can be accessed by all of them. While there is also no need to broadcast among DP ranks, since the saved state_dict is only produced by DP rank 0. For users who can **only place the huggingface model on one device**, we keep the original costly implementation in ``[model]_loader_deprecated``. In this implementation, rank 0 broadcast all weights to each tp and pp rank, and then dp rank 0 broadcast to all dp ranks. There may be at risks of OOM. To use deprecated loader, change the import package of ``load_state_dict_to_megatron_llama``. ================================================ FILE: docs/advance/dpo_extension.rst ================================================ Extend to other RL(HF) algorithms ================================= Last updated: 02/25/2025. We already implemented the complete training pipeline of the PPO algorithms. To extend to other algorithms, we analyze the high-level principle to use verl and provide a tutorial to implement the DPO algorithm. Users can follow the similar paradigm to extend to other RL algorithms. .. note:: **Key ideas**: Single process drives multi-process computation and data communication. Overall Approach ---------------- Step 1: Consider what multi-machine multi-GPU computations are needed for each model, such as ``generate_sequence`` , ``compute_log_prob`` and ``update_policy`` in the actor_rollout model. Implement distributed single-process-multiple-data (SPMD) computation and encapsulate them into APIs Step 2: Based on different distributed scenarios, including FSDP and 3D parallelism in Megatron-LM, implement single-process control of data interaction among multi-process computations. Step 3: Utilize the encapsulated APIs to implement the control flow Example: Online DPO ------------------- We use verl to implement a simple online DPO algorithm. The algorithm flow of Online DPO is as follows: 1. There is a prompt (rollout) generator which has the same weight as the actor model. After a batch of prompts are fed into the generator, it generates N responses for each prompt. 2. Send all the prompts + responses to a verifier for scoring, which can be reward model or a rule-based function. Then sort them in pairs to form a training batch. 3. Use this training batch to train the actor model using DPO. During the process, a reference policy is needed. Step 1: What are the multi-machine multi-GPU computations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **Sample Generator** Implementation details: .. code:: python from verl.single_controller.base import Worker from verl.single_controller.ray import RayWorkerGroup, RayClassWithInitArgs, RayResourcePool import ray @ray.remote class SampleGenerator(Worker): def __init__(self, config): super().__init__() self.config = config def generate_sequences(self, data): pass Here, ``SampleGenerator`` can be viewed as a multi-process pulled up by ``torchrun``, with each process running the same code (SPMD). ``SampleGenerator`` needs to implement a ``generate_sequences`` API for the control flow to call. The implementation details inside can use any inference engine including vllm, sglang and huggingface. Users can largely reuse the code in verl/verl/workers/rollout/vllm_rollout/vllm_rollout.py and we won't go into details here. **ReferencePolicy inference** API: compute reference log probability .. code:: python from verl.single_controller.base import Worker import ray @ray.remote class ReferencePolicy(Worker): def __init__(self): super().__init__() self.model = Model() def infer(self, data): return self.model(data) **Actor update** API: Update actor model parameters .. code:: python from verl.single_controller.base import Worker import ray @ray.remote class DPOActor(Worker): def __init__(self): super().__init__() self.model = Model() self.model = FSDP(self.model) # or other distributed strategy self.optimizer = optim.Adam(self.model.parameters(), lr=1e-3) self.loss_fn = xxx def update(self, data): self.optimizer.zero_grad() logits = self.model(data) loss = self.loss_fn(logits) loss.backward() self.optimizer.step() **Notes: How to distinguish between control processes and distributed computation processes** ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - Control processes are generally functions directly decorated with ``@ray.remote`` - Computation processes are all wrapped into a ``RayWorkerGroup``. Users can reuse most of the distribtued computation logics implemented in PPO algorithm, including FSDP and Megatron-LM backend in verl/verl/trainer/ppo. Step 2: Based on different distributed scenarios, implement single-process control of multi-process data interaction ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **The core problem to solve here is how a single process sends data to multiple processes, drives multi-process computation, and how the control process obtains the results of multi-process computation.** First, we initialize the multi-process ``WorkerGroup`` in the control process. .. code:: python @ray.remote(num_cpus=1) def main_task(config): # construct SampleGenerator resource_pool = RayResourcePool(process_on_nodes=[8] * 2) # 16 GPUs ray_cls = RayClassWithInitArgs(SampleGenerator, config=config) # put SampleGenerator onto resource pool worker_group = RayWorkerGroup(resource_pool, ray_cls) # construct reference policy As we can see, in the control process, multiple processes are wrapped into a ``RayWorkerGroup``. Inside this ``WorkerGroup``, there is a ``self._workers`` member, where each worker is a RayActor (https://docs.ray.io/en/latest/ray-core/actors.html) of SampleGenerator. ray_trainer.md also provide an implementation of ``MegatronRayWorkerGroup``. Assuming the model is distributed using FSDP, and there is a batch of data on the control process, for data parallelism, the underlying calling process is: .. code:: python data = xxx data_list = data.chunk(dp_size) output = [] for d in data_list: # worker_group._workers[i] is a SampleGenerator output.append(worker_group._workers[i].generate_sequences.remote(d)) output = ray.get(output) output = torch.cat(output) Single process calling multiple processes involves the following 3 steps: 1. Split the data into DP parts on the control process. 2. Send the data to remote, call the remote computation through RPC, and utilize multi-process computation. 3. Obtain the computation results of each worker on the control process and merge them. Frequently calling these 3 steps on the controller process greatly hurts code readability. **In verl, we have abstracted and encapsulated these 3 steps, so that the worker's method + dispatch + collect can be registered into the worker_group** .. code:: python from verl.single_controller.base.decorator import register def dispatch_data(worker_group, data): return data.chunk(worker_group.world_size) def collect_data(worker_group, data): return torch.cat(data) dispatch_mode = { 'dispatch_fn': dispatch_data, 'collect_fn': collect_data } @register(dispatch_mode=dispatch_mode) def generate_sequences(self, data): pass In this way, we can directly call the method inside the worker through the ``worker_group`` on the control (driver) process (which is a single process): .. code:: python output = worker_group.generate_sequences(data) This single line includes data splitting, data distribution and computation, and data collection. Furthermore, the model parallelism size of each model is usually fixed, including dp, tp, pp. So for these common distributed scenarios, we have pre-implemented specific dispatch and collect methods,in `decorator.py `_, which can be directly used to wrap the computations. .. code:: python from verl.single_controller.base.decorator import register, Dispatch @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def generate_sequences(self, data: DataProto) -> DataProto: pass Here it requires the data interface to be ``DataProto``. Definition of ``DataProto`` is in `protocol.py `_. Step 3: Main training loop ~~~~~~~~~~~~~~~~~~~~~~~~~~ With the above training flows, we can implement the algorithm's control flow. It is recommended that ``main_task`` is also a ray remote process. .. code:: python @ray.remote(num_cpus=1) def main_task(config): # construct SampleGenerator resource_pool = RayResourcePool(process_on_nodes=[8] * 2) # 16 GPUs ray_cls = RayClassWithInitArgs(SampleGenerator, config=config) # put SampleGenerator onto resource pool sample_gen = RayWorkerGroup(resource_pool, ray_cls) # construct reference policy ray_cls = RayClassWithInitArgs(ReferencePolicy) ref_policy = RayWorkerGroup(resource_pool, ray_cls) # construct actor ray_cls = RayClassWithInitArgs(DPOActor) dpo_policy = RayWorkerGroup(resource_pool, ray_cls) dataloader = DataLoader() for data in dataloader: # generate data data = sample_gen.generate_sequences(data) # generate scores for each data data = generate_scores(data) # generate pairwise data using scores data = generate_pairwise_data(data) # generate ref_log_prob data.batch['ref_log_prob'] = ref_policy.infer(data) # update using dpo dpo_policy.update(data) # logging Here, different ``WorkerGroups`` can be placed in the same resource pool or in different resource pools using ``create_colocated_worker_cls`` similar as in `ray_trainer.py `_. ================================================ FILE: docs/advance/fp8.md ================================================ # FP8 RL in verl Last updated: 03/05/2026 verl supports two FP8 modes for accelerating RL training: | Mode | Training Precision | Rollout Precision | |------|-------------------|-------------------| | **FP8 Rollout Only** | BF16 | FP8 | | **FP8 End-to-End** | FP8 (Megatron) | FP8 (vLLM) | > [!TIP] > For ready-to-run scripts, see the [low-precision recipe directory](https://github.com/verl-project/verl-recipe/low_precision). --- ## FP8 Rollout Only FP8 rollout-only mode keeps training in BF16 and quantizes rollout inference to FP8. This reduces GPU memory during generation and speeds up rollout without affecting training precision. ### Implementation We monkey patch several vLLM functions to enable FP8 rollout for reinforcement learning: 1. **Quantize weights**: Quantize model weights on-the-fly from higher-precision formats to FP8. 2. **Process weights after loading**: For vLLM, we replace the `vllm.model_executor.layers.quantization.fp8.Fp8LinearMethod.process_weights_after_loading` function to handle weight processing after quantization. For SGLang, this patch is not needed as it natively supports loading quantized weights. ### Support Matrix - FP8 blockwise quantization for rollout - Used in Deepseek, which is 1x128 quantization for activations and 128x128 quantization for model weights - Dense models and MoE models - Async rollout interfaces - vLLM 0.10.x & vLLM 0.11 & vLLM 0.12 & SGLang 0.5.5 - FSDP and Megatron training backends ### Usage Enable in config file: ```yaml rollout: quantization: "fp8" ``` Or via command line: ```bash actor_rollout_ref.rollout.quantization=fp8 ``` ### Experiments and Outcomes #### Qwen3-8B-Base Dense Model **Configuration** - DAPO recipe. AIME24 online validation. - vLLM(FP8 spmd rollout) + FSDP - Note that SPMD rollout has been deprecated, so we removed the FP8 SPMD rollout. - Prompt batch size 32, n=16. - Rollout batch size: 32\*3*16 - Train_batch_size & ppo_mini_batch_size 32 - Max response length 20K - Token-level TIS, C=2 - 8*H100 - vLLM 0.10.0+CUDA 12.6 vs vLLM 0.11.0+CUDA 12.9 **Accuracy** ![Qwen3-8b-base_fp8_acc]( https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-8b-base_fp8_acc.png?raw=true) *dark green: BF16, orange: FP8 rollout + token-level TIS, light green: FP8 rollout without TIS* Results and observations: - With TIS, FP8 rollout aligns with BF16 - Obvious accuracy drop when TIS is not enabled - Higher mismatch kl but within acceptable range throughout the training **Performance** ![Qwen3-8b-base_fp8_rollout_perf]( https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-8b-base_fp8_rollout_perf.png?raw=true) *green: BF16, orange: FP8 rollout + CUDA12.6 + DeepGemm, purple: FP8 rollout + CUDA 12.9 + DeepGemm* Results and observations: - FP8 rollout leads to around ~12% rollout speedup with CUDA 12.6 + DeepGemm - When upgrading to CUDA 12.9, speedup can be up to ~18% #### Qwen3-30B-A3B-Base MoE Model **Configuration** - DAPO recipe. AIME24 online validation. - FP8 async rollout, vLLM+FSDP - Prompt batch size 32 - Rollout batch size: 32\*3*16 - Train_batch_size & ppo_mini_batch_size 32 - Max response length 20K - Token-level TIS, C=2 - 2\*8*H100 - vLLM 0.10.0+CUDA 12.6 **Accuracy** ![Qwen3-30b-a3b_fp8_acc]( https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-30b-a3b_fp8_acc.png?raw=true) *grey: BF16 + token-level TIS, red: FP8 rollout + token-level TIS* Results and observations: - Rollout & training distribution mismatch is in general higher for MoE - Rollout correction required even for BF16 - FP8 rollout with token-level TIS aligns with BF16 **Performance** ![Qwen3-30b-a3b_fp8_perf]( https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-30b-a3b_fp8_perf.png?raw=true) *grey: BF16 + token-level TIS, red: FP8 rollout + token-level TIS​* Results and observations: - FP8 rollout : over 35% rollout speedup - Expecting more perf gain with CUDA 12.9 --- ## FP8 End-to-End (Training + Rollout) FP8 E2E applies FP8 to the entire RL pipeline: forward/backward passes via Transformer Engine, FP8 optimizer states, and FP8 rollout inference via vLLM. This maximizes memory savings and throughput. ### Requirements - **CUDA 12.9+** (required for block-wise FP8 scaling) - **Transformer Engine** with block-wise FP8 support - Environment variable: `NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1` ### Key Configuration ```yaml # FP8 training via Transformer Engine actor_rollout_ref.actor.megatron.override_transformer_config: fp8: "hybrid" # FP8 forward + backward; also supports "e4m3" fp8_recipe: "blockwise" # block-wise scaling # FP8 optimizer actor_rollout_ref.actor.optim.override_optimizer_config: fp8_recipe: "blockwise" # FP8 rollout inference (vLLM) actor_rollout_ref.rollout: quantization: fp8 ``` ### Support Matrix - Megatron training backend (via Megatron-Bridge) - Verified on Qwen3-30B-A3B and Qwen3-8B - Block-wise FP8 scaling (`fp8_recipe: "blockwise"`) ### Experiments and Results #### Qwen3-30B-A3B MoE Model **Configuration** - DAPO recipe. AIME24 online validation. - Megatron + Megatron-Bridge, FP8 async rollout with vLLM - MoE router in BF16 for both vLLM and Megatron-Core - Prompt batch size 128, n=16 - Max response length 20K - Token-level TIS, C=2 - 2\*8*H100, CUDA 12.9 ![Qwen3-30b-a3b_fp8_e2e](https://github.com/user-attachments/assets/70fb1396-ec73-40d7-9a43-1d48553c0ad9) *Orange: BF16, Green: FP8 E2E, Red: FP8 rollout + BF16 training* Results and observations: - FP8 E2E achieves comparable accuracy to the BF16 baseline, with the two curves closely aligned throughout training. - The training/inference precision mismatch (measured by KL divergence) follows the ordering: FP8 rollout-only > FP8 E2E > BF16 E2E. This is expected, as FP8 E2E maintains consistent precision across both training and inference, resulting in lower distribution mismatch than the FP8 rollout-only setting where training remains in BF16. --- ## Citation For more extensive experiments, ablation studies, and analysis on FP8 reinforcement learning, please refer to our technical report: ```bibtex @article{qiu2026fp8rl, title={FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning}, author={Qiu, Zhaopeng and Yu, Shuang and Zhang, Jingqi and Zhang, Shuai and Huang, Xue and Yang, Jingyi and Lai, Junjie}, journal={arXiv preprint arXiv:2601.18150}, year={2026}, url={https://arxiv.org/abs/2601.18150} } ``` ================================================ FILE: docs/advance/fsdp_extension.rst ================================================ Add models with the FSDP backend ================================== Last updated: 02/09/2025. Model -------------------------- In principle, our FSDP backend can support any HF model and we can sychronoize the actor model weight with vLLM using `hf_weight_loader.py` under `third_party/vllm`. However, ``hf_weight_loader`` is will gather the full state_dict of a model during synchronization, which may cause OOM. We suggest using ``dtensor_weight_loader`` which gather the full model parameter layer by layer to reduce the peak memory usage. We already support dtensor weight loader for the models below in `dtensor_weight_loader.py` under `third_party/vllm`: - ``GPT2LMHeadModel`` - ``LlamaForCausalLM`` - ``LLaMAForCausalLM`` - ``MistralForCausalLM`` - ``InternLMForCausalLM`` - ``AquilaModel`` - ``AquilaForCausalLM`` - ``Phi3ForCausalLM`` - ``GemmaForCausalLM`` - ``Gemma2ForCausalLM`` - ``GPTBigCodeForCausalLM`` - ``Starcoder2ForCausalLM`` - ``Qwen2ForCausalLM`` - ``DeepseekV2ForCausalLM`` To implement ``dtensor_weight_loader`` of a model that's supported in vLLM, follow the guide of gemma model below: 1. Copy the ``load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]])`` from the vllm model class to ``dtensor_weight_loaders.py`` 2. Modify the arguments to ``(actor_weights: Dict, vllm_model: nn.Module)`` 3. Replace the ``self`` to ``vllm_model`` 4. Add the ``local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)`` before each ``param = params_dict[name]`` and modify the following weight loading using ``local_loaded_weight``. 5. Register the implemented dtensor weight loader to ``__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__``. .. code-block:: diff - def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] - params_dict = dict(self.named_parameters()) + params_dict = dict(vllm_model.named_parameters()) loaded_params = set() - for name, loaded_weight in weights: + for name, loaded_weight in actor_weights.items(): for (param_name, shard_name, shard_id) in stacked_params_mapping: if shard_name not in name: continue name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue + local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader - weight_loader(param, loaded_weight, shard_id) + weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue + local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) - weight_loader(param, loaded_weight) + weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) loaded_params.add(name) unloaded_params = params_dict.keys() - loaded_params if unloaded_params: raise RuntimeError( "Some weights are not initialized from checkpoints: " f"{unloaded_params}") ================================================ FILE: docs/advance/fully_async.md ================================================ # Recipe: Fully Async Policy Trainer **Author:** `https://github.com/meituan-search` Last updated: 02/05/2026. This document introduces a fully asynchronous PPO training system that completely decouples the Trainer and Rollouter, supporting asynchronous sample generation and training. Under this system, we achieved a 2.35x-2.67x performance improvement when training the Qwen2.5-7B model with 128 GPUs, without significantly affecting the results. ## Introduction ### Background The separated rollout and train architecture, compared to the colocate architecture, can allocate resources more flexibly and design more flexible training logic, thereby addressing issues such as low GPU utilization and training efficiency caused by long-tail problems. The one_step_off_policy alleviates the problem of long rollout times and achieves some gains in training efficiency by designing a separated architecture and performing asynchronous training between rollout and train for one round. However, it forcibly uses data from one round of asynchronous training, which is not flexible enough and cannot completely eliminate the impact of long-tail on training efficiency. In other frameworks such as AReaL, Magistral, StreamRL, and AsyncFlow, asynchronous training and streaming training have been implemented based on the separated architecture and have achieved gains. We borrow from their methods and implemented them in VERL. The fully_async_policy supports asynchronous, streaming, and partial rollout training. By reasonably setting parameters such as resource allocation and parameter synchronization frequency, fully_async_policy can significantly improve training efficiency. > Magistral https://arxiv.org/abs/2506.10910 > > AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language > Reasoning https://arxiv.org/abs/2505.24298 > > StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream > Generation https://arxiv.org/abs/2504.15930 > > AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training https://arxiv.org/abs/2507.01663 > ### Core Contributions * **Resource Isolation**: Unlike using hybrid_engine, Rollouter and Trainer use separate computing resources and need to specify the resources they occupy separately. * **Parallel Generation and Training**: While the Trainer is training, the Rollouter is generating new samples. * **Multi-step Asynchronous**: Compared to one step off policy, it supports asynchronous settings from 0.x steps to multiple steps, making the asynchronous solution more flexible. * **NCCL Parameter Synchronization**: Based on the nccl communication primitive, refer to [checkpoint-engine](https://github.com/MoonshotAI/checkpoint-engine) to achieve efficient parameter synchronization between Rollouter and Trainer. * **Stream Inference and Training**: Rollouter generates data sample by sample, and data transmission uses a single sample as the minimum transmission unit. * **Asynchronous Training and Freshness Control**: By setting the parameter async_training.staleness_threshold, it supports training with samples generated by old parameters. * **PartialRollout**: The Rollouter's inference process supports partial rollout logic. During parameter synchronization, by adding `sleep() and resume()` logic, it saves samples from ongoing rollouts and continues using them in the next rollout, reducing the time spent waiting for ongoing tasks to finish during parameter synchronization. Currently, the supported usage mode is megatron/fsdp+vllm. vllm must use the server mode based on AgentLoop. ## Design The overall architecture of fully_async_policy is shown in the figure below. fully_async_policy mainly consists of four parts: Rollouter, MessageQueue, Trainer, and ParameterSynchronizer. ![fully_async_policy_structure]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_structure.svg?raw=true) 1. Rollouter generates sequences sample by sample and puts the generated samples into the MessageQueue, with the production speed controlled by freshness. 2. MessageQueue is used to temporarily store samples generated by Rollouter. 3. Trainer fetches samples from MessageQueue sample by sample. After fetching `require_batches*ppo_mini_batch_size` samples, it will perform training. After training for async_training.trigger_parameter_sync_step rounds, it triggers a parameter synchronization with Rollouter. 4. ParameterSynchronizer implements the NCCL synchronous parameter synchronization capability. The source of benefits compared to the base scheme lies in the fact that in the colocate case, using more resources for rollout cannot solve the idleness caused by long-tail samples. After we perform resource isolation, the time for rollout and train may be longer than before (because fewer resources are used), but the overlap in their time consumption reduces the end-to-end time consumption. ![fully_async_policy_revenue]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_revenue.svg?raw=true) ## Usage ### Parameter Description | super params | implication | |------------------------------------------------------------------|------------------------------------------------------------------------------------------------| | `trainer.nnodes` | Number of nodes for Trainer | | `trainer.n_gpus_per_node` | Number of GPUs per node for Trainer | | `rollout.nnodes` | Number of nodes for Rollouter | | `rollout.n_gpus_per_node` | Number of GPUs per node for Rollouter | | `data.train_batch_size` | In the fully async strategy, this value is not effective (default is 0) | | `data.gen_batch_size` | In the fully async strategy, uses streaming sample production logic (default is 1) | | `rollout.total_rollout_steps` | Total number of rollout samples | | `rollout.test_freq` | How many times Rollouter updates parameters before performing a validation | | `actor_rollout_ref.actor.ppo_mini_batch_size` | The ppo_mini_batch_size is a global num across all workers/gpus | | `actor_rollout_ref.actor.use_rollout_log_probs=True` | Use log_probs generated by rollout | | `algorithm.rollout_correction.bypass_mode` | Whether to compute log_prob using the training model's parameters during the training phase. | | `async_training.require_batches` | Number of ppo_mini_batch_size that FullyAsyncTrainer fetches at once | | `async_training.trigger_parameter_sync_step` | Indicates how many local updates FullyAsyncTrainer performs before a parameter synchronization | | `async_training.staleness_threshold` | Freshness control | | `async_training.partial_rollout` | Whether to perform partial_rollout | | `async_training.use_trainer_do_validate` | Whether use trainer node to do validate process, default `False` | **Further Explanation:** * `rollout.total_rollout_steps` Compared to colocate, the quantity can be aligned by multiplying train_batch_size and step: `rollout.total_rollout_steps = data.train_batch_size * step`. * `async_training.trigger_parameter_sync_step` In the fully async strategy, it indicates how many local updates the Trainer performs (i.e., how many times it fetches `require_batches * ppo_mini_batch_size` samples) before a parameter synchronization with Rollouter. Between every two parameter synchronizations between Rollouter and Trainer, the Trainer will process `trigger_parameter_sync_step* require_batches*ppo_mini_batch_size` samples. To fairly compare speed with colocate, `trigger_parameter_sync_step` should be set to `data.train_batch_size / (require_batches * ppo_mini_batch_size)`. * `async_training.staleness_threshold` In the fully async strategy, it indicates the maximum proportion of stale samples allowed to be used. * `staleness_threshold`=0, indicates synchronous training. Rollouter will generate a fixed number of samples between two parameter updates, the sample count is: `rollout_num = (trigger_parameter_sync_step*require_batches*ppo_mini_batch_size)` * `staleness_threshold`>0, indicates asynchronous training, can be set to a decimal for more flexible asynchronous calls. Rollouter will generate at most the following number of samples between two parameter updates: `rollout_num = (1+staleness_threshold)*(trigger_parameter_sync_step*require_batches*ppo_mini_batch_size) - num_staleness_sample` `num_staleness_sample` represents the number of stale samples generated in excess during the last rollout. Since it's a streaming system, rollout continues to generate and trainer continues to consume. If rollouter is slower, trainer will trigger parameter synchronization earlier, and rollouter will not actually produce rollout_num samples. When rollout is fast enough, setting `staleness_threshold` to 1 is basically equivalent to one_step_off policy. To avoid too many expired samples affecting training accuracy, it is recommended to set this value to less than 1. * `async_training.partial_rollout` partial_rollout only actually takes effect when staleness_threshold>0. * `async_training.require_batches` In streaming training, require_batches should be set to 1, indicating that training is performed after producing enough ppo_mini_batch_size samples. In actual testing, we found that if fewer samples are issued at once, due to the order of data distribution, it can cause training instability and longer response lengths. Here, we additionally provide require_batches for streaming distribution and control the number of samples participating in training at once. * `actor_rollout_ref.actor.use_rollout_log_probs=True` In reinforcement learning algorithms, log_probs have implicit correlations with parameter versions and tokens. Due to the settings of algorithms like PPO/GRPO/DAPO, when calculating importance sampling, old_log_prob must use the log_probs corresponding to the rollout parameters and tokens to ensure algorithm correctness. In the fully async strategy, we default to old_log_prob being calculated by rollout rather than by trainer. * `algorithm.rollout_correction.bypass_mode` > algorithm.rollout_correction.bypass_mode default is True, using rollout log prob. During the training process, we observed that metrics and response lengths may become unstable in the later stages of training. To mitigate this issue, we can use the [Rollout Importance Sampling](https://verl.readthedocs.io/en/latest/advance/rollout_is.html) technique for importance sampling. To utilize Rollout Importance Sampling, we need to compute log_prob using the training engine, which requires enabling this switch. Additionally, when `algorithm.rollout_correction.bypass_mode=False` and Rollout Importance Sampling are enabled under mode d (async stream pipeline with partial rollout), our implementation approximates `Areal's Decoupled PPO`. * `async_training.use_trainer_do_validate` It controls whether to use the trainer's `do_validate` method for validation. If set to True, the trainer will perform validation after each parameter update. It can reduce the validation time overhead and trainer node idle time. If set to False, the trainer will not perform validation. ### Supported Modes 1. on policy pipeline: 1. **trigger_parameter_sync_step=1, staleness_threshold=0** 2. Rollouter produces `require_batches*ppo_mini_batch_size` samples at once, Trainer fetches these samples for training, and after training completes, Trainer and Rollouter perform a parameter synchronization; 3. During the rollout phase, if there are long-tail samples but few rollout samples, shorter samples cannot fill idle resources, causing some resource waste. 4. As shown in figure a; 2. stream off policy pipeline: 1. **trigger_parameter_sync_step>1, staleness_threshold=0** 2. Synchronous streaming training will be performed. Rollouter produces `require_batches*ppo_mini_batch_size*trigger_parameter_sync_step` samples at once, Trainer performs a local training every time it fetches `require_batches*ppo_mini_batch_size` samples, and after training trigger_parameter_sync_step times, Trainer and Rollouter perform a parameter synchronization; 3. Compared to a, since more samples are generated at once, resource idleness will be lower. 4. In one step training, there will be two periods of resource idleness: when fetching the first batch of samples, train waits for `require_batches*ppo_mini_batch_size` samples to be produced, and during the last parameter update, rollout waits for training to complete. 5. As shown in figure b; 3. async stream pipeline with stale samples: 1. **trigger_parameter_sync_step>=1, staleness_threshold>0, partial_rollout=False** 2. After each parameter update, Rollouter will plan to produce at most rollout_num samples (in practice, the number of samples generated may be less than this value depending on rollout speed). 3. If the rollout process is relatively fast, Rollouter will generate some additional samples num_stale_samples before parameter synchronization for immediate use by Trainer after synchronization. When triggering parameter synchronization, if Rollouter has ongoing tasks, it will wait for the tasks to complete and not add new tasks; 4. Compared to b, except for the first step training, subsequent training will not have the time to wait for the first batch rollout to finish, but will have the time to wait for active tasks to finish. 5. As shown in figure c; 4. async stream pipeline with partial rollout: 1. **trigger_parameter_sync_step>=1, staleness_threshold>0, partial_rollout=True** 2. Compared to c, when triggering parameter synchronization, if Rollouter has samples being produced, it will interrupt the rollout process and perform parameter synchronization. The interrupted samples will continue to be generated after synchronization. This reduces the time to wait for active tasks to finish. 3. As shown in figure d; ![fully_async_policy_mode]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_mode.svg?raw=true) ### Key Metrics | metrics | implication | |------------------------------------------------|--------------------------------------------------------------------------------------------------------| | `trainer/idle_ratio` | Trainer idle rate | | `rollouter/idle_ratio` | Rollouter idle rate | | `fully_async/count/stale_samples_processed` | Total number of old samples used in training | | `fully_async/count/stale_trajectory_processed` | Total number of old trajectories used in training (one sample produces rollout.n trajectories) | | `fully_async/partial/total_partial_num` | Number of partial samples processed by Trainer between two trigger_parameter_sync_step | | `fully_async/partial/partial_ratio` | Ratio of partial samples processed by Trainer between two trigger_parameter_sync_step | | `fully_async/partial/max_partial_span` | Maximum parameter span of partial samples processed by Trainer between two trigger_parameter_sync_step | ### Parameter Tuning Recommendations * Resource Allocation and Adjustment: * Reasonable resource allocation is the prerequisite for achieving good training efficiency. The ideal resource allocation should make the rollout time and train time close, thereby minimizing pipeline bubbles in the entire training process, avoiding resource idleness, and ensuring Trainer does not use old samples. In real training scenarios, resource allocation can be adjusted based on the idle time of rollout and train during actual training, which can be obtained from rollouter/idle_ratio and trainer/idle_ratio. If rollouter/idle_ratio is high and trainer/idle_ratio is low, Trainer resources should be increased and Rollouter resources should be reduced, and vice versa. * Key Parameters: * staleness_threshold: Setting it too high will cause more old samples to be used, affecting model performance. It is recommended to set it to less than 1. * require_batches: The closer to 1, the closer to a pure streaming process, the smaller the training bubbles, and the faster the acceleration effect that can be achieved in terms of speed, but it will affect the order of sample processing; * trigger_parameter_sync_step: The smaller the setting, the closer to on policy, but it will cause frequent parameter synchronization. Long-tail samples waste resources that cannot be filled by short samples, resulting in low resource utilization. The larger the setting, the higher the computational efficiency, but the accuracy will be affected by off policy. * rollout.test_freq: It will occupy Rollouter resources and is not recommended to be set too small. * Mode Selection: By adjusting different parameters, the Fully Async architecture supports optimization acceleration at different levels, suitable for tasks in different scenarios. * For small-scale tasks that need to ensure training stability and on-policy nature, and have low speed requirements, the on policy pipeline mode (Mode 1) can be tried. * For scenarios that need to improve training throughput but are sensitive to staleness, the stream off policy pipeline mode can be tried. That is, by setting trigger_parameter_sync_step>1 to improve training efficiency, but still maintaining the synchronization mechanism (staleness_threshold=0) (Mode 2). * For large-scale tasks with high training speed requirements and can tolerate a certain degree of off-policy and staleness, setting staleness_threshold> 0 and partial_rollout=True can improve training efficiency, using the async stream pipeline mode (Mode 3 or 4). ### Quick Start ```shell rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=10 staleness_threshold=0 trigger_parameter_sync_step=16 partial_rollout=False python -m recipe.fully_async_policy.fully_async_main \ train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.actor.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ rollout.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.partial_rollout="${partial_rollout}" ``` ## Experiments ### Asynchronous Training on 7B Model We used Qwen2.5-Math-7B to verify the benefits of the fully async strategy under long candidates and multiple resources. Using the `async stream pipeline with stale samples` strategy, we achieved about 2x performance improvement on 32 cards, 64 cards, and 128 cards without significantly affecting experimental results. * Machine: H20 * Model: Qwen2.5-Math-7B * Rollout length: max_response_length FSDP2: 28K tokens; * Algorithm: DAPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+FSDP2 * rollout.n: 16 * ppo_mini_batch_size: 32 * test_freq: 20 * colocate sync: * step: 400 * train_batch_size: 512 * fully_async_policy * total_rollout_steps: 512*400 * require_batches: 4 * trigger_parameter_sync_step: 4 * staleness_threshold: 0.5 * partial_rollout: True | training mode | resource allocation | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:--------------------:|:---------------------:|:--------:|:--------:|:--------------:|:---------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:-------------------------------:| | colocate sync | 32 | 790.10 | 357.41 | 107.71 | 269.80 | 13h 44m | 1d 3h 43m | 2d 9h 22m | 3d 17h 5m | max: 0.3313
last: 0.2448 | | fully_async_policy | 16:16 | 294.77 | 21.26 | \ | 313.81 | 7h 58m
(1.72x) | 16h 21m
(1.70x) | 1d 0h 53m
(2.31x) | 1d 9h 26m
(2.66x) | max: 0.3302
last: 0.2333 | | colocate sync | 64 | 365.28 | 150.72 | 70.26 | 133.41 | 10h 22m | 20h 45m | 1d 7h 6m | 1d 17h 32m | max: 0.3365
last: 0.2333 | | fully_async_policy | 32:32 | 189.26 | 28.46 | \ | 156.98 | 4h 57m
(2.09x) | 10h 14m
(2.03x) | 16h 58m
(1.83x) | 21h 40m
(1.92x) | max: 0.3677
last: 0.3406 | | colocate sync | 128 | 356.30 | 177.85 | 53.92 | 113.81 | 8h 36m | 17h 56m | 1d 5h 6m | 1d 16h 48m | max: 0.3573
last: 0.2958 | | fully_async_policy | 64:64 | 150.63 | 33.14 | \ | 113.16 | 3h 13m
(2.67x) | 6h 46m
(2.65x) | 10h 53m
(2.67x) | 17h 22m
(2.35x) | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-colocate_async?nw=nwuserhouzg ### 128-card 7B Asynchronous Mode Experiment We used Qwen2.5-Math-7B to verify the effects of various modes supported by fully async. We can see that the benefit brought by streaming is approximately 1.6x, and after combining staleness and partial_rollout, the benefit reaches 2.35x. | mode | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:-------------------------------------------------------------------------------------------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------------:| | colocate sync | 356.30 | 177.85 | 53.92 | 113.81 | 8h 36m | 17h 56m | 1d 5h 6m | 1d 16h 48m | max: 0.3573
last: 0.2958 | | `stream off policy pipeline`
(+fully async: trigger_parameter_sync_step= 4,
require_batches= 4) | 231.34 | 128.47 | \ | 98.77 | 4h 25m | 9h 41m | 15h 2m | 1d 1h 53m | max: 0.2844
last: 0.2604 | | `async stream pipeline with stale samples`
(+staleness_threshold=0.5) | | | | | | | | | | | `async stream pipeline with partial rollout`
(+partial_rollout=True) | 150.63 | 33.14 | \ | 113.16 | 3h 13m | 6h 46m | 10h 53m | 17h 22m | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-stream_stale_partial?nw=nwuserhouzg ### 128-card Stale Ablation Experiment Under the `async stream pipeline with partial rollout` mode, we verified the impact of staleness settings on training efficiency. We found that the larger the staleness, the more obvious the final gains. We also noticed that the times for staleness values of 0.3 and 0.5 are quite close, because as the training steps increase, the response length changes significantly, causing training instability. Further analysis and optimization are needed for this issue. | staleness_threshold | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:---------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:-----------------------------:| | 0 | 231.34 | 128.47 | \ | 98.77 | 4h 25m | 9h 41m | 15h 2m | 1d 1h 53m | max: 0.2844
last: 0.2604 | | 0.1 | 171.30 | 58.17 | \ | 109.12 | 3h 53m | 8h 37m | 14h 25m | 19h 59m | max: 0.3542
last: 0.2979 | | 0.3 | 146.11 | 38.88 | \ | 103.22 | 3h 18m | 6h 49m | 11h 40m | 17h 20m | max: 0.3469
last: 0.2865 | | 0.5 | 150.63 | 33.14 | \ | 113.16 | 3h 13m | 6h 46m | 10h 53m | 17h 22m | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-stream_stale_partial?nw=nwuserhouzg ### 128-card 7B require_batches Ablation Experiment In multiple tests, we found that the number of samples issued each time in streaming affects the response length during training, which in turn affects training time. We verified the impact on results by modifying `async_training.require_batches`. | require_batches | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | acc/mean@1 | |:-----------------:|:--------:|:-------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:-----------------------------:| | 1 | 203.47 | 30.88 | \ | 181.08 | 3h 31m | 8h 29m | 17h 36m | max: 0.349
last: 0.326 | | 2 | 158.72 | 26.32 | \ | 128.08 | 3h 35m | 7h 38m | 13h 57m | max: 0.351
last: 0.3406 | | 4 | 124.64 | 25.62 | \ | 95.06 | 3h 13m | 6h 46m | 10h 53m | max: 0.3521
last: 0.3521 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-ablation_require_batches?nw=nwuserhouzg ### 30B Model Mode Experiment We achieved a 1.7x performance improvement with `async stream pipeline with staleness samples` strategy on the Qwen3-30B-A3B-Base model compared to the colocate setup. It is worth noting that this is far from the upper limit of performance gains achievable through asynchrony. Firstly, the comparative experiments used a maximum response length of only 8k, which is much shorter than the 20k sequence length in previous experiments, resulting in a less pronounced rollout tail effect. Secondly, we adopted a highly skewed resource allocation, with rollout using 96 GPUs and trainer using 32 GPUs, which is not an optimal configuration. During the experiments, we observed that the current verl implementation imposes certain constraints, such as requiring data to be evenly divisible by the number of GPUs, making resource adjustment less flexible. Additionally, as asynchronous training and deployment accelerate, the performance gap is gradually narrowing. Therefore, enabling more flexible resource allocation and dynamic resource adjustment in the future will be our next focus. * Machine: H20 * Model: Qwen3-30B-A3B-Base * Rollout length: max_response_length : 8K tokens; * Algorithm: GRPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+Megatron * rollout.n: 16 * ppo_mini_batch_size: 128 * test_freq: 20 * colocate sync: * step:400 * train_batch_size: 512 * fully_async_policy * total_rollout_steps: 512*400 * trigger_parameter_sync_step: 512/128 = 4 * staleness_threshold: 0.5 * partial_rollout: True | Training Mode | Resource Allocation | Step | Gen | Old Log Prob | Ref | Update Actor | Total Time 100 Step | Total Time 200 Step | Total Time 300 Step | Total Time 400 Step | Acc/Mean@1 | |--------------------|---------------------|--------|--------|--------------|-------|--------------|---------------------|---------------------|---------------------|---------------------|-----------------------------| | Colocate Sync | 128 | 497.89 | 348.05 | 28.73 | 20.86 | 86.27 | 13h 36m | 1d 3h 48m | 1d 19h 4m | 2d 11h 39m | max: 0.3500
last: 0.3208 | | Fully Async Policy | 96:32 | 282.75 | 22.06 | \ | 50.05 | 206.63 | 6h 45m (2.01x) | 14h 48m (1.88x) | 1d 0h 9m (1.78x) | 1d 10h 41m (1.72x) | max: 0.3813
last: 0.3448 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-30B?nw=nwuserhouzg | | | ### checkpoint-engine Ablation Experiment We tested the single-step parameter synchronization time of the checkpoint-engine on three models: Qwen2.5-Math-7B, Qwen3-30B-A3B, and Qwen3-235B-A22B, using default checkpoint-engine configurations. All experiments were performed on H20 machines, and the Megatron engine was used for training. | model | trainer rank | rollout rank | checkpoint-engine | total sync time | |:---------------:|:--------------:|:-------------:|:-------------------:|:-----------------:| | Qwen2.5-Math-7B | 4 | 4 | False | 0.12s | | Qwen2.5-Math-7B | 4 | 4 | True | 0.02s | | Qwen3-30B-A3B | 16 | 16 | False | 15.76s | | Qwen3-30B-A3B | 16 | 16 | True | 4.38s | | Qwen3-235B-A22B | 64 | 64 | False | 58.57s | | Qwen3-235B-A22B | 64 | 64 | True | 23.70s | ### use_trainer_do_validate Experiment We tested the effect of setting `use_trainer_do_validate=True` on the training process. The results show that setting this parameter to True can reduce the validation time overhead and trainer node idle time. We used Qwen2.5-Math-7B to verify the benefits of `use_trainer_do_validate=True` on the training process, we achieved about 2x performance improvement on validation time, and the trainer node idle time is reduced by about 40%. * Machine: H20 * Model: Qwen2.5-Math-7B * Rollout length: max_response_length FSDP2: 10K tokens; * Algorithm: DAPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+FSDP2 * rollout.n: 16 * ppo_mini_batch_size: 32 * test_freq: 10 * fully_async_policy * total_rollout_steps: 512*400 * require_batches: 4 * trigger_parameter_sync_step: 4 * staleness_threshold: 0.5 * partial_rollout: True | training mode | resource allocation | step | gen | old_log_prob | update_actor | validate time | total time
50 step | acc/mean@2 | |:------------------:|:-------------------:|:-------:|:-------:|:------------:|:------------:|:-------------:|:---------------------:|:----------:| | colocate sync | 16 | 484.623 | 52.939 | 0 | 430.263 | 205.080 | 7h9m | 22.6 | | fully_async_policy | 8:8 | 489.953 | 52.622 | 0 | 435.874 | 95.699 | 7h2m | 21.0 | ## Multi-Turn Tool Calling Referencing **recipe/retool** and **ToolAgentLoop**, we implemented **AsyncPartialToolAgentLoop**, a multi-turn tool-calling loop that supports partial_rollout for **fully_async_policy**. ### Core Design `AsyncPartialToolAgentLoop` inherits from `ToolAgentLoop` and is adapted for the asynchronous training mode of `fully_async_policy`. When `partial_rollout=True`, the Rollouter interrupts ongoing generation tasks before synchronizing parameters with the Trainer. `AsyncPartialToolAgentLoop` is capable of: 1. **Interrupting Tasks**: Responding to an interrupt signal to save the current state. Currently, interruptions occur during the `GENERATING` process or after other states have completed. 2. **Resuming Tasks**: Resuming execution from the saved state after parameter synchronization is complete, rather than starting over. ### How to Use RL training with multi-turn tool calling in `fully_async_policy` is similar to `recipe/retool`. It is enabled by specifying `multi_turn` configurations in the config file. 1. **SFT Stage**: First, the model should undergo SFT to learn how to follow tool-calling format instructions. 2. **Multi-turn Configuration**: In the `fully_async_policy` training configuration, set the following parameters: ```yaml actor_rollout_ref: rollout: multi_turn: enable: True # AsyncPartialToolAgentLoop will be used by default in fully_async_policy mode # Other multi_turn related configurations ``` 3. **Async Parameters**: To improve efficiency, enable `partial_rollout` and `staleness_threshold` when using multi-turn tool calling: ```yaml async_training: partial_rollout: True staleness_threshold: 0.5 # Other async parameters ``` 4. **Example**: See `recipe/fully_async_policy/shell/dapo_7b_async_retool.sh`. ### Experimental Results To validate the performance of `fully_async_policy` on multi-turn tool-calling tasks, we compared it with the standard `colocate` synchronous mode. Key parameter settings are as follows. * **SFT Model**: Based on `Qwen2.5-7B-Instruct`, trained for 6 epochs on the `ReTool-SFT` dataset * **RL Algorithm**: DAPO * **Dataset**: * Train: `DAPO-Math-17k` * Test: `aime_2025` * **Resource and Mode Comparison**: * `colocate sync`: 32 H20 gpus * `fully_async_policy`: 16 gpus for Trainer + 16 gpus for Rollouter * **Key Configurations**: 1. **Tool Calling Configuration**: * `multi_turn.enable: True` * `multi_turn.max_user_turns: 16` * `multi_turn.max_assistant_turns: 16` * `multi_turn.tool_config_path: recipe/retool/sandbox_fusion_tool_config.yaml` 2. **`colocate sync` Configuration**: * `ppo_mini_batch_size: 16` * `train_batch_size: 64` 3. **`fully_async_policy` Configuration**: * `ppo_mini_batch_size: 16` * `trigger_parameter_sync_step: 4` * `require_batches: 1` * `staleness_threshold: 1` * `partial_rollout: True` | training mode | Resource allocation | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | aime_2025
acc/mean@30 | |:--------------------:|:---------------------:|:---------:|:---------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:-------------------------------:| | colocate | 32 | 375.47 | 228.03 | 35.19 | 111.84 | 9h 46m | 22h 28m | start:0.1078
last:0.2056 | | fully_async_policy | 16: 16 | 221.36 | 40.59 | \ | 179.58 | 6h 19m
(1.55x) | 14h 4m
(1.60x) | start:0.11
last:0.2044 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-multiturn-tool?nw=nwuserhouzg ## Future Plans * GRPO experiments * Megatron adaptation * SGLang integration * Transfer queue integration * Asynchronous parameter synchronization * AReaL asynchronous algorithm implementation * TPPO algorithm implementation * Multi-turn and Tool support ================================================ FILE: docs/advance/grafana_prometheus.md ================================================ # Use Prometheus and Grafana to Monitor Rollout **Author:** `https://github.com/meituan-search` Last updated: 12/05/2025. Monitor the rollout computation process using Prometheus and Grafana when using verl to enhance system observability and facilitate further performance optimization. We provide an additional training monitoring capability, leveraging Prometheus and Grafana to display rollout information during training and enhance system observability to facilitate further performance optimization. The system automatically configures Prometheus to scrape metrics from rollout servers, eliminating manual configuration steps. ## Overview The figures below show the performance of Qwen235B on the AIME2024 dataset with a response length of 20k, where the emergence of a long-tail problem is clearly observable. ![fully_async_policy_structure](https://github.com/ArronHZG/verl-community/blob/main/docs/grafana_validate.png?raw=true) The following figure presents the fully asynchronous training of the Qwen235B model. Here, resource idleness is distinctly noticeable, indicating that rollout resources can be reduced. ![fully_async_policy_structure](https://github.com/ArronHZG/verl-community/blob/main/docs/grafana_fully_async_train.png?raw=true) Through the above two examples, we also illustrate the necessity of system observability. ## Architecture Overview The overall workflow consists of the following steps: 1. **Multi-node Ray Cluster Setup**: Start Ray cluster across multiple nodes with Grafana and Prometheus information configured in environment variables on the master node 2. **Start Grafana Service**: Launch Grafana on the master node for visualization of monitoring dashboards 3. **Start Prometheus Service**: Launch Prometheus on the master node for metrics collection and storage 4. **verl Async Rollout Mode**: verl uses async rollout mode to obtain rollout server ports and IP addresses 5. **Automatic Prometheus Configuration**: verl automatically rewrites the Prometheus configuration to add monitoring for rollout servers and notifies Prometheus to reload the configuration 6. **Metrics Collection**: After program execution, metrics can be viewed in Prometheus 7. **Dashboard Visualization**: Upload and view monitoring metrics in Grafana dashboards ## Detailed Setup Steps ### Step 1: Environment Variables and Start Ray Cluster First, set the necessary environment variables and start the Ray service. > Reference: [configure-manage-dashboard](https://docs.ray.io/en/latest/cluster/configure-manage-dashboard.html) ```bash # Master node environment variables export GF_SERVER_HTTP_PORT=3000 # Grafana service default port (customizable) export PROMETHEUS_PORT=9090 # Prometheus service default port (customizable) export RAY_HEAD_PORT=6379 # Ray master node port (customizable) export RAY_DASHBOARD_PORT=8265 # Ray dashboard default port (customizable) export GRAFANA_PATHS_DATA=/tmp/grafana # Grafana data storage directory (customizable) export RAY_GRAFANA_HOST="http://${master_ip}:${GF_SERVER_HTTP_PORT}" # Ray-associated Grafana address export RAY_PROMETHEUS_HOST="http://${master_ip}:${PROMETHEUS_PORT}" # Ray-associated Prometheus address # Start Ray on master node ray start --head --port=${RAY_HEAD_PORT} --dashboard-port=${RAY_DASHBOARD_PORT} # Start Ray on worker nodes ray start --address={master_addr}:${RAY_HEAD_PORT} ``` **Verification:** Visit `http://master_ip:8265` to confirm Ray has started successfully. ### Step 2: Start Grafana (Visualization Dashboard) Grafana is used to display metrics collected by Prometheus (such as cache hit rate, throughput, etc.): ```bash # Master node nohup grafana-server \ --config /tmp/ray/session_latest/metrics/grafana/grafana.ini \ --homepath /usr/share/grafana \ web > grafana.log 2>&1 & ``` **Verification:** Visit `http://master_ip:3000` to confirm Grafana has started successfully (default credentials: `admin/admin`). If you need to change the port, modify the `GF_SERVER_HTTP_PORT` environment variable, and grafana-server will automatically recognize it. ### Step 3: Start Prometheus (Metrics Collection) Prometheus is responsible for scraping metrics from vLLM services and storing them as time-series data: ```bash # Master node nohup prometheus \ --config.file /tmp/ray/session_latest/metrics/prometheus/prometheus.yml \ --web.enable-lifecycle \ --web.listen-address=:${PROMETHEUS_PORT} \ > prometheus.log 2>&1 & ``` **Verification:** Visit `http://master_ip:9090` to confirm Prometheus service has started successfully. ### Step 4 & 5: Start verl Training Start verl training with the following parameters configured: **Required Configuration:** - `actor_rollout_ref.rollout.mode="async"` - `actor_rollout_ref.rollout.disable_log_stats=False` - `actor_rollout_ref.rollout.prometheus.enable=True` If use default port, this parameter can be omitted. - `actor_rollout_ref.rollout.prometheus.port=9090` If use default path, this parameter can be omitted. - `actor_rollout_ref.rollout.prometheus.file="/tmp/ray/session_latest/metrics/prometheus/prometheus.yml"` served_model_name uses `model_path.split("/")[-1]` for data statistics by default. Users can also customize other aliases: - `actor_rollout_ref.rollout.prometheus.served_model_name="Qwen3-235B"` **Shell Script Example:** ```bash WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} rollout_mode="async" rollout_name="vllm" # Options: sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Synchronous training ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ --working-dir "${WORKING_DIR}" \ -- python3 -m verl.trainer.main_ppo \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.disable_log_stats=False \ actor_rollout_ref.rollout.prometheus.enable=True ... # Asynchronous training ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ --working-dir "${WORKING_DIR}" \ -- python3 verl.experimental.fully_async_policy.fully_async_main \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.disable_log_stats=False \ actor_rollout_ref.rollout.prometheus.enable=True ... ``` ### Step 6: View Metrics in Prometheus After task execution, verify that Prometheus is correctly collecting metrics. **Verification:** Visit the Prometheus interface at `http://master_ip:9090` and search for `vllm:` or `sglang:` to confirm metrics are being reported correctly. **Troubleshooting:** If no metrics appear: 1. Check logs for `AgentLoopManager` to find the server port 2. Visit `http://master_ip:server_port/metrics` to verify server metrics are available 3. Confirm that `actor_rollout_ref.rollout.disable_log_stats=False` is set ### Step 7: View Metrics in Grafana After task execution, log in to Grafana to view and customize monitoring dashboards. **Login:** Visit `http://master_ip:3000` (default credentials: `admin/admin`) **Import Dashboard:** 1. Select `Dashboards` → `New` → `Import` → `Upload dashboard JSON file` 2. Upload a pre-built dashboard JSON file **Available Dashboards:** - [vLLM Grafana Dashboard style 1](https://github.com/ArronHZG/verl-community/blob/main/docs/grafana/vllm_grafana.json) - [vLLM Grafana Dashboard style 2](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/dashboards/grafana/performance_statistics.json) - [vLLM Grafana Dashboard style 2](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/dashboards/grafana/query_statistics.json) - [SGLang Grafana Dashboard](https://github.com/sgl-project/sglang/blob/main/examples/monitoring/grafana/dashboards/json/sglang-dashboard.json) ## Additional Resources - [Ray Monitoring Documentation](https://docs.ray.io/en/latest/cluster/configure-manage-dashboard.html) - [Prometheus Documentation](https://prometheus.io/docs/) - [Grafana Documentation](https://grafana.com/docs/) - [vLLM GitHub Repository](https://github.com/vllm-project/vllm) - [SGLang GitHub Repository](https://github.com/sgl-project/sglang) ================================================ FILE: docs/advance/megatron_extension.rst ================================================ Add models with the Megatron-LM backend ========================================= Last updated: 04/25/2025. Model ----------- If use latest verl, we have direct support of ``GPTModel`` for Megatron backend. You can use the similar way of using Megatron to pretrain custom models. We list the steps here: 1. Find `model_initializer.py `_ 2. If your model is configurable by ``TransformerLayerSpec`` , you can directly use ``GPTModel``. Otherwise, Please implement a new ``ModelLayerSpec`` and ``ModelLayer`` here. 3. Use the right ``LayerSpec`` , ``TransformerConfig`` and ``HuggingfaceConfig`` as arguments to initialize the GPTModel. 4. Return the model at last. ================================================ FILE: docs/advance/mtp.md ================================================ # Guide to Using MTP in SFT/RL Training and Inference **Author**: `https://github.com/meituan-search` Last updated: 02/15/2026 # 1. Scope of Support Currently, RL training can be performed on mimo-7B-RL, Qwen-next, and Deepseek series models based on the MTP architecture. The support rules for training and inference engines are as follows: - **Training Engine**: Only supports the `mbridge/Megatron-Bridge + megatron` combination; other training engines are not compatible at this time; - **Inference Engine**: Compatible with all engines, but the model must be in the corresponding engine's compatibility list; - **Dependency Versions**: - mbridge: Apply the patches and review suggestions from PR: [#62](https://github.com/ISEEKYAN/mbridge/pull/62) (Already merged into the main branch); - Megatron-Bridge: Apply the patches and review suggestions from PR if you want to try out mimo-7B-RL: [#2387](https://github.com/NVIDIA-NeMo/Megatron-Bridge/pull/2387) (will be merged into the main branch in the future); - megatron: Use the latest dev version (commit: [23e092f41ec8bc659020e401ddac9576c1cfed7e](https://github.com/NVIDIA/Megatron-LM/tree/23e092f41ec8bc659020e401ddac9576c1cfed7e)), which supports MTP + CP training methods. - sglang: Use the specified branch: [https://github.com/ArronHZG/sglang/tree/fix_mtp_update_weights_from_tensor](https://github.com/ArronHZG/sglang/tree/fix_mtp_update_weights_from_tensor), [PR](https://github.com/sgl-project/sglang/pull/17870) , which fix the MTP update weights from tensor OOM issue. # 2. MTP Training Configuration (Core Parameters) The MTP training process can be flexibly controlled through the following configurations. All configurations are based on the `actor_rollout_ref.model.mtp` prefix: | Configuration Scenario | Core Parameters | Description | |------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------| | Load MTP Parameters Only | `enable=True` | VRAM usage will increase, but the exported parameters include the MTP module and can be directly used for online deployment | | Full-Parameter MTP Training | `enable=True`
`enable_train=True`
`mtp_loss_scaling_factor=0.1` | MTP Loss will apply to all model parameters | | MTP Parameter-Only Training | `enable=True`
`enable_train=True`
`detach_encoder=True` | Freeze the Encoder layer, update only MTP module parameters, MTP Loss applies only to MTP parameters | | MTP Accelerated Rollout | 1. vLLM configuration:
`enable=True`
`enable_rollout=True`
`method="mtp"`
`num_speculative_tokens=1`
2. SGLang configuration:
`enable=True`
`enable_rollout=True`
`speculative_algorithm="EAGLE"`
`speculative_num_steps=2`
`speculative_eagle_topk=2`
`speculative_num_draft_tokens=4` | Achieve inference acceleration during the Rollout phase based on MTP | # 3. Experimental Results The experiment was conducted as follows: * model = mimo-7B-math * max_response_length = 8k Experiment chart: ![fully_async_policy_revenue]( https://github.com/ArronHZG/verl-community/blob/main/docs/mimo-7b-mtp.png?raw=true) The wandb link for the graph: [wandb](https://wandb.ai/hou-zg-meituan/mimo-7b-sft-mtp?nw=nwuserhouzg) **Scenarios with No Significant Effect** The following configurations will not have a noticeable impact on training results: 1. The base model does not carry MTP parameters; 2. The base model carries MTP parameters, but the MTP module is not trained; 3. The base model carries MTP parameters and trains MTP, with `mtp_loss_scaling_factor=0`; 4. The base model carries MTP parameters, trains MTP and detaches the encoder, with `mtp_loss_scaling_factor=0.1`. **Scenarios with Significant Effect** Only the following configuration will have a noticeable impact on training results: - The base model carries MTP parameters, MTP Loss applies to all model parameters, and `mtp_loss_scaling_factor=0.1`. **Recommended Training Method** It is recommended to adopt the `detach_encoder=True` approach for MTP training. # 4. Performance Notes for MTP in Rollout Inference Enabling MTP improves the rollout acceptance rate by around 14%. However, on H20 GPUs, overall throughput does not increase and even decreases slightly. ![spec_log]( https://github.com/ArronHZG/verl-community/blob/main/docs/spec_log.png?raw=true) The effectiveness of MTP-accelerated Rollout is significantly affected by **model size** and **inference hardware**. Key reference information is as follows: **Hardware Tensor Core Performance** | Hardware Model | FP16 Performance (TFLOPS) | |----------------|---------------------------| | H20 | 148 | | H800 | 1,671 | | H200 | 1,979 | **Measured Performance and Recommendations** Taking the mimo-7B model deployed separately on H20 hardware using SGLang as an example: After enabling MTP speculative decoding, the Rollout throughput decreases by approximately 50%. - Current priority recommendation: Do not enable MTP acceleration during the inference phase for now; - Future planning: Further optimization of the speculative logic in the Rollout phase will be conducted to improve throughput performance. # 5. SFT training The SFT training with MTP is supported, using the same MTP training configuration as RL training. An example configuration for running SFT can be found in `examples/sft/gsm8k/run_mimo_megatron_mtp.sh` **SFT result** The experiment was conducted using following data: - model = mimo-7B-math - dataset = gsm8k The result: [wandb link](https://wandb.ai/hou-zg-meituan/mimo-7b-sft-mtp?nw=nwuserhouzg) The presence of mtp layer has limited effect on main loss. However, when MTP layer is detached, the mtp_loss converges to a higher value. ================================================ FILE: docs/advance/one_step_off.md ================================================ # Recipe: One Step Off Policy Async Trainer **Author:** `https://github.com/meituan-search` Last updated: 07/17/2025. ## Introduction ### Background The current reinforcement learning training process implemented by verl is synchronous, adhering to the algorithmic workflows of established methods like PPO, GRPO, and DAPO. In each step, training samples are generated by the latest model, and the model is updated after training completes. While this approach aligns with off-policy reinforcement learning and stabilizes RL training, but it suffers from severe efficiency issues. Model updates must wait for the longest output in the generation phase to complete. During the generation of long-tail samples, GPUs remain idle, resulting in significant underutilization. The more severe the long-tail problem in sample generation, the lower the overall training efficiency. For example, in DAPO 32B training, the Rollout phase accounts for approximately 70% of the total time, and increasing resources does not reduce the Rollout duration. ![DAPO 32B Math Performance](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/dapo_32b_math.png) > source data: https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/workspace?nw=nwusertongyuxuan361 ### Solution We have implemented the **One Step Off Async Trainer** to help alleviate this issue. This approach parallelizes the generation and training processes, utilizing samples generated in the previous step for current training. It also involves appropriately partitioning resources, allocating dedicated resources for generation while automatically assigning the remainder to training. By reducing resources allocated to the generation phase, we mitigate GPU idle time during long-tail sample generation. Throughout this process, generation and training parameters maintain a one-step off policy. ![One Step Off Policy Diagram](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/one_step_off_policy.png) > reference: [AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning](https://arxiv.org/abs/2505.24298) Our core contributions include: 1. **Parallel Generation and Training**: Samples for the next batch are asynchronously generated while the current batch is being trained. 2. **Resource Isolation**: Unlike `hybrid_engine`, this method requires explicit resource allocation for rollout, with remaining resources automatically assigned to training. 3. **NCCL Parameter Synchronization**: Employs NCCL communication primitives for seamless parameter transfer between generation and training modules. ### Experimental Results - **Machine Configuration**: 2 nodes with 16 H20 GPUs each - Generation: 4 GPUs - Training: 12 GPUs - **Model**: Qwen2.5-Math-7B - **Rollout Configuration**: - **Max Response Length**: FSDP2: 20,480 tokens; Megatron: 8,192 tokens - **Algorithm**: DAPO - **Rollout Engine**: vLLM | training mode | engine | step | gen | wait_prev_gen | generate_sequences | old_log_prob | update_actor | total time | acc/best@32/mean | acc/maj@32/mean | | ---------------------- | ------------- | ---- | --- | ------------- | ------------------ | ------------ | ------------ | -------------- | ---------------- | --------------- | | colocate sync | VLLM+FSDP2 | 749 | 321 | - | 247 | 88 | 286 | 19h18m | 0.5948 | 0.417 | | one-step-overlap async | VLLM+FSDP2 | 520 | - | 45 | 458 | 108 | 337 | 15h34m（+23%） | 0.6165 | 0.494 | | colocate sync | VLLM+Megatron | 699 | 207 | - | 162 | 119 | 344 | 18h21m | 0.605 | 0.4217 | | one-step-overlap async | VLLM+Megatron | 566 | - | 59 | 501 | 120 | 347 | 13h06m (+40%) | 0.6569 | 0.4038 | - colocate sync: step ≈ gen + old_log_prob + update_actor - one-step-overlap async: step ≈ wait_prev_gen + old_log_prob + update_actor ![One Step Off Megatron Performance](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/one_step_off_megatron.png) > source data: https://wandb.ai/hou-zg-meituan/one-step-off-policy?nw=nwuserhouzg ## Implementation ### One Step Off Policy Async Pipeline Our implemented **One Step Off Policy Async Pipeline** integrates seamlessly into existing training logic at minimal cost, eliminating the need for additional sample storage management. The core mechanism uses `async_gen_next_batch` for asynchronous rollout generation while maintaining continuous operation during epoch transitions via `create_continuous_iterator`. ```python # iterator generator, simplify one-step integration of the training process def _create_continuous_iterator(self): for epoch in range(self.config.trainer.total_epochs): iterator = iter(self.train_dataloader) for batch_dict in iterator: yield epoch, batch_dict # read next batch samples, parameters sync and launch asyn gen_seq def _async_gen_next_batch(self, continuous_iterator): # read train_data try: epoch, batch_dict = next(continuous_iterator) except StopIteration: return None batch = DataProto.from_single_dict(batch_dict) gen_batch = batch_pocess(batch) # sync weights from actor to rollout self.sync_rollout_weights() # async generation gen_batch_output = self.rollout_wg.async_generate_sequences(gen_batch) # future encapsulated return GenerationBatchFuture(epoch, batch, gen_batch_output) continuous_iterator = self._create_continuous_iterator() # run rollout first to achieve one-step-off batch_data_future = self._async_gen_next_batch(continuous_iterator) while batch_data_future is not None: # wait for the gen_seq result from the previous step batch = batch_data_future.get() # launch the next async call to generate sequences batch_data_future = self._async_gen_next_batch(continuous_iterator) # compute advantages batch = critic.compute_values(batch) batch = reference.compute_log_prob(batch) batch = reward.compute_reward(batch) batch = compute_advantages(batch) # model update critic_metrics = critic.update_critic(batch) actor_metrics = actor.update_actor(batch) ``` ### Parameter Synchronization The exciting point is that our nccl based weights updating for rollout model has great performance. At most of time, the latency is under 300ms, which is negligible for RLHF. > **sync_rollout_weights**：The time for synchronizing parameters from actor to rollout is extremely fast and can almost > be ignored because it is implemented with nccl. ```python class ActorRolloutRefWorker: # actor acquires the meta-info of model parameters for parameter sync @register(dispatch_mode=Dispatch.ONE_TO_ALL) def get_actor_weights_info(self): params = self._get_actor_params() ret = [] for key, tensor in params.items(): ret.append((key, tensor.size(), tensor.dtype)) self._weights_info = ret return ret # rollout sets the meta-info of model parameters for parameter sync @register(dispatch_mode=Dispatch.ONE_TO_ALL) def set_actor_weights_info(self, weights_info): self._weights_info = weights_info class AsyncRayPPOTrainer(RayPPOTrainer): def init_workers(self): ... # rollout obtains the meta-info of model parameters from the actor for parameter sync weights_info = self.actor_wg.get_actor_weights_info()[0] self.rollout_wg.set_actor_weights_info(weights_info) # Create an actor-rollout communication group for parameter sync self.create_weight_sync_group ``` ```python # The driving process invokes the actor and rollout respectively to create a weight synchronization group based on nccl/hccl. def create_weight_sync_group(self): master_address = ray.get(self.actor_wg.workers[0]._get_node_ip.remote()) master_port = ray.get(self.actor_wg.workers[0]._get_free_port.remote()) world_size = len(self.actor_wg.workers + self.rollout_wg.workers) self.actor_wg.create_weight_sync_group( master_address, master_port, 0, world_size, ) ray.get( self.rollout_wg.create_weight_sync_group( master_address, master_port, len(self.actor_wg.workers), world_size, ) ) # drive process call the actor and rollout respectively to sync parameters by nccl def sync_rollout_weights(self): self.actor_wg.sync_rollout_weights() ray.get(self.rollout_wg.sync_rollout_weights()) # fsdp model parameter sync @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) def sync_rollout_weights(self): params = self._get_actor_params() if self._is_actor else None if self._is_rollout: inference_model = ( self.rollout.inference_engine.llm_engine.model_executor.driver_worker.worker.model_runner.model ) from verl.utils.vllm.patch import patch_vllm_moe_model_weight_loader patch_vllm_moe_model_weight_loader(inference_model) # Model parameters are broadcast tensor-by-tensor from actor to rollout for key, shape, dtype in self._weights_info: tensor = torch.empty(shape, dtype=dtype, device=get_torch_device().current_device()) if self._is_actor: assert key in params origin_data = params[key] if hasattr(origin_data, "full_tensor"): origin_data = origin_data.full_tensor() if torch.distributed.get_rank() == 0: tensor.copy_(origin_data) from ray.util.collective import collective collective.broadcast(tensor, src_rank=0, group_name="actor_rollout") if self._is_rollout: inference_model.load_weights([(key, tensor)]) ``` ### PPO Correctness To ensure the correctness of the PPO algorithm, we use rollout log_probs for PPO importance sampling. For the related algorithm details, please refer to: https://verl.readthedocs.io/en/latest/algo/rollout_corr_math.html The default mode is `bypass_ppo_clip`, but other modification strategies can also be explored. ### AgentLoop In the current implementation, we no longer provide SPMD model rollout mode. Instead, we have switched to AgentLoop mode, which also supports multi-turn tool calling. ## Usage ### FSDP2 Configuration Example ```shell python3 -m verl.experimental.one_step_off_policy.async_main_ppo \ --config-path=config \ --config-name='one_step_off_ppo_trainer.yaml' \ actor_rollout_ref.actor.strategy=fsdp2 \ # actor and rollout are placed separately actor_rollout_ref.hybrid_engine=False \ # actor and rollout resource trainer.nnodes=1 \ trainer.n_gpus_per_node=6 \ rollout.nnodes=1 \ rollout.n_gpus_per_node=2 ``` ### Megatron Configuration Example ```shell python3 -m verl.experimental.one_step_off_policy.async_main_ppo \ --config-path=config \ --config-name='one_step_off_ppo_megatron_trainer.yaml' \ actor_rollout_ref.actor.strategy=megatron \ # actor and rollout are placed separately actor_rollout_ref.hybrid_engine=False \ # actor and rollout resource trainer.nnodes=1 \ trainer.n_gpus_per_node=6 \ rollout.nnodes=1 \ rollout.n_gpus_per_node=2 ``` ### Configuration Guidelines 1. **Card Number Relationships** Maintain either of these relationships for optimal batch distribution: - `actor_rollout_ref.rollout.n` should be an integer divisor of: `trainer.n_gpus_per_node * trainer.nnodes` - `actor_rollout_ref.rollout.n * data.train_batch_size` should be evenly divisible by: `trainer.n_gpus_per_node * trainer.nnodes` > Rationale: Ensures training samples can be evenly distributed across training GPUs when using partial resources for > generation. 2. **Dynamic Resource Tuning** Adjust `trainer.nnodes` `trainer.n_gpus_per_node` `rollout.nnodes` `rollout.n_gpus_per_node` based on phase durations: - **Ideal state**: Rollout and training phases have comparable durations - **Diagnostic metrics**: - Monitor `wait_prev_gen` duration - Analyze `sequence_length` distribution - **Adjustment strategy**: - High `wait_prev_gen` + uniform sequence lengths → Increase rollout resources - High `wait_prev_gen` + long-tail sequences → Optimize stopping criteria (resource increase won't help) > **wait_prev_gen**：The time consumed waiting for the previous rollout to end (the part that is not fully > overlapped). > **Resource Configuration Strategies:** - **Resource-constrained scenario**: Optimize resource utilization by adjusting GPU allocation ratios, keeping the number of nodes equal to allow training and rollout to share nodes; - Configure `trainer.nnodes = rollout.nnodes` with `trainer.n_gpus_per_node + rollout.n_gpus_per_node = physical_gpus_per_node`. Control rollout resource allocation by adjusting `n_gpus_per_node`. - **Resource-abundant scenario**: Optimize performance by adjusting the number of nodes, keeping the number of GPUs per node equal to enable independent scaling of training and rollout parallelism. - Configure `trainer.n_gpus_per_node = rollout.n_gpus_per_node` and control rollout resource allocation by adjusting `trainer.nnodes` and `rollout.nnodes`to achieve optimal performance. > **Note**: The total number of nodes required by the system is not simply `trainer.nnodes + rollout.nnodes`. The > actual calculation depends on GPU capacity: > > - When `trainer.n_gpus_per_node + rollout.n_gpus_per_node <= physical_gpus_per_node`, > the required node count is `max(trainer.nnodes, rollout.nnodes)` > - When `trainer.n_gpus_per_node + rollout.n_gpus_per_node > physical_gpus_per_node`, > the required node count is `trainer.nnodes + rollout.nnodes` ## Functional Support | Category | Support Situation | | ------------------ | --------------------------------------------------------------------------------------------------------------- | | train engine | FSDP2
Megatron | | rollout engine | vLLM | | AdvantageEstimator | GRPO
GRPO_PASSK
REINFORCE_PLUS_PLUS
RLOO
OPO
REINFORCE_PLUS_PLUS_BASELINE
GPG | | Reward | all | ================================================ FILE: docs/advance/placement.rst ================================================ Ray API Design Tutorial ======================================= Last updated: 10/30/2024. We provide a tutorial for our Ray API design, including: - Ray basic concepts - Resource Pool and RayWorkerGroup - Data Dispatch, Execution and Collection - Initialize the RayWorkerGroup and execute the distributed computation in the given Resource Pool See details in `tutorial.ipynb `_. ================================================ FILE: docs/advance/ppo_lora.rst ================================================ RL(HF) algorithms with LoRA Support =========================================== Last updated: 02/03/2026. We support LoRA (Low-Rank Adaptation) for reinforcement learning algorithms such as PPO, GRPO, and others. LoRA is a parameter-efficient fine-tuning technique that injects trainable low-rank matrices into pre-trained weights (typically linear layers). This reduces memory footprint and compute cost, making it possible to fine-tune large models with limited hardware. The benefits this brings include: - reinforcement learning with very large models (e.g. 70B+) with modest hardware (e.g. 8x80G GPUs), - enable larger batch sizes due to reduced memory usage, - simplify model transfer and deployment, as only LoRA adapters need to be saved, - Combine with techniques like `SLoRA `_ or `CCoE `_ to serve multiple LoRA adapters efficiently This guide explains how to enable LoRA in RL training and configure related parameters. FSDP Backend Usage Guide ------------------------ .. note:: This section applies to **FSDP/FSDP2 backend only**. For Megatron backend, see the :ref:`megatron-lora` section below. 1. Lora is available in the `verl.trainer.ppo.ray_trainer.RayPPOTrainer`. Examples are provided via the `verl.trainer.main_ppo` entry point. 2. Currently, LoRA is supported via huggingface peft, only with fsdp/fsdp2 and vllm backend (sglang support coming soon). - `strategy=fsdp` or `strategy=fsdp2` - `rollout.name=vllm` 3. Required configurations for LoRA: - `actor_rollout_ref.model.lora_rank`: int, set to a reasonable value greater than 0 (e.g., 8, 16, 32, 64) - `actor_rollout_ref.model.lora_alpha`: float, the alpha term in LoRA - `actor_rollout_ref.rollout.load_format="safetensors"`: required. This enables vLLM to load the base model. - `actor_rollout_ref.model.target_modules`: the target modules for LoRA. Typically set to "all-linear". 4. Optional configurations for LoRA: - `actor_rollout_ref.model.lora_adapter_path`: string, path to a pretrained LoRA adapter directory. If provided, loads existing adapter instead of creating new one. Enables multi-stage training from previously saved adapters. Directory need contain `adapter_model.safetensors` and `adapter_config.json`. - `actor_rollout_ref.model.lora.merge`: bool, whether to merge LoRA adapters into the base model weights before transferring to vLLM. If True, it will merge LoRA adapters into the base model weights before transferring to vLLM. If False, it will transfer only adapters to vLLM. This option is currently supported **only for engine-based rollout workers** (i.e. vLLM engine workers using the new worker implementation with ``trainer.use_legacy_worker_impl`` disabled) and is not available when using the legacy worker implementation. 5. Recommend options: - `actor_rollout_ref.model.use_shm=True`: preload the model into `/dev/shm` to improve model loading speed. - `actor_rollout_ref.rollout.layered_summon=True`: this enables the actor-model to gather the FSDP shards per layers when synchronizing the LoRA Adapter to vLLM, thereby reducing GPU peak memory. Recommended if the model is very large (70B+) or the GPU memory is limited (< 48GB) .. _megatron-lora: Megatron Backend Usage Guide ---------------------------- .. warning:: The FSDP-specific config options are **NOT applicable** to Megatron backend, and they will be ignored if set. Only options listed under ``lora`` key are applicable: - ``actor_rollout_ref.model.lora.*`` - ``critic.model.lora.*`` You need to install and enable Megatron-Bridge for Megatron LoRA support. Make sure you use Megatron-Bridge later than 0.2.0, and we recommended using `this commit `_ or later for proper support, and use the following settings to enable Megatron-Bridge: - ``actor_rollout_ref.actor.megatron.use_mbridge=True`` - ``actor_rollout_ref.actor.megatron.vanilla_mbridge=False`` **Key Differences from FSDP LoRA:** 1. **LoRA Implementation**: Verl Megatron backend uses Megatron-Bridge's native LoRA implementation, which differs from HuggingFace PEFT. 2. **Weight Sync / Refit Mechanism**: Currently, Megatron-Bridge can support syncing weights by either merging LoRA adapters into the base model weights before transferring to vLLM (for better inference speed but more refit time and potential precision loss), as well as loading separate adapters. **Configuration for Megatron LoRA:** .. code-block:: yaml actor_rollout_ref: model: lora: # LoRA type: "lora", "vlm_lora", "canonical_lora", or "dora" type: lora # whether to sync weights / refit by either merging LoRA adapters into the base model weights before transferring to vLLM (for better inference speed but more refit time and potential precision loss). If this is False, it will load separate adapters. merge: False # LoRA rank (Dimension of the low-rank projection space.). Set to 0 to disable LoRA rank: 0 # Weighting factor for the low-rank projection. Defaults to 32 alpha: 32 # Dropout rate for the low-rank projection. Defaults to 0.0 dropout: 0.0 # A list of module names to apply LoRA to. # For fused LoRA, Defaults to all linear layers ['linear_qkv', 'linear_proj', 'linear_fc1', 'linear_fc2']. # For canonical LoRA: ["linear_q", "linear_k", "linear_v", "linear_proj", "linear_fc1_up", "linear_fc1_gate", "linear_fc2"] # - 'linear_qkv': Apply LoRA to the fused linear layer used for query, key, and value projections in self-attention # - 'linear_proj': Apply LoRA to the linear layer used for projecting the output of self-attention # - 'linear_fc1': Apply LoRA to the first fully-connected layer in MLP # - 'linear_fc2': Apply LoRA to the second fully-connected layer in MLP # Target modules can also contain wildcards. For example, you can specify # target_modules=['*.layers.0.*.linear_qkv', '*.layers.1.*.linear_qkv'] to add LoRA to only linear_qkv on the first two layers # # Note: # For MLA (e.g., DeepSeek), you should use ["linear_kv_down_proj","linear_kv_up_proj","linear_q_down_proj","linear_q_up_proj","linear_q_proj"] # Instead of "linear_qkv" or ["linear_q","linear_k","linear_v"] # By default, MoE routers are excluded from LoRA adaptation, and you will need to specify "router" in target_modules to include them. target_modules: - linear_qkv - linear_proj - linear_fc1 - linear_fc2 # A list of module names not to apply LoRa to. It will match all nn.Linear & nn.Linear-adjacent modules whose name # does not match any string in exclude_modules. If used, will require target_modules to be empty list or None exclude_modules: [] # Position for applying dropout, can be 'pre' (before the low-rank projection) or 'post' (after). Defaults to 'pre' dropout_position: pre # Initialization method for the low-rank matrix A. Defaults to "xavier". lora_A_init_method: xavier # Initialization method for the low-rank matrix B. Defaults to "zero". lora_B_init_method: zero # Enables the experimental All-to-All (A2A) communication strategy. Defaults to False a2a_experimental: False # Parameter data type for LoRA weights. Default to null, which will use model's dtype. dtype: null # Path to pre-trained LoRA adapter weights (null to train from scratch) adapter_path: null # Whether to fully shard LoRA adapters. Defaults to False # https://docs.vllm.ai/en/latest/api/vllm/config/lora/#vllm.config.lora.LoRAConfig.fully_sharded_loras fully_sharded_loras: bool # VLMLoRA additionally allows the user to specify whether the language or vision models should be frozen. # For example, a common finetuning workload for multimodal models is to apply adapters to language model and fully # finetune the vision model. freeze_vision_model: True freeze_vision_projection: True freeze_language_model: True LoRA training experiment with Qwen3-8B on 8 * H200 single node comparing FSDP and Megatron backend (script adapted from examples/grpo_trainer/run_qwen2-7b_math_megatron_lora.sh): .. image:: https://github.com/user-attachments/assets/0482f423-01a3-4e52-a7ee-8b9cd79b7b1a .. image:: https://github.com/user-attachments/assets/6ce10400-8164-47d8-90a6-c1bf002fb9e8 .. image:: https://github.com/user-attachments/assets/092d3a43-4eba-425e-a584-8d83c1f02de4 Best Practices and Notes ------------------------- 1. **Learning rate**: it is recommended to increase the value of learning rate by an order of magnitude. 2. **LoRA Rank**: - Too small a rank can hurt convergence. - LoRA rank recommendation from @thelongestusernameofall: - A very small lora_rank can lead to slower convergence or worse training performance. It is recommended to set lora_rank to be>=32. Tests have shown that for a 0.5B model, with lora_rank=32,the training convergence speed and final performance are almost identical to non-LoRA training - For a 32B model,with lora_rank=128,the training convergence speed and final performance are also almost identical to non-LoRA training. - More comprehensive reference results are coming soon. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/f2b80b8b26829124dd393b7a795a0640eff11644/docs/lora.jpg?raw=true 3. **FSDP-Specific:** Reference configuration for RL training with the Qwen2.5-72B model using 8 x 80GB GPUs (increase lora_rank if needed): .. code-block:: data.train_batch_size=64 \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.actor.fsdp_config.fsdp_size=8 \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=64 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \ Example Scripts ------------------- For end-to-end examples, refer to the scripts below: **FSDP Examples:** - LoRA training from scratch: examples/grpo_trainer/run_qwen2_5-3b_gsm8k_grpo_lora.sh - LoRA training from adapter path: examples/grpo_trainer/run_qwen2_5-3b_gsm8k_grpo_lora_from_adapter.sh **Megatron Examples:** - LoRA training with Dense: examples/grpo_trainer/run_qwen2-7b_math_megatron_lora.sh - LoRA training with MoE: examples/grpo_trainer/run_qwen3moe-30b_megatron_lora.sh ================================================ FILE: docs/advance/reward_loop.rst ================================================ Reward Loop =========== .. _yyding: https://yyding1.github.io Author: `Yuyang Ding `_ Last updated: 2/10/2026. Introduction ------------ Reward Loop is the default reward computation implementation in verl. It is designed to support efficient, flexible, and easy-to-use reward computation. This document introduces the usage and architectural design. Key features include: 1. **Distributed reward manager**, enabling scalable and efficient reward computation. 2. **Support for hybrid reward settings**, including both generative and discriminative reward models, as well as more complex reward scenarios. 3. **Simple and extensible interface**, for easily defining customized reward functions. Distributed Reward manager -------------------------- .. image:: https://github.com/yyDing1/verl-materials/blob/main/distributed_reward_manager.svg?raw=true How distributed ~~~~~~~~~~~~~~~ Under the single_controller setup, actor rollout and reward computation can be abstracted as: .. code:: python # initalize rollout manager and async reward loop manager async_rollout_manager = AgentLoopManager(config) async_reward_manager = RewardLoopManager(config) # actor rollout using `async_rollout_manager` gen_batch = async_rollout_manager.generate_sequences(batch) # compute reward using `async_reward_manager` reward_batch = async_reward_manager.compute_rm_score(gen_batch) Within the ``RewardLoopManager``, multiple ``RewardWorker`` are launched across all nodes to enable distributed reward computation. The number of parallel workers can be configured via ``config.reward.num_workers``. Upon receiving a batch reward request, the batch is partitioned into smaller chunks and distributed to each reward worker for parallel execution. User only need to invoke ``compute_rm_score``. .. code:: python class RewardLoopManager: """ RewardLoopManager run in single controller. This class will create reward loop workers and manage them. """ def _init_reward_loop_workers(self): self.reward_loop_workers = [...] def compute_rm_score(self, data): chunks = data.chunk(len(self.reward_loop_workers)) outputs = ray.get( [ worker.compute_score_batch.remote(chunk) for worker, chunk in zip(self.reward_loop_workers, chunks, strict=True) ] ) outputs_flat = [item for sublist in outputs for item in sublist] ... This is how the reward manager is parallelized and distributed across all nodes. Streaming Reward with Rollout ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Furthermore, we check whether actor rollout and reward computation can be performed in a streaming manner, where the reward is calculated as soon as each sample is rolled out. .. code:: python # agent_reward_loop: streaming reward computation with actor rollout # two conditions satisfied: (1) rule-based reward, or (2) reward model with extra resource pool enable_agent_reward_loop = not use_rm or config.reward.reward_model.enable_resource_pool # if enable_agent_reward_loop, we directly pass reward_loop_workers to agent loop manager # to stream reward computation with actor rollout reward_loop_worker_handles = async_reward_manager.reward_loop_workers if enable_agent_reward_loop else None async_rollout_manager = AgentLoopManager( config=config, worker_group=actor_rollout_wg, rollout_resource_pool=actor_rollout_resource_pool, reward_loop_worker_handles=reward_loop_worker_handles, ) Hybrid Reward Scenarios Usage ----------------------------- As described above, each ``reward_loop_worker`` is responsible for handling reward requests. The rewards can be categorized as follows: - **Rule-based Reward**: The reward is determined by predefined rules, e.g., checking whether the predicted answer matches the ground truth via string matching. - **Discriminative Reward Model (DisRM)**: The reward is produced by a specified discriminative reward model, such as ``Skywork/Skywork-Reward-Llama-3.1-8B-v0.2``. - **Generative Reward Model (GenRM)**: The reward is obtained using a generative reward model, for example ``dyyyyyyyy/FAPO-GenRM-4B``. - **Hybrid Reward Scenarios**: A combination of the above reward types, e.g., rule + GenRM. .. code:: python class RewardLoopWorker: async def compute_score_batch(self, data: DataProto) -> list[dict]: tasks = [] for i in range(len(data)): tasks.append(asyncio.create_task(self.compute_score(data[i : i + 1]))) outputs = await asyncio.gather(*tasks) return outputs async def compute_score(self, data: DataProto) -> dict: assert len(data) == 1, "RewardLoopWorker only support single data item" if self.config.reward.custom_reward_function.path is not None: # directly use user-customized reward function return await self.reward_manager.run_single(data) else: if self.config.reward.reward_model.enable: # we assume the rm is disrm # genrm must set custom_reward_function return await self.compute_score_disrm(data) else: return await self.reward_manager.run_single(data) Each ``RewardLoopWorker`` will initalize one ``RewardManager``, splits the batch into individual data items and processes them in parallel using asynchronous tasks. Reward Manager ~~~~~~~~~~~~~~ The ``RewardManager`` maintains a reward function and defines its computation logic, including: - **naive**: The simplest implementation. - **dapo**: DAPO implementation with an overlong reward penalty. - **limit**: Restricts the concurrency of the reward function, useful when external API calls are rate-limited. - **remote**: Runs in a separate process, effective for CPU-intensive tasks such as ``Math-Verify``. Users can also customize their own ``RewardManager``, inheriting from ``RewardManagerBase``, and implementing the ``run_single`` function. .. code:: python @register("user_costomized") class UserCostomizedRewardManager(RewardManagerBase): async def run_single(self, data: DataProto) -> dict: assert len(data) == 1, "Only support single data item" # your own reward manager ... After defining it, users can specify their custom reward manager by setting ``reward.reward_manager.name=user_costomized``. Rule-Based Reward ~~~~~~~~~~~~~~~~~ If ``reward.custom_reward_function`` is provided, the user-defined reward function will be used. Otherwise, it falls back to the default reward function. Note that The custom function can be either synchronous or asynchronous; the system automatically detects its type and loads it accordingly. We recommend **using asynchronous functions** when reward computation need to involve external model API calls or sandboxed execution, as they are significantly more efficient. .. code:: python async def compute_score(data_source, solution_str, ground_truth, extra_info): """Compute a score by sending an async request to a remote service.""" # prepare request payload payload = {"messages": [{"role": "user", "content": "check the correcness of the question and response ..."}], ...} # send async HTTP request async with aiohttp.ClientSession() as session: async with session.post("https://api.openai.com/v1/chat/completions", json=payload) as resp: result = await resp.json() # parse and return score score = int(result["choices"][0]["message"]["content"].strip().split("\n")[-1]) return {"score": score} Model-Base Reward ~~~~~~~~~~~~~~~~~ **For discriminative reward model (DisRM)**, we provide a simple implementation: .. code:: python class RewardLoopWorker: async def compute_score_disrm(self, data) -> dict: disrm_prompt = await self._preprocess_reward_inputs(data) payloads = { "model": model_name, "input": disrm_prompt, "activation": False, } output = await self._post_request(payloads, "classify") rm_score = output["data"][-1]["probs"][-1] return {"reward_score": rm_score} pass the question and the model rollout as inputs to the reward model and obtain a reward score. This is also the standard practice for most DisRM. Users should provide ``reward.reward_model.model_path`` to specify the reward model. **For generative reward model (GenRM)** For generative reward model scenarios, users need to specify both ``reward.reward_model.model_path`` and ``reward.custom_reward_function``. The custom reward function should implement the following components: - Convert the question and the model rollout into a GenRM input prompt using a custom prompt template. - Invoke the GenRM to perform generation with custom sampling parameters. For this purpose, the Reward Loop provides an HTTP interface (i.e., ``reward_router_address``) for interacting with GenRM. - Parse the GenRM output using a custom parser and extract the reward score. As these steps are highly customizable and task-dependent, we offer this flexibility entirely to the user-defined reward function. Below we provide an example of a custom reward function using GenRM. .. code:: python async def compute_score_gsm8k( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, reward_router_address: str, # an HTTP router endpoint provided by Reward Loop reward_model_tokenizer: PreTrainedTokenizer, ): """Compute the reward score.""" # Step 1: Prepare prompt and request payload grm_prompt = GRM_PROMPT_TEMPLATE.format(problem=extra_info["question"], solution=solution_str) messages = [{"role": "user", "content": grm_prompt}] sampling_params = {"temperature": 0.7, "top_p": 0.8, "max_tokens": 4096} chat_complete_request = {"messages": messages, **sampling_params} # Step 2: Send async request to the reward model # here, chat_complete sends async http request to the router address result = await chat_complete( router_address=reward_router_address, chat_complete_request=chat_complete_request, ) # Step 3: Parse model response and extract score grm_response = result.choices[0].message.content.strip() try: score_str = grm_response.split("\n\n")[-1].strip() score = int(score_str) except Exception: score = 0 return {"score": score} **For hybrid reward scenarios**, such as combining rule-based rewards with GenRM similarly as above, .. _recipe/fapo: https://github.com/verl-project/verl-recipe/tree/main/fapo A runnable and reproducible example that demonstrates how to use a rule-based reward function together with a GenRM is provided in the `recipe/fapo`_ directory for reference. Welcome to use and cite. Reward Model Arch Design ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ We support multiple execution modes for reward models during: - **Colocate Mode**: The reward model shares the same resource pool as the actor/rollout/reference models. In this setup, all rollouts must complete first, after which the reward model is awakened to perform inference. - **Standalone Mode**: The reward model runs on a separate resource pool, independent from the actor/rollout/reference models. In this setup, each sample is evaluated by the reward model immediately after its rollout finishes. The standalone mode can enable the streaming manner stated above. By default, the system runs in colocate mode. Users can enable standalone mode by setting ``reward.reward_model.enable_resource_pool=True`` and allocating the corresponding resources via ``reward.reward_model.nnodes`` and ``reward.reward_model.n_gpus_per_node``. .. image:: https://github.com/yyDing1/verl-materials/blob/main/reward_loop.svg?raw=true To support flexible and scalable reward model computation, we implement a reward router that coordinates requests among multiple reward model servers. Each reward model runs as an independent server and is registered with the router. This router will forward the requests to the registered reward servers with load balancing and return the results. This design allows us to expose a single unified router address to user-defined reward functions, enabling them to access various reward models seamlessly through the same interface. .. image:: https://github.com/yyDing1/verl-materials/blob/main/reward_loop_full.svg?raw=true .. code:: python class RewardModelManager: """Reward model manager.""" def __init__( self, config: RewardModelConfig, resource_pool: RayResourcePool = None, ): """ Initialize the reward model manager. Args: config (RewardModelConfig): Reward model configuration. resource_pool (RayResourcePool, optional): Resource pool. Defaults to None. """ self.config = config self.resource_pool = resource_pool self._initialize_llm_servers() self._initialize_router() ================================================ FILE: docs/advance/rollout_skip.rst ================================================ RolloutSkip Function Usage Documentation ======================================== Last updated: 08/01/2025. Applicable Scenarios -------------------- The RolloutSkip functionality is designed to accelerate the rollout process in reinforcement learning training by caching and reusing previously generated sequences. This feature is particularly useful when: 1. You need to repeatedly run experiments with the same configuration 2. You want to save time by avoiding redundant sequence generation to come close to the optimal policy API and Usage Example ---------------------- 2.1 Trainer Adaptation ~~~~~~~~~~~~~~~~~~~~~~ Both`RayDAPOTrainer()` (in `verl/recipe/dapo/dapo_ray_trainer.py`) and `RayPPOTrainer()`(in `verl/trainer/ppo/ray_trainer.py``) have already been adapted. This is an example of how to patch rollout_skip in RayPPOTrainer. .. code-block:: python #* Import the RolloutSkip class from verl.utils.rollout_skip import RolloutSkip ... class RayPPOTrainer: ... def fit(self): ... #* Add code as follow: rollout_skip = RolloutSkip(self.config, self.actor_rollout_wg) rollout_skip.wrap_generate_sequences() ... for epoch in range(self.config.trainer.total_epochs): for batch_dict in self.train_dataloader: ... 2.2 Basic Configuration ~~~~~~~~~~~~~~~~~~~~~~~ Then, you should add the following parameters to your config to enable the RolloutSkip feature: .. code-block:: bash actor_rollout_ref.rollout.skip_rollout=True \ actor_rollout_ref.rollout.skip_dump_dir="/tmp/rollout_dump" \ Note: 1. The `skip_dump_dir` is the directory where the cached sequences will be stored. Ensure that this directory is writable and accessible by your training process. And make sure that `skip_dump_dir` is not relative path because ray will store the data in `/tmp/ray/session_/` and the relative path will not be found in the worker. 2. The dumped data path follows this naming pattern `{experiment_name}_{project_name}_TrainGBS{train_gbs}__InferGBS{gen_gbs}__N{n}`, once you change the `experiment_name`, `project_name`, `train_gbs`, `gen_gbs`, or `n`, the cached data will be stored in a new directory. ================================================ FILE: docs/advance/rollout_trace.rst ================================================ Trace Function Usage Instructions ======================================== Last updated: 07/10/2025. Applicable Scenarios -------------------- Agentic RL involves multiple turns of conversations, tool invocations, and user interactions during the rollout process. During the Model Training process, it is necessary to track function calls, inputs, and outputs to understand the flow path of data within the application. The Trace feature helps, in complex multi-round conversations, to view the transformation of data during each interaction and the entire process leading to the final output by recording the inputs, outputs, and corresponding timestamps of functions, which is conducive to understanding the details of how the model processes data and optimizing the training results. The Trace feature integrates commonly used Agent trace tools, including wandb weave and mlflow, which are already supported. Users can choose the appropriate trace tool according to their own needs and preferences. Here, we introduce the usage of each tool. Trace Parameter Configuration ----------------------------- - ``actor_rollout_ref.rollout.trace.backend=mlflow|weave`` # the trace backend type - ``actor_rollout_ref.rollout.trace.token2text=True`` # To show decoded text in trace view - ``actor_rollout_ref.rollout.trace.max_samples_per_step_per_worker=N`` # Limit traces per worker (optional) Limiting Trace Volume ~~~~~~~~~~~~~~~~~~~~~~ By default, all samples are traced, which can generate large amounts of data and incur significant costs with trace backends like Weave or MLflow. To limit trace volume while maintaining representative coverage, use ``max_samples_per_step_per_worker``. Example configuration: .. code-block:: yaml actor_rollout_ref: rollout: trace: backend: weave token2text: False max_samples_per_step_per_worker: 5 # Each worker traces 5 random samples Each agent loop worker independently selects up to N unique samples to trace per training step. For GRPO (``n > 1``), all rollouts for selected samples are traced. Total traces per step = max_samples_per_step_per_worker * num_workers * n. Example: With 4 workers, max_samples_per_step_per_worker=5, and GRPO n=4, you get 4 * 5 * 4 = 80 traces per step instead of tracing all samples. Set to null (default) to trace all samples. Glossary -------- +----------------+------------------------------------------------------------------------------------------------------+ | Object | Explaination | +================+======================================================================================================+ | trajectory | A complete multi-turn conversation includes: | | | 1. LLM output at least once | | | 2. Tool Call | +----------------+------------------------------------------------------------------------------------------------------+ | step | The training step corresponds to the global_steps variable in the trainer | +----------------+------------------------------------------------------------------------------------------------------+ | sample_index | The identifier of the sample, defined in the extra_info.index of the dataset. It is usually a number,| | | but may also be a uuid in some cases. | +----------------+------------------------------------------------------------------------------------------------------+ | rollout_n | In the GROP algorithm, each sample is rolled out n times. rollout_n represents the serial number of | | | the rollout. | +----------------+------------------------------------------------------------------------------------------------------+ | validate | Whether the test dataset is used for evaluation? | +----------------+------------------------------------------------------------------------------------------------------+ Rollout trace functions ----------------------- There are 2 functions used for tracing: 1. ``rollout_trace_op``: This is a decorator function used to mark the functions to trace. In default, only few method has it, you can add it to more functions to trace more infor. 2. ``rollout_trace_attr``: This function is used to mark the entry of a trajectory and input some info to trace. If you add new type of agent, you may need to add it to enable trace. Usage of wandb weave -------------------- 1.1 Basic Configuration ~~~~~~~~~~~~~~~~~~~~~~~ 1. Set the ``WANDB_API_KEY`` environment variable 2. Configuration Parameters 1. ``actor_rollout_ref.rollout.trace.backend=weave`` 2. ``trainer.logger=['console', 'wandb']``: This item is optional. Trace and logger are independent functions. When using Weave, it is recommended to also enable the wandb logger to implement both functions in one system. 3. ``trainer.project_name=$project_name`` 4. ``trainer.experiment_name=$experiment_name`` 5. ``actor_rollout_ref.rollout.mode=async``: Since trace is mainly used for agentic RL, need to enable agent toop using async mode for either vllm or sglang. Note: The Weave Free Plan comes with a default monthly network traffic allowance of 1GB. During the training process, the amount of trace data generated is substantial, reaching dozens of gigabytes per day, so it is necessary to select an appropriate wandb plan. 1.2 View Trace Logs ~~~~~~~~~~~~~~~~~~~ After executing the training, on the project page, you can see the WEAVE sidebar. Click Traces to view it. Each Trace project corresponds to a trajectory. You can filter and select the trajectories you need to view by step, sample_index, rollout_n, and experiment_name. After enabling token2text, prompt_text and response_text will be automatically added to the output of ToolAgentLoop.run, making it convenient to view the input and output content. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/weave_trace_list.png?raw=true 1.3 Compare Trace Logs ~~~~~~~~~~~~~~~~~~~~~~ Weave can select multiple trace items and then compare the differences among them. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/weave_trace_compare.png?raw=true Usage of mlflow --------------- 1. Basic Configuration ~~~~~~~~~~~~~~~~~~~~~~ 1. Set the ``MLFLOW_TRACKING_URI`` environment variable, which can be: 1. Http and https URLs corresponding to online services 2. Local files or directories, such as ``sqlite:////tmp/mlruns.db``, indicate that data is stored in ``/tmp/mlruns.db``. When using local files, it is necessary to initialize the file first (e.g., start the UI: ``mlflow ui --backend-store-uri sqlite:////tmp/mlruns.db``) to avoid conflicts when multiple workers create files simultaneously. 2. Configuration Parameters 1. ``actor_rollout_ref.rollout.trace.backend=mlflow`` 2. ``trainer.logger=['console', 'mlflow']``. This item is optional. Trace and logger are independent functions. When using mlflow, it is recommended to also enable the mlflow logger to implement both functions in one system. 3. ``trainer.project_name=$project_name`` 4. ``trainer.experiment_name=$experiment_name`` 2. View Log ~~~~~~~~~~~ Since ``trainer.project_name`` corresponds to Experiments in mlflow, in the mlflow view, you need to select the corresponding project name, then click the "Traces" tab to view traces. Among them, ``trainer.experiment_name`` corresponds to the experiment_name of tags, and tags corresponding to step, sample_index, rollout_n, etc., are used for filtering and viewing. For example, searching for ``"tags.step = '1'"`` can display all trajectories of step 1. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/mlflow_trace_list.png?raw=true Opening one of the trajectories allows you to view each function call process within it. After enabling token2text, prompt_text and response_text will be automatically added to the output of ToolAgentLoop.run, making it convenient to view the content. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/mlflow_trace_view.png?raw=true Note: 1. mlflow does not support comparing multiple traces 2. rollout_trace can not associate the mlflow trace with the run, so the trace content cannot be seen in the mlflow run logs. ================================================ FILE: docs/advance/rope.rst ================================================ RoPE Scaling override ======================================= Last updated: 05/14/2025. Some models such as `Qwen/Qwen2.5-7B-Instruct `_ support RoPE Scaling but don't have it defined in their config.json file. For example, this model supports this configuration: .. code:: python { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } In order to support a longer context for such models, you must override the model configs when starting the trainer. PPO example: .. code:: bash +actor_rollout_ref.model.override_config.rope_scaling.type=yarn \ +actor_rollout_ref.model.override_config.rope_scaling.factor=4.0 \ +actor_rollout_ref.model.override_config.rope_scaling.original_max_position_embeddings=32768 \ And for the critic model .. code:: bash +critic.model.override_config.rope_scaling.type=yarn \ +critic.model.override_config.rope_scaling.factor=4.0 \ +critic.model.override_config.rope_scaling.original_max_position_embeddings=32768 \ ================================================ FILE: docs/algo/baseline.md ================================================ # Algorithm Baselines Last updated: 06/18/2025. ## Math related datasets ### GSM8k Assuming GSM8k/math dataset is preprocessed via: ```bash python3 examples/data_preprocess/*.py ``` Refer to the table below to reproduce RL training from different pre-trained checkpoints. Below is the performance on the GSM8k dataset if not specified otherwise. More comprehensive benchmark results areavailable in the recipe folder. | Hardware | Model | Method | Test score | Details | | ---------- | -------------------------------- | --------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | NVIDIA GPU | google/gemma-2-2b-it | hf checkpoint | 23.9 | [Huggingface](https://huggingface.co/google/gemma-2-2b-it#benchmark-results) | | NVIDIA GPU | google/gemma-2-2b-it | SFT | 52.06 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/gemma-2-2b-it-sft-0.411.log) | | NVIDIA GPU | google/gemma-2-2b-it | SFT + PPO | 64.02 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/gemma-2-2b-it-ppo-bsz512_4-prompt1024-resp-512-0.640.log), [wandb](https://api.wandb.ai/links/verl-team/h7ux8602) | | NVIDIA GPU | Qwen/Qwen2.5-0.5B-Instruct | hf checkpoint | 49.6 | [Qwen blog](https://qwen.ai/blog?id=qwen2.5-llm) | | NVIDIA GPU | Qwen/Qwen2.5-0.5B-Instruct | PPO | 56.7 | [command and log](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-0.5B-bsz256_2-prompt1024-resp512-0.567.log) | | NVIDIA GPU | Qwen/Qwen2.5-0.5B-Instruct | PRIME | 58.7 | [script](https://github.com/verl-project/verl-recipe/blob/main//prime/run_prime_qwen.sh), [wandb](https://api.wandb.ai/links/zefan-wang-thu-tsinghua-university/rxd1btvb) | | NVIDIA GPU | Qwen/Qwen2.5-0.5B-Instruct | GRPO-LoRA | 54.3 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-0.5B-bsz64_2-prompt512-resp1024-lorarank32-score0.543.log) | | NVIDIA GPU | Qwen/Qwen2.5-1.5B-Instruct | GRPO-LoRA | 77.9 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-1.5B-bsz64_2-prompt512-resp1024-lorarank32-score0.779.log) | | NVIDIA GPU | Qwen/Qwen2.5-3B-Instruct | GRPO-LoRA | 86.1 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-3B-bsz64_2-prompt512-resp1024-lorarank32-score0.861.log) | | NVIDIA GPU | deepseek-ai/deepseek-llm-7b-chat | PPO (Megatron) | 69.5 [1] | [log](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/deepseek-llm-7b-chat-megatron-bsz256_4-prompt512-resp512-0.695.log), [wandb](https://wandb.ai/verl-team/verl_megatron_gsm8k_examples/runs/10fetyr3) | | NVIDIA GPU | Qwen/Qwen2-7B-Instruct | GRPO | 89 | [script](https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/examples/grpo_trainer/run_qwen2-7b_seq_balance.sh) | | NVIDIA GPU | Qwen/Qwen2-7B-Instruct | GRPO (FSDP2) | 89.8 | [log](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/qwen2-7b-fsdp2.log) | | NVIDIA GPU | Qwen/Qwen2-7B-Instruct | GRPO (Megatron) | 89.6 | [log](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/qwen2-7b_math_megatron.log) | | NVIDIA GPU | Qwen/Qwen2.5-7B-Instruct | ReMax | 97 | [script](https://github.com/eric-haibin-lin/verl/blob/main/examples/remax_trainer/run_qwen2.5-3b_seq_balance.sh), [wandb](https://wandb.ai/liziniu1997/verl_remax_example_gsm8k/runs/vxl10pln) | | NVIDIA GPU | Qwen/Qwen2.5-7B-Instruct | SPPO | 65.6 (MATH) | [SPPO script](https://github.com/verl-project/verl-recipe/tree/main/sppo/README.md) | | NVIDIA GPU | Qwen/Qwen2.5-7B-Instruct | GRPO-LoRA | 93.4 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-7B-bsz64_8-prompt512-resp1024-lorarank32-score0.934.log) | | NVIDIA GPU | Mixtral-8x22B-Instruct-v0.1 | Instruct model | 83.7 | [Qwen Blog](https://qwen.ai/blog?id=qwen2.5-llm) | | NVIDIA GPU | Mixtral-8x22B-Instruct-v0.1 | RLOO (Megatron) | 92.3 | [wandb](https://api.wandb.ai/links/ppo_dev/sbuiuf2d) | | NVIDIA GPU | Qwen/Qwen2.5-7B-Instruct | SPIN | 92 | [script](https://github.com/verl-project/verl-recipe/tree/main/spin/README.md) | | NVIDIA GPU | Qwen/Qwen2-7B-Instruct | GPG | 88 | [log](https://github.com/diqiuzhuanzhuan/verldata/blob/main/run_logs/qwen2-7b_math.log), [wandb](https://wandb.ai/diqiuzhuanzhuan/verl_gpg_example_gsm8k_math/runs/ab86c4va) | | NVIDIA GPU | Qwen/Qwen2-7B-Instruct | GPG (Megatron) | 88 | [log](https://github.com/diqiuzhuanzhuan/verldata/blob/main/run_logs/qwen2-7b_math_megatron.log), [wandb](https://wandb.ai/diqiuzhuanzhuan/verl_gpg_example_gsm8k_math/runs/yy8bheu8) | | NVIDIA GPU | Qwen/Qwen2.5-VL-7B-Instruct | GRPO (Megatron) | 65.4 (GEO3k) | [script](https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_qwen2_5_vl-7b-megatron.sh), [wandb](https://api.wandb.ai/links/megatron-core-moe-dev/1yngvkek) | | AMD MI300 | deepseek-ai/deepseek-llm-7b-chat | PPO | 70.5 [1] | [log](https://github.com/yushengsu-thu/verl_training_log/blob/main/gsm8k/ppo_run_deepseek7b_llm.log) | | AMD MI300 | deepseek-ai/deepseek-llm-7b-chat | GRPO | 71.4 [1] | [log](https://github.com/yushengsu-thu/verl_training_log/blob/main/gsm8k/grpo_run_deepseek7b_llm.log) | | NVIDIA GPU | Qwen/Qwen2.5-14B-Instruct | GRPO-LoRA | 94.6 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-14B-bsz64_8-prompt512-resp1024-lorarank32-score0.946.log) | | NVIDIA GPU | Qwen/Qwen2.5-32B-Instruct | GRPO-LoRA | 95.8 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-32B-bsz64_8-prompt512-resp1024-lorarank32-score0.958.log) | | NVIDIA GPU | Qwen/Qwen2.5-72B-Instruct | GRPO-LoRA | 96.0 | [command and logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-72B-bs64_8-prompt512-resp1024-lorarank32-score0.960.log) | ### DAPO math-17k - Training DAPO math-17k dataset: https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k - Testing: AIME'24: https://huggingface.co/datasets/BytedTsinghua-SIA/AIME-2024 Note: - For Qwen/Qwen2.5-Math-7B, we directly modify the max_position_embeddings to 32768 without observing performance degradation in order to train longer response length. | Hardware | Model | Method | Test score | Details | | ---------- | -------------------------- | ----------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | NVIDIA GPU | Qwen/Qwen2.5-Math-7B (32k) | DAPO | 36.3 | [command](https://github.com/verl-project/verl-recipe/blob/main//dapo/test_dapo_7b_math.sh), [logs](https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/runs/ow47vvon?nw=nwusertongyuxuan361) | | NVIDIA GPU | Qwen/Qwen2.5-7B-Instruct | DAPO + Code Interpreter | 40.0 | [command](https://github.com/verl-project/verl-recipe/blob/main//retool/run_qwen2_7b_dapo.sh) | ## Coding related datasets Below is the result on leetcode if not specified otherwise. | Hardware | Model | Method | Test score | Details | | ---------- | ----------------------- | ------ | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | NVIDIA GPU | PRIME-RL/Eurus-2-7B-SFT | RPIME | 36.1 | [script](https://github.com/verl-project/verl-recipe/blob/main//prime/run_prime_qwen_code.sh), [swanlab](https://swanlab.cn/@wangzefan/prime_example/runs/7f541qhspgmy8nmhdlx35/chart) | ### Notes [1] During evaluation, we have only extracted answers following the format `"####"`. A more flexible answer extraction, longer response length, and better prompt engineering may lead to a higher score. [2] The default value of `actor_rollout_ref.actor.entropy_coeff` is set to `0.0` since verl 0.3.x on 2025-05-30, which is different from previous versions. ================================================ FILE: docs/algo/collabllm.md ================================================ # Recipe: CollabLLM Last updated: 09/22/2025. > Open-Source Algorithm Implementation & Expriement Running: [Haiquan Chen](https://github.com/chenhaiq), [Shirley Wu](https://github.com/Wuyxin) 🏠 [Homepage](https://aka.ms/CollabLLM) | 📝 [Paper](https://arxiv.org/pdf/2502.00640) | 🤗 [Datasets & Models](https://huggingface.co/collabllm) | ⭐️ [Original Implementation](https://github.com/Wuyxin/collabllm) `verl` provides a recipe for the Outstanding Paper at ICML 2025, **"CollabLLM: From Passive Responders to Active Collaborators"**. [CollabLLM](https://aka.ms/CollabLLM) is a unified fine-tuning framework that optimizes LLMs for effective and efficient multiturn collaboration with users. **Core Idea:** Models are rewarded based on how well their responses enable effective *future* collaboration with users. Paper Authors: [Shirley Wu](https://cs.stanford.edu/~shirwu/), [Michel Galley](https://www.microsoft.com/en-us/research/people/mgalley/), Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, [James Zou](https://www.james-zou.com/), [Jure Leskovec](https://cs.stanford.edu/people/jure/), [Jianfeng Gao](https://www.microsoft.com/en-us/research/people/jfgao/) --- ## Quick Start ### 0. Environment Make sure the required packages for `verl` are installed. Additionally, install `litellm` and export the required API keys. The API model will be used for user simulators and, optionally, LLM Judges (see the Configuration section below). ### 1. Prepare Your Dataset First, process your dataset using the provided script (see example commands and usage in `process_dataset.py`): ```bash python process_dataset.py --dataset <> ... --dataset_type ``` **Requirements:** - Input: A Hugging Face multiturn dataset. Existing datasets: `collabllm/collabllm-multiturn-$DATASET`, with `DATASET` in one of [`math-hard(-large)`, `medium(-large)`, `bigcodebench(-large)`] (*-large are the datasets used in the CollabLLM paper) - Example format: See [collabllm-multiturn-math-hard](https://huggingface.co/datasets/collabllm/collabllm-multiturn-math-hard) - To generate your own dataset: Use [build_dataset.py](https://github.com/Wuyxin/collabllm/blob/main/scripts/engine/build_dataset.py) from the original CollabLLM repository ### 2. Train Your Model **(Optional) For Supervised Fine-Tuning (SFT):** ```bash bash train_sft_collabllm.sh ``` **For Reinforcement Learning (RL):** ```bash bash train_rl_collabllm.sh ``` The RL script shows an example to train CollabLLM on `math-hard-large`. - The config to sample future conversations are in `recipe/collabllm/config/collabllm_interaction_config.yaml`. - The Multiturn-aware Reward is aggregated from these three conversational-level rewards: ``` +reward_model.reward_kwargs.metric_weights.accuracy=1 \ +reward_model.reward_kwargs.metric_weights.interactivity=1 \ +reward_model.reward_kwargs.metric_weights.token_amount=-0.0001 \ ``` You can remove, add, or modify the weights depending on your task. A list of implemented metrics you can already add are under `recipe/collabllm/metrics`. For example, on `medium-large`, you can replace `accuracy` with `bleu_score` via ``` +reward_model.reward_kwargs.metric_weights.bleu_score=1 ``` which will instead apply bleu score on the sampled future conversations. ## Algorithm | Step | Name | Description | |------|-------------------------------|-----------------------------------------------------------------------------| | 1 | Model response generation | The model generates multiple responses for each prompt in a batch. | | 2 | Collaborative simulation | A user simulator (e.g., GPT or Claude) samples `num_repeat_rollouts` conversations for up to `max_user_turns` additional turns. | | 3 | Compute Multiturn-aware Reward | Customized conversational reward functions are applied to the sampled conversations. Rewards are aggregated, then averaged across rollouts. | | 4 | Update model | The model weights are updated using the computed multiturn-aware rewards. | --- ## Configuration The primary configuration is managed through the launch script `train_rl_collabllm.sh` and the YAML file `recipe/collabllm/config/collabllm_interaction_config.yaml`. Key configuration sections: | Section | Key Parameters / Notes | |----------------------|-----------------------------------------------------------------------------------------| | `data` | Paths to training/validation files, batch sizes, sequence lengths. | | `actor_rollout_ref` (common) | Base model path (used for actor + initial reference), FSDP settings, optimization (LR, scheduler). | | `actor_rollout_ref` (CollabLLM-specific) | Hyperparameters under `actor_rollout_ref.rollout.multi_turn`: `max_user_turns`, `max_assistant_turns`, `num_repeat_rollouts`. | | `interaction` | Defined in `collabllm_interaction_config.yaml`. Specifies user simulator and hyperparameters. Requires exported API keys. | | `reward_model` | Manager set to `collabllm` by default. Modify `reward_model.reward_kwargs.metric_weights` for conversational rewards and weights. LLM Judge hyperparameters (e.g., `model`, `temperature`) go under `reward_model.reward_kwargs.llm_judge_kwargs`. | | `algorithm` | GRPO-specific hyperparameters such as `actor_rollout_ref.rollout.n`. | | `trainer` | Distributed training (nodes, GPUs per node), logging (WandB), checkpointing frequency. | --- ## Key Files | File Path | Purpose | |-----------|---------| | `recipe/collabllm/collabllm_agent_loop.py` | Main logic to sample future conversations, using `CollabLLMInteraction` from `verl/interactions/collabllm_interaction.py`. | | `verl/workers/reward_manager/collabllm.py` | Computes rewards for future conversations, leveraging `recipe/collabllm/reward_function.py` to apply each metric. | --- ## Acknowledgement We sincerely thank the `verl` community and advisors for their contributions and guidance! ================================================ FILE: docs/algo/dapo.md ================================================ # Recipe: Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) Last updated: 06/19/2025. > Open-Source Algorithm Implementation & Expriement Running: [Yuxuan Tong](https://tongyx361.github.io/), [Guangming Sheng](https://hk.linkedin.com/in/guangming-sheng-b50640211) 🏠 [Homepage](https://dapo-sia.github.io/) | 📝 [Paper@arXiv](https://arxiv.org/abs/2503.14476) | 🤗 [Datasets&Models@HF](https://huggingface.co/collections/BytedTsinghua-SIA/dapo-67d7f1517ee33c8aed059da0) | 🐱 [Code@GitHub](https://github.com/verl-project/verl-recipe/tree/main/dapo/recipe/dapo) | 🐱 [Repo@GitHub](https://github.com/BytedTsinghua-SIA/DAPO) > We propose the **D**ecoupled Clip and Dynamic s**A**mpling **P**olicy **O**ptimization (DAPO) algorithm. By making our work publicly available, we provide the broader research community and society with practical access to scalable reinforcement learning, enabling all to benefit from these advancements. Our system is based on the awesome [verl](https://github.com/volcengine/verl) framework. Thanks for their great work! Applying DAPO training to Qwen2.5-32B base model proves to outperform the previous state-of-the-art DeepSeek-R1-Zero-Qwen-32B on AIME 2024, achieving **50%** accuracy with **50%** less training steps. > > ![dapo-main-result](https://dapo-sia.github.io/static/images/score.png) ## Quickstart 1. Prepare the datasets **on the Ray cluster**: ```bash bash prepare_dapo_data.sh # This downloads the datasets to ${HOME}/verl/data by default ``` 2. Submit the job to the Ray cluster **from any machine**: ```bash cd verl # Repo root export RAY_ADDRESS="http://${RAY_IP:-localhost}:8265" # The Ray cluster address to connect to export WORKING_DIR="${PWD}" # The local directory to package to the Ray cluster # Set the runtime environment like env vars and pip packages for the Ray cluster in yaml export RUNTIME_ENV="./recipe/dapo/runtime_env.yaml" # This sets environment variables for the Ray cluster bash recipe/dapo/run_dapo_qwen2.5_32b.sh # or other scripts ``` ## Reproduction Runs | Setup | AIME 2024 Acc. | Hardware | Image | Commit | Environment Variables | Training Script | Training Record | | -------------------------------------------- | -------------- | --------- | -------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | | DAPO | 52% | 16x8xH800 | `hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0` | [`4f80e4`](https://github.com/volcengine/verl/tree/4f80e465c2ec79ab9c3c30ec74b9745de61d0490) | [runtime_env.yaml](https://github.com/volcengine/verl/blob/4f80e465c2ec79ab9c3c30ec74b9745de61d0490/recipe/dapo/runtime_env.yaml) | [run_dapo_qwen2.5_32b.sh](https://github.com/volcengine/verl/blob/4f80e465c2ec79ab9c3c30ec74b9745de61d0490/recipe/dapo/run_dapo_qwen2.5_32b.sh) | [W&B](https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/workspace?nw=wmb4qxfht0n) | | DAPO w/o Dynamic Sampling | 50% | 16x8xH800 | `hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0` | [`4f80e4`](https://github.com/volcengine/verl/tree/4f80e465c2ec79ab9c3c30ec74b9745de61d0490) | [runtime_env.yaml](https://github.com/volcengine/verl/blob/4f80e465c2ec79ab9c3c30ec74b9745de61d0490/recipe/dapo/runtime_env.yaml) | [run_dapo_wo_ds_qwen2.5_32b.sh](https://github.com/volcengine/verl/blob/4f80e465c2ec79ab9c3c30ec74b9745de61d0490/recipe/dapo/run_dapo_wo_ds_qwen2.5_32b.sh) | [W&B](https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/workspace?nw=wmb4qxfht0n) | | DAPO w/o Token-level Loss & Dynamic Sampling | 44% | 16x8xH20 | `hiyouga/verl:ngc-th2.5.1-cu120-vllm0.7.4-hotfix` | [`4f80e4`](https://github.com/volcengine/verl/tree/4f80e465c2ec79ab9c3c30ec74b9745de61d0490) | [runtime_env.yaml](https://github.com/volcengine/verl/blob/4f80e465c2ec79ab9c3c30ec74b9745de61d0490/recipe/dapo/runtime_env.yaml) | [run_dapo_early_qwen2.5_32b.sh](https://github.com/volcengine/verl/blob/4f80e465c2ec79ab9c3c30ec74b9745de61d0490/recipe/dapo/run_dapo_early_qwen2.5_32b.sh) | [W&B](https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/workspace?nw=wmb4qxfht0n) | > [!IMPORTANT] > > **📢 Call for Contribution!** > > Welcome to submit your reproduction runs and setups! ## Configuration ### Separated Clip Epsilons (-> Clip-Higher) An example configuration: ```yaml actor_rollout_ref: actor: clip_ratio_low: 0.2 clip_ratio_high: 0.28 ``` `clip_ratio_low` and `clip_ratio_high` specify the $\varepsilon_{\text {low }}$ and $\varepsilon_{\text {high }}$ in the DAPO objective. Core relevant code: ```python pg_losses1 = -advantages * ratio pg_losses2 = -advantages * torch.clamp(ratio, 1 - cliprange_low, 1 + cliprange_high) pg_losses = torch.maximum(pg_losses1, pg_losses2) ``` ### Dynamic Sampling (with Group Filtering) An example configuration: ```yaml data: gen_batch_size: 1536 train_batch_size: 512 algorithm: filter_groups: enable: True metric: acc # score / seq_reward / seq_final_reward / ... max_num_gen_batches: 10 # Non-positive values mean no upper limit ``` Setting `filter_groups.enable` to `True` will filter out groups whose outputs' `metric` are all the same, e.g., for `acc`, groups whose outputs' accuracies are all 1 or 0. The trainer will repeat sampling with `gen_batch_size` until there are enough qualified groups for `train_batch_size` or reaching the upper limit specified by `max_num_gen_batches`. Core relevant code: ```python prompt_bsz = self.config.data.train_batch_size if num_prompt_in_batch < prompt_bsz: print(f'{num_prompt_in_batch=} < {prompt_bsz=}') num_gen_batches += 1 max_num_gen_batches = self.config.algorithm.filter_groups.max_num_gen_batches if max_num_gen_batches <= 0 or num_gen_batches < max_num_gen_batches: print(f'{num_gen_batches=} < {max_num_gen_batches=}. Keep generating...') continue else: raise ValueError( f'{num_gen_batches=} >= {max_num_gen_batches=}. Generated too many. Please check your data.' ) else: # Align the batch traj_bsz = self.config.data.train_batch_size * self.config.actor_rollout_ref.rollout.n batch = batch[:traj_bsz] ``` ### Flexible Loss Aggregation Mode (-> Token-level Loss) An example configuration: ```yaml actor_rollout_ref: actor: loss_agg_mode: "token-mean" # / "seq-mean-token-sum" / "seq-mean-token-mean" # NOTE: "token-mean" is the default behavior ``` Setting `loss_agg_mode` to `token-mean` will mean the (policy gradient) loss across all the tokens in all the sequences in a mini-batch. Core relevant code: ```python if loss_agg_mode == "token-mean": loss = verl_F.masked_mean(loss_mat, loss_mask) elif loss_agg_mode == "seq-mean-token-sum": seq_losses = torch.sum(loss_mat * loss_mask, dim=-1) # token-sum loss = torch.mean(seq_losses) # seq-mean elif loss_agg_mode == "seq-mean-token-mean": seq_losses = torch.sum(loss_mat * loss_mask, dim=-1) / torch.sum(loss_mask, dim=-1) # token-mean loss = torch.mean(seq_losses) # seq-mean else: raise ValueError(f"Invalid loss_agg_mode: {loss_agg_mode}") ``` ### Overlong Reward Shaping An example configuration: ```yaml data: max_response_length: 20480 # 16384 + 4096 reward_model: overlong_buffer: enable: True len: 4096 penalty_factor: 1.0 ``` Setting `overlong_buffer.enable` to `True` will penalize the outputs whose lengths are overlong but still within the hard context limit. Specifically, the penalty increases linearly from `0` to `overlong_buffer.penalty_factor` when the length of the output exceeds the `max_response_length - overlong_buffer.len` by `0` to `overlong_buffer.len` tokens. Core relevant code: ```python if self.overlong_buffer_cfg.enable: overlong_buffer_len = self.overlong_buffer_cfg.len expected_len = self.max_resp_len - overlong_buffer_len exceed_len = valid_response_length - expected_len overlong_penalty_factor = self.overlong_buffer_cfg.penalty_factor overlong_reward = min(-exceed_len / overlong_buffer_len * overlong_penalty_factor, 0) reward += overlong_reward ``` ## FAQ ### Where is the "Overlong Filtering" in the paper? Most experiments in the paper, including the best-performant one, are run without Overlong Filtering because it's somehow overlapping with Overlong Reward Shaping in terms of properly learning from the longest outputs. So we don't implement it here. ### What's the difference between [the `recipe/dapo` directory in the `main` branch](https://github.com/verl-project/verl-recipe/tree/main/dapo) and the [`recipe/dapo` branch](https://github.com/verl-project/verl-recipe/tree/main/dapo/recipe/dapo)? [The `recipe/dapo` branch](https://github.com/verl-project/verl-recipe/tree/main/dapo/recipe/dapo) is for **as-is reproduction** and thus won't be updated with new features. [The `recipe/dapo` directory in the `main` branch](https://github.com/verl-project/verl-recipe/tree/main/dapo) works as an example of how to extend the latest `verl` to implement an algorithm recipe, which will be maintained with new features. ### Why can't I produce similar results after modifications? RL infrastructures nowadays still have inherent unrobustness, on which we are still working hard to improve. We strongly recommend to only modify one thing at a time. We also list some known problems here: 1. Enabling CUDA graph (`enforce_eager=False`) might cause model performance degradation, whose cause is still under investigation. ================================================ FILE: docs/algo/dppo.md ================================================ # Divergence Proximal Policy Optimization (DPPO) Last updated: 02/25/2026.
## Rethinking the Trust Region in LLM Reinforcement Learning [![Paper](https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white )](https://arxiv.org/pdf/2602.04879) [![Github](https://img.shields.io/badge/Stable_RL-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white)](https://github.com/sail-sg/Stable-RL) [![Twitter](https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=twitter&logoColor=white)](https://x.com/QPHutu/status/2019435642539897303)
## ✨Getting started 1. Prepare the datasets by running [prepare_dapo_data.sh](https://github.com/verl-project/verl-recipe/blob/3490a22a0a3adeb7e4787fe70b1060b642efbae4/dapo/prepare_dapo_data.sh): ```bash bash prepare_dapo_data.sh # This downloads the datasets to ${HOME}/verl/data by default ``` 2. Prepare the model: ```bash hf download Qwen/Qwen3-30B-A3B-Base --local-dir ${HOME}/verl/models/Qwen3-30B-A3B-Base ``` 3. Run the script: ```bash # run DPPO-Binary-KL LOSS_MODE=dppo_kl bash examples/dppo_trainer/run_qwen30b_dppo.sh # run DPPO-Binary-TV LOSS_MODE=dppo_tv bash examples/dppo_trainer/run_qwen30b_dppo.sh # run GRPO baseline LOSS_MODE=vanilla CLIP_LOW=0.2 CLIP_HIGH=0.2 bash examples/dppo_trainer/run_qwen30b_dppo.sh # or GRPO with clip higher LOSS_MODE=vanilla CLIP_LOW=0.2 CLIP_HIGH=0.28 bash examples/dppo_trainer/run_qwen30b_dppo.sh ``` ## 📖Introduction
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Comparison of **PPO** and the proposed **DPPO** (the Binary-TV variant). **(Left)** The surrogate objective and corresponding masks for PPO and DPPO. PPO (and variants like GRPO) employs a heuristic mask based on the probability ratio. In contrast, DPPO utilizes a more principled mask based on a direct approximation of policy divergence (e.g., Total Variation), ensuring updates stay within a theoretically grounded trust region. **(Right)** Experimental results on the AIME24 using Qwen3-30B-A3B-Base. DPPO significantly outperforms GRPO baselines, achieving superior training stability and final performance even without rollout routing replay (R3).
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DPPO variants achieve stable training while controlling the training-inference mismatch at a low level. In contrast, methods without a trust region (PG-IS, CISPO) or with a misspecified one (MiniRL) suffer from growing mismatch and eventual collapse.
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The plots show numerical differences between a training and an inference engine for Qwen3-30B-A3B-Base with identical parameters. **(Left)** The probability ratio (used in PPO) is highly volatile for low-probability tokens. **(Right)** In contrast, the TV divergence is more stable. This highlights a key flaw of PPO's clipping mechanism: it **over-penalizes low-probability tokens**, which can slow down learning; and **under-penalizes high-probability tokens**, which can permit large, destabilizing updates.
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The most frequently clipped tokens (by GRPO) are important to the reasoning task! They are dominated by: - numbers, like 1, 4 - mathematical symbols, like +, -, = - reasoning and structural Words: Wait, Thus, Next ## Top-K divergence approximation We only implement the DPPO-Binary-TV/DPPO-Binary-KL here due to their simplicity. For the TopK divergence approximation, please refer to the [the original repo](https://github.com/sail-sg/Stable-RL) for a complete implementation. ## Citation If you find our works useful for your research, please consider citing: ```bibtex @article{qi2026dppo, title={Rethinking the Trust Region in LLM Reinforcement Learning}, author={Qi, Penghui and Zhou, Xiangxin and Liu, Zichen and Pang, Tianyu and Du, Chao and Lin, Min and Lee, Wee Sun}, journal={arXiv preprint arXiv:2602.04879}, year={2026} } ``` ## 🌻Acknowledgement We implement our reinforcement learning algorithm extending from [verl](https://github.com/volcengine/verl). We utilize [vLLM](https://github.com/vllm-project/vllm) and [sglang](https://github.com/sgl-project/sglang) for inference. Our models are trained primarily on [Qwen3 family](https://huggingface.co/collections/Qwen/qwen3). Our training data is built from [DAPO-MATH](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k). Thanks for their great contributions! ================================================ FILE: docs/algo/entropy.md ================================================ # Recipe: Entropy Mechanism Last updated: 06/27/2025.
The Entropy Mechanism of Reinforcement Learning for Large Language Model Reasoning. [![Paper](https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/pdf/2505.22617) [![Github](https://img.shields.io/badge/PRIME-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white)](https://github.com/PRIME-RL/Entropy-Mechanism-of-RL) [![alphaXiv](https://img.shields.io/badge/discussion-A42C25?style=for-the-badge&logo=arxiv&logoColor=white&color=blue )](https://www.alphaxiv.org/abs/2505.22617) [![Twitter](https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=twitter&logoColor=white)](https://x.com/stingning/status/1928088554166505667) [![Twitter](https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=twitter&logoColor=white)](https://x.com/charlesfornlp/status/1928089451080585283) [![Twitter-ak](https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=twitter&logoColor=white)](https://x.com/_akhaliq/status/1928077929105268861)
## 🎉News - **[2025/05/29]** 🎉 Ranked **#1** of the day on [Huggingface Daily Papers](https://huggingface.co/papers?date=2025-05-29). - **[2025/05/29]** Released our Paper on arXiv. See [here](https://arxiv.org/pdf/2505.22617). We provide insights into the entropy mechanism of RL for LLMs and propose two simple yet effective strategies to alleviate the entropy collapse. ## ✨Getting started After preparing the training data, for training Qwen2.5-7B on a single node, taking the KL-Cov approach as an example, you can simply run: ``` cd verl conda activate your_env bash recipe/dapo/7b_kl_cov.sh ``` While for training Qwen2.5-32B on multi nodes, you can run the following commands: ``` cd verl conda activate your_env bash recipe/dapo/32b_kl_cov.sh ``` ## 📖Introduction
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This paper addresses the entropy collapse issue in scaling reinforcement learning (RL) for large language models (LLMs), where policy entropy drops sharply during training, leading to overconfidence and performance saturation. We empirically establish a relationship between entropy ($H$) and performance ($R$): $R=−aexp(H)+b$, showing performance is bottlenecked by entropy exhaustion.
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Theoretically, we find entropy changes are driven by the covariance between action probability and logit updates, which correlates with advantage in Policy Gradient methods. High-probability, high-advantage actions reduce entropy, while rare, high-advantage actions increase it. Empirically, the covariance term remains positive, explaining entropy’s monotonic decline. To mitigate this, we propose ​​Clip-Cov​​ and ​​KL-Cov​​, which restrict updates for high-covariance tokens. These methods effectively prevent entropy collapse, and improve performance. ## 📃Evaluation
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Our method is able to maintain a considerably higher level of entropy throughout training. For example, when the baseline's entropy reaches a plateau and can no longer be consumed, the KL-Cov method still sustains an entropy level over 10 times higher. Meanwhile, the response length of the policy model steadily increases, and its performance on the test set consistently surpasses that of the baseline. This indicates that our model is able to explore more freely during training, learning better policy through RL. | **Method** | **AIME24** | **AIME25** | **AMC** | **MATH-500** | **OMNI-MATH** | **OlympiadBench** | **Minerva** | **Avg.** | | ----------------- | ---------: | ---------: | -------: | -----------: | ------------: | ----------------: | ----------: | -------: | | *Qwen2.5-7B* | | | | | | | | | | GRPO | 21.2 | 9.6 | 58.7 | 78.8 | 27.9 | 40.7 | 36.7 | 38.6 | | w. Clip-higher | 18.1 | 11.5 | 56.6 | 79.2 | 29.8 | 43.3 | 40.4 | 38.8 | | w. **`CLIP-Cov`** | 22.1 | **15.8** | 58.2 | 80.4 | **30.5** | **44.1** | **41.1** | 40.4 | | w. **`KL-Cov`** | **22.6** | 12.9 | **61.4** | **80.8** | 29.1 | 42.6 | 38.2 | **40.6** | | *Qwen2.5-32B* | | | | | | | | | | GRPO | 21.8 | 16.2 | 69.7 | 84.2 | 35.2 | 43.6 | 45.5 | 45.8 | | w. Clip-higher | 35.6 | 22.3 | 69.5 | 77.2 | 35.1 | 42.5 | 43.0 | 47.2 | | w. **`CLIP-Cov`** | 32.3 | 22.7 | 67.2 | **87.0** | **42.0** | **57.2** | 46.0 | 50.3 | | w. **`KL-Cov`** | **36.8** | **30.8** | **74.5** | 84.6 | 39.1 | 49.0 | **46.3** | **52.2** | Our two approaches both achieve non-trivial improvements across all benchmarks. Compared to GRPO, our method outperforms it by 2.0% on average for the 7B model and by 6.4% for the 32B model. Moreover, we observe that our method yields more substantial gains on the larger Qwen2.5-32B. Specifically, our method achieves improvements of 15.0% and 14.6% compared to GRPO on the most challenging benchmarks, AIME24 and AIME25, respectively. ## 🎈Citation If you find this paper or repo helpful, please cite us. ```bibtex @article{cui2025entropy, title={The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models}, author={Cui, Ganqu and Zhang, Yuchen and Chen, Jiacheng and Yuan, Lifan and Wang, Zhi and Zuo, Yuxin and Li, Haozhan and Fan, Yuchen and Chen, Huayu and Chen, Weize and others}, journal={arXiv preprint arXiv:2505.22617}, year={2025} } ``` ## 🌻Acknowledgement We implement our reinforcement learning algorithm extending from [verl](https://github.com/volcengine/verl). We utilize [vLLM](https://github.com/vllm-project/vllm) for inference. Our models are trained primarily on [Qwen2.5 family](https://github.com/QwenLM/Qwen2.5). Our training data is built from [DAPO-MATH](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k). Thanks for their great contributions! ## 📬 Contact For questions, discussion, or collaboration opportunities, feel free to contact: - Ganqu Cui: cuiganqu@pjlab.org.cn - Yuchen Zhang: yuchen.zhang2003@gmail.com - Jiacheng Chen: jackchan9345@gmail.com - Ning Ding: ningding.cs@gmail.com ================================================ FILE: docs/algo/gpg.md ================================================ # GPG: Group Policy Gradient Last updated: 07/03/2025. Group Policy Gradient (GPG) is a minimalist reinforcement learning (RL) method that enhances the reasoning ability of large language models without relying on supervised fine-tuning or complex tricks. GPG revisits traditional policy gradients and directly optimizes the RL objective—no surrogate losses, no KL penalties, no critic, and no reference model. Compared to GRPO, GPG is simpler, more efficient, and achieves better results on many tasks. For more details, please refer to the original paper [GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning ](https://arxiv.org/abs/2504.02546). ## Key Components - Use a corrected advantage function to improve policy gradient accuracy and training efficiency. - By eliminating the critic and reference models, avoiding KL divergence constraints, significantly simplifies the training process compared to Group Relative Policy Optimization (GRPO) ## Configuration To configure GPG within the framework, use the following YAML settings. ```yaml algorithm: adv_estimator: gpg actor_rollout_ref: actor: policy_loss: loss_mode: "gpg" ``` ## Advanced Extensions GPG is a simple and strong baseline for model reasoning. Although it avoids using KL loss in its original form, you can still use KL loss to further improve the performance. ```yaml algorithm: adv_estimator: gpg actor_rollout_ref: actor: use_kl_loss: True # enable kl regularization kl_loss_coef: 0.01 policy_loss: loss_mode: "gpg" ``` ================================================ FILE: docs/algo/grpo.md ================================================ # Group Relative Policy Optimization (GRPO) Last updated: 05/31/2025. In reinforcement learning, classic algorithms like PPO rely on a "critic" model to estimate the value of actions, guiding the learning process. However, training this critic model can be resource-intensive. GRPO simplifies this process by eliminating the need for a separate critic model. Instead, it operates as follows: - Group Sampling: For a given problem, the model generates multiple possible solutions, forming a "group" of outputs. - Reward Assignment: Each solution is evaluated and assigned a reward based on its correctness or quality. - Baseline Calculation: The average reward of the group serves as a baseline. - Policy Update: The model updates its parameters by comparing each solution's reward to the group baseline, reinforcing better-than-average solutions and discouraging worse-than-average ones. This approach reduces computational overhead by avoiding the training of a separate value estimation model, making the learning process more efficient. For more details, refer to the original paper [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://arxiv.org/pdf/2402.03300) ## Key Components - No Value Function (Critic-less): unlike PPO, GRPO does not train a separate value network (critic) - Group Sampling (Grouped Rollouts): instead of evaluating one rollout per input, GRPO generates multiple completions (responses) from the current policy for each prompt. This set of completions is referred to as a group. - Relative Rewards: within each group, completions are scored (e.g., based on correctness), and rewards are normalized relative to the group. ## Configuration Note that all configs containing `micro_batch_size` are used to configure the maximum sample or token count per forward or backward pass to avoid GPU OOMs, whose value should not change algorithmic/convergence behavior. Despite that many configurations start with the `ppo_` prefix, they work across different RL algorithms in verl, as the GRPO training loop is similar to that of PPO (without critic). ![image](https://github.com/user-attachments/assets/16aebad1-0da6-4eb3-806d-54a74e712c2d) - `actor_rollout.ref.rollout.n`: For each prompt, sample n times. Default to 1. For GRPO, please set it to a value larger than 1 for group sampling. - `data.train_batch_size`: The global batch size of prompts used to generate a set of sampled trajectories/rollouts. The number of responses/trajectories is `data.train_batch_size * actor_rollout.ref.rollout.n` - `actor_rollout_ref.actor.ppo_mini_batch_size`: The set of sampled trajectories is split into multiple mini-batches with batch_size=ppo_mini_batch_size for PPO actor updates. The ppo_mini_batch_size is a global size across all workers. - `actor_rollout_ref.actor.ppo_epochs`: Number of epochs for GRPO updates on one set of sampled trajectories for actor - `actor_rollout_ref.actor.clip_ratio`: The GRPO clip range. Default to 0.2 - `algorithm.adv_estimator`: Default is gae. Please set it to grpo instead - `actor_rollout_ref.actor.loss_agg_mode`: Default is "token-mean". Options include "token-mean", "seq-mean-token-sum", "seq-mean-token-mean". The original GRPO paper takes the sample-level loss (seq-mean-token-mean), which may be unstable in long-CoT scenarios. All GRPO example scripts provided in verl uses the default configuration "token-mean" for loss aggregation instead. Instead of adding KL penalty in the reward, GRPO regularizes by directly adding the KL divergence between the trained policy and the reference policy to the loss: - `actor_rollout_ref.actor.use_kl_loss`: To use kl loss in the actor. When used, we are not applying KL in the reward function. Default is False. Please set it to True for GRPO. - `actor_rollout_ref.actor.kl_loss_coef`: The coefficient of kl loss. Default is 0.001. - `actor_rollout_ref.actor.kl_loss_type`: Support kl(k1), abs, mse(k2), low_var_kl(k3) and full. Appending "+" in the end (e.g., 'k1+' and 'k3+') would apply straight through to employ k2 for unbiased gradient estimation, regardless of the kl value estimation (see https://github.com/volcengine/verl/pull/2953#issuecomment-3162113848 for more details). How to calculate the kl divergence between actor and reference policy. See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html ## Advanced Extensions ### DrGRPO [Understanding R1-Zero-Like Training: A Critical Perspective](https://arxiv.org/pdf/2503.20783) claims there's optimization bias in GRPO, which leads to artificially longer responses, especially for incorrect outputs. This inefficiency stems from the way GRPO calculates advantages using group-based reward normalization. Instead, DrGRPO aggregates token-level losses by normalizing with a global constant to eliminate length bias. Configure the following to enable DrGRPO, with all other parameters the same as GRPO's: - `actor_rollout_ref.actor.loss_agg_mode`: "seq-mean-token-sum-norm", which turns off seq-dim averaging - `actor_rollout_ref.actor.loss_scale_factor`: (Optional) Set to a constant integer (e.g., max response length) to ensure consistent normalization throughout training. If not set, uses the current batch's response length. - `actor_rollout_ref.actor.use_kl_loss`: Please set it to False for DrGRPO - `algorithm.norm_adv_by_std_in_grpo`: False, which turns off standard deviation norm ## Reference Example Qwen2.5 GRPO training log and commands: [link](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/qwen2-7b-fsdp2.log) ```bash bash examples/grpo_trainer/run_qwen3-8b.sh ``` For more reference performance, please see https://verl.readthedocs.io/en/latest/algo/baseline.html ================================================ FILE: docs/algo/opo.md ================================================ # On-Policy RL with Optimal Reward Baseline (OPO) Last updated: 06/02/2025. Loose on-policy constraints and suboptimal baselines in reinforcement learning often lead to training instability such as large policy shifts and entropy collapse. OPO addresses these challenges by using exact on-policy training with the theretically optimal reward baseline for advantage estimation. It achieves lower policy shifts and higher output entropy, encouraging more diverse and less repetitive responses. OPO uses group sampling to generate multiple outputs for each input like GRPO. Unlike group-based algorithms which typically use the mean reward of a group as its baseline, OPO employs a theoretically optimal baseline: the length-weighted reward of the group. It also omits the standard deviation normalization. By adopting these two key components, OPO enables the training of a single policy model with the objective of maximizing only the expected reward. For more detailes, refer to the original paper [On-Policy RL with Optimal Reward Baseline](https://arxiv.org/pdf/2505.23585). ## Key Components - Exact On-Policy Training: always generates responses from the current policy, without using any pre-generated data or off-policy data. - Optimal Reward Baseline: uses a length-weighted reward of the group as the baseline for normalizing the rewards. ## Configuration To configure OPO within the framework, use the following YAML settings. These parameters are crucial for enabling exact on-policy training and activating the optimal reward baseline. ```yaml algorithm: adv_estimator: opo # Use OPO for optimal reward baseline data: train_batch_size: 1024 actor_rollout_ref: actor: ppo_mini_batch_size: 1024 # ppo_mini_batch_size should equal to train_batch_size to enable exact on-policy training entropy_coeff: 0 # disable entropy regularization use_kl_loss: False # disable kl regularization kl_loss_coef: 0 ``` ## Advanced Extensions OPO can also be extended to other algorithms like RLOO and Reinforce++. It just needs to adjust their configurations to enable exact on-policy training and incorporate the optimal length-weighted reward baseline with minimal modifications to their advantage estimation functions. ================================================ FILE: docs/algo/otb.md ================================================ # Optimal Token Baseline (OTB) Last updated: 02/23/2026. 📝 [ArXiv](https://www.arxiv.org/abs/2602.07078) | 📒 [Blog](https://richardli.xyz/optimal-token-baseline) | 🤗 [Datasets](https://huggingface.co/datasets/Jiawei415/DPAO_filter) Optimal Token Baseline (OTB) is a dynamic token-level baseline for gradient variance reduction in policy-gradient reinforcement learning. It weights updates with the "Realized Energy" statistic that tracks how much uncertainty has accumulated up to each token, so noisy regions get downweighted while confident regions carry more weight. ## Key properties - _Token-level baselines:_ OTB adapts per token by tracking realized energy, avoiding the padding artifacts that appear when group means dilute the signal with `EOS` tokens. - _Forward-only overhead:_ The realized-energy statistic is computed via the **Logit-Gradient Proxy**, so OTB requires no extra backward passes or gradient-norm kernels. ## Logit-Gradient Proxy Computing true uncertainty per token would normally mandate per-token backward passes. OTB sidesteps this by estimating realized energy entirely from forward probabilities, so it introduces negligible runtime overhead in practice. ## Mechanics at a glance For each prompt group of size `N`, OTB computes rewards-to-go `G_t` and cumulative variance weights `W_t`. The optimal baseline per token is ``` B*_t = (Σ_i G_t^{(i)} · W_t^{(i)}) / (Σ_i W_t^{(i)} + ε), W_t = Σ_{j=1}^t (1 - 2π_j + Σπ_j²), Σπ_j² = exp(logsumexp(2·logits_j) - 2·logsumexp(logits_j)). ``` The final advantage is `(G_t - B*_t) · mask_t`, so padding tokens stay at zero. ## Integration in VERL - `AdvantageEstimator.OPTIMAL_TOKEN_BASELINE` registers `compute_optimal_token_baseline_advantage`, invoked whenever `algorithm.adv_estimator` is set to `optimal_token_baseline`. - `ActorRolloutRefWorker.compute_log_prob` emits an additional tensor `sum_pi_squared` (Σπ² per token) when `actor.calculate_sum_pi_squared=True`. This requires disabling fused log-prob kernels, because they do not surface logits. - Trainers assert `sum_pi_squared` exists, regroup trajectories by `non_tensor_batch["uid"]`, and run the OTB calculation. If rollout IS is active, they rescale the weights by `rollout_is_weights**2` before aggregating. - In Ulysses sequence-parallel setups, the actor gathers, unpads, and returns Σπ² in the same way it handles log-probabilities, so OTB supports sharded sequence-parallel models out of the box. - `sum_pi_squared_checkpointing` is available to trade compute for memory when Σπ² tensors become large (e.g., lengthy chain-of-thought reasoning). ## Configuration checklist - `actor_rollout_ref.actor.calculate_sum_pi_squared: true` (mandatory). - `actor_rollout_ref.model.use_fused_kernels: false` (required until fused kernels emit logits). - `algorithm.adv_estimator: optimal_token_baseline` for single-turn RL and `tir_optimal_token_baseline` for multi-turn RL. - Group sampling (`actor_rollout_ref.rollout.n > 1`) to unlock OTB’s variance reduction; with `n=1` the baseline collapses to returns. Example OmegaConf overlay: ```yaml algorithm: adv_estimator: optimal_token_baseline actor_rollout_ref: actor: calculate_sum_pi_squared: true sum_pi_squared_checkpointing: false # optional memory saver rollout: n: 8 ``` ## Example script See `examples/otb_trainer/run_qwen2_5-7b.sh` for a reference training loop. ## Gradient Variance Proxy Metrics All gradient-variance analysis in the Optimal Token Baseline work starts from the variance identity ``` Var(ĝ) = E[||ĝ||²] - ||E[ĝ]||², ``` which states that the variance of any stochastic gradient equals the mean squared magnitude minus the squared norm of its expectation. For a trajectory `τ`, the policy-gradient estimator is ``` ĝ(τ) = ∇ log π_θ(τ) · A(τ), A(τ) = R(τ) - B. ``` The logit-gradient proxy approximates the squared gradient norm without an extra backward pass: ``` ||ĝ(τ)||² ≈ Ŵ(τ) · A(τ)², ``` where `Ŵ(τ)` is the realized energy built. Given a mini-batch `{τ_i}` of size `N`, we decompose its statistics into three diagnostics: - **Signal strength (squared norm of the mean gradient)** ``` S = || (1/N) · Σ ĝ(τ_i) ||² ``` - **Total power (signal + noise)** ``` P_total = (1/N) · Σ Ŵ(τ_i) · A(τ_i)² ``` - **Pure noise (estimated variance of the batch mean)** ``` Var_proxy = (1/(N-1)) · (P_total - S) ``` `verl/trainer/ppo/metric_utils.py#L306` implements these diagnostics via `compute_variance_proxy_metrics`, emitting `variance_proxy/proxy1_signal_strength`, `variance_proxy/proxy2_total_power`, and `variance_proxy/proxy3_pure_noise`. Tracking these metrics provides a forward-only, low-overhead view of gradient health for any advantage estimator that supplies `sum_pi_squared`. ================================================ FILE: docs/algo/ppo.md ================================================ # Proximal Policy Optimization (PPO) Last updated: 06/19/2025. Proximal Policy Optimization (PPO) is a family of policy gradient methods for reinforcement learning, proposed by OpenAI in 2017. PPO strikes a balance between simplicity, stability, and performance, making it one of the most widely used algorithms in modern RL applications, including large-scale language model fine-tuning. Traditional policy gradient methods like REINFORCE or Vanilla Policy Gradient suffer from: - High variance and sample inefficiency. - Instability due to large policy updates. PPO addresses this problem using a clipped surrogate objective that avoids overly large updates without requiring second-order derivatives. For more technical details regarding PPO, we suggest reading the introduction in the [OpenAI spinning up tutorial](https://spinningup.openai.com/en/latest/algorithms/ppo.html), and the paper [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347). ## Key Components - Actor-Critic Architecture: PPO requires both an actor model (policy) and a critic model (value function). This differs from other algorithms like GRPO and RLOO that don't require a critic model. - Generalized Advantage Estimation (GAE): PPO uses GAE for computing advantage values, which helps reduce variance in policy gradient estimates while maintaining low bias. - Clipped Surrogate Objective: The core of PPO is implemented through the clipped surrogate objective function that limits policy updates. ## Configuration Note that all configs containing `micro_batch_size` are used to configure the maximum sample or token count per forward or backward pass to avoid GPU OOMs, whose value should not change algorithmic/convergence behavior. Most critic configs are similar to those of actors. Note that the critic model is omitted from the figure below. ![image](https://github.com/user-attachments/assets/16aebad1-0da6-4eb3-806d-54a74e712c2d) - `data.train_batch_size`: The global batch size of prompts used to generate a set of sampled trajectories/rollouts. The number of responses/trajectories is `data.train_batch_size * actor_rollout.ref.rollout.n` - `actor_rollout_ref.actor.ppo_mini_batch_size`: The set of sampled trajectories is split into multiple mini-batches with batch_size=ppo_mini_batch_size for PPO actor updates. The ppo_mini_batch_size is a global size across all workers - `critic.ppo_mini_batch_size`: The set of sampled trajectories is split into multiple mini-batches with batch_size=ppo_mini_batch_size for PPO critic updates. The ppo_mini_batch_size is a global size across all workers - `actor_rollout_ref.actor.clip_ratio`: The PPO clip range. Default to 0.2 - `actor_rollout_ref.actor.ppo_epochs`: Number of epochs for PPO updates on one set of sampled trajectories for actor - `critic.ppo_epochs`: Number of epochs for PPO updates on one set of sampled trajectories for critic. Defaults to `actor_rollout_ref.actor.ppo_epochs` - `algorithm.gemma`: discount factor - `algorithm.lam`: The lambda term that trades off between bias and variance in the GAE estimator - `algorithm.adv_estimator`: Support gae, grpo, reinforce_plus_plus, reinforce_plus_plus_baseline, rloo ## Advanced Extensions ### KL Divergence Control Options to prevent the policy from diverging too far from a reference policy. Two mechanisms are available: KL reward penalty and KL loss. For more technical details, see [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) Options to use KL loss for KL divergence control: - `actor_rollout_ref.actor.use_kl_loss`: to use kl loss in the actor. When used, we are not applying KL in the reward function. Default is False - `actor_rollout_ref.actor.kl_loss_coef`: The coefficient of kl loss. Default is 0.001. - `actor_rollout_ref.actor.kl_loss_type`: Support kl(k1), abs, mse(k2), low_var_kl(k3) and full. Appending "+" in the end (e.g., 'k1+' and 'k3+') would apply straight through to employ k2 for unbiased gradient estimation, regardless of the kl value estimation (see https://github.com/volcengine/verl/pull/2953#issuecomment-3162113848 for more details). How to calculate the kl divergence between actor and reference policy. See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html Options to use KL penalty in the reward: - `algorithm.use_kl_in_reward`: Whether to enable in-reward kl penalty. Default is False. - `algorithm.kl_penalty`: Support kl(k1), abs, mse(k2), low_var_kl(k3) and full. This defines the way to calculate the kl divergence between actor and reference policy. For specific options, refer to `kl_penalty` in core_algos.py. See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html - `algorithm.kl_ctrl.kl_coef`: The (initial) coefficient of in-reward kl_penalty. Default is 0.001. - `algorithm.kl_ctrl.type`: 'fixed' for FixedKLController and 'adaptive' for AdaptiveKLController. - `algorithm.kl_ctrl.horizon`: See source code of AdaptiveKLController for details. - `algorithm.kl_ctrl.target_kl`: See source code of AdaptiveKLController for details. ### Dual-clip PPO The Dual-Clip PPO introduces a approach by applying a lower bound to the policy ratio when the advantage is less than zero, when multiplied by a large raito, does not exceed a specified lower bound. ![image](https://github.com/user-attachments/assets/fc232181-d8b0-4307-8dd2-4dc0a4c1c139) - `actor_rollout_ref.actor.clip_ratio_c`: lower bound of the value for Dual-clip PPO, defaults to 3.0 ## Reference Example Qwen2.5 training log and commands: [link](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-0.5B-bsz256_2-prompt1024-resp512-0.567.log) ```bash bash run_gemma.sh trainer.n_gpus_per_node=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ trainer.logger=console \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ data.train_batch_size=256 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size=2 \ critic.ppo_micro_batch_size=2 ``` Reference performance with verl v0.2: | Model | Method | Score | Link | |-------------------------------|------------------|-------|------------------------------------------------------------------------------------------------| | Qwen/Qwen2.5-0.5B-Instruct | pretrained model | 36.4 | [Qwen Blog](https://qwenlm.github.io/blog/qwen2.5-llm/) | | Qwen/Qwen2.5-0.5B-Instruct | PPO | 56.7 | [PPO Command and Logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-0.5B-bsz256_2-prompt1024-resp512-0.567.log) | ================================================ FILE: docs/algo/rollout_corr.md ================================================ # Rollout Correction **Author:** [Yingru Li](https://richardli.xyz/) Last updated: 10/30/2025. --- > **📖 Documentation Structure** > > - **This document** - Practical usage guide: configurations, presets, troubleshooting > - **[Mathematical Formulations](rollout_corr_math.md)** - Theoretical foundations, derivations, and algorithmic details > > Start here for implementation, refer to the math doc for theory and design rationale. --- This document provides a comprehensive overview of the Rollout Correction implementation in verl. **Note on Naming**: This feature is called "Rollout Correction" to reflect the complete functionality: importance sampling (IS) weights and rejection sampling (RS). The internal variable `rollout_is_weights` retains its name as it specifically refers to the IS weights component. ### BibTeX Citation ```bibtex @online{liu-li-2025-rl-collapse, title = {When Speed Kills Stability: Demystifying {RL} Collapse from the Training-Inference Mismatch}, author = {Liu, Jiacai and Li, Yingru and Fu, Yuqian and Wang, Jiawei and Liu, Qian and Shen, Yu}, year = {2025}, month = sep, url = {https://richardli.xyz/rl-collapse} } @article{li2025trust, title={Trust Region Masking for Long-Horizon LLM Reinforcement Learning}, author={Li, Yingru and Liu, Jiacai and Xu, Jiawei and Tong, Yuxuan and Li, Ziniu and Liu, Qian and Wang, Baoxiang}, journal={arXiv preprint arXiv:2512.23075}, year={2025} } ``` ### Blog Series - Main blog post: https://richardli.xyz/rl-collapse - [Part 1: Why Mismatch Breaks LLM-RL](https://richardli.xyz/rl-collapse-1) (analytical framework using TV distance for bias and χ²-divergence for variance) - [Part 2: The Gradient Estimator Trials](https://richardli.xyz/rl-collapse-2) (token-level vs sequence-level correction bias-variance tradeoff) - [Part 3: When Math Meets Reality—Toxic Tails and Length Traps](https://richardli.xyz/rl-collapse-3) (why rejection over clipping, and geometric-level RS) - Latest Paper: https://arxiv.org/abs/2512.23075 ## Overview Rollout Correction provides a unified framework to handle **general off-policy problems** in RL training. Any scenario where the data collection distribution differs from the training distribution can benefit from these methods. **Common off-policy scenarios:** 1. **Policy Mismatch** (Implementation Differences) - Different precision: FP8 vs FP16 vs BF16 vs FP32 - Different backends: vLLM vs SGLang vs FSDP vs Megatron - Different implementations even with identical weights 2. **Temporal Lag** (Model Staleness) - Rollout uses older checkpoint while training has progressed - Asynchronous rollout workers with stale parameters - Common in distributed/async RL systems 3. **Replay Buffers** - Training on historical trajectories from earlier iterations - Experience replay from different policy versions - Data augmentation or resampling strategies 4. **Off-Policy Algorithms** - Behavioral cloning from expert demonstrations - DAPO (data from auxiliary policies) - Any algorithm using trajectories from a different policy 5. **Data Quality Filtering** - Reweighting or filtering collected data - Preference learning with modified distributions - Curriculum learning with distribution shifts These off-policy gaps can cause training instability and policy collapse. Rollout Correction uses importance sampling (IS) weights and rejection sampling (RS) to correct for any distribution shift between data collection and training. **Important Note on Common Implementation Mistakes:** Many LLM-RL implementations incorrectly apply PPO by **ignoring the actual rollout policy** π_rollout and assuming the training reference policy π_old is the behavior policy. This is mathematically incorrect when π_rollout ≠ π_old (which is typical in LLM-RL due to precision/backend differences between rollout and training). **This is not PPO's fault** - PPO itself is mathematically correct. The issue is the incorrect assumption that π_old = π_rollout in naive implementations. This critical implementation mistake that leads to RL training collapse was identified in the blog post ["When Speed Kills Stability: Demystifying RL Collapse from the Training-Inference Mismatch"](https://richardli.xyz/rl-collapse) and motivated the development of this rollout correction framework. **Mathematically correct approaches:** - **Decoupled mode**: Three policies (π_rollout, π_old, π_θ) with IS correction from π_rollout to π_old - **Bypass mode**: Two policies (π_rollout = π_old, π_θ) using actual rollout policy as PPO anchor - **Bypass + Policy Gradient mode**: Two policies (π_rollout, π_θ) with IS/RS correction and no PPO clipping See [Mathematical Formulations](rollout_corr_math.md#37-common-implementation-mistake) for detailed explanation. ### Key Design Principle: Separation of IS Weights and Rejection Sampling The implementation cleanly separates two orthogonal mechanisms: 1. **IS Weights** (`rollout_is_weights`): Continuous reweighting for gradient correction - Policy ratio: π_old/π_rollout (decoupled) or π_θ/π_rollout (bypass) - **Safety-bounded**: Clamped to [exp(-20), exp(20)] ≈ [2e-9, 5e8] to prevent overflow - Token level: Bounds per-token ratios - Sequence level: Bounds product of ratios (broadcast to all tokens) - **Truncated**: Upper clamped via `.clamp(max=rollout_is_threshold)` (TIS: Truncated Importance Sampling) - **Zeroed at padding**: Multiplied by response_mask to zero out padding positions - Used to weight policy gradients (variance reduction) 2. **Rejection Sampling** (`modified_response_mask`): Binary filtering for outlier exclusion - Creates binary mask: 1 = keep, 0 = reject - Rejects tokens/sequences with IS ratios outside [lower_threshold, upper_threshold] - Modifies response_mask to exclude rejected samples from training This separation ensures: - ✅ IS weights provide continuous reweighting (reduce variance) - ✅ Rejection sampling provides hard filtering (remove extreme outliers) - ✅ Both mechanisms can be enabled independently or together - ✅ Safety bounds prevent numerical overflow in all cases ## Quick Start: Using Verified Presets **NEW**: We now provide typed configuration with verified presets for common scenarios. These presets have been validated with tens of thousands of GPU hours across various models and training scenarios. ### Python API ```python from verl.trainer.config.algorithm import RolloutCorrectionConfig # === Decoupled PPO mode (3 policies: π_rollout, π_old, π_θ) === # IS weights correct for gap between π_old and π_rollout config = RolloutCorrectionConfig.decoupled_token_is() # Token-TIS config = RolloutCorrectionConfig.decoupled_seq_is() # Seq-TIS config = RolloutCorrectionConfig.decoupled_seq_is_rs() # Seq-MIS config = RolloutCorrectionConfig.decoupled_geo_rs() # Geo-RS (ratio mode) config = RolloutCorrectionConfig.decoupled_geo_rs_token_tis() # Geo-RS + Token-TIS # === K3 KL Estimator presets (more stable for small KL) === config = RolloutCorrectionConfig.decoupled_k3_rs() # K3-RS only config = RolloutCorrectionConfig.decoupled_k3_rs_token_tis() # K3-RS + Token-TIS # === Bypass PPO mode (2 policies: π_rollout = π_old, π_θ) - fast === # PPO ratio handles IS, so no explicit IS weights needed config = RolloutCorrectionConfig.bypass_ppo_clip() # PPO-clip only config = RolloutCorrectionConfig.bypass_ppo_clip_geo_rs() # PPO-clip + Geo-RS config = RolloutCorrectionConfig.bypass_ppo_clip_k3_rs() # PPO-clip + K3-RS # === Bypass PG mode (2 policies, no PPO clipping) - fast === # IS weights computed on-the-fly as π_θ / π_rollout config = RolloutCorrectionConfig.bypass_pg_is() # Seq-TIS + PG config = RolloutCorrectionConfig.bypass_pg_geo_rs() # Geo-RS + PG config = RolloutCorrectionConfig.bypass_pg_geo_rs_token_tis() # Geo-RS + Token-TIS + PG # === Other === config = RolloutCorrectionConfig.disabled() # Metrics only (no correction) ``` ### YAML Configuration (Advanced) For advanced customization or YAML-based configs: ```yaml algorithm: rollout_correction: rollout_is: token # IS weights: "token", "sequence", or null rollout_is_threshold: 2.0 # Upper threshold for IS weights rollout_is_batch_normalize: false # Batch normalize IS weights to mean=1.0 rollout_rs: null # Rejection sampling: comma-separated canonical options (e.g. "token_k1,seq_max_k2") rollout_rs_threshold: null # Threshold spec: float(s) or "lower_upper" string(s) bypass_mode: false # Skip old_log_prob computation (sets π_old = π_rollout) loss_type: ppo_clip # Loss type in bypass mode: "ppo_clip" (default) or "reinforce" # REQUIRED: Enable log prob calculation actor_rollout_ref: rollout: calculate_log_probs: true ``` ## Files ### **Core Implementation** - `verl/trainer/ppo/rollout_corr_helper.py` - Contains `compute_rollout_correction_and_rejection_mask()` and `compute_offpolicy_metrics()` - `verl/trainer/ppo/core_algos.py` - Rollout Correction integration with PPO and REINFORCE modes (`compute_policy_loss_bypass_mode()`, `compute_policy_loss_reinforce()`) - `verl/trainer/ppo/ray_trainer.py` - Bypass mode implementation (skips `old_log_prob` computation) - `verl/workers/actor/dp_actor.py` - Mode selection logic and metrics collection ### **Configuration Files** - `verl/trainer/config/algorithm.py` - Rollout Correction parameters in `RolloutCorrectionConfig` - `verl/workers/config/actor.py` - Rollout Correction parameters in `PolicyLossConfig` - `verl/trainer/config/actor/actor.yaml` - Rollout Correction configuration section - `verl/trainer/config/ppo_trainer.yaml` - Algorithm config with Rollout Correction ### **Documentation** - `docs/examples/config.rst` - Configuration parameter descriptions ### **Example Scripts** - `recipe/dapo/run_dapo_qwen2.5_32b_rollout_corr.sh` - DAPO example with Rollout Correction - `examples/rollout_correction/run_with_rollout_corr.sh` - Basic example - `examples/rollout_correction/run_with_rollout_corr_multi_rs.sh` - Multi-RS example ### **Tests** - `tests/trainer/ppo/test_rollout_corr.py` - Unit tests for IS/RS mechanisms - `tests/trainer/ppo/test_rollout_corr_integration.py` - Integration tests ## Configuration Parameters All parameters are under `algorithm.rollout_correction`: ### `rollout_is` (str or null) Importance sampling weights aggregation level: - `null` = No IS weights computed (metrics-only mode) - `"token"`: Per-token IS weights - **Decoupled mode**: ρ_t = π_old(t)/π_rollout(t) - **Bypass/Pure IS mode**: ρ_t = π_θ(t)/π_rollout(t) - Independent truncation per token - Typical threshold: 1.5 - 5.0 - `"sequence"`: Per-sequence weight ρ_seq = ∏_t ρ_t - Multiplicative aggregation across sequence - Typical threshold: 2.0 - 10.0 All IS weights are safety-bounded to [exp(-20), exp(20)] ≈ [2e-9, 5e8] ### `rollout_is_threshold` (float) Upper threshold for IS weight truncation. Default: `2.0` - Truncates IS weights via `.clamp(max=rollout_is_threshold)` (TIS: Truncated Importance Sampling) - Applied to IS weights for variance reduction - Separate from rejection sampling (controlled by `rollout_rs` parameters) ### `rollout_is_batch_normalize` (bool) Apply batch normalization to IS weights. Default: `False` - `True`: Normalize IS weights to have mean=1.0 within each batch - **Token-level IS**: Normalizes over all token weights - **Sequence-level IS**: Normalizes over sequence means (one weight per sequence) - `False`: Use raw (truncated) IS weights - Reduces variance by ensuring average weight is 1.0 per batch - Applied AFTER truncation to preserve truncation semantics - Only affects IS weight values, not rejection sampling ### `rollout_rs` (str or null) Rejection sampling aggregation modes. Supply a comma-separated string (spaces optional) using the canonical options implemented in `rollout_corr_helper`: - `token_k1`: Token-level rejection with `-log r` bounds (ratio thresholds supplied as `lower_upper`). Example: `"0.6_1.4"` - `token_k2`: Token-level rejection with `0.5 * (log r)^2` (upper bound only) - `token_k3`: Token-level rejection with `exp(log r) - 1 - log r` (upper bound only) - `seq_sum_k1`: Sequence-level rejection with sum of `-log r` (ratio bounds) - `seq_sum_k2`: Sequence-level rejection with sum of `0.5 * (log r)^2` (upper bound only) - `seq_sum_k3`: Sequence-level rejection with sum of `exp(log r) - 1 - log r` (upper bound only) - `seq_mean_k1`: Sequence-level rejection with mean of `-log r` (ratio bounds) - `seq_mean_k2`: Sequence-level rejection with mean of `0.5 * (log r)^2` (upper bound only) - `seq_mean_k3`: Sequence-level rejection with mean of `exp(log r) - 1 - log r` (upper bound only) - `seq_max_k2`: Sequence-level rejection with max of `0.5 * (log r)^2` (upper bound only) - `seq_max_k3`: Sequence-level rejection with max of `exp(log r) - 1 - log r` (upper bound only) ### `rollout_rs_threshold` (str, float, or null) Threshold specification for rejection sampling. - Provide **one entry per option**, separated by commas. A single entry is broadcast to every option. - **K1 KL modes (`*k1`)**: Use `"lower_upper"` strings (e.g. `"0.7_1.3"`). Supplying a float implies only the upper bound; the lower bound defaults to its reciprocal. - **K2/K3 KL modes (`*k2`/`*k3`)**: Supply positive upper bounds (float or numeric string). - Set to `null` to disable thresholds entirely (only valid when `rollout_rs` is null). ## Understanding the Framework: Components and Combinations The rollout correction framework is built from **orthogonal components** that can be combined flexibly. Understanding these components helps you choose the right configuration for your scenario. ### Key Components 1. **Operating Mode** (Section: [Operation Modes](#operation-modes)) - **Decoupled**: Three policies (π_rollout, π_old, π_θ) with separate π_old computation - **Bypass**: Two policies (π_rollout = π_old, π_θ), skips π_old computation 2. **Loss Function** (in bypass mode, controlled by `loss_type`) - **PPO-clip** (`loss_type="ppo_clip"`, default): PPO clipped objective (IS handled by ratio) - **REINFORCE** (`loss_type="reinforce"`): Policy gradient with explicit IS weights (no clipping) 3. **IS/RS Aggregation Level** - **Token**: Per-token IS weights/rejection - **Sequence**: Sequence-level IS weights/rejection See [Mathematical Formulations](rollout_corr_math.md#3-algorithmic-components-and-combinations) for detailed theory. --- ## Preset Configuration Guide This section provides detailed guidance on choosing and using the verified presets. Each preset is a specific combination of components optimized for common scenarios. ### Understanding the Presets #### Available Preset Methods | Preset Method | Estimator | Mode | IS Level | RS Level | Properties | | ------------------------------------------------------------------------------ | ---------------- | ------------------ | -------- | -------- | --------------------------------------- | | **Decoupled PPO Mode** (3 policies: π_rollout, π_old, π_θ) | | `decoupled_token_is()` | Token-TIS | Decoupled | token | - | Token-level IS weights | | `decoupled_seq_is()` | Seq-TIS | Decoupled | sequence | - | Sequence-level IS weights | | `decoupled_seq_is_rs()` | Seq-MIS | Decoupled | sequence | sequence | Sequence IS + seq_sum_k1 RS | | `decoupled_geo_rs()` | Geo-RS | Decoupled | - | sequence | Geometric RS (seq_mean_k1) | | `decoupled_geo_rs_token_tis()` | Geo-RS-Token-TIS | Decoupled | token | sequence | Geometric RS + token IS | | **K3 KL Estimator** (more stable for small KL values) | | `decoupled_k3_rs()` | K3-RS | Decoupled | - | sequence | seq_mean_k3 RS | | `decoupled_k3_rs_token_tis()` | K3-RS-Token-TIS | Decoupled | token | sequence | seq_mean_k3 RS + token IS | | **Bypass Mode (PPO-clip)** (2 policies; ratio handles IS, RS masks outliers) | | `bypass_ppo_clip()` | - | Bypass (PPO-clip) | - | - | PPO-clip only | | `bypass_ppo_clip_geo_rs()` | Geo-RS | Bypass (PPO-clip) | - | sequence | PPO-clip + Geo-RS | | `bypass_ppo_clip_k3_rs()` | K3-RS | Bypass (PPO-clip) | - | sequence | PPO-clip + K3-RS | | **Bypass Mode (REINFORCE)** (2 policies; explicit IS weights, no PPO clipping) | | `bypass_pg_is()` | Seq-TIS | Bypass (REINFORCE) | sequence | - | REINFORCE with explicit IS | | `bypass_pg_geo_rs()` | Geo-RS | Bypass (REINFORCE) | - | sequence | REINFORCE with Geo-RS | | `bypass_pg_geo_rs_token_tis()` | Geo-RS-Token-TIS | Bypass (REINFORCE) | token | sequence | REINFORCE + Geo-RS + token IS | | **Other** | | `disabled()` | - | - | - | - | Metrics only, no correction | **Note:** - **Bypass mode** sets π_old = π_rollout and uses `loss_type` to select the loss function: - `"ppo_clip"` (default): PPO clipped objective where ratio = π_θ/π_rollout already handles IS - `"reinforce"`: REINFORCE with explicit IS weights as π_θ/π_rollout - Both loss types benefit from rejection sampling (RS) which masks out-of-distribution samples. - All estimators (Token-TIS, Seq-TIS, Seq-MIS, Geo-RS, ...) are compatible with Decoupled and Bypass modes. #### Other Supported Combinations (Manual Configuration Required) **Other supported combinations without preset methods:** - Token IS + Token RS: Token-level IS weights + Token-level RS mask - Pure token RS: Token-level RS only, no IS weights - Pure sequence RS: Sequence-level RS only, no IS weights See [detailed configuration examples below](#additional-useful-configurations-not-exposed-as-presets) for manual configurations. **Key properties:** - Any aggregation level (token/sequence) works in either decoupled or bypass mode - All combinations are fully supported by the implementation - Rejection sampling is independent of IS weighting - Pure RS (`bypass_pg_rs`) uses bypass + geometric RS with `loss_type="reinforce"` (no IS weights) --- ### 1. Decoupled Mode with Token-level Importance Sampling (`decoupled_token_is`) **Configuration:** ```python config = RolloutCorrectionConfig.decoupled_token_is(threshold=2.0) ``` **Components:** - **Operating Mode**: Decoupled (3 policies) - **Loss**: PPO with clipping (only for the second drift correction) - **IS Aggregation**: Token-level - **RS**: None (can be added separately) **Equivalent YAML:** ```yaml algorithm: rollout_correction: rollout_is: token rollout_is_threshold: 2.0 rollout_rs: null bypass_mode: false # Decoupled mode ``` **Properties:** - Independent truncation per token - Lower variance than sequence-level (product of ratios bounded individually) - Typical threshold: 1.5 - 5.0 **Theory:** See [rollout_corr_math.md §3.3.1](rollout_corr_math.md#331-token-level-aggregation) --- ### 2. Decoupled Mode with Sequence-level Importance Sampling (`decoupled_seq_is`) **Also known as: Seq-TIS (Sequence-Level Truncated IS)** **Configuration:** ```python config = RolloutCorrectionConfig.decoupled_seq_is(threshold=2.0) ``` **Components:** - **Operating Mode**: Decoupled (3 policies) - **Loss**: PPO with clipping (only for the second drift correction) - **IS Aggregation**: Sequence-level (Seq-TIS) - **RS**: None (can be added separately) **Equivalent YAML:** ```yaml algorithm: rollout_correction: rollout_is: sequence rollout_is_threshold: 2.0 rollout_rs: null bypass_mode: false # Decoupled mode ``` **Properties:** - Multiplicative aggregation across sequence - More sensitive to outliers than token-level - Typical threshold: 2.0 - 10.0 (higher than token-level) **Theory:** See [rollout_corr_math.md §3.3.2](rollout_corr_math.md#332-sequence-level-aggregation) --- ### 3. Decoupled Mode with Sequence-level IS + Rejection Sampling (`decoupled_seq_is_rs`) **Also known as: Seq-MIS (Sequence-Level Masked IS)** **Configuration:** ```python config = RolloutCorrectionConfig.decoupled_seq_is_rs(is_threshold=2.0, rs_threshold="0.5_2.0") ``` **Components:** - **Operating Mode**: Decoupled (3 policies) - **Loss**: PPO with clipping (only for the second drift correction) - **IS Aggregation**: Sequence-level (Seq-TIS) - **RS**: Sequence-level rejection (Seq-MIS) **Equivalent YAML:** ```yaml algorithm: rollout_correction: rollout_is: sequence rollout_is_threshold: 2.0 rollout_rs: seq_sum_k1 rollout_rs_threshold: 0.5_2.0 bypass_mode: false # Decoupled mode ``` **Properties:** - Double mechanism: IS reweighting (Seq-TIS) + rejection filtering (Seq-MIS) - Lower effective sample size (rejects outliers) - For severe off-policy gaps or when the distribution tail is "toxic" (garbage/adversarial samples) **When to use Seq-MIS over Seq-TIS:** - **Seq-TIS (clipping only)**: Maximizes information efficiency; extracts signal from all samples. Use when data is clean and mismatch is moderate. - **Seq-MIS (rejection)**: Maximizes safety; acts as a hard trust region filter. Use when mismatch is severe or when high-weight samples are likely garbage rather than signal. **Theory:** See [rollout_corr_math.md §3.5](rollout_corr_math.md#35-rejection-sampling-rs) --- ### 6. Bypass Mode with PPO-clip (`bypass_ppo_clip`) **Configuration:** ```python config = RolloutCorrectionConfig.bypass_ppo_clip() ``` **Components:** - **Operating Mode**: Bypass (2 policies: π_rollout = π_old, π_θ) - **Loss**: PPO-clip (IS handled by ratio, no explicit IS weights) - **IS Aggregation**: None (PPO ratio handles it) - **RS**: None **Equivalent YAML:** ```yaml rollout_correction: rollout_is: null rollout_rs: null bypass_mode: true loss_type: ppo_clip ``` **Properties:** - PPO clipped objective in bypass mode - The PPO ratio = π_θ/π_rollout already handles IS (no explicit IS weights needed) - Skips `actor.compute_log_prob()` forward pass (2 policies instead of 3) - No rejection sampling - use `bypass_ppo_clip_geo_rs()` for RS **Configuration requirement:** - Set `actor_rollout_ref.rollout.calculate_log_probs: true` **Additional requirements for bypass mode:** - Set `actor_rollout_ref.actor.use_rollout_log_probs: true` - Set `actor_rollout_ref.actor.policy_loss.loss_mode: bypass_mode` - Set rollout correction config via `actor_rollout_ref.actor.policy_loss.rollout_correction` **Theory:** See [rollout_corr_math.md §3.1.2](rollout_corr_math.md#312-bypass-mode-two-policies) --- ### 7. REINFORCE with IS (`bypass_pg_is`) **Configuration:** ```python config = RolloutCorrectionConfig.bypass_pg_is(threshold=2.0) ``` **Components:** - **Operating Mode**: Bypass (2 policies: π_rollout, π_θ) - **Loss**: REINFORCE (policy gradient with explicit IS weights, no PPO clipping) - **IS Aggregation**: Sequence-level - **RS**: None **Equivalent YAML:** ```yaml rollout_correction: rollout_is: sequence rollout_is_threshold: 2.0 rollout_rs: null bypass_mode: true loss_type: reinforce # REINFORCE with explicit IS weights ``` **Properties:** - REINFORCE loss with explicit IS weights (no PPO clipping) - Single forward pass (skips old_log_prob computation) - IS weights computed on-the-fly in loss function **Theory:** See [rollout_corr_math.md §3.2.2](rollout_corr_math.md#322-policy-gradient-loss-with-isrs-correction) --- ## Additional Useful Configurations (Not Exposed as Presets) These configurations are **fully supported** but don't have convenience preset methods yet. ### 1. Token IS + Token RS (`token_is_rs`) Token-level IS weights with token-level RS mask. **Python:** ```python config = RolloutCorrectionConfig( rollout_is="token", rollout_is_threshold=2.0, rollout_rs="token_k1", rollout_rs_threshold=2.0, ) ``` **Properties:** Per-token IS weights + per-token RS mask. ### 2. Pure Token RS (`token_rs`) Token-level RS only, no IS weights. **Python:** ```python config = RolloutCorrectionConfig( rollout_is=None, rollout_rs="token_k1", rollout_rs_threshold=2.0, ) ``` **Properties:** Token-level RS mask, no IS reweighting. ### 3. Pure Sequence RS (`seq_rs`) Sequence-level RS only, no IS weights. **Python:** ```python config = RolloutCorrectionConfig( rollout_is=None, rollout_rs="seq_sum_k1", rollout_rs_threshold="0.5_2.0", ) ``` **Properties:** Sequence-level RS mask, no IS reweighting. --- ### Summary: How IS Weights are Processed IS weights (`rollout_is_weights`) go through a fixed processing pipeline: **Stage 1: Safety Bound (Prevent Overflow)** - Token level: `exp(clamp(log_ratio, -20, 20))` per token → bounds each token to [2e-9, 5e8] - Sequence level: `exp(clamp(sum(log_ratio), -20, 20))` → bounds product to [2e-9, 5e8], broadcast to all tokens **Stage 2: Truncation (Reduce Variance)** - `.clamp(max=rollout_is_threshold)` → caps weights at upper threshold (TIS: Truncated Importance Sampling) - No lower truncation (preserves unbiasedness for small weights) **Stage 3: Padding Zeroing (Correct Aggregation)** - `weights * response_mask` → zeros out padding positions **Stage 4: Optional Batch Normalization** - If `rollout_is_batch_normalize=True`: Normalize weights to mean=1.0 within batch - Applied after truncation to preserve truncation semantics **Rejection Sampling (Separate Mechanism)** Rejection sampling modifies `response_mask` (NOT weights) through `compute_rollout_rejection_mask()`: - Computes safety-bounded ratios independently - Creates binary mask: tokens/sequences outside [lower_threshold, upper_threshold] → 0 (rejected) - Modified mask used for loss aggregation ## Operation Modes The framework provides **two operating modes** for computing π_old, which can be combined with different loss functions. ### Operating Modes and Configuration | Configuration | `bypass_mode` | `loss_type` | Operating Mode | Loss Function | Description | | ---------------------- | ------------- | ---------------------- | -------------- | ------------- | ----------------------------------------------------------------- | | **Decoupled** | `false` | N/A | Decoupled | PPO | Computes `old_log_prob` separately via `actor.compute_log_prob()` | | **Bypass + PPO-clip** | `true` | `"ppo_clip"` (default) | Bypass | PPO-clip | PPO clipped objective (IS handled by ratio) | | **Bypass + REINFORCE** | `true` | `"reinforce"` | Bypass | REINFORCE | Policy gradient with explicit IS weights (no PPO clipping) | ### Operating Mode Details #### Decoupled Mode (Three Policies) **Policy setup:** - π_rollout: Behavior policy (data collection) - π_old: Proximal policy (computed via `actor.compute_log_prob()` at start of training epoch) - π_θ: Current policy (being updated) **Configuration:** `bypass_mode = false` **Properties:** - ✅ Achieves batch size invariance - ✅ Separately corrects Drift 1 (rollout→old) and Drift 2 (old→current) - ✅ Efficient stale data utilization - ❌ Extra forward pass needed (`actor.compute_log_prob()`) **Theory:** See [rollout_corr_math.md §3.1.1](rollout_corr_math.md#311-decoupled-mode-three-policies) #### Bypass Mode (Two Policies) **Policy setup:** - π_rollout: Behavior policy (data collection) - π_old = π_rollout: Proximal policy equals behavior policy - π_θ: Current policy (being updated) **Configuration:** `bypass_mode = true` **Properties:** - ✅ Skips `actor.compute_log_prob()` call (faster) - ✅ Handles off-policy correction via IS/RS (when using policy gradient with IS/RS) - ✅ Uses two policies instead of three (π_rollout = π_old) - ⚠️ Does not separate proximal policy from behavior policy (unlike decoupled mode) **Theory:** See [rollout_corr_math.md §3.1.2](rollout_corr_math.md#312-bypass-mode-two-policies) --- ### IS/RS Aggregation Levels (Orthogonal to Operating Mode) The aggregation level can be chosen **independently** of the operating mode. Any aggregation level works in either decoupled or bypass mode. | `rollout_is` | `rollout_rs` | Behavior | | ------------------------- | ------------------------------------------------------------------ | --------------------------------------------------------------------------------- | | `null` | `null` | **Disabled**: No computation, no metrics, no rejection | | `null` | `"token_k1"`, `"seq_sum_k1"`, `"seq_mean_k1"`, `"seq_max_k2"`, etc | **Rejection only**: Compute metrics, NO weight correction, YES rejection sampling | | `"token"` or `"sequence"` | `null` | **IS weights only**: Weight correction enabled, NO rejection sampling | | `"token"` or `"sequence"` | `"token_k1"`, `"seq_sum_k1"`, `"seq_mean_k1"`, `"seq_max_k2"`, etc | **Full correction**: Both weight correction and rejection sampling enabled | ### Key Insights - ✅ Any IS/RS aggregation level (token/sequence/geometric) can be used in **either** decoupled or bypass mode - ✅ You can use **rejection sampling alone** without IS weight correction (`rollout_is=null, rollout_rs="token_k1"`) - ✅ You can use **IS weights alone** without outlier rejection (`rollout_is="token", rollout_rs=null`) - ✅ You can use **both together** (`rollout_is="token", rollout_rs="token_k1"`) - ✅ You can **monitor metrics only** without any correction by setting both to `null` but still providing rollout_log_probs **Theory:** See [rollout_corr_math.md §3.3](rollout_corr_math.md#33-isrs-aggregation-levels) for details on aggregation levels. ### Example Workflow **Recommended: Bypass Mode** This workflow uses bypass mode for efficiency. 1. **Start with metrics only** to understand the off-policy gap: ```yaml rollout_correction: rollout_is: null rollout_rs: null bypass_mode: true # Bypass mode (recommended) loss_type: ppo_clip # Default: PPO clipped objective ``` Monitor `rollout_corr/kl`, `rollout_corr/log_ppl_abs_diff`, `rollout_corr/chi2_token` to assess off-policy gap. 2. **Enable rejection sampling** if you see high outlier fractions: ```yaml rollout_correction: rollout_is: null rollout_rs: sequence # or "geometric" for higher sensitivity rollout_rs_threshold: 2.0 bypass_mode: true # Bypass mode loss_type: ppo_clip # or "reinforce" for explicit IS weights ``` This excludes outliers from training without modifying gradients. 3. **Enable full IS correction** (with REINFORCE loss) once comfortable with metrics: ```yaml rollout_correction: rollout_is: sequence # Recommended: unbiased, suitable for most cases rollout_is_threshold: 2.0 rollout_rs: sequence # or "geometric" for more aggressive filtering rollout_rs_threshold: 2.0 bypass_mode: true # Bypass mode loss_type: reinforce # REINFORCE with explicit IS weights ``` **Benefits of bypass mode:** - ✅ Skips expensive `actor.compute_log_prob()` forward pass (faster) - ✅ `loss_type` controls the loss function: "ppo_clip" (default) or "reinforce" - ✅ PPO-clip: IS handled by ratio (no explicit weights), RS mask applied - ✅ REINFORCE: Explicit IS weights computed on-the-fly (π_θ / π_rollout) - ✅ Both loss types work with all IS/RS combinations ## Usage ### Basic Setup ```yaml algorithm: rollout_correction: rollout_is: token # Enable IS weights at token level rollout_is_threshold: 2.0 # Threshold for IS weights rollout_rs: null # No rejection sampling actor_rollout_ref: rollout: calculate_log_probs: true # Required! ``` ### Additional Configurations for Bypass Mode - Set `actor_rollout_ref.actor.use_rollout_log_probs: true` - Set `actor_rollout_ref.actor.policy_loss.loss_mode: bypass_mode` - Set rollout correction config via `actor_rollout_ref.actor.policy_loss.rollout_correction` ### Metrics All metrics are prefixed with `rollout_corr/` in logs. For example, `rollout_is_mean` appears as `rollout_corr/rollout_is_mean`. These metrics cover both: - **Diagnostic metrics**: KL divergence, perplexity differences (measuring off-policy gap) - **Correction statistics**: IS weights, rejection rates (measuring correction applied) #### **Core IS Weight Metrics** - **`rollout_is_mean`**: Mean importance sampling weight across all valid tokens - Value close to 1.0 indicates minimal off-policy gap - **`rollout_is_std`**: Standard deviation of IS weights - Higher values indicate greater variance in IS weights - **`rollout_is_min`**: Minimum IS weight observed - Shows the most underweighted token/sequence - For sequence/geometric: computed from unclamped log-space ratios (true minimum) - For token: computed from safety-bounded weights - **`rollout_is_max`**: Maximum IS weight observed - Shows the most overweighted token/sequence - For sequence/geometric: computed from unclamped log-space ratios (true maximum before safety bound) - For token: computed from safety-bounded weights (before threshold clamping) - Compare with `rollout_is_threshold` to see truncation impact #### **Effective Sample Size** - **`rollout_is_eff_sample_size`**: Effective sample size after IS weighting - **Formula**: `1 / mean(weights²)` where weights are normalized - **Range**: 0.0 to 1.0 (as fraction of original batch) - Lower values indicate weight concentration on fewer samples #### **Threshold Exceedance Metrics** - **`rollout_is_ratio_fraction_high`**: Fraction of weights exceeding upper threshold - Shows how often truncation/masking occurs on high end - For sequence/geometric: computed from unclamped log-space ratios (true exceedance) - For token: computed from safety-bounded weights (before threshold clamping) - **`rollout_is_ratio_fraction_low`**: Fraction of weights below lower threshold (1/upper_threshold) - Diagnostic metric showing how many weights are below the reciprocal threshold - For sequence/geometric: computed from unclamped log-space ratios (true exceedance) - For token: computed from safety-bounded weights (before truncation) #### **Sequence-Level Metrics** (for sequence aggregation) - **`rollout_is_seq_mean`**: Mean IS weight at sequence level - Should match `rollout_is_mean` for sequence-level aggregation - **`rollout_is_seq_std`**: Standard deviation of sequence-level IS weights - **`rollout_is_seq_min`**: Minimum sequence-level IS weight - **`rollout_is_seq_max`**: Maximum sequence-level IS weight - **`rollout_is_seq_max_deviation`**: Maximum absolute deviation from 1.0 at sequence level - Shows worst-case sequence off-policy gap - **`rollout_is_seq_fraction_high`**: Fraction of sequences exceeding upper threshold - **`rollout_is_seq_fraction_low`**: Fraction of sequences below lower threshold #### **Rejection Sampling Metrics** (when `rollout_rs` is enabled) - **`rollout_rs_masked_fraction`**: Fraction of tokens rejected via rejection sampling - **Important**: Rejection sampling modifies `response_mask` (sets rejected tokens to 0) - **Separate from IS weights**: IS weights are still truncated; rejection is an independent filtering step - Only present when `rollout_rs` is enabled (token/sequence/geometric) - **`rollout_rs_seq_masked_fraction`**: Fraction of sequences with at least one rejected token - Shows sequence-level impact of rejection sampling - Token-level RS: sequence rejected if ANY token is outside [lower, upper] - Sequence-level RS: entire sequence rejected or accepted based on sequence-level ratio - Geometric RS: entire sequence rejected or accepted based on geometric mean #### **Off-Policy Diagnostic Metrics** (Training vs Rollout Policy) **Note on terminology:** These metrics use "training" to refer to the training reference policy and "rollout" to refer to π_rollout (the behavior policy used for data collection). - **Decoupled mode**: "training" = π_old (computed at start of training epoch) - **Bypass/Pure IS mode**: "training" = π_θ (current policy being trained) In bypass/pure IS mode, metrics measure the drift between π_θ and π_rollout directly. - **`training_ppl`**: Perplexity of training reference policy (π_old in decoupled mode, π_θ in bypass/pure IS mode) - **Formula**: `exp(-mean(log_probs))` - Lower values indicate higher model confidence - **`rollout_ppl`**: Perplexity of rollout policy π_rollout (e.g., vLLM BF16) - **`ppl_ratio`**: Ratio of training PPL to rollout PPL - **Formula**: `exp(mean(log(training_ppl / rollout_ppl)))` - **Meaning**: > 1.0 means training is less confident than rollout - **`training_log_ppl`**: Log perplexity of training policy - Useful for identifying trends (linear scale) - **`rollout_log_ppl`**: Log perplexity of rollout policy - **`log_ppl_diff`**: Mean difference in log perplexities - **Formula**: `mean(log_ppl_rollout - log_ppl_training)` - Sign indicates which policy is more confident - **`log_ppl_abs_diff`**: Mean absolute log perplexity difference - Magnitude of off-policy gap regardless of direction - **`log_ppl_diff_max`**: Maximum log perplexity difference across sequences - Identifies worst-case sequence - **`log_ppl_diff_min`**: Minimum log perplexity difference across sequences - **`kl`**: KL divergence KL(π_rollout || π_training) - **Formula**: `mean(log_prob_rollout - log_prob_training)` - **Note**: Can be negative (rollout is less confident) - **`k3_kl`**: K3 divergence (equals KL(π_rollout || π_training) in expectation) - **Formula**: `mean(exp(log_ratio) - log_ratio - 1)` - More stable than direct KL (non-negative per token) - Always >= 0 - **`chi2_token`**: Chi-squared divergence at token level - **Formula**: `mean(ratio²) - 1` where ratio = π_training/π_rollout - Measures second moment of IS weight distribution - Always non-negative - **`chi2_seq`**: Chi-squared divergence at sequence level - **Formula**: `mean((∏_t ratio_t)²) - 1` - Sequence-level second moment of IS weights - More sensitive than token-level chi-squared #### **Example: Accessing Metrics in Code** ```python # Metrics are returned from compute_rollout_correction_and_rejection_mask from verl.trainer.ppo.rollout_corr_helper import compute_rollout_correction_and_rejection_mask # Returns 3 values (weights, modified_response_mask, metrics) weights_proto, modified_response_mask, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=training_log_probs, # from training policy rollout_log_prob=rollout_log_probs, # from rollout policy response_mask=response_mask, rollout_is="token", # Enable IS weights at token level rollout_is_threshold=2.0, rollout_rs="token_k1", rollout_rs_threshold="0.5_2.0", ) # Extract IS weights (processed, zeroed at padding) is_weights = weights_proto.batch["rollout_is_weights"] # IS weights processing (with IS enabled at token level): # 1. Safety-bounded: exp(clamp(log_ratio, -20, 20)) per token # 2. Truncated: .clamp(max=2.0) to cap extreme weights # 3. Zeroed at padding positions # Note: Truncation is ALWAYS applied to IS weights (TIS: Truncated Importance Sampling) # modified_response_mask has rejection applied (since rollout_rs="token_k1"): # 1. RS rejection: tokens outside [0.5, 2.0] masked to 0 via response_mask # Note: RS and IS are separate mechanisms - both can be enabled independently # All metrics have 'rollout_corr/' prefix print(f"Mean IS weight: {metrics['rollout_corr/rollout_is_mean']:.3f}") print(f"Effective sample size: {metrics['rollout_corr/rollout_is_eff_sample_size']:.3f}") print(f"RS masked fraction: {metrics['rollout_corr/rollout_rs_masked_fraction']:.3f}") print(f"KL divergence: {metrics['rollout_corr/kl']:.3f}") # Check IS weights for valid tokens (non-padding) valid_weights = is_weights[response_mask.bool()] print(f"\n✓ IS weights min (valid tokens): {valid_weights.min():.4f}") print(f"✓ IS weights max (valid tokens): {valid_weights.max():.4f}") print(f"✓ All valid IS weights > 0: {(valid_weights > 0).all()}") print(f"✓ IS weights are capped at threshold: {(valid_weights <= 2.0).all()}") # Check rejection via response_mask rejected_tokens = (response_mask == 1) & (modified_response_mask == 0) print(f"\n✓ Rejected {rejected_tokens.sum()} tokens via response_mask") print(f"✓ Rejection sampling modifies response_mask (separate from IS weight truncation)") print(f"✓ IS weights are always truncated to [0, threshold] after safety bounding") # Check for warning conditions if metrics['rollout_corr/rollout_is_mean'] < 0.5 or metrics['rollout_corr/rollout_is_mean'] > 2.0: print("⚠️ Warning: Mean IS weight far from 1.0, significant off-policy gap detected") if metrics['rollout_corr/rollout_is_eff_sample_size'] < 0.3: print("⚠️ Warning: Low effective sample size, high weight concentration") ``` #### **Example: Monitoring Metrics During Training** ```python # In your training loop for epoch in range(num_epochs): for batch_idx, batch in enumerate(dataloader): # ... rollout phase ... # Compute IS weights and get metrics rollout_corr_config = config.algorithm.get("rollout_correction", None) if rollout_corr_config is not None: weights_proto, modified_response_mask, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=batch.old_log_prob, rollout_log_prob=batch.rollout_log_prob, response_mask=batch.response_mask, rollout_is=rollout_corr_config.get("rollout_is", None), rollout_is_threshold=rollout_corr_config.get("rollout_is_threshold", 2.0), rollout_rs=rollout_corr_config.get("rollout_rs", None), rollout_rs_threshold=rollout_corr_config.get("rollout_rs_threshold", None), ) # Log to tensorboard/wandb for metric_name, metric_value in metrics.items(): logger.log_scalar(metric_name, metric_value, step=global_step) # IMPORTANT: Update batch response_mask with rejection applied batch.response_mask = modified_response_mask # Use IS weights in training (always safety-bounded, zeroed at padding) is_weights = weights_proto.batch["rollout_is_weights"] # ... apply weights to policy gradient ... ``` #### **Example: Conditional Alerting Based on Metrics** ```python def check_rollout_correction_health(metrics, config): """Check if Rollout Correction metrics indicate healthy training.""" warnings = [] # Check mean IS weight mean_weight = metrics['rollout_corr/rollout_is_mean'] if mean_weight < 0.5 or mean_weight > 2.0: warnings.append(f"Mean IS weight {mean_weight:.3f} is far from 1.0") # Check effective sample size ess = metrics['rollout_corr/rollout_is_eff_sample_size'] if ess < 0.3: warnings.append(f"Effective sample size {ess:.3f} is too low") # Check standard deviation std = metrics['rollout_corr/rollout_is_std'] if std > 1.0: warnings.append(f"IS weight std {std:.3f} is too high") # Check KL divergence kl = metrics['rollout_corr/kl'] if abs(kl) > 0.1: warnings.append(f"KL divergence {kl:.3f} indicates significant off-policy gap") # Check chi-squared divergence if 'rollout_corr/chi2_token' in metrics: chi2_token = metrics['rollout_corr/chi2_token'] if chi2_token > 1.0: warnings.append(f"Chi-squared divergence (token) {chi2_token:.3f} indicates severe distribution shift") if warnings: print("⚠️ Rollout Correction Health Warnings:") for warning in warnings: print(f" - {warning}") return False else: print("✅ Rollout Correction metrics look healthy") return True # Use in training _, _, metrics = compute_rollout_correction_and_rejection_mask(...) is_healthy = check_rollout_correction_health(metrics, config) if not is_healthy: # Consider adjusting config or investigating issues print("Consider:") print(" - Tightening rollout_is_threshold") print(" - Switching to geometric aggregation level") print(" - Checking if rollout and training policies are too different") ``` ### Running Examples Start with the basic token-level truncate configuration: ```bash bash examples/rollout_correction/run_with_rollout_corr.sh ``` Monitor metrics for 1-2 epochs before adjusting parameters. ## Configuration Examples ### Example 1: IS Weights Only (Token Level) ```yaml algorithm: rollout_correction: rollout_is: token rollout_is_threshold: 2.0 rollout_rs: null # No rejection sampling ``` ### Example 2: Rejection Sampling Only (No IS Weights) ```yaml algorithm: rollout_correction: rollout_is: null # No IS weights rollout_rs: token_k1 rollout_rs_threshold: "0.5_2.0" ``` ### Example 3: Both IS and RS (Token RS) ```yaml algorithm: rollout_correction: rollout_is: token rollout_is_threshold: 2.0 rollout_rs: token_k1 rollout_rs_threshold: "0.5_2.0" ``` ### Example 5: Bypass Mode with PPO-clip (Default) ```yaml algorithm: rollout_correction: rollout_is: token rollout_is_threshold: 2.0 rollout_rs: token_k1 rollout_rs_threshold: "0.5_2.0" bypass_mode: true # Skip old_log_prob computation loss_type: ppo_clip # PPO clipped objective (default) ``` **Skips expensive `actor.compute_log_prob()` forward pass. PPO ratio = π_θ/π_rollout handles IS.** ### Example 6: Bypass Mode with REINFORCE ```yaml rollout_correction: rollout_is: sequence # Explicit IS correction in loss rollout_is_threshold: 2.0 rollout_rs: null # Optional: can add rejection sampling bypass_mode: true loss_type: reinforce # REINFORCE with explicit IS weights ``` **No PPO clipping, pure policy gradient with IS correction** ### Example 7: Bypass Mode with PPO-clip + Rejection Sampling ```yaml rollout_correction: rollout_is: sequence # Computed for metrics rollout_is_threshold: 2.0 rollout_rs: seq_max_k2 # Sequence max χ²/2 guard rollout_rs_threshold: 2.5 bypass_mode: true loss_type: ppo_clip # PPO clipped objective (IS handled by ratio) ``` **PPO clipping with rejection sampling. IS handled by PPO ratio (no explicit IS weights).** ## Troubleshooting ### Issue: High spread in IS weights **Symptoms:** `rollout_is_std` > 1.0, `rollout_is_eff_sample_size` < 0.3 **Solutions:** 1. Switch from `sequence` to `geometric` level 2. Tighten thresholds 3. Verify rollout and training aren't too different ### Issue: Mean IS weight far from 1.0 **Symptoms:** `rollout_is_mean` < 0.5 or > 2.0 **Solutions:** 1. Verify `calculate_log_probs=True` is set 2. Check rollout_log_probs are correctly passed 3. Check for systematic distribution shift ### Debugging: Visualizing Metrics **Example: Plot IS weight distribution** ```python import matplotlib.pyplot as plt import numpy as np def plot_is_metrics(metrics_history): """Plot rollout IS metrics over training steps.""" fig, axes = plt.subplots(2, 3, figsize=(15, 10)) # Plot 1: Mean IS weight over time axes[0, 0].plot(metrics_history['rollout_corr/rollout_is_mean']) axes[0, 0].axhline(y=1.0, color='r', linestyle='--', label='Ideal') axes[0, 0].set_title('Mean IS Weight') axes[0, 0].set_xlabel('Step') axes[0, 0].legend() # Plot 2: Effective sample size axes[0, 1].plot(metrics_history['rollout_corr/rollout_is_eff_sample_size']) axes[0, 1].axhline(y=0.5, color='g', linestyle='--', label='Good') axes[0, 1].axhline(y=0.3, color='r', linestyle='--', label='Warning') axes[0, 1].set_title('Effective Sample Size') axes[0, 1].set_xlabel('Step') axes[0, 1].legend() # Plot 3: KL divergence over time axes[1, 0].plot(metrics_history['rollout_corr/kl'], label='KL') axes[1, 0].plot(metrics_history['rollout_corr/k3_kl'], label='K3 KL') axes[1, 0].axhline(y=0, color='g', linestyle='--', alpha=0.3) axes[1, 0].set_title('KL Divergence') axes[1, 0].set_xlabel('Step') axes[1, 0].legend() # Plot 4: PPL ratio over time axes[1, 1].plot(metrics_history['rollout_corr/ppl_ratio']) axes[1, 1].axhline(y=1.0, color='r', linestyle='--', label='Ideal') axes[1, 1].set_title('PPL Ratio (Training/Rollout)') axes[1, 1].set_xlabel('Step') axes[1, 1].legend() # Plot 5: Chi-squared divergence if 'rollout_corr/chi2_token' in metrics_history: axes[1, 2].plot(metrics_history['rollout_corr/chi2_token'], label='Token-level') if 'rollout_corr/chi2_seq' in metrics_history: axes[1, 2].plot(metrics_history['rollout_corr/chi2_seq'], label='Seq-level') axes[1, 2].axhline(y=1.0, color='r', linestyle='--', label='Warning') axes[1, 2].set_title('Chi-squared Divergence') axes[1, 2].set_xlabel('Step') axes[1, 2].legend() else: axes[1, 2].axis('off') plt.tight_layout() plt.savefig('rollout_is_metrics.png', dpi=150) print("Saved plot to rollout_is_metrics.png") ``` **Example: Metric collection during training** ```python # Collect metrics over time metrics_history = { 'rollout_corr/rollout_is_mean': [], 'rollout_corr/rollout_is_eff_sample_size': [], 'rollout_corr/kl': [], 'rollout_corr/k3_kl': [], 'rollout_corr/ppl_ratio': [], 'rollout_corr/chi2_token': [], 'rollout_corr/chi2_seq': [], } # In training loop for step in range(num_steps): # ... compute IS weights and rejection mask ... _, _, metrics = compute_rollout_correction_and_rejection_mask(...) # Store metrics for key in metrics_history.keys(): if key in metrics: metrics_history[key].append(metrics[key]) # Plot every 100 steps if step % 100 == 0: plot_is_metrics(metrics_history) ``` ## Performance Impact - **Memory overhead**: ~1% of model memory - **Computational overhead**: 1-3% depending on level - **Training stability**: Significantly improved when off-policy gap exists ## Testing Run the test suite to verify everything works: ```bash # Basic unit tests python tests/trainer/ppo/test_rollout_corr.py # Integration tests (if pytest is available) pytest tests/trainer/ppo/test_rollout_corr_integration.py -v ``` Expected output: All tests pass ✓ ## Additional Resources - **Implementation**: `verl/trainer/ppo/rollout_corr_helper.py` - **Examples**: `examples/rollout_correction/` - **DAPO Example**: `recipe/dapo/run_dapo_qwen2.5_32b_rollout_corr.sh` ## Summary Rollout Correction provides a unified framework for handling general off-policy problems in RL: - ✅ Corrects ANY distribution shift between data collection and training - ✅ Supports diverse scenarios: policy mismatch, staleness, replay buffers, off-policy algorithms - ✅ Numerical stability with safety bounds and rejection mechanisms - ✅ Comprehensive diagnostics: KL, perplexity, χ² divergence - ✅ Flexible methods from token-level to sequence-level aggregation - ✅ Memory-efficient implementation ## References - **[Mathematical Formulations](rollout_corr_math.md)** - Detailed mathematical theory and derivations for all rollout correction methods - [Your Efficient RL Framework Secretly Brings You Off-Policy RL Training](https://fengyao.notion.site/off-policy-rl) ================================================ FILE: docs/algo/rollout_corr_math.md ================================================ # Mathematical Formulations of Rollout Correction Methods in `verl` **Author:** [Yingru Li](https://richardli.xyz) **Last updated:** 2025-11-04 --- > **📖 Documentation Structure** > - **This document** - Mathematical theory: formulations, derivations, and algorithmic foundations > - **[Rollout Correction Usage Guide](rollout_corr.md)** - Practical implementation: configurations, presets, troubleshooting > > Start here for theory and design rationale, refer to the usage guide for implementation. --- ### BibTeX Citation ```bibtex @online{liu-li-2025-rl-collapse, title = {When Speed Kills Stability: Demystifying {RL} Collapse from the Training-Inference Mismatch}, author = {Liu, Jiacai and Li, Yingru and Fu, Yuqian and Wang, Jiawei and Liu, Qian and Shen, Yu}, year = {2025}, month = sep, url = {https://richardli.xyz/rl-collapse} } @article{li2025trust, title={Trust Region Masking for Long-Horizon LLM Reinforcement Learning}, author={Li, Yingru and Liu, Jiacai and Xu, Jiawei and Tong, Yuxuan and Li, Ziniu and Liu, Qian and Wang, Baoxiang}, journal={arXiv preprint arXiv:2512.23075}, year={2025} } ``` ### Blog Series - Main blog post: https://richardli.xyz/rl-collapse - [Part 1: Why Mismatch Breaks LLM-RL](https://richardli.xyz/rl-collapse-1) (analytical framework using TV distance for bias and χ²-divergence for variance) - [Part 2: The Gradient Estimator Trials](https://richardli.xyz/rl-collapse-2) (token-level vs sequence-level correction bias-variance tradeoff) - [Part 3: When Math Meets Reality—Toxic Tails and Length Traps](https://richardli.xyz/rl-collapse-3) (why rejection over clipping, and geometric-level RS) - Latest Paper: https://arxiv.org/abs/2512.23075 ## Abstract This document provides the definitive mathematical formulations for rollout correction methods in `verl`, following the natural progression from **REINFORCE** to **PPO** to **Decoupled PPO**. Rollout correction provides a unified framework to handle **general off-policy problems** in RL training - any scenario where the data collection distribution differs from the training distribution. **Applicable scenarios include:** - **Policy mismatch**: Different precision (FP8 vs FP16 vs BF16 vs FP32), different backends (vLLM vs SGLang vs FSDP vs Megatron) - **Temporal lag**: Model staleness, asynchronous rollout workers - **Replay buffers**: Training on historical trajectories from earlier policy versions - **Off-policy algorithms**: Behavioral cloning, DAPO, expert demonstrations - **Data filtering**: Reweighting, preference learning, curriculum learning --- ## Table of Contents 1. [Theoretical Foundation: From REINFORCE to Decoupled PPO](#1-theoretical-foundation-from-reinforce-to-decoupled-ppo) 2. [Implementation in verl: The Three-Policy Framework](#2-implementation-in-verl-the-three-policy-framework) 3. [Algorithmic Components and Combinations](#3-algorithmic-components-and-combinations) 4. [Off-Policy Diagnostic Metrics](#4-off-policy-diagnostic-metrics) 5. [Summary and Decision Guide](#5-summary-and-decision-guide) 6. [Implementation References](#6-implementation-references) --- ## 1. Theoretical Foundation: From REINFORCE to Decoupled PPO This section establishes the theoretical progression that `verl` implements. ### 1.1 REINFORCE: Policy Gradient Baseline The REINFORCE algorithm ([Williams, 1992](https://doi.org/10.1007/BF00992696)) is the foundation of policy gradient methods. **Vanilla REINFORCE (On-Policy)** For trajectories $\tau = (s_0, a_0, s_1, a_1, \ldots, s_T, a_T)$ sampled from the current policy $\pi_\theta$, the policy gradient is: $$ \nabla_\theta J(\theta) = \mathbb{E}_{\tau \sim \pi_\theta} \left[ \sum_{t=0}^T \nabla_\theta \log \pi_\theta(a_t|s_t) \cdot A_t \right] $$ where $A_t$ is the advantage function at timestep $t$. **Off-Policy REINFORCE** When trajectories are sampled from a different behavior policy $\mu$, we apply importance sampling over the **joint trajectory distribution**: $$ \nabla_\theta J(\theta) = \mathbb{E}_{\tau \sim \mu} \left[ \frac{P_{\pi_\theta}(\tau)}{P_\mu(\tau)} \sum_{t=0}^T \nabla_\theta \log \pi_\theta(a_t|s_t) \cdot A_t \right] $$ where the trajectory-level importance weight is: $$ \frac{P_{\pi_\theta}(\tau)}{P_\mu(\tau)} = \frac{p(s_0) \prod_{t=0}^T \pi_\theta(a_t|s_t) p(s_{t+1}|s_t, a_t)}{p(s_0) \prod_{t=0}^T \mu(a_t|s_t) p(s_{t+1}|s_t, a_t)} = \prod_{t=0}^T \frac{\pi_\theta(a_t|s_t)}{\mu(a_t|s_t)} $$ The transition dynamics $p(s_{t+1}|s_t, a_t)$ and initial state $p(s_0)$ cancel out, leaving only the product of per-step action probability ratios. **Key properties:** - **Off-policy capable**: Can learn from any behavior policy via importance sampling - **No trust region**: Policy updates not constrained **Implementation in verl:** The `bypass_pg_is` preset implements off-policy REINFORCE with truncated importance sampling. ### 1.2 PPO: Adding Trust Region Control Proximal Policy Optimization ([Schulman et al., 2017](https://arxiv.org/abs/1707.06347)) adds a clipped surrogate objective: $$ L_{\text{PPO}}(\theta) = -\mathbb{E}_{(s,a) \sim \mu} \left[ \min\left( r_t(\theta) A_t, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon) A_t \right) \right] $$ where $r_t(\theta) = \frac{\pi_\theta(a_t|s_t)}{\mu(a_t|s_t)}$ and $\epsilon$ is the clip range (typically 0.2). **Key properties:** - **Two policies**: $\mu$ (reference for clipping) and $\pi_\theta$ (being updated) - **Trust region via clipping**: Limits policy update magnitude via ratio $r_t(\theta) = \frac{\pi_\theta}{\mu}$ ### 1.3 Decoupled PPO: Achieving Batch Size Invariance Decoupled PPO ([Hilton et al., 2021](https://arxiv.org/abs/2110.00641)) solves PPO's batch size sensitivity by **decoupling two roles**: 1. **Proximal policy** $\pi_{\text{prox}}$: The anchor policy for PPO clipping (controls policy update size) 2. **Behavior policy** $\mu$: The policy that collected the data (for off-policy correction via importance sampling) **The problem**: Standard PPO controls policy update size via the ratio $\frac{\pi_\theta}{\pi_{\text{old}}}$, where $\pi_{\text{old}}$ is assumed to be both the proximal policy *and* the behavior policy. This coupling makes the algorithm sensitive to batch size because aggregating data from multiple workers or using replay buffers changes the effective behavior policy. **The solution**: Decouple these two roles, leading to a **three-policy formulation**: $$ L_{\text{DecoupledPPO}}(\theta) = -\mathbb{E}_{(s,a) \sim \mu} \left[ w_t \cdot \min\left( r_t(\theta) A_t, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon) A_t \right) \right] $$ where: - $w_t = \frac{\pi_{\text{prox}}(a_t|s_t)}{\mu(a_t|s_t)}$: Importance sampling weight (corrects for behavior policy $\mu$). Here $\pi_{\text{prox}}$ is frozen during training, so $w_t$ is constant (no stopgrad operator needed). - $r_t(\theta) = \frac{\pi_\theta(a_t|s_t)}{\pi_{\text{prox}}(a_t|s_t)}$: PPO ratio (controls policy update size against proximal policy $\pi_{\text{prox}}$) **Key properties**: By decoupling: - **Batch size invariance**: Policy update control (via $\pi_{\text{prox}}$) is independent of data aggregation - **Flexible behavior policy**: Any $\mu$ can be used (different workers, replay buffers, or stale checkpoints) - **Stale data utilization**: Older trajectories can be corrected via importance sampling - **Clipping preserved**: Clipping against $\pi_{\text{prox}}$ limits update magnitude **This is the algorithm that `verl` implements via its three-policy framework.** --- ## 2. Implementation in verl: The Three-Policy Framework The `verl` library implements decoupled PPO using three distinct policies, each serving a specific role. ### 2.1 Policy Roles and Notation **$\pi_{\text{rollout}}$ (Behavior Policy $\mu$)** The policy used for data collection. This is the behavior distribution $\mu$ from theory. - **When created**: During rollout/data collection phase - **Purpose**: Generate trajectories for training - **Common sources**: - Policy mismatch: Same weights, different implementation (precision, backend) - Temporal lag: Stale checkpoint from async workers - Replay buffer: Historical data from earlier iterations - Off-policy algorithms: Expert demonstrations, auxiliary policies (DAPO) - Data filtering: Reweighted or filtered data - **Fixed**: Frozen during training on a batch **$\pi_{\text{old}}$ (Proximal Policy $\pi_{\text{prox}}$)** The reference policy for PPO clipping. This is the "proximal policy" from decoupled PPO theory. - **When created**: - **Decoupled mode**: Computed at start of training epoch via `actor.compute_log_prob()` - **Bypass mode**: Set equal to $\pi_{\text{rollout}}$ (skips separate computation) - **Purpose**: - Anchor point for PPO clipping (controls policy update size) - When separate from $\pi_{\text{rollout}}$: Enables batch size invariance and efficient use of stale data - **Fixed**: Frozen during all PPO update epochs on the same batch **$\pi_{\theta}$ (Current Policy)** The policy being actively optimized during training. - **Updated**: Every gradient step - **Purpose**: The policy we're improving ### 2.2 Operating Modes The three-policy framework can operate in two modes: **Decoupled Mode (Three Policies)** - Computes $\pi_{\text{old}}$ separately at the start of each training epoch - **Algorithm**: Full decoupled PPO with three policies (mathematically correct) - **Properties**: Achieves batch size invariance; separately corrects Drift 1 (rollout→old) and Drift 2 (old→current) **Bypass Mode (Two Policies)** - Sets $\pi_{\text{old}} = \pi_{\text{rollout}}$ (skips separate computation) - **Algorithm**: Uses $\pi_{\text{rollout}}$ as both behavior policy and proximal policy (mathematically correct) - **Key difference**: Proximal policy equals behavior policy, so no IS correction needed between them - **Properties**: Faster (skips `actor.compute_log_prob()` call); does not achieve batch size invariance ### 2.3 Two Distribution Shifts The three-policy framework handles two types of distribution drift: **Drift 1: $\pi_{\text{rollout}} \to \pi_{\text{old}}$ (Off-Policy Gap)** This is the distribution shift between the data collection policy and the training reference policy. - **Nature**: Ranges from negligible (same checkpoint, minor differences) to severe (replay buffers, expert data) - **Correction**: Importance sampling weight $w_t = \frac{\pi_{\text{old}}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$ - **Optional**: Can be ignored (bypass mode) when negligible **Drift 2: $\pi_{\text{old}} \to \pi_{\theta}$ (Policy Update Drift)** This is the drift from policy parameter updates during training. - **Nature**: Occurs as $\pi_\theta$ is updated via gradient descent - **Correction**: PPO clipping on ratio $r_t(\theta) = \frac{\pi_\theta(a_t|s_t)}{\pi_{\text{old}}(a_t|s_t)}$ - **Universal**: Applies to both on-policy and off-policy training ### 2.4 Notation Summary - $\pi_{\text{rollout}}$: Behavior policy (data collection) - $\pi_{\text{old}}$: Proximal policy (PPO anchor) - $\pi_{\theta}$: Current policy (being updated) - $\rho_t = \frac{\pi_{\text{old}}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$: Per-token IS ratio (corrects Drift 1) - $r_t(\theta) = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{old}}(a_t|s_t)}$: PPO ratio (corrects Drift 2) - $A_t$: Advantage at token $t$ - $T$: Set of valid tokens in a sequence - $C_{\text{IS}}$: Upper threshold for IS weights (e.g., 2.0) - $C_{\text{RS-upper}}$: Upper threshold for RS mask (e.g., 2.0) - $C_{\text{RS-lower}}$: Lower threshold for RS mask (typically $1/C_{\text{RS-upper}}$) - $\epsilon$: PPO clip range (typically 0.2) --- ## 3. Algorithmic Components and Combinations The rollout correction framework in `verl` is built from **orthogonal components** that can be combined flexibly: 1. **Operating Mode**: How $\pi_{\text{old}}$ is computed (Decoupled vs Bypass) 2. **Loss Function**: PPO (with clipping) vs Pure IS (policy gradient only) 3. **IS/RS Aggregation Level**: Token, Sequence, or Geometric This section explains each component and their valid combinations. ### 3.1 Operating Modes: Decoupled vs Bypass The operating mode determines how the proximal policy $\pi_{\text{old}}$ is computed. #### 3.1.1 Decoupled Mode (Three Policies) **Configuration:** `bypass_mode = false` **Policy setup:** - $\pi_{\text{rollout}}$: Behavior policy (data collection) - $\pi_{\text{old}}$: Proximal policy (computed via `actor.compute_log_prob()` at start of training epoch) - $\pi_{\theta}$: Current policy (being updated) **IS ratio:** $\rho_t = \frac{\pi_{\text{old}}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$ (corrects Drift 1: rollout→old) **PPO ratio:** $r_t(\theta) = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{old}}(a_t|s_t)}$ (corrects Drift 2: old→current) **Properties:** - ✅ Achieves batch size invariance - ✅ Separately corrects two distribution drifts - ✅ Efficient stale data utilization - ❌ Extra forward pass needed (`actor.compute_log_prob()`) #### 3.1.2 Bypass Mode (Two Policies) **Configuration:** `bypass_mode = true` **Policy setup:** - $\pi_{\text{rollout}}$: Behavior policy (data collection) - $\pi_{\text{old}} = \pi_{\text{rollout}}$: Proximal policy equals behavior policy - $\pi_{\theta}$: Current policy (being updated) **Ratios:** - **With PPO-clip loss** (`loss_type = "ppo_clip"`, default): PPO ratio $r_t(\theta) = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$ clips against rollout policy (IS handled by ratio) - **With REINFORCE loss** (`loss_type = "reinforce"`): IS ratio $\rho_t = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$ computed on-the-fly in loss function **Properties:** - ✅ Skips `actor.compute_log_prob()` call (faster) - ✅ Handles off-policy correction via IS/RS (when using policy gradient with IS/RS) - ✅ Uses two policies instead of three (π_rollout = π_old) - ⚠️ Does not separate proximal policy from behavior policy (unlike decoupled mode) --- ### 3.2 Loss Functions: PPO vs Policy Gradient #### 3.2.1 PPO Loss (with Clipping) **Configuration:** `loss_type = "ppo_clip"` (default in bypass mode) **Loss function:** $$ L_{\text{PPO}}(\theta) = -\mathbb{E}_t \left[ w_t \cdot \min\left( r_t(\theta) A_t, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon) A_t \right) \right] $$ where: - $w_t$: IS weight (depends on aggregation level, see Section 3.3). In decoupled mode, $w_t = \frac{\pi_{\text{old}}}{\pi_{\text{rollout}}}$ where $\pi_{\text{old}}$ is frozen, so $w_t$ is constant (no stopgrad needed). In bypass mode with PPO loss, no separate IS weights are typically computed. - $r_t(\theta) = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{old}}(a_t|s_t)}$: PPO ratio - $\epsilon$: Clip range (typically 0.2) **Properties:** - Trust region control via clipping - Limits policy update magnitude - Standard in RL training #### 3.2.2 Policy Gradient Loss (with IS/RS Correction) **Configuration:** `loss_type = "reinforce"` (requires `bypass_mode = true`) **Loss function** (example with sequence-level IS): $$ L_{\text{PG}}(\theta) = -\mathbb{E}_{(s,a) \sim \pi_{\text{rollout}}} \left[ \text{stopgrad}(w_{\text{seq}}(\theta)) \cdot \sum_{t \in T} \log \pi_{\theta}(a_t|s_t) \cdot A_t \right] $$ where: - $w_{\text{seq}}(\theta)$: Sample weight (IS or RS, see §3.3-3.4 for details) - For IS: $w_{\text{seq}}(\theta) = \min\left( \prod_{t \in T} \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}, C_{\text{IS}} \right)$ - For RS: $w_{\text{seq}}(\theta) \in \{0, 1\}$ (binary rejection mask) - **stopgrad operator**: The weight $w_{\text{seq}}(\theta)$ is computed using $\pi_\theta$ but treated as a **constant coefficient** when computing $\nabla_\theta L$. This is essential for importance sampling correctness (see theoretical justification below). **Effective gradient:** $$ \nabla_\theta L_{\text{PG}} = -\mathbb{E}_{(s,a) \sim \pi_{\text{rollout}}} \left[ \text{stopgrad}(w_{\text{seq}}(\theta)) \cdot \sum_{t \in T} \nabla_\theta \log \pi_{\theta}(a_t|s_t) \cdot A_t \right] $$ **Theoretical Justification for stopgrad:** The stopgrad operator is **mathematically required** by importance sampling theory, not an implementation detail. Here's why: **The fundamental principle**: Importance sampling is a technique to **change the measure** (reweight samples from one distribution to estimate expectations under another), not to optimize the reweighting function itself. **Formal derivation**: 1. **Original objective**: We want to optimize $J(\theta) = \mathbb{E}_{\tau \sim \pi_\theta}[\sum_t A_t]$. 2. **Off-policy setting**: We only have samples from $\pi_{\text{rollout}}$, so we use importance sampling: $$ J(\theta) = \mathbb{E}_{\tau \sim \pi_{\text{rollout}}} \left[ \underbrace{\frac{P_{\pi_\theta}(\tau)}{P_{\pi_{\text{rollout}}}(\tau)}}_{w(\tau;\theta)} \sum_t A_t \right] $$ 3. **Computing the policy gradient**: The correct gradient uses the **policy gradient theorem BEFORE importance sampling**: $$ \begin{aligned} \nabla_\theta J(\theta) &= \nabla_\theta \mathbb{E}_{\tau \sim \pi_\theta}\left[\sum_t A_t\right] \\ &= \mathbb{E}_{\tau \sim \pi_\theta} \left[\sum_t A_t \nabla_\theta \log \pi_\theta(a_t|s_t) \right] \quad \text{(policy gradient theorem)} \\ &= \mathbb{E}_{\tau \sim \pi_{\text{rollout}}} \left[ w(\tau;\theta) \sum_t A_t \nabla_\theta \log \pi_\theta(a_t|s_t) \right] \quad \text{(change of measure)} \end{aligned} $$ In the final line, $w(\tau;\theta)$ appears as a **multiplicative coefficient** from the change of measure, not as something we differentiate. 4. **What goes wrong without stopgrad**: If we naively compute $\nabla_\theta \left[w(\theta) \log \pi_\theta \right]$ in the loss, we get: $$ \nabla_\theta \left[w(\theta) \log \pi_\theta \right] = \underbrace{\log \pi_\theta \cdot \nabla_\theta w(\theta)}_{\text{WRONG: bias term}} + \underbrace{w(\theta) \cdot \nabla_\theta \log \pi_\theta}_{\text{CORRECT: IS-weighted gradient}} $$ The first term $\log \pi_\theta \cdot \nabla_\theta w(\theta)$ is an artifact of the computational trick (using loss times log-prob), not part of the true policy gradient. It biases the gradient estimator and optimizes a different objective than $J(\theta)$. 5. **Implementation requirement**: In PyTorch, to compute only the second term, we must use: ```python loss = -advantages * log_prob * rollout_is_weights.detach() # stopgrad on weights ``` Without `.detach()`, autograd computes both terms, giving an incorrect gradient. **Intuition**: The IS weight $w(\theta)$ tells us "how much to trust this sample" for estimating the gradient under $\pi_\theta$. We update $\theta$ to maximize the reweighted objective, but we don't update $\theta$ to maximize the weight itself—that would be circular reasoning (optimizing the correction factor instead of the actual objective). **Properties:** - **Algorithm**: Off-policy policy gradient with IS/RS correction - **Loss types** (`loss_type` config option in bypass mode): - `"ppo_clip"` (default): PPO clipped objective - $L = -\mathbb{E}[\min(r \cdot A, \text{clip}(r) \cdot A)]$ where $r = \pi_\theta / \pi_{\text{rollout}}$ - Note: IS weights NOT applied (PPO ratio already handles it; would be double-counting) - `"reinforce"`: Pure policy gradient with explicit IS weights, no PPO clipping - $L = -\mathbb{E}[w \cdot \log \pi_\theta(a|s) \cdot A]$ where $w = \pi_\theta / \pi_{\text{rollout}}$ - **Always uses bypass mode**: Direct $\pi_\theta$ to $\pi_{\text{rollout}}$ comparison - **Fast**: Single forward pass **Implementation:** `compute_policy_loss_bypass_mode()` and `compute_policy_loss_reinforce()` in [core_algos.py](../../verl/trainer/ppo/core_algos.py) --- ### 3.3 IS/RS Aggregation Levels The aggregation level determines how per-token probability ratios are combined into IS weights and/or rejection masks. This choice is **orthogonal to the operating mode** - you can use any aggregation level in either decoupled or bypass mode. #### 3.3.1 Token-Level Aggregation **IS weights:** $w_t = \min(\rho_t, C_{\text{IS}})$ where $\rho_t = \frac{\pi_{\text{old}}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$ (decoupled) or $\rho_t = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$ (bypass/pure IS) **Configuration:** ```python rollout_is = "token" # IS weights rollout_rs = "token_k1" # Optional: rejection sampling (ratio bounds) ``` **Properties:** - Independent truncation per token - Lower variance than sequence-level (product of ratios bounded individually) - **Bias-variance tradeoff**: Token-level correction has $O(T^2 \Delta_{\max})$ bias where $T$ is sequence length and $\Delta_{\max}$ is maximum per-token policy divergence. This bias becomes significant when the rollout policy deviates substantially from the training policy. Sequence-level correction is unbiased but has higher variance. - Typical threshold: 1.5 - 5.0 - Optional batch normalization [§3.4](rollout_corr_math.md#34-batch-normalization): Normalizes over all token weights to ensure $\mathbb{E}[\tilde{w}_t] = 1$ (reduces variance) - **When to use**: Token-level works well when rollout policy stays within the trust region of training policy. When mismatch is significant, the bias becomes intolerable and sequence-level correction is preferred. **Loss function (REINFORCE + Token IS):** $$ L_{\text{REINFORCE+TIS}}(\theta) = -\mathbb{E}_t \left[ \text{stopgrad}(w_t) \cdot \log \pi_\theta(a_t|s_t) \cdot A_t \right] $$ where $w_t = \min(\rho_t, C_{\text{IS}})$ are the truncated token-level IS weights. The stopgrad operator ensures that when computing $\nabla_\theta L$, the weights are treated as constants (see §3.2.2 for theoretical justification). This formulation can also be combined with PPO clipping by replacing the REINFORCE gradient with the clipped surrogate objective. **Implementation:** - IS weights: `compute_rollout_correction_weights()` in [rollout_corr_helper.py](../../verl/trainer/ppo/rollout_corr_helper.py#L325-L402) - Loss: `compute_policy_loss()` in [core_algos.py](../../verl/trainer/ppo/core_algos.py#L812-L884) #### 3.3.2 Sequence-Level Aggregation **IS weights:** $w_{\text{seq}} = \min\left( \prod_{t \in T} \rho_t, C_{\text{IS}} \right) = \min\left( \exp\left(\sum_{t \in T} \log \rho_t\right), C_{\text{IS}} \right)$ (broadcast to all tokens) **Configuration:** ```python rollout_is = "sequence" # IS weights rollout_rs = "seq_sum_k1" # Optional: rejection sampling ``` **Properties:** - Multiplicative aggregation across sequence - More sensitive to outliers than token-level - Typical threshold: 2.0 - 10.0 - Optional batch normalization [§3.4](rollout_corr_math.md#34-batch-normalization): Normalizes over sequence means (one weight per sequence) **Terminology Note:** - **Seq-TIS (Sequence-Level Truncated IS)**: Clips the sequence ratio $\rho(\tau) \to \min(\rho(\tau), C)$. Maximizes information efficiency by extracting signal from all samples. Best for clean data with moderate mismatch. - **Seq-MIS (Sequence-Level Masked IS)**: Rejects (masks) sequences with $\rho(\tau) > C$ instead of clipping. Acts as a hard trust region filter. Best for severe mismatch or when the distribution tail is "toxic" (contains garbage/adversarial samples rather than signal). **Loss function (REINFORCE + Sequence IS):** $$ L_{\text{REINFORCE+SeqIS}}(\theta) = -\mathbb{E}_t \left[ \text{stopgrad}(w_{\text{seq}}) \cdot \log \pi_\theta(a_t|s_t) \cdot A_t \right] $$ where $w_{\text{seq}}$ is broadcast to all tokens in the sequence. The stopgrad operator ensures correct IS gradient computation (see §3.2.2). This formulation can also be combined with PPO clipping. #### 3.3.3 Geometric Mean Aggregation (Geo-RS) **Geometric mean ratio:** $\rho_{\text{geo}} = \exp\left( \frac{1}{|T|} \sum_{t \in T} \log \rho_t \right) = \left(\prod_{t \in T} \rho_t\right)^{1/|T|}$ (broadcast to all tokens) **Configuration:** ```python rollout_is = null # No IS weights, pure rejection rollout_rs = "seq_mean_k1" # Geometric mean rejection sampling (ratio bounds) ``` **Properties:** - Length-invariant (normalizes by sequence length) - Ideal ratio = 1.0 (policies match) - Typical bounds: `"0.999_1.001"` (~±0.1%) - **Used for rejection sampling only, not IS weighting** **The Length Trap Problem:** Standard IS estimators have a systematic **length bias** that penalizes long sequences. The importance ratio $\rho(y)$ is multiplicative: $$ \rho(y) = \prod_{t=1}^T \frac{\pi(y_t|y_{= 0 per token (equals 0 when ρ = 1) - More stable than geometric ratio checks because each token term is non-negative - Only upper threshold applies (no lower threshold since K3 >= 0) - Typical threshold: 0.001 - 0.01 **Why K3 over geometric ratio?** - Geometric ratio uses average log-ratio; small numerical bias can flip sign - K3 = E[ρ - log ρ - 1] is non-negative per token, offering a smoother detector - Both estimate the same quantity: KL(π_rollout || π_old) - For small divergences, K3 ≈ 0.5 × Var(log_ratio) **Combined Estimator (K3-RS-Token-TIS):** For best results, combine K3 filter with token-level IS weights: $$ \hat{g}_{\text{k3-rs-token-tis}}(y) = \underbrace{\mathbb{I}\left( K3_{\text{seq}} \le C_{\text{k3}} \right)}_{\text{K3 Filter}} \cdot \prod_t \min(\rho_t, C) \cdot f(y) $$ This is implemented by combining `rollout_rs="seq_mean_k3"` with `rollout_is="token"`. --- ### 3.4 Batch Normalization An optional variance reduction technique that normalizes IS weights to have mean 1.0 within each batch. **Configuration:** ```python rollout_is_batch_normalize = True # Default: False ``` **Normalization formula (aggregation-aware):** For **token-level IS** (§3.3.1): $$ \tilde{w}_t = \frac{w_t}{\frac{1}{\sum_{i,t} m_{i,t}} \sum_{i,t} w_{i,t} \cdot m_{i,t}} $$ where $w_{i,t}$ are truncated token IS weights, $m_{i,t}$ is the response mask, and normalization is over **all tokens**. For **sequence-level IS** (§3.3.2): $$ \tilde{w}_i = \frac{w_i}{\frac{1}{B}\sum_{j=1}^B \bar{w}_j} $$ where $\bar{w}_j = \frac{1}{T_j}\sum_{t=1}^{T_j} w_{j,t} \cdot m_{j,t}$ is the per-sequence mean (all tokens in a sequence have the same weight), and normalization is over **sequences**. **Properties:** - Applied **after** truncation to preserve truncation semantics - Ensures $\mathbb{E}[\tilde{w}] = 1$ within each batch - **Aggregation-aware**: Token-level normalizes over tokens; sequence-level normalizes over sequences - Uses `masked_mean` to respect padding tokens - Reduces gradient magnitude variance by removing random batch-level scale fluctuations **Metrics:** - `rollout_is_batch_norm_factor`: The normalization factor applied (batch mean before normalization) **Implementation:** [rollout_corr_helper.py](../../verl/trainer/ppo/rollout_corr_helper.py#L401-L421) --- ### 3.5 Rejection Sampling (RS) Rejection sampling can be added to **any combination** of operating mode and aggregation level. It modifies the `response_mask` to exclude outlier tokens/sequences. **Configuration examples:** ```python rollout_rs = "token_k1" # Token-level ratio bounds rollout_rs_threshold = "0.6_1.6" rollout_rs = "seq_sum_k1" # Sequence sum of log ratios rollout_rs_threshold = "0.5_2.0" rollout_rs = "seq_mean_k3" # Sequence mean of K3 divergence rollout_rs_threshold = 0.01 ``` **Acceptance set:** - **Token-level**: $\mathcal{A}_{\text{token}} = \{ t : C_{\text{RS-lower}} \leq \rho_t \leq C_{\text{RS-upper}} \}$ - **Sequence-level**: $\mathcal{A}_{\text{seq}} = \{ \text{seq} : C_{\text{RS-lower}} \leq \prod_{t \in T} \rho_t \leq C_{\text{RS-upper}} \}$ - **Geometric**: $\mathcal{A}_{\text{geo}} = \{ \text{seq} : C_{\text{RS-lower}} \leq \rho_{\text{geo}} \leq C_{\text{RS-upper}} \}$ **Properties:** - Separate from IS weighting (can use RS without IS) - Reduces effective sample size - Filters extreme outliers **Implementation:** `compute_rollout_rejection_mask()` in [rollout_corr_helper.py](../../verl/trainer/ppo/rollout_corr_helper.py#L80-L188) --- ### 3.6 Combination Matrix **Key insight:** Estimators (how IS/RS is computed) and operating modes (decoupled PPO vs bypass PG) are **orthogonal**. Any estimator can be combined with any operating mode. #### Estimator × Operating Mode | Estimator | Configuration | Compatible Modes | |-----------|---------------|------------------| | **Token-TIS** | `rollout_is="token"` | Decoupled PPO, Bypass PG | | **Seq-TIS** | `rollout_is="sequence"` | Decoupled PPO, Bypass PG | | **Seq-MIS** | `rollout_is="sequence"` + `rollout_rs="seq_sum_k1"` | Decoupled PPO, Bypass PG | | **Geo-RS** | `rollout_rs="seq_mean_k1"` (geometric mean) | Decoupled PPO, Bypass PG | | **Geo-RS-Token-TIS** | `rollout_is="token"` + `rollout_rs="seq_mean_k1"` | Decoupled PPO, Bypass PG | | **K3-RS** | `rollout_rs="seq_mean_k3"` | Decoupled PPO, Bypass PG | | **K3-RS-Token-TIS** | `rollout_is="token"` + `rollout_rs="seq_mean_k3"` | Decoupled PPO, Bypass PG | **Note:** In bypass mode, `loss_type` controls the loss function. Use "ppo_clip" (default) or "reinforce". #### Available Preset Methods | Preset Method | Estimator | Mode | Properties | |---------------|-----------|------|------------| | **Decoupled PPO Mode** (3 policies: π_rollout, π_old, π_θ) | | `decoupled_token_is()` | Token-TIS | Decoupled PPO | Per-token IS weights | | `decoupled_seq_is()` | Seq-TIS | Decoupled PPO | Sequence-level IS weights | | `decoupled_seq_is_rs()` | Seq-MIS | Decoupled PPO | Sequence IS + sequence RS | | `decoupled_geo_rs()` | Geo-RS | Decoupled PPO | Geometric RS | | `decoupled_geo_rs_token_tis()` | Geo-RS-Token-TIS | Decoupled PPO | Geometric filter + token IS | | **K3 KL Estimator** (more stable for small KL values) | | `decoupled_k3_rs()` | K3-RS | Decoupled PPO | K3 rejection, no IS weights | | `decoupled_k3_rs_token_tis()` | K3-RS-Token-TIS | Decoupled PPO | K3 filter + token clipped weight | | **Bypass Mode (PPO-clip)** (ratio handles IS, RS masks outliers) | | `bypass_ppo_clip()` | - | Bypass (PPO-clip) | PPO-clip only | | `bypass_ppo_clip_geo_rs()` | Geo-RS | Bypass (PPO-clip) | PPO-clip + Geo-RS (ratio) | | `bypass_ppo_clip_k3_rs()` | K3-RS | Bypass (PPO-clip) | PPO-clip + K3-RS | | **Bypass Mode (REINFORCE)** (explicit IS weights, no PPO clipping) | | `bypass_pg_is()` | Seq-TIS | Bypass (REINFORCE) | REINFORCE + Seq IS | | `bypass_pg_geo_rs()` | Geo-RS | Bypass (REINFORCE) | REINFORCE + Geo-RS (ratio) | | `bypass_pg_geo_rs_token_tis()` | Geo-RS-Token-TIS | Bypass (REINFORCE) | REINFORCE + Geo filter + token IS | | **Other** | | `disabled()` | - | - | Metrics only | **Note:** Bypass mode sets π_old = π_rollout and uses `loss_type` to select the loss function. #### Additional Supported Combinations (Manual Configuration) These combinations are **fully supported** but require manual configuration: **1. Token IS + Token RS** ```python config = RolloutCorrectionConfig( rollout_is="token", rollout_is_threshold=2.0, rollout_rs="token_k1", rollout_rs_threshold="0.5_2.0", ) ``` **Properties:** Token-level IS weights + token-level RS mask. **2. Pure Token RS** ```python config = RolloutCorrectionConfig( rollout_is=None, rollout_rs="token_k1", rollout_rs_threshold="0.5_2.0", ) ``` **Properties:** Token-level RS mask only, no IS weights. **3. Pure Sequence RS** ```python config = RolloutCorrectionConfig( rollout_is=None, rollout_rs="seq_sum_k1", rollout_rs_threshold="0.5_2.0", ) ``` **Properties:** Sequence-level RS mask only, no IS weights. **Key properties:** - Any IS aggregation level (token/sequence) can be used in either decoupled or bypass mode - Rejection sampling can be added to any combination - Geometric aggregation is typically used for RS only (not IS weighting) - Pure RS (`bypass_pg_rs`) uses bypass + geometric RS with `loss_type="reinforce"` for REINFORCE (no IS weights) - All combinations in the table above are valid and supported by the implementation --- ### 3.7 Common Implementation Mistake #### Incorrect LLM-RL Implementation (PPO Without Rollout Correction) **Theory:** Naive LLM-RL implementation that incorrectly applies PPO by **ignoring the actual rollout policy** and assuming $\pi_{\text{old}} = \pi_{\text{rollout}}$. **Note:** This incorrect implementation pattern was identified in [Liu, Li, et al. (2025)](https://richardli.xyz/rl-collapse) as a key cause of training instability in LLM-RL systems, motivating the development of this rollout correction framework. **Loss Function:** $$ L_{\text{PPO}}(\theta) = -\mathbb{E}_t \left[ \min\left( r_t(\theta) A_t, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon) A_t \right) \right] $$ where $r_t(\theta) = \frac{\pi_{\theta}(a_t|s_t)}{\pi_{\text{old}}(a_t|s_t)}$ (ignores $\pi_{\text{rollout}}$). **Why it's wrong:** - **Ignores $\pi_{\text{rollout}}$**: Uses $\pi_{\text{old}}$ as behavior policy instead of actual $\pi_{\text{rollout}}$ - **Policy mismatch**: In LLM-RL, rollout typically uses different precision/backend/checkpoint than training, causing $\pi_{\text{rollout}} \neq \pi_{\text{old}}$ even with same model weights - **Not PPO's fault**: PPO itself is correct; the issue is the incorrect assumption **Correct alternatives:** 1. **Decoupled mode**: Three policies with IS correction from $\pi_{\text{rollout}}$ to $\pi_{\text{old}}$ 2. **Bypass mode**: Two policies using $\pi_{\text{rollout}}$ as both behavior policy and proximal policy 3. **Bypass + Policy Gradient mode**: Two policies with IS/RS correction and no PPO clipping **Implementation:** `compute_policy_loss()` in [core_algos.py](../../verl/trainer/ppo/core_algos.py#L812-L884) --- ## 4. Off-Policy Diagnostic Metrics These metrics quantify the severity of off-policy drift. **Note on notation:** Metrics use $\rho_t = \frac{\pi_{\text{old}}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$. In bypass mode, $\pi_{\text{old}} = \pi_{\text{rollout}}$, so metrics measure rollout→current drift using $\rho_t = \frac{\pi_{\theta}}{\pi_{\text{rollout}}}$ instead. ### 4.1 KL Divergence **Direct KL estimator:** $$ \text{KL}(\pi_{\text{rollout}} \| \pi_{\text{old}}) = \mathbb{E}_{t \sim \pi_{\text{rollout}}} \left[ \log \pi_{\text{rollout}}(a_t|s_t) - \log \pi_{\text{old}}(a_t|s_t) \right] $$ **K3 KL estimator** (alternative formulation): $$ \text{KL}_{\text{K3}} = \mathbb{E}_{t \sim \pi_{\text{rollout}}} \left[ \rho_t - \log \rho_t - 1 \right] $$ where $\rho_t = \frac{\pi_{\text{old}}(a_t|s_t)}{\pi_{\text{rollout}}(a_t|s_t)}$. ### 4.2 Perplexity **Old policy perplexity:** $$ \text{PPL}_{\text{old}} = \exp\left( -\frac{1}{|T|} \sum_{t \in T} \log \pi_{\text{old}}(a_t|s_t) \right) $$ **Rollout policy perplexity:** $$ \text{PPL}_{\text{rollout}} = \exp\left( -\frac{1}{|T|} \sum_{t \in T} \log \pi_{\text{rollout}}(a_t|s_t) \right) $$ **PPL ratio** (inverse of geometric mean IS weight): $$ \text{PPL}_{\text{ratio}} = \frac{\text{PPL}_{\text{old}}}{\text{PPL}_{\text{rollout}}} = \exp\left( -\frac{1}{|T|} \sum_{t \in T} \log \rho_t \right) = \left(\prod_{t \in T} \rho_t\right)^{-1/|T|} $$ **Interpretation:** Values > 1 mean $\pi_{\text{old}}$ assigns lower probability than $\pi_{\text{rollout}}$ to the observed actions (distribution shift). ### 4.3 Chi-squared Divergence Measures the second moment of the IS weight distribution. **Token-level:** $$ \chi^2_{\text{token}} = \mathbb{E}_{t \sim \pi_{\text{rollout}}} \left[ \rho_t^2 \right] - 1 $$ **Sequence-level:** $$ \chi^2_{\text{seq}} = \mathbb{E}_{\text{seq} \sim \pi_{\text{rollout}}} \left[ \left(\prod_{t \in T} \rho_t\right)^2 \right] - 1 $$ **Interpretation:** - $\chi^2 = 0$: Policies are identical - $\chi^2 > 0$: Higher values indicate more severe off-policy distribution shift **Implementation:** `compute_offpolicy_metrics()` in [rollout_corr_helper.py](../../verl/trainer/ppo/rollout_corr_helper.py#L670-L776) --- ## 5. Summary and Decision Guide ### 5.1 Method Summary Table | Method | Theory | Policies | PPO Clip | IS Correction | Correctness | Speed | |--------|--------|----------|----------|---------------|-------------|-------| | **Bypass Mode** (π_old = π_rollout, `loss_type` selects algorithm) | | `loss_type="ppo_clip"` (default) | PPO (ratio = π_θ/π_rollout) | 2 (rollout, θ) | ✅ | RS mask only (ratio handles IS) | ✅ Correct | **Fast** | | `loss_type="reinforce"` | Off-policy REINFORCE | 2 (rollout, θ) | ❌ | ✅ (explicit IS weights) | ✅ Correct | **Fast** | | **Bypass Mode Presets (PPO-clip)** | | `bypass_ppo_clip` | PPO only | 2 (rollout, θ) | ✅ | - | ✅ Correct | **Fast** | | `bypass_ppo_clip_geo_rs` | PPO + Geo-RS | 2 (rollout, θ) | ✅ | Geo-RS mask (ratio) | ✅ Correct | **Fast** | | **Bypass Mode Presets (REINFORCE)** | | `bypass_pg_is` | REINFORCE + Seq-TIS | 2 (rollout, θ) | ❌ | ✅ Seq-TIS | ✅ Correct | **Fast** | | `bypass_pg_geo_rs` | REINFORCE + Geo-RS | 2 (rollout, θ) | ❌ | Geo-RS only (ratio) | ✅ Correct | **Fast** | | `bypass_pg_geo_rs_token_tis` | REINFORCE + Geo RS + Token IS | 2 (rollout, θ) | ❌ | ✅ Geo-RS-Token-TIS | ✅ Correct | **Fast** | | **Decoupled PPO Mode** (IS weights = π_old / π_rollout) | | `decoupled_token_is` | Decoupled PPO | 3 (rollout, old, θ) | ✅ | ✅ Token-TIS | ✅ Correct | Standard | | `decoupled_seq_is` | Decoupled PPO | 3 (rollout, old, θ) | ✅ | ✅ Seq-TIS | ✅ Correct | Standard | | `decoupled_seq_is_rs` | Decoupled PPO + RS | 3 (rollout, old, θ) | ✅ | ✅ Seq-MIS | ✅ Correct | Standard | | `decoupled_geo_rs` | Decoupled PPO + Geo-RS | 3 (rollout, old, θ) | ✅ | Geo-RS only (ratio) | ✅ Correct | Standard | | `decoupled_geo_rs_token_tis` | Decoupled PPO + Geo RS + Token IS | 3 (rollout, old, θ) | ✅ | ✅ Geo-RS-Token-TIS | ✅ Correct | Standard | | **Incorrect (for reference)** | | Naive LLM-RL | Incorrect PPO usage | 2 (old, θ) | ✅ | ❌ | ⚠️ Incorrect | Standard | **Notes:** - **Bypass mode** sets π_old = π_rollout and uses `loss_type` to select the loss function: - `"ppo_clip"` (default): PPO clipped ratio (IS handled by ratio = π_θ/π_rollout, no explicit IS weights to avoid double-counting) - `"reinforce"`: Explicit IS weights applied as $w \cdot \log \pi \cdot A$ - Both loss types benefit from rejection sampling (RS) which masks out-of-distribution samples ### 5.2 Estimator Hierarchy These estimators define **how IS weights and rejection masks are computed**. They are orthogonal to the operating mode (decoupled PPO vs bypass policy gradient) and can be combined with either. | Estimator | Configuration | Mechanism | Best For | |-----------|---------------|-----------|----------| | **Token-TIS** | `rollout_is="token"` | Clips per-token ratios | Lower variance IS with acceptable bias | | **Seq-TIS** | `rollout_is="sequence"` | Clips sequence ratio $\rho(\tau) \to \min(\rho(\tau), C)$ | Clean data with moderate mismatch; unbiased | | **Seq-MIS** | `rollout_is="sequence"` + `rollout_rs="seq_sum_k1"` | Rejects sequences with $\rho(\tau) > C$ | Severe mismatch; filters "toxic tail" (garbage data) | | **Geo-RS** | `rollout_rs="seq_mean_k1"` | Rejects on geometric mean ratio exp(E[log(r)]) | Length-invariant trust region | | **Geo-RS-Token-TIS** | `rollout_is="token"` + `rollout_rs="seq_mean_k1"` | Geometric filter + token IS weights | Ratio-based length normalization + lower variance IS | | **K3-RS** | `rollout_rs="seq_mean_k3"` | Rejects on K3 KL divergence | Small KL values; smooth detector | | **K3-RS-Token-TIS** | `rollout_is="token"` + `rollout_rs="seq_mean_k3"` | K3 filter + token IS weights | Small KL + lower variance IS | **Note:** Each estimator can be used with either: - **Decoupled PPO** (`bypass_mode=false`): Three policies with PPO clipping - **Bypass Mode** (`bypass_mode=true`): Two policies with configurable loss type - `loss_type="ppo_clip"` (default): PPO clipped objective (IS via ratio, RS mask applied) - `loss_type="reinforce"`: REINFORCE with explicit IS weights ### 5.3 Method Characteristics by Scenario **Choosing estimator by off-policy severity:** - **Negligible** (same checkpoint, minor differences): No IS correction needed; use bypass mode for efficiency - **Moderate** (async workers, slight staleness): Token-TIS provides per-token IS correction with lower variance - **Severe** (replay buffers, old data): Seq-TIS or Seq-MIS provides sequence-level IS correction; use Seq-MIS when high-weight samples are likely garbage **Choosing estimator by sequence length:** - **Short sequences** (standard chat): Seq-TIS is optimal - **Long sequences** (CoT, agents): K1-RS or K1-RS-Token-TIS to avoid Length Trap **Choosing operating mode:** - **Batch size invariance needed**: Use decoupled mode (`bypass_mode=false`) - **Computational efficiency needed**: Use bypass mode (`bypass_mode=true`) to skip `old_log_prob` computation - **No PPO clipping**: Use bypass mode with `loss_type="reinforce"` ### 5.4 Decoupled Mode vs Bypass Mode **Decoupled mode** (computes `old_log_prob` separately): - Implements full decoupled PPO with three policies (mathematically correct) - Separately measures and corrects Drift 1 (rollout→old) and Drift 2 (old→current) - Achieves batch size invariance and efficient stale data utilization - Enables accurate off-policy metrics monitoring **Bypass mode** (sets $\pi_{\text{old}} = \pi_{\text{rollout}}$): - Uses $\pi_{\text{rollout}}$ as both behavior policy and proximal policy (mathematically correct) - Computational efficiency: Skips separate `old_log_prob` computation - Does not achieve batch size invariance (proximal policy depends on data collection) --- ## 6. Implementation References - **[Rollout Correction Usage Guide](rollout_corr.md)** - Practical configuration and troubleshooting - **Config:** [verl/trainer/config/algorithm.py](../../verl/trainer/config/algorithm.py) - **IS/RS Helper:** [verl/trainer/ppo/rollout_corr_helper.py](../../verl/trainer/ppo/rollout_corr_helper.py) - **PPO Loss:** [verl/trainer/ppo/core_algos.py](../../verl/trainer/ppo/core_algos.py) - **Tests:** [tests/trainer/ppo/test_rollout_corr.py](../../tests/trainer/ppo/test_rollout_corr.py) --- ## References - **Williams, R. J. (1992).** "Simple statistical gradient-following algorithms for connectionist reinforcement learning." *Machine Learning*, 8(3-4), 229-256. https://doi.org/10.1007/BF00992696 - **Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017).** "Proximal policy optimization algorithms." *arXiv preprint arXiv:1707.06347.* https://arxiv.org/abs/1707.06347 - **Hilton, J., Cobbe, K., & Schulman, J. (2021).** "Batch size-invariance for policy optimization." *arXiv preprint arXiv:2110.00641.* https://arxiv.org/abs/2110.00641 - Introduced decoupled PPO: separating proximal policy (for controlling policy update size) from behavior policy (for off-policy correction) to achieve batch size invariance ================================================ FILE: docs/algo/spin.md ================================================ # Recipe: Self-Play Fine-Tuning (SPIN) Last updated: 05/31/2025. `verl` provides a recipe inspired by the paper **"Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models"** (SPIN). SPIN is a language model finetuning algorithm that enables iterative self-improvement through a self-play mechanism inspired by game theory. **Core Idea:** Models learn by playing against themselves, reducing reliance on external preference datasets or stronger teacher models: 1. **Synthetic Data Generation:** The current model generates responses, creating its own training data from previous iterations. 2. **Two-Player Game Setup:** A game involving two players acted by a single LLM. 3. **Iterative Training:** The model progressively improves by refining its policy, with each iteration's model becoming the opponent for the next iteration. Paper Authors: [Zixiang Chen](https://github.com/uclaml/SPIN)\*, [Yihe Deng](https://github.com/uclaml/SPIN)\*, [Huizhuo Yuan](https://scholar.google.com/citations?user=8foZzX4AAAAJ)\*, [Kaixuan Ji](https://scholar.google.com/citations?user=FOoKDukAAAAJ), [Quanquan Gu](https://web.cs.ucla.edu/~qgu/) [[Webpage](https://uclaml.github.io/SPIN/)] [[Huggingface](https://huggingface.co/papers/2401.01335)] [[Paper](https://arxiv.org/abs/2401.01335)] [[Original Implementation](https://github.com/uclaml/SPIN)] verl Implementation Authors: [Chendong Wang](https://cdwang96.github.io/), [Chenyang Zhao](https://github.com/zhaochenyang20) --- ## Key Function (compute_online_dpo_loss) and Related works SPIN (Chen et al., 2024) proposes an iterative self-play mechanism to fine-tune language models. In each iteration, SPIN's training objective, when using a logistic loss function, is equivalent to Direct Preference Optimization (DPO) loss (Rafailov et al., 2023). This `verl` recipe realizes SPIN's core concept by using DPO loss iteratively (Xu et al., 2023; Xiong et al., 2023; Snorkel AI, 2024). This means that in each iteration, we fine-tune the LLM using DPO loss for preference optimization. Notably, Xu et al. (2023) explored iterative preference optimization with pairwise cringe loss, while Xiong et al. (2023) discussed how to bridge theory and practice for RLHF under KL constraints using iterative training. The concept of iterative preference learning was also explored in online DPO (Guo et al., 2024), which focuses on direct alignment from online AI feedback. In online DPO, preference data is dynamically updated during training, allowing the model to learn from its own generated data. Specifically, we developed the **`compute_online_dpo_loss`** function and built this SPIN recipe on top of it. By incorporating online preference generation, this approach enables continuously refining language models without relying on fixed external preference datasets. **Reference Papers:** * [Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models](https://arxiv.org/abs/2401.01335) (Chen et al., 2024) * [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/abs/2305.18290) (Rafailov et al., 2023) * [Somethings are more cringe than others: Preference optimization with the pairwise cringe loss](https://arxiv.org/abs/2312.16682) (Xu et al., 2023) * [Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint](https://arxiv.org/abs/2312.11456) (Xiong et al., 2023) * [Snorkel-Mistral-PairRM-DPO](https://huggingface.co/snorkelai/Snorkel-Mistral-PairRM-DPO) (Snorkel AI, 2024) * [Direct language model alignment from online ai feedback](https://arxiv.org/abs/2402.04792) (Guo et al., 2024) ## Our Online DPO Implementation Our `compute_online_dpo_loss` function adapts `verl`'s existing PPO infrastructure (based on `verl` v0.3.0.post1) for this iterative online DPO. Key aspects of our implementation include: * **No Critic:** Unlike PPO, we omit the value function critic. * **Dynamic Reference Model:** An explicit reference policy (`ref_policy_wg`) is used for DPO loss. This reference model's weights can be periodically updated from the actor (`ref_update_freq`), providing a dynamic baseline. * **Online Preference Generation:** The `compute_onlineDPO_pref` function (in `core_algos.py`) dynamically creates chosen/rejected pairs based on a reward source (e.g., rule-based ranking for math problems). * **DPO Loss Integration:** We replace PPO's policy loss with our `compute_online_dpo_loss` (in `core_algos.py`) within the actor update (`dp_actor.py`), directly optimizing the policy using the generated preferences. * **Iterative Training Orchestration:** The `SpinTrainer` (in `spin_trainer.py`) manages the entire self-play loop: generation, preference labeling, optional reference model updates, and policy updates, enabling continuous self-improvement aligned with SPIN's principles. --- ## Algorithm This recipe implements an Online algorithm adapted to the `verl` Reinforcement Learning framework, which provides an alternative to PPO for fine-tuning language models. **Online Loop:** Instead of maximizing a scalar reward signal in PPO, this approach directly optimizes the policy model to align with preference data generated *online* during training: 1. **Generation:** The current model generates multiple responses for each prompt in a batch. 2. **Preference Labeling:** A function evaluates these generated responses to determine which one is preferred (chosen) and which is dispreferred (rejected). This can be done using a reward function or implicit ranking based on specific rules. (In this recipe, we use rule-based ranking on the math problem). 3. **Update:** This preference tuple (`prompt`, `chosen_response`, `rejected_response`) is used to update the actor model using `compute_online_dpo_loss`, comparing against a reference model. **Connection with SPIN:** Instead of only using a fixed target data distribution, the online generation loop in step 2 will dynamically change the target data distribution by using a certain Preference Labeling method (rule-based ranking on the math problem by selecting the better one in this recipe). This explores the direction mentioned in SPIN's paper Section 7 about "dynamically changing target data distribution" to potentially elevate LLM performance beyond the fixed human-annotated data ceiling. --- ## Reproduce the Experiment (Example Setup) The following steps outline how to set up the environment and run the SPIN recipe, based on the provided test log using GSM8K and Qwen2.5-3B-Instruct. 1. **Setup Environment (Example using Docker):** ```bash # Start a container with GPU access and shared memory docker run -it --name spin_test --gpus all \ --shm-size=32g \ --ipc=host \ -v /path/to/host/.cache:/root/.cache \ -e HF_TOKEN= \ lmsysorg/sglang:latest \ /bin/bash # Inside the container or on your host machine: # Ensure /tmp is writable mkdir -p /tmp chmod 1777 /tmp # Install Python 3.10 (if not present) and venv sudo apt update sudo apt install -y python3.10 python3.10-venv tmux python3 -m ensurepip --upgrade # Create and activate a virtual environment python3 -m venv ~/.python/spin_env source ~/.python/spin_env/bin/activate # Install uv (fast package installer) python3 -m pip install uv ``` 2. **Install verl and Dependencies:** ```bash # Clone the verl repository and checkout the spin branch cd ~ git clone git@github.com:volcengine/verl.git && cd verl # Install flash-attn (handle potential build issues) python3 -m uv pip install wheel packaging python3 -m uv pip install flash-attn --no-build-isolation --no-deps # Install verl with sglang extras python3 -m uv pip install -e ".[sglang]" ``` *Note: If `flash-attn` installation fails, try the manual steps again or consult its documentation.* 3. **Login & Download Data/Model:** ```bash # Login to Weights & Biases (optional, for logging) export WANDB_API_KEY= # wandb login # Download the GSM8K dataset python3 examples/data_preprocess/gsm8k.py --local_save_dir ~/data/gsm8k # Adjusted path # Download the base model (Example: Qwen2.5-3B-Instruct) hf download Qwen/Qwen2.5-3B-Instruct --local-dir $HOME/models/Qwen2.5-3B-Instruct ``` 4. **Configure:** * Modify the configuration file (e.g., `config/spin_trainer.yaml` or the one specified in the run script) with correct paths to your downloaded model, data, desired hyperparameters (`dpo_beta`, learning rate, etc.), and distributed training settings (nodes, GPUs per node). * Pay attention to `actor_rollout_ref.model`, `data` paths, `reward_model` config (if using one), and `trainer.ref_update_freq`. 5. **Run Training:** ```bash # Set CUDA visible devices (adjust based on your hardware and config) export CUDA_VISIBLE_DEVICES=0,1,2,3 # Launch the training script (e.g., test.sh or a custom script) # Ensure test.sh points to the correct config and main script bash recipe/spin/run_spin.sh ``` --- ## Configuration * The primary configuration is typically managed through a YAML file specified in the launch script (e.g., `config/spin_trainer.yaml`). * Key configuration sections: * `data`: Paths to training/validation prompt files, batch sizes, sequence lengths. * `actor_rollout_ref`: Paths to the base model (used for actor and initial reference), FSDP settings, optimization parameters (learning rate, scheduler). * `reward_model`: Configuration for the reward model used for online preference labeling (path, batch size, etc.). Can be omitted if using a simpler reward function. * `algorithm`: DPO-specific hyperparameters like `dpo_beta`, `dpo_loss_type`. * `trainer`: Distributed training settings (nodes, GPUs per node), logging (WandB), checkpointing frequency, and `ref_update_freq` (set > 0 to enable periodic reference model updates from the actor). --- ## Key Files * `main_spin.py`: Main entry point using Hydra to load the config and launch the `SpinTrainer`. * `spin_trainer.py`: Defines the `SpinTrainer` class, orchestrating the Online DPO training loop. * `fsdp_workers.py`: Implements Ray workers (Actor, Reference) potentially using FSDP. * `dp_actor.py`: Contains the actor class, including the DPO policy update logic. * `core_algos.py`: Includes helper functions for `compute_online_dpo_loss` and `compute_onlineDPO_pref`. * `config/spin_trainer.yaml` (or similar): Main Hydra configuration file for the recipe. * `run_spin.sh` (or similar): Example bash script for launching a training run. * `README.md`: This file. --- ## Acknowledgement We sincerely thank the contribution and guidance from the `verl` community and advisors, including (adapted from SPPO): * [Zixiang Chen](https://sites.google.com/view/zxchen) * [Yuhao Yang](https://github.com/yhyang201) * [Yifan Zhang](https://github.com/yifanzhang-pro) * [Yongan Xiang](https://github.com/BearBiscuit05) * [Junrong Lin](https://github.com/ocss884) * [Yuxuan Tong](https://github.com/tongyx361) * [Guangming Shen](https://github.com/PeterSH6) * [Biao He](https://www.linkedin.com/in/biao-he/) * [Qingquan Song](https://qingquansong.github.io/) * [Chenyang Zhao](https://zhaochenyang20.github.io/Chayenne/) * [Quanquan Gu](https://web.cs.ucla.edu/~qgu/) ================================================ FILE: docs/algo/sppo.md ================================================ # Recipe: Self-Play Preference Optimization (SPPO) Last updated: 05/28/2025. verl provides a community recipe implementation for the paper [Self-Play Preference Optimization for Language Model Alignment](https://arxiv.org/abs/2405.00675). SPPO can significantly enhance the performance of an LLM without strong external signals such as responses or preferences from GPT-4. It can outperform the model trained with iterative direct preference optimization (DPO), among other methods. SPPO is theoretically grounded, ensuring that the LLM can converge to the von Neumann winner (i.e., Nash equilibrium) under general, potentially intransitive preference, and empirically validated through extensive evaluations on multiple datasets. Paper Authors: [Yue Wu](https://yuewu.us/)\*, [Zhiqing Sun](https://www.cs.cmu.edu/~zhiqings/)\*, [Huizhuo Yuan](https://scholar.google.com/citations?user=8foZzX4AAAAJ)\*, [Kaixuan Ji](https://scholar.google.com/citations?user=FOoKDukAAAAJ), [Yiming Yang](https://www.cs.cmu.edu/~yiming/), [Quanquan Gu](https://web.cs.ucla.edu/~qgu/) verl Implementation Authors: [Yuhao Yang](https://github.com/yhyang201), [Chenyang Zhao](https://github.com/zhaochenyang20) [[Webpage](https://uclaml.github.io/SPPO/)] [[Huggingface](https://huggingface.co/papers/2405.00675)] [[Paper](https://arxiv.org/abs/2405.00675)][[Original Implementation](https://github.com/uclaml/SPPO)] ## Reproduce the Experiment We evaluate the performance of SPPO on the MATH dataset. Starting from an initial score of 46.6 with Qwen2.5-7B-Instruct, we achieve a score of 65.6 after 20 epochs of training, placing our model approximately in the top 20 on the [MATH leaderboard](https://paperswithcode.com/sota/math-word-problem-solving-on-math). It's important to note that verl's internal evaluation metrics may not perfectly align with the official evaluation methodology for Qwen2.5-7B-Instruct. Therefore, for consistency and fair comparison, we report only the results based on verl's evaluation framework. ``` git clone git@github.com:volcengine/verl.git cd verl python3 -m uv pip install -e ".[sglang]" export WANDB_API_KEY= python3 examples/data_preprocess/math_dataset.py --local_dir ~/data/math hf download Qwen/Qwen2.5-7B-Instruct --local-dir $HOME/models/Qwen2.5-7B-Instruct export CUDA_VISIBLE_DEVICES=0,1,2,3 bash recipe/sppo/run_qwen2.5-7b_rm.sh ``` Note that the installation would occasionally fail to install flash-attn. If this happens, you can install it manually by running: ```bash python3 -m uv pip install wheel python3 -m uv pip install packaging python3 -m uv pip install flash-attn --no-build-isolation --no-deps ``` ## Acknowledgement We sincerely thank the contribution and guidance from: - [Yue Wu](https://yuewu.us/) - [Chendong Wang](https://cdwang96.github.io/) - [Yifan Zhang](https://github.com/yifanzhang-pro) - [Yongan Xiang](https://github.com/BearBiscuit05) - [Junrong Lin](https://github.com/ocss884) - [Yuxuan Tong](https://github.com/tongyx361) - [Guangming Shen](https://github.com/PeterSH6) - [Biao He](https://www.linkedin.com/in/biao-he/) - [Qingquan Song](https://qingquansong.github.io/) - [Quanquan Gu](https://web.cs.ucla.edu/~qgu/) ================================================ FILE: docs/amd_tutorial/amd_build_dockerfile_page.rst ================================================ Getting started with AMD (ROCM Kernel) ===================================================== Last updated: 07/06/2025. Author: `Yusheng Su `_ Setup ----- If you run on AMD GPUs (MI300) with ROCM platform, you cannot use the previous quickstart to run verl. You should follow the following steps to build a docker and set ``RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES`` or ``RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES`` when starting ray in verl's RLHF training. docker/Dockerfile.rocm ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash FROM "rlsys/rocm-6.3.4-patch:rocm6.3.4-numa-patch_ubuntu-22.04" SHELL ["/bin/bash", "-ceuxo", "pipefail"] ENV MAX_JOBS=512 ENV PATH="/usr/local/python3.12/bin:$PATH" RUN ln -sf /usr/bin/python3.12 /usr/bin/python && \ ln -sf /usr/bin/pip3.12 /usr/bin/pip ############################################ RUN apt-get update RUN apt-get install -y pkg-config liblzma-dev ############################################ ########################################### ##########Install TransformerEngine######## ########################################### WORKDIR /workspace/ # transformer-engine install # https://github.com/ROCm/TransformerEngine RUN rm -rf TransformerEngine RUN git clone --recursive https://github.com/ROCm/TransformerEngine.git WORKDIR /workspace/TransformerEngine git checkout 236178e5 # git checkout bb061ade # git checkout 864405c ENV NVTE_FRAMEWORK=pytorch ENV NVTE_ROCM_ARCH=gfx942 ENV NVTE_USE_HIPBLASLT=1 ENV NVTE_USE_ROCM=1 # export CMAKE_PREFIX_PATH="/opt/rocm:/opt/rocm/hip:/usr/local:/usr:${CMAKE_PREFIX_PATH:-}" ENV CMAKE_PREFIX_PATH="/opt/rocm:/opt/rocm/hip:/usr/local:/usr" RUN MAX_JOBS=$(MAX_JOBS) pip install . -vvv WORKDIR /workspace/ ########################################### ########################################### ########################################### #################################################################################### ################Install vllm - sglang require vllm 0.6.7 dependency################# #################################################################################### #### Require vllm 0.6.7 - checkout 113274a0 WORKDIR /workspace/ RUN rm -rf vllm RUN pip uninstall -y vllm # Refer to here (down-grade vllm to 0.6.3): https://docs.vllm.ai/en/v0.6.3/getting_started/amd-installation.html RUN git clone https://github.com/ROCm/vllm.git # git clone https://github.com/vllm-project/vllm.git WORKDIR /workspace/vllm RUN git checkout 113274a0 ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" #ENV MAX_JOBS=512 ENV MAX_JOBS=${MAX_JOBS} RUN pip install "boto3>=1.26.0" RUN pip install setuptools_scm # will add src into py. You can delete the repo RUN python3 setup.py install WORKDIR /workspace/ #################################################################################### #################################################################################### #################################################################################### ########################################### ############For hack docker################ ########################################### RUN pip install setuptools==75.8.0 ########################################### ########################################### ########################################### ########################################### ############build sgalng################### ########################################### # Set environment variables ENV BASE_DIR=/sgl-workspace ENV BUILD_TYPE=all ENV SGL_REPO=https://github.com/sgl-project/sglang ENV SGL_BRANCH=v0.4.6.post5 ENV TRITON_REPO=https://github.com/ROCm/triton.git ENV TRITON_COMMIT=improve_fa_decode_3.0.0 ENV AITER_REPO=https://github.com/ROCm/aiter.git ENV AITER_COMMIT=v0.1.2 # v0.1.2 version - commit id: 9d11f47 # ENV AITER_COMMIT=9d11f47 ENV HIP_FORCE_DEV_KERNARG=1 ENV HSA_NO_SCRATCH_RECLAIM=1 ENV SGLANG_SET_CPU_AFFINITY=1 ENV SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 ENV NCCL_MIN_NCHANNELS=112 ENV MOE_PADDING=1 ENV VLLM_FP8_PADDING=1 ENV VLLM_FP8_ACT_PADDING=1 ENV VLLM_FP8_WEIGHT_PADDING=1 ENV VLLM_FP8_REDUCE_CONV=1 ENV TORCHINDUCTOR_MAX_AUTOTUNE=1 ENV TORCHINDUCTOR_MAX_AUTOTUNE_POINTWISE=1 ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942" ENV AMDGPU_TARGETS=gfx942 ENV ROCM_ARCH=gfx942 ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" # Switch to working directory WORKDIR /sgl-workspace # Clean and create directory RUN rm -rf /sgl-workspace && mkdir -p /sgl-workspace # Clone and build sglang RUN git clone ${SGL_REPO} \ && cd sglang \ && git checkout ${SGL_BRANCH} || echo "Using default branch" \ && cd sgl-kernel \ && rm -f pyproject.toml \ && mv pyproject_rocm.toml pyproject.toml \ && python setup_rocm.py install \ && cd .. \ && if [ "$BUILD_TYPE" = "srt" ]; then \ python -m pip --no-cache-dir install -e "python[srt_hip]"; \ else \ python -m pip --no-cache-dir install -e "python[all_hip]"; \ fi \ && cd /sgl-workspace \ && cp -r /sgl-workspace/sglang /sglang \ && python -m pip cache purge # Install common Python packages RUN pip install IPython orjson python-multipart torchao pybind11 # Rebuild Triton RUN pip uninstall -y triton || true \ && git clone ${TRITON_REPO} \ && cd triton \ && git checkout ${TRITON_COMMIT} \ && cd python \ && python3 setup.py install \ && cd /sgl-workspace # ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942 --amdgpu-lower-module-lds-strategy=1" # ENV HIPCC_COMPILE_FLAGS_APPEND="--offload-arch=gfx942" # Build aiter #version: Commit 9d11f47 # && git checkout ${AITER_COMMIT} \ RUN pip uninstall -y aiter || true RUN git clone ${AITER_REPO} \ && cd aiter \ && git checkout ${AITER_COMMIT} \ && git submodule sync \ && git submodule update --init --recursive \ && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py install \ && cd /sgl-workspace # Copy MI300X config RUN find /sgl-workspace/sglang/python/sglang/srt/layers/quantization/configs/ \ /sgl-workspace/sglang/python/sglang/srt/layers/moe/fused_moe_triton/configs/ \ -type f -name '*MI300X*' | \ xargs -I {} sh -c 'vf_config=$(echo "$1" | sed "s/MI300X/MI300X_VF/"); cp "$1" "$vf_config"' -- {} # Environment setup complete. RUN echo "Environment setup complete." WORKDIR /workspace/ ########################################### ########################################### ########################################### ########################################### ###############vllm v0.8.5################# ########################################### WORKDIR /workspace/ ENV VLLM_TARGET_DEVICE=rocm ENV ROCM_PATH=/opt/rocm ENV SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev # Find the repo path in: DockerFile/Dockerfile.rocm_yang # RUN git clone https://github.com/RLFoundation/vllm-patch.git RUN pip uninstall -y vllm || true RUN rm -rf vllm-patch RUN git clone https://github.com/RLFoundation/vllm-patch.git \ && cd vllm-patch \ && git checkout v0.8.5-sleep-numa \ && rm -rf build/ dist/ *.egg-info \ && ln -sf /opt/rocm/lib/libamdhip64.so /usr/lib/libamdhip64.so \ && SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev PYTORCH_ROCM_ARCH="gfx90a;gfx942" MAX_JOBS=${MAX_JOBS} python3 setup.py install # RUN SETUPTOOLS_SCM_PRETEND_VERSION=0.8.5.dev PYTORCH_ROCM_ARCH="gfx90a;gfx942" MAX_JOBS=${MAX_JOBS} python3 setup.py develop WORKDIR /workspace/ ########################################### ########################################### ########################################### ######################################### #### Install megatron-core############### ######################################### RUN pip uninstall -y megatron-core && \ git clone https://github.com/yushengsu-thu/Megatron-LM-amd_version.git && \ cd Megatron-LM-amd_version && \ pip install -vvv -e . && \ cd /workspace/ ######################################### ######################################### ######################################### ####################################### ################apex################### ####################################### WORKDIR /workspace/ RUN pip uninstall -y apex && \ git clone git@github.com:ROCm/apex.git && \ cd apex && \ python setup.py install && \ cd /workspace/ ####################################### ####################################### ####################################### ################################################################################ ###########################Add torch_memory_saver############################### ################################################################################ # Set environment variables ENV HIPCC_COMPILE_FLAGS_APPEND="--amdgpu-target=gfx90a;gfx942 -D__HIP_PLATFORM_AMD__" ENV CFLAGS="-D__HIP_PLATFORM_AMD__" ENV CXXFLAGS="-D__HIP_PLATFORM_AMD__" RUN pip install "git+https://github.com/YangWang92/torch_memory_saver_numa.git@numa" ################################################################################ ################################################################################ ################################################################################ ######################################## ######Install ray####################### ######################################## # need to add this patch: https://github.com/ray-project/ray/pull/53531/files RUN pip uninstall ray -y RUN pip install "ray[data,train,tune,serve]>=2.47.0" ######################################## ######################################## ######################################## ########################################## #######Install other dependencies######### ########################################## RUN pip install "tensordict==0.6.2" --no-deps && \ pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ torchdata \ wandb \ orjson \ pybind11 WORKDIR /workspace/ RUN git clone https://github.com/volcengine/verl.git && \ cd verl && \ pip install -e . ########################################## ########################################## ########################################## WORKDIR /workspace/ CMD ["/usr/bin/bash"] Build the image: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash docker docker/build -t verl-rocm . Run the container ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Note: You can pull the docker from this DockerHub: [RLSys Foundation](https://hub.docker.com/u/yushengsuthu) Pull the image: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash docker pull rlsys/verl:verl-0.4.1_ubuntu-22.04_rocm6.3.4-numa-patch_vllm0.8.5_sglang0.4.6.post4 docker tag rlsys/verl:verl-0.4.1_ubuntu-22.04_rocm6.3.4-numa-patch_vllm0.8.5_sglang0.4.6.post4 verl-rocm:latest Run the container ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Optional: Running without root and with user permissions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: bash docker run --rm -it \ --device /dev/dri \ --device /dev/kfd \ -p 8265:8265 \ --group-add video \ --cap-add SYS_PTRACE \ --security-opt seccomp=unconfined \ --privileged \ -v $HOME/.ssh:/root/.ssh \ -v $HOME:$HOME \ --shm-size 128G \ -w $PWD \ verl-rocm \ /bin/bash (Optional): If you do not want to root mode and require assign yourself as the user Please add ``-e HOST_UID=$(id -u)`` and ``-e HOST_GID=$(id -g)`` into the above docker launch script. Example ------- Due to to special setting in AMD (ROCM) torch, 1. If your ``ray>=2.45.0`` (default), you need to set ``RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES`` when starting ray in verl's RLHF training and add this [patch](https://github.com/ray-project/ray/pull/53531/files). 2. If your ``ray<2.45.0``, you need to set ``RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES`` when starting ray in verl's RLHF training. Inference ``$ENGINE`` can be ``vllm`` or ``sglang``. We choose ``vllm`` as default in the following examples. PPO ~~~ .. code-block:: bash YOUR_PROJECT_NAME=r1-verl-ppo-upstream YOUR_RUN_NAME=r1-training_ppo-upstream # export HYDRA_FULL_ERROR=1 export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 # [ray] < 2.45.0 #export RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1 # [ray] >= 2.45.0 export RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES=1 # Patch with https://github.com/ray-project/ray/pull/52794 GPUS_PER_NODE=8 MODEL_PATH=Qwen/Qwen2.5-0.5B-Instruct python3 examples/data_preprocess/gsm8k.py --local_save_dir data/gsm8k python3 -c "import transformers; transformers.pipeline('text-generation', model='$MODEL_PATH')" ENGINE=vllm #sglang PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.val_batch_size=1312 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=$MODEL_PATH \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=console \ trainer.project_name=$YOUR_PROJECT_NAME \ trainer.experiment_name=$YOUR_RUN_NAME \ trainer.val_before_train=False \ trainer.n_gpus_per_node=$GPUS_PER_NODE \ trainer.nnodes=1 \ trainer.save_freq=10 \ trainer.test_freq=10 \ trainer.total_epochs=15 #2>&1 | tee verl_demo.log GRPO ~~~~ .. code-block:: bash YOUR_PROJECT_NAME=r1-verl-grpo-upstream YOUR_RUN_NAME=r1-training_grpo-upstream # export HYDRA_FULL_ERROR=1 # export FSDP_VERBOSE=1 #export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 # [ray] < 2.45.0 #export RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1 # [ray] >= 2.45.0 export RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES=1 # Patch with https://github.com/ray-project/ray/pull/52794 GPUS_PER_NODE=8 MODEL_PATH=Qwen/Qwen2.5-0.5B-Instruct # MODEL_PATH=Qwen/Qwen2-7B-Instruct python3 examples/data_preprocess/gsm8k.py --local_save_dir data/gsm8k python3 -c "import transformers; transformers.pipeline('text-generation', model='$MODEL_PATH')" ENGINE=vllm #sglang python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.val_batch_size=1312 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=Flase \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.fsdp_config.param_offload=False \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name=$YOUR_PROJECT_NAME \ trainer.experiment_name=$YOUR_RUN_NAME \ trainer.n_gpus_per_node=$GPUS_PER_NODE \ trainer.val_before_train=False \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 Multi-node training: slurm with Docker/Podman container --------------------------------------------------------------------------------------- If you want to run multi-node training with slurm, you can use the following script. .. note:: 1. You need to use ``podman`` or ``docker`` in the following script. We will release the apptainer script later. 2. If you want to use ``podman``, you just replace ``docker`` with ``podman`` in the following script. The script includes the following steps: 1. SLURM Configuration 2. Environment Setup 3. Docker/Podman Container Setup 4. Ray Cluster Initialization 5. Data Preprocessing 6. Model Setup 7. Training Launch slurm_script.sh ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash #!/bin/bash #SBATCH --job-name=verl-ray-on-slurm #SBATCH --nodes=2 #SBATCH --ntasks-per-node=2 #SBATCH --mem=200G #SBATCH --time=30-00:00:00 #SBATCH --gpus-per-node=8 #SBATCH --cpus-per-task=28 #SBATCH --output=../verl_log/slurm-%j.out #SBATCH --error=../verl_log/slurm-%j.err #SBATCH --nodelist=gpu-[0,1] # load necessary modules ### Run this setup # [Cluster]: Use docker # docker pull docker.io/rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 ########################################################################## ###The following setting should be set in different project and cluster### ########################################################################## ### Project CONTAINER_NAME="multinode_verl_training" IMG="verl.rocm" DOCKERFILE="docker/Dockerfile.rocm" # echo $PWD verl_workdir="${HOME}/projects/verl_upstream" export TRANSFORMERS_CACHE="${HOME}/.cache/huggingface" export HF_HOME=$TRANSFORMERS_CACHE ### Cluster Network Setting export NCCL_DEBUG=TRACE export GPU_MAX_HW_QUEUES=2 export TORCH_NCCL_HIGH_PRIORITY=1 export NCCL_CHECKS_DISABLE=1 # export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7 export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9 export NCCL_IB_GID_INDEX=3 export NCCL_CROSS_NIC=0 export CUDA_DEVICE_MAX_CONNECTIONS=1 export NCCL_PROTO=Simple export RCCL_MSCCL_ENABLE=0 export TOKENIZERS_PARALLELISM=false export HSA_NO_SCRATCH_RECLAIM=1 ########################################################################## ## Assign using GPUs export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ### For rocm and training script # [ray] < 2.45.0 #export RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1 # [ray] >= 2.45.0 export RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES=1 # Patch with https://github.com/ray-project/ray/pull/52794 # Build and launch the Docker container srun bash -c " # Exit on any error set -e # Clean up dangling images (images with tag) docker image prune -f # Need to pull the docker first docker pull rlsys/verl:verl-0.4.1_ubuntu-22.04_rocm6.3.4-numa-patch_vllm0.8.5_sglang0.4.6.post4 if ! docker images --format "{{.Repository}}:{{.Tag}}" | grep -q "${IMG}"; then echo \"Building ${IMG} image...\" docker build -f \"${DOCKERFILE}\" -t \"${IMG}\" . else echo \"${IMG} image already exists, skipping build\" fi # Removing old container if exists docker rm \"${CONTAINER_NAME}\" 2>/dev/null || true # Checking network devices ibdev2netdev # Launch the docker docker run --rm -d \ -e HYDRA_FULL_ERROR=1 \ -e RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1 \ -e RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES=1 \ -e NCCL_DEBUG=${NCCL_DEBUG} \ -e GPU_MAX_HW_QUEUES=${GPU_MAX_HW_QUEUES} \ -e TORCH_NCCL_HIGH_PRIORITY=${TORCH_NCCL_HIGH_PRIORITY} \ -e NCCL_CHECKS_DISABLE=${NCCL_CHECKS_DISABLE} \ -e NCCL_IB_HCA=${NCCL_IB_HCA} \ -e NCCL_IB_GID_INDEX=${NCCL_IB_GID_INDEX} \ -e NCCL_CROSS_NIC=${NCCL_CROSS_NIC} \ -e CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS} \ -e NCCL_PROTO=${NCCL_PROTO} \ -e RCCL_MSCCL_ENABLE=${RCCL_MSCCL_ENABLE} \ -e TOKENIZERS_PARALLELISM=${TOKENIZERS_PARALLELISM} \ -e HSA_NO_SCRATCH_RECLAIM=${HSA_NO_SCRATCH_RECLAIM} \ -e TRANSFORMERS_CACHE=${TRANSFORMERS_CACHE} \ -e HF_HOME=${HF_HOME} \ --network host \ --device /dev/dri \ --device /dev/kfd \ --device /dev/infiniband \ --group-add video \ --cap-add SYS_PTRACE \ --security-opt seccomp=unconfined \ --privileged \ -v \${HOME}:\${HOME} \ -v \${HOME}/.ssh:/root/.ssh \ -w "${verl_workdir}" \ --shm-size 128G \ --name \"${CONTAINER_NAME}\" \ \"${IMG}\" \ tail -f /dev/null echo \"Container setup completed\" " # (Optional): If you do not want to root mode and require assign yuorself as the user # Please add `-e HOST_UID=$(id -u)` and `-e HOST_GID=$(id -g)` into the above docker launch script. ### Ray launch the nodes before training # Getting the node names nodes_array=($(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')) head_node=${nodes_array[0]} head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address) # if we detect a space character in the head node IP, we'll # convert it to an ipv4 address. This step is optional. if [[ "$head_node_ip" == *" "* ]]; then IFS=' ' read -ra ADDR <<<"$head_node_ip" if [[ ${#ADDR[0]} -gt 16 ]]; then head_node_ip=${ADDR[1]} else head_node_ip=${ADDR[0]} fi echo "IPV6 address detected. We split the IPV4 address as $head_node_ip" fi port=6379 ip_head=$head_node_ip:$port export ip_head echo "IP Head: $ip_head" # make sure we set environment variables before Ray initialization # Print out all env variables printenv echo "Starting HEAD at $head_node" srun --nodes=1 --ntasks=1 -w "$head_node" \ docker exec "${CONTAINER_NAME}" \ ray start --head --node-ip-address="$head_node_ip" --port=$port \ --dashboard-port=8266 \ --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & # optional, though may be useful in certain versions of Ray < 1.0. sleep 10 # number of nodes other than the head node worker_num=$((SLURM_JOB_NUM_NODES - 1)) for ((i = 1; i <= worker_num; i++)); do node_i=${nodes_array[$i]} echo "Debug: Starting worker on node_i = ${node_i}" if [ -z "$node_i" ]; then echo "Error: Empty node name for worker $i" continue fi echo "Starting WORKER $i at $node_i" srun --nodes=1 --ntasks=1 -w "$node_i" \ docker exec "${CONTAINER_NAME}" \ ray start --address "$ip_head" --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & sleep 5 done # Ray initlization test (See whether any error in the above execution) echo "Testing Ray initialization in the slurm nodes..." docker exec "${CONTAINER_NAME}" python3 -c ' import ray try: ray.init(address="auto") print("\n=== Ray Cluster Status ===") print(f"Number of nodes: {len(ray.nodes())}") for node in ray.nodes(): print("Node: {}, Status: {}".format(node["NodeManagerHostname"], node["Alive"])) # print(f"Node: {node}") ray.shutdown() print("Ray initialization successful!") except Exception as e: print(f"Ray initialization failed: {str(e)}") ' echo "=== Ray test completed ===" ###### # Run data preprocessing echo "Starting data preprocessing..." docker exec "${CONTAINER_NAME}" \ python3 "examples/data_preprocess/gsm8k.py" "--local_save_dir" "../data/gsm8k" echo "Starting data preprocessing..." docker exec "${CONTAINER_NAME}" \ python3 "examples/data_preprocess/math_dataset.py" "--local_dir" "../data/math" train_files="../data/gsm8k/train.parquet" val_files="../data/gsm8k/test.parquet" # Download and test model echo "Loading model..." docker exec "${CONTAINER_NAME}" \ python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2-7B-Instruct')" MODEL_PATH="Qwen/Qwen2-7B-Instruct" # Set model path after pipeline test MODEL_PATH="Qwen/Qwen2.5-0.5B-Instruct" echo "== Data and model loading Done ==" echo "Start to train..." docker exec "${CONTAINER_NAME}" \ python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2-7B-Instruct')" MODEL_PATH="Qwen/Qwen2-7B-Instruct" PYTHONUNBUFFERED=1 srun --overlap --nodes=${SLURM_NNODES} --ntasks=1 -w "$head_node" \ docker exec "${CONTAINER_NAME}" \ python3 -m verl.trainer.main_ppo \ data.train_files=$train_files \ data.val_files=$val_files \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=$MODEL_PATH \ critic.model.enable_gradient_checkpointing=False \ critic.ppo_micro_batch_size_per_gpu=8 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.kl_ctrl.kl_coef=0.0001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example' \ trainer.experiment_name='Qwen2.5-32B-Instruct_function_rm' \ trainer.n_gpus_per_node=${SLURM_GPUS_PER_NODE} \ trainer.val_before_train=False \ trainer.nnodes=${SLURM_NNODES} \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 Run slurm_script.sh ~~~~~~~~~~~~~~~~~~~~ Just sbatch your slurm_script.sh .. code-block:: bash sbatch slurm_script.sh ================================================ FILE: docs/amd_tutorial/amd_vllm_page.rst ================================================ verl performance tuning for AMD (ROCm Kernel) ===================================================== Last updated: 11/13/2025. Author: `Yang Wang `_, `Songlin Jiang `_ Use vLLM Sleep Mode for AMD MI3xx series GPUs -------------------------------------------------------------- By default, verl requires vLLM to enable sleep mode, which allows vLLM to offload GPU memory to CPU memory after rollout. This feature has been merged into the main branch of vLLM for version later than 0.11.0. For now, you can use the vLLM main branch and build it from the source code, or you can directly install vLLM from the pre-built ROCm wheels for vLLM version later than 0.11.0 when it's available. 1. Clone the vLLM repository and build it with the following commands: .. code-block:: bash git clone https://github.com/vllm-project/vllm.git cd vllm git reset --hard 4ca5cd5740c0cd7788cdfa8b7ec6a27335607a48 # You can also use a later commit as you wish python -m pip install -r requirements/rocm.txt VLLM_TARGET_DEVICE=rocm ROCM_PATH=/opt/rocm/ python3 setup.py develop 2. Additionally, we recommend you to use the ROCm version later than or equal to ROCm 7.0. After the upgrade, you can verify whether sleep mode is working by trying out `these scripts `_. If sleep mode is working, you should see the memory usage reduce after sleep. After applying the vLLM patch and completing the installation, you can enable sleep mode in verl to reduce memory overhead. This allows verl to offload unused GPU memory during rollout, significantly lowering the memory footprint during long-context training or multi-node reinforcement learning. Enable CUDA Graph and Bypass ROCm-related issues -------------------------------------------------------------- Due to potential issues with CUDA graph capture in ROCm, we've found that vLLM's CUDA graph feature cannot be enabled on multiple nodes in verl on AMD platforms with vLLM V1 mode. This leads to significantly slower rollout performance. Our investigation shows that ROCm may trigger an unexpected crash when attempting to capture large batches with CUDA graph. One workaround is to set ``actor_rollout_ref.rollout.cudagraph_capture_sizes`` to values such as ``[1, 2, 4, 8, 16, 32, 64]`` (change depending on your GPU memory size). Then, you can choose to enable CUDA graph by setting ``actor_rollout_ref.rollout.enforce_eager`` to ``False`` in your verl configuration file. ================================================ FILE: docs/api/data.rst ================================================ Data interface ========================= Last updated: 05/19/2025 (API docstrings are auto-generated). DataProto is the interface for data exchange. The :class:`verl.DataProto` class contains two key members: - batch: a :class:`tensordict.TensorDict` object for the actual data - meta_info: a :class:`Dict` with additional meta information TensorDict ~~~~~~~~~~~~ :attr:`DataProto.batch` is built on top of :class:`tensordict`, a project in the PyTorch ecosystem. A TensorDict is a dict-like container for tensors. To instantiate a TensorDict, you must specify key-value pairs as well as the batch size. .. code-block:: python >>> import torch >>> from tensordict import TensorDict >>> tensordict = TensorDict({"zeros": torch.zeros(2, 3, 4), "ones": torch.ones(2, 3, 5)}, batch_size=[2,]) >>> tensordict["twos"] = 2 * torch.ones(2, 5, 6) >>> zeros = tensordict["zeros"] >>> tensordict TensorDict( fields={ ones: Tensor(shape=torch.Size([2, 3, 5]), device=cpu, dtype=torch.float32, is_shared=False), twos: Tensor(shape=torch.Size([2, 5, 6]), device=cpu, dtype=torch.float32, is_shared=False), zeros: Tensor(shape=torch.Size([2, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([2]), device=None, is_shared=False) One can also index a tensordict along its batch_size. The contents of the TensorDict can be manipulated collectively as well. .. code-block:: python >>> tensordict[..., :1] TensorDict( fields={ ones: Tensor(shape=torch.Size([1, 3, 5]), device=cpu, dtype=torch.float32, is_shared=False), twos: Tensor(shape=torch.Size([1, 5, 6]), device=cpu, dtype=torch.float32, is_shared=False), zeros: Tensor(shape=torch.Size([1, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([1]), device=None, is_shared=False) >>> tensordict = tensordict.to("cuda:0") >>> tensordict = tensordict.reshape(6) For more about :class:`tensordict.TensorDict` usage, see the official tensordict_ documentation. .. _tensordict: https://pytorch.org/tensordict/stable/overview.html Core APIs ~~~~~~~~~~~~~~~~~ .. autoclass:: verl.DataProto :members: to, select, union, make_iterator, concat ================================================ FILE: docs/api/single_controller.rst ================================================ Single Controller interface ============================ Last updated: 05/27/2025 (API docstrings are auto-generated). The Single Controller provides a unified interface for managing distributed workers using Ray or other backends and executing functions across them. It simplifies the process of dispatching tasks and collecting results, particularly when dealing with data parallelism or model parallelism. Core APIs ~~~~~~~~~~~~~~~~~ .. autoclass:: verl.single_controller.Worker :members: __init__, __new__, get_master_addr_port, get_cuda_visible_devices, world_size, rank .. autoclass:: verl.single_controller.WorkerGroup :members: __init__, world_size .. autoclass:: verl.single_controller.ClassWithInitArgs :members: __init__, __call__ .. autoclass:: verl.single_controller.ResourcePool :members: __init__, world_size, local_world_size_list, local_rank_list .. autoclass:: verl.single_controller.ray.RayWorkerGroup :members: __init__ .. autofunction:: verl.single_controller.ray.create_colocated_worker_cls ================================================ FILE: docs/api/trainer.rst ================================================ Trainer Interface ================================ Last updated: 06/08/2025 (API docstrings are auto-generated). Trainers drive the training loop. Introducing new trainer classes in case of new training paradiam is encouraged. .. autosummary:: :nosignatures: verl.trainer.ppo.ray_trainer.RayPPOTrainer Core APIs ~~~~~~~~~~~~~~~~~ .. autoclass:: verl.trainer.ppo.ray_trainer.RayPPOTrainer :members: __init__, init_workers, fit .. automodule:: verl.utils.tokenizer :members: hf_tokenizer .. automodule:: verl.trainer.ppo.core_algos :members: agg_loss, kl_penalty, compute_policy_loss, kl_penalty .. automodule:: verl.trainer.ppo.reward :members: load_reward_manager, compute_reward, compute_reward_async .. autoclass:: verl.workers.reward_manager.NaiveRewardManager .. autoclass:: verl.workers.reward_manager.DAPORewardManager ================================================ FILE: docs/api/utils.rst ================================================ Utilities ============ Last updated: 05/19/2025 (API docstrings are auto-generated). This section documents the utility functions and classes in the VERL library. Python Functional Utilities ------------------------------ .. automodule:: verl.utils.py_functional :members: append_to_dict File System Utilities ------------------------ .. automodule:: verl.utils.fs :members: copy_to_local Tracking Utilities --------------------- .. automodule:: verl.utils.tracking :members: Tracking Metrics Utilities --------------------- .. automodule:: verl.utils.metric :members: reduce_metrics Checkpoint Management ------------------------ .. automodule:: verl.utils.checkpoint.checkpoint_manager :members: find_latest_ckpt_path .. automodule:: verl.utils.checkpoint.fsdp_checkpoint_manager :members: FSDPCheckpointManager Dataset Utilities --------------------- .. automodule:: verl.utils.dataset.rl_dataset :members: RLHFDataset, collate_fn Torch Functional Utilities ----------------------------- .. automodule:: verl.utils.torch_functional :members: get_constant_schedule_with_warmup, masked_whiten, masked_mean, logprobs_from_logits Sequence Length Balancing ---------------------------- .. automodule:: verl.utils.seqlen_balancing :members: get_reverse_idx, rearrange_micro_batches Ulysses Utilities -------------------- .. automodule:: verl.utils.ulysses :members: gather_outputs_and_unpad, ulysses_pad_and_slice_inputs FSDP Utilities ------------------ .. automodule:: verl.utils.fsdp_utils :members: get_fsdp_wrap_policy, get_init_weight_context_manager, init_fn, load_fsdp_model_to_gpu, load_fsdp_optimizer, offload_fsdp_model_to_cpu, offload_fsdp_optimizer, Debug Utilities ------------------- .. automodule:: verl.utils.profiler :members: log_gpu_memory_usage, GPUMemoryLogger ================================================ FILE: docs/ascend_tutorial/contribution_guide/ascend_ci_guide_zh.rst ================================================ NPU-CI 添加指导 =========== Last updated: 02/02/2026. 我们在 verl 上增加基于华为昇腾设备的CI用例添加指导。 verl 仓库使用 GitHub Actions 作为 CI 平台，通过分层测试架构保障代码质量与系统稳定性。 NPU 相关的工作流主要包括： * ``npu_unit_test.yml``：运行单元测试。 * 以 ``_ascend.yml`` 结尾的文件：运行针对 Ascend NPU 的端到端测试或专项测试。 添加新用例指南 ----------------------------------- 1. 数据集与权重 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 流水机器上的权重与绝对路径： +---------------------------------------+-------------------------------------------------------------------+ | 模型名称 | 绝对路径 | +=======================================+===================================================================+ | Qwen3-30B-A3B-Instruct-2507 | ``${HOME}/.cache/models/Qwen/Qwen3-30B-A3B-Instruct-2507`` | +---------------------------------------+-------------------------------------------------------------------+ | Qwen2.5-VL-3B-Instruct | ``${HOME}/.cache/models/Qwen/Qwen2.5-VL-3B-Instruct`` | +---------------------------------------+-------------------------------------------------------------------+ | Qwen2.5-0.5B | ``${HOME}/.cache/models/Qwen/Qwen2.5-0.5B`` | +---------------------------------------+-------------------------------------------------------------------+ | Qwen2.5-0.5B-Instruct | ``${HOME}/.cache/models/Qwen/Qwen2.5-0.5B-Instruct`` | +---------------------------------------+-------------------------------------------------------------------+ | Qwen2.5-1.5B-Instruct | ``${HOME}/.cache/models/Qwen/Qwen2.5-1.5B-Instruct`` | +---------------------------------------+-------------------------------------------------------------------+ | Skywork-Reward-V2-Llama-3.2-1B | ``${HOME}/.cache/models/Skywork/Skywork-Reward-V2-Llama-3.2-1B`` | +---------------------------------------+-------------------------------------------------------------------+ 流水机器上的数据集与绝对路径： +--------------+---------------------------------------------------+ | 数据集名称 | 绝对路径 | +==============+===================================================+ | gsm8k | ``${HOME}/.cache/datasets/openai/gsm8k`` | +--------------+---------------------------------------------------+ | geo3k | ``${HOME}/.cache/datasets/hiyouga/geometry3k`` | +--------------+---------------------------------------------------+ **Note** {HOME}是root gpu用例中权重在~/models/路径下，如需适配可以用软链接，``ln -s /root/.cache/models ~/models`` 此处为原始数据集，按需进行数据处理，如下。 ``python examples/data_preprocess/gsm8k_multiturn_sft.py --local_dataset_path ${HOME}/.cache/datasets/openai/gsm8k`` 2. 工作流 YAML 模板 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 如需新增一个工作流，可参考以下模板创建 ``.github/workflows/your_yml_ascend.yml`` 文件。 主要修改部分包括： * 工作流名称（``name``） * 触发条件（``on``） * 运行环境（``runs-on``） * 容器镜像（``container.image``） * 具体执行步骤（``jobs..steps``） .. code-block:: yaml :linenos: name: your_yml_ascend # 工作流唯一标识 # 触发条件配置 on: push: branches: - main - v0.* pull_request: branches: - main paths: - ".github/workflows/your_yml_ascend.yml" # 必须包含此工作流文件路径 - "path/to/affected_files" # 需监控的相关代码路径 # 并发控制策略 concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} # 仅非main分支取消进行中的任务 permissions: contents: read # 最小权限原则 jobs: your_job_name: # 任务唯一标识 if: github.repository_owner == 'verl-project' # 仅在主仓库运行 runs-on: linux-aarch64-a2-4 # 硬件规格：a2实例，4卡NPU timeout-minutes: 60 # 任务超时阈值（分钟） container: #运行镜像 该示例为vllm的镜像 image: swr.ap-southeast-1.myhuaweicloud.com/base_image/ascend-ci/verl/verl:verl-8.5.0-910b-ubuntu22.04-py3.11-latest options: >- --shm-size 16g # 共享内存配置 env: HF_ENDPOINT: "https://hf-mirror.com" HF_HUB_ENABLE_HF_TRANSFER: "0" steps: - name: Check npu and CANN info run: | cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info npu-smi info - name: Check initial pip list from image run: pip list - name: Checkout repository uses: actions/checkout@v4 with: fetch-depth: 0 clean: true - name: Install dependencies run: | pip install -r requirements-npu.txt pip install -e . - name: Verify environment run: pip list # 以下为具体测试步骤（根据需求定制） - name: Preprocess dataset run: python examples/data_preprocess/your_script.py --local_dataset_path ${HOME}/.cache/datasets/your_dataset - name: Execute NPU test run: | ray stop --force bash tests/special_npu/your_test_script.sh **Note** ${HOME}/.cache/文件夹内一旦添加新内容，不会因CI跑完容器销毁而删除，请避免往该文件夹添加内容。 3. 添加单元测试 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 步骤： (1) 在 ``tests/`` 目录下创建或修改单元测试文件（例如 ``test_xxx.py``）。 (2) 若测试文件路径未被 ``npu_unit_test.yml`` 中的 ``--ignore-glob`` 规则排除，则会在以下命令中自动执行： .. code-block:: yaml pytest -s -x --ignore-glob="xxx" --ignore-glob="xxx" tests/ (3) 若测试路径在 ``--ignore-glob`` 排除范围内，需在 ``npu_unit_test.yml`` 中新增一个 step 来显式运行该测试。 (4) 如新增一批相关用例，建议单独创建专门的工作流文件以保持清晰。 4. 添加端到端测试脚本 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 步骤： (1) 在 ``tests/special_npu/`` 目录下创建端到端测试脚本。 (2) 在 ``.github/workflows/`` 目录中找到功能最接近的以 ``_ascend.yml`` 结尾的工作流文件，在其中添加一个 step 调用该脚本。 (3) 若测试场景独立或较复杂，可考虑单独创建新的工作流文件。 5. 测试策略建议 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * **单元测试**：覆盖核心函数、类与方法，确保逻辑正确。 * **集成/端到端测试**：覆盖典型训练、推理 pipeline，验证多模块协同与硬件适配。 * **资源管理**：一个workflow里的多个job为并行运行，请合理设置超时时间，避免任务长时间挂起，请控制单个 job 的运行时间在 40min 以内。 通过以上步骤，可系统化地为 verl 仓库添加 NPU 相关的自动化测试，确保代码变更在合并前经过充分验证。 ================================================ FILE: docs/ascend_tutorial/examples/ascend_performance_analysis_guide.md ================================================ # Ascend Performance Analysis Guide Last updated: 02/24/2026. ## 背景介绍 随着DeepSeek-R1的发布，大模型强化学习（RL）训练受到广泛关注。在昇腾NPU环境下，verl框架已积累了丰富的性能调优经验。本文系统总结了包括性能数据采集与分析在内的方法论，旨在帮助开发者更高效地运用MindStudio工具链，实现强化学习场景下的性能优化。 ### 强化学习计算流程概述 1. **Rollout**：策略（actor）模型基于输入的prompt序列，推理生成回答（response序列） 2. **ref logprob**：基于prompt和生成的response，reference模型计算ref logprob用于KL散度计算 3. **logprob**：基于prompt和生成的response，actor模型计算logprob用于重要性采样 4. **reward**：基于prompt和生成的response，奖励模型评估奖励值R_N。 5. **update**：基于计算得到的R_N、ref logprob、logprob计算优化函数和策略梯度，对actor模型进行更新 ![rl_data_stream](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/rl_data_stream.png) ## profilling工具使能 ### 使能方法 使能和配置教程可参考：[verl/docs/ascend_tutorial/profiling/ascend_profiling_zh.rst at main · verl-project/verl](https://github.com/verl-project/verl/raw/main/docs/ascend_tutorial/profiling/ascend_profiling_zh.rst) ## 性能分析方法论 ### 整体性能概览分析 #### 1. 长耗时任务与资源空泡分析 - **操作**：使用MindStudio Insight加载profiling数据，自动识别不同计算阶段，通过RL页签流水图定位长耗时任务与NPU资源空泡 - **价值**：快速掌握不同阶段耗时占比 - **效果展示**： ![Bubble_analysis](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/Bubble_analysis.png) #### 2. 负载均衡分析 - **操作**：通过MindStudio Insight直接查看MSTX打点数据，观察Rollout阶段不同DP Rank的负载均衡情况 - **价值**：快速识别负载不均问题 - **效果展示：** ![Load_Balancing_Analysis](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/Load_Balancing_Analysis.gif) #### 3. 集群整体性能分析 - **操作**：结合MSTT的rl_analysis功能，生成集群Timeline缩略图，观察各阶段整体耗时 - **价值**：宏观掌握集群性能瓶颈 - **操作指南**：[rl_analysis使用文档](https://gitcode.com/Ascend/mstt/raw/pre-research/profiler/msprof_analyze/docs/features/rl_analysis.md) - **效果展示**： ![Cluster%20Performance%20Analysis](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/Cluster%20Performance%20Analysis.png) ### 细粒度分析 #### 性能分析 - **操作**：可通过 MindStudio Insight Windows 或 Linux 版本加载 Profiling 数据 - **价值**：MindStudio Insight 支持分析任务调度效率、算子执行性能、计算资源利用率、集合通信性能等。其 Timeline 视图具备任务拆解与 Overlap 分析功能（**为 MindStudio 独有核心特性，在 NV 及其他竞品中不具备，是 AI 调优的必备工具**），并支持鼠标交互式分析。 - **效果展示**： ![performance%20analysis](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/performance%20analysis.png) #### 内存分析 ##### **通过 Profiling 结合调用栈分析系统内存变化** - **操作**：采集数据时开启调用栈和内存视图功能。 - **价值**：观察框架、CANN内存申请释放情况，可结合调用栈跟踪到前端python代码。 - **效果展示**：结合调用栈进行内存变化分析。效果如下所示： ![in-memory%20analytics](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/in-memory%20analytics.gif) ##### **使用 msleaks 工具进行深层次内存分析** - **操作步骤**：参考 [msleaks 工具使用指南](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/83RC1alpha003/devaids/msleaks/atlas_msleaks_0001.html)。 - **价值**：可以查看框架内存申请总量折线图/内存块图，并直接对应调用栈，可深层次分析框架内存使用情况。 - **效果展示**： ![msleaks](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/msleaks.gif) ## 性能分析案例 要做具体的性能分析，profiling要开启**level1**，否则算子的关键信息会缺失。 ### 1.host bound诊断 host bound是指CPU任务量综合大于NPU，导致NPU执行出现空泡的现象。可以通过看Host2Device的同步连线来判断，如果连线都是歪的，那证明这里的set信号早于wait信号，NPU一ready就执行了，那也是device bound： ![host_bound_1](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/host_bound_1.png) 如果确诊为host bound，那么我们可以打开CPU侧，找出各算子的下发耗时。注意找的时候需要找出所有CPU耗时的累加值，而不能找单层，因为首次调用的耗时是很长的。例如下图的GmmSwigluQuant，CPU上首次调用需要1ms，后续每次只需要200us。 ![host_bound_2](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/host_bound_2.png) 此时有的算子在负重前行，有的算子拖了后腿，后者多于了前者。我们优先**找出来host耗时大于device的top算子，这些算子是拖后腿的**，可以交予算子团队重点分析。 ### 2.组网合理性分析 有的时候，模型组网没有按照最高效的方式来，这一点在profiling中是非常易于识别的，下面会介绍一下分析思路并给出例子。 通常来讲，LLM中的大的热点算子是Attention和FFN中的矩阵乘计算，二者加起来在prefill下可能达到计算耗时的70%+，decode下可能达到50%+。如果整体的耗时比例不符合预期，或者profiling中出现了一些新面孔，或者拼接类算子太多了，这都值得我们去分析一下模型组网，是不是使用算子的方式错了？尤其是拼接类算子，是值得我们逐一分析的。 对于slice/split/concat这样的拼接类算子，还有transpose/cast这种转换算子，他们的存在往往是前后算子不直接配套造成的。如果前一个算子可以直接对输出做好尾处理，往往可以节省一个算子的启动开销和一次冗余读写。但这样的改变不一定符合算子的基本设计原则。 举一个正例，对于某次Matmul的输出shape为[m, n0 + n1]，在这后面我们接了两个slice，输入均为这个[m, n0 + n1]的tensor，输出分别为[m, n0]和[m, n1]。第一个优化的思路是将两个slice改为一个split，这样耗时可以基本减半，[m, n0 + n1]的显存也可以尽早释放。进一步优化的思路是将矩阵乘的权重从[k, n0 + n1]分割为[k, n0]和[k, n1]，将原来的矩阵乘任务分成两个（前提是这两个的耗时加起来不比之前的劣化太多，分核策略不能出问题），从而彻底消除这个slice/split操作。 ![network_1](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/network_1.png) 举一个反例，Rmsnorm(fp16)+Cast(fp16->fp32)+Matmul(fp32)，Rmsnorm虽然输入输出都是fp16，但考虑到累加运算的精度，内部是fp32做计算的。如果将Cast融到Rmsnorm内，本就内部使用fp32做计算的Rmsnorm就可以省去一个末尾fp32->fp16的cast，加上我们干掉的Cast，总共节省两个cast的同时避免了一次精度丢失。虽然这样看起来精度性能双收了，但fp16进，fp32出的Rmsnorm是反原则的（核心输入和输出需要是同数据类型），除非我们能在广大开源模型中频繁找到这样的结构，证明它的普适性，否则算子团队是不允许做这样的算子的。 ![network_2](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/network_2.png) ### 3.算子性能初诊 需要利用`".\ASCEND_PROFILER_OUTPUT\operator_details.csv"`来做分析，从而判断算子识否有性能问题。 Profiling工具会统计这些流水线在不同核上的平均繁忙时间（xxx_time），与最慢核的完整kernel耗时（task_duration）做除法，得到流水线利用率（xxx_ratio）。这些流水线之间虽然互有依赖，且搬运类流水线会互抢带宽，但算子只要设计得当，是可以做到互相掩盖的。因此我们可以初步认为，**当算子的执行耗时大到一定程度上，算子应当在某一条流水线上形成bound**，即利用率要高到一定程度。经验上，在单算子耗时达到50μ时，就可以认为算子应当在bound流水线上，达成80%+的占用率了。 以下图为例，第一行是一个FA算子，第二行是一个Matmul算子，FA在vec流水线上达到了88.1%的利用率，Matmul算子在mac流水线上达到了89.8%的利用率，他们的性能可以认为是合格的。 ![Operator%20performance](https://github.com/chengminhua/verl_data/raw/main/MindStudio_Insight_use/Operator%20performance.png) ### 4.亲和shape调整 对于一个模型而言，超参是我们控制不了的，但我们可以控制并发度、权重格式、切分策略等因素来迎合算子，使其发挥出最大的性能，这一节主要从算子搬运效率和负载均衡两个方面出发，讨论模型侧值得尝试的调整方向。 #### 4.1 搬运效率亲和的shape mte2是一个自身效率严重受shape影响的流水线。要想让mte2保证最大搬运效率，我们需要保障如下两个条件至少满足其一： **（1）被搬运的矩阵使用nz作为format（最优） （2）被搬运的矩阵的尾轴512B对齐，且不为16KB的整数倍（近似最优）** 对于权重矩阵来说，推理阶段尤其是decode，我们通常满足（1），训练阶段我们通常满足（2）。**如果我们做不到（1），我们就要迎合（2）**。典型的手段有： 1，如果没达成B的矩阵的首轴是亲和的而尾轴不亲和，那么对它做transpose 2，调整TP切分策略，避免出现不亲和的尾轴 #### 4.2 负载均衡亲和的shape 在算子shape不大时，受制于算子语义，我们有可能不能把所有核都利用起来，或者即使开满核，负载均衡却很差。这一小节主要是对decode阶段的小shape做分析。 首先，我们明确出当前NPU卡是多少核的，如果不清楚，跑出来的profiling里都是20，40这样的数，就说明是20核，反之是24核。这里我的24核其实是代表了一个cube和两个vector组成的小组，我们可以认为是一个cube作为主核，带了两个vector作为从核。如果一个算子是纯vector算子，那么就不再有组的概念，40或48个vector核会作为主核直接独立去拿逻辑任务。 对于LLM中的vector算子，它的一种常见分核策略有可能是分在最高维，也就是batch维，常见于对低维（也叫尾轴）有规约操作的norm类、动态量化类等算子；另一种是整体拍平，允许算子切分的非常细的算子，如elementwse算子。对于第一种，我们就可以在模型侧关注它的负载均衡问题。例如我们打48batch，而硬件却是个40个vector核，那这40个核会循环2次，第二次有多数的核会无事可做，这个batch数就可以认为是不友好的。如果将batch打到64或80，性能可以预见会是无损的。同样的情况下，如果是48核的卡，那我们可以认为这就是个非常友好的batch数。 对于cube类算子，它常见的分核策略是以base快去切分M和N（K轴是累加轴，对它分核会引入确定性问题）。最常见的分块是baseM=128，baseN=256。在decode阶段，我们的耗时基本可以看做都是在搬权重，这是因为激活的M极小，M方向大概率只分了一块，那么右矩阵就只需要搬一次。所以我们在M≤128的范围内可以尽情提高M，对性能都基本是无损的，如果M大于128，可以认为(128, 256]是下一个性能分档。 除了M外，N轴切分的任务也影响算子亲和性，以deepseekR1中的MLA预处理为例，它会使用同一个激活（shape为[batch_size, 7168]）与两个权重做矩阵乘(shape为[7168, 1536]和[7168, 576])。在batch_size打不大的情况下，即使baseN缩短为128，N轴都不能用满核数，所以此时这两个矩阵乘各自的耗时，会约等于将他们权重N轴拼起来乘(shape为[7168, 2112])的矩阵乘的耗时。如果仅考虑模型竞争力，我们更希望对这两个权重做合并，否则两个小的矩阵乘带宽利用率都会非常差。 对于Attention算子，它常见的分核策略是q_seqlen、batch_size和kv_headnum。增量阶段q_seqlen会以MTP和GQA倍数做合并，但是通常也不会大过128，划分不出第二个任务，那么并行度基本就是batch_size * kv_headnum。 总的来说，我们可以依据shape信息和算子类别，对算子是否有负载均衡问题作出识别，从而对我们切分策略选择，最高吞吐量的batch策略作出预判。 ================================================ FILE: docs/ascend_tutorial/examples/ascend_retool_best_pratice.rst ================================================ Ascend Retool Best Practice =================================== Last updated: 03/01/2026. 引言 ---------------------------------- Retool论文参考([Retool](https://arxiv.org/pdf/2504.11536)) 集成代码解释器工具，通过多轮实时代码执行进行策略部署，并教会模型根据结果反馈学习何时以及如何调用工具。 1. 环境构建 2. 模型训练 用例模型脚本以及其需要的硬件条件各自如下： =============== ============ ============ =============== 模型 NPU型号 节点数量 训推后端 =============== ============ ============ =============== ``Qwen2.5-7B`` Atlas 900 A2 1 ``vllm + FSDP`` =============== ============ ============ =============== 环境构建 ----------------------------------- 1.从自定义Conda环境进行构建 ============ ============================================================ software version ============ ============================================================ Python ``>= 3.10, <3.12`` CANN ``== 8.3.RC1`` torch ``== 2.7.1`` torch_npu ``== 2.7.1`` verl ``v0.6.1 commitId=d62da4950573d7a4b7ef2362337952e7ab59e78d`` vllm ``v0.11.0`` vllm-ascend ``v0.11.0-dev`` transformers ``4.57.6`` ============ ============================================================ 模型训练与评估 ----------------------------------- 1.模型数据准备 ^^^^^^^^^^^ `Qwen2.5-7B` ^^^^^^^^^^^ **下载模型权重** --local-dir: 模型保存路径 .. code-block:: bash git clone https://huggingface.co/Qwen/Qwen2.5-7B-Instruct **下载训练数据集** .. code-block:: bash git clone https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k **下载评估数据集** .. code-block:: bash git clone https://huggingface.co/datasets/Maxwell-Jia/AIME_2024 **下载预训练数据集** .. code-block:: bash python3 recipe/retool/retool_sft_preprocess.py *注:自动下载ReTool-SFT，最后生成数据默认保存在~/ReTool-SFT/data目录下* **执行预训练脚本** .. code-block:: bash bash recipe/retool/run_qwen2_7b_sft_npu.sh # 需适配脚本中路径 **合并预训练权重生成checkpoint** .. code-block:: bash python3 -m verl.model_merger merge --backend fsdp \ --local_dir ${DATASETS}/checkpoint/multiturn-sft-qwen-2.5-7b-instruct/global_step_372 \ --target_dir ${DATASETS}/checkpoint/multiturn-sft-qwen-2.5-7b-instruct/global_step_372/huggingface 2.代码沙箱准备 开源沙箱代码及部署参考 https://github.com/bytedance/SandboxFusion **沙箱代码下载** .. code-block:: bash git clone -b main https://github.com/bytedance/SandboxFusion.git **沙箱安装** .. code-block:: bash cd SandboxFusion conda create -n sandbox -y python=3.11 conda activate sandbox pip install poetry poetry lock poetry install mkdir -p docs/build cd runtime/python bash install-python-runtime.sh cd ../../ make run-online 3.训练 示例配置文件如下，在recipe/retool目录下创建一个run_qwen2.5_7b_dapo_npu.sh 根据开发者实际路径配置情况修改模型训练脚本中的以下参数 .. code-block:: bash set -x export VLLM_USE_V1=1 export TORCHDYNAMO_DISABLE=1 export VLLM_ASCEND_ENABLE_NZ=0 export TASK_QUEUE_ENABLE=1 export VLLM_ENABLE_GRAPH_MODE=1 export HCCL_OP_EXPANSION_MODE="AIV" export VLLM_ASCEND_ENABLE_MLP_OPTIMIZE=1 export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2 # ================= data/model/tool ================= HDFS_ROOT=${HDFS_ROOT:-"${PWD}"} DATA_ROOT=${DATA_ROOT:-"${PWD}"} dapo_math_17k=$DATA_ROOT/dataset/BytedTsinghua-SIA/DAPO-Math-17k aime_2024=$DATA_ROOT/dataset/Maxwell-Jia/AIME_2024 #aime_2025=$DATA_ROOT/dataset/yentinglin/aime_2025 model_path=$DATA_ROOT/dataset/checkpoint/multiturn-sft-qwen-2.5-7b-instruct/global_step_372/huggingface train_files="['$dapo_math_17k']" test_files="['$aime_2024']" # tool tool_config_path=recipe/retool/sandbox_fusion_tool_config.yaml # wandb project_name=retool experiment_name=qwen2.5-7b_dapo default_local_dir=$DATA_ROOT/checkpoint/$experiment_name # 创建日志文件 export TIMESTAMP=$(date +%Y%m%d_%H%M%S) LOG_DIR="$HDFS_ROOT/verl/logs/$project_name/$experiment_name" # 判断路径是否存在 if [ ! -d "$LOG_DIR" ]; then # 路径不存在，创建路径 mkdir -p "$LOG_DIR" echo "Directory $LOG_DIR created." else echo "Directory $LOG_DIR already exists." fi LOG_FILE="${LOG_DIR}/${TIMESTAMP}.log" touch "$LOG_FILE" echo "Log file $LOG_FILE created." # ================= algorithm ================= adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_turns=16 max_prompt_length=2048 max_response_length=20480 actor_lr=1e-6 train_batch_size=32 ppo_mini_batch_size=16 n_resp_per_prompt=16 n_resp_per_prompt_val=30 # ================= performance ================= infer_tp=2 # vllm train_sp=4 # train offload=True actor_max_token_len_per_gpu=$(( (max_prompt_length + max_response_length) * 1 )) log_prob_max_token_len_per_gpu=$(( actor_max_token_len_per_gpu * 4 )) PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=$adv_estimator \ algorithm.use_kl_in_reward=$use_kl_in_reward \ algorithm.kl_ctrl.kl_coef=$kl_coef \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.return_raw_chat=True \ data.train_batch_size=$train_batch_size \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.custom_cls.path=recipe/retool/retool.py \ data.custom_cls.name=CustomRLHFDataset \ custom_reward_function.path=recipe/retool/retool.py \ custom_reward_function.name=compute_score \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.clip_ratio_low=$clip_ratio_low \ actor_rollout_ref.actor.clip_ratio_high=$clip_ratio_high \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.optim.lr=$actor_lr \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$actor_max_token_len_per_gpu \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$train_sp \ actor_rollout_ref.actor.fsdp_config.param_offload=$offload \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=$offload \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=$log_prob_max_token_len_per_gpu \ actor_rollout_ref.rollout.max_num_batched_tokens=$actor_max_token_len_per_gpu \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.max_num_seqs=1024 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$infer_tp \ actor_rollout_ref.rollout.multi_turn.enable=True \ actor_rollout_ref.rollout.multi_turn.max_user_turns=$max_turns \ actor_rollout_ref.rollout.multi_turn.max_assistant_turns=$max_turns \ actor_rollout_ref.rollout.multi_turn.tool_config_path=$tool_config_path \ actor_rollout_ref.rollout.multi_turn.format=hermes \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.n=$n_resp_per_prompt \ actor_rollout_ref.rollout.val_kwargs.top_p=0.6 \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.n=$n_resp_per_prompt_val \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enforce_eager=False \ trainer.logger=['console'] \ trainer.project_name=$project_name \ trainer.experiment_name=$experiment_name \ trainer.n_gpus_per_node=8 \ trainer.val_before_train=False \ trainer.log_val_generations=20 \ trainer.nnodes=1 \ trainer.save_freq=100 \ trainer.default_local_dir=$default_local_dir \ trainer.test_freq=20 \ trainer.device=npu \ actor_rollout_ref.actor.entropy_from_logits_with_chunking=True \ actor_rollout_ref.ref.entropy_from_logits_with_chunking=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.actor.entropy_checkpointing=True \ actor_rollout_ref.ref.entropy_checkpointing=True \ actor_rollout_ref.ref.use_torch_compile=False \ trainer.total_epochs=1 $@ > $LOG_FILE 2>&1 & ================================================ FILE: docs/ascend_tutorial/examples/ascend_sglang_best_practices.rst ================================================ Ascend SGLang Best Practice =================================== Last updated: 01/27/2026. .. _Qwen3-30B: https://github.com/verl-project/verl/blob/main/examples/grpo_trainer/run_qwen3moe-30b_sglang_megatron_npu.sh .. _Qwen2.5-32B: https://github.com/verl-project/verl/blob/main/examples/grpo_trainer/run_qwen2-32b_sglang_fsdp_npu.sh 引言 ---------------------------------- SGLang 是当前主流的高性能开源推理引擎, 昇腾已经全面原生支持该推理引擎在verl中使用, 仅需简单的构建流程，开发者即可完成环境构建，本文将提供两个经典用例来帮助开发者了解以下内容： 1. 环境构建 2. 模型训练与评估 3. 性能采集 两个用例模型脚本以及其需要的硬件条件各自如下： +----------------------+---------------------+----------+------------------------+ | 模型 | NPU型号 | 节点数量 | 训推后端 | +======================+=====================+==========+========================+ | `Qwen3-30B`_ | Atlas 800T A3 | 1 | SGLang + Megatron | +----------------------+---------------------+----------+------------------------+ | `Qwen2.5-32B`_ | Atlas 900 A2 | 2 | SGLang + FSDP | +----------------------+---------------------+----------+------------------------+ 环境构建 ----------------------------------- 我们在quickstart中提供了两种构建环境的方法, 1.从镜像文件DockerFile进行构建 2.从自定义Conda环境进行构建 在本实践中, 我们额外指定verl 的commit id 以避免引入其他问题 .. code-block:: bash cd verl git checkout c98cb8cc 模型训练与评估 ----------------------------------- 1.模型数据准备 ^^^^^^^^^^^ `Qwen3-30B`_ ^^^^^^^^^^^ **下载模型权重** Qwen3-30B: https://huggingface.co/Qwen/Qwen3-30B-A3B **下载数据集** DAPO-Math-17k: https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k **HuggingFace To Megatron权重转换(可选)** .. code-block:: bash python scripts/converter_hf_to_mcore.py \ --hf_model_path Qwen/Qwen3-30B-A3B \ --output_path Qwen/Qwen3-30B-A3B-mcore \ --use_cpu_initialization # Only work for MoE models *注:verl当前已支持mbridge进行灵活的hf和mcore之间的权重转换,可以修改以下相关参数直接加载hf权重* .. code-block:: bash actor_rollout_ref.actor.megatron.use_dist_checkpointing=False actor_rollout_ref.actor.megatron.use_mbridge=True `Qwen2.5-32B`_ ^^^^^^^^^^^ **下载模型权重** --local-dir: 模型保存路径 .. code-block:: bash export HF_ENDPOINT=https://hf-mirror.com hf download --resume-download Qwen/Qwen2.5-32B --local-dir /path/to/local_dir **下载及处理数据集** .. code-block:: bash wget https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset/resolve/main/deepscaler.json python recipe/r1_ascend/json_to_parquet.py --output_dir ./data/deepscaler --json_path path/to/deepscaler.json --train_data_ratio 0.9 2.训练 ^^^^^^^^^^^ 根据开发者实际路径配置情况修改模型训练脚本中的以下参数 .. code-block:: bash # Model Weights Paths MODEL_PATH=Qwen/Qwen3-30B-A3B MCORE_MODEL_PATH=Qwen/Qwen3-30B-A3B-mcore RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet #保存频率，-1默认不保存，如需评测请修改此参数 trainer.save_freq=-1 对于单机任务 `Qwen3-30B`_ , 可以直接bash执行verl仓上示例脚本 .. code-block:: bash bash examples/grpo_trainer/run_qwen3moe-30b_sglang_megatron_npu.sh 对于多节点任务 `Qwen2.5-32B`_ ，我们推荐使用以下脚本进行大规模多节点训练拉起 .. code-block:: bash pkill -9 python ray stop --force rm -rf /tmp/ray export RAY_DEDUP_LOGS=0 export HYDRA_FULL_ERROR=1 # TASK_QUEUE_ENABLE，下发优化，图模式设置为1，非图模式设置为2 export TASK_QUEUE_ENABLE=1 export HCCL_ASYNC_ERROR_HANDLING=0 export HCCL_EXEC_TIMEOUT=3600 export HCCL_CONNECT_TIMEOUT=3600 export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050 export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050 export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8 # 修改为当前需要跑的用例路径 DEFAULT_SH="./run_*.sh" echo "Use $DEFAULT_SH" ulimit -n 32768 mkdir logs NNODES=2 NPUS_PER_NODE=8 # 修改为对应主节点IP MASTER_ADDR="IP FOR MASTER NODE" # 修改为当前节点的通信网卡 SOCKET_IFNAME="Your SOCKET IFNAME" export HCCL_SOCKET_IFNAME="SOCKET IFNAME FOR CURRENT NODE" export GLOO_SOCKET_IFNAME="SOCKET IFNAME FOR CURRENT NODE" # 获取当前IP CURRENT_IP=$(ifconfig $SOCKET_IFNAME | grep -Eo 'inet (addr:)?([0-9]{1,3}\.){3}[0-9]{1,3}' | awk '{print $NF}') if [ "$MASTER_ADDR" = "$CURRENT_IP" ]; then # 主节点启动 ray start --head --port 6766 --dashboard-host=$MASTER_ADDR --node-ip-address=$CURRENT_IP --dashboard-port=8260 --resources='{"NPU": '$NPUS_PER_NODE'}' while true; do ray_status_output=$(ray status) npu_count=$(echo "$ray_status_output" | grep -oP '(?<=/)\d+\.\d+(?=\s*NPU)' | head -n 1) npu_count_int=$(echo "$npu_count" | awk '{print int($1)}') device_count=$((npu_count_int / $NPUS_PER_NODE)) # 判断device_count 是否与 NNODES 相等 if [ "$device_count" -eq "$NNODES" ]; then echo "Ray cluster is ready with $device_count devices (from $npu_count NPU resources), starting Python script." ray status bash $DEFAULT_SH break else echo "Waiting for Ray to allocate $NNODES devices. Current device count: $device_count" sleep 5 fi done else # 子节点尝试往主节点注册 ray 直到成功 while true; do # 尝试连接 ray 集群 ray start --address="$MASTER_ADDR:6766" --resources='{"NPU": '$NPUS_PER_NODE'}' --node-ip-address=$CURRENT_IP # 检查连接是否成功 ray status if [ $? -eq 0 ]; then echo "Successfully connected to the Ray cluster!" break else echo "Failed to connect to the Ray cluster. Retrying in 5 seconds..." sleep 5 fi done fi sleep 600 DEFAULT_SH:修改为训练所用配置 sh 文件路径。在此案例中修改为 `Qwen2.5-32B`_ 路径。 NNODES 和 NPUS_PER_NODE:修改为使用节点数量和每个节点 NPU 数量。在此案例中分别为2和8。 MASTER_ADDR:修改为对应主节点 IP。即所有节点的 MASTER_ADDR 应该相同。 SOCKET_IFNAME, HCCL_SOCKET_IFNAME, GLOO_SOCKET_IFNAME: 修改为对应通信网卡，通信网卡可以通过以下命令获取： .. code-block:: bash ifconfig |grep "$(hostname -I |awk '{print $1}'|awk -F '.' '{print $0}')" -B 1|awk -F ':' '{print$1}' | head -1 | tail -1 3.模型评估 ^^^^^^^^^^^ 不同模型步骤一致,仅以Qwen3-30b为例列举 我们通过 AISBenchmark 评估模型,该工具支持vllm/sglang多种推理后端的评估 **安装方法** .. code-block:: bash git clone https://gitee.com/aisbench/benchmark.git cd benchmark pip install -e . **下载评估数据集** .. code-block:: bash cd path/to/benchmark/ais_bench/datasets wget http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/math.zip unzip math.zip rm math.zip **修改AISBench配置代码使能sglang推理评测** 打开 benchmark/ais_bench/benchmark/configs/models/vllm_api/vllm_api_stream_chat.py 文件，这是推理配置文件 .. code-block:: bash from ais_bench.benchmark.models import VLLMCustomAPIChatStream from ais_bench.benchmark.utils.model_postprocessors import extract_non_reasoning_content from ais_bench.benchmark.clients import OpenAIChatStreamClient, OpenAIChatStreamSglangClient models = [ dict( attr="service", type=VLLMCustomAPIChatStream, abbr='sgl-api-stream-chat', path="/path/to/Qwen3-30B", # 修改为 Qwen3-30B 模型路径 model="qwen3-30b", request_rate = 0, max_seq_len=2048, retry = 2, host_ip = "localhost", # 推理服务的IP host_port = 8005, # 推理服务的端口 max_out_len = 8192, # 最大输出tokens长度 batch_size=48, # 推理的最大并发数 trust_remote_code=False, custom_client=dict(type=OpenAIChatStreamSglangClient), #使用sglang客户端 generation_kwargs = dict( temperature = 0, seed = 1234, ), pred_postprocessor=dict(type=extract_non_reasoning_content) ) ] **启动sglang_server服务** .. code-block:: bash python -m sglang.launch_server --model-path "/path/to/Qwen3-30B" --tp-size 4 --dp-size 1 --port 8005 **启动sglang_client评测** .. code-block:: bash ais_bench --models vllm_api_stream_chat --datasets math500_gen_0_shot_cot_chat_prompt **评测结果** 经过训练,模型在Math-500上的评分显著上升 +------+----------------------+---------+----------+------+----------------------+ | iter | dataset | version | metric | mode | sgl-api-stream-chat | +======+======================+=========+==========+======+======================+ | 0 | math_prm800k_500 | c4b6f0 | accuracy | gen | 84.4 | +------+----------------------+---------+----------+------+----------------------+ | 150 | math_prm800k_500 | c4b6f0 | accuracy | gen | 91.7 | +------+----------------------+---------+----------+------+----------------------+ 性能采集 ----------------------------------- 关于NPU profiling的详细文档请参考 `ascend_profiling_zh `_ 在 `Qwen3-30B`_ 的脚本中提供了基本的采集性能选项PROF_CONFIG，默认设置 global_profiler.steps=null 关闭采集， 开发者可根据实际需要进行参数修改 采集完成后，开发者可以使用 `MindStudio Insight `_ 进行数据解析 注: verl框架侧进行采集全量 Profiling 产生海量且重复的算子记录，可以根据文档修改代码仅采集关键阶段 ================================================ FILE: docs/ascend_tutorial/examples/dapo_multi_model_optimization_practice.md ================================================ # DAPO multi model optimization practice ## DAPO 介绍 Last updated: 03/04/2026. DAPO的论文可以参考：[DAPO](https://arxiv.org/pdf/2503.14476)，其中包含以下几个关键技术。 * ​**Clip-Higher**​: 通过对重要性采样比的上限剪裁促进了系统的多样性并避免了熵坍缩（Entropy Collapse）。 * ​**Dynamic Sampling**​: 提高了训练效率和稳定性。DAPO出了一种执行动态采样的策略，并过滤掉准确率等于1和0的提示组，从而保持批次间具有有效梯度的提示数量一致。 * ​**Token-level Policy Gradient Loss**​: 在长链思维强化学习 (long-CoT RL) 场景中至关重要。 * ​**Overlong Reward Shaping**​: 减少奖励噪声并稳定了训练。 在verl中，可以进行如下设置，从而进行DAPO算法的运行。 - **奖励模型的管理策略为 DAPO** 在dapo算法中，必须配置成dapo。 ``` reward_model.reward_manager.name=dapo ``` - **Clip-Higher 更高裁剪** `clip_ratio_low` 和 `clip_ratio_high` 用于指定 DAPO 目标函数中的 $\varepsilon_{\text {low }}$ 和 $\varepsilon_{\text {high }}$。 ``` clip_ratio_low=0.2 # 裁剪比例下限，默认值为0.2 clip_ratio_high=0.28 # 裁剪比例上限，默认值为0.28 ``` - **动态采样的相关配置** 将 `filter_groups.enable` 设置为 `True` 会过滤掉输出 `metric` 完全相同的组，例如对于 `acc` 指标，过滤掉输出准确率全部为 1 或 0 的组。 训练器会使用 `gen_batch_size` 进行重复采样，直到生成足够数量的符合条件的组，或者达到 `max_num_gen_batches` 所指定的上限为止。 ``` data.gen_batch_size=${gen_prompt_bsz} algorithm.filter_groups.enable=${enable_filter_groups} # 动态采样开关 algorithm.filter_groups.metric=${filter_groups_metric} # 使用准确率作为过滤标准 algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} # 最大生成批次数量,最多重复生成数据的次数 ``` - **Token-level Loss** 将 `loss_agg_mode` 设置为 `token-mean` 意味着计算一个批次中所有序列内所有 token 的（策略梯度）损失的平均值。 ``` actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} # 注意：“token-mean”是默认行为。 ``` - **奖励模型对超长回答的惩罚配置** 将 `overlong_buffer.enable` 设置为 `True` 将对输出长度过长但仍未超过硬上下文限制的输出进行惩罚。具体来说，当输出的长度超过 `max_response_length - overlong_buffer.len` 且超出 `0` 到 `overlong_buffer.len` 个 token 时，惩罚值会从 `0` 线性增加到 `overlong_buffer.penalty_factor`。 ``` reward_model.overlong_buffer.enable=${enable_overlong_buffer} # 启用超长缓冲区惩罚,开启对超长输出的惩罚机制 reward_model.overlong_buffer.len=${overlong_buffer_len} # 缓冲区长度,定义缓冲区的toke,最大惩罚强度 reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} #惩罚因子,最大惩罚强度 ``` 相关参数涉及的代码可以参考：[Recipe: Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO)](https://github.com/verl-project/verl-recipe/blob/main/dapo/README.md) ## 硬件要求 当前支持Atlas 800T A3 与 Atlas 900 A3 SuperPoD。完成跑完本次最佳实践需要 2台Atlas 800T A3。关键软件版本可以参考：[Ascend Quickstart](https://github.com/volcengine/verl/blob/main/docs/ascend_tutorial/quick_start/ascend_quick_start.rst) ## 安装基础环境 | software | version| | --- | --- | | Python| >= 3.10, <3.12 | | CANN | == 8.3.RC1 | | torch | == 2.7.1 | | torch_npu | == 2.7.1 | | verl | main分支 commitId=252d76908b903ad8fb6969eb3a5e5f873c95ea2b | | vllm | v0.11.0 | | vllm-ascend | v0.11.0-dev| | transformers | 4.57.3| 在本实践中, 我们通过指定 verl 的commit id 以避免引入其他问题 ``` cd verl git checkout 252d76908b903ad8fb6969eb3a5e5f873c95ea2b # 指定相应的recipe版本 git submodule update --init --recursive recipe cd recipe git checkout main ``` ## 模型训练 ### 数据集准备 Geometry3k 数据集是由加利福尼亚大学洛杉矶分校与浙江大学联合研发的几何领域专用数据集，核心面向视觉问答（VQA）任务展开研究与模型训练。该数据集总计包含 3002 个样本，采用图像和文本两种模态数据形式构建，其中文本模态涵盖各类几何问题描述，图像则以可视化图表呈现问题中的几何图形信息，包括三角形、圆形、四边形等基础几何形状，以及不同图形间的位置、嵌套、相交等关联关系。可以从Hugging Face库下载对应的原始数据集：[Geometry3k ](https://huggingface.co/datasets/hiyouga/geometry3k) ```python # 下载原始数据并预处理 python ./examples/data_preprocess/geo3k.py --local_dir=./data/geo3k ``` ### 权重下载 从Hugging Face库下载对应的模型权重：[Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct/tree/main ) ### jemalloc安装 为了确保 Ray 进程能够正常回收内存，需要安装并使能 jemalloc 库进行内存管理。 #### Ubuntu 操作系统 通过操作系统源安装jemalloc（注意： 要求ubuntu版本>=20.04）： ```shell sudo apt install libjemalloc2 ``` 在启动任务前执行如下命令通过环境变量导入jemalloc，需先通过 **find /usr -name libjemalloc.so.2** 确认文件是否存在 ： ```shell # arm64架构 export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2 # x86_64架构 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2 ``` #### OpenEuler 操作系统 执行如下命令重操作系统源安装jemalloc ```shell yum install jemalloc ``` 如果上述方法无法正常安装，可以通过源码编译安装 前往jemalloc官网下载最新稳定版本，官网地址:https://github.com/jemalloc/jemalloc/releases/ ```shell tar -xvf jemalloc-{version}.tar.bz2 cd jemalloc-{version} ./configure --prefix=/usr/local make make install ``` ### 全局变量导入 - 为了确保 Ray 进程能够正常回收内存，需要安装并使能 jemalloc 库进行内存管理，用于更好管理内存，避免长跑过程中内存 OOM。 ``` # 根据实际安装路径设置 jemalloc 环境变量，例如安装路径为:/usr/local/lib/libjemalloc.so.2(可通过 find /usr -name libjemalloc.so.2 确认文件是否存在) export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2 ``` - 某些模型是通过 vllm ascend 进行优化的。但在某些情况下，优化后的模型可能并不适用。此时，将此值设置为 0 即可禁用优化后的模型。 ``` export USE_OPTIMIZED_MODEL=0 ``` - 启用vLLM V1 ``` export VLLM_USE_V1=1 ``` - 昇腾多卡通信的兜底配置，延长连接超时时间，避免集群环境下训练启动因连接慢而失败 ``` export HCCL_CONNECT_TIMEOUT=5400 ``` - 控制 vLLM 在昇腾芯片上是否启用NZ优化 ``` export VLLM_ASCEND_ENABLE_NZ=0 ``` ### 训练 ``` # Model Weights Paths MODEL_PATH=hf_weights/Qwen3-VL-30B-A3B-Instruct RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=$RAY_DATA_HOME/datasets/geo3k/train.parquet TEST_FILE=$RAY_DATA_HOME/datasets/geo3k/test.parquet # 保存频率，-1默认不保存，如需评测请修改此参数 trainer.save_freq=-1 ``` 对于单机任务 Qwen3-VL-30B , 修改脚本中参数`trainer.nnodes`为 1， `trainer.n_gpus_per_node` 为16，然后直接bash执行verl仓上示例脚本 ``` bash recipe/dapo/run_dapo_qwen3_vl_30b_fsdp2_npu.sh ``` 对于多节点任务 Qwen3-VL-30B ，我们推荐使用以下脚本进行大规模多节点训练拉起 ``` pkill -9 python ray stop --force rm -rf /tmp/ray export VLLM_USE_V1=1 export HCCL_CONNECT_TIMEOUT=5400 export VLLM_ASCEND_ENABLE_NZ=0 export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2 # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. export USE_OPTIMIZED_MODEL=0 # 修改为当前需要跑的用例路径 DEFAULT_SH="./run_*.sh" echo "Use $DEFAULT_SH" ulimit -n 32768 mkdir logs NNODES=2 NPUS_PER_NODE=8 # 修改为对应主节点IP MASTER_ADDR="IP FOR MASTER NODE" # 修改为当前节点的通信网卡 SOCKET_IFNAME="Your SOCKET IFNAME" export HCCL_SOCKET_IFNAME="SOCKET IFNAME FOR CURRENT NODE" export GLOO_SOCKET_IFNAME="SOCKET IFNAME FOR CURRENT NODE" # 获取当前IP CURRENT_IP=$(ifconfig $SOCKET_IFNAME | grep -Eo 'inet (addr:)?([0-9]{1,3}\.){3}[0-9]{1,3}' | awk '{print $NF}') if [ "$MASTER_ADDR" = "$CURRENT_IP" ]; then # 主节点启动 ray start --head --port 6766 --dashboard-host=$MASTER_ADDR --node-ip-address=$CURRENT_IP --dashboard-port=8260 --resources='{"NPU": '$NPUS_PER_NODE'}' while true; do ray_status_output=$(ray status) npu_count=$(echo "$ray_status_output" | grep -oP '(?<=/)\d+\.\d+(?=\s*NPU)' | head -n 1) npu_count_int=$(echo "$npu_count" | awk '{print int($1)}') device_count=$((npu_count_int / $NPUS_PER_NODE)) # 判断device_count 是否与 NNODES 相等 if [ "$device_count" -eq "$NNODES" ]; then echo "Ray cluster is ready with $device_count devices (from $npu_count NPU resources), starting Python script." ray status bash $DEFAULT_SH break else echo "Waiting for Ray to allocate $NNODES devices. Current device count: $device_count" sleep 5 fi done else # 子节点尝试往主节点注册 ray 直到成功 while true; do # 尝试连接 ray 集群 ray start --address="$MASTER_ADDR:6766" --resources='{"NPU": '$NPUS_PER_NODE'}' --node-ip-address=$CURRENT_IP # 检查连接是否成功 ray status if [ $? -eq 0 ]; then echo "Successfully connected to the Ray cluster!" break else echo "Failed to connect to the Ray cluster. Retrying in 5 seconds..." sleep 5 fi done fi sleep 600 ``` DEFAULT_SH: 修改为训练所用配置 sh 文件路径。在此案例中修改为 [Qwen3_VL_30B](https://github.com/verl-project/verl-recipe/blob/main/dapo/run%20dapo_qwen3_vl_30b_fsdp2_npu.sh) 路径。 NNODES 和 NPUS_PER_NODE: 修改为使用节点数量和每个节点 NPU 数量。在此案例中分别为2和8。 MASTER_ADDR:修改为对应主节点 IP。即所有节点的 MASTER_ADDR 应该相同。 SOCKET_IFNAME, HCCL_SOCKET_IFNAME, GLOO_SOCKET_IFNAME: 修改为对应通信网卡，通信网卡可以通过以下命令获取： ``` ifconfig |grep "$(hostname -I |awk '{print $1}'|awk -F '.' '{print $0}')" -B 1|awk -F ':' '{print$1}' | head -1 | tail -1 ``` ## 优化参考 - **启动动态批次大小** 根据单 GPU 的最大 Token 总数（ppo_max_token_len_per_gpu）动态调整批次大小 ``` actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} ``` - **单个 GPU 能处理的最大 Token 总数** 当`use_dynamic_bsz=True`时，单 GPU 在一个微批次中能处理的最大 Token 数量 ``` actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} ``` - **单个 GPU 微批次大小** 当`use_dynamic_bsz=True`时，框架会以该值为​初始批次大小​，再根据`ppo_max_token_len_per_gpu`向上 / 向下调整 ``` actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 ``` - **启用 FSDP2 框架** “将模型参数、梯度、优化器状态分片存储在不同 GPU 上”，避免单卡加载全量模型导致显存溢出。 ``` # 启用 FSDP2 框架 actor_rollout_ref.actor.strategy=fsdp2 actor_rollout_ref.ref.strategy=fsdp2 critic.strategy=fsdp2 # 仅用于 FSDP2：前向传播后重新分片以减少内存占用。 actor_rollout_ref.actor.fsdp_config.reshard_after_forward=True # 仅用于 FSDP2：是否在模型前向传播后重新分片以节省内存。 actor_rollout_ref.ref.fsdp_config.reshard_after_forward=True ``` - **启用专家并行配置** 指定有多少个 GPU用于并行计算不同的专家网络 ``` # MoE 架构 Actor 模型的专家并行配置 actor_rollout_ref.rollout.expert_parallel_size=8 ``` ================================================ FILE: docs/ascend_tutorial/examples/gspo_optimization_practice.md ================================================ # NPU Qwen3-32B GSPO Optimization Practice Last updated: 02/26/2026. 本文章对应脚本地址：[qwen3_32b_gspo_npu](https://github.com/volcengine/verl/blob/main/examples/gspo_trainer/run_qwen3_32b_gspo_npu.sh) ## 算法适配 GSPO通过将优化颗粒度从**token级**提升到**sequence级**，规避了GRPO会遇到的**方差急剧增大**导致训练不稳定的情况，增加了训练的稳定性，同时该算法也在一定程度上提升了算法的收敛速度。 想要成功在verl仓库中成功调用到GSPO算法，需要进行如下的必要配置 ~~~python # 核心算法配置 algorithm.adv_estimator=grpo \ # 使用GRPO优势估计器 algorithm.use_kl_in_reward=False \ # 不在奖励中添加KL惩罚 # GSPO策略损失模式 actor_rollout_ref.actor.policy_loss.loss_mode=gspo \ # 启用GSPO策略损失 # 极小裁剪范围（GSPO特色） actor_rollout_ref.actor.clip_ratio_low=0.0003 \ # 裁剪下界，论文推荐值 actor_rollout_ref.actor.clip_ratio_high=0.0004 \ # 裁剪上界，论文推荐值 # KL配置（GSPO不使用KL loss） actor_rollout_ref.actor.use_kl_loss=False \ # 禁用KL损失 actor_rollout_ref.actor.kl_loss_coef=0.0 \ # KL损失系数设为0 # 序列级损失聚合模式（GSPO核心） actor_rollout_ref.actor.loss_agg_mode=seq-mean-token-mean \ # 序列级平均，GSPO论文推荐 # 批次配置 actor_rollout_ref.rollout.n=16 \ # 每个prompt生成16个响应（组采样） ~~~ 一般选择入口函数为`verl.trainer.main_ppo` ## 基础环境 当前支持Atlas 800T A3 与 Atlas 900 A3 SuperPoD。完成跑完本次最佳实践需要 4台Atlas 800T A3。关键软件版本可以参考：[Ascend Quickstart](https://github.com/volcengine/verl/blob/main/docs/ascend_tutorial/quick_start/ascend_quick_start.rst) ### 安装基础环境 | software | version | | ------------ | ---------------------------------------------------------- | | Python | >= 3.10, <3.12 | | CANN | == 8.3.RC1 | | torch | == 2.7.1 | | torch_npu | == 2.7.1 | | verl | main分支 commitId=252d76908b903ad8fb6969eb3a5e5f873c95ea2b | | vllm | v0.11.0 | | vllm-ascend | v0.11.0-dev | | transformers | 4.57.3 | 在本实践中, 我们通过指定 verl 的commit id 以避免引入其他问题 ~~~bash cd verl git checkout 252d76908b903ad8fb6969eb3a5e5f873c95ea2b # 指定相应的recipe版本 git submodule update --init --recursive recipe ~~~ ### 权重获取 从Hugging Face库下载对应的模型权重：[Qwen/Qwen3-32B · Hugging Face](https://huggingface.co/Qwen/Qwen3-32B) ### 数据集准备 ~~~bash # 下载math-17k数据集 git clone https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k # 下载AIME_2024测试数据集 git clone https://huggingface.co/datasets/Maxwell-Jia/AIME_2024 ~~~ ### jemalloc安装 为了确保 Ray 进程能够正常回收内存，需要安装并使能 jemalloc 库进行内存管理。 #### Ubuntu 操作系统 通过操作系统源安装jemalloc（注意： 要求ubuntu版本>=20.04）： ```shell sudo apt install libjemalloc2 ``` 在启动任务前执行如下命令通过环境变量导入jemalloc，需先通过 **find /usr -name libjemalloc.so.2** 确认文件是否存在 ： ```shell # arm64架构 export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2 # x86_64架构 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2 ``` #### OpenEuler 操作系统 执行如下命令重操作系统源安装jemalloc ```shell yum install jemalloc ``` 如果上述方法无法正常安装，可以通过源码编译安装 前往jemalloc官网下载最新稳定版本，官网地址:https://github.com/jemalloc/jemalloc/releases/ ```shell tar -xvf jemalloc-{version}.tar.bz2 cd jemalloc-{version} ./configure --prefix=/usr/local make make install ``` 在启动任务前执行如下命令通过环境变量导入jemalloc： ```shell #根据实际安装路径设置环境变量，例如安装路径为:/usr/local/lib/libjemalloc.so.2,可通过以下命令来设置环境变量(可通过 find /usr -name libjemalloc.so.2 确认文件是否存在) export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2 ``` ### 多机任务拉起 针对本实践提供的多机任务，可用下面的脚本拉起 ~~~bash pkill -9 python ray stop --force rm -rf /tmp/ray export RAY_DEDUP_LOGS=0 export HYDRA_FULL_ERROR=1 export TASK_QUEUE_ENABLE=1 export HCCL_EXEC_TIMEOUT=3600 export HCCL_CONNECT_TIMEOUT=3600 export HCCL_ASYNC_ERROR_HANDLING=0 export CPU_AFFINITY_CONF=1 export VLLM_USE_V1=1 export VLLM_ATTENTION_BACKEND=XFORMERS export VLLM_ASCEND_ENABLE_FLASHCOMM=1 export VLLM_ASCEND_ENABLE_PREFETCH_MLP=1 export VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE=1 export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2 # 修改为当前需要跑的用例路径 DEFAULT_SH="./run_*.sh" echo "Use $DEFAULT_SH" ulimit -n 32768 mkdir logs NNODES=4 NPUS_PER_NODE=16 # 修改为对应主节点IP MASTER_ADDR="IP FOR MASTER NODE" # 修改为当前节点的通信网卡 SOCKET_IFNAME="Your SOCKET IFNAME" export HCCL_SOCKET_IFNAME="SOCKET IFNAME FOR CURRENT NODE" export GLOO_SOCKET_IFNAME="SOCKET IFNAME FOR CURRENT NODE" # 获取当前IP CURRENT_IP=$(ifconfig $SOCKET_IFNAME | grep -Eo 'inet (addr:)?([0-9]{1,3}\.){3}[0-9]{1,3}' | awk '{print $NF}') if [ "$MASTER_ADDR" = "$CURRENT_IP" ]; then # 主节点启动 ray start --head --port 6766 --dashboard-host=$MASTER_ADDR --node-ip-address=$CURRENT_IP --dashboard-port=8260 --resources='{"NPU": '$NPUS_PER_NODE'}' while true; do ray_status_output=$(ray status) npu_count=$(echo "$ray_status_output" | grep -oP '(?<=/)\d+\.\d+(?=\s*NPU)' | head -n 1) npu_count_int=$(echo "$npu_count" | awk '{print int($1)}') device_count=$((npu_count_int / $NPUS_PER_NODE)) # 判断device_count 是否与 NNODES 相等 if [ "$device_count" -eq "$NNODES" ]; then echo "Ray cluster is ready with $device_count devices (from $npu_count NPU resources), starting Python script." ray status bash $DEFAULT_SH break else echo "Waiting for Ray to allocate $NNODES devices. Current device count: $device_count" sleep 5 fi done else # 子节点尝试往主节点注册 ray 直到成功 while true; do # 尝试连接 ray 集群 ray start --address="$MASTER_ADDR:6766" --resources='{"NPU": '$NPUS_PER_NODE'}' --node-ip-address=$CURRENT_IP # 检查连接是否成功 ray status if [ $? -eq 0 ]; then echo "Successfully connected to the Ray cluster!" break else echo "Failed to connect to the Ray cluster. Retrying in 5 seconds..." sleep 5 fi done fi sleep 600 ~~~ DEFAULT_SH:修改为训练所用配置 sh 文件路径。在此案例中修改为 [Qwen2.5-32B](https://github.com/volcengine/verl/blob/main/examples/gspo_trainer/run_qwen3_32b_gspo_npu.sh) 路径。 NNODES 和 NPUS_PER_NODE:修改为使用节点数量和每个节点 NPU 数量。在此案例中分别为4和16。 MASTER_ADDR:修改为对应主节点 IP。即所有节点的 MASTER_ADDR 应该相同。 SOCKET_IFNAME, HCCL_SOCKET_IFNAME, GLOO_SOCKET_IFNAME: 修改为对应通信网卡，通信网卡可以通过以下命令获取： ``` ifconfig |grep "$(hostname -I |awk '{print $1}'|awk -F '.' '{print $0}')" -B 1|awk -F ':' '{print$1}' | head -1 | tail -1 ``` ## 性能调优 优化从训练、推理、调度和其他四个方面入手。 ### 训练 #### 动态bsz ~~~bash actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) ~~~ **这个优化点主要调整上面这两个参数，不过需要注意这两个参数调整的太大会导致OOM** **主要调整**`actor_ppo_max_token_len`,调大了会降低训练的耗时，调整`infer_ppo_max_token_len`没有明显的收益，可以不动 **这两个参数的作用介绍如下：** **这两个参数用于控制动态批处理(dynamic batch size)模式下每个GPU处理的最大token数量** - **`actor_ppo_max_token_len`**: Actor模型在PPO更新(前向+反向传播)时每个GPU能处理的最大token数 - **`infer_ppo_max_token_len`**: 推理阶段(Reference policy和Rollout)计算log概率时每个GPU能处理的最大token数 ### 推理 #### ACLgraph+FULL_DECODE_ONLY 推理算子下发方面的优化，平均能有`15%~20%`左右的性能收益。 先看单开**ACLgraph**，如下： ~~~bash # 开启ACLgraph+FULL_DECODE_ONLY（注意：当设置此参数为False时，TASK_QUEUE_ENABLE必须设置为1，不然会报错） actor_rollout_ref.rollout.enforce_eager=False actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_capture_sizes='[8,16,32,64,128]' \ actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode='FULL_DECODE_ONLY' \ ~~~ `FULL_DECODE_ONLY`开启成功后有如下输出： ![FULL_DECODE_ONLY result](https://github.com/wucong25/verl-data/blob/main/ascend_acl_graph.png) **`cudagraph_capture_sizes`参数设置指南** cudagraph_capture_sizes设置的值对应的是批大小，这里的批大小不是配置里的DP域对应的那个批次大小，这里是相较于vllm来说的批大小，单位为**token** 默认生成的算法如下，可做参考 ![cudagraph_capture_sizes](https://github.com/wucong25/verl-data/blob/main/ascend_set_cudagraph_sizes.png) ##### 推理后端切换 使用方式：`export VLLM_ATTENTION_BACKEND=XFORMERS` ![VLLM_ATTENTION_BACKEND](https://github.com/wucong25/verl-data/blob/main/ascend_vllm_attn_backend.png) 注：需要注意某些后端在一些比较老的vllm-ascend版本内并不支持 ##### 使能vllm v1版本 使用方式：`export VLLM_USE_V1=1` 可以常开，一般都是正收益。 ### 调度 #### AIV 打开方式：设置`export HCCL_OP_EXPANSION_MODE="AIV"` HCCL_OP_EXPANSION_MODE环境变量用于配置通信算法的编排展开位置，支持如下取值： - AI_CPU：代表通信算法的编排展开位置在Device侧的AI CPU计算单元。 - AIV：代表通信算法的编排展开位置在Device侧的Vector Core计算单元。 - HOST：代表通信算法的编排展开位置为Host侧CPU，Device侧根据硬件型号自动选择相应的调度器。 - HOST_TS：代表通信算法的编排展开位置为Host侧CPU，Host向Device的Task Scheduler下发任务，Device的Task Scheduler进行任务调度执行。 下面介绍两种展开机制 ##### HOST展开 image-20260113194257095 - 软件栈工作在hostcpu，通信算法展开一个个task - 每个task调用runtime接口，下发到device的rtsqueue - STARS从rstqueue上顺序拿取task - 根据task类型分别调用掉SDMA和RDMA引擎。 **单算子瓶颈**：hostbound 每个task提交是2~5us，一个通信算子有几百个task，单算子场景不会在device上缓存，下发一个执行一个 ##### AICpu机制展开 image-20260113194333218 - host侧不下发一个个task，把通信算子作为一个个kernel，放在通信算子kernel的队列上去。 - STARS调度kernel队列流上的kernel，把kernel放到AiCPU上去执行。 - AICPU调用函数（kernel），用一个线程执行kernel 函数，在函数内把通信task展开，把task放到rstqueue上，STARS调用。 - 降低host和aicpu交互，由几百次降低为一次。 - task的提交在AICPU上提交，做了提交的部分合并。 #### TASK_QUEUE_ENABLE **使用方式：**`export TASK_QUEUE_ENABLE=2` TASK_QUEUE_ENABLE，下发优化，图模式设置为1（即开启图模式的时候这个要设置为1），非图模式设置为2 示意图： ![ascend task queue](https://github.com/wucong25/verl-data/blob/main/ascend_task_queue2.png) ##### 绑核优化 **使用方式：**`export CPU_AFFINITY_CONF=1` 详细设置原理可看：https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0059.html ### 其他 以下内容汇总了若干全局环境变量的调优配置。由于这些参数在训练阶段与推理阶段往往都能带来正向收益，且目前尚缺乏足够精细的消融实验来严格区分它们各自对训练或推理的贡献占比，故统一归拢在此，供后续持续监控与进一步拆解分析。 #### 使能jemalloc 使用方式（注意需要先安装jemalloc库）：`export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2` **安装使用教程：**[MindSpeed-RL/docs/install_guide.md · Ascend/MindSpeed-RL - AtomGit | GitCode](https://gitcode.com/Ascend/MindSpeed-RL/blob/master/docs/install_guide.md#高性能内存库-jemalloc-安装) #### 多流复用 内存方面有优化 使能方式：`export MULTI_STREAM_MEMORY_REUSE=1` 原理介绍：https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0040.html #### VLLM_ASCEND_ENABLE_FLASHCOMM 使用方式：`export VLLM_ASCEND_ENABLE_FLASHCOMM=1` 启用昇腾 NPU 特有的FLASHCOMM高速通信优化技术 地址：https://vllm-ascend.readthedocs.io/zh-cn/latest/user_guide/release_notes.html #### VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE 使用方式：`export VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE=1` 启用昇腾 NPU针对大模型推理的稠密计算优化 地址：https://vllm-ascend.readthedocs.io/zh-cn/latest/user_guide/release_notes.html #### VLLM_ASCEND_ENABLE_PREFETCH_MLP 使用方式：`export VLLM_ASCEND_ENABLE_PREFETCH_MLP=1` 启用 MLP 层的权重预取机制 image-20251124173132677 ### verl框架参数设置 主要是内存方面的一些设置开关（注意，这个里面的优化都或多或少会导致吞吐量有一定程度的劣化） ~~~bash # 梯度检查点 (Gradient Checkpointing) # 作用: 通过重新计算激活值来节省显存,以计算换内存。在前向传播时不保存中间激活值,反向传播时重新计算,可以显著降低显存占用,允许使用更大的batch size。 actor_rollout_ref.model.enable_gradient_checkpointing=True # 参数卸载 (Parameter Offload) # 作用: 将模型参数卸载到CPU内存,训练时再加载回GPU。 actor_rollout_ref.actor.fsdp_config.param_offload=${offload} # True actor_rollout_ref.ref.fsdp_config.param_offload=${offload} # True # 优化器状态卸载 (Optimizer Offload) # 作用: 将优化器状态(如Adam的动量)卸载到CPU。优化器状态通常占用大量显存(对于Adam,每个参数需要额外8字节),卸载可以节省显存。 actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} # True # 释放推理引擎缓存 (Free Cache Engine) # 作用: 在训练阶段释放推理引擎的KV cache和权重。这是3D-HybridEngine的核心优化,允许在同一GPU上交替进行推理和训练,显著降低显存需求。 actor_rollout_ref.rollout.free_cache_engine=True # 熵计算优化 # entropy_checkpointing: 在训练时对熵计算启用重计算,降低显存峰值 # entropy_from_logits_with_chunking: 分块处理logits张量(如2048 tokens一组),避免一次性加载整个[bsz*seq_len, vocab]张量 actor_rollout_ref.actor.entropy_checkpointing=True actor_rollout_ref.ref.entropy_checkpointing=True actor_rollout_ref.actor.entropy_from_logits_with_chunking=True actor_rollout_ref.ref.entropy_from_logits_with_chunking=True # 推理引擎显存配置 # gpu_memory_utilization: 控制vLLM使用的GPU显存比例(0.90 = 90%) # enforce_eager=False: 启用CUDA graphs加速推理,但会占用额外显存 actor_rollout_ref.rollout.gpu_memory_utilization=0.90 actor_rollout_ref.rollout.enforce_eager=False ~~~ ## NPU调优参考文章 环境变量相关：[环境变量列表-Ascend Extension for PyTorch6.0.0-昇腾社区](https://www.hiascend.com/document/detail/zh/Pytorch/600/apiref/Envvariables/Envir_001.html) 社区性能调优教程：[性能调优流程-Ascend Extension for PyTorch6.0.0-昇腾社区](https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0001.html) ================================================ FILE: docs/ascend_tutorial/examples/run_qwen3_32B_megatron_1k_256k_npu.md ================================================ # Long Sequence Qwen3-32B 1k-to-256k Example Last updated: 6/3/2026. 本章对Qwen3-32B进行了长序列开发。Qwen3-32B的模型能力为最长推到40k ## 全层实验 对Qwen3-32B进行了长序列开发，脚本如下： ```bash set -x export USE_OPTIMIZED_MODEL=0 export VLLM_USE_V1=1 export VLLM_ASCEND_ENABLE_NZ=0 export VLLM_VERSION="0.13.0" export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2 export PYTORCH_NPU_ALLOC_CONF="max_split_size_mb:2048" PROJECT_NAME="GRPO-Qwen3-32B" EXPERIMENT_NAME="GRPO-Qwen3-32B-megatron-gsm8k" SAVE_CHECKPOINT_DIR=$HOME/verl_checkpoints math_train_path=$HOME/datasets/gsm8k/train.parquet math_test_path=$HOME/datasets/gsm8k/test.parquet train_files="['$math_train_path']" test_files="['$math_test_path']" use_dynamic_bsz=False enable_chunked_prefill=True tp_size=8 max_prompt_length=1024 max_response_length=$((1024*256)) actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / tp_size)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / tp_size)) cp_size=4 python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.shuffle=False \ data.validation_shuffle=False \ data.train_batch_size=64 \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.filter_overlong_prompts=False \ data.truncation='error' \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.path=$HOME/hf_weights/Qwen3-32B \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=16 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=8 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=1 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.megatron.context_parallel_size=${CP} \ +actor_rollout_ref.actor.megatron.override_transformer_config.context_parallel_size=${CP} \ +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.enable_chunked_prefill=${enable_chunked_prefill} \ actor_rollout_ref.rollout.enable_prefix_caching=True \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.ref.megatron.param_offload=True \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=False \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=${SAVE_CHECKPOINT_DIR} \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=False \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=${SAVE_CHECKPOINT_DIR} \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.n_gpus_per_node=16 \ trainer.nnodes=2 \ trainer.save_freq=100 \ trainer.test_freq=-1 \ trainer.total_training_steps=100 \ trainer.device=npu \ trainer.project_name=${PROJECT_NAME} \ trainer.experiment_name=${EXPERIMENT_NAME} \ trainer.total_epochs=30 ``` - 相关实验结果 ![qwen3-32b-perfo](https://github.com/ChibiQuest/verl_data/blob/main/qwen3-32B-1k-256k/performance.png) ## 减层实验 在实际推理中，我们发现其最大在20k左右，因此对其进行减层实验，其response能到达到40k。 在权重的`config.json`文件中，我们将`num_hidden_layers`从64减层到16 ``` { "architectures": [ "Qwen3ForCausalLM" ], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151645, "head_dim": 128, "hidden_act": "silu", "hidden_size": 5120, "initializer_range": 0.02, "intermediate_size": 25600, "max_position_embeddings": 40960, "max_window_layers": 64, "model_type": "qwen3", "num_attention_heads": 64, "num_hidden_layers": 16, "num_key_value_heads": 8, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 1000000, "sliding_window": null, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.51.0", "use_cache": true, "use_sliding_window": false, "vocab_size": 151936 } ``` - 其实验结果如下： ![qwen3-32b-function](https://github.com/ChibiQuest/verl_data/blob/main/qwen3-32B-1k-256k/function.png) ================================================ FILE: docs/ascend_tutorial/faq/faq.rst ================================================ Last updated: 03/16/2026. ================================================ FILE: docs/ascend_tutorial/features/ascend_backend_features.md ================================================ # Ascend Backend Features Guide ================================================================================== Last updated: 03/03/2026. 昇腾全面支持verl生态建设，本文将介绍NPU上对于verl的适配工作及后端特性支持供开发者进行参考 --- ## 推理后端 当前verl支持vllm/sglang这两种主流推理后端，均可在昇腾NPU上运行。 ### 1. vllm: 昇腾通过vllm-ascend插件来支持vllm推理后端，该插件是 vLLM 社区支持 Ascend 后端的推荐方法。它遵循[[RFC]](https://github.com/vllm-project/vllm/issues/11162)，提供了一个可插拔接口，将 Ascend NPU 与 vLLM 解耦。 ##### 参数特性支持 | vllm参数| verl对应通用参数 | 简介| | --- | --- | --- | | `model_path` | `actor_rollout_ref.model.path` |模型权重文件的路径| | `gpu_memory_utilization` | `actor_rollout_ref.rollout.gpu_memory_utilization` |用于控制每个阶段可使用的 GPU 内存量。它被指定为一个介于 0.0 和 1.0 之间的分数，其中：- 0.8 表示 GPU 总内存的 80%- 1.0 表示 GPU 总内存的 100%（不推荐，没有预留缓冲）| | `enforce_eager`| `actor_rollout_ref.rollout.enforce_eager` |禁用图模式，verl默认为False| | `enable_chunked_prefill`| `actor_rollout_ref.rollout.enable_chunked_prefill` | 分块预填充允许将大预填充分块成更小的块，并将它们与解码请求一起批处理。| | `free_cache_engine`| `actor_rollout_ref.rollout.free_cache_engine` |在部署生成阶段之后卸载 KVCache，默认值为 True。| | `max_model_len` | `actor_rollout_ref.rollout.max_model_len` | 模型能够处理的最大序列长度。它限制了单个输入序列的最大长度 | | `tp_size`| `actor_rollout_ref.rollout.tensor_model_parallel_size * data_parallel_size`|TP并行度| | `dp_size`| `actor_rollout_ref.rollout.data_parallel_size`|DP并行度| | `ep_size`| `actor_rollout_ref.rollout.expert_parallel_size`|EP并行度| | `node_rank`| `无，根据实际实例和卡数自动计算` |实例中的节点排序| | `load_format`| `actor_rollout_ref.rollout.load_format` |要加载的模型权重格式| | `disable_log_stats`| `actor_rollout_ref.rollout.disable_log_stats`|记录抢占请求的累积数量 | | `nnodes `| `无，根据实际实例和卡数自动计算` | 每个实例包含的节点数量` | | `trust_remote_code`| `actor_rollout_ref.model.trust_remote_code`|是否允许在 Hub 上定义自定义模型，并将其写入自己的建模文件中| | `max_num_seqs` | `actor_rollout_ref.rollout.max_num_seqs` |正在运行的请求的最大数量| | `max_num_batched_tokens`| `actor_rollout_ref.rollout.max_num_batched_tokens` |在一次批处理（batch）中可以处理的最大总Token数| | `skip_tokenizer_init`| `actor_rollout_ref.rollout.skip_tokenizer_init` |跳过初始化分词器并将 input_ids 传递到推理请求中| | `enable_prefix_caching` | `actor_rollout_ref.rollout.enable_prefix_caching`|`用于启用自动前缀缓存` | | `quantization`| `actor_rollout_ref.rollout.quantization，默认为None`|`量化方法`| | `enforce_eager`|`actor_rollout_ref.rollout.enforce_eager`|标志用于强制使用PyTorch的eager执行模式，而非默认的图执行模式| ### 2. sglang: 对于sglang推理后端，昇腾通过直接向sglang社区进行持续建设与维护来支持相关功能。 此外在verl中使用sglang还涉及以下组件, 我们在[quick start](https://github.com/verl-project/verl/blob/main/docs/ascend_tutorial/quick_start/ascend_sglang_quick_start.rst)中提供详细说明与一键安装脚本。 | 组件| 描述| | --- | --- | | [sgl_kernel_npu](https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/sgl_kernel_npu/README.md) | Ascend NPU SGL 优化推理内核集合，包括注意力机制、归一化、激活函数、LoRA 适配器等。 | | [deepep](https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/deep_ep/README.md) | DeepEP的 Ascend 实现，为MoE模型提供高度优化的专家并行 (EP) 通信内核 | ##### 参数特性支持 verl中通过rollout config管理推理后端参数使能，包含通用参数和engine_kwargs自定义传参。 以下列举在verl中常见设置的sglang特性参数，更多参数介绍请参考 [sglang社区NPU特性支持](https://docs.sglang.io/platforms/ascend_npu_support_features.html) | sglang参数| verl对应通用参数 | 简介| | --- | --- | --- | | model_path | actor_rollout_ref.model.path|模型权重文件的路径| | mem_fraction_static| actor_rollout_ref.rollout.gpu_memory_utilization |用于静态分配（模型权重和键值缓存内存池）的内存比例| | disable_cuda_graph| actor_rollout_ref.rollout.enforce_eager|禁用图模式，verl默认为False| | enable_memory_saver| 无，verl中默认设置为True | 允许使用 release_memory_occupation 和 resume_memory_occupation 来节省内存 | base_gpu_id| 无，根据实际实例和卡数自动计算 |用于分配每个实例上计算卡资源时的的初始ID | gpu_id_step| 无，默认设置为1| 使用的连续计算卡ID 之间的差值 | tp_size| actor_rollout_ref.rollout.tensor_model_parallel_size * data_parallel_size|TP并行度| | dp_size| actor_rollout_ref.rollout.data_parallel_size|DP并行度| | ep_size| actor_rollout_ref.rollout.expert_parallel_size|EP并行度| | node_rank| 无，根据实际实例和卡数自动计算 |实例中的节点排序| | load_format| actor_rollout_ref.rollout.load_format|要加载的模型权重格式| | dist_init_addr| 无，自动计算|用于初始化分布式后端的主机地址| | nnodes| 无，根据实际实例和卡数自动计算|每个实例包含的节点数量| | trust_remote_code| actor_rollout_ref.model.trust_remote_code|是否允许在 Hub 上定义自定义模型，并将其写入自己的建模文件中| | max_running_requests| actor_rollout_ref.rollout.max_num_seqs |正在运行的请求的最大数量| | log_level| 无，默认设置为error |日志记录器的日志级别| | skip_tokenizer_init| actor_rollout_ref.rollout.skip_tokenizer_init |跳过初始化分词器并将 input_ids 传递到推理请求中| | skip_server_warmup| 无，默认设置为True |跳过预热| | quantization| actor_rollout_ref.rollout.quantization，默认为None|量化方法| | attention_backend|actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend|attention内核,NPU应该设置为ascend| --- ## 训练后端 ### 1. FSDP 昇腾通过torch_npu提供FSDP相关支持能力，当前pytorch api支持度参照[版本说明](https://www.hiascend.com/document/detail/zh/Pytorch/730/apiref/PyTorchNativeapi/docs/zh/native_apis/pytorch_2-7-1/torch-distributed-fsdp.md)。 #### FSDP1 ##### 参数特性支持 | verl参数 | 简介| | --- | --- | | `actor_rollout_ref.actor.fsdp_config.param_offload` |是否卸载模型权重到CPU，默认值为False| | `actor_rollout_ref.actor.fsdp_config.optimizer_offload` |是否卸载优化器状态到CPU，默认值为False| | `actor_rollout_ref.actor.fsdp_config.reshard_after_forward` |控制前向计算后的参数行为，平衡内存与通信。默认值为True：前向后重新分片参数，反向时重新全收集| | `actor_rollout_ref.actor.fsdp_config.fsdp_size` | 每个FSDP分片组中的NPU数量；默认值-1表示自动。| | `actor_rollout_ref.actor.fsdp_config.forward_prefetch` |在前向计算完成前预取下一次前向传播的 all-gather，仅用于FSDP1，默认值为False| | `actor_rollout_ref.actor.fsdp_config.use_orig_params` | FSDP是否会使用module的原始参数来初始化，仅用于FSDP1，默认值为False| | `actor_rollout_ref.actor.ulysses_sequence_parallel_size`|Ulysses序列并行大小| | `actor_rollout_ref.actor.entropy_from_logits_with_chunking`|通过分块计算熵以减少显存峰值，默认值为False| | `actor_rollout_ref.actor.fsdp_config.entropy_checkpointing`|在训练时对熵计算启用重计算,降低显存峰值，默认值为False| | `actor_rollout_ref.actor.fsdp_config.forward_only` |是否只进行前向计算，默认值为False| #### FSDP2 ##### 参数特性支持 | verl参数 | 简介| | --- | --- | | `actor_rollout_ref.actor.fsdp_config.param_offload` |是否卸载模型权重到CPU，默认值为False| | `actor_rollout_ref.actor.fsdp_config.optimizer_offload` |是否卸载优化器状态到CPU，默认值为False| | `actor_rollout_ref.actor.fsdp_config.reshard_after_forward` |控制前向计算后的参数行为，平衡内存与通信。默认值为True：前向后重新分片参数，反向时重新全收集| | `actor_rollout_ref.actor.fsdp_config.fsdp_size` | 每个FSDP分片组中的NPU数量；默认值-1表示自动。| | `actor_rollout_ref.actor.ulysses_sequence_parallel_size`|Ulysses序列并行大小| | `actor_rollout_ref.actor.entropy_from_logits_with_chunking`|通过分块计算熵以减少显存峰值，默认值为False| | `actor_rollout_ref.actor.fsdp_config.entropy_checkpointing`|在训练时对熵计算启用重计算,降低显存峰值，默认值为False| | `actor_rollout_ref.actor.fsdp_config.forward_only` |是否只进行前向计算，默认值为False| ### 2. Megatron Megatron 是 NVIDIA 推出的一个专注于模型并行的训练框架仓库。如果一个仓库（例如 Verl）的训练后端使用了 Megatron，同时又希望在 NPU 上运行该仓库，那么就需要额外安装 MindSpeed 来提供底层支持。下文将介绍 MindSpeed 是如何实现无感替换 Megatron 中的关键组件，从而使其能够适配 NPU 的。 MindSpeed 底层的替换原理采用了 Monkey Patch 技术 * MindSpeed Moneky Patch框架 在verl里面通过`from mindspeed.megatron_adaptor import repatch `触发patch，调用栈如下： ~~~ from mindspeed.megatron_adaptor import repatch ├── 执行 megatron_adaptor.py 模块导入 ├── 导入 features_manager 模块 ├── 执行 mindspeed/features_manager/__init__.py ├── @AutoExecuteFunction 装饰器触发 ├── patch_features() 自动执行 └── 进行`apply_features_pre_patches`和`apply_features_patches`操作 ~~~ `Patch`类是整个patch系统的核心，实现了函数/类的动态替换 ~~~python class Patch ~~~ `parse_path`方法实现了动态模块导入和创建 ~~~python def parse_path(module_path, function_name, create_dummy) ~~~ patch系统支持多层装饰器叠加 ~~~ def apply_patch(self): final_patch_func = self.orig_func if self.patch_func is not None: final_patch_func = self.patch_func # 应用所有装饰器 for wrapper in self.wrappers: final_patch_func = wrapper(final_patch_func) ~~~ * MindSpeedPatchesManager类 `MindSpeedPatchesManager`作为全局单例管理所有patch ~~~python class MindSpeedPatchesManager: patches_info: Dict[str, Patch] = {} ~~~ * Feature集成模式 各个Feature通过继承`MindSpeedFeature`基类集成patch系统 ~~~python class MindSpeedFeature: """Base class for mindspeed features.""" def __init__(self, feature_name: str, optimization_level: int = 2): self.feature_name = feature_name.lower().strip().replace('-', '_') self.optimization_level = optimization_level self.default_patches = self.optimization_level == 0 def is_need_apply(self, args): """Check the feature is need to apply.""" return (self.optimization_level <= args.optimization_level and getattr(args, self.feature_name, None)) \ or self.default_patches def register_args(self, parser: ArgumentParser): """Register cli arguments to enable the feature.""" pass def pre_validate_args(self, args: Namespace): """Validate the arguments of mindspeed before megatron args validation and store some arguments of the mindspeed temporarily, incase that megatron validate faile. for example: ```python origin_context_parallel_size = args.context_parallel_size args.context_parallel_size = 1 ``` """ pass def validate_args(self, args: Namespace): """Restore the arguments of the mindspeed. for example: ```python args.context_parallel_size = origin_context_parallel_size ``` """ pass def post_validate_args(self, args: Namespace): """validate mindspeed arguments after megatron arguments validation.""" pass def pre_register_patches(self, patch_manager: MindSpeedPatchesManager, args: Namespace): """Register all patch functions before import megatron""" pass def register_patches(self, patch_manager: MindSpeedPatchesManager, args: Namespace): """Register all patch functions the feature is related.""" pass def incompatible_check(self, global_args, check_args): """Register all incompatible functions the feature is related.""" if getattr(global_args, self.feature_name, None) and getattr(global_args, check_args, None): raise AssertionError('{} and {} are incompatible.'.format(self.feature_name, check_args)) def dependency_check(self, global_args, check_args): """Register all dependency functions the feature is related.""" if getattr(global_args, self.feature_name, None) and not getattr(global_args, check_args, None): raise AssertionError('{} requires {}.'.format(self.feature_name, check_args)) @staticmethod def add_parser_argument_choices_value(parser, argument_name, new_choice): """Add a new choice value to the existing choices of a parser argument.""" for action in parser._actions: exist_arg = isinstance(action, argparse.Action) and argument_name in action.option_strings if exist_arg and action.choices is not None and new_choice not in action.choices: action.choices.append(new_choice) ~~~ ##### 参数特性支持 | verl参数 | 简介| | --- | --- | | `actor_rollout_ref.actor.megatron.optimizer_offload` |是否卸载模型优化器到CPU，默认值为False| | `actor_rollout_ref.actor.megatron.use_mbridge` |是否使用mbridge进行权重转换| | `actor_rollout_ref.actor.megatron.param_offload` |是否卸载模型权重到CPU，默认值为False| | `actor_rollout_ref.actor.megatron.tensor_model_parallel_size` | 张量并行大小；默认值为1。| | `actor_rollout_ref.actor.megatron.pipeline_model_parallel_size` |流水并行大小，默认值为1| | `actor_rollout_ref.actor.megatron.expert_model_parallel_size` | 专家并行大小，默认值为1| | `actor_rollout_ref.actor.megatron.expert_tensor_parallel_size`|TP拓展EP大小，默认值为null| | `actor_rollout_ref.actor.context_parallel_size`|序列并行大小，默认值为False| | `actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs`|张量在发送到下一个pp stage后,输出数据被释放，降低显存峰值，默认值为False| | `actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm` |是否使用持久化 LayerNorm，默认值为False| | `actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm` |是否使用持Group GEMM，默认值为False| | `actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype` |用于路由和专家输出加权平均的数据类型。使用 fp32 或 fp64 可以提高稳定性，尤其是在专家数量较多时，默认值为fp32| | `actor_rollout_ref.actor.megatron.override_transformer_config.account_for_loss_in_pipeline_split` |如果设置为 True，在流水线并行的划分和放置策略中，loss 层会被视为一个标准的 Transformer 层来处理。默认为False。| | `actor_rollout_ref.actor.megatron.override_transformer_config.account_for_embedding_in_pipeline_split` |如果设置为 True，在流水线并行的划分和放置策略中，输入embedding 层会被视为一个标准的 Transformer 层来处理。默认为False。| | `actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity` |重新计算激活的粒度，可选项为'full', 'selective' and 'none'。其中full代表重新计算整个transformer layer，selective代表只计算transformer layer中的核心注意力部分。默认为'none'。| | `actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method` |该参数需将recompute_granularity设置为'full'才生效，可选项为'uniform', 'block'。默认为None。| | `actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers` |该参数需将recompute_granularity设置为'full'才生效，默认为None。若recompute_method设置为uniform，该参数含义为每个均匀划分的重新计算单元的transformer layers数量。例如你可以指定为--recompute_granularity full --recompute_method uniform --recompute_num_layers 4。recompute_num_layers越大，显存占用越小，计算成本越大。注意：当前进程中的模型层数需能被recompute_num_layers整除。默认为None。| | `actor_rollout_ref.actor.megatron.use_dist_checkpointing` |是否使用分布式权重，默认值为False| | `actor_rollout_ref.actor.megatron.dist_checkpointing_path` |分布式权重路径，默认值为null| | `actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn` |是否使用fa，默认值为true| | `actor_rollout_ref.actor.megatron.override_transformer_config.use_fused_rotary_pos_emb` |是否使用融合旋转位置编码，默认值为False| | `actor_rollout_ref.actor.megatron.override_transformer_config.use_fused_swiglu` |是否使用融合swiglu，默认值为False| | `actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_first_pipeline_stage` |第一个pipeline stage 的层数，默认值为none| | `actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage` |最后一个pipeline stage 的层数，默认值为none| ================================================ FILE: docs/ascend_tutorial/features/ascend_consistency.rst ================================================ 推理一致性指导 ==================================== 在昇腾设备上对齐verl和vLLM两个框架下的推理结果。 Last updated: 11/17/2025. 这是一份在昇腾设备上对齐verl和vLLM两个框架下推理结果的教程。 环境变量配置 ~~~~~~~~~~~~ 在多卡通信情况下： - HCCL通信下(默认场景): - export CLOSE_MATMUL_K_SHIFT=1 - export ATB_MATMUL_SHUFFLE_K_ENABLE=0 - export HCCL_DETERMINISTIC="true" - export VLLM_ENABLE_V1_MULTIPROCESSING=0 - LCCL通信下(通过export HCCL_OP_EXPANSION_MODE="AIV"使能）: - export CLOSE_MATMUL_K_SHIFT=1 - export ATB_MATMUL_SHUFFLE_K_ENABLE=0 - export LCCL_DETERMINISTIC=1 - export ATB_LLM_LCOC_ENABLE=0 - export VLLM_ENABLE_V1_MULTIPROCESSING=0 在单卡无通信情况下： - HCCL和LCCL通信下: - export CLOSE_MATMUL_K_SHIFT=1 - export ATB_MATMUL_SHUFFLE_K_ENABLE=0 - export VLLM_ENABLE_V1_MULTIPROCESSING=0 vLLM初始化参数 ~~~~~~~~~~~~ 需要对 SamplingParams 参数里单独设置seed, 保持vLLM和verl推理结果一致, 举例修改如下： .. code:: yaml sampling_params = SamplingParams(n=1, logprobs=0, # can be set to 0 and let actor to recompute max_tokens=config.response_length, repetition_penalty=config.get("repetition_penalty", 1.0), seed=1234) ================================================ FILE: docs/ascend_tutorial/profiling/ascend_profiling_en.rst ================================================ Profiling Data Collection Guide ========================================================================================== Last updated: 12/20/2025. This is a tutorial for data collection using the GRPO or DAPO algorithm based on FSDP or MindSpeed(Megatron) on Ascend devices. Configuration ------------- Leverage two levels of configuration to control data collection: - **Global profiler control**: Use parameters in ``verl/trainer/config/ppo_trainer.yaml`` (FSDP) or ``verl/trainer/config/ppo_megatron_trainer.yaml`` (MindSpeed) to control the collection mode and steps. - **Role profile control**: Use parameters in each role's ``profile`` field to control various parameters. Global collection control ~~~~~~~~~~~~~~~~~~~~~~~~~ Use parameters in ppo_trainer.yaml to control the collection mode and steps. - global_profiler: Control the ranks and mode of profiling - tool: The profiling tool to use, options are nsys, npu, torch, torch_memory. - steps: This parameter can be set as a list that has collection steps, such as [2, 4], which means it will collect steps 2 and 4. If set to null, no collection occurs. - save_path: The path to save the collected data. Default is "outputs/profile". Role collection control ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In each role's ``profiler`` field, you can control the collection mode for that role. - enable: Whether to enable profiling for this role. - all_ranks: Whether to collect data from all ranks. - ranks: A list of ranks to collect data from. If empty, no data is collected. - tool_config: Configuration for the profiling tool used by this role. Use parameters in each role's ``profiler.tool_config.npu`` to control npu profiler behavior: - level: Collection level—options are level_none, level0, level1, and level2 - level_none: Disables all level-based data collection (turns off profiler_level). - level0: Collect high-level application data, underlying NPU data, and operator execution details on NPU. After balancing data volume and analytical capability, Level 0 is recommended as the default configuration. - level1: Extends level0 by adding CANN-layer AscendCL data and AI Core performance metrics on NPU. - level2: Extends level1 by adding CANN-layer Runtime data and AI CPU metrics. - contents: A list of options to control the collection content, such as npu, cpu, memory, shapes, module, stack. - npu: Whether to collect device-side performance data. - cpu: Whether to collect host-side performance data. - memory: Whether to enable memory analysis. - shapes: Whether to record tensor shapes. - module: Whether to record framework-layer Python call stack information. It is recommended to use 'module' instead of 'stack' for recording call stack information, as it costs less performance overhead. - stack: Whether to record operator call stack information. - analysis: Enables automatic data parsing. - discrete: Whether to enable discrete mode. Examples -------- Disabling collection ~~~~~~~~~~~~~~~~~~~~ .. code:: yaml global_profiler: steps: null # disable profile End-to-End collection ~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml global_profiler: steps: [1, 2, 5] save_path: ./outputs/profile actor_rollout_ref: actor: # Set actor role profiler collection configuration parameters profiler: enable: True all_ranks: True tool_config: npu: discrete: False contents: [npu, cpu] # Control collection list, default cpu, npu, can configure memory, shapes, module, etc. # rollout & ref follow actor settings Discrete Mode Collection ~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml global_profiler: steps: [1, 2, 5] save_path: ./outputs/profile actor_rollout_ref: actor: profiler: enable: True # Set to True to profile training all_ranks: False ranks: [0] # Global Rank 0 tool_config: npu: discrete: True contents: [npu, cpu] rollout: profiler: enable: True # Set to True to profile inference all_ranks: False ranks: [0] # In Agent Loop mode, this is the Replica Rank (e.g., 0-th instance) tool_config: npu: discrete: True # Must be enabled in Agent Loop mode # ref follow actor settings **Agent Loop Mode Description**: When Rollout runs in `Agent Loop <../advance/agent_loop.rst>`_ mode, performance data for the Rollout phase **must be collected using discrete mode**. In this case, the Profiler is triggered by the inference engine backend. 1. Rank Definition: ranks in the Rollout configuration refers to Replica Rank (inference instance index), not Global Rank. 2. Inference Engine Support: Currently, vLLM and SGLang engines are supported without additional settings. Specific details are as follows: - vLLM Engine: Automatically collects AsyncLLM scheduling stacks and inference process performance data. Does not support setting analysis (defaults to no analysis, requires offline analysis) and profiler_level (defaults to level1). - SGLang Engine: Automatically collects inference process performance data. Does not support the memory option in contents. Does not support setting analysis (defaults to enabled) and profiler_level (defaults to level0). Visualization ------------- Collected data is stored in the user-defined save_path and can be visualized by using the `MindStudio Insight `_ tool. Additionally, in a Linux environment, the MindStudio Insight tool is provided in the form of a `JupyterLab Plugin `_ ，offering a more intuitive and highly interactive user interface. The advantages of the JupyterLab plugin are as follows: - Seamless integration: Supports running the MindStudio Insight tool directly within the Jupyter environment, eliminating the need to switch platforms or copy data from the server, enabling data to be collected and used immediately. - Fast startup: Allows MindStudio Insight to be launched quickly via the JupyterLab command line or graphical interface. - Smooth operation: In a Linux environment, launching MindStudio Insight through JupyterLab effectively alleviates performance lag compared to the full-package communication mode, significantly improving the user experience. - Remote access: Supports remotely launching MindStudio Insight. Users can connect to the service via a local browser for direct visual analysis, reducing the difficulty of uploading and downloading data during large-model training or inference. If the analysis parameter is set to False, offline parsing is required after data collection: .. code:: python import torch_npu # Set profiler_path to the parent directory of the "localhost.localdomain___ascend_pt" folder torch_npu.profiler.profiler.analyse(profiler_path=profiler_path) Advanced Guide: Fine-grained Collection --------------------------------------- Background and Challenges ~~~~~~~~~~~~~~~~~~~~~~~~~ Although the configuration-based collection method mentioned above is convenient, it faces challenges in training scenarios with **long sequences (Long Context)** or **large global batch sizes (Large Global Batch Size)**. Within a complete training step (Step), model computation exhibits high-frequency and repetitive characteristics: 1. **Rollout phase**: Sequence generation (Generate Sequence) is an autoregressive process involving thousands of forward computations of the Decoder model. 2. **Training phase**: To control peak memory usage, verl typically adopts a Micro-Batch strategy, dividing large data streams into multiple micro-batches for computation. - **compute_log_prob (Actor/Ref)**: Involves multiple rounds of pure forward propagation. - **update_policy (Actor/Critic)**: Involves multiple rounds of forward and backward propagation. This characteristic leads to massive and repetitive operator records from full profiling. As shown in the image below: .. image:: https://raw.githubusercontent.com/mengchengTang/verl-data/master/verl_ascend_profiler.png Even with ``discrete`` mode enabled, performance data files for a single stage can still reach several TB, leading to **parsing failures** or **visualization tool lag**. Solution: Critical Path Sampling ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To solve the above problems, we can adopt a **critical path sampling** strategy: Based on the API interface provided by `torch_npu.profiler `_, directly modify Python source code to collect only representative data segments (such as specific Decode Steps or the first Micro-Batch). **Important Notes** 1. This chapter involves direct source code modification. It is recommended to back up files before modification and restore them after debugging. 2. When using code instrumentation for collection, be sure to **disable global collection** (``global_profiler: steps: null``) in ``ppo_trainer.yaml`` or ``ppo_megatron_trainer.yaml`` to avoid Profiler conflicts. 1. Fine-grained Collection in Rollout Phase ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For vLLM or SGLang inference engines, we can control the ``schedule`` parameter to collect model forward propagation performance data for specific tokens. **vLLM Engine** - **Reference Version**: vLLM v0.11.0, vLLM-Ascend v0.11.0rc1 - **Modified File**: ``vllm-ascend/vllm_ascend/worker/worker_v1.py`` .. code-block:: diff class NPUWorker(WorkerBase): def __init__(self, *args, **kwargs): # ... existing code ... + # Initialize profiler + import torch_npu + experimental_config = torch_npu.profiler._ExperimentalConfig( + profiler_level=torch_npu.profiler.ProfilerLevel.Level1, + export_type=torch_npu.profiler.ExportType.Db, # You can choose torch_npu.profiler.ExportType.Text format + ) + self.profiler_npu = torch_npu.profiler.profile( + activities=[torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU], + with_modules=False, # Collect call stack + profile_memory=False, # Collect memory + experimental_config=experimental_config, + # Skip first step, warmup one step, collect 3 steps, repeat 1 time. If you want to collect decode steps 30~70, set schedule=torch_npu.profiler.schedule(wait=29, warmup=1, active=30, repeat=1) + schedule=torch_npu.profiler.schedule(wait=1, warmup=1, active=3, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/vllm_profile", analyse_flag=True) # Data save path and whether to parse online + ) + self.profiler_npu.start() # ... existing code ... def execute_model(self, scheduler_output=None, intermediate_tensors=None, **kwargs): # ... existing code ... output = self.model_runner.execute_model(scheduler_output, intermediate_tensors) + self.profiler_npu.step() # Drive schedule to collect partial decode steps # ... existing code ... **SGLang Engine** - **Reference Version**: SGLang master branch - **Modified File**: ``sglang/python/sglang/srt/model_executor/model_runner.py`` .. code-block:: diff # ... existing imports ... + import torch_npu class ModelRunner: def __init__(self, *args, **kwargs): # ... existing init code ... + # Initialize profiler (same configuration as above, omitted) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.profiler_npu = torch_npu.profiler.profile( + # ... + # Skip first step, warmup one step, collect 3 steps, repeat 1 time. + schedule=torch_npu.profiler.schedule(wait=1, warmup=1, active=3, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/sglang_profile", analyse_flag=True) + ) + self.profiler_npu.start() def forward(self, forward_batch, **kwargs): # ... existing code ... + self.profiler_npu.step() # Drive schedule to collect partial decode steps return output 2. Fine-grained Collection in compute_log_prob (Actor & Ref) Phase ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This phase computes probability distributions for new and old policies. **FSDP Backend** The FSDP backend allows fine-grained control at the Micro-Batch level. - **Modified File**: ``verl/workers/actor/dp_actor.py`` .. code-block:: diff # ... import dependencies ... + import torch_npu class DataParallelPPOActor(BasePPOActor): def compute_log_prob(self, data: DataProto, calculate_entropy=False) -> torch.Tensor: + role = "Ref" if self.actor_optimizer is None else "Actor" + # Prepare profiler (same configuration as above, omitted) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.prof_npu = torch_npu.profiler.profile( + # ... + # wait=0, warmup=0, active=1: directly collect first micro-batch + schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler(f"./outputs/{role}_compute_log_prob", analyse_flag=True) + ) + # This function is shared by ref and actor, set role flag to distinguish. If you want to collect actor_compute_log_prob, set if role=="Actor": + if role=="Ref": + self.prof_npu.start() for micro_batch in micro_batches: # ... original computation logic ... with torch.no_grad(): entropy, log_probs = self._forward_micro_batch(...) + # Drive schedule to collect micro batch + if role=="Ref": + self.prof_npu.step() # ... **Megatron Backend** The Micro-Batch scheduling in the Megatron backend is managed internally by the framework and does not currently support fine-grained collection at the Micro-Batch level through simple code instrumentation. It is recommended to use global configuration for collection. 3. Fine-grained Collection in update_policy (Actor & Critic) Phase ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The Update phase includes forward and backward propagation. **FSDP Backend** The FSDP backend supports collection at both Mini-Batch and Micro-Batch granularities. - **Modified File**: ``verl/workers/actor/dp_actor.py`` .. code-block:: diff # ... import dependencies ... + import torch_npu class DataParallelPPOActor(BasePPOActor): def update_policy(self, data: DataProto): + # Prepare profiler (same configuration as above, omitted) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.prof_npu = torch_npu.profiler.profile( + # ... + # Only collect first Mini Batch (including all Micro-Batch computations and one optimizer update) + schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/fsdp_actor_update_profile", analyse_flag=True) + ) + self.prof_npu.start() # ... PPO Epochs loop ... for _ in range(self.config.ppo_epochs): # ... Mini Batch loop ... for batch_idx, mini_batch in enumerate(mini_batches): # ... mini_batches split ... for i, micro_batch in enumerate(micro_batches): # ... Original Forward & Backward logic ... # ... loss.backward() ... pass grad_norm = self._optimizer_step() + # Drive schedule to collect mini batch, if you want micro batch collection, move self.prof_npu.step() inside the micro_batch loop + self.prof_npu.step() **Megatron Backend** The Megatron backend supports collection at the Mini-Batch granularity. - **Modified File**: ``verl/workers/actor/megatron_actor.py`` .. code-block:: diff class MegatronPPOActor(BasePPOActor): def update_policy(self, dataloader: Iterable[DataProto]) -> dict: # ... + # Prepare profiler (same configuration as above, omitted) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.prof_npu = torch_npu.profiler.profile( + # ... + # Only collect computation of first Mini Batch (including all Micro-Batches) and one optimizer update + schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/megatron_actor_update_profile", analyse_flag=True) + ) + self.prof_npu.start() for data in dataloader: # ... internally calls self.forward_backward_batch for computation ... # ... metric_micro_batch = self.forward_backward_batch(...) # ... self.actor_optimizer.step() ... + # Drive schedule to collect mini batch + self.prof_npu.step() ================================================ FILE: docs/ascend_tutorial/profiling/ascend_profiling_zh.rst ================================================ Profiling采集指导 ================================================================================== Last updated: 12/20/2025. 这是一份在昇腾设备上基于FSDP或MindSpeed(Megatron)后端，使用GRPO或DAPO算法进行数据采集的教程。 配置 ---- 使用两级profile设置来控制数据采集 - 全局采集控制：使用verl/trainer/config/ppo_trainer.yaml(FSDP)，或verl/trainer/config/ppo_megatron_trainer.yaml(MindSpeed)中的配置项控制采集的模式和步数。 - 角色profile控制：通过每个角色中的配置项控制等参数。 全局采集控制 ~~~~~~~~~~~~ 通过 ppo_trainer.yaml 中的参数控制采集步数和模式： - global_profiler: 控制采集的rank和模式 - tool: 使用的采集工具，选项有 nsys、npu、torch、torch_memory。 - steps: 此参数可以设置为包含采集步数的列表，例如 [2, 4]，表示将采集第2步和第4步。如果设置为 null，则不进行采集。 - save_path: 保存采集数据的路径。默认值为 "outputs/profile"。 角色profiler控制 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 在每个角色的 ``profiler`` 字段中，您可以控制该角色的采集模式。 - enable: 是否为此角色启用性能分析。 - all_ranks: 是否从所有rank收集数据。 - ranks: 要收集数据的rank列表。如果为空，则不收集数据。 - tool_config: 此角色使用的性能分析工具的配置。 通过每个角色的 ``profiler.tool_config.npu`` 中的参数控制具体采集行为： - level: 采集级别—选项有 level_none、level0、level1 和 level2 - level_none: 禁用所有基于级别的数据采集（关闭 profiler_level）。 - level0: 采集高级应用数据、底层NPU数据和NPU上的算子执行详情。在权衡数据量和分析能力后，level0是推荐的默认配置。 - level1: 在level0基础上增加CANN层AscendCL数据和NPU上的AI Core性能指标。 - level2: 在level1基础上增加CANN层Runtime数据和AI CPU指标。 - contents: 控制采集内容的选项列表，例如 npu、cpu、memory、shapes、module、stack。 - npu: 是否采集设备端性能数据。 - cpu: 是否采集主机端性能数据。 - memory: 是否启用内存分析。 - shapes: 是否记录张量形状。 - module: 是否记录框架层Python调用栈信息。相较于stack，更推荐使用module记录调用栈信息，因其产生的性能膨胀更低。 - stack: 是否记录算子调用栈信息。 - analysis: 启用自动数据解析。 - discrete: 使用离散模式。 示例 ---- 禁用采集 ~~~~~~~~~~~~~~~~~~~~ .. code:: yaml global_profiler: steps: null # disable profile 端到端采集 ~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml global_profiler: steps: [1, 2, 5] save_path: ./outputs/profile actor_rollout_ref: actor: # 设置 actor role 的 profiler 采集配置参数 profiler: enable: True all_ranks: True tool_config: npu: discrete: False contents: [npu, cpu] # 控制采集列表，默认cpu、npu，可配置memory、shapes、module等 # rollout & ref follow actor settings 离散模式采集 ~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml global_profiler: steps: [1, 2, 5] save_path: ./outputs/profile actor_rollout_ref: actor: profiler: enable: True # 设置为 True 以采集训练阶段 all_ranks: False ranks: [0] # 全局 Rank 0 tool_config: npu: discrete: True contents: [npu, cpu] rollout: profiler: enable: True # 设置为 True 以采集推理阶段 all_ranks: False ranks: [0] # 在 Agent Loop 模式下，此处指推理实例的 Replica Rank (例如第 0 个实例) tool_config: npu: discrete: True # Agent Loop 模式下必须开启离散模式 # ref follow actor settings **Agent Loop 模式说明**： 在 `Agent Loop <../advance/agent_loop.rst>`_ 模式下，Rollout 阶段的性能数据 **必须使用离散模式** 采集，此时 Profiler 由推理引擎后端触发。 1. Rank 定义：Rollout 配置中的 ranks 指代 Replica Rank（推理实例索引），而非全局 Rank。 2. 推理引擎支持：当前支持vLLM和SGLang引擎，无需额外设置。具体说明如下： - vLLM 引擎：自动采集 AsyncLLM 调度栈及推理进程性能数据。不支持设置 analysis（默认不解析，需离线解析）和 profiler_level（默认 level1）。 - SGLang 引擎：自动采集推理进程性能数据。不支持 contents 中的 memory 配置项。不支持设置 analysis（默认解析）和 profiler_level（默认 level0）。 可视化 ------ 采集后的数据存放在用户设置的save_path下，可通过 `MindStudio Insight `_ 工具进行可视化。 另外在Linux环境下，MindStudio Insight工具提供了 `JupyterLab插件 `_ 形态，提供更直观和交互式强的操作界面。JupyterLab插件优势如下： - 无缝集成：支持在Jupyter环境中直接运行MindStudio Insight工具，无需切换平台，无需拷贝服务器上的数据，实现数据即采即用。 - 快速启动：通过JupyterLab的命令行或图形界面，可快速启动MindStudio Insight工具。 - 运行流畅：在Linux环境下，通过JupyterLab环境启动MindStudio Insight，相较于整包通信，有效解决了运行卡顿问题，操作体验显著提升。 - 远程访问：支持远程启动MindStudio Insight，可通过本地浏览器远程连接服务直接进行可视化分析，缓解了大模型训练或推理数据上传和下载的困难。 如果analysis参数设置为False，采集之后需要进行离线解析： .. code:: python import torch_npu # profiler_path请设置为"localhost.localdomain___ascend_pt"目录的上一级目录 torch_npu.profiler.profiler.analyse(profiler_path=profiler_path) 进阶指南：精细化采集 -------------------- 背景与挑战 ~~~~~~~~~~ 上述基于配置文件的采集方式虽然便捷，但在 **长序列 (Long Context)** 或 **大全局批量 (Large Global Batch Size)** 的训练场景中面临挑战。 在一个完整的训练步 (Step) 内，模型计算呈现出高频次、重复性的特征： 1. Rollout 阶段：序列生成 (Generate Sequence) 是一个自回归过程，涉及成千上万次 Decoder 模型的前向计算。 2. Training 阶段：为了控制显存峰值，verl 通常采用 Micro-Batch 策略，将庞大的数据流切分为多个微批次进行计算。 - compute_log_prob (Actor/Ref)：涉及多轮纯前向传播。 - update_policy (Actor/Critic)：涉及多轮前向与反向传播。 这种特性会导致全量 Profiling 产生海量且重复的算子记录。如下图所示： .. image:: https://raw.githubusercontent.com/mengchengTang/verl-data/master/verl_ascend_profiler.png 即使使用了 ``discrete`` 模式，单个阶段的性能数据文件仍可能达到数 TB，导致 **解析失败** 或 **可视化工具卡顿** 。 解决方案：关键路径采样 ~~~~~~~~~~~~~~~~~~~~~~ 为了解决上述问题，我们可以采用 **关键路径采样** 策略：基于 `torch_npu.profiler `_ 提供的API接口，直接修改 Python 源码，仅采集具有代表性的数据片段（如特定 Decode Step 或首个 Micro-Batch）。 **重要提示** 1. 本章节涉及直接修改源码。建议修改前备份文件，调试完成后恢复。 2. 使用代码插桩采集时，请务必在 ``ppo_trainer.yaml`` 或 ``ppo_megatron_trainer.yaml`` 中**禁用全局采集** (``global_profiler: steps: null``)，以避免 Profiler 冲突。 1. Rollout 阶段精细化采集 ~~~~~~~~~~~~~~~~~~~~~~~~~ 对于 vLLM 或 SGLang 推理引擎，我们可以通过控制 ``schedule`` 参数来控制采集模型在特定token的前向传播性能数据。 **vLLM 引擎** - **参考版本**：vLLM v0.11.0, vLLM-Ascend v0.11.0rc1 - **修改文件**：``vllm-ascend/vllm_ascend/worker/worker_v1.py`` .. code-block:: diff class NPUWorker(WorkerBase): def __init__(self, *args, **kwargs): # ... existing code ... + # Initialize profiler + import torch_npu + experimental_config = torch_npu.profiler._ExperimentalConfig( + profiler_level=torch_npu.profiler.ProfilerLevel.Level1, + export_type=torch_npu.profiler.ExportType.Db, # 可选择torch_npu.profiler.ExportType.Text格式 + ) + self.profiler_npu = torch_npu.profiler.profile( + activities=[torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU], + with_modules=False, # 采集调用栈 + profile_memory=False, # 采集内存 + experimental_config=experimental_config, + # 跳过第一步，warmup一步，采集3步，重复1次。如果想采集第30~70个decode step，可以设置为schedule=torch_npu.profiler.schedule(wait=29, warmup=1, active=30, repeat=1) + schedule=torch_npu.profiler.schedule(wait=1, warmup=1, active=3, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/vllm_profile", analyse_flag=True) # 采集数据保存路径，是否在线解析 + ) + self.profiler_npu.start() # ... existing code ... def execute_model(self, scheduler_output=None, intermediate_tensors=None, **kwargs): # ... existing code ... output = self.model_runner.execute_model(scheduler_output, intermediate_tensors) + self.profiler_npu.step() # 驱动 schedule，对部分decode step进行采集 # ... existing code ... **SGLang 引擎** - **参考版本**：SGLang master 分支 - **修改文件**：``sglang/python/sglang/srt/model_executor/model_runner.py`` .. code-block:: diff # ... existing imports ... + import torch_npu class ModelRunner: def __init__(self, *args, **kwargs): # ... existing init code ... + # Initialize profiler (配置同上，略) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.profiler_npu = torch_npu.profiler.profile( + # ... + # 跳过第一步，warmup一步，采集3步，重复1次。 + schedule=torch_npu.profiler.schedule(wait=1, warmup=1, active=3, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/sglang_profile", analyse_flag=True) + ) + self.profiler_npu.start() def forward(self, forward_batch, **kwargs): # ... existing code ... + self.profiler_npu.step() # 驱动 schedule，对部分decode step进行采集 return output 2. compute_log_prob (Actor & Ref) 阶段精细化采集 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 该阶段计算新旧策略的概率分布。 **FSDP 后端** FSDP 后端允许在 Micro-Batch 级别进行精细控制。 - **修改文件**：``verl/workers/actor/dp_actor.py`` .. code-block:: diff # ... 引入依赖 ... + import torch_npu class DataParallelPPOActor(BasePPOActor): def compute_log_prob(self, data: DataProto, calculate_entropy=False) -> torch.Tensor: + role = "Ref" if self.actor_optimizer is None else "Actor" + # 准备 profiler (配置同上，略) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.prof_npu = torch_npu.profiler.profile( + # ... + # wait=0, warmup=0, active=1: 直接采集第一个 micro-batch + schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler(f"./outputs/{role}_compute_log_prob", analyse_flag=True) + ) + # 此函数ref和actor共用，设置role标志位来区分。如果想采集actor_compute_log_prob，可设置if role=="Actor": + if role=="Ref": + self.prof_npu.start() for micro_batch in micro_batches: # ... 原始计算逻辑 ... with torch.no_grad(): entropy, log_probs = self._forward_micro_batch(...) + # 驱动 schedule，对micro batch进行采集 + if role=="Ref": + self.prof_npu.step() # ... **Megatron 后端** Megatron 后端的 Micro-Batch 调度由框架内部管理，暂不支持通过简单的代码插桩进行 Micro-Batch 级别的精细化采集。建议使用全局配置进行采集。 3. update_policy (Actor & Critic) 阶段精细化采集 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Update 阶段包含前向和反向传播。 **FSDP 后端** FSDP 后端支持设置对 Mini-Batch 和 Micro-Batch 的粒度进行采集。 - **修改文件**：``verl/workers/actor/dp_actor.py`` .. code-block:: diff # ... 引入依赖 ... + import torch_npu class DataParallelPPOActor(BasePPOActor): def update_policy(self, data: DataProto): + # 准备 profiler (配置同上，略) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.prof_npu = torch_npu.profiler.profile( + # ... + # 仅采集第一个 Mini Batch（包含所有 Micro-Batch 的计算和一次优化器更新） + schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/fsdp_actor_update_profile", analyse_flag=True) + ) + self.prof_npu.start() # ... PPO Epochs 循环 ... for _ in range(self.config.ppo_epochs): # ... Mini Batch 循环 ... for batch_idx, mini_batch in enumerate(mini_batches): # ... mini_batches 切分 ... for i, micro_batch in enumerate(micro_batches): # ... 原始 Forward & Backward 逻辑 ... # ... loss.backward() ... pass grad_norm = self._optimizer_step() + # 驱动 schedule，对mini batch进行采集，如果想对micro batch进行，则将self.prof_npu.step()移动到micro_batch的循环内 + self.prof_npu.step() **Megatron 后端** Megatron 后端支持以 Mini-Batch 的粒度进行采集。 - **修改文件**：``verl/workers/actor/megatron_actor.py`` .. code-block:: diff class MegatronPPOActor(BasePPOActor): def update_policy(self, dataloader: Iterable[DataProto]) -> dict: # ... + # 准备 profiler (配置同上，略) + experimental_config = torch_npu.profiler._ExperimentalConfig(...) + self.prof_npu = torch_npu.profiler.profile( + # ... + # 仅采集第一个 Mini Batch 的计算（含所有 Micro-Batch）和一次优化器更新 + schedule=torch_npu.profiler.schedule(wait=0, warmup=0, active=1, repeat=1), + on_trace_ready=torch_npu.profiler.tensorboard_trace_handler("./outputs/megatron_actor_update_profile", analyse_flag=True) + ) + self.prof_npu.start() for data in dataloader: # ... 内部会调用 self.forward_backward_batch 进行计算 ... # ... metric_micro_batch = self.forward_backward_batch(...) # ... self.actor_optimizer.step() ... + # 驱动 schedule，对mini batch进行采集 + self.prof_npu.step() ================================================ FILE: docs/ascend_tutorial/quick_start/ascend_quick_start.rst ================================================ Ascend Quickstart =================================== Last updated: 03/03/2026. 关键更新 ---------------------------------- 2025/12/11：verl 存量场景目前支持自动识别 NPU 设备类型， GPU 脚本在昇腾上运行，原则上不再需要显式设置 trainer.device=npu 参数，新增特性通过设置 trainer.device 仍可优先使用，逐步适配自动识别能力。 [说明] 自动识别 NPU 设备类型的前提，是运行程序所在环境包含 torch_npu 软件包。如不包含该软件包，仍需显式指定 trainer.device=npu 参数。 硬件支持 ----------------------------------- Atlas 200T A2 Box16 Atlas 900 A2 PODc Atlas 800T A3 安装流程 ----------------------------------- DockerFile镜像构建 & 获取 & 使用 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 如需要通过 DockerFile 构建镜像，或希望使用基于 verl 构建的镜像，请参考 `文档 `_ 如果想直接获取镜像，请前往`quay.io/ascend/verl `_ 进行获取，镜像中已包含基础环境和依赖软件包。 安装基础环境 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. 基础环境涉及以下软件包，请参考 `文档 `_ 安装。 +---------------+----------------------+ | software | version | +---------------+----------------------+ | Python | >= 3.10, <3.12 | +---------------+----------------------+ | CANN | == 8.5.0 | +---------------+----------------------+ | torch | == 2.8.0 | +---------------+----------------------+ | torch_npu | == 2.8.0 | +---------------+----------------------+ 2. （可选）在 x86 平台安装时，pip 需要配置额外的源，指令如下： .. code-block:: bash pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/" 安装其他软件包 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 基础环境准备完毕后，需要通过指令安装以下软件包： +---------------+----------------------+ | torchvision | == 0.22.1 | +---------------+----------------------+ | triton-ascend | == 3.2.0 | +---------------+----------------------+ | transformers | == 4.57.6 | +---------------+----------------------+ tips: verl is not support transformers 5.0.0 or higher 安装指令： .. code-block:: bash # 安装torchvision，版本需要和torch匹配 pip install torchvision==0.22.1 # 清理环境上可能存在的历史triton/triton-ascend软件包残留 pip uninstall -y triton triton-ascend # 安装triton-ascend，不需要单独安装triton pip install triton-ascend==3.2.0 安装 vllm & vllm-ascend ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. 需确保CANN ascend-toolkit 和 nnal 环境变量被激活，对于CANN默认安装路径 /usr/local/Ascend 而言，激活指令如下： .. code-block:: source /usr/local/Ascend/ascend-toolkit/set_env.sh source /usr/local/Ascend/nnal/atb/set_env.sh 2. vllm 源码安装指令： .. code-block:: bash git clone --depth 1 --branch v0.13.0 https://github.com/vllm-project/vllm.git cd vllm && pip install -r requirements/build.txt VLLM_TARGET_DEVICE=empty pip install -v -e. && cd .. 3. vllm-ascend 源码安装指令： .. code-block:: bash git clone -b releases/v0.13.0 https://github.com/vllm-project/vllm-ascend.git cd vllm-ascend && pip install -r requirements.txt export COMPILE_CUSTOM_KERNELS=1 && pip install -v -e . && cd .. 安装 MindSpeed ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ MindSpeed 源码安装指令： .. code-block:: bash # 下载 MindSpeed，切换到指定commit-id，并下载 Megatron-LM git clone https://gitcode.com/Ascend/MindSpeed.git cd MindSpeed && git checkout 2.3.0_core_r0.12.1 && cd .. git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git # 安装 MindSpeed & Megatron pip install -e MindSpeed pip install -e Megatron-LM # 安装 mbridge pip install mbridge MindSpeed 对应 Megatron-LM 后端使用场景，使用方式如下： 1. 使能 verl worker 模型 ``strategy`` 配置为 ``megatron`` ，例如 ``actor_rollout_ref.actor.strategy=megatron``。 2. MindSpeed 自定义入参可通过 ``override_transformer_config`` 参数传入，例如对 actor 模型开启 FA 特性可使用 ``+actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True``。 3. 更多特性信息可参考 `MindSpeed & verl 文档 `_ 。 安装verl ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: bash git clone --recursive https://github.com/volcengine/verl.git cd verl && pip install -r requirements-npu.txt && pip install -v -e . && cd .. # （可选）提示：为了更佳的使用体验，最好将recipe子模块更新至最新commit cd recipe && git checkout main && cd .. 昇腾暂不支持生态库说明 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ verl 中昇腾暂不支持生态库如下： +---------------+----------------+ | software | description | +---------------+----------------+ | flash_attn | not supported | +---------------+----------------+ | liger-kernel | not supported | +---------------+----------------+ 1. 不支持通过 flash_attn 使能 flash attention 加速，支持通过 transformers 使用。 2. 不支持 liger-kernel 使能。 快速开始 ----------------------------------- 正式使用前，建议您通过对Qwen2.5-0.5B GRPO的训练尝试以检验环境准备和安装的正确性。 1.下载数据集并将数据集预处理为parquet格式，以便包含计算RL奖励所需的必要字段 .. code-block:: bash python3 examples/data_preprocess/gsm8k.py --local_save_dir ~/data/gsm8k 2.执行训练 .. code-block:: bash set -x export VLLM_ATTENTION_BACKEND=XFORMERS python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=128 \ data.max_prompt_length=512 \ data.max_response_length=128 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=5e-7 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.entropy_coeff=0.001 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=20 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 算法支持现状 ----------------------------------- **表1** RL类算法 +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | algorithm | model | download link | actor.strategy | rollout.name | shell location | hardware | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen2.5-7B-instruct |`7B `_ | FSDP | vllm-ascend |`qwen2_5_7b_grpo_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen2.5-32B-instruct |`32B `_ | FSDP | vllm-ascend |`qwen2_5_32b_grpo_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen2.5-VL-3B-instruct |`3B `_ | FSDP | vllm-ascend |`qwen2_5_vl_3b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen2.5-VL-7B-instruct |`7B `_ | FSDP | vllm-ascend |`qwen2_5_vl_7b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen2.5-VL-32B-instruct |`32B `_ | FSDP | vllm-ascend |`qwen2_5_vl_32b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen3-4B |`4B `_ | FSDP | vllm-ascend |`qwen3-4B_npu `_ | Atlas 800T A3 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen3-8B |`8B `_ | FSDP | vllm-ascend |`qwen3_8b_vllm_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen3-8B |`8B `_ | FSDP | sglang |`qwen3_8b_sglang_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | Qwen3-32B |`32B `_ | FSDP | vllm-ascend |`qwen3-32B_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | GRPO | DeepSeekv3-671B |`671B `_ | Megatron | vllm-ascend |`deepseek_v3_megatron_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | DAPO | Qwen2.5-7B-instruct |`7B `_ | FSDP | vllm-ascend |`qwen2.5_7b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | DAPO | Qwen2.5-32B |`32B `_ | FSDP | vllm-ascend |`qwen2.5_32b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | DAPO | Qwen3-8B-base |`8B `_ | FSDP | vllm-ascend |`qwen3_8b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | DAPO | Qwen3-14B-base |`14B `_ | FSDP | vllm-ascend |`qwen3_14b_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | DAPO | Qwen3-30B-A3B-base |`30B `_ | FSDP | vllm-ascend |`qwen3_30b_fsdp_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | DAPO | Qwen3-30B-A3B-base |`30B `_ | Megatron | vllm-ascend |`qwen3_30b_megatron_npu `_ | Atlas 200T A2 Box16 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | PPO | Qwen3-8B |`8B `_ | FSDP | vllm-ascend |`qwen3_8b_ppo_npu `_ | Atlas 900 A2 PODc | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ | One_Step_Off_Policy | Qwen3-8B |`8B `_ | FSDP2 | vllm-ascend |`qwen3_8b_fsdp2_npu `_ | Atlas 800T A3 | +-----------------------+-------------------------+------------------------------------------------------------------+-------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+--------------------------+ **表2** SFT类算法 +-----------+-------------------------+------------------------------------------------------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------+ | algorithm | model | download link | actor.strategy | shell location | hardware | +-----------+-------------------------+------------------------------------------------------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------+ | SFT-PEFT | Qwen3-8B |`8B `_ | FSDP |`sft_peft_sp2_npu `_ | Atlas 900 A2 PODc | +-----------+-------------------------+-------------------------+----------------------------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------+ | ReTool-SFT| Qwen2-7B-instruct |`7B `_ | FSDP |`qwen2_7b_sft_npu `_ | Atlas 900 A2 PODc | +-----------+-------------------------+-------------------------+----------------------------------------+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------+----------------------+ 声明 ----------------------------------- verl中提供的ascend支持代码、Dockerfile、镜像皆为参考样例，如在生产环境中使用请通过官方正式途径沟通，谢谢。 ================================================ FILE: docs/ascend_tutorial/quick_start/ascend_sglang_quick_start.rst ================================================ Ascend Quickstart with SGLang Backend =================================== Last updated: 01/27/2026. 我们在 verl 上增加对华为昇腾设备的支持。 硬件支持 ----------------------------------- Atlas 200T A2 Box16 Atlas 900 A2 PODc Atlas 800T A3 安装 ----------------------------------- 关键支持版本 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +-----------+-----------------+ | software | version | +===========+=================+ | Python | == 3.11 | +-----------+-----------------+ | HDK | >= 25.3.RC1 | +-----------+-----------------+ | CANN | >= 8.3.RC1 | +-----------+-----------------+ | torch | >= 2.7.1 | +-----------+-----------------+ | torch_npu | >= 2.7.1.post2 | +-----------+-----------------+ | sglang | v0.5.8 | +-----------+-----------------+ 从 Docker 镜像进行安装 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 我们提供了DockerFile进行构建,详见 `dockerfile_build_guidance `_ ，请根据设备自行选择对应构建文件 从自定义环境安装 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **1. 安装HDK&CANN依赖并激活** 异构计算架构CANN(Compute Architecture for Neural Networks)是昇腾针对AI场景推出的异构计算架构, 为了使训练和推理引擎能够利用更好、更快的硬件支持, 我们需要安装以下 `先决条件 `_ +-----------+-------------+ | HDK | >= 25.3.RC1 | +-----------+-------------+ | CANN | >= 8.3.RC1 | +-----------+-------------+ 安装完成后请激活环境 .. code-block:: bash source /usr/local/Ascend/ascend-toolkit/set_env.sh source /usr/local/Ascend/nnal/atb/set_env.sh **2. 创建conda环境** .. code-block:: bash # create conda env conda create -n verl-sglang python==3.11 conda activate verl-sglang **3. 然后，执行我们在 verl 中提供的脚本** `install_sglang_mcore_npu.sh `_ 如果在此步骤中遇到错误，请检查脚本并手动按照脚本中的步骤操作。 .. code-block:: bash git clone https://github.com/volcengine/verl.git # Make sure you have activated verl conda env # NPU_DEVICE=A3 or A2 depends on your device # USE_MEGATRON=1 if you need to install megatron backend NPU_DEVICE=A3 USE_MEGATRON=1 bash verl/scripts/install_sglang_mcore_npu.sh **4. 安装verl** .. code-block:: bash cd verl pip install --no-deps -e . pip install -r requirements-npu.txt 快速开始 ----------------------------------- **1.当前NPU sglang脚本一览** .. _Qwen3-30B: https://github.com/verl-project/verl/blob/main/examples/grpo_trainer/run_qwen3moe-30b_sglang_megatron_npu.sh .. _Qwen2.5-32B: https://github.com/verl-project/verl/blob/main/examples/grpo_trainer/run_qwen2-32b_sglang_fsdp_npu.sh .. _Qwen3-8B-1k: https://github.com/verl-project/verl/blob/main/examples/grpo_trainer/run_qwen3_8b_grpo_sglang_1k_spmd_npu.sh .. _Qwen3-8B-32k: https://github.com/verl-project/verl/blob/main/examples/grpo_trainer/run_qwen3_8b_grpo_sglang_32k_spmd_npu.sh +-----------------+----------------+----------+-------------------+ | 模型 | 推荐NPU型号 | 节点数量 | 训推后端 | +=================+================+==========+===================+ | `Qwen3-30B`_ | Atlas 800T A3 | 1 | SGLang + Megatron | +-----------------+----------------+----------+-------------------+ | `Qwen2.5-32B`_ | Atlas 900 A2 | 2 | SGLang + FSDP | +-----------------+----------------+----------+-------------------+ | `Qwen3-8B-1k`_ | Atlas A3/A2 | 1 | SGLang + FSDP | +-----------------+----------------+----------+-------------------+ | `Qwen3-8B-32k`_ | Atlas A3/A2 | 1 | SGLang + FSDP | +-----------------+----------------+----------+-------------------+ **2.最佳实践** 我们提供基于verl+sglang `Qwen3-30B`_ 以及 `Qwen2.5-32B`_ 的 `最佳实践 `_ 作为参考 **3.环境变量与参数** 当前NPU上支持sglang后端必须添加以下环境变量 .. code-block:: bash #支持NPU单卡多进程 https://www.hiascend.com/document/detail/zh/canncommercial/850/commlib/hcclug/hcclug_000091.html export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050 export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050 #规避ray在device侧调用无法根据is_npu_available接口识别设备可用性 export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 #根据当前设备和需要卡数定义 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 #使能推理EP时需要 export SGLANG_DEEPEP_BF16_DISPATCH=1 当前verl已解析推理常见参数, 详见 `async_sglang_server.py `_ 中 ServerArgs初始化传参,其他 `sglang参数 `_ 均可通过engine_kwargs 进行参数传递 vllm后端推理脚本转换为sglang, 需要添加修改以下参数 .. code-block:: bash #必须 actor_rollout_ref.rollout.name=sglang +actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend="ascend" #可选 #使能推理EP，详细使用方法见 https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/deep_ep/README_CN.md ++actor_rollout_ref.rollout.engine_kwargs.sglang.deepep_mode="auto" ++actor_rollout_ref.rollout.engine_kwargs.sglang.moe_a2a_backend="deepep" #Moe模型多DP时必须设置为True +actor_rollout_ref.rollout.engine_kwargs.sglang.enable_dp_attention=False #chunked_prefill默认关闭 +actor_rollout_ref.rollout.engine_kwargs.sglang.chunked_prefill_size=-1 ================================================ FILE: docs/ascend_tutorial/quick_start/dockerfile_build_guidance.rst ================================================ Ascend Dockerfile Build Guidance =================================== Last updated: 03/03/2025. 镜像获取 & 公开镜像地址 -------------------- 昇腾在 `quay.io/ascend/verl `_ 中托管每日构建的 A2/A3 镜像，基于上述 Dockerfile 构建。 每日构建镜像名格式：verl-{CANN版本}-{NPU设备类型}-{操作系统版本}-{python版本}-latest verl release版本镜像名格式：verl-{CANN版本}-{NPU设备类型}-{操作系统版本}-{python版本}-{verl release版本号} 镜像硬件支持 ----------------------------------- Atlas 200T A2 Box16 Atlas 900 A2 PODc Atlas 800T A3 镜像内各组件版本信息清单 ---------------- ================= ============ 组件 版本 ================= ============ 基础镜像 Ubuntu 22.04 Python 3.11 CANN 8.5.0 torch 2.8.0 torch_npu 2.8.0 torchvision 0.22.1 vLLM 0.13.0 vLLM-ascend 0.13.0 Megatron-LM v0.12.1 MindSpeed 2.3.0_core_r0.12.1 triton-ascend 3.2.0 mbridge latest version SGLang v0.5.8 sgl-kernel-npu (46b73de) ================= ============ Dockerfile构建镜像脚本清单 --------------------------- ============== ============== ============== ============================================================== 设备类型 基础镜像版本 推理后端 参考文件 ============== ============== ============== ============================================================== A2 8.2.RC1 vLLM `Dockerfile.ascend_8.2.rc1_a2 `_ A2 8.3.RC1 vLLM `Dockerfile.ascend_8.3.rc1_a2 `_ A2 8.5.0 vLLM `Dockerfile.ascend_8.5.0_a2 `_ A2 8.3.RC1 SGLang `Dockerfile.ascend.sglang_8.3.rc1_a2 `_ A3 8.2.RC1 vLLM `Dockerfile.ascend_8.2.rc1_a3 `_ A3 8.3.RC1 vLLM `Dockerfile.ascend_8.3.rc1_a3 `_ A3 8.5.0 vLLM `Dockerfile.ascend_8.5.0_a3 `_ A3 8.3.RC1 SGLang `Dockerfile.ascend.sglang_8.3.rc1_a3 `_ ============== ============== ============== ============================================================== 镜像构建命令示例 -------------------- .. code:: bash # Navigate to the directory containing the Dockerfile cd {verl-root-path}/docker/ascend # Build the image # vLLM docker build -f Dockerfile.ascend_8.3.rc1_a2 -t verl-ascend:8.3.rc1-a2 . # SGLang docker build -f Dockerfile.ascend.sglang_8.3.rc1_a2 -t verl-ascend-sglang:8.3.rc1-a2 . 声明 -------------------- verl中提供的ascend相关Dockerfile、镜像皆为参考样例，可用于尝鲜体验，如在生产环境中使用请通过官方正式途径沟通，谢谢。 ================================================ FILE: docs/blog/v0.7.md ================================================ # verl 0.7 release blog **Author:** verl team Last updated: 01/03/2026. ## Overview verl adopts a Hybrid-Controller architecture (also known as HybridFlow). Sharing design principles with asynchronous sharded dataflow systems like Google Pathways, verl models Reinforcement Learning (RL) algorithms, such as PPO, GRPO, DAPO, and others, as a multi-stage, multi-model and parallelizable dataflow graph. To balance flexibility with performance, verl unifies two distinct programming models: **High-Level Single-Controller (MPMD)**: At the orchestration level, a single process `RLTrainer` manages the global computation graph. It handles macro-tasks such as scheduling rollout generation, triggering reward scoring, and dispatching distributed training jobs. **Internal Multi-Controller (SPMD)**: Internally, the Model Engine operates in standard distributed training mode. Workers execute identical programs, via trainer backends like FSDP, Megatron, or VeOmni, or rollout executors (not rollout server) like vLLM/SGLang/TensorRT-LLM, to perform heavy distributed computation, synchronizing via collective communication.
hybridflow.png
This hybrid approach offers significant advantages: **Flexible Orchestration**: The single-controller design allows verl to dynamically manage complex constraints within the computation graph, including flexible data dependencies, diverse resource allocation and model placement, and fine-grained asynchronous staleness control. **Abstraction of Complexity**: We encapsulate complex parallel strategies—such as 5D parallelism (DP, TP, CP, PP, and EP)—strictly within the Model Engine. This allows users to focus entirely on RL algorithm implementation without getting bogged down by the details of distributed training. Furthermore, leveraging Ray placement groups, verl provides `ResourcePool` and `WorkerGroup` abstractions. These enable flexible GPU sharing among the various roles in the RL process—such as actor, critic, reward, and rollout—allowing components to share resources efficiently while remaining isolated. As illustrated in the diagram below, the overall architecture of verl is divided into two layers: - **verl-core**: provides four components required for the RL pipeline: model engine, rollout engine, checkpoint engine, and transfer queue. Each component exposes abstract interfaces, making them both extensible and pluggable. - **verl-trainer**: builds upon these components, construct various RL pipelines—such as on-policy, one-step-off-policy, and fully asynchronous—tailored to meet the demands of diverse scenarios.
verl-arch.png
## verl-core ### Model Engine The Model Engine serves as verl's core training engine, defining a set of abstract interfaces that support pluggable backends. It operates in SPMD mode: - SFT: Workers are launched via torchrun. - RL: Workers are executed via the WorkerGroup API, invoked by the single-controller. The abstract interfaces include methods like `initialize`, `forward`, `optimizer_step`, and `load`/`offload`. Integrating a new training engine simply requires inheriting and implementing these interfaces. Crucially, because all backends adhere to this unified abstraction, adding a new Model Engine requires absolutely no code modification on the caller side. The RLTrainer remains completely agnostic to the backend's specific parallel strategy when calling these interfaces, while the WorkerGroup automatically handles data dispatch and collection based on the underlying parallelism. Currently, the Model Engine supports the following backends (more backend maybe supported in future, e.g torchtitan): |Backend|Parallelism|Performance|Support Model|New Model Support Time |-----|-----|----|----|----| |FSDP| FSDP+SP|Dense medium/MoE low| all transformer models|Day 0 |MCore| DP+TP+PP+EP+CP|High| see [Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) support model list|few weeks or month |VeOmni| FSDP+SP+EP|Medium| see [VeOmni](https://github.com/ByteDance-Seed/VeOmni) support model list|~1 week ```python class BaseEngine: def initialize(self): """Instantiate or load the model, optimizer, and learning rate scheduler.""" raise NotImplementedError def optimizer_zero_grad(self): """Zero the gradients of the optimizer.""" raise NotImplementedError def optimizer_step(self): """Perform an optimization step using the optimizer.""" raise NotImplementedError def lr_scheduler_step(self): """Advance the learning rate scheduler by one step.""" raise NotImplementedError def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False) -> Any: """Perform a forward pass and optionally a backward pass on a batch of data.""" raise NotImplementedError def get_per_tensor_param(self) -> tuple[Generator[tuple[str, torch.Tensor], None, None], Optional[dict]]: """Get a generator that yields per-tensor parameters and optional peft config.""" raise NotImplementedError def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): """Move model parameters, optimizer states, or both to the specified device.""" raise NotImplementedError ``` ### Rollout Engine As LLM reinforcement learning evolves from single-turn, static tasks to multi-turn, dynamic, and interactive agentic tasks, the legacy SPMD rollout mode previously used by verl has become insufficient. Consequently, in verl v0.7, we have removed the SPMD rollout mode and switched to rollout server mode by default.
rollout_engine.png
In the server mode, the LLM server operates as online serving rather than the traditional offline batch inference. Clients send per-sample requests to the server, enabling the engine to utilize dynamic batching. This significantly enhances throughput efficiency for multi-turn conversation. Furthermore, the server-based approach eliminates the need for intrusive modifications to the LLM inference engine, allowing for the seamless integration of modern inference backends such as vLLM, SGLang, and TensorRT-LLM. On the client side, verl introduces an extensible **AgentLoop** abstraction designed to define custom agentic task loops. This abstraction manages the cycle of requesting responses from the LLM server and interacting with external environments to obtain feedback. We provide two default implementations: - **SingleTurnAgentLoop**: Designed for standard single-turn tasks. - **ToolAgentLoop**: Designed for classic ReAct architectures involving multi-turn tool invocation. Users can implement custom AgentLoop logic tailored to their specific needs, such as [SWEAgentLoop](https://github.com/volcengine/verl/pull/4080) or GUIAgentLoop. ```python class AgentLoopBase(ABC): @abstractmethod async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput: """Run agent loop to interact with LLM server and environment. Args: sampling_params (Dict[str, Any]): LLM sampling params. **kwargs: dataset fields from `verl.utils.dataset.RLHFDataset`. Returns: AgentLoopOutput: Agent loop output. """ raise NotImplementedError ``` ### TransferQueue As mentioned, verl uses a global single-controller RLTrainer to orchestrate the computation graph. A major limitation in the current implementation is that the RLTrainer handles both control and data flow, creating a bottleneck when dispatching data between components. This issue is amplified by the massive data volumes in multimodal training (images, video, audio) and complex algorithms like router replay, which requires transmitting large tensors per sample. Our earlier attempt to solve this using the Ray object store yielded poor performance due to the lack of tensor optimization and fine-grained column access.
transfer_queue.png
In v0.7, we experimentally introduced **TransferQueue** to decouple control flow from data flow. The RLTrainer now only dispatch instructions and metadata, while TransferQueue handles data transmission via reference passing. TransferQueue is specifically optimized for PyTorch tensors (supporting zero-copy and RDMA) and allows for backend extensions like ZeroMQ, NIXL, and Ray RDT. We plan to make this the default transmission method in v0.8. ```python # In PPOTrainer def fit(self): batch = next(dataloader) gen_batch: BatchMeta = self.rollout_manager.generate_sequences(batch) output: BatchMeta = self.actor_rollout_wg.compute_log_prob(gen_batch) gen_batch = gen_batch.union(output) output = self.actor_rollout_wg.update_actor(gen_batch) # In Worker def compute_log_prob(self, batch: BatchMeta) -> BatchMeta: data = tq.get(batch) output = self.actor.infer_batch(data=data) return tq.put(output) ``` ### Checkpoint Engine With the increase in LLM context lengths and the evolution of agentic tasks, the "long-tail" problem in rollout has become prominent, limiting the overall efficiency of RL training. To mitigate this, a viable strategy is moving from on-policy synchronous training to off-policy asynchronous training, e.g [Laminar](https://arxiv.org/abs/2510.12633), [Areal](https://arxiv.org/abs/2505.24298), [StreamRL](https://arxiv.org/abs/2504.15930), [LlamaRL](https://arxiv.org/pdf/2505.24034), [PipelineRL](https://arxiv.org/abs/2509.19128). This involves separating the rollout and model engines onto different nodes (a disaggregated architecture, as opposed to colocated), with data transmitted via queues. This separation alleviates the rollout long-tail issue and enables rollout elastic scaling, fault tolerance, and heterogeneous hardware. However, it introduces a new challenge: efficient cross-node parameter synchronization.
checkpoint_engine.png
To address this, we introduce the Checkpoint Engine: a unified abstraction layer designed to synchronize weights between various training and inference backends. - It provides three unified APIs to implement the streaming transmission of parameters. - Users can extend the Transport Layer implementation based on their specific infrastructure requirements (device, network, local cache, etc.). Currently, we provide two transport backends: NCCL (for broadcast collective communication) and NIXL (for P2P point-to-point communication). ```python class CheckpointEngine(ABC): @abstractmethod async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ raise NotImplementedError @abstractmethod async def receive_weights(self) -> Generator[tuple[str, torch.Tensor], None, None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ raise NotImplementedError ``` ## verl-trainer Building upon the four core components provided by verl-core, verl-trainer constructs several RL training pipelines tailored to specific scenarios. These pipelines are designed to address training efficiency challenges across varying scales and requirements: **On-policy (Synchronous)** - Main Features: Executes rollout and training serially, typically sharing GPU resources (Colocate). It strictly adheres to standard on-policy algorithm definitions, where training must wait for all samples to be generated. - Scenarios: Best for baseline implementations, scenarios where strict algorithmic correctness is prioritized over training throughput. **One-step-off-policy (Async)** - Main Features: Parallelizes generation and training by overlapping the current training step with the next batch's generation. It employs resource isolation and uses parameters from the previous step for rollout to minimize GPU idle time. - Scenarios: Ideal for scenarios requiring moderate efficiency gains (20%–40%) while maintaining training stability very close to strict on-policy methods. **Fully async (Decoupled & Streaming)** - Main Features: Completely decouples the Trainer and Rollouter onto separate nodes. It utilizes streaming data transfer, staleness control, and partial rollout mechanisms to maximize throughput and mitigate long-tail generation latency. - Scenarios: Essential for large-scale training (e.g., 128+ GPUs) or complex reasoning tasks (e.g., long chain-of-thought) where generation latency significantly bottlenecks performance.
fully_async.png
## roadmap ### v0.7 release **Model Engine** - Integrate Megatron-Bridge and support LoRA/PEFT, see blog post: [How We Build Trillion Parameter Reasoning RL with 10% GPUs](https://macaron.im/mindlab/research/building-trillion-parameter-reasoning-rl-with-10-gpus) - Support experimental fp8 training for megatron backend - Support new model for megatron backend: GPT-OSS, Qwen3-Next - Comprehensive support for new mode engine, FSDP and Megatron engine are production ready. - Dispatch tensordict with nested tensor instead of padded DataProto - Add TrainingWorker that resembles Tinker-like API - Add VLM support for model engine, SFT and RL trainer - Add model engine based critic model - Implement ActorRolloutRefWorker by TrainingWorker, support different backend in one worker - New VeOmni engine added, still in alpha status. **Rollout Engine** - Remove SPMD rollout mode - Support blockwise fp8 rollout for vllm and sglang; support online quant for vllm with torchao - Experimental router replay support for vllm - Optimize multi-modal data fetch and preprocess, support video input - Upgrade to vllm==0.12.0; sglang==0.5.6 **Reward** - Support hybrid reward scenarios, including generative, discriminative, rule-based rewards, and their combinations. - Refactor reward models into server mode, supporting both colocated and standalone deployments. - Introduce new reward managers to handle more complex scenarios, limited mode for request rate control and remote mode for CPU-intensive tasks. **Algorithm** - Add [CISPO](https://arxiv.org/pdf/2506.13585): Clipped IS-weight Policy Optimization - Add [SAPO](https://arxiv.org/abs/2511.20347): Soft Adaptive Policy Optimization **Recipe** - [NEW] VLA: add experimental support for VLA model - [NEW] [rhymerl](https://arxiv.org/abs/2508.18588): History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL - TransferQueue: support multiple data partition and optimize tensor zero-copy serialization - One-step-off-policy/Fully async: optimize weight synchronization by checkpoint engine with bucket and pipeline support. ### v0.8 **Model Engine** - Deprecate DataProto by Tensordict for zero padding transmission - Switch default to new model engine, mark legacy engine (fsdp_workers.py, megatron_workers.py) as deprecated - Feature parity between new and legacy model engine: LoRA/PEFT, etc - Polish VeOmni engine to production ready status - Support MTP RL training - Optimize GPU memory for long context: fine-grained activation recompuation/offload - New model support: DeepSeek V3.2, etc **Rollout Engine** - New rollout engine TensorRT-LLM - Separate vllm worker from trainer process, update weights by cuda ipc **TransferQueue** - Merge TransferQueue recipe into main - Optimize e2e image/video vlm training pipeline by TransferQueue - Optimize router replay transmission by TransferQueue **Checkpoint Engine** - Add checkpoint engine abstract interface - Add NCCL and NIXL transport backend - Add more transport backend ### v0.9 **Trainer** - Merge Full async into main: refactor with verl-core component **Model Engine** - Remove legacy model engine (fsdp_workers.py, megatron_workers.py) - Support omni-model RL training: Qwen3-Omni, BAGEL, etc **Rollout Engine** - New rollout engine vllm-omni **More agentic training recipe** - SWEAgent - GUIAgent ================================================ FILE: docs/conf.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- Project information ----------------------------------------------------- project = "verl" copyright = "2024 ByteDance Seed Foundation MLSys Team" author = "Guangming Sheng, Chi Zhang, Yanghua Peng, Haibin Lin" # -- General configuration --------------------------------------------------- # The master toctree document. master_doc = "index" # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "myst_parser", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.autosectionlabel", "sphinx.ext.napoleon", "sphinx.ext.viewcode", ] # MyST-Parser settings myst_enable_extensions = [ "dollarmath", # Enables $...$ and $$...$$ syntax "amsmath", # Enables amsmath environments ] # Use Google style docstrings instead of NumPy docstrings. napoleon_google_docstring = True napoleon_numpy_docstring = False # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: source_suffix = { ".rst": "restructuredtext", ".md": "markdown", } # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # Add the JavaScript file html_js_files = [ "js/runllm-widget.js", "js/resizable-sidebar.js", ] # Add custom CSS file for full-width layout html_css_files = [ "custom.css", ] exclude_patterns += ["README.md", "README_vllm0.7.md"] suppress_warnings = ["ref.duplicate", "ref.myst"] ================================================ FILE: docs/data/transfer_queue.md ================================================ # TransferQueue Data System Last updated: 01/07/2026. This doc introduce [TransferQueue](https://gitcode.com/Ascend/TransferQueue), an asynchronous streaming data management system for efficient post-training. 🔥 **Now TransferQueue is formally open-sourced at [GitCode](https://gitcode.com/Ascend/TransferQueue). We will soon provide a [Github Mirror Repo](https://github.com/Ascend/TransferQueue) for community contributions. You are welcome to submit contributions or propose new ideas on either platform!** > At the mean time, the early development history remains accessible at: https://github.com/TransferQueue/TransferQueue.

Overview

TransferQueue is a high-performance data storage and transfer module with panoramic data visibility and streaming scheduling capabilities, optimized for efficient dataflow in post-training workflows.

TransferQueue offers **fine-grained, sample-level** data management and **load-balancing** (on the way) capabilities, serving as a data gateway that decouples explicit data dependencies across computational tasks. This enables a divide-and-conquer approach, significantly simplifies the algorithm controller design.

Updates

- **Dec 30, 2025**: **TransferQueue x verl** integration is tested with the DAPO algorithm at scale **(64 nodes, 1024 cards)**. It significantly optimizes host memory utilization and accelerates data transfers. Stay tuned for more details! - **Dec 20, 2025**: 🔥 The official [tutorial](https://github.com/TransferQueue/TransferQueue/tree/main/tutorial) is released! Feel free to check it out. - **Nov 10, 2025**: We disentangle the data retrieval logic from TransferQueueController [PR#101](https://github.com/TransferQueue/TransferQueue/pull/101). Now you can implement your own `Sampler` to control how to consume the data. - **Nov 5, 2025**: We provide a `KVStorageManager` that simplifies the integration with KV-based storage backends [PR#96](https://github.com/TransferQueue/TransferQueue/pull/96). The first available KV-based backend is [Yuanrong](https://gitee.com/openeuler/yuanrong-datasystem). - **Nov 4, 2025**: Data partition capability is available in [PR#98](https://github.com/TransferQueue/TransferQueue/pull/98). Now you can define logical data partitions to manage your train/val/test datasets. - **Oct 25, 2025**: We make storage backends pluggable in [PR#66](https://github.com/TransferQueue/TransferQueue/pull/66). You can try to integrate your own storage backend with TransferQueue now! - **Oct 21, 2025**: Official integration into verl is ready [verl/pulls/3649](https://github.com/volcengine/verl/pull/3649). Following PRs will optimize the single controller architecture by fully decoupling data & control flows. - **July 22, 2025**: We present a series of Chinese blogs on Zhihu 1, 2. - **July 21, 2025**: We started an RFC on verl community [verl/RFC#2662](https://github.com/volcengine/verl/discussions/2662). - **July 2, 2025**: We publish the paper [AsyncFlow](https://arxiv.org/abs/2507.01663).

Components

### Control Plane: Panoramic Data Management In the control plane, `TransferQueueController` tracks the **production status** and **consumption status** of each training sample as metadata. When all the required data fields are ready (i.e., written to the `TransferQueueStorageManager`), we know that this data sample can be consumed by downstream tasks. For consumption status, we record the consumption records for each computational task (e.g., `generate_sequences`, `compute_log_prob`, etc.). Therefore, even when different computation tasks require the same data field, they can consume the data independently without interfering with each other.

To make the data retrieval process more customizable, we provide a `Sampler` class that allows users to define their own data retrieval and consumption logic. Refer to the [Customize](#customize) section for details. > In the future, we plan to support **load-balancing** and **dynamic batching** capabilities in the control plane. Additionally, we will support data management for disaggregated frameworks where each rank manages the data retrieval by itself, rather than coordinated by a single controller. ### Data Plane: Distributed Data Storage In the data plane, we provide a pluggable design that enables TransferQueue to integrate with different storage backends according to user requirements. Specifically, we provide a `TransferQueueStorageManager` abstraction class that defines the core APIs as follows: - `async def put_data(self, data: TensorDict, metadata: BatchMeta) -> None` - `async def get_data(self, metadata: BatchMeta) -> TensorDict` - `async def clear_data(self, metadata: BatchMeta) -> None` This class encapsulates the core interaction logic within the TransferQueue system. You only need to write a simple subclass to integrate your own storage backend. Refer to the [Customize](#customize) section for details. Currently, we support the following storage backends: - SimpleStorageUnit: A basic CPU memory storage with minimal data format constraints and easy usability. - [Yuanrong](https://gitcode.com/openeuler/yuanrong-datasystem) (beta, [#PR107](https://github.com/TransferQueue/TransferQueue/pull/107), [#PR96](https://github.com/TransferQueue/TransferQueue/pull/96)): An Ascend native data system that provides hierarchical storage interfaces including HBM/DRAM/SSD. - [Mooncake Store](https://github.com/kvcache-ai/Mooncake) (alpha, [#PR162](https://github.com/TransferQueue/TransferQueue/pull/162)): A high-performance, KV-based hierarchical storage that supports RDMA transport between GPU and DRAM. - [Ray Direct Transport](https://docs.ray.io/en/master/ray-core/direct-transport.html) (alpha, [#PR167](https://github.com/TransferQueue/TransferQueue/pull/167)): Ray's new feature that allows Ray to store and pass objects directly between Ray actors. Among them, `SimpleStorageUnit` serves as our default storage backend, coordinated by the `AsyncSimpleStorageManager` class. Each storage unit can be deployed on a separate node, allowing for distributed data management. `SimpleStorageUnit` employs a 2D data structure as follows: - Each row corresponds to a training sample, assigned a unique index within the corresponding global batch. - Each column represents the input/output data fields for computational tasks. This data structure design is motivated by the computational characteristics of the post-training process, where each training sample is generated in a relayed manner across task pipelines. It provides an accurate addressing capability, which allows fine-grained, concurrent data read/write operations in a streaming manner.

### User Interface: Asynchronous & Synchronous Client The interaction workflow of TransferQueue system is as follows: 1. A process sends a read request to the `TransferQueueController`. 2. `TransferQueueController` scans the production and consumption metadata for each sample (row), and dynamically assembles a micro-batch metadata according to the load-balancing policy. This mechanism enables sample-level data scheduling. 3. The process retrieves the actual data from distributed storage units using the metadata provided by the controller. To simplify the usage of TransferQueue, we have encapsulated this process into `AsyncTransferQueueClient` and `TransferQueueClient`. These clients provide both asynchronous and synchronous interfaces for data transfer, allowing users to easily integrate TransferQueue into their framework. > In the future, we will provide a `StreamingDataLoader` interface for disaggregated frameworks as discussed in [issue#85](https://github.com/TransferQueue/TransferQueue/issues/85) and [verl/RFC#2662](https://github.com/volcengine/verl/discussions/2662). Leveraging this abstraction, each rank can automatically get its own data like `DataLoader` in PyTorch. The TransferQueue system will handle the underlying data scheduling and transfer logic caused by different parallelism strategies, significantly simplifying the design of disaggregated frameworks.

🔥 Showcases

### General Usage The primary interaction points are `AsyncTransferQueueClient` and `TransferQueueClient`, serving as the communication interface with the TransferQueue system. Core interfaces: - `(async_)get_meta(data_fields: list[str], batch_size:int, partition_id: str, mode: str, task_name:str, sampling_config: Optional[dict[str, Any]]) -> BatchMeta` - `(async_)get_data(metadata: BatchMeta) -> TensorDict` - `(async_)put(data: TensorDict, metadata: Optional[BatchMeta], partition_id: Optional[str])` - `(async_)clear_partition(partition_id: str)` and `(async_)clear_samples(metadata: BatchMeta)` **Refer to our [tutorial](https://github.com/TransferQueue/TransferQueue/tree/main/tutorial) for detailed examples.** ### verl Example The primary motivation for integrating TransferQueue to verl now is to **alleviate the data transfer bottleneck of the single controller `RayPPOTrainer`**. Currently, all `DataProto` objects must be routed through `RayPPOTrainer`, resulting in a single point bottleneck of the whole post-training system. ![verl_dataflow_DataProto](https://github.com/TransferQueue/community_doc/blob/main/docs/verl_workflow.jpeg?raw=true) Leveraging TransferQueue, we separate experience data transfer from metadata dispatch by - Replacing `DataProto` with `BatchMeta` (metadata) and `TensorDict` (actual data) structures - Preserving verl's original Dispatch/Collect logic via BatchMeta (maintaining single-controller debuggability) - Accelerating data transfer by TransferQueue's distributed storage units ![verl_dataflow_TransferQueue](https://github.com/TransferQueue/community_doc/blob/main/docs/verl_workflow_with_tq.jpeg?raw=true) You may refer to the [recipe](https://github.com/TransferQueue/TransferQueue/tree/dev/recipe/simple_use_case), where we mimic the verl usage in both async & sync scenarios. Official integration to verl is also available now at [verl/pulls/3649](https://github.com/volcengine/verl/pull/3649) (with subsequent PRs to further optimize the integration). ### Use Python package ```bash pip install TransferQueue ``` ### Build wheel package from source code Follow these steps to build and install: 1. Clone the source code from the GitHub repository ```bash git clone https://github.com/TransferQueue/TransferQueue/ cd TransferQueue ``` 2. Install dependencies ```bash pip install -r requirements.txt ``` 3. Build and install ```bash python -m build --wheel pip install dist/*.whl ```

📊 Performance

> Note: The above benchmark for TransferQueue is based on our naive `SimpleStorageUnit` backend. By introducing high-performance storage backends and optimizing serialization/deserialization, we expect to achieve even better performance. Warmly welcome contributions from the community! For detailed performance benchmarks, please refer to [this blog](https://www.yuque.com/haomingzi-lfse7/hlx5g0/tml8ke0zkgn6roey?singleDoc#). We also provide a [stress test report](https://www.yuque.com/haomingzi-lfse7/hlx5g0/ydbwgo5k2umaag78?singleDoc#) that demonstrates **768 concurrent clients writing 1.4 TB of data** into TransferQueue across 4 nodes. The system remains stable without any crashes or data loss, achieving 80% bandwidth.

🛠️ Customize TransferQueue

### Define your own data retrieval logic We provide a `BaseSampler` abstraction class, which defines the following interface: ```python3 @abstractmethod def sample( self, ready_indexes: list[int], batch_size: int, *args: Any, **kwargs: Any, ) -> tuple[list[int], list[int]]: """Sample a batch of indices from the ready indices. Args: ready_indexes: List of global indices for which all required fields of the corresponding samples have been produced, and the samples are not labeled as consumed in the corresponding task. batch_size: Number of samples to select *args: Additional positional arguments for specific sampler implementations **kwargs: Additional keyword arguments for specific sampler implementations Returns: List of sampled global indices of length batch_size List of global indices of length batch_size that should be labeled as consumed (will never be retrieved in the future) Raises: ValueError: If batch_size is invalid or ready_indexes is insufficient """ raise NotImplementedError("Subclasses must implement sample") ``` In this design, we separate data retrieval and data consumption through the two return values, which enables us to easily control sample replacement. We have implemented two reference designs: `SequentialSampler` and `GRPOGroupNSampler`. The `Sampler` class or instance should be passed to the `TransferQueueController` during initialization. During each `get_meta` call, you can provide dynamic sampling parameters to the `Sampler`. ```python3 from transfer_queue import TransferQueueController, TransferQueueClient, GRPOGroupNSampler, process_zmq_server_info # Option 1: Pass the sampler class to the TransferQueueController controller = TransferQueueController.remote(GRPOGroupNSampler) # Option 2: Pass the sampler instance to the TransferQueueController (if you need custom configuration) your_own_sampler = YourOwnSampler(config) controller = TransferQueueController.remote(your_own_sampler) # Use the sampler batch_meta = client.get_meta( data_fields=["input_ids", "attention_mask"], batch_size=8, partition_id="train_0", task_name="generate_sequences", sampling_config={"n_samples_per_prompt": 4} # Put the required sampling parameters here ) ``` **Refer to [tutorial/04_custom_sampler.py](https://github.com/TransferQueue/TransferQueue/blob/main/tutorial/04_custom_sampler.py) for more details.** ### How to integrate a new storage backend The data plane is organized as follows: ```text transfer_queue/ ├── storage/ │ ├── __init__.py │ │── simple_backend.py # Default distributed storage backend (SimpleStorageUnit) by TQ │ ├── managers/ # Managers are upper level interfaces that encapsulate the interaction logic with TQ system. │ │ ├── __init__.py │ │ ├──base.py # TransferQueueStorageManager, KVStorageManager │ │ ├──simple_backend_manager.py # AsyncSimpleStorageManager │ │ ├──yuanrong_manager.py # YuanrongStorageManager │ │ ├──mooncake_manager.py # MooncakeStorageManager │ │ └──factory.py # TransferQueueStorageManagerFactory │ └── clients/ # Clients are lower level interfaces that directly manipulate the target storage backend. │ │ ├── __init__.py │ │ ├── base.py # TransferQueueStorageKVClient │ │ ├── yuanrong_client.py # YuanrongStorageClient │ │ ├── mooncake_client.py # MooncakeStorageClient │ │ ├── ray_storage_client.py # RayStorageClient │ │ └── factory.py # TransferQueueStorageClientFactory ``` To integrate TransferQueue with a custom storage backend, start by implementing a subclass that inherits from `TransferQueueStorageManager`. This subclass acts as an adapter between the TransferQueue system and the target storage backend. For KV-based storage backends, you can simply inherit from `KVStorageManager`, which can serve as the general manager for all KV-based backends. Distributed storage backends often come with their own native clients serving as the interface of the storage system. In such cases, a low-level adapter for this client can be written, following the examples provided in the `storage/clients` directory. Factory classes are provided for both `StorageManager` and `StorageClient` to facilitate easy integration. Adding necessary descriptions of required parameters in the factory class helps enhance the overall user experience.

✏️ Contribution Guide

**Contributions are warmly welcome!** New ideas, feature suggestions, and user experience feedback are all encouraged—feel free to submit issues or PRs. We will respond as soon as possible. We recommend using pre-commit for better code format. ```bash # install pre-commit pip install pre-commit # run the following command in your repo folder, then fix the check before committing your code pre-commit install && pre-commit run --all-files --show-diff-on-failure --color=always ```

Citation

Please kindly cite our paper if you find this repo is useful: ```bibtex @article{han2025asyncflow, title={AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training}, author={Han, Zhenyu and You, Ansheng and Wang, Haibo and Luo, Kui and Yang, Guang and Shi, Wenqi and Chen, Menglong and Zhang, Sicheng and Lan, Zeshun and Deng, Chunshi and others}, journal={arXiv preprint arXiv:2507.01663}, year={2025} } ``` ================================================ FILE: docs/examples/config.rst ================================================ .. _config-explain-page: Config Explanation =================== Last updated: 06/18/2025. ppo_trainer.yaml for RL FSDP Backend ------------------------------------- Data ~~~~ .. code:: yaml data: tokenizer: null train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 # set to -1 to use full dataset val_max_samples: -1 # set to -1 to use full dataset prompt_key: prompt max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 return_raw_input_ids: False # This should be set to true when the tokenizer between policy and rm differs return_raw_chat: False return_full_prompt: False shuffle: True seed: 42 filter_overlong_prompts: False filter_overlong_prompts_workers: 1 truncation: error image_key: images trust_remote_code: True custom_cls: path: null name: null - ``data.train_files``: Training set parquet. Can be a list or a single file. The program will read all files into memory, so it can't be too large (< 100GB). The path can be either local path or HDFS path. For HDFS path, we provide utils to download it to DRAM and convert the HDFS path to local path. - ``data.val_files``: Validation parquet. Can be a list or a single file. - ``data.train_max_samples``: Maximum number of samples to use from the training dataset. Set to -1 to use the full dataset. - ``data.val_max_samples``: Maximum number of samples to use from the validation dataset. Set to -1 to use the full dataset. - ``data.prompt_key``: The field in the dataset where the prompt is located. Default is 'prompt'. - ``data.max_prompt_length``: Maximum prompt length. All prompts will be left-padded to this length. An error will be reported if the length is too long - ``data.max_response_length``: Maximum response length. Rollout in RL algorithms (e.g. PPO) generates up to this length - ``data.train_batch_size``: Batch size sampled for one training iteration of different RL algorithms. - ``data.return_raw_input_ids``: Whether to return the original input_ids without adding chat template. This is mainly used to accommodate situations where the reward model's chat template differs from the policy. It needs to be decoded first, then apply the RM's chat template. If using a model-based RM, and the policy and RM chat_templates are different, this flag needs to be set - ``data.return_raw_chat``: Whether to return the original chat (prompt) without applying chat template. - ``data.return_full_prompt``: Whether to return the full prompt with chat template - ``data.shuffle``: Whether to shuffle the data in the dataloader. - ``data.seed``: An integer seed to use when shuffling the data. If not set or set to `null`, the data shuffling will not be seeded, resulting in a different data order on each run. - ``data.filter_overlong_prompts``: Default don't filter. - ``data.filter_overlong_prompts_workers``: For large-scale dataset, filtering overlong prompts could be timeconsuming. You cat set the ``filter_overlong_prompts_workers`` to use multiprocessing for speed up. Default to 1. - ``data.truncation``: Truncate the input_ids or prompt length if they exceed max_prompt_length. Default is 'error', not allow exceed the max_prompt_length. The users should increase the max_prompt_length if throwing the error. You can also set ``left``, ``right`` and ``middle``. When ``middle`` is selected, the logic splits the allowed max length roughly in half and keeps the head and tail of the sequence, effectively discarding the middle section. - ``data.image_key``: The field in the multi-modal dataset where the image is located. Default is 'images'. - ``data.trust_remote_code``: If the remote tokenizer has python file, we can use this field to allow using remote tokenizer. For example: moonshotai/Moonlight-16B-A3B-Instruct Customized Dataset ~~~~~~~~~~~~~~~~~~~~~~~~~~ Customized dataset extension is implemented for the SFT trainer and can be extended to other trainers with similar changes. .. code:: yaml custom_cls: path: null name: null - ``data.custom_cls.path``: The path to the file containing your customized dataset class. If not specified, pre-implemented dataset will be used. - ``data.custom_cls.name``: The name of the dataset class within the specified file. Actor/Rollout/Reference Policy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml actor_rollout_ref: hybrid_engine: True model: path: ~/models/deepseek-llm-7b-chat external_lib: null override_config: attn_implementation: flash_attention_2 # or eager, sdpa - attention implementation override model_config: {} moe_config: # Megatron only, can adjust moe configuration freeze_moe_router: False # Megatron only, can freeze moe router (no grad) enable_gradient_checkpointing: False enable_activation_offload: False trust_remote_code: False use_remove_padding: False actor: strategy: fsdp # This is for backward-compatibility ppo_mini_batch_size: 256 ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: 8 use_dynamic_bsz: False ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length} grad_clip: 1.0 clip_ratio: 0.2 entropy_coeff: 0.0 use_kl_loss: False # True for GRPO # Rollout Correction (corrects distribution mismatch between rollout and training) rollout_correction: rollout_is: token # IS weights rollout_is_threshold: 2.0 # Upper threshold for IS weights rollout_rs: null # Rejection sampling rollout_rs_threshold: null # RS upper threshold use_torch_compile: True # False to disable torch compile kl_loss_coef: 0.001 # for grpo kl_loss_type: low_var_kl # for grpo ppo_epochs: 1 data_loader_seed: null shuffle: False ulysses_sequence_parallel_size: 1 # sp size optim: lr: 1e-6 lr_warmup_steps: -1 # Prioritized. Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: 0.0 # only used with cosine lr scheduler, default to 0.0 num_cycles: 0.5 # only used with cosine lr scheduler, default to 0.5 lr_scheduler_type: constant # select from constant/cosine total_training_steps: -1 # must be override by program fsdp_config: wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 param_offload: False optimizer_offload: False fsdp_size: -1 checkpoint: # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] # For more flexibility, you can specify the contents to load from the checkpoint. load_contents: ${actor_rollout_ref.actor.checkpoint.save_contents} ref: fsdp_config: param_offload: False wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: 16 log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size rollout: name: vllm temperature: 1.0 top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1 prompt_length: ${data.max_prompt_length} # not use for opensource response_length: ${data.max_response_length} # for vllm rollout dtype: bfloat16 # should align with FSDP gpu_memory_utilization: 0.5 ignore_eos: False enforce_eager: True free_cache_engine: True load_format: dummy_dtensor tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_num_seqs: 1024 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: 16 log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} # for hf rollout do_sample: True engine_kwargs: # inference engine parameters, please refer vllm/sglang official doc for detail vllm: {} sglang: {} n: 1 # for each prompt, sample n responses (i.e. num sample times). set it to values > 1 for grpo, rloo calculate_log_probs: False # set to True for computing log probs via rollouts val_kwargs: # sampling parameters for validation top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1.0 temperature: 0 n: 1 do_sample: False # default eager for validation agent: custom_async_server: # Use custom async server implementation for rollout path: null name: null **Common config for actor, rollout and reference model** - ``actor_rollout_ref.hybrid_engine``: Whether it's a hybrid engine, currently only supports hybrid engine - ``actor_rollout_ref.model.path``: Huggingface model path. This can be either local path or HDFS path. For HDFS path, we provide utils to download it to DRAM and convert the HDFS path to local path. - ``actor_rollout_ref.model.external_libs``: Additional Python packages that need to be imported. Used to register models or tokenizers into the Huggingface system. - ``actor_rollout_ref.model.override_config``: Used to override some of the model's original configurations. Common overrides include: - ``attn_implementation``: Override the attention implementation. Default is ``flash_attention_2``. Supported values: ``flash_attention_2``, ``eager``, ``sdpa``. Use ``eager`` for debugging or compatibility issues. See :ref:`attention-implementation-override` for detailed usage. - ``actor_rollout_ref.model.enable_gradient_checkpointing``: FSDP only, decide Whether to enable gradient checkpointing for the actor, Megatron uses recompute options in ``override_transformer_config`` to set this - ``actor_rollout_ref.model.enable_activation_offload``: Whether to enable activation offloading for the actor - ``actor_rollout_ref.model.trust_remote_code``: Whether to enable loading a remote code model - ``actor_rollout_ref.model.use_fused_kernels``: Whether to use fused kernels in the model. If set to True, the following parameters will be used. - ``actor_rollout_ref.model.fused_kernel_options.impl_backend``: The implementation backend for fused kernels. Options: "triton" or "torch". Default is "torch". While in megatron, we only support "triton" as the implementation backend, so there is no need for this option. - ``actor_rollout_ref.model.use_remove_padding``: Whether to use remove padding in the model. If set to True, the model will remove padding tokens in the input_ids and response_ids. This helps a lot in improving model running efficiency. - ``actor_rollout_ref.model.tiled_mlp``: TiledMLP configuration for memory-efficient MLP computation. Reduces peak memory by processing MLP forward/backward in tiles. Only compatible with FSDP2 (requires ``actor_rollout_ref.actor.strategy=fsdp2``). - ``actor_rollout_ref.model.tiled_mlp.enabled``: Whether to enable TiledMLP. Default is False. - ``actor_rollout_ref.model.tiled_mlp.num_shards``: Number of shards to split the input. Higher values reduce peak memory but may slightly impact performance. Default is 4. **Actor model** - ``actor_rollout_ref.actor.strategy``: fsdp or megatron. In this example, we use fsdp backend. - ``actor_rollout_ref.actor.ppo_mini_batch_size``: One sample is split into multiple sub-batches with batch_size=ppo_mini_batch_size for PPO updates. The ppo_mini_batch_size is a global num across all workers/gpus - ``actor_rollout_ref.actor.ppo_micro_batch_size``: [Will be deprecated, use ppo_micro_batch_size_per_gpu] Similar to gradient accumulation, the micro_batch_size_per_gpu for one forward pass, trading speed for GPU memory. The value represent the global view. - ``actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu``: Similar to gradient accumulation, the micro_batch_size_per_gpu for one forward pass, trading speed for GPU memory. The value represent the local num per gpu. - ``actor_rollout_ref.actor.grad_clip``: Gradient clipping for actor updates - ``actor_rollout_ref.actor.use_kl_loss``: to use kl loss in actor. When used, we are not applying KL in the reward function. - ``actor_rollout_ref.actor.clip_ratio``: PPO clip ratio - ``actor_rollout_ref.actor.use_torch_compile``: Whether to use torch compile in actor - ``actor_rollout_ref.actor.entropy_coeff``: The weight of entropy when calculating PPO loss. The default value is changed to 0.0 since v0.3.x - ``actor_rollout_ref.actor.ppo_epochs``: Number of epochs for PPO updates on one set of sampled data - ``actor_rollout_ref.actor.data_loader_seed``: From torch 2.6.0 Megatron backend can get wrong seed generated by pytorch between cp ranks and cause misalignment between data on these ranks, so we shall manually set the seed to avoid hanging issue. if ``actor_rollout_ref.actor.shuffle`` is not null, this must be set. - ``actor_rollout_ref.actor.shuffle``: Whether to shuffle data when there are multiple epochs - ``actor_rollout_ref.actor.optim``: Actor's optimizer parameters - ``actor_rollout_ref.actor.fsdp_config``: FSDP config for actor training - ``wrap_policy``: FSDP wrap policy. By default, it uses Huggingface's wrap policy, i.e., wrapping by DecoderLayer - No need to set transformer_layer_cls_to_wrap, so we comment it. - ``*_offload``: Whether to enable parameter, gradient and optimizer offload - Trading speed for GPU memory. - ``actor_rollout_ref.actor.use_kl_loss``: Whether to enable kl loss. Default is False. - ``actor_rollout_ref.actor.kl_loss_coef``: The coefficient of kl loss. Default is 0.001. - ``actor_rollout_ref.actor.kl_loss_type``: Support ``kl`` (``k1``), ``abs``, ``mse`` (``k2``), ``low_var_kl`` (``k3``) and ``full``. Appending ``+`` in the end (e.g., ``k1+`` and ``k3+``) would use straight-through to employ ``k2`` for unbiased gradient estimation, regardless of the kl value estimation (see https://github.com/volcengine/verl/pull/2953#issuecomment-3162113848 for more details). How to calculate the kl divergence between actor and reference policy. For specific options, refer to `kl_penalty()` in `core_algos.py `_ . See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html - ``actor_rollout_ref.actor.checkpoint``: The configurations of checkpoint function in actor - ``save_contents``: The contents to save in the checkpoint. By default, we save model, optimizer and extra information in the checkpoint. The extra information includes Rng states currently, FSDP supported lr_scheduler, and Megatron opt_param_scheduler will coming soon. We do not store hf_model in checkpoint by default, but we provide a tool in ``scripts/model_merge.py`` to convert checkpoint format to hf format. - ``load_contents``: The contents to load in the checkpoint, you can specify different checkpoint loading contents. By default, it is the same with ``save_checkpoint``. **Reference Model** Reference model will be enabled when ``actor.use_kl_loss`` or/and ``algorithm.use_kl_in_reward`` is/are True. - ``actor_rollout_ref.ref``: FSDP config same as actor. **For models larger than 7B, it's recommended to turn on offload for ref by default** - ``actor_rollout_ref.ref.log_prob_micro_batch_size``: [Will be deprecate, use log_prob_micro_batch_size_per_gpu] The batch size for one forward pass in the computation of ``ref_log_prob``. The value represent the global num. - ``actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu``: The batch size for one forward pass in the computation of ``ref_log_prob``. The value represent the local num per gpu. **Rollout Model** - ``actor_rollout_ref.rollout.name``: hf/vllm/sglang. - Rollout (Auto-regressive) parameters. The key should be equal to the property name in vLLM's ``SamplingParams``. - ``temperature``, ``top_k``, ``top_p`` and others: Sampling parameters in ``SamplingParams``. - ``actor_rollout_ref.rollout.dtype``: Rollout model parameters type. This should be align with the actor model parameter type in FSDP/Megatron backend. - ``actor_rollout_ref.rollout.gpu_memory_utilization``: - For vLLM v0.7.0 and later: The fraction of **total** GPU memory to be used for the vLLM instance. - For SGLang: Corresponding to ``mem_fraction_static``, the fraction of the free GPU memory used for **static** memory like model weights and KV cache. - ``actor_rollout_ref.rollout.tensor_model_parallel_size``: TP size for rollout. Only effective for vllm. - ``actor_rollout_ref.rollout.log_prob_micro_batch_size``: [Will be deprecate, use log_prob_micro_batch_size_per_gpu] The batch size for one forward pass in the computation of ``log_prob``. The value represent the global num. - ``actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu``: Micro batch size per gpu (The batch size for one forward pass) for recalculating ``log_prob``. The value represent the local num per gpu. - ``actor_rollout_ref.rollout.do_sample``: Whether to sample during training rollout. If set to False, the rollout model will perform greedy sampling. - ``actor_rollout_ref.rollout.val_kwargs```: Sampling parameters used specifically during validation. - ``top_k``: Top-k sampling parameter. Default to -1 for vLLM rollout or 0 for HF rollout. - ``top_p``: Top-p sampling parameter. Default is 1.0 (disabled). - ``temperature``: Sampling temperature. Default is 0 (deterministic greedy). - ``n``: Number of responses to generate during validation. Default is 1. - ``do_sample``: Whether to use sampling during validation. Default is False for deterministic outputs. When set to True, the rollout will use the ``actor_rollout_ref.rollout.val_kwargs`` parameters (top_k, top_p, temperature) to control the sampling behavior. - ``actor_rollout_ref.rollout.engine_kwargs.vllm``: extra vllm engine args, please refer vllm official doc for detail - ``actor_rollout_ref.rollout.engine_kwargs.sglang``: extra sglang engine args, please refer sglang official doc for detail - ``actor_rollout_ref.rollout.ignore_eos``: Whether to ignore the EOS token and continue generating tokens after the EOS token is generated. - ``actor_rollout_ref.rollout.free_cache_engine``: Offload the KVCache after rollout generation stage. Default is True. When set to True, for vllm v0.5.4 and v0.6.3, we need to disable the usage of CUDAGraph (set ``enforce_eager`` to True.) - ``actor_rollout_ref.rollout.enforce_eager``: Whether to use CUDAGraph in vLLM generation. Default set to True to disable CUDAGraph. - ``actor_rollout_ref.rollout.load_format``: Which weight loader to use to load the actor model weights to the rollout model. - ``auto``: Use Megatron weight loader. - ``megatron``: Use Megatron weight loader. Deployed with Megatron backend. The input model ``state_dict()`` is already partitioned along TP dimension and already gathered along PP dimension. This weight loader requires that the Rollout model and Actor model's parameters shape and name should be identical. - ``dtensor``: Default solution when using Huggingface weight loader. Deployed with FSDP backend and the state_dict_type is ``StateDictType.SHARDED_STATE_DICT``. Recommend to use this weight loader - ``hf``: Use Huggingface weight loader. Deployed with FSDP backend and the state_dict_type is ``StateDictType.FULL_STATE_DICT``. This solution doesn't need to rewrite the weight loader for each model implemented in vLLM but it results in larger peak memory usage. - ``dummy_hf``, ``dummy_megatron``, ``dummy_dtensor``: Random initialization. .. note:: **NOTED**: In this config field, users only need to select from ``dummy_megatron``, ``dummy_dtensor``, ``dummy_hf`` for rollout initialization and our hybrid engine will select the corresponding weight loader (i.e., ``megatron``, ``dtensor``, ``hf``) during actor/rollout weight synchronization. Megatron Optimizer and Optimizer Parameter Scheduler ____________________________________________________ .. code:: yaml optim: optimizer: adam lr: 1e-6 clip_grad: 1.0 total_training_steps: -1 # must be override by program lr_warmup_init: 0.0 # initial learning rate for warmup, default to 0.0 lr_warmup_steps: -1 # Prioritized. Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime lr_decay_steps: null lr_decay_style: constant # select from constant/linear/cosine/inverse_square_root min_lr: 0.0 # minimum learning rate, default to 0.0 weight_decay: 0.01 weight_decay_incr_style: constant # select from constant/linear/cosine lr_wsd_decay_style: exponential # select from constant/exponential/cosine lr_wsd_decay_steps: null use_checkpoint_opt_param_scheduler: False # use checkpoint optimizer parameter scheduler Notice that there are some differences in APIs between Megatron optimizer and FSDP optimizer. - Megatron optimizer scheduler names the period after lr_warmup as lr_decay_steps, so the ``lr_scheduler_type`` actually means the style of lr decay after warmup. - Megatron optimizer also support weight decay decay mechanism - ``use_checkpoint_opt_param_scheduler`` determines whether to use the checkpoint optimizer parameter scheduler. If set to True, the optimizer parameter scheduler will be saved in the checkpoint and loaded from the checkpoint during resuming training. For learning rate decay, original Megatron pretrain default option of ``lr_decay_style`` is ``linear``, meaning that the learning rate will be linearly decayed from the initial learning rate to ``min_lr`` within the ``lr_decay_steps``. However, in verl, to align with FSDP's default behavior, we set the default ``lr_decay_style`` to ``constant``, meaning that the learning rate will be kept constant after the warmup stage. Critic Model ~~~~~~~~~~~~ Most parameters for Critic are similar to Actor Model. Reward Model ~~~~~~~~~~~~ .. code:: yaml reward_model: enable: False model: input_tokenizer: ${actor_rollout_ref.model.path} # set this to null if the chat template is identical path: ~/models/Anomy-RM-v0.1 external_lib: ${actor_rollout_ref.model.external_lib} trust_remote_code: False fsdp_config: min_num_params: 0 param_offload: False micro_batch_size_per_gpu: 16 max_length: null reward_manager: naive - ``reward_model.enable``: Whether to enable reward model. If False, we compute the reward only with the user-defined reward functions. In GSM8K and Math examples, we disable reward model. For RLHF alignment example using full_hh_rlhf, we utilize reward model to assess the responses. If False, the following parameters are not effective. - ``reward_model.model`` - ``input_tokenizer``: Input tokenizer. If the reward model's chat template is inconsistent with the policy, we need to first decode to plaintext, then apply the rm's chat_template. Then score with RM. If chat_templates are consistent, it can be set to null. - ``path``: RM's HDFS path or local path. Note that RM only supports AutoModelForSequenceClassification. Other model types need to define their own RewardModelWorker and pass it from the code. - ``trust_remote_code``: Whether to enable loading a remote code model, default to False. - ``reward_model.reward_manager``: Reward Manager. This defines the mechanism of computing rule-based reward and handling different reward sources. Default is ``naive``. If all verification functions are multiprocessing-safe, the reward manager can be set to ``prime`` for parallel verification. Customized Reward Function ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml custom_reward_function: path: null name: compute_score - ``custom_reward_function.path``: The path to the file containing your customized reward function. If not specified, pre-implemented reward functions will be used. - ``custom_reward_function.name`` (Optional) : The name of the reward function within the specified file. Default is 'compute_score'. Algorithm ~~~~~~~~~ .. code:: yaml algorithm: gamma: 1.0 lam: 1.0 adv_estimator: gae use_kl_in_reward: False kl_penalty: kl # how to estimate kl divergence kl_ctrl: type: fixed kl_coef: 0.005 horizon: 10000 target_kl: 0.1 # Rollout Correction rollout_correction: rollout_is: null # IS weights rollout_is_threshold: 2.0 # Upper threshold for IS weights rollout_rs: null # Rejection sampling rollout_rs_threshold: null # RS upper threshold - ``gamma``: discount factor - ``lam``: Trade-off between bias and variance in the GAE estimator - ``adv_estimator``: Support ``gae``, ``grpo``, ``reinforce_plus_plus``, ``reinforce_plus_plus_baseline``, ``rloo``, ``rloo_vectorized``, ``grpo_vectorized`` - ``use_kl_in_reward``: Whether to enable in-reward kl penalty. Default is False. - ``kl_penalty``: Support ``kl``, ``abs``, ``mse``, ``low_var_kl`` and ``full``. How to calculate the kl divergence between actor and reference policy. For specific options, refer to `kl_penalty()` in `core_algos.py `_ . - ``kl_ctrl``: Config for in-reward kl_penalty controller - ``kl_coef``: The (initial) coefficient of in-reward kl_penalty. Default is 0.001. - ``type``: 'fixed' for FixedKLController and 'adaptive' for AdaptiveKLController. - ``horizon`` and ``target_kl``: See source code of AdaptiveKLController for details. - ``rollout_correction``: Rollout Correction configuration (nested dict). Set to ``null`` to disable. When enabled, contains: - ``rollout_is``: IS weights aggregation level, ``null`` to disable IS weights. - ``rollout_is_threshold``: Upper threshold for IS weights (e.g., 2.0). - ``rollout_rs``: Rejection sampling mode, ``null`` to disable RS. - ``rollout_rs_threshold``: RS upper threshold. Note: Rollout Correction requires setting ``actor_rollout_ref.rollout.calculate_log_probs=True``. Trainer ~~~~~~~ .. code:: yaml trainer: total_epochs: 30 project_name: verl_examples experiment_name: gsm8k logger: ['console', 'wandb'] log_val_generations: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 val_before_train: True test_freq: 2 critic_warmup: 0 default_hdfs_dir: null # hdfs checkpoint path default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} # local checkpoint path resume_mode: auto # or disable or resume_path if resume_from_path is set resume_from_path: null remove_previous_ckpt_in_save: False del_local_ckpt_after_load: False ray_wait_register_center_timeout: 300 - ``trainer.total_epochs``: Number of epochs in training. - ``trainer.project_name``: For wandb, swanlab, mlflow - ``trainer.experiment_name``: For wandb, swanlab, mlflow - ``trainer.logger``: Support console and wandb, swanlab, mlflow, tensorboard, trackio - ``trainer.log_val_generations``: The number of logged generation during validation (default ``0``) - ``trainer.nnodes``: Number of nodes used in the training. - ``trainer.n_gpus_per_node``: Number of GPUs per node. - ``trainer.save_freq``: The frequency (by iteration) to save checkpoint of the actor and critic model. - ``trainer.val_before_train``: Whether to run validation before training. - ``trainer.test_freq``: The validation frequency (by iteration). - ``trainer.critic_warmup``: The number of iteration to train the critic model before actual policy learning. - ``trainer.resume_mode``: The mode of resuming training. Support ``disable``, ``auto`` and ``resume_path``. If set to ``auto`` as default, the program will automatically resume from the latest checkpoint in the ``default_local_dir``. If set to ``resume_path``, the program will resume from the path specified in ``resume_from_path``. - ``trainer.resume_from_path``: The path to resume training from. Only effective when ``resume_mode`` is set to ``resume_path``. - ``trainer.remove_previous_ckpt_in_save``: Whether to remove previous checkpoints in the save directory. Default is False. - ``trainer.del_local_ckpt_after_load``: Whether to delete local checkpoints after loading them. Default is False. - ``trainer.ray_wait_register_center_timeout``: The timeout for waiting for the ray register center to be ready. Default is 300 seconds. This figure illustrates how the configurations affect the training. https://excalidraw.com/#json=pfhkRmiLm1jnnRli9VFhb,Ut4E8peALlgAUpr7E5pPCA .. image:: https://github.com/user-attachments/assets/16aebad1-0da6-4eb3-806d-54a74e712c2d evaluation.yaml --------------- Data ~~~~ .. code:: yaml data: path: /tmp/math_Qwen2-7B-Instruct.parquet prompt_key: prompt response_key: responses data_source_key: data_source reward_model_key: reward_model - ``data.path``: Path to the dataset file (Parquet format). - ``data.prompt_key``: The field in the dataset where the prompt is located. Default is 'prompt'. - ``data.response_key``: The key holds the generated responses. This should be a list of strings representing the responses. Default is 'responses'. - ``data.data_source_key``: This is used to separate metric calculations for different data sources, ensuring that metrics are calculated independently for each source. - ``data.reward_model_key``: The key holds the reference answers. These reference answers typically serve as the ground truth or test cases for the task. Customized Reward Function ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: yaml custom_reward_function: path: null name: compute_score - ``custom_reward_function.path``: The path to the file containing your customized reward function. If not specified, pre-implemented reward functions will be used. - ``custom_reward_function.name`` (Optional) : The name of the reward function within the specified file. Default is 'compute_score'. sft_trainer.yaml for SFT FSDP Backend -------------------------------------- Optim ~~~~~~~ .. code:: yaml optim: optimizer: AdamW optimizer_impl: torch.optim lr: 1e-5 weight_decay: 0.01 lr_warmup_steps_ratio: 0.1 clip_grad: 1.0 lr_scheduler: cosine override_optimizer_config: null - ``optimizer``: Optimizer class name (e.g., ``"AdamW"``, ``"AdamW8bit"``, ``"_AdamW"``). The class name as it appears in the module. - ``optimizer_impl``: Module path to import optimizer from (e.g., ``"torch.optim"``, ``"torchao.optim"``, ``"bitsandbytes.optim"``). - ``optim.lr``: Learning rate for the optimizer. - ``optim.weight_decay``: Weight decay for the optimizer. - ``optim.lr_warmup_steps_ratio``: Ratio of warmup steps to total training steps. - ``optim.clip_grad``: Gradient clipping value. - ``optim.lr_scheduler``: Learning rate scheduler type. Options: - ``cosine``: Cosine learning rate scheduler with warmup (default). - ``wsd``: Warmup-Stable-Decay scheduler that provides a stable learning rate phase between warmup and decay phases. - ``override_optimizer_config``: Dictionary of additional optimizer-specific keyword arguments. For example, to use ``torchao.optim``'s ``_AdamW`` with BF16 stochastic rounding: ``{"bf16_stochastic_round": true}`` Model ~~~~~~~~~~~~ Most parameters for Model are similar to Reward Model. .. code:: yaml model: partial_pretrain: ~/models/gemma-1.1-7b-it fsdp_config: model_dtype: fp32 wrap_policy: min_num_params: 0 cpu_offload: False offload_params: False external_lib: null enable_gradient_checkpointing: False trust_remote_code: False lora_rank: 0 lora_alpha: 16 target_modules: all-linear use_liger: False - ``partial_pretrain``: HDFS path or local path for the pretrained model. - ``fsdp_config`` - ``model_dtype``: Model parameters type, default to ``fp32``. Support: ``bf16``, ``fp16``, ``fp32``. - ``cpu_offload``: Whether to enable CPU offloading for FSDP. If True, the offload_params will be used as argument. - ``offload_params``: Whether to offload parameters to CPU when not involved in computation. If True, then this offloads gradients to CPU as well, meaning that the optimizer step runs on CPU. - ``lora_rank``: The rank of the LoRA model, default to 0. If ``lora_rank``>0, we will train LoRA modules instead of tuning the full model. - ``lora_alpha``: The alpha parameter for LoRA scaling, default to 16. - ``target_modules``: The names of the modules to apply the adapter to, default to ``all-linear``. See `peft docs `_ for detail. - ``use_liger``: Whether to enable Liger kernel, default to False. If True, we apply Liger kernel to the model (depends on `liger-kernel`). ================================================ FILE: docs/examples/gsm8k_example.rst ================================================ GSM8K Example ============= Last updated: 03/25/2025. Introduction ------------ In this example, we train an LLM to tackle the GSM8k task. Paper: https://arxiv.org/pdf/2110.14168 Dataset: https://huggingface.co/datasets/openai/gsm8k Note that the original paper mainly focuses on training a verifier (a reward model) to solve math problems via Best-of-N sampling. In this example, we train an RLHF agent using a rule-based reward model. Dataset Introduction -------------------- GSM8k is a math problem dataset. The prompt is an elementary school problem. The LLM model is required to answer the math problem. The training set contains 7473 samples and the test set contains 1319 samples. **An example** Prompt Katy makes coffee using teaspoons of sugar and cups of water in the ratio of 7:13. If she used a total of 120 teaspoons of sugar and cups of water, calculate the number of teaspoonfuls of sugar she used. Solution The total ratio representing the ingredients she used to make the coffee is 7+13 = <<7+13=20>>20 Since the fraction representing the number of teaspoons she used is 7/20, she used 7/20\ *120 = <<7/20*\ 120=42>>42 #### 42 Step 1: Prepare dataset ----------------------- .. code:: bash cd examples/data_preprocess python3 gsm8k.py --local_save_dir ~/data/gsm8k Step 2: Download Model ---------------------- There're three ways to prepare the model checkpoints for post-training: - Download the required models from huggingface or modelscope .. code:: bash hf download deepseek-ai/deepseek-math-7b-instruct --local-dir ~/models/deepseek-math-7b-instruct --local-dir-use-symlinks False # or modelscope download --model deepseek-ai/deepseek-math-7b-instruct --local_dir ~/models/deepseek-math-7b-instruct - Already store your store model in the local directory or HDFS path. - Also, you can directly use the model name in huggingface (e.g., deepseek-ai/deepseek-math-7b-instruct) in ``actor_rollout_ref.model.path`` and ``critic.model.path`` field in the run script. You can also download models from modelscope by setting environmental variable ``VERL_USE_MODELSCOPE=True``. See examples/ppo_trainer/run_deepseek7b_llm_modelscope.sh for example. Noted that users should prepare checkpoints for actor, critic and reward model. [Optional] Step 3: SFT your Model --------------------------------- We provide a SFT Trainer using PyTorch FSDP in `sft_trainer.py `_. Users can customize their own SFT script using our FSDP SFT Trainer. We also provide various training scripts for SFT on GSM8K dataset in `gsm8k sft directory `_. .. code:: shell set -x torchrun -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=8 \ model.path=deepseek-ai/deepseek-coder-6.7b-instruct \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-deepseek-coder-6.7b-instruct \ trainer.total_epochs=4 \ trainer.logger='["console","wandb"]' If you use AMD GPUs (ROCm kernel), you need to add the following environment variables into the run script: .. code-block:: bash export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export ROCR_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES export CUDA_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES Step 4: Perform PPO training with your model on GSM8K Dataset ------------------------------------------------------------- - Prepare your own run.sh script. Here's an example for GSM8k dataset and deepseek-llm-7b-chat model. - Users could replace the ``data.train_files`` ,\ ``data.val_files``, ``actor_rollout_ref.model.path`` and ``critic.model.path`` based on their environment. - See :doc:`config` for detailed explanation of each config field. **Reward Model/Function** We use a rule-based reward model. We force the model to produce a final answer following 4 “#” as shown in the solution. We extract the final answer from both the solution and model's output using regular expression matching. We compare them and assign a reward of 1 to correct answer, 0.1 to incorrect answer and 0 to no answer. **Training Script** The training script example for FSDP and Megatron-LM backend are stored in examples/ppo_trainer directory. .. code:: bash cd ../ppo_trainer bash run_deepseek7b_llm.sh The script of run_deepseek7b_llm.sh .. code:: bash set -x python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=1 \ trainer.total_epochs=15 $@ If you use AMD GPUs (ROCm kernel), you need to add the following environment variables into the run script: .. code-block:: bash export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export ROCR_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES export CUDA_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES If you encounter any issues in using AMD GPUs running VeRL, feel free to contact me - `Yusheng Su `_. ================================================ FILE: docs/examples/multi_modal_example.rst ================================================ Multi-Modal Example Architecture ================================= Last updated: 04/28/2025. Introduction ------------ Now, verl has supported multi-modal training. You can use fsdp and vllm/sglang to start a multi-modal RL task. Megatron supports is also on the way. Follow the steps below to quickly start a multi-modal RL task. Step 1: Prepare dataset ----------------------- .. code:: python # it will be saved in the $HOME/data/geo3k folder python examples/data_preprocess/geo3k.py Step 2: Download Model ---------------------- .. code:: bash # download the model from huggingface python3 -c "import transformers; transformers.pipeline(model='Qwen/Qwen2.5-VL-7B-Instruct')" Step 3: Perform GRPO training with multi-modal model on Geo3K Dataset --------------------------------------------------------------------- .. code:: bash # run the task bash examples/grpo_trainer/run_qwen2_5_vl-7b.sh ================================================ FILE: docs/examples/ppo_code_architecture.rst ================================================ PPO Example Architecture ======================== Last updated: 02/17/2025. Let's start with the Proximal Policy Optimization algorithm, which is most widely used algorithm in LLM post-training. The main entry point of the PPO algorithm example is: `main_ppo.py `_. In this tutorial, we will go through the code architecture in `main_ppo.py `_. Define the data --------------- Users need to preprocess and store the dataset in parquet files. And we implement `RLHFDataset` to load and tokenize the parquet files. For ``RLHFDataset`` (Default), at least 1 fields are required: - ``prompt``: Contains the string prompt We already provide some examples of processing the datasets to parquet files in `data_preprocess directory `_. Currently, we support preprocess of GSM8k, MATH, Hellasage, Full_hh_rlhf datasets. See :doc:`../preparation/prepare_data` for more information. Define the reward functions for different datasets -------------------------------------------------- In this main entry point, the users only need to define their own reward function based on the datasets (or applications) utilized in PPO training. For example, we already provide reward functions for `GSM8k `_ and `MATH `_ datasets in the ``_select_rm_score_fn``. In the ``RewardManager``, we will compute the reward score based on the data_source to select corresponding reward functions. For some RLHF datasets (e.g., full_hh_rlhf), the reward model is utilized to assess the responses without any reward functions. In this case, the ``RewardManager`` will return the ``rm_score`` computed by the reward model directly. See `reward functions `_ for detailed implementation. Define worker classes --------------------- .. code:: python if config.actor_rollout_ref.actor.strategy in {"fsdp", "fsdp2"}: # for FSDP backend assert config.critic.strategy in {"fsdp", "fsdp2"} from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker from verl.single_controller.ray import RayWorkerGroup ray_worker_group_cls = RayWorkerGroup elif config.actor_rollout_ref.actor.strategy == 'megatron': # for Megatron backend assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup ray_worker_group_cls = NVMegatronRayWorkerGroup # Ray worker class for Megatron-LM else: raise NotImplementedError from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role role_worker_mapping = { Role.ActorRollout: ActorRolloutRefWorker, Role.Critic: CriticWorker, Role.RefPolicy: ActorRolloutRefWorker } global_pool_id = 'global_pool' resource_pool_spec = { global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes, } mapping = { Role.ActorRollout: global_pool_id, Role.Critic: global_pool_id, Role.RefPolicy: global_pool_id, } Step 1: Construct the mapping between roles and workers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A role represents a group of workers in the same process. We have pre-defined several roles in `ray_trainer.py `_. .. code:: python class Role(Enum): """ To create more roles dynamically, you can subclass Role and add new members """ Actor = 0 # This worker only has Actor Rollout = 1 # This worker only has Rollout ActorRollout = 2 # This worker has both actor and rollout, it's a HybridEngine Critic = 3 # This worker only has critic RefPolicy = 4 # This worker only has reference policy RewardModel = 5 # This worker only has reward model ActorRolloutRef = 6 # This worker contains actor, rollout and reference policy simultaneously Step 2: Define the worker class corresponding to this role ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - We have pre-implemented the ``ActorRolloutRefWorker``. Through different configs, it can be a standalone actor, a standalone rollout, an ActorRollout HybridEngine, or an ActorRolloutRef HybridEngine - We also pre-implemented workers for ``Actor``, ``Rollout``, ``Critic``, ``Reward Model`` and ``Reference model`` on two different backend: PyTorch FSDP and Megatron-LM. See `FSDP Workers `_ and `Megatron-LM Workers `_ for more information. Step 3: Define resource pool id and resource pool spec ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - Resource pool is a division of global GPU resources, ``resource_pool_spec`` is a dict, mapping from id to # of GPUs - In the above example, we defined a global resource pool: global_pool_id, and then put all roles on this one resource pool with all the GPUs in this post-training task. This refers to *co-locate* placement where all the models share the same set of GPUs. - See resource pool and placement for advance usage. Defining reward model/function ------------------------------ .. code:: python # we should adopt a multi-source reward function here # - for rule-based rm, we directly call a reward score # - for model-based rm, we call a model # - for code related prompt, we send to a sandbox if there are test cases # - finally, we combine all the rewards together # - The reward type depends on the tag of the data if config.reward_model.enable: from verl.workers.fsdp_workers import RewardModelWorker role_worker_mapping[Role.RewardModel] = RewardModelWorker mapping[Role.RewardModel] = global_pool_id reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0) # Note that we always use function-based RM for validation val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1) resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) Since not all tasks use model-based RM, users need to define here whether it's a model-based RM or a function-based RM - If it's a model-based RM, directly add the ``RewardModel`` role in the resource mapping and add it to the resource pool mapping. - Note that the pre-defined ``RewardModelWorker`` only supports models with the structure of huggingface ``AutoModelForSequenceClassification``. If it's not this model, you need to define your own RewardModelWorker in `FSDP Workers `_ and `Megatron-LM Workers `_. - If it's a function-based RM, the users are required to classified the reward function for each datasets. .. code:: python def _select_rm_score_fn(data_source): if data_source == 'openai/gsm8k': return gsm8k.compute_score elif data_source == 'lighteval/MATH': return math.compute_score else: raise NotImplementedError See reward functions implemented in `directory `_ for more information. Define, init and run the PPO Trainer ------------------------------------ .. code:: python trainer = RayPPOTrainer(config=config, tokenizer=tokenizer, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, reward_fn=reward_fn, val_reward_fn=val_reward_fn) trainer.init_workers() trainer.fit() - We first initialize the ``RayPPOTrainer`` with user config, tokenizer and all the above worker mapping, resource pool, worker group and reward functions - We first call the ``trainer.init_workers()`` to initialize the models on the allocated GPUs (in the resource pool) - The actual PPO training will be executed in ``trainer.fit()`` verl can be easily extended to other RL algorithms by reusing the Ray model workers, resource pool and reward functions. See :doc:`extension<../advance/dpo_extension>` for more information. Details of the ``RayPPOTrainer`` is discussed in :doc:`Ray Trainer<../workers/ray_trainer>`. ================================================ FILE: docs/examples/sandbox_fusion_example.rst ================================================ Sandbox Fusion Example ============================ Last updated: 06/27/2025. Introduction ------------ Sandbox Fusion is a remote code sandbox service that provides a secure environment for running and evaluating code generated by Large Language Models (LLMs). This example demonstrates how to train an LLM and use Sandbox Fusion to verify generated code, enhancing both security and performance. By leveraging a remote code sandbox service with greater CPU resources for concurrent code verification, you can reduce the reward stage time by 10-30%, depending on the quality of the generated code. Step 1: Prepare the Dataset --------------------------- We use the Eurus-2-RL-Data dataset for training. This dataset combines math and code questions, making it suitable for LLM training tasks. You can download it from HuggingFace: `Eurus-2-RL-Data Dataset `_. Step 2: Set Up the Sandbox Fusion Service ----------------------------------------- Sandbox Fusion is a remote code sandbox service designed to securely run and evaluate LLM-generated code. To use it: 1. **Access Full Documentation**: For detailed setup instructions, refer to the `Sandbox Fusion Documentation `_. 2. **Deploy the Service**: Choose one of the following deployment methods: - **Local Deployment**: Follow the guide `here `_. - **FaaS Instance (Volcengine)**: Create an instance using the `Volcengine Documentation `_. After deployment, you will receive an API endpoint in the format: ``https:///run_code``. Step 3: Configure the Training Script ------------------------------------- To integrate Sandbox Fusion into your training script, configure the following parameters: **Key Settings for Sandbox Fusion** - ``reward_model.sandbox_fusion.url=''``: Enable Sandbox Fusion by specifying the API endpoint (must end with ``/run_code``). - ``reward_model.sandbox_fusion.max_concurrent=256``: Set the maximum number of concurrent API requests to the Sandbox Fusion service. - ``reward_model.sandbox_fusion.memory_limit_mb=1024``: Set the memory limit (in MB) for each sandbox instance. Defaults to 1024MB if not specified. **Additional Optimization** To further reduce code verification time, enable parallel processing with: - ``reward_model.reward_manager=prime``: The Prime reward manager verifies code across multiple subprocesses concurrently. **Example Script** For a practical implementation, refer to the example script: ``examples/ppo_trainer/run_deepseek7b_llm_sandbox_fusion.sh`` Once you’ve set your API endpoint in the script, you can start the training job. ================================================ FILE: docs/examples/skypilot_examples.rst ================================================ SkyPilot Examples ================= Last updated: 09/04/2025. This guide provides examples of running VERL reinforcement learning training on Kubernetes clusters or cloud platforms with GPU nodes using `SkyPilot `_. Installation and Configuration ------------------------------- Step 1: Install SkyPilot ~~~~~~~~~~~~~~~~~~~~~~~~~ Choose the installation based on your target platform: .. code-block:: bash # For Kubernetes only pip install "skypilot[kubernetes]" # For AWS pip install "skypilot[aws]" # For Google Cloud Platform pip install "skypilot[gcp]" # For Azure pip install "skypilot[azure]" # For multiple platforms pip install "skypilot[kubernetes,aws,gcp,azure]" Step 2: Configure Your Platform ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ See https://docs.skypilot.co/en/latest/getting-started/installation.html Step 3: Set Up Environment Variables ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Export necessary API keys for experiment tracking: .. code-block:: bash # For Weights & Biases tracking export WANDB_API_KEY="your-wandb-api-key" # For HuggingFace gated models (if needed) export HF_TOKEN="your-huggingface-token" Examples -------- All example configurations are available in the `examples/skypilot/ `_ directory on GitHub. See the `README `_ for additional details. PPO Training ~~~~~~~~~~~~ .. code-block:: bash sky launch -c verl-ppo verl-ppo.yaml --secret WANDB_API_KEY -y Runs PPO training on GSM8K dataset using Qwen2.5-0.5B-Instruct model across 2 nodes with H100 GPUs. Based on examples in ``examples/ppo_trainer/``. `View verl-ppo.yaml on GitHub `_ GRPO Training ~~~~~~~~~~~~~ .. code-block:: bash sky launch -c verl-grpo verl-grpo.yaml --secret WANDB_API_KEY -y Runs GRPO (Group Relative Policy Optimization) training on MATH dataset using Qwen2.5-7B-Instruct model. Memory-optimized configuration for 2 nodes. Based on examples in ``examples/grpo_trainer/``. `View verl-grpo.yaml on GitHub `_ Multi-turn Tool Usage Training ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash sky launch -c verl-multiturn verl-multiturn-tools.yaml \ --secret WANDB_API_KEY --secret HF_TOKEN -y Single-node training with 8xH100 GPUs for multi-turn tool usage with Qwen2.5-3B-Instruct. Includes tool and interaction configurations for GSM8K. Based on examples in ``examples/sglang_multiturn/`` but uses vLLM instead of sglang. `View verl-multiturn-tools.yaml on GitHub `_ Configuration ------------- The example YAML files are pre-configured with: - **Infrastructure**: Kubernetes clusters (``infra: k8s``) - can be changed to ``infra: aws`` or ``infra: gcp``, etc. - **Docker Image**: VERL's official Docker image with CUDA 12.6 support - **Setup**: Automatically clones and installs VERL from source - **Datasets**: Downloads required datasets during setup phase - **Ray Cluster**: Configures distributed training across nodes - **Logging**: Supports Weights & Biases via ``--secret WANDB_API_KEY`` - **Models**: Supports gated HuggingFace models via ``--secret HF_TOKEN`` Launch Command Options ---------------------- - ``-c ``: Cluster name for managing the job - ``--secret KEY``: Pass secrets for API keys (can be used multiple times) - ``-y``: Skip confirmation prompt Monitoring Your Jobs -------------------- Check Cluster Status ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash sky status View Logs ~~~~~~~~~ .. code-block:: bash sky logs verl-ppo # View logs for the PPO job SSH into Head Node ~~~~~~~~~~~~~~~~~~ .. code-block:: bash ssh verl-ppo Access Ray Dashboard ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash sky status --endpoint 8265 verl-ppo # Get dashboard URL Stop a Cluster ~~~~~~~~~~~~~~ .. code-block:: bash sky down verl-ppo ================================================ FILE: docs/faq/faq.rst ================================================ Frequently Asked Questions ==================================== Last updated: 09/24/2025. Ray related ------------ How to add breakpoint for debugging with distributed Ray? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Please checkout the official debugging guide from Ray: https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html "Unable to register worker with raylet" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The cause of this issue is due to some system setting, e.g., SLURM added some constraints on how the CPUs are shared on a node. While `ray.init()` tries to launch as many worker processes as the number of CPU cores of the machine, some constraints of SLURM restricts the `core-workers` seeing the `raylet` process, leading to the problem. To fix this issue, you can set the config term ``ray_init.num_cpus`` to a number allowed by your system. Distributed training ------------------------ How to run multi-node post-training with Ray? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can start a ray cluster and submit a ray job, following the official guide from Ray: https://docs.ray.io/en/latest/ray-core/starting-ray.html Then in the configuration, set the ``trainer.nnode`` config to the number of machines for your job. How to use verl on a Slurm-managed cluster? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Ray provides users with `this `_ official tutorial to start a Ray cluster on top of Slurm. We have verified the :doc:`GSM8K example<../examples/gsm8k_example>` on a Slurm cluster under a multi-node setting with the following steps. 1. [Optional] If your cluster support `Apptainer or Singularity `_ and you wish to use it, convert verl's Docker image to an Apptainer image. Alternatively, set up the environment with the package manager available on your cluster or use other container runtimes (e.g. through `Slurm's OCI support `_) available to you. .. code:: bash apptainer pull /your/dest/dir/vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3.sif docker://verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3 2. Follow :doc:`GSM8K example<../examples/gsm8k_example>` to prepare the dataset and model checkpoints. 3. Modify `examples/slurm/ray_on_slurm.slurm `_ with your cluster's own information. 4. Submit the job script to the Slurm cluster with `sbatch`. Please note that Slurm cluster setup may vary. If you encounter any issues, please refer to Ray's `Slurm user guide `_ for common caveats. If you changed Slurm resource specifications, please make sure to update the environment variables in the job script if necessary. Install related ------------------------ NotImplementedError: TensorDict does not support membership checks with the `in` keyword. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Detail error information: .. code:: bash NotImplementedError: TensorDict does not support membership checks with the `in` keyword. If you want to check if a particular key is in your TensorDict, please use `key in tensordict.keys()` instead. Cause of the problem: There is no suitable version of tensordict package for the linux-arm64 platform. The confirmation method is as follows: .. code:: bash pip install tensordict==0.6.2 Output example: .. code:: bash ERROR: Could not find a version that satisfies the requirement tensordict==0.6.2 (from versions: 0.0.1a0, 0.0.1b0, 0.0.1rc0, 0.0.2a0, 0.0.2b0, 0.0.3, 0.1.0, 0.1.1, 0.1.2, 0.8.0, 0.8.1, 0.8.2, 0.8.3) ERROR: No matching distribution found for tensordict==0.6.2 Solution 1st: Install tensordict from source code: .. code:: bash pip uninstall tensordict git clone https://github.com/pytorch/tensordict.git cd tensordict/ git checkout v0.6.2 python setup.py develop pip install -v -e . Solution 2nd: Temperally modify the error takeplace codes: tensordict_var -> tensordict_var.keys() Illegal memory access --------------------------------- If you encounter the error message like ``CUDA error: an illegal memory access was encountered`` during rollout, please check the vLLM documentation for troubleshooting steps specific to your vLLM version. Checkpoints ------------------------ If you want to convert the model checkpoint into huggingface safetensor format, please refer to ``verl/model_merger``. Triton ``compile_module_from_src`` error ------------------------------------------------ If you encounter triton compilation error similar to the stacktrace below, please set the ``use_torch_compile`` flag according to https://verl.readthedocs.io/en/latest/examples/config.html to disable just-in-time compilation for fused kernels. .. code:: bash File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/jit.py", line 345, in return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 338, in run return self.fn.run(*args, **kwargs) File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/jit.py", line 607, in run device = driver.active.get_current_device() File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/driver.py", line 23, in __getattr__ self._initialize_obj() File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/driver.py", line 20, in _initialize_obj self._obj = self._init_fn() File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/driver.py", line 9, in _create_driver return actives[0]() File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/backends/nvidia/driver.py", line 371, in __init__ self.utils = CudaUtils() # TODO: make static File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/backends/nvidia/driver.py", line 80, in __init__ mod = compile_module_from_src(Path(os.path.join(dirname, "driver.c")).read_text(), "cuda_utils") File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/backends/nvidia/driver.py", line 57, in compile_module_from_src so = _build(name, src_path, tmpdir, library_dirs(), include_dir, libraries) File "/data/lbh/conda_envs/verl/lib/python3.10/site-packages/triton/runtime/build.py", line 48, in _build ret = subprocess.check_call(cc_cmd) File "/data/lbh/conda_envs/verl/lib/python3.10/subprocess.py", line 369, in check_call raise CalledProcessError(retcode, cmd) What is the meaning of train batch size, mini batch size, and micro batch size? ------------------------------------------------------------------------------------------ This figure illustrates the relationship between different batch size configurations. https://excalidraw.com/#json=pfhkRmiLm1jnnRli9VFhb,Ut4E8peALlgAUpr7E5pPCA .. image:: https://github.com/user-attachments/assets/16aebad1-0da6-4eb3-806d-54a74e712c2d How to generate ray timeline to analyse performance of a training job? ------------------------------------------------------------------------------------------ To generate the ray timeline file, you can set the config term ``ray_init.timeline_json_file`` to a json file path. For example: .. code:: bash ray_init.timeline_json_file=/tmp/ray_timeline.json The file will be generated in the specified path at the end of a training job. You can use tools like chrome://tracing or the Perfetto UI and view the ray timeline file. This figure shows the ray timeline file generated by from a training job on 1 node with 4 GPUs .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray_timeline.png?raw=true How to set proxy only for wandb? ------------------------------------------------------------------------------------------ If you need a proxy to access wandb, you can add below config in your training job script. Comparing to using global https_proxy env variable, this approach won't mess up other http requests, such as ChatCompletionScheduler. .. code:: bash +trainer.wandb_proxy=http:// Missmatch between inference and training sequence (high actor/grad_norm) ------------------------------------------------------------------------------------------ If you encounter the issue of actor/grad_norm metric continuously increasing during training, it might be caused by a significant precision mismatching between the inference engine and training. You can use the following parameter to confirm this: .. code:: bash actor_rollout_ref.rollout.calculate_log_probs=True This parameter will add metrics like training/rollout_probs_diff_mean , which can be used to verify if there is a precision difference between inference and training. Under normal circumstances, the value of training/rollout_probs_diff_mean should be below 0.005. If you observe this value to be higher than 0.01, it indicates a precision issue from the inference engine. The precision issue is known to occur under the following conditions: 1. Using non-Hopper architecture GPUs, such as A100, L20, B200, etc. 2. Using vLLM `with issue 22103 `_ as the inference engine. 3. The input and output texts are long, for example, in multi-turn scenarios using reasioning models like Qwen3 for RL training. If all three conditions above are met and you observe that rollout_probs_diff_mean is too high, it is recommended to add the following parameter to resolve the precision issue: .. code:: bash +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_cascade_attn=True The root cause of this issue is a bug in the flash attention used by vLLM. Although it has been fixed, the fix has not yet been released in the latest version of vLLM (v0.10.2). For a more detailed explanation of this issue, please refer to `Fix LSE output error in FA2 kv-split `_. Until vLLM releases a new version with this fix, it is recommended to use the configuration above to disable cascade attention as a workaround. ================================================ FILE: docs/hybrid_flow.rst ================================================ ========================================================= HybridFlow Programming Guide ========================================================= Last updated: 06/02/2025. .. _vermouth: https://github.com/vermouth1992 Author: `Chi Zhang `_ verl is an open source implementation of the paper `HybridFlow `_ [1]_. In this section, we will introduce the basic concepts of HybridFlow, the motivation and how to program with verl APIs. Motivation and Design ------------------------ We use dataflow to represent RL systems. [4]_. DataFlow ~~~~~~~~~~~~~~~~~~~~ Dataflow is an abstraction of computations. Neural Network training is a typical dataflow. It can be represented by computational graph. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/dataflow.jpeg?raw=true :alt: The dataflow graph from CS231n 2024 lecture 4 This figure [2]_ represents the computation graph of a polynomial function followed by a sigmoid function. In the data flow of neural network computation, each node represents an operator, and each edge represents the direction of forward/backward propagation. The computation graph determines the architecture of the neural network. RL as a dataflow problem ++++++++++++++++++++++++++++++++++++++++++++++ Reinforcement learning (RL) training can also be represented as a dataflow. Below is the dataflow graph that represents the PPO algorithm used in RLHF [3]_: .. image:: https://picx.zhimg.com/70/v2-cb8ab5ee946a105aab6a563e92682ffa_1440w.avis?source=172ae18b&biz_tag=Post :alt: PPO dataflow graph, credit to Zhihu 低级炼丹师 However, the dataflow of RL has fundamental differences compared with dataflow of neural network training as follows: +--------------------------+--------------------------------------------------+---------------------+ | Workload | Node | Edge | +--------------------------+--------------------------------------------------+---------------------+ | Neural Network Training | Operator (+/-/matmul/softmax) | Tensor movement | +--------------------------+--------------------------------------------------+---------------------+ | Reinforcement Learning | High-level operators (rollout/model forward) | Data Movement | +--------------------------+--------------------------------------------------+---------------------+ In the case of tabular reinforcement learning, each operator is a simple scalar math operation (e.g., bellman update). In deep reinforcement learning(DRL), each operator is a high-level neural network computation such as model inference/update. This makes RL a two-level dataflow problem: - Control flow: defines how the high-level operators are executed (e.g., In PPO, we first perform rollout. Then, we perform advantage computation. Finally, we perform training). It expresses the **core logics of RL algorithms**. - Computation flow: defines the dataflow of **neural network computation** (e.g., model forward/backward/optimizer). Design Choices ~~~~~~~~~~~~~~~~~~~~ The model size used in DRL before the LLM era is typically small. Thus, the high-level neural network computation can be done in a single process. This enables embedding the computation flow inside the control flow as a single process. However, in the LLM era, the computation flow (e.g., training neural network) becomes a multi-process program. This naturally leads to two design choices: 1. Convert the control flow into a multi-process program as well. Then colocate with computation flow (unified multi-controller) - Advantages: - Achieves the **optimal performance** under fixed computation flow and control flow as the communication overhead in both training and data transfer is minimized. - Disadvantages: - The computation and/or control flow is **hard to reuse** from software perspective as computation code is coupled with specific controller code. For example, the training loop of PPO is generic. Say we have an PPO training flow implemented with a specific computation flow such as FSDP. Neither the control flow or computation flow can be reused if we want to switch the computation flow from FSDP to Megatron, due to the coupling of control and computation flows. - Requires more efforts from the user under flexible and dynamic control flows, due to the multi-process nature of the program. 2. Separate the flows: single process for the control flow and multi-process for computation flow - Advantages: - The computation flow defined elsewhere can be **easily reused** after the decoupling. - The controller runs on a single process. Implementing a new RL algorithm with a **different control flow is simple and easy**. - Disadvantages: - Additional **data communication overhead** each time the controller process and computatation processes interact. The data has to be sent back and forth. In verl, the latter strategy with separate control flow and computation flow is adopted. verl is designed to decouple the control flow of RL algorithms, and the implementation of computation engines. Overall Execution Diagram ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Below is a simplified diagram denoting the execution of a reinforcement learning job. In the diagram, the controller runs on a single process, while the generator/actor workers, critic workers run on multiple processes, placed with specific resource groups. For rollout, the controller passes the data to the generator to perform sample generation. When the rollout is done, the data is passed back to controller for the next step of the algorithm. Similar execution is done for other workers. With the hybrid controller design, the data flow and computation is decoupled to provide both efficiency in computation and flexibility in defining algorithm training loops. .. figure:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/driver_worker.png?raw=true :alt: The execution diagram Codebase walkthrough (PPO) ------------------------------------------------ Entry function ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Code: https://github.com/volcengine/verl/blob/main/verl/trainer/main_ppo.py In this file, we define a remote function `main_task` that serves as the controller (driver) process as shown in the above figure. We also define a ``RewardManager``, where users can customize their reward function based on the data source in the dataset. Note that `RewardManager` should return the final token-level reward that is optimized by RL algorithms. Note that users can combine model-based rewards and rule-based rewards. The ``main_task`` constructs a RayPPOTrainer instance and launch the fit. Note that ``main_task`` **runs as a single process**. We highly recommend that the ``main_task`` is NOT scheduled on the head of the ray cluster because ``main_task`` will consume a lot of memory but the head usually contains very few resources. Ray trainer ~~~~~~~~~~~~~~~~~~~~ Code: https://github.com/volcengine/verl/blob/main/verl/trainer/ppo/ray_trainer.py The RayPPOTrainer manages - Worker and WorkerGroup construction - Runs the main loop of PPO algorithm Note that, the fit function of RayPPOTrainer **runs as a single process**. Worker and WorkerGroup construction ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Each workerGroup manages a list of workers that runs remotely. Note that the worker group runs in the process of its constructor. Each worker inside the WorkerGroup runs on a GPU. The worker group serves as a proxy for the controller process to interact with a list of workers, in order to perform certain computations. **In order to do so, we have to bind the methods of the worker into the method of the WorkerGroup and define the data dispatch and data collection**. This is done via simple decoration that will be introduced in the Worker definition section. For example, in PPO, we define 3 worker groups: - ActorRolloutRef: manages actor, rollout and reference policy. ActorRolloutRefWorker can be instantiated as a single actor, a single rollout, a single reference policy, a combined actor/rollout or a combined actor/rollout/ref. This design is aimed for the maximum code reuse in various scenarios. The reason for colocating actor and rollout is for fast weight transfer using nccl. The reason for coloating actor and reference is to implement an efficient lora PPO as the reference policy is simply the base model of PPO in lora. The colocation is done via ``verl.single_controller.ray.base.create_colocated_worker_cls``, where it creates a single ray remote class exposing all class methods from these roles. - Critic: manages the critic model - Reward: manages the reward model The worker group will be constructed on the resource pool it designates. The resource pool is a set of GPUs in the ray cluster. Worker definition ~~~~~~~~~~~~~~~~~~~~ .. _ActorRolloutRefWorker: https://github.com/volcengine/verl/blob/main/verl/workers/fsdp_workers.py We take `ActorRolloutRefWorker `_ for an example. The APIs it should expose to the controller process are: - init_model: build the underlying model - generate_sequences: given prompts, generate responses - compute_log_prob: compute the log-probability of a generated sequence using actor - compute_ref_log_prob: compute the log-probability of a generated sequence using reference policy - save_checkpoint: save the checkpoint Note that these methods are defined in the worker that can only be invoked via remote calls. For example, if the controller process wants to initialize the model, it has to call .. code-block:: python for worker in actor_rollout_ref_wg: worker.init_model.remote() If the controller process wants to generate sequences, it has to call .. code-block:: python data = xxx # split the data into dp chunks data_dp_lst = data.split(dp_size) output_dp_lst = [] for i, worker in enumerate(actor_rollout_ref_wg): output_future = worker.generate_sequences.remote(data_dp_lst[i]) output_dp_lst.append(output_future) output = torch.cat(ray.get(output_dp_lst), dim=0) We observe that controller process calling worker group methods in general can be divided into 3 parts: - Split the data into data parallel sizes - Dispatch the corresponding data into each worker - Collect and concatenate the data when the computation finishes In verl, we design a syntax sugar to encapsulate the 3 processes into a single call from the controller process. .. code-block:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def generate_sequences(data): ... # on the driver output = actor_rollout_ref_wg.generate_sequences(data) We decorate the method of the worker with a ``register`` that explicitly defines how the input data should be split and dispatched to each worker, and how the output data should be collected and concatenated by the controller. For example, ``Dispatch.DP_COMPUTE_PROTO`` splits the input data into dp chunks, dispatch each data to each worker, collect the output and concatenate the results. Note that this function requires the input and output to be a DataProto defined here (https://github.com/volcengine/verl/blob/main/verl/protocol.py). PPO main loop ~~~~~~~~~~~~~~~~~~~~ With the aforementioned APIs, we can implement the main loop of PPO as if it is a single process program .. code-block:: python for prompt in dataloader: output = actor_rollout_ref_wg.generate_sequences(prompt) old_log_prob = actor_rollout_ref_wg.compute_log_prob(output) ref_log_prob = actor_rollout_ref_wg.compute_ref_log_prob(output) values = critic_wg.compute_values(output) rewards = reward_wg.compute_scores(output) # compute_advantages is running directly on the control process advantages = compute_advantages(values, rewards) output = output.union(old_log_prob) output = output.union(ref_log_prob) output = output.union(values) output = output.union(rewards) output = output.union(advantages) # update actor actor_rollout_ref_wg.update_actor(output) critic.update_critic(output) Takeaways ~~~~~~~~~~~~~~~~~~~~ - This programming paradigm enables users to use different computation backend without modification of the control process. - This programming paradigm enables flexible placement (by changing the mapping of WorkerGroup and ResourcePool) without modification of the control process. Repository organization ------------------------------------------------ Important code files in the repository are organized as below: .. code-block:: bash verl # the verl package trainer main_ppo.py # the entrypoint for RL training ppo ray_trainer.py # the training loop for RL algorithms such as PPO sft_trainer.py # the SFT trainer with FSDP backend config generation.yaml # configuration template for rollout ppo_trainer.yaml # configuration template for the RL trainer workers protocol.py # the interface of DataProto fsdp_workers.py # the FSDP worker interfaces: ActorRolloutRefWorker, CriticWorker, RewardModelWorker megatron_workers.py # the Megatron worker interfaces: ActorRolloutRefWorker, CriticWorker, RewardModelWorker actor dp_actor.py # data parallel actor with FSDP backend megatron_actor.py # nD parallel actor with Megatron backend critic dp_critic.py # data parallel critic with FSDP backend megatron_critic.py # nD parallel critic with FSDP backend reward_model megatron reward_model.py # reward model with Megatron backend rollout vllm vllm_rollout.py # rollout with vllm backend hf_rollout.py # rollout with huggingface TGI backend sharding_manager fsdp_ulysses.py # data and model resharding when using FSDP + ulysses fsdp_vllm.py # data and model resharding when using FSDP + ulysses + vllm megatron_vllm.py # data and model resharding when using Megatron + vllm utils dataset # datasets for SFT/RM/RL reward_score # function based reward gsm8k.py # reward function for gsm8k dataset math.py # reward function for math dataset seqlen_balancing.py # the sequence balance optimization models llama # Megatron implementation for llama, deepseek, mistral, etc transformers # ulysses integration with transformer models such as llama, qwen, etc weight_loader_registery.py # registry of weight loaders for loading hf ckpt into Megatron third_party vllm # adaptor for vllm's usage in RL vllm_spmd # vllm >= v0.7 adaptor examples # example scripts tests # integration and unit tests .github # the configuration of continuous integration tests .. [1] HybridFlow: A Flexible and Efficient RLHF Framework: https://arxiv.org/abs/2409.19256v2 .. [2] Data flow graph credit to CS231n 2024 lecture 4: https://cs231n.stanford.edu/slides/2024/lecture_4.pdf .. [3] PPO dataflow graph credit to 低级炼丹师 from Zhihu​: https://zhuanlan.zhihu.com/p/635757674 .. [4] RLFlow ================================================ FILE: docs/index.rst ================================================ Welcome to verl's documentation! ================================================ verl is a flexible, efficient and production-ready RL training framework designed for large language models (LLMs) post-training. It is an open source implementation of the `HybridFlow `_ paper. verl is flexible and easy to use with: - **Easy extension of diverse RL algorithms**: The hybrid programming model combines the strengths of single-controller and multi-controller paradigms to enable flexible representation and efficient execution of complex Post-Training dataflows. Allowing users to build RL dataflows in a few lines of code. - **Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as PyTorch FSDP, Megatron-LM, vLLM and SGLang. Moreover, users can easily extend to other LLM training and inference frameworks. - **Flexible device mapping and parallelism**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes. - Ready integration with popular HuggingFace models verl is fast with: - **State-of-the-art throughput**: By seamlessly integrating existing SOTA LLM training and inference frameworks, verl achieves high generation and training throughput. - **Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases. -------------------------------------------- .. _Contents: .. toctree:: :maxdepth: 2 :caption: Quickstart start/install start/quickstart start/multinode start/ray_debug_tutorial start/more_resources start/agentic_rl .. toctree:: :maxdepth: 2 :caption: Programming guide hybrid_flow single_controller .. toctree:: :maxdepth: 1 :caption: Data Preparation preparation/prepare_data preparation/reward_function .. toctree:: :maxdepth: 2 :caption: Configurations examples/config .. toctree:: :maxdepth: 1 :caption: PPO Example examples/ppo_code_architecture examples/gsm8k_example examples/multi_modal_example examples/skypilot_examples .. toctree:: :maxdepth: 1 :caption: Algorithms algo/ppo.md algo/grpo.md algo/collabllm.md algo/dapo.md algo/spin.md algo/sppo.md algo/entropy.md algo/opo.md algo/baseline.md algo/gpg.md algo/rollout_corr.md algo/rollout_corr_math.md algo/otb.md algo/dppo.md .. toctree:: :maxdepth: 1 :caption: PPO Trainer and Workers workers/ray_trainer workers/fsdp_workers workers/megatron_workers workers/automodel_workers workers/sglang_worker workers/trtllm_worker workers/model_engine .. toctree:: :maxdepth: 1 :caption: Performance Tuning Guide perf/dpsk.md perf/best_practices perf/perf_tuning perf/perf_tuning_on_ascend.rst README_vllm0.8.md perf/device_tuning perf/verl_profiler_system.md perf/nsight_profiling.md perf/torch_profiling.md .. toctree:: :maxdepth: 1 :caption: Adding new models advance/fsdp_extension advance/megatron_extension .. toctree:: :maxdepth: 1 :caption: Advanced Features advance/checkpoint advance/rope advance/attention_implementation advance/ppo_lora.rst sglang_multiturn/multiturn.rst sglang_multiturn/interaction_system.rst advance/placement advance/dpo_extension examples/sandbox_fusion_example advance/rollout_trace.rst advance/rollout_skip.rst advance/one_step_off advance/agent_loop advance/reward_loop advance/fully_async data/transfer_queue.md advance/grafana_prometheus.md advance/fp8.md advance/async-on-policy-distill advance/mtp.md .. toctree:: :maxdepth: 2 :caption: Hardware Support amd_tutorial/amd_build_dockerfile_page.rst amd_tutorial/amd_vllm_page.rst ascend_tutorial/contribution_guide/ascend_ci_guide_zh.rst ascend_tutorial/quick_start/ascend_quick_start.rst ascend_tutorial/quick_start/dockerfile_build_guidance.rst ascend_tutorial/quick_start/ascend_sglang_quick_start.rst ascend_tutorial/features/ascend_consistency.rst ascend_tutorial/features/ascend_backend_features.md ascend_tutorial/profiling/ascend_profiling_zh.rst ascend_tutorial/profiling/ascend_profiling_en.rst ascend_tutorial/examples/gspo_optimization_practice.md ascend_tutorial/examples/ascend_performance_analysis_guide.md ascend_tutorial/examples/dapo_multi_model_optimization_practice.md ascend_tutorial/examples/ascend_sglang_best_practices.rst ascend_tutorial/examples/ascend_retool_best_pratice.rst ascend_tutorial/examples/run_qwen3_32B_megatron_1k_256k_npu.md ascend_tutorial/faq/faq.rst .. toctree:: :maxdepth: 1 :caption: API References api/data api/single_controller.rst api/trainer.rst api/utils.rst .. toctree:: :maxdepth: 1 :caption: Blog blog/v0.7.md .. toctree:: :maxdepth: 2 :caption: FAQ faq/faq .. toctree:: :maxdepth: 1 :caption: Development Notes sglang_multiturn/sandbox_fusion.rst Contribution ------------- verl is free software; you can redistribute it and/or modify it under the terms of the Apache License 2.0. We welcome contributions. Join us on `GitHub `_, `Slack `_ and `Wechat `_ for discussions. Contributions from the community are welcome! Please check out our `project roadmap `_ and `good first issues `_ to see where you can contribute. Code Linting and Formatting ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ We use pre-commit to help improve code quality. To initialize pre-commit, run: .. code-block:: bash pip install pre-commit pre-commit install To resolve CI errors locally, you can also manually run pre-commit by: .. code-block:: bash pre-commit run Adding CI tests ^^^^^^^^^^^^^^^^^^^^^^^^ If possible, please add CI test(s) for your new feature: 1. Find the most relevant workflow yml file, which usually corresponds to a ``hydra`` default config (e.g. ``ppo_trainer``, ``ppo_megatron_trainer``, ``sft_trainer``, etc). 2. Add related path patterns to the ``paths`` section if not already included. 3. Minimize the workload of the test script(s) (see existing scripts for examples). We are HIRING! Send us an `email `_ if you are interested in internship/FTE opportunities in MLSys/LLM reasoning/multimodal alignment. ================================================ FILE: docs/perf/best_practices.rst ================================================ Verl LLM Best Practices (DAPO + Qwen3-235B) =========================================== Last updated: 11/03/2025. Purpose ------- This guide uses DAPO training on Qwen3-235B as a concrete example. We unpack every parameter that appears in the optimization objective, map it to Verl configuration entries, and share field-tested recommendations so you can derive sensible settings for your own workloads. .. note:: 1. The guide only covers the subset of parameters required to reproduce the DAPO experiments discussed here. For the full list, refer to the ``config`` components in the Verl source tree: https://github.com/volcengine/verl/tree/main/verl/trainer/config 2. PPO and GRPO introduce KL-constrained policies. We therefore include that setup in the explanations below. You can treat all configurations mentioned here as a DAPO pipeline augmented with a KL penalty. Optimization Objectives ----------------------- DAPO objective ~~~~~~~~~~~~~~ .. math:: \begin{aligned} \mathcal{J}_{\mathrm{DAPO}}(\theta)= & \mathbb{E}_{(q, a) \sim \mathcal{D},\left\{o_i\right\}_{i=1}^G \sim \pi_{\theta_{\text {old }}}(\cdot \mid q)} \ {\left[\frac{1}{\sum_{i=1}^G\left|o_i\right|} \sum_{i=1}^G \sum_{t=1}^{\left|o_i\right|} \min \left(r_{i, t}(\theta) \hat{A}_{i, t}, \operatorname{clip}\left(r_{i, t}(\theta), 1-\varepsilon_{\text {low }}, 1+\varepsilon_{\text {high }}\right) \hat{A}_{i, t}\right)\right] } \\ \end{aligned} .. math:: \text { s.t. } \quad 0<\mid\left\{o_i \mid \text { is_equivalent }\left(a, o_i\right)\right\} \mid 2 * model_parameters`` (bf16/fp16). Increase TP gradually to expand KV cache capacity while watching communication cost—especially once TP > 8. - ``actor_rollout_ref.rollout.temperature`` / ``top_p`` / ``top_k``: Sampling knobs for rollout. Keep enough randomness; ``temperature=1.0``, ``top_p=1.0``, ``top_k=-1`` are good defaults. - ``actor_rollout_ref.rollout.val_kwargs.temperature`` / ``top_p`` / ``top_k`` / ``do_sample`` / ``n``: Sampling options for validation. Set ``temperature > 0`` to prevent repetitive thinking chains. For small test sets (e.g., AIME24) raise ``n`` (64 is a common choice) to reduce variance. A practical starting point is ``temperature=1.0``, ``top_p=0.7``, ``top_k=-1``, ``do_sample=True``, ``n=1`` and then increase ``n`` as needed. - ``+actor_rollout_ref.rollout.engine_kwargs.vllm.*`` / ``+actor_rollout_ref.rollout.engine_kwargs.sglang.*``: Extra backend options injected via the ``+`` syntax. Consult backend docs for exact semantics. Some switches (for example ``pipeline_parallel_size``) may not be supported yet; when TP=32, ``enable_expert_parallel=True`` can even slow down DeepSeek-V3 rollout, so benchmark carefully. :math:`\pi_\theta` - ``data.train_batch_size``: Total batch size per training iteration. Each rollout produces ``train_batch_size * n`` samples. Larger values reduce the number of rollouts but increase off-policy drift. - ``actor_rollout_ref.actor.ppo_mini_batch_size``: Mini-batch size per optimization step. Tune it the same way you would for standard deep learning workloads. - ``actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu``: Samples processed per forward pass on one GPU group (a Megatron group contains TP * PP * CP GPUs). Keep it ≤ ``ppo_mini_batch_size`` and as large as memory allows. - ``actor_rollout_ref.actor.use_dynamic_bsz``: Enable dynamic batch sizing to adapt to sequence length and improve throughput. - ``actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu``: Maximum tokens per GPU when computing log probabilities under dynamic batching. Set it to at least a multiple of ``max_prompt_length + max_response_length`` to prevent truncation. - Megatron parallelism parameters (``pipeline_model_parallel_size`` / ``tensor_model_parallel_size`` / ``expert_model_parallel_size`` / ``expert_tensor_parallel_size`` / ``context_parallel_size``): Balance PP/TP/EP/ETP/CP to match memory and network constraints. In bf16/fp16, each parameter consumes roughly ``2 / TP`` bytes; if you keep FP32 master weights or skip optimizer offload, reserve another 4–8 bytes for Adam. Activations scale with ``micro_batch_size × sequence_length × hidden_size`` and can be mitigated with gradient checkpointing, dynamic batches, or offload. Prefer increasing TP first, add PP when necessary, extend sequence capacity with CP, align EP/ETP with TP for MoE models, and keep DP minimal on constrained clusters while combining with offload. Always align the setup with hardware topology and communication cost. - ``actor_rollout_ref.model.use_fused_kernels``: Enable Verl’s fused kernels for supported models to squeeze out additional performance. :math:`\hat{A}_{i,t}` - ``algorithm.adv_estimator``: Advantage estimator. Set to ``grpo`` for DAPO/GRPO. :math:`R_i` - ``reward_model.reward_manager``: Reward aggregation strategy. Use ``dapo`` for DAPO and ``naive`` for GRPO. :math:`D_{KL}` - ``algorithm.use_kl_in_reward``: Whether to add a KL term to the reward. ``True`` for PPO, ``False`` for GRPO and DAPO. - ``actor_rollout_ref.actor.use_kl_loss``: Whether to include a KL loss term. ``False`` for PPO, ``True`` for GRPO, ``False`` for DAPO. :math:`\beta` - ``actor_rollout_ref.actor.kl_loss_coef``: Weight of the KL loss. Start around 0.001. Larger values curb reward hacking but reduce exploration. - ``algorithm.kl_ctrl.kl_coef``: KL coefficient applied within the reward. Adjust to match your tolerance for divergence. :math:`\pi_{old}` - ``actor_rollout_ref.rollout.log_prob_use_dynamic_bsz``: Enable dynamic batching when the old policy computes log-probabilities. Recommended. :math:`\pi_{ref}` - ``actor_rollout_ref.ref.log_prob_use_dynamic_bsz``: Enable dynamic batching for the reference policy. Recommended. - Reference Megatron parallelism: Keep ``pipeline_model_parallel_size``, ``tensor_model_parallel_size``, ``expert_model_parallel_size``, ``expert_tensor_parallel_size``, and ``context_parallel_size`` in sync with the actor. - ``actor_rollout_ref.ref.megatron.param_offload``: Offload reference parameters to CPU when the actor does so. Even without gradients or optimizer states, parity helps with capacity planning. :math:`o_i` / :math:`|o_i|` - ``actor_rollout_ref.actor.loss_agg_mode``: Loss aggregation mode. Token-level ``token-mean`` matches the recommendations from Dr.GRPO and DAPO; use ``seq-mean-token-mean`` to reproduce the original GRPO behavior. :math:`\pi_\theta(o_{i,t} \mid q_i,o_{i,`_ - `SimonHuang `_ 1.5B ~~~ .. list-table:: :widths: auto :header-rows: 1 * - Tag - Model - Task - Resource - MaxBatch - Train - Infer - Link - Contributor * - MIN - Qwen2.5-1.5B - GRPO-LoRA - 1*H100 - 128 - fsdp - vllm0.8.3 - `qwen2-1.5b_grpo-lora_1_h100_fsdp_vllm.sh `_ - `SimonHuang `_ 3B ~~~ .. list-table:: :widths: auto :header-rows: 1 * - Tag - Model - Task - Resource - MaxBatch - Train - Infer - Link - Contributor * - MIN - Qwen2.5-3B - GRPO-LoRA - 1*H100 - 62 - fsdp - vllm0.8.3 - `qwen2-3b_grpo-lora_1_h100_fsdp_vllm.sh `_ - `SimonHuang `_ 7B ~~~ .. list-table:: :widths: auto :header-rows: 1 * - Tag - Model - Task - Resource - MaxBatch - Train - Infer - Link - Contributor * - MIN - Qwen2-7B - GRPO - 2*H800 - \ - fsdp - vllm0.8.2 - `qwen2-7b_grpo_2_h800_fsdp_vllm `_ - `Xiangyongan `_ * - MIN - Qwen2.5-7B - GRPO-LoRA - 1*H100 - 16 - fsdp - vllm0.8.3 - `qwen2-7b_grpo-lora_1_h100_fsdp_vllm.sh `_ - `SimonHuang `_ 14B ~~~ .. list-table:: :widths: auto :header-rows: 1 * - Tag - Model - Task - Resource - MaxBatch - Train - Infer - Link - Contributor * - MIN - Qwen2-14B - GRPO - 4*H800 - \ - fsdp - vllm0.8.2 - `qwen2-14b_grpo_4_h800_fsdp_vllm `_ - `Xiangyongan `_ * - MIN - Qwen2.5-14B - GRPO-LoRA - 2*H100 - 116 - fsdp - vllm0.8.3 - `qwen2-14b_grpo-lora_2_h100_fsdp_vllm.sh `_ - `SimonHuang `_ 32B ~~~ .. list-table:: :widths: auto :header-rows: 1 * - Tag - Model - Task - Resource - MaxBatch - Train - Infer - Link - Contributor * - MIN - Qwen2-32B - GRPO - 8*H20 - \ - megatron - vllm0.8.2 - `qwen2-32b_grpo_8_h20_megatron_vllm `_ - `Xiangyongan `_ * - MIN - Qwen2.5-32B - GRPO-LoRA - 4*H100 - 180 - fsdp - vllm0.8.3 - `qwen2-32b_grpo-lora_4_h100_fsdp_vllm.sh `_ - `SimonHuang `_ 70B ~~~ .. list-table:: :widths: auto :header-rows: 1 * - Tag - Model - Task - Resource - MaxBatch - Train - Infer - Link - Contributor * - MIN - Qwen2-70B - GRPO - 32*H20 - \ - fsdp - vllm0.8.2 - `qwen2-70b_grpo_32_h20_fsdp_vllm `_ - `Xiangyongan `_ * - MIN - Qwen2-70B - GRPO - 32*H800 - \ - fsdp - vllm0.8.3 - `qwen2-70b_grpo_32_h800_fsdp_vllm `_ - `Xiangyongan `_ * - MIN - Qwen2.5-72B - GRPO-LoRA - 8*H100 - 176 - fsdp - vllm0.8.3 - `qwen2-72b_grpo-lora_8_h100_fsdp_vllm.sh `_ - `SimonHuang `_ 405B ~~~~ .. table:: :widths: auto ====== ====== ====== ======== ======== ====== ====== ====== tag model task resource MaxBatch train infer link ====== ====== ====== ======== ======== ====== ====== ====== \ \ \ \ \ \ \ ====== ====== ====== ======== ======== ====== ====== ====== 671B ~~~~ .. table:: :widths: auto ====== ====== ====== ======== ======== ====== ====== ====== tag model task resource MaxBatch train infer link ====== ====== ====== ======== ======== ====== ====== ====== \ \ \ \ \ \ \ ====== ====== ====== ======== ======== ====== ====== ====== ================================================ FILE: docs/perf/dpsk.md ================================================ # Training DeepSeek 671b Last updated: 08/20/2025. verl integrates Megatron to support large MoE models such as `Qwen3-235B-A22B` and `deepseek-ai/DeepSeek-V3`. This is an ongoing community effort. In the journey the community added the following features and optimizations that enable verl with larger models: - per tensor weight resharding between rollout and training - context parallelism and expert parallelism enabled via megatron - dynamic batch size (sequence balance) for megatron - reduced ray-related serialization overhead - optimizer offloading, recomputation, and efficient kernels - various debugging metrics and utils - hybrid optimizer and the megatron backend now has a wider list of models supported: - DeepSeek-V3 - Moonlight - Qwen3 - Qwen2.5-VL (to be merged soon) - Qwen2 - Mixtral ## Getting Started ### preparation The recommended image with pre-built Megatron dependency is `verlai/verl:app-verl0.4-vllm0.8.5-mcore0.13.0-preview`, which is built using the Dockerfile at [docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.vllm.mcore0.13.preview](https://github.com/volcengine/verl/blob/main/docker/verl0.4-cu124-torch2.6-fa2.7.4/Dockerfile.app.vllm.mcore0.13.preview). The image is build in Hopper GPUs with DeepEP. It does not support None-Hopper GPUs, such as A100. You may need to reinstall DeepEP to work with A100. With `OFFLOAD_FRACTION=1`, the system's minimum requirements are lowered. It can run on as few as 96 H20 (96GB) GPUs for DeepSeek-V3, and on as few as 32 H20 (96GB) GPUs for Qwen3-235B-A22B. However, this configuration will use 1.6TB CPU memory per node. If you run out of CPU memory or require faster training speed, you can add more nodes. ### DeepSeek 671b For DeepSeek-V3 671b, please refer to [examples/grpo_trainer/run_deepseek671b_math_megatron_96gb.sh](https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_deepseek671b_math_megatron_96gb.sh). MTP and quantilization is disabled during RL training. To train your project, configure the following environment variables based on the number of available GPUs. These are recommended settings and can be adjusted based on your specific hardware. | num gpus | NNODES | TP | PP | EP | OFFLOAD_FRACTION | OFFLOAD_OPTIM | LAST_LAYER | | -- | -- | -- | -- | -- | -- | -- | -- | | 96 | 12 | 8 | 12 | 8 | 1. | False | 6 | | 128 | 16 | 8 | 16 | 8 | 0.5 | True | 1 | | 256 | 32 | 8 | 16 | 8 | 0. | True | 1 | | 512 | 64 | 1 | 16 | 32 | 0 | True | 1 | ### Qwen3 235b For Qwen3-235b, please refer to [examples/grpo_trainer/run_qwen3-235b_megatron_96gb.sh](https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_qwen3-235b_megatron_96gb.sh). To train your project, configure the following environment variables based on the number of available GPUs. These are recommended settings and can be adjusted based on your specific hardware. | num gpus | NNODES | TP | PP | EP | OFFLOAD_FRACTION | OFFLOAD_OPTIM | LAST_LAYER | | -- | -- | -- | -- | -- | -- | -- | -- | | 32 | 4 | 4 | 8 | 4 | 1. | False | 6 | | 64 | 8 | 4 | 8 | 4 | 0.5 | True | 6 | | 128 | 16 | 4 | 8 | 4 | 0 | True | 6 | | 256 | 32 | 4 | 8 | 4 | 0 | True | 6 | ### Benchmark Here are some benchmark results for DeepSeek / Qwen3-235B. All configurations match the recommended settings based on the number of GPUs. | model | num gpus | mean response length | rollout time(s) | GPU memory(GB) | CPU memory(GB) | MFU | step time(s) | | -- | -- | -- | -- | -- | -- | -- | -- | | DeepSeek 671b | 96 | 1960 | 1050 | 66 | 1500 | 0.19 | 1700 | ### Qwen3-30B-A3B MOE For Qwen3-30b, please refer to [examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh](https://github.com/volcengine/verl/blob/main/examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh). To train your project, configure the following environment variables based on the number of available GPUs. These are recommended settings and can be adjusted based on your specific hardware. | num gpus | NNODES | TP | PP | EP | OFFLOAD_FRACTION | OFFLOAD_OPTIM | MFU | | -- | -- | -- | -- | -- | -- | -- | -- | | 8 | 1 | 1 | 1 | 8 | 1. | True | 0.4 | | 16 | 2 | 1 | 1 | 8 | 1. | True | 0.37 | | 32 | 4 | 1 | 1 | 8 | 1. | True | 0.31 | ## Upcoming Optimizations The community continue to optimize large MoE models further, ongoing efforts include: - further optimizing memory consumption, and provide recommended/tuned configurations with various machine types - optimizing long context RL training performance - performance improvement with SGLang x Megatron We invite the community to try and improve verl together. Get connected with us on [slack](https://join.slack.com/t/verlgroup/shared_invite/zt-2w5p9o4c3-yy0x2Q56s_VlGLsJ93A6vA)/[wechat](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/WeChat.JPG)/[Github issues](https://github.com/volcengine/verl/issues/708)! ## Acknowledgement @vermouth1992 @ISEEKYAN @ETOgaosion @yzlnew @ShareLer @BearBiscuit05 @ccclyu @ann-qin-lu @SwordFaith @zzong2006 @zhaochenyang20 @ocss884 @eric-haibin-lin @chenhaiq @techkang ================================================ FILE: docs/perf/nsight_profiling.md ================================================ # NVIDIA Nsight Systems profiling in verl Last updated: 06/20/2025. This guide explains how to use NVIDIA Nsight Systems for profiling verl training runs. ## Configuration Profiling in verl can be configured through several parameters in the trainer configuration file (ppo_trainer.yaml or other files like dapo_trainer.yaml): ### Prerequisites Nsight Systems version is important, please reference `docker/Dockerfile.vllm.sglang.megatron` for the version we used. ### Global profiling control verl has one single controller process and multiple worker processes. Both controller and worker processes can be profiled. Since the controller process can be executed in any nodes in the cluster, there is a message printed in the logging to indicate the controller process node hostname and process id. In `global_profiler`, three new config entries control the profiler behaviors: * **`global_profiler.steps`**. List of step numbers at which profiling should be performed. For example: [1, 2, 5] will profile steps 1, 2, and 5. And ``null`` means no profiling. * **`global_profiler.profile_continuous_steps`**. If true, and the following `global_profiler.discrete==False`, then the continuous steps in `global_profiler.steps` will be combined into one database. For example the above step 1 and 2 are in one database, and 5 in another. If false, every step occupies at least one database. The reason for this config is to observe the program behaviors between steps. Nsys options in controller nodes and worker nodes are configured in `global_profiler.global_tool_config.nsys`: * **`global_profiler.global_tool_config.nsys.controller_nsight_options`**. This config group is for the single controller. All fields in this config group will be just sent to Nsight Systems when Ray starts the controller process. `ppo_trainer.yaml` provides a workable example. Users can reference [Nsight Systems manual](https://docs.nvidia.com/nsight-systems/UserGuide/index.html) and [Ray user guide](https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html) for more details. * **`global_profiler.global_tool_config.nsys.worker_nsight_options`**. This config group is for the worker processes. Similarly all fields in this config group will be just sent to Nsight Systems when Ray starts the controller process. Capture range is used to control the profiler when to start and stop. So `capture-range: "cudaProfilerApi"` is fixed and does not change it. Users can change `capture-range-end` with some accurate calculation or just leave it `null`. ### Worker process profiling Verl manages mulitiple RL roles, _Actor_, _Ref_, _Rollout_, _Critic_, _Reward_, which are implemented in different Worker classes. And these workers can be combined into one Ray Actor, running in a process group. Each RL role has its own profiling config group, `profiler`, which consists of three fields: * **`all_ranks` and `ranks`**. When `all_ranks` is set `True` then all ranks will be profiled; when set `False`, `ranks` will be profiled. By default, verl profiles the whole training process in a series ` worker_process_..nsys-rep` files for each process rank. PID is the process ID; RID is the capture range ID. * **`discrete`**. When set `False`, all the roles actions in one training step will be dumped in one database. When set `True`, the actions annotated by `DistProfiler.annotate` will be dumped into a discrete database. In this case, each role's action occupies one ``. * **Verl collocate mode**. Verl can combine two Worker sub classes to one Worker Actor. In this case, the user should take care that the combined Workers have consistent `discrete`. The Nsight Systems profiler uses a `torch.cuda.profiler.start()` and `stop()` pair to dump a `` database anyway. ### where to find the profiling data By default the `*.nsys-rep` files are saved in the directory `/tmp/ray/session_latest/logs/nsight/` at each node. According to the Ray manual, this default directory is not changeable. ["however, Ray preserves the `--output` option of the default config"](https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html). Some users may think it is not convenient, but it is understandable that Ray may start hundreds of processes and it would be a big network file system pressure if we save the files in one central place. ## Usage Example To enable profiling for specific components and steps, modify your ppo_trainer.yaml like this: ### Disable profiler ```yaml profiler: steps: null # disable profile ``` ### Enable profiler and one database for one training step ```yaml global_profiler: steps: [1, 2, 5] discrete: False actor_rollout_ref: actor: profiler: enable: True all_ranks: True # rollout & ref follow actor settings critic: profiler: enable: True all_ranks: True reward_model: profiler: enable: True all_ranks: True ``` ### Enable profiler and multiple databases for one training step ```yaml profiler: steps: [1, 2, 5] discrete: True ``` ## Profiling Output When profiling is enabled, verl will generate Nsight Systems profiles for the specified components and steps. The profiles will include: - CUDA kernel execution - Memory operations - CPU-GPU synchronization - NVTX markers for key operations Nsight Systems supports multi-report view, to open multiple databases together. In this mode, different processes and steps can be aligned in one time line for better analysis. ================================================ FILE: docs/perf/perf_tuning.rst ================================================ Performance Tuning Guide ============================== Last updated: 07/17/2025. Author: `Guangming Sheng `_, `Jiali Zheng `_ In this section, we will discuss how to tune the performance of all the stages in verl, including: 1. Rollout generation throughput. 2. Enable ``use_remove_padding=True`` for sequence packing (i.e., data packing and remove padding). 3. Batch size tuning for forward and backward computation 4. Enable ``use_dynamic_bsz=True`` for higher throughput. 5. Utilize Ulysses Sequence Parallel for Long Context Training 6. LigerKernel for SFT performance optimization 7. Forward prefetch in FSDP training backend 8. Memory optimization for entropy calculation from logits Rollout Generation Tuning -------------------------- verl currently supports two rollout backends: vLLM and TGI (with SGLang support coming soon). Below are key factors for tuning vLLM-based rollout. Before tuning, we recommend setting ``actor_rollout_ref.rollout.disable_log_stats=False`` so that rollout statistics are logged. - Increase ``gpu_memory_utilization``. - For vLLM v0.7.0 and later, the vLLM instance will only use gpu_memory_utilization of the **total** memory. - For SGLang, it's the fraction of the free GPU memory used for **static** memory like model weights and KV cache. However, the remaining (1-gpu_memory_utilization) will also be used during inference. However, if model parameters and optimizer states are not offloaded, using too high a fraction can lead to OOM. A value between 0.5 and 0.7 often strikes a good balance between high throughput and avoiding OOM. Note: since the definition of ``gpu_memory_utilization`` varies across inference engines, a value that works well for one engine may cause OOM for another. - Adjust ``max_num_seqs`` or ``max_num_batched_tokens``. If the GPU cache utilization is relatively low in the log, increase ``max_num_seqs`` or ``max_num_batched_tokens`` can enlarge the effective batch size in the decoding stage, allowing more concurrent requests per batch. We recommend setting ``max_num_batched_tokens > 2048`` for higher throughput. - Use a smaller ``tensor_parallel_size``. When GPU resources allow, a smaller tensor parallel size spawns more vLLM replicas. Data parallelism (DP) can yield higher throughput than tensor parallelism (TP), but also increases KVCache consumption. Carefully balance the trade-off between more replicas and higher memory usage. Our experiment in Sec. 8.4 of `HybridFlow paper `_ evaluate this trade-off. - Balance performance and memory using ``cudagraph_capture_sizes``. If ``cudagraph_capture_sizes`` is set, vLLM will try to capture the model execution graph for different batch sizes. Since cudagraph memory can not be offloaded to cpu, The memory stay in gpu when update actor is running. Using smaller batch sizes can avoid OOM but slightly reduce throughput. Must to set ``enforce_eager=False`` to use ``cudagraph_capture_sizes``. More tuning details such as dealing with Preemption and Chunked-prefill can be found in `vLLM official tuning guide `_ For optimal performance, we recommend using vLLM v0.8.3 or later. See https://github.com/volcengine/verl/blob/main/docs/README_vllm0.8.md for details. Enable remove padding (sequence packing) ----------------------------------------- Currently, for llama, mistral, gemma1 and qwen based models, users can enable `use_remove_padding=True` to utilize the sequence packing implementation provided by transformers library. For other models, transformers library may also support it but we haven't tested it yet. Users can add the desired model config to the `test_transformer.py `_ file. And test its functionality by running the following command: .. code-block:: bash pytest -s tests/models/test_transformer.py If the test passes, you can add your desired model into the model `registry.py `_ file. Then, you can enjoy the performance boost of sequence packing and welcome to PR your tested model to verl! Batch Size Tuning ----------------- To achieve higher throughput in experience preparation (i.e., model fwd) and model update (i.e., actor/critic fwd/bwd), users may need to tune the ``*micro_batch_size_per_gpu`` for different computation. In verl, the core principle for setting batch sizes is: - **Algorithmic metrics** (train batch size, PPO mini-batch size) are *global* (from a single-controller perspective), normalized in each worker. See the `normalization code `_. - **Performance-related parameters** (micro batch size, max token length for dynamic batch size) are *local* parameters that define the per-GPU data allocations. See the `normalization code `_. .. note:: In your training script, please use ``*micro_batch_size_per_gpu`` instead of ``*micro_batch_size``. So that you don't need to consider the normalization of the ``micro_batch_size`` and ``micro_batch_size`` will be deprecated. Batch Size Tuning tips """""""""""""""""""""" Therefore, users may need to tune the ``*micro_batch_size_per_gpu`` to accelerate training. Here're some tips: 1. **Enable gradient checkpointing**: Set ``actor_rollout_ref.model.enable_gradient_checkpointing=True`` and ``critic.model.enable_gradient_checkpointing=True``. This often allows for larger micro-batch sizes and will be beneficial for large mini-batch training. 2. Increase the ``*micro_batch_size_per_gpu`` as much as possible till equals to normalized ``mini_batch_size``. 3. **Use larger forward-only parameters**: Forward only parameter, such as ``actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu``, ``actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu``, ``critic.forward_micro_batch_size_per_gpu`` could be larger (e.g., 2x) than training related micro batch sizes, such as ``actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu``, ``critic.ppo_micro_batch_size_per_gpu``. 4. **Allow larger micro-batch sizes for Critic and Reward models**: micro batch size of Critic and Reward model could be larger than Actor model. This is because the actor model has much larger vocab size in the final layer. 5. **Enable activation offloading**: Set ``actor_rollout_ref.model.enable_activation_offload=True`` and ``critic.model.enable_activation_offload=True``. This often works together with gradient checkpointing to get larger micro-batch sizes and it's only available in FSDP backend now. Tuning for Dynamic Batch Size ----------------------------- Dynamic batch size is a technique that allows the model to process similar number of tokens in a single forward pass (with different actual batch sizes). This can significantly improve the training efficiency and reduce the memory usage. To utilize this technique, users can set ``use_dynamic_bsz=True`` in actor, ref, critic and reward models. With ``use_dynamic_bsz=True``, users don't need to tune ``*micro_batch_size_per_gpu``. Instead, users should tune the following parameters: - ``actor_rollout_ref.actor.ppo_max_token_len_per_gpu``, ``critic.ppo_max_token_len_per_gpu``: The maximum number of tokens to be processed in fwd and bwd of ``update_policy`` and ``update_critic``. - ``actor_rollout_ref.ref.log_prob_max_token_len_per_gpu`` and ``actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu``: The maximum number of tokens to be processed in a the fwd computation of ``compute_log_prob`` and ``compute_ref_log_prob``. - ``critic.forward_micro_batch_size_per_gpu``, ``reward_model.forward_micro_batch_size_per_gpu``: The maximum number of tokens to be processed in a the fwd computation of ``compute_values``, ``compute_rm_score``. Dynamic Batch Size Tuning tips """""""""""""""""""""""""""""" Here're some tips to tune the above parameters: 1. **Increase** ``actor_rollout_ref.actor.ppo_max_token_len_per_gpu`` Make it at least 2 x (max_prompt_length + max_response_length). We set it to 3x in `run_qwen2-7b_rm_seq_balance.sh `_. Try to increase it to get higher throughput. 2. **Forward-only parameters can be larger**: Similar to the non-dynamic-batch scenario, forward-only token limits can exceed those used in forward/backward operations. 3. **Use larger limits for Critic and Reward models**: Critic and Reward parameters can be set at least 2× the Actor’s limits. For instance, we set them to 4× here: `run_qwen2-7b_rm_seq_balance.sh `_ .. :math:`\text{critic.ppo_max_token_len_per_gpu} = 2 \times \text{actor.ppo_max_token_len_per_gpu})`. Ulysses Sequence Parallel for Long Context Training ---------------------------------------------------- To utilize this technique, users can set ``ulysses_sequence_parallel_size>1`` in actor, ref, critic and reward models. We support different model utilize different ulysses_sequence_parallel_size sizes. To train long sequence (>32k), users may need to decrease the ``*micro_batch_size_per_gpu`` and ``*max_token_len_per_gpu`` to avoid OOM. LigerKernel for SFT ---------------------- LigerKernel is a high-performance kernel for Supervised Fine-Tuning (SFT) that can improve training efficiency. To enable LigerKernel in your SFT training: 1. Install liger-kernel via ``pip3 install liger-kernel``. In your SFT configuration file (e.g., ``verl/trainer/config/sft_trainer.yaml``), set the ``use_liger`` parameter: .. code-block:: yaml model: use_liger: True # Enable LigerKernel for SFT 2. The default value is ``False``. Enable it only when you want to use LigerKernel's optimizations. 3. LigerKernel is particularly useful for improving training performance in SFT scenarios. Forward prefetch in FSDP training backend ---------------------- During the training phase, users can enable forward prefetching in FSDP by setting ``fsdp_config.forward_prefetch=True``. For example, ``actor_rollout_ref.actor.fsdp_config.forward_prefetch=True``. This configuration prefetches the next forward-pass all-gather operation before completing the current forward computation, overlapping communication with computation and improving efficiency. For further details, refer to the `FSDP forward_prefetch `_ documentation. .. note:: Backward prefetch is unsupported because the ``BACKWARD_POST`` policy may prefetch incorrectly in nested-module cases. For details, see the `FSDP documentation `_ Migrating to FSDP2 ---------------------- FSDP2 offers notable improvements over FSDP1. According to `PyTorch TorchTitan benchmarks `_: - 7% lower GPU memory usage on average - 1.5% throughput improvement with BF16 training - Better composability with DTensor and per-parameter sharding **Enabling FSDP2 in VERL:** .. code-block:: python # Enable FSDP2 in actor configuration actor_rollout_ref.actor.strategy="fsdp2" .. note:: FSDP2 requires PyTorch 2.1+ and is recommended for models with transformer architecture. Memory optimization for entropy calculation from logits ---------------------- The ``logits`` tensor (typically of shape ``[bsz*seq_len, voc]``) can consume significant memory. When using ``compute_entropy_from_logits``, memory usage reaches approximately ``[bsz*seq_len, voc] × (4 bytes (float32) + 2 bytes (autocast for softmax+logsumexp) + 1 byte (softmax output))``. To reduce this memory peak, enable chunked computation by setting: ``actor_rollout_ref.ref.entropy_from_logits_with_chunking = True`` This processes the tensor in chunks of shape ``[chunk_size, voc]`` (e.g., 2048) rather than the full sequence length, exclusively during the model's forward pass. Additionally, during training, standard gradient checkpointing (``enable_gradient_checkpointing=True``) does not apply to entropy calculations. To reduce memory peaks in this context, set: ``actor_rollout_ref.actor.entropy_checkpointing = True`` This enables entropy recomputation specifically for the entropy calculation, lowering memory usage during training. ================================================ FILE: docs/perf/perf_tuning_on_ascend.rst ================================================ Performance Tuning Guide on Ascend ==================================== Last updated: 01/29/2026. Author: `Xiaobo Hu `_, `Haozhe Li `_ `Perf Tuning `_ 中介绍的性能调优方法在昇腾设备中同样适用。本文重点介绍了昇腾特有的一些调优手段，包括融合算子优化、特定硬件配置和昇腾亲和特性等。 融合算子 -------------------------- 常用融合算子列表 ********************************** 融合算子的优化原理为，通过数学意义上的等价替换，将多个算子融为一个算子的计算，减少冗余计算，同时减少下发次数，从而提高性能。几个典型的NPU融合算子列举如下，目前均已在 npu_patch.py 中对 Qwen2、Qwen3 系列模型完成替换。 当前verl中使用的全量融合算子请查阅 `npu_patch.py `_ Matrix Computation-Communication operator fusion (MC2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MC2 是 CANN 中一系列计算通信融合算子的统称，这些算子将原本串行的通信和计算操作融合在一起，通过内部的切分和流水线并行执行来优化性能。 在 vllm-ascend 中，可以通过指定环境变量： .. code-block:: sh export VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE=1 在前向计算的 ``RowParallelLinear`` 中使能 ``torch_npu.npu_mm_all_reduce_base`` ，将分离的 ``matmul`` 和 ``allreduce`` 合并为一个融合算子。 `RotaryMul&RotaryMulGrad `_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ torch_npu 接口: ``torch_npu.npu_rotary_mul(x, r1, r2)`` 参数说明: - x: q，k，shape要求输入为4维，一般为 ``[B, N, S, D]`` 或 ``[B, S, N, D]`` 或 ``[S, B, N, D]`` 。 - r1: cos值 ，shape要求输入为4维，一般为 ``[1, 1, S, D]`` 或 ``[1, S, 1, D]`` 或 ``[S, 1, 1, D]`` 。 - r2: sin 值，shape要求输入为4维，一般为 ``[1, 1, S, D]`` 或 ``[1, S, 1, D]`` 或 ``[S, 1, 1, D]`` 。 `RmsNorm&RmsNormGrad `_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ torch_npu 接口: ``torch_npu.npu_rms_norm(self, gamma, epsilon=1e-06) -> (Tensor, Tensor)`` 参数说明: - self: Tensor 类型，shape 支持 1-8 维。 - gamma: Tensor 类型，通常为weight，shape 要求与 self 的后几维保持一致。 - epsilon: Float 数据类型，用于防止除 0 错误。 输出说明: - 第 1 个输出为 Tensor，计算公式的最终输出y。 - 第 2 个输出为 Tensor， rms_norm 的中间结果 rstd ，用于反向计算。 `Swiglu `_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ torch_npu 接口: ``torch_npu.npu_swiglu(Tensor self, int dim=-1) -> (Tensor)`` 参数说明: - self: Tensor 类型，shape支持 1-8 维。 - dim: Int 类型，默认为 -1。 输出说明: - 输出为 Tensor，计算公式的最终输出 y。 `GroupMatMul `_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 函数原型： .. code:: python npu_grouped_matmul( x, weight, *, bias=None, scale=None, offset=None, antiquant_scale=None, antiquant_offset=None, per_token_scale=None, group_list=None, activation_input=None, activation_quant_scale=None, activation_quant_offset=None, split_item=0, group_type=None, group_list_type=0, act_type=0, output_dtype=None, tuning_config=None ) -> List[Tensor] 详细使用方法见标题文档链接 FSDP后端融合算子使用方法 ********************************** 在 ``verl/models/transformers/npu_patch.py`` 目录中，已经把可用的融合算子通过 patch 的形式进行替换，无需进行其他操作即可默认进行使用 Megatron后端融合算子使用方法 ********************************** Megatron 的融合算子集成在 MindSpeed 中，需要添加特定参数开启: 1. **Flash Attention（必须开启）** :: +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True ++actor_rollout_ref.ref.megatron.override_transformer_config.use_flash_attn=True 2. **RotaryMul** :: +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True +actor_rollout_ref.actor.megatron.override_transformer_config.use_fused_rotary_pos_emb=True 3. **RMSNorm** :: +actor_rollout_ref.actor.megatron.override_transformer_config.use_fused_rmsnorm=True 4. **GroupMatMul** :: +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True 5. **Swiglu** :: +actor_rollout_ref.actor.megatron.override_transformer_config.use_fused_swiglu=True 6. **Permute/Unpermute** :: +actor_rollout_ref.actor.megatron.override_transformer_config.fused_permute_unpermute=True 7. **MC2** :: +actor_rollout_ref.actor.megatron.override_transformer_config.use_ascend_mc2=True 昇腾通用配置 -------------------------- `算子下发 `_ ************************************************************************************************************************************************************************************************************ 通过 ``TASK_QUEUE_ENABLE`` 可配置 task_queue 算子下发队列优化等级，默认为 Level 1 优化。该配置可以减少host下发时间，可用于缓解由下发导致的整体free过大问题。 .. image :: https://github.com/verl-project/verl-data/blob/main/images/ascend/perf_tuning_task_queue.png :width: 500px Level 0 : 不开启下发流水优化。 Level 1 : \ 将算子下发任务分为两段，一部分任务（主要是 aclnn 算子的调用）放在新增的二级流水上，一、二级流水通过算子队列传递任务，相互并行，通过部分掩盖减少整体的下发耗时，提升端到端性能。 Level 2 : \ 基于 Level 1 的优化进一步平衡了一、二级流水的任务负载，主要是将 workspace 相关任务迁移至二级流水，掩盖效果更好，性能收益更大。该配置仅在二进制场景生效，建议配置值为 Level 2 优化。 `通讯算法编排展开 `_ ************************************************************************************************************************************************************************************************************ 使用环境变量 ``HCCL_OP_EXPANSION_MODE=AIV`` 用于配置通信算法的编排展开位置，支持如下取值: - **AI_CPU:** 代表通信算法的编排展开位置在 Device 侧的 AI CPU，Device 侧根据硬件型号自动选择相应的调度器。 - **AIV:** 代表通信算法的编排展开位置在 Device 侧的 Vector Core，执行也在 Vector Core。 - **HOST:** 代表通信算法的编排展开位置为 Host 侧 CPU，Device 侧根据硬件型号自动选择相应的调度器。 - **HOST_TS:** 代表通信算法的编排展开位置为 Host 侧 CPU，Host 向 Device 的 Task Scheduler 下发任务，Device 的 Task Scheduler 进行任务调度执行。 推理阶段调优 -------------------------- Chunked Prefill in V1 *************************** VLLM 当前版本已默认启用 VLLM V1，使用以下配置启用 Chunked Prefill： .. code-block:: sh actor_rollout_ref.rollout.enable_chunked_prefill=True 原理参考 `VLLM 官方文档 `_。 Graph Mode *************************** 与 CUDA 类似，NPU 通过以下配置启用 **ACL Graph**： .. code-block:: sh actor_rollout_ref.rollout.enforce_eager=False 文档：`ACL Graph `_ .. note:: ACL Graph 与 ``taskqueue Level 2`` 原理冲突，**二者无法同时开启**。 训练阶段调优 -------------------------- FSDP ********************************** .. csv-table:: :header: "FSDP", "说明" :widths: 30, 60 "/","仅切分优化器(Zero-1)" SHARD_GRAD_OP,切分梯度和优化器(Zero-2) "HYBRID_SHARD","切分权重、梯度和优化器(Zero-3)" "2D device_mesh+HYBRID_SHARD","又称HSDP（FSDP+DDP）例如device_mesh=[2,8], 每8个rank为一个FSDP组，组内进行FSDP切分，共有两个组，两个组间进行DDP，通过allreduce同步梯度。" "2D device_mesh+HYBRID_SHARD_ZERO2","HSDP的Zero2版本" NO_SHARD,DDP FSDP 不支持 Zero-1， VeRL中会根据卡数和 ``actor_rollout_ref.actor.fsdp_config.fsdp_size`` 来决定 device mesh 的取值，默认使用 Zero-3 进行切分；如果模型较小（建议小于 7B 时），可以通过控制参数 ``actor_rollout_ref.actor.fsdp_config.reshard_after_forward`` 为 ``True`` 在 FSDP/FSDP2 上使用 Zero-2 来优化性能. Megatron ********************************** 在模型较大时，使用 Megatron 作为训练后端可以更灵活的进行性能调优。 当 DP 并行显存无法容纳模型时，优先开启 TP 来切分模型权重，如果模型仍然过大，再开启 PP 来进一步切分；如果序列过长导致激活太大，则可以开启 CP 和 SP 来进行优化；在 MoE 模型中则可以额外开启 EP 来控制对专家的切分，如果专家过小，为了避免将权重切的果味细碎，则可以开启 ETP 来避免 MoE 部分的 TP 切分，而将多个完整的专家分布到 DP 和 TP 上。 TP、PP、EP、ETP和 Megatron 使用方式一样，CP 和 SP 在 NPU 上开启方式: - SP: ``Sequence Parallel`` 在 Tensor Parallel 的基础上进一步提高计算效率，是一种通过将输入数据的序列维度进行切分的并行计算方式。在 NPU 上通过 MindSpeed 来调用SP: :: actor_rollout_ref.actor.megatron.override_transformer_config.sequence_parallel=True - CP: ``Context Parallel`` 是一种在多个 GPU/NPU 上并行处理神经网络激活值的方法，他通过在序列维度上对输入张量进行划分来实现。在 NPU 上通过 MindSpeed 来调用 CP （两个参数必须同时添加）: :: actor_rollout_ref.actor.megatron.context_parallel_size actor_rollout_ref.actor.megatron.override_transformer_config.context_parallel_size Megatron-distributed optimizer ********************************** 在面对较大尺寸模型时，通常需要将优化器分片到一个 DP 域内的每张卡上来节省显存。Megatron 后端下在 NPU 上开启分布式优化器: :: +actor_rollout_ref.actor.megatron.override_transformer_config.use_distributed_optimizer=True ================================================ FILE: docs/perf/torch_profiling.md ================================================ # PyTorch Profiling in verl Last updated: 01/13/2026. This guide explains how to use the native [PyTorch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) for profiling verl training runs. ## Configuration Profiling in verl can be configured through parameters in the trainer configuration file (e.g., `ppo_trainer.yaml`). ### Global Profiling Control In `global_profiler`, you can control when and how profiling occurs globally: * **`global_profiler.steps`**: List of step numbers to profile. E.g., `[1, 2, 5]` profiles steps 1, 2, and 5. Set to `null` to disable. * **`global_profiler.save_path`**: Directory to save the profiling results. Default is `outputs/profile`. ### Role Profiling Control Each RL role (Actor, Critic, etc.) has its own `profiler` configuration: * **`enable`**: Whether to enable profiling for this role. * **`all_ranks`**: If `True`, profiles all ranks. * **`ranks`**: List of specific ranks to profile if `all_ranks` is `False`. * **`tool_config.torch`**: Configuration specific to the PyTorch Profiler. #### PyTorch Profiler Options (`tool_config.torch`) You can customize the PyTorch Profiler behavior using the following fields under `tool_config.torch`: * **`contents`**: List of contents to profile. * **`cpu`**: Profile CPU activities. * **`cuda`**: Profile CUDA activities. * **`memory`**: Track tensor memory allocation/free. * **`shapes`**: Record shapes of operator inputs. * **`stack`**: Record source code file and line number. * **`schedule`**: (Advanced) configuration for `wait`, `warmup`, `active`, `repeat` cycles. ## Examples ### 1. End-to-End Collection Collects performance data for all steps in a single trace file. ```yaml global_profiler: steps: [1, 2, 5] save_path: ./outputs/profile actor_rollout_ref: actor: profiler: enable: True all_ranks: True tool_config: torch: discrete: False contents: [cpu, cuda] # rollout & ref follow actor settings ``` ### 2. Discrete Mode Collection Discrete mode saves separate trace files for each step. This is useful for detailed analysis and is **mandatory** when using Agent Loop. **Configuration Example** This configuration supports profiling both Training (Actor) and Inference (Rollout). You can enable/disable them independently. ```yaml actor_rollout_ref: actor: profiler: enable: True # Set to True to profile training all_ranks: False ranks: [0] # Global Rank 0 tool_config: torch: discrete: True contents: [cpu, cuda] rollout: profiler: enable: True # Set to True to profile inference all_ranks: False ranks: [0] # In Agent Loop, this is the Replica Rank (e.g. 0-th instance) tool_config: torch: discrete: True # REQUIRED # ref follow actor settings ``` **Agent Loop Mode Description** When Rollout runs in [Agent Loop](../advance/agent_loop.rst) mode, performance data for the Rollout phase **must be collected using discrete mode**. In this case, the Profiler is triggered by the inference engine backend. 1. Rank Definition: ranks in the Rollout configuration refers to Replica Rank (inference instance index), not Global Rank. 2. Inference Engine Support: Currently, vLLM and SGLang engines are supported without additional settings. Specific details are as follows: * **vLLM Engine**: Automatically collects AsyncLLM scheduling stacks and inference process performance data. * **SGLang Engine**: Automatically collects inference process performance data. Does not support the memory option in contents. ## Visualization Collected trace files (usually `.json` or `.json.gz`) are stored in the configured `save_path`. You can visualize them using: 1. **Chrome Tracing**: Open `chrome://tracing` in a Chrome browser and load the JSON file. 2. **Perfetto**: Open [ui.perfetto.dev](https://ui.perfetto.dev/) and load the file (recommended for large traces). 3. **TensorBoard**: If using the TensorBoard plugin for PyTorch Profiler. ================================================ FILE: docs/perf/verl_profiler_system.md ================================================ # verl Profiler System Last updated: 08/18/2025. ## Architecture The architecture of verl profiler system is like below: ![verl-profiler-arch](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/2bc7ed0ba2f37f21707bfac3b241eca4b86d1bc6/docs/verl_profiler_arch.png) There is a global profiler and tool configuration to set some common config in single controller level, deciding - `tool`: which tool to use - `steps`: which steps to profile - `save_path`: results saving path When some tool need to profile behavior of each role, configurations in role-level is needed: - `tool`: which tool to use - `enable`: whether enable profiling on this role - rank info: `all_ranks` and `rank` to decide which rank to profile or log output For tool config in role-level, there are some detailed behavior needed to control, like the `discrete` mode in nsys profiler. Every role has a profiler config, and by default, rollout/ref/reward models follow the Actor's behavior. ## To Add a new profiling tool New added profiling tool shall reuse the current APIs as much as possible. 1. The logic of **whether to use the tool**: `tool == [new tool]`. 2. Add the global and local tool config to `ppo_trainer.yaml`/`ppo_megatron_trainer.yaml` and each `[role].yaml`, under `global_tool_config.[new tool]` and `tool_config.[new tool]` 3. The tool config should be implemented in `verl/utils/profiler/config.py`, inherit the `BaseConfig` class. 4. Implement profiling tool initialization logic using configurations in `global_profiler.global_tool_config.[new tool]` and the results saving logics (can also save in role-level profile) 5. For role function-level profiling, please follow the nsys profiler way in `nvtx_profiler.py`, implement a profiler class inherit `DistProfiler` and import new profiler in `verl/utils/profiler/__init__.py` 6. Add unit test and examples for others to use in convinience. ================================================ FILE: docs/preparation/prepare_data.rst ================================================ Prepare Data for Post-Training ======================================== Last updated: 02/09/2025. Before starting the post-training job, we need to prepare the data for the policy training. The data should be stored in the parquet format. We provide several data preprocess scripts for different datasets, including GSM8K, MATH, HelloSwag, Full_hh_rlhf. To prepare other datasets, we need to follow the following steps: The data preprocess script can be divided into two parts: 1. The first part is the common part, which loads the dataset from huggingface's ``datasets`` package. Then preprocess the datasets with the ``make_map_fn`` and then store in the parquet format. .. code:: python import re import os import datasets from verl.utils.hdfs_io import copy, makedirs import argparse # To extract the solution for each prompts in the dataset # def extract_solution(solution_str): # ... if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--local_dir', default='/opt/tiger/gsm8k') parser.add_argument('--hdfs_dir', default=None) args = parser.parse_args() num_few_shot = 5 data_source = 'openai/gsm8k' dataset = datasets.load_dataset(data_source, 'main') train_dataset = dataset['train'] test_dataset = dataset['test'] # Construct a `def make_map_fn(split)` for the corresponding datasets. # ... train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True) local_dir = args.local_dir hdfs_dir = args.hdfs_dir train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet')) test_dataset.to_parquet(os.path.join(local_dir, 'test.parquet')) makedirs(hdfs_dir) copy(src=local_dir, dst=hdfs_dir) 2. The users are required to implement the ``make_map_fn()`` function (as well as the ``extract_solution``) on their own to support different datasets or tasks. We already implemented the data preprocess of GSM8k, MATH, Hellaswag and Full_hh_rlhf datasets. And we take the GSM8k dataset as an example: **GSM8K** In the ``make_map_fn``, each data field should consist of the following 5 fields: 1. ``data_source``: The name of the dataset. To index the corresponding reward function in the ``RewardModel`` 2. ``prompt``: This field should be constructed in the format of huggingface chat_template. The tokenizer in ``RLHFDataset`` will apply chat template and tokenize the prompt. 3. ``ability``: Define the task category. 4. ``reward_model``: Currently, we only utilize the ``ground_truth`` field during evaluation. The ``ground_truth`` is computed by the ``extract_solution`` function. **NOTED** that the implementation of the corresponding reward function should align with this extracted ``ground_truth``. 5. ``extra_info``: Record some information of the current prompt. Not use for now. .. code:: python def extract_solution(solution_str): solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) # extract the solution after #### assert solution is not None final_solution = solution.group(0) final_solution = final_solution.split('#### ')[1].replace(',', '') return final_solution instruction_following = "Let's think step by step and output the final answer after \"####\"." # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question = example.pop('question') question = question + ' ' + instruction_following answer = example.pop('answer') solution = extract_solution(answer) data = { "data_source": data_source, "prompt": [{ "role": "user", "content": question }], "ability": "math", "reward_model": { "style": "rule", "ground_truth": solution }, "extra_info": { 'split': split, 'index': idx } } return data return process_fn ================================================ FILE: docs/preparation/reward_function.rst ================================================ Implement Reward Function for Dataset ====================================== Last updated: 06/02/2025. For each dataset, we need to implement a reward function or utilize a reward model to compute the rewards for the generated responses. We already pre-implemented some reward functions in `reward_score directory `_. You can also use customized reward functions. Currently, we support reward functions for GSM8k and MATH datasets. For RLHF datasets (e.g., full_hh_rlhf) and Code Generation (e.g., APPS), we utilize reward model and SandBox (will opensource soon) for evaluation respectively. RewardManager ------------- In the entrypoint of the PPO Post-Training script `main_ppo.py `_, we implement a ``RewardManager`` that utilize pre-implemented reward functions to compute the scores for each response. In the ``RewardManager``, we implemented a ``__call__`` function to compute the score for each response. All the reward functions are executed by ``compute_score_fn``. The input is a ``DataProto``, which includes: - ``input_ids``, ``attention_mask``: ``input_ids`` and ``attention_mask`` after applying chat_template, including prompt and response - ``responses``: response tokens - ``ground_truth``: The ground truth string of the current prompt. Stored in ``non_tensor_batch`` in the ``DataProto``, which should be preprocessed in the parquet files. - ``data_source``: The dataset name of the current prompt. Stored in ``non_tensor_batch`` in the ``DataProto``, which should be preprocessed in the parquet files. After detokenize the responses, the responses string and the ground truth string will be input to the ``compute_score_fn`` to compute the score for each response. Reward Functions ---------------- Pre-implemented ~~~~~~~~~~~~~~~ We already pre-implemented some reward functions in `reward_score directory `_. - In the `GSM8k example `_, we force the response to output the final answer after four ####, then use string matching to compare with the ground truth. If completely correct, score 1 point; if the format is correct, score 0.1 points; if the format is incorrect, score 0 points. - In the `MATH example `_, we follow the implementation in `lm-evaluation-harness repository `_. Customized ~~~~~~~~~~ You can implement customized reward functions in a separate file and specify them using ``custom_reward_function.path`` and ``custom_reward_function.name``. For the set of them, please refer to :ref:`config-explain-page`. The parameters of your reward function should be ``data_source``, ``solution_str``, ``ground_truth``, and ``extra_info``. For example: .. code:: python def my_reward_fn(data_source, solution_str, ground_truth, extra_info=None): return len(solution_str)/100 If you are testing only a single customized reward function, you can simply name it 'compute_score' and leave ``custom_reward_function.name`` unset. To run multiple tests with different customized reward functions, you can modify both ``custom_reward_function.path`` and ``custom_reward_function.name`` for each trial. For instance, you might create a single `my_reward.py` file and implement multiple reward functions within it. This way, for different trials, you only need to adjust ``custom_reward_function.name``, making it more convenient to conduct multiple tests within scripts. ================================================ FILE: docs/requirements-docs.txt ================================================ # markdown support recommonmark myst_parser # markdown table support sphinx-markdown-tables # theme default rtd # crate-docs-theme sphinx-rtd-theme # pin tokenizers version to avoid env_logger version req tokenizers==0.21 ================================================ FILE: docs/sglang_multiturn/interaction_system.rst ================================================ Interaction System for Multi-turn RL Training ============================================= Last updated: 06/25/2025. Overview -------- The verl interaction system enables dynamic, multi-turn conversational feedback during reinforcement learning training. This system allows models to engage in iterative problem-solving scenarios where interaction agents can provide corrective feedback, guidance, or evaluation based on the model's responses. **New in Multi-Interaction Support**: The system now supports multiple named interactions within a single training session, enabling sophisticated training scenarios where different samples can use different interaction strategies. This allows for curriculum learning, domain-specific feedback, and flexible agent switching at the sample level. Key features: - **Async-based Architecture**: Non-blocking interaction processing for distributed training - **Instance Management**: Stateful session handling with unique instance IDs for concurrent interactions - **SGLang Integration**: Seamless integration with SGLang rollout system for multi-turn conversations - **Configuration-driven**: Dynamic agent loading via YAML configuration files - **Multi-Interaction Support**: Registry system enabling multiple named interactions per rollout - **Sample-Level Selection**: Each sample can specify which interaction to use via configuration - **Reward Integration**: Turn-level scoring mechanism integrated with verl's reward system Architecture ------------ The interaction system follows a plugin-based architecture with clear separation of concerns: .. code-block:: Interaction Registry System ↓ BaseInteraction (Abstract Interface) ↓ Multiple Named Interactions (e.g., Gsm8kInteraction, CustomInteraction) ↓ SGLang Rollout Integration (interaction_map) ↓ Sample-Level Interaction Selection ↓ Async Request Lifecycle Management Core Components ~~~~~~~~~~~~~~~ **Interaction Registry System** The interaction registry system allows loading and managing multiple named interactions: .. code-block:: python from verl.interactions.utils.interaction_registry import initialize_interactions_from_config # Load multiple interactions from config interaction_map = initialize_interactions_from_config("config.yaml") # Access specific interaction by name gsm8k_interaction = interaction_map["gsm8k"] custom_interaction = interaction_map["custom_solver"] **BaseInteraction Interface** All interaction agents must implement the ``BaseInteraction`` abstract class: .. code-block:: python from verl.interactions.base import BaseInteraction from typing import Dict, Any, List, Tuple, Optional class BaseInteraction: def __init__(self, config: Dict[str, Any]): self.config = config self.name: str = config.get("name", "interaction_agent") async def start_interaction(self, instance_id: Optional[str] = None, **kwargs) -> str: """Initialize interaction session, return instance_id""" async def generate_response(self, instance_id: str, messages: List[Dict[str, Any]], **kwargs) -> Tuple[bool, str, float, Dict[str, Any]]: """Generate response, return (should_terminate, response, score, metadata)""" async def calculate_score(self, instance_id: str, **kwargs) -> float: """Calculate turn-level score for RL training""" async def finalize_interaction(self, instance_id: str, **kwargs) -> None: """Clean up resources""" **Request Lifecycle** The interaction system integrates with SGLang's async rollout via state management: 1. ``PENDING`` → Initialize interaction via ``start_interaction()`` 2. ``GENERATING`` → Model generates response 3. ``INTERACTING`` → Process response via ``generate_response()`` 4. ``GENERATING`` → Continue if not terminated, otherwise ``COMPLETED`` Configuration ------------- **Basic Setup** Enable interaction in your rollout configuration: .. code-block:: yaml actor_rollout_ref: rollout: multi_turn: enable: true interaction_config_path: "path/to/interaction_config.yaml" max_user_turns: 10 max_assistant_turns: 10 **Interaction Configuration File** Create an interaction configuration file (e.g., ``interaction_config.yaml``): **Single Interaction (Legacy Format)** .. code-block:: yaml interaction: - name: "gsm8k" class_name: "verl.interactions.gsm8k_interaction.Gsm8kInteraction" config: {} **Multiple Interactions (New Format)** .. code-block:: yaml interaction: - name: "gsm8k" class_name: "verl.interactions.gsm8k_interaction.Gsm8kInteraction" config: {} - name: "custom_solver" class_name: "custom.interactions.CustomInteraction" config: solver_type: "advanced" timeout: 30 - name: "code_verifier" class_name: "verl.interactions.base.BaseInteraction" config: verification_mode: "strict" **Automatic Name Generation** If no ``name`` field is provided, the system will automatically generate one from the class name: .. code-block:: yaml interaction: - class_name: "verl.interactions.gsm8k_interaction.Gsm8kInteraction" config: {} # Automatically generates name: "gsm8k" The system will dynamically load all specified interaction classes and make them available by name. Implementation Example: GSM8K ----------------------------- The GSM8K interaction demonstrates a complete implementation for math problem-solving scenarios: .. code-block:: python from verl.interactions.base import BaseInteraction from verl.utils.reward_score import gsm8k from uuid import uuid4 class Gsm8kInteraction(BaseInteraction): def __init__(self, config: dict): super().__init__(config) self._instance_dict = {} async def start_interaction(self, instance_id=None, ground_truth=None, **kwargs): if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "ground_truth": ground_truth, "reward": 0.0, } return instance_id async def generate_response(self, instance_id, messages, **kwargs): # Extract last assistant message content content = "" for item in reversed(messages): if item.get("role") == "assistant": content = item.get("content", "") break # Ensure GSM8K format (#### prefix) self._instance_dict[instance_id]["response"] = content reward = await self.calculate_score(instance_id) if reward == 1.0: return True, "Your response is correct!", 1.0, {} else: return False, "Your response is incorrect! You need to reflect on your answer and try again.", 0.0, {} async def calculate_score(self, instance_id, **kwargs): return gsm8k.compute_score( self._instance_dict[instance_id]["response"], self._instance_dict[instance_id]["ground_truth"], method="strict", format_score=0.0, score=1.0, ) async def finalize_interaction(self, instance_id, **kwargs): del self._instance_dict[instance_id] Training Integration -------------------- **Training Script Configuration** Include interaction configuration in your training command: .. code-block:: bash python3 -m verl.trainer.main_ppo \\ --config-path="$CONFIG_PATH" \\ --config-name='gsm8k_multiturn_grpo_w_interaction' \\ algorithm.adv_estimator=grpo \\ data.train_batch_size=512 \\ data.return_raw_chat=True \\ actor_rollout_ref.rollout.name=sglang \\ actor_rollout_ref.rollout.multi_turn.interaction_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/interaction_config/gsm8k_interaction_config.yaml" \\ trainer.total_epochs=15 **Data Requirements** Ensure your dataset includes interaction parameters with the ``name`` field for interaction selection: .. code-block:: python # Dataset should include interaction_kwargs in non_tensor_batch interaction_kwargs = [ {"name": "gsm8k", "query": "What is 2+2?", "ground_truth": "4"}, {"name": "custom_solver", "query": "Solve: x^2 + 5x + 6 = 0", "ground_truth": "x = -2, -3"}, {"name": "gsm8k", "query": "What is 3+3?", "ground_truth": "6"}, ] **Sample-Level Interaction Selection** Each sample can specify which interaction to use via the ``name`` field. This enables flexible training scenarios where different samples use different interaction strategies: .. code-block:: python # Example: Math problems use GSM8K interaction, code problems use code verifier data_samples = [ { "prompt": "What is 15% of 200?", "interaction_kwargs": { "name": "gsm8k", "query": "What is 15% of 200?", "ground_truth": "30" } }, { "prompt": "Write a function to check if a number is prime", "interaction_kwargs": { "name": "code_verifier", "code_type": "python", "expected_behavior": "return True for prime numbers" } } ] **Backward Compatibility** If no ``name`` field is provided in ``interaction_kwargs``, the system defaults to ``"gsm8k"`` for backward compatibility. Best Practices -------------- **Resource Management** - Always implement proper cleanup in ``finalize_interaction()`` - Use unique instance IDs to avoid conflicts in concurrent training - Handle edge cases like empty messages or malformed content **Performance Optimization** - Keep interaction logic lightweight to avoid blocking training - Use async/await properly to maintain non-blocking behavior - Consider caching expensive computations within interaction instances **Testing** Comprehensive testing is essential for interaction systems: .. code-block:: python import pytest from unittest.mock import patch @pytest.mark.asyncio async def test_interaction_workflow(): interaction = YourInteraction({}) # Test complete workflow instance_id = await interaction.start_interaction(ground_truth="expected_answer") messages = [{"role": "user", "content": "user_content"}, {"role": "assistant", "content": "assistant_content"}] should_terminate, response, reward, metadata = await interaction.generate_response(instance_id, messages) assert should_terminate in [True, False] assert isinstance(reward, float) await interaction.finalize_interaction(instance_id) Advanced Usage -------------- **Multi-Interaction Training Strategies** You can design sophisticated training scenarios using multiple interactions: .. code-block:: python # Example: Progressive difficulty with different interaction agents class MathTrainingPipeline: def create_interaction_config(self): return { "interaction": [ { "name": "basic_math", "class_name": "verl.interactions.gsm8k_interaction.Gsm8kInteraction", "config": {"difficulty": "easy"} }, { "name": "advanced_math", "class_name": "custom.interactions.AdvancedMathInteraction", "config": {"difficulty": "hard", "allow_hints": True} }, { "name": "competition_math", "class_name": "custom.interactions.CompetitionMathInteraction", "config": {"time_limit": 300, "show_steps": False} } ] } def create_curriculum_data(self, epoch): if epoch < 5: return [{"name": "basic_math", ...} for _ in samples] elif epoch < 10: return [{"name": "advanced_math", ...} for _ in samples] else: return [{"name": "competition_math", ...} for _ in samples] **Custom Scoring Functions** You can integrate custom reward functions: .. code-block:: python async def calculate_score(self, instance_id, **kwargs): response = self._instance_dict[instance_id]["response"] ground_truth = self._instance_dict[instance_id]["ground_truth"] # Custom evaluation logic if custom_evaluation_function(response, ground_truth): return 1.0 else: return 0.0 **Multi-step Interactions** For complex scenarios requiring multiple feedback rounds: .. code-block:: python async def generate_response(self, instance_id, messages, **kwargs): instance = self._instance_dict[instance_id] instance["attempts"] += 1 # Evaluate current response reward = await self.calculate_score(instance_id) if reward > 0.8: return True, "Excellent work!", reward, {} elif instance["attempts"] < 3: return False, "Good attempt, but try to improve...", reward, {} else: return True, "Maximum attempts reached.", reward, {} Troubleshooting --------------- **Common Issues** 1. **Instance ID Conflicts**: Ensure unique instance IDs across concurrent sessions 2. **Memory Leaks**: Always call ``finalize_interaction()`` to clean up resources 3. **Blocking Operations**: Keep interaction logic async and non-blocking 4. **Configuration Errors**: Verify interaction config path and class name are correct 5. **Interaction Name Conflicts**: Ensure all interactions have unique names in the configuration 6. **Missing Interaction**: Verify the ``name`` field in ``interaction_kwargs`` matches available interactions 7. **Backward Compatibility**: When migrating from single to multi-interaction, add ``name`` fields to existing data **Debugging** Enable debug logging to trace interaction flow: .. code-block:: bash export VERL_LOGGING_LEVEL=DEBUG **Performance Monitoring** Monitor interaction performance impact on training throughput and adjust accordingly. Related Documentation -------------------- - :doc:`multiturn`: Basic multi-turn rollout configuration - :doc:`sandbox_fusion`: Tool integration with SGLang - :doc:`search_tool_example`: Search tool implementation example ================================================ FILE: docs/sglang_multiturn/multiturn.rst ================================================ Multi-turn Rollout Support ========================== Last updated: 06/27/2025. Basic Configuration ~~~~~~~~~~~~~~~~~~~ To enable multi-turn rollout, make sure to configure the following fields in your rollout configuration: .. code-block:: yaml actor_rollout_ref: rollout: multi_turn: True name: "sglang" These configuration activates the sglang engine for multi-turn interaction during rollout. Custom Tool Configuration ~~~~~~~~~~~~~~~~~~~~~~~~~ For custom environment interaction tools, you can implement your own tools based on ``verl.tools.base_tool.BaseTool``. Then, specify your tool configurations in a YAML file: .. code-block:: yaml tools: - class_name: "" config: type: native tool_schema: You may refer to GSM8KTool_example_configuration_, which is one example of the tool configurations. Its implementation can be found in gsm8k_tool.py_. Finally, set the ``tools_config_file`` in your rollout config: .. code-block:: yaml actor_rollout_ref: rollout: tool_kwargs: tools_config_file: This allows integration of customized tool behaviors during actor rollout steps. If you want rollout with simulated interaction, you can set the ``interaction_config_file`` in your rollout config: .. code-block:: yaml interaction: - class_name: "" config: {} .. code-block:: yaml actor_rollout_ref: rollout: interaction_config_file: If your tool creates multi-modal inputs, you should return a list of multi-modal inputs in your tool.execute() implementation. Image and video should be processed before returning. For example, if you are using Qwen2.5-VL, you can use the following code to get the representations: .. code-block:: python async def create(self, ...) -> tuple[str, ToolResponse]: ... from verl.utils.dataset.vision_utils import process_image, process_video img1 = process_image(img1) video1 = process_video(video1) # due to the (image | video) key is ("image" | "video") instead of ("images" | "videos") in vllm, we need to use ("image" | "video") to specify list of images/videos # link: https://github.com/vllm-project/vllm/blob/3c545c0c3b98ee642373a308197d750d0e449403/vllm/multimodal/parse.py#L205 return instance_id, ToolResponse(image=[img1, ...], video=[video1, ...], text="...") async def execute(self, ...) -> Tuple[str | Dict[str, Any], float, dict]: ... from verl.utils.dataset.vision_utils import process_image, process_video img1 = process_image(img1) video1 = process_video(video1) # due to the (image | video) key is ("image" | "video") instead of ("images" | "videos") in vllm, we need to use ("image" | "video") to specify list of images/videos # link: https://github.com/vllm-project/vllm/blob/3c545c0c3b98ee642373a308197d750d0e449403/vllm/multimodal/parse.py#L205 return ToolResponse(image=[img1, ...], video=[video1, ...], text="..."), 0, {} remeber to set ``return_multi_modal_inputs: False`` in your dataset config in order to process the multi-modal inputs in the rollout correctly. Refer to the `Handling Multi-Modal Inputs in Datasets`_ section for more details. MCP Tool Configuration ~~~~~~~~~~~~~~~~~~~~~~ For MCP interaction tools, you can flexibly configure them using a YAML file. The typical setup is as follows: .. code-block:: yaml tools: - class_name: "" config: type: mcp mcp: mcp_servers_config_path: ./mcp_server.json tool_selected_list: {} The ``tool_selected_list`` field is optional and specifies which tools to use from the servers. If you want to enable all available tools, simply omit this attribute. Besides, ``mcp_servers_config_path`` points to a JSON file containing the MCP server configurations. For example: .. code-block:: json { "mcpServers": { "SSE Server": { "url": "your_server_url", "auth_token": "your_server_api_token" }, "STDIO Server": { "command": "npx", "args": ["-y", "server-mcp@0.2.1"], "env": { "SERVER_API_KEY": "your_server_api_token" } } } } Since the content formats returned by the MCP server may vary, users can inherit from ``MCPBaseTool`` and override the ``_parse_tool_result`` method to implement custom parsing logic. .. code-block:: python class MCPYourTool(MCPBaseTool): def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): super().__init__(config, tool_schema) def _parse_tool_result(self, content: list) -> Tuple[str, dict]: ... Overall, you may refer to mcp_search_tool.py_ and mcp_tool_config.yaml_ for custom implementation and configuration. Multi-turn Tokenization ~~~~~~~~~~~~~~~~~~~~~~~ Tokenizing multi-turn rollouts poses a challenge: after applying the chat template and tokenizing the full message list, it's hard to identify which tokens belong to assistant messages. Since the token list is flat, it lacks direct alignment with the message roles. To address this, we adopt a **delta-based tokenization** strategy. Each time the LLM generates a new message, we: 1. Apply the chat template to all prior messages (`messages[:i]`). 2. Apply the chat template again including the latest message (`messages[:i+1]`). 3. Tokenize only the *delta* between these two serialized message strings. This ensures that only tokens generated by the assistant are included in the loss mask. .. code-block:: python # When using tokenizer # Exclude the assistant prompt (e.g., "<|im_start|>assistant") from the loss by setting add_generation_prompt=True prev = tokenizer.apply_chat_template(messages[:i], add_generation_prompt=True, tokenize=False) curr = tokenizer.apply_chat_template(messages[:i+1], add_generation_prompt=False, tokenize=False) token_ids += tokenizer.encode(curr[len(prev):], add_special_tokens=False) loss_mask += [1] * len(token_ids) # Mask only the new assistant tokens .. code-block:: python # When using processor # Exclude the assistant prompt (e.g., "<|im_start|>assistant") from the loss by setting add_generation_prompt=True prev = processor.apply_chat_template(messages[:i], add_generation_prompt=True, tokenize=False) prev_model_inputs = processor(text=prev, images=images, videos=videos, return_tensors="pt")[0].tolist() curr = processor.apply_chat_template(messages[:i+1], add_generation_prompt=False, tokenize=False) curr_model_inputs = processor(text=curr, images=images, videos=videos, return_tensors="pt")[0].tolist() token_ids += curr_model_inputs["input_ids"][len(prev_model_inputs["input_ids"]):] loss_mask += [1] * len(token_ids) # Mask only the new assistant tokens While we've validated this produces consistent results with full message tokenization, future models' chat template could break compatibility. To guard against silent inconsistencies, we compare the delta-based tokenization with full-tokenization results by default at the end of each rollout. If you see the following warning, you can check the mismatched substring in the log: .. code-block:: Inconsistent training and inference tokenization detected. This may lead to unexpected behavior during training. Please review your chat template to determine if this is intentional. For more information, refer to the multiturn README.md. The tokenization sanity check mode can be configured using the ``actor_rollout_ref.rollout.multi_turn.tokenization_sanity_check_mode`` parameter, which accepts the following values: - ``strict`` (default): Performs strict comparison between delta-based and full tokenization results, raising warnings for any differences. - ``ignore_strippable``: Ignores differences in whitespace characters (``\n``, ``\t``, ``\r``, spaces) while still checking for meaningful text mismatches. This is useful when debugging chat template issues where whitespace variations are expected and acceptable. - ``disable``: Completely disables the tokenization sanity check. Only use this if you have thoroughly validated that tokenization discrepancies are expected and won't impact training. Example configuration: .. code-block:: yaml actor_rollout_ref: rollout: multi_turn: tokenization_sanity_check_mode: "ignore_strippable" # Choose from: "disable", "ignore_strippable", "strict" Handling Multi-Modal Inputs in Datasets ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If your dataset includes multi-modal inputs (such as images or videos), you can control whether these are pre-processed and included in each sample by setting the return_multi_modal_inputs flag in your dataset config (used by RLHFDataset). - ``return_multi_modal_inputs: True`` (default): The dataset will pre-process and include a multi_modal_inputs dictionary for each sample. This dict contains the model-ready representations (e.g., image tensors, video tensors, etc.) as produced by your processor. This is useful for single-turn or SFT-style training, where the model expects all modalities to be present in the batch. - ``return_multi_modal_inputs: False``: The dataset will not include the multi_modal_inputs field. This is recommended for multi-turn RL or tool-augmented rollouts, where the model may generate new multi-modal inputs dynamically during rollout, and you want to avoid conflicts or redundant data in the batch. Special Cases ^^^^^^^^^^^^^ Some models (e.g., Qwen/QwQ-32B and Qwen3 series) remove internal reasoning content during chat template rendering. As a result, the message content can vary across turns, making the delta-based tokenization inaccurate. For example, for the following conversation: .. code-block:: python messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is 2 + 2?"}, {"role": "assistant", "content": "user asked about a simple math question. 2 + 2 = 4."}, {"role": "user", "content": "Explain why."}, {"role": "assistant", "content": "user wants to know the reasoning behind the answer. Search for a good explanation", "tool_calls": [{"id": "tool1", "type": "search", "arguments": {"query": "Why is 2 + 2 = 4?"}}]}, {"role": "tool", "content": "The sum of two and two is four because it is a basic arithmetic operation."}, {"role": "assistant", "content": "The tool provided a good explanation.The sum of two and two is four because it is a basic arithmetic operation."} ] 1. Qwen/QwQ-32B will remove all reasoning content except the last assistant message after applying the chat template. .. code-block:: text <|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user What is 2 + 2?<|im_end|> <|im_start|>assistant 2 + 2 = 4.<|im_end|> <|im_start|>user Explain why.<|im_end|> <|im_start|>assistant {"name": "", "arguments": {"query": "Why is 2 + 2 = 4?"}} <|im_end|> <|im_start|>user The sum of two and two is four because it is a basic arithmetic operation. <|im_end|> <|im_start|>assistant The tool provided a good explanation. The sum of two and two is four because it is a basic arithmetic operation.<|im_end|> 2. Qwen3 series will remove all reasoning content before the last user message. .. code-block:: text <|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user What is 2 + 2?<|im_end|> <|im_start|>assistant 2 + 2 = 4.<|im_end|> <|im_start|>user Explain why.<|im_end|> <|im_start|>assistant user wants to know the reasoning behind the answer. Search for a good explanation {"name": "", "arguments": {"query": "Why is 2 + 2 = 4?"}} <|im_end|> <|im_start|>user The sum of two and two is four because it is a basic arithmetic operation. <|im_end|> <|im_start|>assistant The tool provided a good explanation. The sum of two and two is four because it is a basic arithmetic operation.<|im_end|> To handle this, we fall back to a **fixed base conversation** containing only a single system and user message. Since this base doesn't include assistant messages or reasoning content, it remains consistent across turns. .. code-block:: python BASE_CHAT_HISTORY = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "I am a user."} ] prev = tokenizer.apply_chat_template(BASE_CHAT_HISTORY, add_generation_prompt=True, tokenize=False) curr = tokenizer.apply_chat_template([*BASE_CHAT_HISTORY, messages[i]], add_generation_prompt=False, tokenize=False) token_ids += tokenizer.encode(curr[len(prev):], add_special_tokens=False) loss_mask += [1] * len(token_ids) This method works well for Qwen3 series. However, Qwen/QwQ-32B currently has a bug in its chat template. A fix_ has been proposed but not yet adopted. Until then, use the following command to download the fixed model revision: .. code-block:: bash pip install huggingface_hub hf download Qwen/QwQ-32B --revision refs/pr/81 .. _fix: https://huggingface.co/Qwen/QwQ-32B/discussions/81 Discrepancy Between Training and Inference Templates ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Although the above approach fixes the delta mismatch issue, the removal of reasoning content in the inference-time chat template introduces a new discrepancy: training uses the full reasoning content, while inference does not. This mismatch can affect model performance in unpredictable ways. To avoid it, we default to using the full response (including reasoning) for both training and rollout. However, this approach comes with trade-offs: 1. Long reasoning contents can easily exceed the model's context window, especially in multi-turn rollout. 2. There's a mismatch between rollout and production environment now—models will not have reasoning content from past turns if you use the default chat template in production. We are still evaluating the impact of these issues. If you experience context length problems or prefer rollouts that match production (i.e., exclude reasoning), you can enable: ``actor_rollout_ref.rollout.multi_turn.use_inference_chat_template = True`` GSM8K Multi-turn Training Performance ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ See the training performance of multi-turn rollout on the GSM8K task HERE_. .. _HERE: https://wandb.ai/zhaochenyang20/gsm8k_async_rl/runs/1ro1r7om?nw=nwuserzhaochenyang20 .. _GSM8KTool_example_configuration: https://github.com/volcengine/verl/blob/main/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml .. _gsm8k_tool.py: https://github.com/volcengine/verl/blob/main/verl/tools/gsm8k_tool.py .. _mcp_search_tool.py: https://github.com/volcengine/verl/blob/main/verl/tools/mcp_search_tool.py .. _mcp_tool_config.yaml: https://github.com/volcengine/verl/blob/main/examples/sglang_multiturn/config/tool_config/mcp_tool_config.yaml Interaction System ~~~~~~~~~~~~~~~~~~ For dynamic conversational feedback during RL training, see: .. toctree:: :maxdepth: 1 interaction_system Search Tool Integration ~~~~~~~~~~~~~~~~~~~~~~~ .. toctree:: :maxdepth: 1 search_tool_example Code Walkthrough ~~~~~~~~~~~~~~~~~~~~~~~ If you want to learn more in depth about the code execution flow, please read https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/tree/main/rlhf/verl/multi-turn/code-walk-through ================================================ FILE: docs/sglang_multiturn/sandbox_fusion.rst ================================================ =============================== Sandbox Fusion Tool Integration =============================== Last updated: 06/10/2025. Motivations =========== - As users of verl, we want to allow the model to call certain tools during Actor rollout, incorporating the results into the training process. - A colleague from ByteDance proposed a paper aimed at enhancing model capability through code execution tools. - We aim to support tool-calling capabilities of inference engines using `sandbox-fusion` as the code execution system, providing the community with a reimplementation of `retools`. Reward Compute with Sandbox Fusion + FaaS Integration ===================================================== - In current datasets and tasks, similar work already exists (e.g., Prime), which uses local processes as runners to execute model-generated code for reward computation. - On this basis, #1429 has advanced the design by integrating FaaS as the runner for reward computation. Goals ===== - Adapt to the `sglang` tool-calling protocol and define tools for sandbox fusion. - Integrate with the `async-rollout` process, ensuring sandbox fusion tools follow asyncIO conventions. - Design and implement a basic rate limiter to prevent issues such as 429 errors. Non-Goals ========= - Training effectiveness is out of scope. - Observability metrics are not considered. - Distributed failover and component fault tolerance are not addressed. Design Details ============== Tool Schema Definition ---------------------- - Currently, only code execution is considered, requiring a `code` field in the JSON from the model. - Only Python code is supported for now, so no `language` parameter is defined. .. code-block:: python OpenAIFunctionToolSchema( type="function", function=OpenAIFunctionSchema( name="code_interpreter", description="A tool for executing code.", parameters=OpenAIFunctionParametersSchema( type="object", properties={ "code": OpenAIFunctionPropertySchema( type="string", description="The code to execute.", enum=None, ) }, required=["code"], ), strict=False, ) ) Configuration Parameters -------------------------- +----------------------------+--------------------------------------------------------------+ | Parameter Name | Description | +============================+==============================================================+ | `num_workers` | Number of worker threads/processes per DP to request runner. | +----------------------------+--------------------------------------------------------------+ | `rate_limit` | Global limit of concurrent code executions. Default: 10 | +----------------------------+--------------------------------------------------------------+ | `default_timeout` | Timeout (in seconds) for each code execution. Default: 30 | +----------------------------+--------------------------------------------------------------+ | `default_language` | Default programming language. Default: "python" | +----------------------------+--------------------------------------------------------------+ | `enable_global_rate_limit` | Whether to enable global rate limiting. Default: True | +----------------------------+--------------------------------------------------------------+ | `sandbox_fusion_url` | URL for the veFaas sandbox execution service | +----------------------------+--------------------------------------------------------------+ Rate Limiting Design ----------------------- Objective: - Limit the number of inflight requests using a token bucket model. - Ensure ordered submission to code runners to avoid starvation due to backoff. Design Highlights: - Use Ray Global Actor as a singleton distributed counter at cluster level. - Semaphore used for counting, with `acquire` and `release` in separate thread pools to preserve order. - Use Ray’s cloud-pickle to serialize functions for decoupled `ExecutionWorker`. .. code-block:: python @ray.remote(concurrency_groups={"acquire": 1,"release": 10}) class TokenBucketWorker: def __init__(self, rate_limit: int): self.rate_limit = rate_limit self.current_count = 0 self._semaphore = threading.Semaphore(rate_limit) @ray.method(concurrency_group="acquire") def acquire(self): self._semaphore.acquire() self.current_count += 1 @ray.method(concurrency_group="release") def release(self): self._semaphore.release() self.current_count -= 1 def get_current_count(self): return self.current_count class ExecutionWorker: def __init__(self, enable_global_rate_limit=True, rate_limit=10): self.rate_limit_worker = self._init_rate_limit(rate_limit) if enable_global_rate_limit else None def _init_rate_limit(self, rate_limit): return TokenBucketWorker.options(name="rate-limiter", get_if_exists=True).remote(rate_limit) def execute(self, fn: Callable[..., T], *fn_args, **fn_kwargs) -> T: with ExitStack() as stack: stack.callback(self.rate_limit_worker.release.remote) ray.get(self.rate_limit_worker.acquire.remote()) try: return fn(*fn_args, **fn_kwargs) except Exception as e: logger.warning(f"Error when executing code: {e}") def init_execution_pool(num_workers: int, enable_global_rate_limit=True, rate_limit=10, mode: PoolMode=PoolMode.ThreadMode): if mode == PoolMode.ThreadMode: return ray.remote(ExecutionWorker).options(max_concurrency=num_workers).remote( enable_global_rate_limit=enable_global_rate_limit, rate_limit=rate_limit ) else: raise NotImplementedError("Process mode is not implemented yet") Tool Implementation ------------------- - Use `instance_id` to identify requests across multiple dialogue rounds. - Use `execution_pool` to implement async invocation. - Cleanup state after rollout completion. .. code-block:: python class SandboxFusionTool(BaseTool): def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): ... self.execution_pool = init_execution_pool(...) ... async def create(self, instance_id: Optional[str] = None, ...): ... async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> Tuple[str, float, dict]: code = parameters.get("code", "") timeout = parameters.get("timeout", self.default_timeout) language = parameters.get("language", self.default_language) if not isinstance(code, str): code = str(code) result = await self.execution_pool.execute.remote(self.execute_code,instance_id,code,timeout,language) self._instance_dict[instance_id]["reward"].append(result.strip()) return result, result, {} def execute_code(self,instance_id,code,timeout=30,language="python"): result_status, metadata = _process_single_case(0, None, None,self.sandbox_fusion_url, code, timeout, language) # we should always expect this since we don't have correct answer if metadata["run_status"] == "Finished": actual_output = metadata["stdout"] if metadata["stdout"] is not None else "" return actual_output else: return "no stdout here" async def calc_reward(self, instance_id: str, ...): ... async def release(self, instance_id: str, ...): ... Test Plan ========= Unit Tests ---------- - **test_tools_registration**: Test tool registration and initialization. - **test_rollout_req_creation**: Validate that `AsyncRolloutReq` is built correctly. - **test_over_size_case**: Ensure rollout terminates early when exceeding `max_seq_len`. - **test_tool_call_basic_case**: Mock `sglang` output, validate tool call and result. - **test_tool_call_batch_case**: Test batch processing of tool calls. - **test_basic_multi_process_init**: Validate Ray global actor behaves as singleton. - **TestSingleNodeRateLimiterCase**: Verify rate limiter works in single-node mode. - **test_rotten_execution**: Ensure rate limiter recovers from function errors. - **TestMultiNodeRateLimiterCase**: Verify behavior in multi-node environments. e2e Tests ---------- we provide e2e test scripts in `tests/special_e2e` folder, named `tests/special_e2e/run_gsm8k_fsdp_sgl_multiturn_sf_tool.sh` by setting 'trainer.rollout_data_dir' you can dump the rollout data to local disk. here is an sample taken from the rollout data: .. code-block:: python { "input": " system\nYou are a math expert. You are given a question and you need to solve it step by step. Reasoning step by step before any tool call. You should use the `calc_gsm8k_reward` tool after step by step solving the question, before generate final answer at least once and refine your answer if necessary. Put your final answer in the format of `#### `.\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n\n{\"type\": \"function\", \"function\": {\"name\": \"code_interpreter\", \"description\": \"A tool for executing code.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"code\": {\"type\": \"string\", \"description\": \"The code to execute.\", \"enum\": null}}, \"required\": [\"code\"]}, \"strict\": false}}\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n\n user\nJohn gets a bonus that's the same percentage every year. Last year he made $100,000 and got a $10,000 bonus. This year he makes $200,000. How much will John make this year when adding both his total pay and bonus together? Let's think step by step and output the final answer after `####`.\n assistant\n", "output": "assistant\n\nOkay, let's see. John gets a bonus that's the same percentage every year. Last year, he made $100,000 and got a $10,000 bonus. This year, he makes $200,000. The question is asking how much he'll make this year when adding both his total pay and bonus together.\n\nFirst, I need to figure out the percentage bonus he gets. Since the bonus is the same percentage each year, if I can find that percentage based on last year's details, I can apply it to this year's earnings to find this year's bonus. Then, I'll add that bonus to his total pay this year.\n\nLast year, he earned $100,000 and got a $10,000 bonus. To find the percentage, I can divide the bonus by his total earnings. So, 10,000 divided by 100,000. Let me calculate that.\n\n10,000 / 100,000 equals 0.1, which is 10% as a percentage. So, his bonus is 10% of his pay each year.\n\nThis year, he makes $200,000. So, the bonus this year would be 10% of 200,000. Let me compute that using the code interpreter to ensure accuracy.\n \n{\"name\": \"code_interpreter\", \"arguments\": {\"code\": \"total_pay_this_year = 200000\\nbonus_percentage = 10 / 100\\nbonus_this_year = total_pay_this_year * bonus_percentage\\ntotal_income_this_year = total_pay_this_year + bonus_this_year\\nprint(total_income_this_year)\", \"executes\": \"True\"}}\n\n tool\n220000.0\n\n assistant\n\nSo the code shows that John's total income this year would be $220,000. This is consistent with the 10% bonus on last year's pay of $100,000, which was $10,000. Therefore, this year's bonus of $20,000 added to his total pay of $200,000 results in a total income of $220,000.\n\nI need to verify that there are no other factors at play. The problem states it's a constant percentage bonus each year, so there's no mention of changing percentages or other variables. Therefore, applying a straightforward percentage of 10% on this year's partial salary amount would result in $20,000. Adding that to his total pay gives $220,000. The calculation is correct and aligns with the question's parameters.\n\n\n#### 220000.0", "score": 0, "step": 1 } here is the readable format version: .. code-block:: python [system] You are a math expert. You are given a question and you need to solve it step by step. Reasoning step by step before any tool call. You should use the `calc_gsm8k_reward` tool after step by step solving the question, before generate final answer at least once and refine your answer if necessary. Put your final answer in the format of `#### `. # Tools You may call one or more functions to assist with the user query. You are provided with function signatures within XML tags: {"type": "function", "function": {"name": "code_interpreter", "description": "A tool for executing code.", "parameters": {"type": "object", "properties": {"code": {"type": "string", "description": "The code to execute.", "enum": null}}, "required": ["code"]}, "strict": false}} For each function call, return a json object with function name and arguments within XML tags: {"name": , "arguments": } [user] John gets a bonus that's the same percentage every year. Last year he made $100,000 and got a $10,000 bonus. This year he makes $200,000. How much will John make this year when adding both his total pay and bonus together? Let's think step by step and output the final answer after `####`. [assistant] Okay, let's see. John gets a bonus that's the same percentage every year. Last year, he made $100,000 and got a $10,000 bonus. This year, he makes $200,000. The question is asking how much he'll make this year when adding both his total pay and bonus together. First, I need to figure out the percentage bonus he gets. Since the bonus is the same percentage each year, if I can find that percentage based on last year's details, I can apply it to this year's earnings to find this year's bonus. Then, I'll add that bonus to his total pay this year. Last year, he earned $100,000 and got a $10,000 bonus. To find the percentage, I can divide the bonus by his total earnings. So, 10,000 divided by 100,000. Let me calculate that. 10,000 / 100,000 equals 0.1, which is 10% as a percentage. So, his bonus is 10% of his pay each year. This year, he makes $200,000. So, the bonus this year would be 10% of 200,000. Let me compute that using the code interpreter to ensure accuracy. {"name": "code_interpreter", "arguments": {"code": "total_pay_this_year = 200000\nbonus_percentage = 10 / 100\nbonus_this_year = total_pay_this_year * bonus_percentage\ntotal_income_this_year = total_pay_this_year + bonus_this_year\nprint(total_income_this_year)", "executes": "True"}} [tool] 220000.0 [assistant] So the code shows that John's total income this year would be $220,000. This is consistent with the 10% bonus on last year's pay of $100,000, which was $10,000. Therefore, this year's bonus of $20,000 added to his total pay of $200,000 results in a total income of $220,000. I need to verify that there are no other factors at play. The problem states it's a constant percentage bonus each year, so there's no mention of changing percentages or other variables. Therefore, applying a straightforward percentage of 10% on this year's partial salary amount would result in $20,000. Adding that to his total pay gives $220,000. The calculation is correct and aligns with the question's parameters. #### 220000.0 You can also use the `RolloutViewer` TUI tool to view the dumped rollout data: .. code-block:: bash python scripts/rollout_viewer.py ${trainer.rollout_data_dir} .. image:: https://github.com/user-attachments/assets/e34e5157-2880-4a21-afb2-73885d0dfb11 :alt: RolloutViewer screenshot ================================================ FILE: docs/sglang_multiturn/search_tool_example.rst ================================================ ======================= Search Tool Integration ======================= Last updated: 05/30/2025. Introduction ------------ - We have added a search tool calling function to Multi-Turn RL, enabling the model to initiate retrieval requests during Actor rollout and directly use retrieval results for training. **We support using a local dense retriever as the retrieval tool, as well as integrating with your own local retrieval engine.** Quick Reproduction ------------------ Create a New Docker Container ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: bash docker run \ -it \ --shm-size 32g \ --gpus all \ -v {Huggingface-Cache-Path}:/root/.cache \ --ipc=host \ --network=host \ --privileged \ --name sglang_{your-name} \ lmsysorg/sglang:dev \ /bin/zsh If you need to restart after exiting the container: .. code:: bash docker start -i sglang_{your-name} Update Python and Configure the Virtual Environment using uv ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: bash apt update apt install -y python3.10 python3.10-venv # Create a virtual environment python3 -m venv ~/.python/verl-multiturn-rollout # Activate the virtual environment source ~/.python/verl-multiturn-rollout/bin/activate # Install uv python3 -m pip install uv Install verl Upstream ~~~~~~~~~~~~~~~~~~~~~ .. code:: bash cd ~ git clone https://github.com/volcengine/verl.git cd verl # Install verl python3 -m uv pip install . python3 -m uv pip install -r ./requirements_sglang.txt # Manually install flash-attn python3 -m uv pip install wheel python3 -m uv pip install packaging python3 -m uv pip install flash-attn --no-build-isolation --no-deps Set Up a Local Retrieval Engine ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you are using your own local retrieval service, you can skip this step. We chose the local dense retriever provided in the search-R1 example; detailed instructions are in the `searchR1 docs `__. In brief: - The GPU version offers higher accuracy and speed; each GPU uses about 5–7 GB of memory. - The CPU version can be used for simple testing but has lower retrieval precision, which will degrade training performance. See the `retriever documentation `__ in search-R1 for details. - Recommend using Conda to install faiss-gpu=1.8.0; venv may cause errors. **Note**: To start both the training process and the local retrieval service, we launch two separate Python environments. The training uses uv in the verl-multiturn-rollout environment, while the retriever uses conda to install ``faiss-gpu``. .. code:: bash # Download the Miniconda installer script wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh # Install to $HOME/miniconda3 in batch mode bash ~/miniconda.sh -b -p $HOME/miniconda3 # Activate conda (only in the current shell) eval "$($HOME/miniconda3/bin/conda shell.bash hook)" # (Optional) Add conda to your default shell startup conda init # Reload shell config source ~/.bashrc # Create and activate the retriever environment with Python 3.10 conda create -n retriever python=3.10 -y conda activate retriever # Install PyTorch (with GPU support) and related libraries conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y # Install other Python packages pip install transformers datasets pyserini huggingface_hub # Install the GPU version of faiss conda install faiss-gpu=1.8.0 -c pytorch -c nvidia -y # Install the API service framework pip install uvicorn fastapi Download the Indexing and Corpus ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The local retrieval files are large—prepare sufficient disk space. Downloading is about 60–70 GB, and uncompressed takes about 132 GB: .. code:: bash conda activate retriever save_path=/the/path/to/save python examples/sglang_multiturn/search_r1_like/local_dense_retriever/download.py --save_path $save_path cat $save_path/part_* > $save_path/e5_Flat.index gzip -d $save_path/wiki-18.jsonl.gz Start the Local flat e5 Retrieval Server ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. The first startup will download models and load the index. 2. Apart from the download, startup takes about 1–2 minutes. 3. After startup, each GPU uses about 5–7 GB of memory, leaving the rest for multi-turn RL training. .. code:: bash conda activate retriever index_file=$save_path/e5_Flat.index corpus_file=$save_path/wiki-18.jsonl retriever_name=e5 retriever_path=intfloat/e5-base-v2 python examples/sglang_multiturn/search_r1_like/local_dense_retriever/retrieval_server.py \ --index_path $index_file \ --corpus_path $corpus_file \ --topk 3 \ --retriever_name $retriever_name \ --retriever_model $retriever_path \ --faiss_gpu Set Up WANDB_API_KEY ~~~~~~~~~~~~~~~~~~~~ .. code:: bash export WANDB_API_KEY={YOUR_WANDB_API_KEY} # Define a timestamp function function now() { date '+%Y-%m-%d-%H-%M' } **Preprocess the Dataset** ~~~~~~~~~~~~~~~~~~~~~~~~~~ **Note:** The following data processing and training commands must be run in the verl-multiturn-rollout environment. .. code:: bash python3 examples/data_preprocess/preprocess_search_r1_dataset.py Testing on 8 x H20 ~~~~~~~~~~~~~~~~~~ .. code:: bash # Ensure the now() function is defined # Create a logs directory mkdir -p logs # Set GPUs and run with a suitable log path export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 nohup bash examples/sglang_multiturn/search_r1_like/run_qwen2.5-3b_instruct_search_multiturn.sh \ trainer.experiment_name=qwen2.5-3b-it_rm-searchR1-like-sgl-multiturn-$(now) \ > logs/searchR1-like$(now).log 2>&1 & Custom Search Configuration --------------------------- To enable multi-turn reasoning, set the following fields in your config: .. code:: yaml actor_rollout_ref: rollout: name: "sglang" multi_turn: enable: True You must specify ``retrieval_service_url`` in ``examples/sglang_multiturn/config/tool_config/search_tool_config.yaml``, and properly configure concurrency. For more details on concurrency, refer to the Sandbox Fusion example: .. code:: yaml tools: - class_name: verl.tools.search_tool.SearchTool config: retrieval_service_url: http://127.0.0.1:8000/retrieve num_workers: 120 rate_limit: 120 timeout: 30 The retriever input/output formats are as follows. If your service parameters match, only modify ``retrieval_service_url``. You can also customize in ``search_r1_like_utils.py``. .. code:: python Input format: { "queries": ["What is Python?", "Tell me about neural networks."], "topk": 3, "return_scores": true } Output format (when return_scores=True, similarity scores are returned): { "result": [ [ # Results for each query { "document": doc, "score": score }, # ... more documents ], # ... results for other queries ] } Notes ----- 1. The total training time is about 27 hours; meanwhile, the validation dataset is very large (51 k), and each validation takes about 6000 s. (Therefore, ``val_before_train=False`` by default) ================================================ FILE: docs/single_controller.rst ================================================ The Design of ``verl.single_controller`` ============================================== Last updated: 05/21/2025. **Author:**\ `Wang Zhang `__ Preface ------- We prepared this document for developers of ``verl``, particularly those interested in understanding or contributing to the ``verl.single_controller`` module. It is not intended for end users, but for contributors seeking to understand the architectural rationale and internal mechanics. -------------- Origin ------ The ``single_controller`` module originated from a request I received — to adapt a toy single-process RLHF script into a distributed system with minimal changes, while maintaining ease of debugging. Common practice — such as using PyTorch’s Distributed Data Parallel (DDP) — typically involves wrapping ``nn.Module`` and launching multiple processes that execute the same function under different ranks. However, this approach presents two main limitations in the context of distributed RLHF: - Difficulty representing multiple DAGs as required by PPO; - Difficulty inspecting intermediate tensors during training. To maintain debuggability, we opted for a different approach — breaking the training loop into well-defined stages like ``generate_sequences``, ``compute_advantages``, and so on. We selected `Ray `__ as the initial backend for ``verl`` due to its ability to expose Python class methods as RPC endpoints. However, Ray’s default model only supports **one method call, one RPC**, while training LLMs typically requires coordination across multiple processes. To hide this multi-Ray actors invocation for a single method from users, we introduced the following components: - ``WorkerGroup`` – manages a group of remote workers and provides a unified interface for multi-process distributed computation; - ``ResourcePool`` – binds computational resources to worker processes; - ``ClassWithArgs`` – enables delayed remote instantiation with specified initialization arguments. -------------- A Running Example: ``generate_sequences`` ----------------------------------------- To illustrate the design, we walk through how the ``generate_sequences`` method in the ``ActorRolloutRefWorker`` class is registered and invoked across distributed workers. -------------- Step 1: Register with a Decorator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The first step is to define the ``generate_sequences`` and decorate it with ``@register`` as it will be called in driver script. **Source:** `fsdp_workers.py `__ .. code:: python class ActorRolloutRefWorker(Worker): ... @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def generate_sequences(self, prompts: DataProto): prompts = prompts.to(torch.cuda.current_device()) ... The ``@register`` decorator adds metadata to the ``generate_sequences`` method. Currently, it doesn’t alter functionality, but attaches attributes via a magic key (``MAGIC_ATTR``): **Source:** `decorator.py `__ .. code:: python def register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.ALL, blocking=True, materialize_futures=True): ... def decorator(func): @wraps(func) def inner(*args, **kwargs): if materialize_futures: args, kwargs = _materialize_futures(*args, **kwargs) return func(*args, **kwargs) attrs = {"dispatch_mode": dispatch_mode, "execute_mode": execute_mode, "blocking": blocking} setattr(inner, MAGIC_ATTR, attrs) return inner return decorator As the code shows, values of ``dispatch_mode``, ``execute_mode`` and ``blocking`` is attached the ``generate_sequences`` method. -------------- Step 2: Binding During Initialization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ These attached attributes are extracted and utilized when ``ActorRolloutRefWorker``, wrapped in a ``RayClassWithArgs``, is passed into a ``RayWorkerGroup``. **Source:** `main_generation.py `__ .. code:: python ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(ActorRolloutRefWorker), config=config, role="rollout") resource_pool = RayResourcePool(process_on_nodes=[config.trainer.n_gpus_per_node] * config.trainer.nnodes) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init) During the `initialization `__ of ``RayWorkerGroup``, two key steps occur: 1. Worker instances (Ray actors) are created: `RayWorkerGroup._init_with_resource_pool `__ 2. Methods decorated with ``@register`` are bound to ``RayWorkerGroup``: `RayWorkerGroup._bind_worker_method `__ .. figure:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/worker_group_init.png?raw=true :alt: initialization_and_binding_of_worker_group initialization_and_binding_of_worker_group The binding procedure is the heart of ``verl.single_controller``. **Key function:** `WorkerGroup._bind_worker_method `__ .. code:: python def _bind_worker_method(self, user_defined_cls, func_generator): ... for method_name in dir(user_defined_cls): try: method = getattr(user_defined_cls, method_name) assert callable(method) except Exception: continue # Skip properties <<>> When a method has the ``MAGIC_ATTR``, the attributes set by ``@register`` are extracted: .. code:: python <<>> if hasattr(method, MAGIC_ATTR): attribute = getattr(method, MAGIC_ATTR) dispatch_mode = attribute["dispatch_mode"] execute_mode = attribute["execute_mode"] blocking = attribute["blocking"] <<>> As show in the flow chart above, these attributes are fed into ``func_generator``. However, ``func_generator`` takes ``method_name``, ``dispatch_fn``, ``collect_fn``, ``execute_fn``, ``blocking``. We need to find the corresponding ``dispatch_fn`` and ``collect_fn`` associated with the ``dispatch_mode`` (``DP_COMPUTE_PROTO``) from `DISPATCH_MODE_FN_REGISTRY `__: .. code:: python3 DISPATCH_MODE_FN_REGISTRY = { Dispatch.ONE_TO_ALL: { "dispatch_fn": dispatch_one_to_all, "collect_fn": collect_all_to_all, }, ... Dispatch.DP_COMPUTE_PROTO: { "dispatch_fn": dispatch_dp_compute_data_proto, "collect_fn": collect_dp_compute_data_proto, }, ... } Similarly, the ``execute_fn`` is selected by ``execute_mode`` and extracted by: .. code:: python <<>> # get execute_fn_name execute_mode = get_predefined_execute_fn(execute_mode=execute_mode) wg_execute_fn_name = execute_mode["execute_fn_name"] # get execute_fn from string try: execute_fn = getattr(self, wg_execute_fn_name) assert callable(execute_fn), "execute_fn must be callable" except Exception: print(f"execute_fn {wg_execute_fn_name} is invalid") raise <<>> In this ``generate_sequences`` cases: - ``dispatch_mode = Dispatch.DP_COMPUTE_PROTO`` - ``dispatch_fn = dispatch_dp_compute_data_proto`` - ``collect_fn = collect_dp_compute_data_proto`` - ``execute_fn = RayWorkerGroup.execute_all`` ONE_TO_ALL v.s. DP_COMPUTE_PROTO ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``dispatch_mode`` is associated with a ``dispatch_fn`` and a ``collect_fn``. As the name implies, ``dispatch_fn`` processes the input arguments in ``WorkerGroup`` and generate a batch (list) of input arguments, each of which will be fed into a worker attached to the ``WorkerGroup``. ``dispatch_fn`` of ``ONE_TO_ALL`` is `dispatch_one_to_all `__, which just duplicates all the input arguments into N replicas, where N equals the number of Workers attached to the ``worker_group``: .. code:: python def dispatch_one_to_all(worker_group, *args, **kwargs): args = tuple([arg] * worker_group.world_size for arg in args) kwargs = {k: [v] * worker_group.world_size for k, v in kwargs.items()} return args, kwargs ``dispatch_fn`` of ``DP_COMPUTE_PROTO`` is `dispatch_dp_compute_data_proto `__, which uses ``DataProto.chunk`` to split a large ``DataProto`` into N smaller ``DataProto``, where N equals the world_size (number of the workers) of the ``worker_group``: .. code:: python def dispatch_dp_compute_data_proto(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) # Note: enable auto padding for dp compute DatapProto splitted_args, splitted_kwargs = _split_args_kwargs_data_proto_with_auto_padding( worker_group.world_size, *args, **kwargs, ) return splitted_args, splitted_kwargs The ``collect_fn`` follows the same pattern and process a batch (list) of returned value from all workers of a ``WorkerGroup`` and merge it into a list as ``collect_all_to_all`` does or a large ``DataProto`` as ``collect_dp_compute_data_proto`` does. Finally, a new method is dynamically generated using ``func_generator`` and added to the ``WorkerGroup`` instance: .. code:: python <<>> # bind a new method to the RayWorkerGroup func = func_generator( self, method_name, dispatch_fn=dispatch_fn, collect_fn=collect_fn, execute_fn=execute_fn, blocking=blocking, ) try: setattr(self, method_name, func) method_names.append(method_name) except Exception as e: raise ValueError(f"Fail to set method_name {method_name}") from e This makes the method invocable via the ``WorkerGroup`` interface. -------------- Step 3: Call Chain ~~~~~~~~~~~~~~~~~~ All the machinery above ensures that distributed calls feel identical to single-process ones. In the original single-process script, the code looks like: .. code:: python rollout = Rollout() rollout.generate_sequences(batch) With ``verl``, the multiprocess program becomes: .. code:: python rollout = RayWorkerGroup(resource_pool=[4], RayClassWithArgs(Rollout)) rollout.generate_sequences(batch) .. figure:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/call_generate_sequences.png?raw=true :alt: call_chain_of_generate_sequences call_chain_of_generate_sequences Behind this simple call: - ``dispatch_fn`` splits input across workers - ``execute_fn`` performs the actual remote invocation - ``collect_fn`` gathers the results All of this is abstracted away, enabling developers to write distributed code with minimal changes to their existing logic. -------------- Beyond RL Post-Training: Generalizing ``verl.single_controller`` ---------------------------------------------------------------- The ``verl.single_controller`` module generalizes well beyond reinforcement learning. It provides a clean abstraction to batch-process remote method calls, with automatic input/output handling. By minimizing the gap between single-process and multi-process scripts, ``verl.single_controller`` opens the door to distributed computing in broader domains — not limited to RL post-training. We hope this design inspires more examples and extensions from the community. ================================================ FILE: docs/start/agentic_rl.rst ================================================ Agentic RL Training =================== Last updated: 07/15/2025. Overview ---------- The goal of Agentic RL is to improve the performance of backend models from reinforcement learning to the Agent. During the training process, a series of features are developed: 1. Server-based asynchronous rollout 2. Multi-turn conversations and tool calls 3. LangGraph-based Agent This document explains the system principles and usage involved to help users implement Agentic RL. Server-based Asynchronous Rollout --------------------------------- Since Agents need to interact with the environment through various tool calls, in order to avoid GPU idling while waiting for tool call return results, an asyncio based co-routing mechanism is utilized to execute each rollout requests asynchronously, thereby improving training performance. To support asynchronous rollout, the inference engine (server) and the agent (client) are architecturally separated, implementing a server-based system with the following objectives: 1. Enabling load balancing mechanisms to balance loads across multiple GPUs and reduce the impact of long-tail requests on performance. For this purpose, scheduling capabilities in stream mode (recipe\stream_mode) are implemented as a recipe. 2. Preventing agent specific features such as tracing from affecting the inference engine. System Architecture ~~~~~~~~~~~~~~~~~~~ .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/agent_loop.png?raw=true For more detail on internal design, please refer to :doc:`Agent Loop<../advance/agent_loop>`. System Components ~~~~~~~~~~~~~~~~~ +--------------------------+----------------------------------------------------------------------------+ | Component | Role | +==========================+============================================================================+ | AgentLoop | Client, implements Agent functions | +--------------------------+----------------------------------------------------------------------------+ | AsyncLLMServerManager | Inference gateway, provides generate interface for AgentLoop | +--------------------------+----------------------------------------------------------------------------+ | AsyncServer | Server, each instance is connected to one DP group of the inference engine | +--------------------------+----------------------------------------------------------------------------+ **"generate" Interface** The "generate" function based on ray actor is used between the Client and Server instead of the standard chat completion API. This is because the conversion between tokens and text can be irreversible. For example, the token converted from "" will be different from that generated by the LLM. During the training phase, it is necessary to strictly use the tokens generated by LLM inference to avoid inaccurate in computing advantage, which may affect model performance. Having the Server provide a token-based API helps the Client maintain the relationship between the text generated by tool calls and the tokens returned by the LLM, so as to output correct tokens for training. **Inference Engine Adaptation** AsyncServer uniformly provides a generate function to the upper layer, with separate implementations for SGLang and vLLM to hide underlying differences: 1. The SGLang AsyncServer uses the async_generate interface of the SGLang engine, which is located on the first GPU of each TP group. Therefore, AsyncServer needs to remotely call async_generate through ray actor. 2. The vLLM AsyncServer uses the generate interface of the vLLM engine, which can communicate with the GPUs in the TP group through ZMQ and can be directly called in AsyncServer. Usage Example ~~~~~~~~~~~~~ Follow :doc:`GSM8K example<../examples/gsm8k_example>` to prepare the dataset and model checkpoints. There are two options required to use agent loop: - `data.return_raw_chat=True` - `actor_rollout_ref.rollout.mode=async` This example uses the sglang inference engine by default, and you can also modify rollout_name to use vllm. .. code-block:: bash bash examples/grpo_trainer/run_qwen2-7b_seq_balance.sh Multi-turn Conversations and Tool Calls --------------------------------------- Follow :doc:`Multi-turn Rollout Support<../sglang_multiturn/multiturn>` to prepare tool and configuration files. The Tool Agent Loop has an additional requirement: adding an "agent_name" field to the dataset. During rollout, it will choose to use tool_agent_loop or single_turn_agent (default) based on this field. Usage Example ~~~~~~~~~~~~~ .. code-block:: bash # install mlflow to view toolcall and llm trace pip install mlflow # This will download and preprocess the GSM8K dataset into ~/data/gsm8k/ and add the "agent_name" field. python examples/data_preprocess/gsm8k_tool_agent_loop.py # Start training with tool calls and enabled mlflow based trace helping to debug the rollout details bash examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_tool_agent_mlflow.sh # When training is done, start a mlflow server to view trace mlflow ui -h 0.0.0.0 -p 5000 --backend-store-uri sqlite:////tmp/mlruns.db # then you can open http://:5000 from browser to view trace Note: During training, because the model may sometimes fail to generate correct toolcall tags, an error message "Failed to decode tool call" will be output to the console, which does not indicate an abnormality in training. Follow :doc:`Rollout trace<../advance/rollout_trace>` to known more about trace feature. Agent Framework --------------- System Architecture ~~~~~~~~~~~~~~~~~~~ .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/langgraph_agent.png?raw=true System Components ~~~~~~~~~~~~~~~~~ +--------------------------+-----------------------------------------------------------------------------------------------+ | Component | Role | +==========================+===============================================================================================+ | ChatModel | LLM object of LangChain, used to adapt to the “generate” api provided by AsyncLLMServerManager| +--------------------------+-----------------------------------------------------------------------------------------------+ | ReactAgentLoop | Agent adaptation layer, which by default supports a naive LangGraph Agentic. | | | New classes can be derived to support user-defined Agents, and the run function needs to be | | | implemented to complete Agent calls. | +--------------------------+-----------------------------------------------------------------------------------------------+ | AsyncServer | Server, each instance is connected to one DP group of the inference engine. | +--------------------------+-----------------------------------------------------------------------------------------------+ Follow doc "recipe/langgraph_agent/example/README.md" for more details. ================================================ FILE: docs/start/install.rst ================================================ Installation ============ Requirements ------------ - **Python**: Version >= 3.10 - **CUDA**: Version >= 12.8 verl supports various backends. Currently, the following configurations are available: - **FSDP** and **Megatron-LM** (optional) for training. - **SGLang**, **vLLM** and **TGI** for rollout generation. Choices of Backend Engines ---------------------------- 1. Training: We recommend using **FSDP** backend to investigate, research and prototype different models, datasets and RL algorithms. The guide for using FSDP backend can be found in :doc:`FSDP Workers<../workers/fsdp_workers>`. For users who pursue better scalability, we recommend using **Megatron-LM** backend. Currently, we support `Megatron-LM v0.13.1 `_. The guide for using Megatron-LM backend can be found in :doc:`Megatron-LM Workers<../workers/megatron_workers>`. 2. Inference: For inference, vllm 0.8.3 and later versions have been tested for stability. We recommend turning on env var `VLLM_USE_V1=1` for optimal performance. For SGLang, refer to the :doc:`SGLang Backend<../workers/sglang_worker>` for detailed installation and usage instructions. SGLang rollout is under extensive development and offers many advanced features and optimizations. We encourage users to report any issues or provide feedback via the `SGLang Issue Tracker `_. For huggingface TGI integration, it is usually used for debugging and single GPU exploration. Install from docker image ------------------------- Start from v0.6.0, we use vllm and sglang release image as our base image. Base Image :::::::::: - vLLM: https://hub.docker.com/r/vllm/vllm-openai - SGLang: https://hub.docker.com/r/lmsysorg/sglang Application Image ::::::::::::::::: Upon base image, the following packages are added: - flash_attn - Megatron-LM - Apex - TransformerEngine - DeepEP Latest docker file: - `Dockerfile.stable.vllm `_ - `Dockerfile.stable.sglang `_ All pre-built images are available in dockerhub: `verlai/verl `_. For example, ``verlai/verl:sgl055.latest``, ``verlai/verl:vllm011.latest``. You can find the latest images used for development and ci in our github workflows: - `.github/workflows/vllm.yml `_ - `.github/workflows/sgl.yml `_ Installation from Docker :::::::::::::::::::::::: After pulling the desired Docker image and installing desired inference and training frameworks, you can run it with the following steps: 1. Launch the desired Docker image and attach into it: .. code:: bash docker create --runtime=nvidia --gpus all --net=host --shm-size="10g" --cap-add=SYS_ADMIN -v .:/workspace/verl --name verl sleep infinity docker start verl docker exec -it verl bash 2. If you use the images provided, you only need to install verl itself without dependencies: .. code:: bash # install the nightly version (recommended) git clone https://github.com/volcengine/verl && cd verl pip3 install --no-deps -e . [Optional] If you hope to switch between different frameworks, you can install verl with the following command: .. code:: bash # install the nightly version (recommended) git clone https://github.com/volcengine/verl && cd verl pip3 install -e ".[vllm]" pip3 install -e ".[sglang]" Install from custom environment --------------------------------------------- We recommend to use docker images for convenience. However, if your environment is not compatible with the docker image, you can also install verl in a python environment. .. note:: - Dockerfile provides more details than this installation instructions. You can find examples in each Dockerfile, for example `verl0.6-cu128-torch2.8.0-fa2.7.4 Dockerfile.base `_ . Pre-requisites :::::::::::::: For training and inference engines to utilize better and faster hardware support, CUDA/cuDNN and other dependencies are required, and some of the dependencies are easy to be overridden when installing other packages, so we put them in the :ref:`Post-installation` step. .. note:: - The installation steps below are recommended configurations for the latest version of verl. If you are trying to customize your own environment, please ignore the strict constraints. We need to install the following pre-requisites: - **CUDA**: Version >= 12.8 - **cuDNN**: Version >= 9.10.0 - **Apex** CUDA above 12.8 is recommended to use as the docker image, please refer to `NVIDIA's official website `_ for other version of CUDA. .. code:: bash # change directory to anywher you like, in verl source code directory is not recommended wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda-repo-ubuntu2204-12-8-local_12.8.1-570.124.06-1_amd64.deb dpkg -i cuda-repo-ubuntu2204-12-8-local_12.8.1-570.124.06-1_amd64.deb cp /var/cuda-repo-ubuntu2204-12-8-local/cuda-*-keyring.gpg /usr/share/keyrings/ apt-get update apt-get -y install cuda-toolkit-12-8 update-alternatives --set cuda /usr/local/cuda-12-8 cuDNN can be installed via the following command, please refer to `NVIDIA's official website `_ for other version of cuDNN. .. code:: bash # change directory to anywher you like, in verl source code directory is not recommended wget https://developer.download.nvidia.com/compute/cudnn/9.10.2/local_installers/cudnn-local-repo-ubuntu2204-9.10.2_1.0-1_amd64.deb dpkg -i cudnn-local-repo-ubuntu2204-9.10.2_1.0-1_amd64.deb cp /var/cudnn-local-repo-ubuntu2204-9.10.2/cudnn-*-keyring.gpg /usr/share/keyrings/ apt-get update apt-get -y install cudnn-cuda-12 Install dependencies :::::::::::::::::::: .. note:: We recommend to use a fresh new conda environment to install verl and its dependencies. **Notice that the inference frameworks often strictly limit your pytorch version and will directly override your installed pytorch if not paying enough attention.** As a countermeasure, it is recommended to install inference frameworks first with the pytorch they needed. For vLLM, if you hope to use your existing pytorch, please follow their official instructions `Use an existing PyTorch installation `_ . 1. First of all, to manage environment, we recommend using conda: .. code:: bash conda create -n verl python==3.12 conda activate verl 2. Then, execute the ``install.sh`` script that we provided in verl: .. code:: bash # Make sure you have activated verl conda env # If you need to run with megatron bash scripts/install_vllm_sglang_mcore.sh # Or if you simply need to run with FSDP USE_MEGATRON=0 bash scripts/install_vllm_sglang_mcore.sh If you encounter errors in this step, please check the script and manually follow the steps in the script. [Optional] NVIDIA Apex is recommended for Megatron-LM training, but it's not needed if you only use FSDP backend. You can install it via the following command, but notice that this steps can take a very long time. It is recommended to set the ``MAX_JOBS`` environment variable to accelerate the installation process, but do not set it too large, otherwise the memory will be overloaded and your machines may hang. .. code:: bash # change directory to anywher you like, in verl source code directory is not recommended git clone https://github.com/NVIDIA/apex.git && \ cd apex && \ MAX_JOB=32 pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ Install verl :::::::::::: For installing the latest version of verl, the best way is to clone and install it from source. Then you can modify our code to customize your own post-training jobs. .. code:: bash git clone https://github.com/volcengine/verl.git cd verl pip install --no-deps -e . Post-installation ::::::::::::::::: Please make sure that the installed packages are not overridden during the installation of other packages. The packages worth checking are: - **torch** and torch series - **vLLM** - **SGLang** - **pyarrow** - **tensordict** - **nvidia-cudnn-cu12**: For Magetron backend If you encounter issues about package versions during running verl, please update the outdated ones. Install with AMD GPUs - ROCM kernel support ------------------------------------------------------------------ When you run on AMD GPUs (MI300) with ROCM platform, you cannot use the previous quickstart to run verl. You should follow the following steps to build a docker and run it. If you encounter any issues in using AMD GPUs running verl, feel free to contact me - `Yusheng Su `_. Find the docker for AMD ROCm: `docker/Dockerfile.rocm `_ :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .. code-block:: bash # Build the docker in the repo dir: # docker build -f docker/Dockerfile.rocm -t verl-rocm:03.04.2015 . # docker images # you can find your built docker FROM rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 # Set working directory # WORKDIR $PWD/app # Set environment variables ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" # Install vllm RUN pip uninstall -y vllm && \ rm -rf vllm && \ git clone -b v0.6.3 https://github.com/vllm-project/vllm.git && \ cd vllm && \ MAX_JOBS=$(nproc) python3 setup.py install && \ cd .. && \ rm -rf vllm # Copy the entire project directory COPY . . # Install dependencies RUN pip install "tensordict<0.6" --no-deps && \ pip install accelerate \ codetiming \ datasets \ dill \ hydra-core \ liger-kernel \ numpy \ pandas \ datasets \ peft \ "pyarrow>=15.0.0" \ pylatexenc \ "ray[data,train,tune,serve]" \ torchdata \ transformers \ wandb \ orjson \ pybind11 && \ pip install -e . --no-deps Build the image :::::::::::::::::::::::: .. code-block:: bash docker build -t verl-rocm . Launch the container :::::::::::::::::::::::::::: .. code-block:: bash docker run --rm -it \ --device /dev/dri \ --device /dev/kfd \ -p 8265:8265 \ --group-add video \ --cap-add SYS_PTRACE \ --security-opt seccomp=unconfined \ --privileged \ -v $HOME/.ssh:/root/.ssh \ -v $HOME:$HOME \ --shm-size 128G \ -w $PWD \ verl-rocm \ /bin/bash If you do not want to root mode and require assign yourself as the user, Please add ``-e HOST_UID=$(id -u)`` and ``-e HOST_GID=$(id -g)`` into the above docker launch script. verl with AMD GPUs currently supports FSDP as the training engine, vLLM and SGLang as the inference engine. We will support Megatron in the future. ================================================ FILE: docs/start/more_resources.rst ================================================ More Resources ============== Last updated: 06/30/2025. - Introduction to verl (`Slides `_) - verl Code Walkthrough (`Slides `_, `Talk in Chinese `_) ================================================ FILE: docs/start/multinode.rst ================================================ Multinode Training ================== Last updated: 06/10/2025. .. _wuxibin89: https://github.com/wuxibin89 Author: `Xibin Wu `_, `Yusheng Su `_. Option 1: Launch Manually ------------------------------ Set up multinode ray cluster ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Start head node with ``ray start --head --dashboard-host=0.0.0.0``, there're 2 address you should care about: - GCS address: ``ray start --address=
``, where worker node should connect to. - Dashboard address: ``
:8265``, where you should submit job to the cluster. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/head.png?raw=true 2. Start worker node with ``ray start --address=
`` you get above. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/worker.png?raw=true 3. Now you should see the cluster have 2 nodes with ``ray status``. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/status.png?raw=true 4. Additionally, you can access dashboard in the browser with the address you get above. *Firewall rules maybe need configure to access the dashboard, if there's any trouble, please contact your network administrator.* .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/overview.png?raw=true Submit job to ray cluster ~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Submit ray job to cluster with the dashboard address you get above. .. code-block:: bash ray job submit --address="http://127.0.0.1:8265" \ --runtime-env=verl/trainer/runtime_env.yaml \ --no-wait \ -- \ python3 -m verl.trainer.main_ppo \ trainer.n_gpus_per_node=8 \ trainer.nnodes=2 \ ... .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/submit.png?raw=true 2. Then you can check the job status with the following commands: - ray job list: list all jobs submitted to the cluster. - ray job logs : query the logs of the job. - ray job status : query the status of the job. - ray job stop : request the job to be stopped. - ray job list | grep submission_id | grep JobStatus | grep RUNNING | grep -oP 'raysubmit_[^'\''"]+' | head -n 1: get the latest job submission ID of the running job. - ray job logs --follow: added ``--follow`` parameter to ray job logs command to enable continuous log streaming. 3. You can also access driver/task/actor logs in ``/tmp/ray/session_latest/logs/``, driver log is ``job-driver-raysubmit_.log``. 4. We strongly recommend you to view job detail from dashboard in multinode training, because it provide more structure way to view the job information. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/job.png?raw=true .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/job_detail.png?raw=true Option 2: Launch via SkyPilot on Kubernetes or clouds ------------------------------------------------------ .. note:: Ready-to-use SkyPilot example configurations are available in the `examples/skypilot/ `_ directory: - ``verl-ppo.yaml`` - PPO training with GSM8K dataset - ``verl-grpo.yaml`` - GRPO training with MATH dataset - ``verl-multiturn-tools.yaml`` - Multi-turn tool usage training See the `SkyPilot examples README `_ for detailed usage instructions. Step 1: Setup SkyPilot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SkyPilot can support different clouds, here we use GCP as example. `install skypilot `_ .. code-block:: bash conda create -y -n sky python=3.10 conda activate sky pip install "skypilot[gcp]" conda install -c conda-forge google-cloud-sdk gcloud init # Run this if you don't have a credential file. # This will generate ~/.config/gcloud/application_default_credentials.json. gcloud auth application-default login # Check if the GCP credential is correctly setup. sky check gcp .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/setup_skypilot.png?raw=true Step 2: Prepare dataset ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash git clone https://github.com/volcengine/verl.git cd examples/data_preprocess python3 gsm8k.py --local_save_dir ~/data/gsm8k Step 3: Submit a job with SkyPilot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Create a SkyPilot YAML ``verl-cluster.yml`` with the following content: .. parsed-literal:: workdir: . will sync all the data in the current dir to the remote cluster. .. code-block:: yaml resources: accelerators: L4:1 # every node has 1 L4 GPU image_id: docker:verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.0-fa2.7.4 memory: 64+ # every node has 64 GB memory ports: 8265 # expose port for ray dashboard num_nodes: 2 # cluster size # --------------- Work Directory Synchronization (workdir) --------------- # Defines the local working directory to be synchronized to the remote cluster. # Here, '.' means synchronizing the directory where the sky submit command is currently run. workdir: . # --------------- (secrets) --------------- secrets: ## your wandb api key ## WANDB_API_KEY: null # --------------- File Mounts/Data Upload (file_mounts) --------------- # If your dataset (gsm8k folder) is local, it needs to be uploaded to the remote cluster. file_mounts: # Remote path (relative to remote user's home directory): Local path # /remote/dir1/file: /local/dir1/file data/gsm8k: ~/data/gsm8k # --------------- Environment Setup (setup) --------------- # Commands run on each node of the remote cluster to set up the environment (e.g., install dependencies). These are run directly inside Docker. setup: | rm -rf verl git clone https://github.com/volcengine/verl.git cd verl pip3 install -v -e .[vllm] # --------------- Run Command (run) --------------- # The actual task commands to be executed on the remote cluster. # This script will first start the Ray cluster (different ray start commands are executed on Head and Worker nodes). # Then, your training script will only be run on the Head node (SKYPILOT_NODE_RANK == 0). run: | # Get the Head node's IP and total number of nodes (environment variables injected by SkyPilot). head_ip=`echo "$SKYPILOT_NODE_IPS" | head -n1` num_nodes=`echo "$SKYPILOT_NODE_IPS" | wc -l` # Here num_nodes should be equal to 2. # login wandb python3 -c "import wandb; wandb.login(relogin=True, key='$WANDB_API_KEY')" # Start Ray based on node role (Head=0, Worker>0). # This logic is a standard Ray cluster startup script. if [ "$SKYPILOT_NODE_RANK" == "0" ]; then # Head node starts Ray Head. echo "Starting Ray head node..." # Check if a Ray Head is already running to avoid duplicate starts. ps aux | grep ray | grep 6379 &> /dev/null || ray start --head --disable-usage-stats \ --port=6379 \ --dashboard-host=0.0.0.0 \ --dashboard-port=8265 # Wait for all worker nodes to join the cluster. while [ $(ray nodes | grep NODE_ID | wc -l) -lt $num_nodes ]; do echo "Waiting for all nodes to join... ($(ray nodes | grep NODE_ID | wc -l)/$num_nodes)" sleep 5 done # Head node executes the training script. echo "Executing training script on head node..." python3 -m verl.trainer.main_ppo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=['console','wandb'] \ trainer.val_before_train=False \ trainer.default_hdfs_dir=null \ trainer.n_gpus_per_node=1 \ trainer.nnodes=2 \ trainer.save_freq=20 \ trainer.test_freq=20 \ trainer.total_epochs=2 \ trainer.project_name=verl_examples \ trainer.experiment_name=experiment_name_gsm8k else # Wait for Ray Head to start. sleep 10 # Increase waiting time to ensure Head finishes starting. # Worker node starts Ray Worker. echo "Starting Ray worker node..." # Check if a Ray Worker is already running to avoid duplicate starts. ps aux | grep ray | grep $head_ip:6379 &> /dev/null || ray start --address $head_ip:6379 --disable-usage-stats # Add sleep to after `ray start` to give ray enough time to daemonize sleep 5 # Ensure Worker successfully connects to Head. fi # No commands are added to the Worker node here; the Worker's main task is to start Ray and wait for the Head node to assign tasks. echo "Node setup and Ray start script finished for rank $SKYPILOT_NODE_RANK." .. code-block:: bash export WANDB_API_KEY= sky launch -c verl --secret WANDB_API_KEY verl-cluster.yml .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/running_job.png?raw=true .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/running_job_1.png?raw=true .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/finished.png?raw=true **Check the cluster on GCP** .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/gcp_instances.png?raw=true **Check Ray Dashboard** We can see the cluster on the RAY Dashboard with the GCP head node: ```console $ sky status --endpoint 8265 verl 1.2.3.4:8265 ``` .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/ray_dashboard_overview.png?raw=true .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/ray_dashboard_jobs.png?raw=true .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/ray_dashboard_cluster.png?raw=true **Check the checkpoint of model** .. code-block:: bash # login the head node ssh verl # The global step will vary. Find the correct path from the training logs. cd ~/sky_workdir/checkpoints/verl_examples/gsm8k/ # Then list contents to find the checkpoint, e.g.: ls -R . .. image:: https://github.com/yottalabsai/open-source/blob/main/static/verl/saved_model.png?raw=true Option 3: Launch via Slurm ------------------------------ Ray provides users with `this `_ official tutorial to start a Ray cluster on top of Slurm. We have verified the :doc:`GSM8K example<../examples/gsm8k_example>` on a Slurm cluster under a multi-node setting with the following steps. 1. [Optional] If your cluster support `Apptainer or Singularity `_ and you wish to use it, convert verl's Docker image to an Apptainer image. Alternatively, set up the environment with the package manager available on your cluster or use other container runtimes (e.g. through `Slurm's OCI support `_) available to you. .. code:: bash apptainer pull /your/dest/dir/vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3.sif docker://verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3 2. Follow :doc:`GSM8K example<../examples/gsm8k_example>` to prepare the dataset and model checkpoints. 3. Modify `examples/slurm/ray_on_slurm.slurm `_ with your cluster's own information. 4. Submit the job script to the Slurm cluster with `sbatch`. Please note that Slurm cluster setup may vary. If you encounter any issues, please refer to Ray's `Slurm user guide `_ for common caveats. If you changed Slurm resource specifications, please make sure to update the environment variables in the job script if necessary. Option 4: Launch via dstack ------------------------------ `dstackai/dstack `_ is an open-source container orchestrator that simplifies distributed training across cloud providers and on-premises environments without the need to use K8S or Slurm. Prerequisite ~~~~~~~~~~~~ Once dstack is `installed `_, initialize the directory as a repo with ``dstack init``. .. code-block:: bash mkdir myproject && cd myproject dstack init **Create a fleet** Before submitting distributed training jobs, create a `dstack` `fleet `_. Run a Ray cluster task ~~~~~~~~~~~~~~~~~~~~~~ Once the fleet is created, define a Ray cluster task, e.g. in ``ray-cluster.dstack.yml``: .. code-block:: yaml type: task name: ray-verl-cluster nodes: 2 env: - WANDB_API_KEY - PYTHONUNBUFFERED=1 - CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 image: verlai/verl:app-verl0.6-transformers4.56.1-sglang0.5.2-mcore0.13.0-te2.2 commands: - git clone https://github.com/volcengine/verl - cd verl - pip install --no-deps -e . - pip install hf_transfer hf_xet - | if [ $DSTACK_NODE_RANK = 0 ]; then python3 examples/data_preprocess/gsm8k.py --local_save_dir ~/data/gsm8k python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2.5-7B-Instruct')" ray start --head --port=6379; else ray start --address=$DSTACK_MASTER_NODE_IP:6379 fi # Expose Ray dashboard port ports: - 8265 resources: gpu: 80GB:8 shm_size: 128GB # Save checkpoints on the instance volumes: - /checkpoints:/checkpoints Now, if you run this task via `dstack apply`, it will automatically forward the Ray's dashboard port to `localhost:8265`. .. code-block:: bash dstack apply -f ray-cluster.dstack.yml As long as the `dstack apply` is attached, you can use `localhost:8265` to submit Ray jobs for execution Submit Ray jobs ~~~~~~~~~~~~~~~ Before you can submit Ray jobs, ensure to install `ray` locally: .. code-block:: shell pip install ray Now you can submit the training job to the Ray cluster which is available at ``localhost:8265``: .. code-block:: shell $ RAY_ADDRESS=http://localhost:8265 $ ray job submit \ -- python3 -m verl.trainer.main_ppo \ data.train_files=/root/data/gsm8k/train.parquet \ data.val_files=/root/data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-7B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.project_name=ppo_training \ trainer.experiment_name=qwen-2.5-7B \ trainer.val_before_train=False \ trainer.n_gpus_per_node=8 \ trainer.nnodes=2 \ trainer.default_local_dir=/checkpoints \ trainer.save_freq=10 \ trainer.test_freq=10 \ trainer.total_epochs=15 2>&1 | tee verl_demo.log \ trainer.resume_mode=disable For more details on how `dstack` works, check out its `documentation `_. How to debug? --------------------- Ray Distributed Debugger VSCode Extension (Recommended) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Starting with Ray 2.39, Anyscale has introduced the `Ray Distributed Debugger `_ VSCode extension. Follow the extension’s installation instructions, then add your cluster using the dashboard URL you obtained earlier. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/debugger.png?raw=true :alt: Ray Distributed Debugger VSCode extension screenshot 2. Prerequisites. Ensure the following are installed (see the extension README for more detail): - Visual Studio Code - `ray[default]` >= 2.9.1 - `debugpy` >= 1.8.0 .. image:: https://github.com/aoshen524/verl/blob/main/docs/start/c7098b755ff689859837773a916c857.png?raw=true :alt: VSCode with Ray prerequisites 3. Environment Variables. To enable post‑mortem debugging, set: .. code-block:: bash export RAY_DEBUG_POST_MORTEM=1 .. admonition:: Note :class: important Be sure to remove any legacy flags before starting Ray: - `RAY_DEBUG=legacy` - `--ray-debugger-external` 4. Configuring BreakpointsSet up breakpoint() in your code, and submit job to cluster. Then the extension will show the breakpoint information. 1. Insert `breakpoint()` calls into your remote functions. 2. Submit your job to the cluster. The extension will detect active breakpoints and display them in VSCode. .. image:: https://github.com/aoshen524/verl/blob/main/docs/start/4ddad74395c79a1402331c0ce73316f.png?raw=true :alt: Detected breakpoint in VSCode **Note:** Breakpoints are only supported inside functions decorated with `@ray.remote`. 5. Launching the Debugger. Run your job directly from the command line (do not use a `launch.json`): .. code-block:: bash python job.py 6. Attaching to a Breakpoint. Once the process hits the first `breakpoint()`, click the Ray Distributed Debugger icon in the VSCode sidebar to attach the debugger. .. image:: https://github.com/aoshen524/verl/blob/main/docs/start/4ddad74395c79a1402331c0ce73316f.png?raw=true :alt: Attaching VSCode debugger to Ray process 7. Debugging With Multiple breakpoint(). For each subsequent task, first disconnect the current debugger session, then click the extension icon again to attach to the next breakpoint. .. image:: https://github.com/aoshen524/verl/blob/main/docs/start/6e83c910a62c82fecb89c6619e001cd.png?raw=true :alt: Disconnecting and reconnecting the debugger Legacy Ray Debugger ~~~~~~~~~~~~~~~~~~~ 1. Ray has a builtin legacy `debugger `_ that allows you to debug your distributed applications. To enable debugger, start ray cluster with ``RAY_DEBUG=legacy`` and ``--ray-debugger-external``. .. code-block:: bash # start head node RAY_DEBUG=legacy ray start --head --dashboard-host=0.0.0.0 --ray-debugger-external # start worker node RAY_DEBUG=legacy ray start --address='10.124.46.192:6379' --ray-debugger-external 2. Set up breakpoint in your code, and submit job to cluster. Then run ``ray debug`` to wait breakpoint: .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/legacy.png?raw=true Multi-node training on AMD clusters --------------------------------------------------------------------------------------- If you want to run multi-node training with slurm with Docker/Podman container on AMD Cluster, you can use the following script. If you encounter any issues in using AMD GPUs running verl, please contact `Yusheng Su `_. .. note:: 1. You need to use ``podman`` or ``docker`` in the following script. We will release the apptainer script later. 2. If you want to use ``podman``, you just replace ``docker`` with ``podman`` in the following script. The script includes the following steps: 1. SLURM Configuration 2. Environment Setup 3. Docker/Podman Container Setup 4. Ray Cluster Initialization 5. Data Preprocessing 6. Model Setup 7. Training Launch slurm_script.sh ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash #!/bin/bash #SBATCH --job-name=verl-ray-on-slurm #SBATCH --nodes=2 #SBATCH --ntasks-per-node=2 #SBATCH --mem=200G #SBATCH --time=30-00:00:00 #SBATCH --gpus-per-node=8 #SBATCH --cpus-per-task=28 #SBATCH --output=../verl_log/slurm-%j.out #SBATCH --error=../verl_log/slurm-%j.err #SBATCH --nodelist=gpu-[0,1] # load necessary modules ### Run this setup # [Cluster]: Use docker # docker pull docker.io/rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 ########################################################################## ###The following setting should be set in different project and cluster### ########################################################################## ### Project CONTAINER_NAME="multinode_verl_training" IMG="verl.rocm" DOCKERFILE="docker/Dockerfile.rocm" # echo $PWD verl_workdir="${HOME}/projects/verl_upstream" export TRANSFORMERS_CACHE="${HOME}/.cache/huggingface" export HF_HOME=$TRANSFORMERS_CACHE ### Cluster Network Setting export NCCL_DEBUG=TRACE export GPU_MAX_HW_QUEUES=2 export TORCH_NCCL_HIGH_PRIORITY=1 export NCCL_CHECKS_DISABLE=1 # export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7 export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9 export NCCL_IB_GID_INDEX=3 export NCCL_CROSS_NIC=0 export CUDA_DEVICE_MAX_CONNECTIONS=1 export NCCL_PROTO=Simple export RCCL_MSCCL_ENABLE=0 export TOKENIZERS_PARALLELISM=false export HSA_NO_SCRATCH_RECLAIM=1 ########################################################################## ### For rocm and training script export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export ROCR_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES export CUDA_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES # Build and launch the Docker container srun bash -c " # Exit on any error set -e # Clean up dangling images (images with tag) docker image prune -f # Need to pull the docker first docker pull docker.io/rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 if ! docker images --format "{{.Repository}}:{{.Tag}}" | grep -q "${IMG}"; then echo \"Building ${IMG} image...\" docker build -f \"${DOCKERFILE}\" -t \"${IMG}\" . else echo \"${IMG} image already exists, skipping build\" fi # Removing old container if exists docker rm \"${CONTAINER_NAME}\" 2>/dev/null || true # Checking network devices ibdev2netdev # Launch the docker docker run --rm -d \ -e HYDRA_FULL_ERROR=1 \ -e HIP_VISIBLE_DEVICES=${HIP_VISIBLE_DEVICES} \ -e ROCR_VISIBLE_DEVICES=${ROCR_VISIBLE_DEVICES} \ -e CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} \ -e NCCL_DEBUG=${NCCL_DEBUG} \ -e GPU_MAX_HW_QUEUES=${GPU_MAX_HW_QUEUES} \ -e TORCH_NCCL_HIGH_PRIORITY=${TORCH_NCCL_HIGH_PRIORITY} \ -e NCCL_CHECKS_DISABLE=${NCCL_CHECKS_DISABLE} \ -e NCCL_IB_HCA=${NCCL_IB_HCA} \ -e NCCL_IB_GID_INDEX=${NCCL_IB_GID_INDEX} \ -e NCCL_CROSS_NIC=${NCCL_CROSS_NIC} \ -e CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS} \ -e NCCL_PROTO=${NCCL_PROTO} \ -e RCCL_MSCCL_ENABLE=${RCCL_MSCCL_ENABLE} \ -e TOKENIZERS_PARALLELISM=${TOKENIZERS_PARALLELISM} \ -e HSA_NO_SCRATCH_RECLAIM=${HSA_NO_SCRATCH_RECLAIM} \ -e TRANSFORMERS_CACHE=${TRANSFORMERS_CACHE} \ -e HF_HOME=${HF_HOME} \ --network host \ --device /dev/dri \ --device /dev/kfd \ --device /dev/infiniband \ --group-add video \ --cap-add SYS_PTRACE \ --security-opt seccomp=unconfined \ --privileged \ -v \${HOME}:\${HOME} \ -v \${HOME}/.ssh:/root/.ssh \ -w "${verl_workdir}" \ --shm-size 128G \ --name \"${CONTAINER_NAME}\" \ \"${IMG}\" \ tail -f /dev/null echo \"Container setup completed\" " # (Optional): If you do not want to root mode and require assign yuorself as the user # Please add `-e HOST_UID=$(id -u)` and `-e HOST_GID=$(id -g)` into the above docker launch script. ### Ray launch the nodes before training # Getting the node names nodes_array=($(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')) head_node=${nodes_array[0]} head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address) # if we detect a space character in the head node IP, we'll # convert it to an ipv4 address. This step is optional. if [[ "$head_node_ip" == *" "* ]]; then IFS=' ' read -ra ADDR <<<"$head_node_ip" if [[ ${#ADDR[0]} -gt 16 ]]; then head_node_ip=${ADDR[1]} else head_node_ip=${ADDR[0]} fi echo "IPV6 address detected. We split the IPV4 address as $head_node_ip" fi port=6379 ip_head=$head_node_ip:$port export ip_head echo "IP Head: $ip_head" # make sure we set environment variables before Ray initialization # Print out all env variables printenv echo "Starting HEAD at $head_node" srun --nodes=1 --ntasks=1 -w "$head_node" \ docker exec "${CONTAINER_NAME}" \ ray start --head --node-ip-address="$head_node_ip" --port=$port \ --dashboard-port=8266 \ --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & # optional, though may be useful in certain versions of Ray < 1.0. sleep 10 # number of nodes other than the head node worker_num=$((SLURM_JOB_NUM_NODES - 1)) for ((i = 1; i <= worker_num; i++)); do node_i=${nodes_array[$i]} echo "Debug: Starting worker on node_i = ${node_i}" if [ -z "$node_i" ]; then echo "Error: Empty node name for worker $i" continue fi echo "Starting WORKER $i at $node_i" srun --nodes=1 --ntasks=1 -w "$node_i" \ docker exec "${CONTAINER_NAME}" \ ray start --address "$ip_head" --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & sleep 5 done # Ray initlization test (See whether any error in the above execution) echo "Testing Ray initialization in the slurm nodes..." docker exec "${CONTAINER_NAME}" python3 -c ' import ray try: ray.init(address="auto") print("\n=== Ray Cluster Status ===") print(f"Number of nodes: {len(ray.nodes())}") for node in ray.nodes(): print("Node: {}, Status: {}".format(node["NodeManagerHostname"], node["Alive"])) # print(f"Node: {node}") ray.shutdown() print("Ray initialization successful!") except Exception as e: print(f"Ray initialization failed: {str(e)}") ' echo "=== Ray test completed ===" ###### # Run data preprocessing echo "Starting data preprocessing..." docker exec "${CONTAINER_NAME}" \ python3 "examples/data_preprocess/gsm8k.py" "--local_save_dir" "../data/gsm8k" echo "Starting data preprocessing..." docker exec "${CONTAINER_NAME}" \ python3 "examples/data_preprocess/math_dataset.py" "--local_dir" "../data/math" train_files="../data/gsm8k/train.parquet" val_files="../data/gsm8k/test.parquet" # Download and test model echo "Loading model..." docker exec "${CONTAINER_NAME}" \ python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2-7B-Instruct')" MODEL_PATH="Qwen/Qwen2-7B-Instruct" # Set model path after pipeline test MODEL_PATH="Qwen/Qwen2.5-0.5B-Instruct" echo "== Data and model loading Done ==" echo "Start to train..." docker exec "${CONTAINER_NAME}" \ python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2-7B-Instruct')" MODEL_PATH="Qwen/Qwen2-7B-Instruct" PYTHONUNBUFFERED=1 srun --overlap --nodes=${SLURM_NNODES} --ntasks=1 -w "$head_node" \ docker exec "${CONTAINER_NAME}" \ python3 -m verl.trainer.main_ppo \ data.train_files=$train_files \ data.val_files=$val_files \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=$MODEL_PATH \ critic.model.enable_gradient_checkpointing=False \ critic.ppo_micro_batch_size_per_gpu=8 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.kl_ctrl.kl_coef=0.0001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example' \ trainer.experiment_name='Qwen2.5-32B-Instruct_function_rm' \ trainer.n_gpus_per_node=${SLURM_GPUS_PER_NODE} \ trainer.val_before_train=False \ trainer.nnodes=${SLURM_NNODES} \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 Run multi-node training with above slurm_script.sh ~~~~~~~~~~~~~~~~~~~~ Just sbatch your slurm_script.sh .. code-block:: bash sbatch slurm_script.sh ================================================ FILE: docs/start/quickstart.rst ================================================ .. _quickstart: ========================================================= Quickstart: PPO training on GSM8K dataset ========================================================= Post-train a LLM using GSM8K dataset. Introduction ------------ .. _hf_dataset_gsm8k: https://huggingface.co/datasets/openai/gsm8k In this example, we train an LLM to tackle the `GSM8k `_ task with function-based rewards. [1]_ Prerequisite: - the latest version of ``verl`` and its dependencies installed following the installation guide. Using the docker image is recommended. - a GPU with at least 24 GB HBM Dataset Introduction -------------------- GSM8k is a math problem dataset. The prompt is an elementary school problem. The LLM model is asked to solve the math problem. Below is an example: Prompt Katy makes coffee using teaspoons of sugar and cups of water in the ratio of 7:13. If she used a total of 120 teaspoons of sugar and cups of water, calculate the number of teaspoonfuls of sugar she used. Solution The total ratio representing the ingredients she used to make the coffee is 7+13 = <<7+13=20>>20 Since the fraction representing the number of teaspoons she used is 7/20, she used 7/20\ *120 = <<7/20*\ 120=42>>42 #### 42 Step 1: Prepare the dataset ---------------------------- We preprocess the dataset in parquet format so that (1) it contains necessary fields for computing RL rewards and (2) is faster to read. .. code-block:: bash python3 examples/data_preprocess/gsm8k.py --local_save_dir ~/data/gsm8k Step 2: Download a model for post-training ------------------------------------------- In this example, we start with the ``Qwen2.5-0.5B-Instruct`` model. If you want to perform SFT before RL, refer to the :doc:`Complete GSM8K Example<../examples/gsm8k_example>`, the `sft directory `_ and `SFT Trainer `_ for further details. .. code-block:: bash python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2.5-0.5B-Instruct')" Step 3: Perform PPO training with the instruct model ---------------------------------------------------------------------- **Reward Model/Function** We use a pre-defined rule-based reward model. We force the model to produce a final answer following 4 “#” as shown in the solution. We extract the final answer from both the solution and model's output using regular expression matching. We assign a reward of 1 to correct answer, 0.0 to incorrect answer and 0 to no answer. For more details, please refer to `verl/utils/reward_score/gsm8k.py `_. **Training Script** Now let's run PPO training with the dataset and model above. [2]_ Set the ``data.train_files`` ,\ ``data.val_files``, ``actor_rollout_ref.model.path`` and ``critic.model.path`` based on your dataset and model names or paths. You may set ``VERL_USE_MODELSCOPE=True`` to download models from `modelscope `_ instead of `huggingface `_. .. code-block:: bash PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=512 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=console \ trainer.val_before_train=False \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=10 \ trainer.test_freq=10 \ trainer.total_epochs=15 2>&1 | tee verl_demo.log You are expected to see the following logs, indicating training in progress. The key metric ``val/test_score/openai/gsm8k`` is computed every ``trainer.test_freq`` steps: .. code-block:: bash step:0 - timing/gen:21.470 - timing/ref:4.360 - timing/values:5.800 - actor/reward_kl_penalty:0.000 - actor/reward_kl_penalty_coeff:0.001 - timing/adv:0.109 - timing/update_critic:15.664 - critic/vf_loss:14.947 - critic/vf_clipfrac:0.000 - critic/vpred_mean:-2.056 - critic/grad_norm:1023.278 - critic/lr(1e-4):0.100 - timing/update_actor:20.314 - actor/entropy_loss:0.433 - actor/pg_loss:-0.005 - actor/pg_clipfrac:0.000 - actor/ppo_kl:0.000 - actor/grad_norm:1.992 - actor/lr(1e-4):0.010 - critic/score/mean:0.004 - critic/score/max:1.000 - critic/score/min:0.000 - critic/rewards/mean:0.004 - critic/rewards/max:1.000 - critic/rewards/min:0.000 - critic/advantages/mean:-0.000 - critic/advantages/max:2.360 - critic/advantages/min:-2.280 - critic/returns/mean:0.003 - critic/returns/max:0.000 - critic/returns/min:0.000 - critic/values/mean:-2.045 - critic/values/max:9.500 - critic/values/min:-14.000 - response_length/mean:239.133 - response_length/max:256.000 - response_length/min:77.000 - prompt_length/mean:104.883 - prompt_length/max:175.000 - prompt_length/min:68.000 step:1 - timing/gen:23.020 - timing/ref:4.322 - timing/values:5.953 - actor/reward_kl_penalty:0.000 - actor/reward_kl_penalty:0.001 - timing/adv:0.118 - timing/update_critic:15.646 - critic/vf_loss:18.472 - critic/vf_clipfrac:0.384 - critic/vpred_mean:1.038 - critic/grad_norm:942.924 - critic/lr(1e-4):0.100 - timing/update_actor:20.526 - actor/entropy_loss:0.440 - actor/pg_loss:0.000 - actor/pg_clipfrac:0.002 - actor/ppo_kl:0.000 - actor/grad_norm:2.060 - actor/lr(1e-4):0.010 - critic/score/mean:0.000 - critic/score/max:0.000 - critic/score/min:0.000 - critic/rewards/mean:0.000 - critic/rewards/max:0.000 - critic/rewards/min:0.000 - critic/advantages/mean:0.000 - critic/advantages/max:2.702 - critic/advantages/min:-2.616 - critic/returns/mean:0.000 - critic/returns/max:0.000 - critic/returns/min:0.000 - critic/values/mean:-2.280 - critic/values/max:11.000 - critic/values/min:-16.000 - response_length/mean:232.242 - response_length/max:256.000 - response_length/min:91.000 - prompt_length/mean:102.398 - prompt_length/max:185.000 - prompt_length/min:70.000 Checkout ``Algorithm Baselines`` page for full training and validation logs for reference. The checkpoint is saved at the following dir by default: ``checkpoints/${trainer.project_name}/${trainer.experiment_name}``. You can merge the saved checkpoints to huggingface model using ``verl.model_merger`` module, for example: .. code-block:: bash python3 -m verl.model_merger merge \ --backend fsdp \ --local_dir checkpoints/${trainer.project_name}/${trainer.experiment_name}/global_step_1/actor \ --target_dir checkpoints/${trainer.project_name}/${trainer.experiment_name}/global_step_1/actor/huggingface For more details about checkpoint and model merging, please refer to :ref:`checkpoint-page`. To enable ``wandb`` for experiment tracking, set the following configs: .. code-block:: bash trainer.logger='["console","wandb"]' \ trainer.project_name=$YOUR_PROJECT_NAME \ trainer.experiment_name=$YOUR_RUN_NAME \ If you encounter out of memory issues with HBM less than 32GB, enable the following configs would help: .. code-block:: bash actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ critic.ppo_micro_batch_size_per_gpu=1 \ For the full set of configs, please refer to :ref:`config-explain-page` for detailed explanation and performance tuning. .. [1] The original paper (https://arxiv.org/pdf/2110.14168) mainly focuses on training a verifier (a reward model) to solve math problems via Best-of-N sampling. In this example, we train an RL agent using a rule-based reward model. .. [2] More training script examples for FSDP and Megatron-LM backend are stored in `examples/ppo_trainer `_ directory. ================================================ FILE: docs/start/ray_debug_tutorial.rst ================================================ Ray Debug Tutorial ================== Last updated: 04/23/2025 .. _wuxibin89: https://github.com/wuxibin89 Author: `Ao Shen `_. How to debug? --------------------- Ray Distributed Debugger VSCode Extension (Recommended) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Starting with Ray 2.39, Anyscale has introduced the `Ray Distributed Debugger `_ VSCode extension. Follow the extension’s installation instructions, then add your cluster using the dashboard URL you obtained earlier. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/debugger.png?raw=true :alt: Ray Distributed Debugger VSCode extension screenshot 2. Prerequisites. Ensure the following are installed (see the extension README for more detail): - Visual Studio Code - `ray[default]` >= 2.9.1 - `debugpy` >= 1.8.0 .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/readme.png?raw=true :alt: VSCode with Ray prerequisites 3. Environment Variables. To enable post‑mortem debugging, set: .. code-block:: bash export RAY_DEBUG_POST_MORTEM=1 .. admonition:: Note :class: important Be sure to remove any legacy flags before starting Ray: - `RAY_DEBUG=legacy` - `--ray-debugger-external` 4. Configuring BreakpointsSet up breakpoint() in your code, and submit job to cluster. Then the extension will show the breakpoint information. 1. Insert `breakpoint()` calls into your remote functions. 2. Submit your job to the cluster. The extension will detect active breakpoints and display them in VSCode. **Note:** Breakpoints are only supported inside functions decorated with `@ray.remote`. 5. Launching the Debugger. Run your job directly from the command line (do not use a `launch.json`): .. code-block:: bash python job.py 6. Attaching to a Breakpoint. Once the process hits the first `breakpoint()`, click the Ray Distributed Debugger icon in the VSCode sidebar to attach the debugger. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/launch.png?raw=true :alt: Attaching VSCode debugger to Ray process 7. Debugging With Multiple breakpoint(). For each subsequent task, first disconnect the current debugger session, then click the extension icon again to attach to the next breakpoint. .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/disconnect.png?raw=true :alt: Disconnecting and reconnecting the debugger Legacy Ray Debugger ~~~~~~~~~~~~~~~~~~~ 1. Ray has a builtin legacy `debugger `_ that allows you to debug your distributed applications. To enable debugger, start ray cluster with ``RAY_DEBUG=legacy`` and ``--ray-debugger-external``. .. code-block:: bash # start head node RAY_DEBUG=legacy ray start --head --dashboard-host=0.0.0.0 --ray-debugger-external # start worker node RAY_DEBUG=legacy ray start --address='10.124.46.192:6379' --ray-debugger-external 2. Set up breakpoint in your code, and submit job to cluster. Then run ``ray debug`` to wait breakpoint: .. image:: https://github.com/eric-haibin-lin/verl-community/blob/main/docs/ray/legacy.png?raw=true ================================================ FILE: docs/workers/automodel_workers.rst ================================================ Automodel Backend ================= Last updated: 03/07/2026. We support the Automodel (nemo_automodel) backend by implementing the ``AutomodelEngine`` and ``AutomodelEngineWithLMHead`` engine classes. The Automodel backend delegates model building, parallelization, optimizer sharding, LR scheduling, gradient clipping, and checkpointing to nemo_automodel's infrastructure while using verl's training loop, data pipeline, and loss function. **Requirements** - Automodel r0.3.0 - transformers v5.0.0 **Pros** - Supports FSDP2 and TP distributed strategies out of the box. - Native support for Mixture-of-Experts (MoE) models with Expert Parallelism (EP) via DeepEP. - TransformerEngine (TE) integration for optimized attention, linear layers, and RMSNorm. - Readily supports any HuggingFace model without checkpoint conversion. **Cons** - Pipeline parallelism is not yet supported. SFT Examples ------------ We provide example SFT training scripts using the Automodel backend in `examples/sft/gsm8k/ `_. Basic: Qwen2.5-0.5B with FSDP2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A minimal example using ``Qwen/Qwen2.5-0.5B-Instruct`` with FSDP2 and no parallelism: .. code:: shell bash examples/sft/gsm8k/run_qwen_05_automodel.sh 4 /tmp/automodel_sft_test See `run_qwen_05_automodel.sh `_. Advanced: Qwen3-30B MoE with Expert Parallelism ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A larger-scale example using ``Qwen/Qwen3-30B-A3B-Base`` (MoE model) with Expert Parallelism (EP=8), DeepEP, TransformerEngine backend, and torch_mm experts backend: .. code:: shell bash examples/sft/gsm8k/run_qwen3_30b_automodel.sh 8 /tmp/automodel_sft_30b See `run_qwen3_30b_automodel.sh `_. ================================================ FILE: docs/workers/fsdp_workers.rst ================================================ PyTorch FSDP Backend ====================== Last updated: 12/01/2025. We support PyTorch FSDP Backend by implementing various workers for actor, critic, reference, rollout and reward models. **Pros** - Readily support various models. - Users only need to implement the corresponding ``dtensor_weight_loader`` for weight synchronization between FSDP and vLLM. While for ``hf_weight_loader``, users can directly apply any models supported both in HF and vLLM without any code change. - Easy to organize the forward and backward computation for each model. **Cons** - Poor scalability when it comes to large-scale models (e.g. Llama 70B and 405B) - The resharding overhead between actor and rollout could be larger than Megatron-LM backend. Due to the simplicity, we recommend using FSDP backend for algorithm research and prototyping. FSDP Workers -------------- ActorRolloutRefWorker ^^^^^^^^^^^^^^^^^^^^^ Actor/Rollout HybridEngine '''''''''''''''''''''''''' 1. HybridEngine, Actor and Rollout initialization API. .. code:: python @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): ``ONE_TO_ALL``: when calling the ``init_model`` function from the driver process, each worker (on a GPU) will execute the following model initialization process. The initialization details of HybridEngine, Actor and Rollout are highlighted below: 1. ``DataParallelPPOActor`` implements the simple PPO computation logics when the model is built with FSDP, including compute log prob, model update. 2. ``vLLMRollout`` support generation with vLLM. We modify the vLLM Engine and make it executed under SPMD to fit into our ``WorkerGroup`` design. See `source code `_. for more information. 1. Generate sequence and recompute log prob .. code:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def generate_sequences(self, prompts: DataProto): - ``Dispatch.DP_COMPUTE_PROTO``: The data will be dispatched and collected along the DP dimension - In this function, the rollout model will perform auto-regressive generation and the actor model will recompute the old log prob for the generated response. 3. Update actor model .. code:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_actor(self, data: DataProto): - Update the actor model weight using PPO & entropy loss. ReferenceModel '''''''''''''' 1. Reference model initialization The reference model is initialized using the same function as the actor model without initializing the HybridEngine and Optimizer. Then the actor model is also wrapped by the ``DataParallelPPOActor``. 2. Compute reference log prob .. code:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_ref_log_prob(self, data: DataProto): - In this function, the reference model will call the compute log prob function in ``DataParallelPPOActor`` to compute the reference log prob. CriticWorker and RewardWorker ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. Model initialization Quite similar to reference model. The CriticWorker will perform additional initialization for the Optimizer. 2. Compute Values for CriticWorker .. code:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_values(self, data: DataProto): 3. Update Critic .. code:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_critic(self, data: DataProto): 4. Compute Reward .. code:: python @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_rm_score(self, data: DataProto): HybridShard ------------ We didn't support FSDP `HybridShard`. To support this, we may need to construct a 2D device mesh and test the corresponding ``dtensor_weight_loader`` and ``hf_weight_loader`` for each model. ================================================ FILE: docs/workers/megatron_workers.rst ================================================ Megatron-LM Backend =================== Last updated: 12/01/2025. We support Megatron Backend by implementing various workers for actor, critic, reference, rollout and reward models. We also implement the ``3DHybridEngine`` using Megatron-LM and vLLM/SGLang in `megatron_vllm.py `_ and `megatron_sglang.py `_. **Pros** - Support 5D parallelism (TP, EP, CP, DP, PP) and sequence parallelism for best scalablility and throughput. - 3D HybridEngine can significantly reduce peak memory usage and reduce weight synchronize overhead between actor and rollout. **Cons** - Huggingface Models and Megatron checkpoints need tools for conversion. Development Progress -------------------- Note that [Deprecated] means that the feature is not supported in the latest version of verl. [To-Optimize] means that the feature is implemented but not optimized yet. [WIP] means that the feature is working in progress. [In-Release] means that the feature is ready and in review process, coming at any time. +---------------+-----------------------------------------------------------+ | [Deprecated] | Megatron 3D Parallelism with custom models | +---------------+-----------------------------------------------------------+ | [Done] | Megatron 0.11.0 ``GPTModel`` support | +---------------+-----------------------------------------------------------+ | [Done] | Megatron GRPO support | +---------------+-----------------------------------------------------------+ | [Done] | Megatron with vLLM 0.8.2, with per-tensor weights loading | +---------------+-----------------------------------------------------------+ | [Done] | Megatron with Context Parallel | +---------------+-----------------------------------------------------------+ | [Done] | Qwen2MoE model support | +---------------+-----------------------------------------------------------+ | [To-Optimize] | Megatron dist Checkpoint | +---------------+-----------------------------------------------------------+ | [To-Optimize] | Huggingface and Megatron Checkpoint Converter | +---------------+-----------------------------------------------------------+ | [To-Optimize] | Efficient fused linear, entropy and cross entropy | +---------------+-----------------------------------------------------------+ | [Done] | Megatron offload(param, grad, optimizer) | +---------------+-----------------------------------------------------------+ | [Done] | Megatron Profiler | +---------------+-----------------------------------------------------------+ | [In-Release] | Megatron 0.12.0, TE 2.2 with vLLM 0.8.3 and Fused Attn | +---------------+-----------------------------------------------------------+ | [WIP] | Moonlight/DeepSeek-V3 model support | +---------------+-----------------------------------------------------------+ | [WIP] | Expert Parallel support | +---------------+-----------------------------------------------------------+ | [WIP] | Megatron support dynamic batch size | +---------------+-----------------------------------------------------------+ | [To-Do] | Performance tuning | +---------------+-----------------------------------------------------------+ | [MileStone] | Runnable with DeepSeek-V3 671B post-training | +---------------+-----------------------------------------------------------+ Utils of Megatron Workers ------------------------- MegatronWorker ^^^^^^^^^^^^^^ ``MegatronWorker`` is the base class of different megatron worker classes. In this class, ``get_megatron_global_info`` and ``get_megatron_rank_info`` function to retrieve the 3D parallel world size and rank of each ``Worker`` running on specific GPU. These information will be used in transfer protocol for Megatron Backend. The following ``Worker`` class for different models will be utilized to construct the ``WorkerGroup`` . We implement various of APIs for each ``Worker`` class decorated by the ``@register(dispatch_mode=)`` . These APIs can be called by the ray driver process. The data can be correctly collect and dispatch following the ``dispatch_mode`` on each function. The supported dispatch_model (i.e., transfer protocols) can be found in `decorator.py `_. ActorRolloutRefWorker ^^^^^^^^^^^^^^^^^^^^^ This class is implemented for Actor/Rollout HybridEngine or for the reference model to initialize their model and perform computation. Actor/Rollout HybridEngine '''''''''''''''''''''''''' 1. HybridEngine, Actor and Rollout initialization API. .. code:: python @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): ``ONE_TO_ALL``: when calling the ``init_model`` function from the driver process, each worker (on a GPU) will execute the following model initialization process. The initialization details of HybridEngine, Actor and Rollout are highlighted below: 1. ``MegatronPPOActor`` implements the simple PPO computation logics when the model is built with Megatron, including compute log prob, model update. 2. ``vLLMRollout`` support generation with vLLM. We modify the vLLM Engine and make it executed under SPMD to fit into our ``WorkerGroup`` design. See `source code `_ for more information. .. code:: python # build actor model self.actor = MegatronPPOActor(config=self.config.actor, model_config=self.actor_model_config, megatron_config=megatron_config, actor_module=self.actor_module, actor_optimizer=self.actor_optimizer, actor_optimizer_config=self.actor_optim_config) # build rollout # rollout initialization rollout = vLLMRollout(actor_module=params, config=self.config.rollout, tokenizer=self.tokenizer, model_hf_config=self.actor_model_config, train_tp=mpu.get_tensor_model_parallel_world_size()) ... 1. Generate sequence and recompute log prob .. code:: python @register(dispatch_mode=Dispatch.MEGATRON_PP_AS_DP_PROTO) def generate_sequences(self, prompts: DataProto): - ``Dispatch.MEGATRON_PP_AS_DP_PROTO``: The PP dimension of the actor model will be regarded as DP dimension. Then the driver process will dispatch and collect the data according to this reorganization. This is because, in HybridEngine, the actor weight, which usually applied larger 3D parallel sizes, will be gathered along the PP dimension and TP dimension. Therefore, the corresponding data should be dispatched and collected through the 3D parallel group of the rollout model, rather than the actor model. However, the world_size and rank information can only be retrieved from ``get_megatron_global_info`` and ``get_megatron_rank_info``, which records the 3D information for the actor model. Moreover, the data resharding inside TP dimension will be processed within the HybridEngine. - In this function, the rollout model will perform auto-regressive generation and the actor model will recompute the old log prob for the generated response. 3. Update actor model .. code:: python @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def update_actor(self, data: DataProto): - ``Dispatch.MEGATRON_COMPUTE_PROTO``: User passes the data partitioned by DP dimension. The data is dispatched to all tp/pp ranks within the same dp group, and ultimately only collects output data from tp=0 and the last pp. - Update the actor model weight using PPO & entropy loss. ..note:: Currently, training Tensor Parallel Size can be different from inference Tensor Parallel Size. ReferenceModel '''''''''''''' 1. Reference model initialization The reference model is initialized using the same function as the actor model without initializing the HybridEngine and Optimizer. Then the actor model is also wrapped by the ``MegatronPPOActor``. 2. Compute reference log prob .. code:: python @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_ref_log_prob(self, data: DataProto): - In this function, the reference model will call the compute log prob function in ``MegatronPPOActor`` to compute the reference log prob. CriticWorker and RewardWorker ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. Model initialization Quite similar to reference model. The CriticWorker will perform additional initialization for the Optimizer. 2. Compute Values for CriticWorker .. code:: python @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_values(self, data: DataProto): 3. Update Critic .. code:: python @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def update_critic(self, data: DataProto): 4. Compute Reward .. code:: python @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_rm_score(self, data: DataProto): Utils of Train Optimization --------------------------- Offload ^^^^^^^ When resources are tight, the offload method can lower GPU memory usage, helping training and inference frameworks work well under verl. It moves parameters, gradients, and optimizers to CPU memory and only loads them back to the GPU when needed. If you want to use the offload, you can add the following parameters for the actor and ref separately. .. code:: python # For the actor actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ # For the ref w/o grad and optimizer actor_rollout_ref.ref.megatron.param_offload=True \ For the critic, you can include these parameters. .. code:: python # For the critic critic.megatron.param_offload=True \ critic.megatron.grad_offload=True \ critic.megatron.optimizer_offload=True \ Related MCore Document ---------------------- There is also a detailed document of using MCore to train different kinds of models, please refer to `MCore Document `_. ================================================ FILE: docs/workers/model_engine.rst ================================================ Model Engine ============ .. _vermouth: https://github.com/vermouth1992 Author: `Chi Zhang `_ Last updated: 09/25/2025. Current Support Matrix ---------------------- +----------+-----------+--------------+-------------+--------------------------+ | Backends | Model | Scalability | Model | Pain points | | | Supported | | Definition | | | | | | | | +==========+===========+==============+=============+==========================+ | FSDP | Day 1 | - Dense is OK| Huggingface | Monkey patch can be | | + | support | | + monkey | easily impacted by | | ulysses | HF model | - MoE is bad | patch | transformers version | +----------+-----------+--------------+-------------+--------------------------+ | MCore | Limited | Best | GPTModel | Supporting new models is | | | | | (One model | difficult | | | | | for all) | | +----------+-----------+--------------+-------------+--------------------------+ - We monkey patch attention function to support ulysses - We monkey patch VLM models to support FSDP with mixed data with and without images Class Hierarchy --------------- Note that all the workers and trainers run in **SPMD** mode. SFT/DPO/RM trainer is directly invoked by ``torchrun``. The Actor/Critic worker can also be invoked by a RayWorkerGroup and provides APIs to a single controller. - Base Engine level: implement model init, optimizer init, lr scheduler init, sharding, checkpoint manager. - Full Engine level: subclass base engine and implement ``forward_step``. - Worker/SPMD trainer level: **engine agnostic**, implement training logics using abstract engine APIs RL trainer utilizes workers to construct HybridFlow program. This is out of the scope of model engine. Existing Model Types -------------------- ========== ====================== ====================== Model type Language model Value model ========== ====================== ====================== Input text/image/video/audio text/image/video/audio Output logits for next token logits as value ========== ====================== ====================== Currently, we have two model types: language model and value model. We expect to expand the category to include Qwen-Omni family (output both text and audio) and VLA models. Data Format ----------- Currently, verl adopts left-right padding data format in RL trainer. This creates massive padding when the discrepancy between response length is large. We will start to implement no-padding format throughout the whole system. .. image:: https://github.com/vermouth1992/verl-data/blob/master/images/data_format.png?raw=true :alt: Data Format Here is the migration plan: - Implement no-padding format in engine - Add a transformation layer in Actor/Critic worker. - Replace Actor/Critic Worker in RL trainer - Implement no-padding throughput system Checkpoint System ----------------- .. image:: https://github.com/vermouth1992/verl-data/blob/master/images/verl-ckpt.png?raw=true :alt: Model Engine Checkpoint System The engine constructs the model using huggingface config, then load weights from huggingface checkpoint. If the engine directly uses huggingface model definition, it can use function provided by ``transformers``. Otherwise, each engine has to write their own checkpoint load logic (e.g., `mbridge `__). During model training, each engine has to implement save_checkpoint and load_checkpoint that save/load intermediate sharded checkpoint including model, optimizer and lr scheduler states. Each engine has to implement a checkpoint merge script, that merges the intermediate sharded checkpoint back to huggingface format. API --- A tentative model engine API can be found: https://github.com/volcengine/verl/blob/main/verl/workers/engine/base.py#L24 Extension --------- Add a new backend ~~~~~~~~~~~~~~~~~ - Start a new folder under ``verl/workers/engine``. Then, implement ``transformer_impl.py``. If you want to implement a non-transformer model, please contact us in advance. - Add the engine config to the GSM8k SFT trainer script: https://github.com/volcengine/verl/blob/main/tests/special_e2e/sft/run_sft_engine_gsm8k.sh - Invoke the tests with your backend: https://github.com/volcengine/verl/blob/main/tests/special_e2e/sft/test_sft_engine_all.sh. This test script will run various backends and various configurations, and compare the loss and grad norm of the first step to make sure they are close. Add a new model type ~~~~~~~~~~~~~~~~~~~~ - This is mainly reserved for models whose the output is not just text (e.g., Qwen3-Omni). Please discuss with us before you proceed. ================================================ FILE: docs/workers/ray_trainer.rst ================================================ PPO Ray Trainer =============== Last updated: 02/12/2025. We implement the RayPPOTrainer, which is a trainer runs on the driver process on a single CPU/GPU node (default is CPU). The PPORayTrainer include 3 core functions for data preparation, WorkerGroup initialization and PPO training loop. Data Preparation ---------------- The ``PPORayTrainer``, as a single process, is responsible for loading a complete batch of samples (prompts) from the dataset and then dispatch to different worker_groups running on different GPUs. To generalize the data loading, we implement the ``RLHFDataset`` class to load the preprocessed parquet files, apply chat templates to the prompts, add padding, truncate prompts that exceed max prompt length and then tokenize. .. code:: python self.train_dataset = RLHFDataset(data_files=self.config.data.train_files, tokenizer=self.tokenizer, config=self.config.data) Then, the dataloader will iterate the dataset under PPO mini batch size. WorkerGroup Initialization -------------------------- We first introduce a basic implementation of initializing the ``WorkerGroup`` of the actor model on a given set of GPUs. .. code:: python # max_colocate_count means the number of WorkerGroups (i.e. processes) in each RayResourcePool # For FSDP backend, we recommend using max_colocate_count=1 that merge all WorkerGroups into one. # For Megatron backend, we recommend using max_colocate_count>1 that can utilize different WorkerGroup for differnt models resource_pool = RayResourcePool(process_on_nodes=[config.trainer.n_gpus_per_node] * config.trainer.nnodes, use_gpu=True, max_colocate_count=1) # define actor rollout cls to be init on remote actor_rollout_cls = RayClassWithInitArgs(cls=ActorRolloutWorker) # define actor_rollout worker group actor_rollout_worker_group = MegatronRayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=actor_rollout_cls, default_megatron_kwargs=config.actor_rollout.megatron) Different WorkerGroups, like ``actor_rollout_worker_group`` , ``critic_worker_group`` and ``ref_worker_group`` lies on a separate process in the above implementation. The driver process can then call the distributed compute function within the ``actor_rollout_worker_group`` and other roles to construct the RL training loop. For models colocated in the same set of GPUs, we further provide a fine-grain optimization, which merge the ``worker_group`` of different roles in the same process. This optimization can save the redundant CUDA/distributed context in different processes. .. code:: python # initialize WorkerGroup # NOTE: if you want to use a different resource pool for each role, which can support different parallel size, # you should not use `create_colocated_worker_cls`. Instead, directly pass different resource pool to different worker groups. # See TODO(url) for more information. all_wg = {} for resource_pool, class_dict in self.resource_pool_to_cls.items(): worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = self.ray_worker_group_cls(resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) if self.use_critic: self.critic_wg = all_wg['critic'] self.critic_wg.init_model() if self.use_reference_policy: self.ref_policy_wg = all_wg['ref'] self.ref_policy_wg.init_model() if self.use_rm: self.rm_wg = all_wg['rm'] self.rm_wg.init_model() # we should create rollout at the end so that vllm can have a better estimation of kv cache memory self.actor_rollout_wg = all_wg['actor_rollout'] self.actor_rollout_wg.init_model() .. note:: For megatron backend, if we merge the ``worker_groups`` into the same processes, all the roles will utilize the same 3D parallel size. To optimize this, we may need to maintain several 3D process groups for each role in the same distributed context. If you want to use different 3D parallel size for different roles, please follow the similar architecture of the first code block to initialize each role's ``worker_group`` PPO Training Loop ----------------- We implement the PPO training loop by calling the functions in worker_group of each role. The input and output data of each function is a ``DataProto`` object implemented in `protocol.py `_. In the training loop, trainer will dispatch/collect the data to/from different GPUs following the transfer protocols wrapped in the workers' functions. The computation of PPO micro batches is processed in ``update_actor`` and ``update_critic`` functions. To extend to other RLHF algorithms, such as DPO, GRPO, please refer to :doc:`../advance/dpo_extension`. .. code:: python def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ from verl.utils.tracking import Tracking from omegaconf import OmegaConf logger = Tracking(project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True)) global_steps = 0 # perform validation before training # currently, we only support validation using the reward_function. if self.val_reward_fn is not None: val_metrics = self._validate() pprint(f'Initial validation metrics: {val_metrics}') for epoch in range(self.config.trainer.total_epochs): for batch_dict in self.train_dataloader: metrics = {} batch: DataProto = DataProto.from_single_dict(batch_dict) # batch = batch.to('cuda') # pop those keys for generation gen_batch = batch.pop(batch_keys=['input_ids', 'attention_mask', 'position_ids']) # generate a batch with Timer(name='gen', logger=None) as timer: gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch) metrics['timing/gen'] = timer.last batch = batch.union(gen_batch_output) if self.use_reference_policy: # compute reference log_prob with Timer(name='ref', logger=None) as timer: ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch) batch = batch.union(ref_log_prob) metrics['timing/ref'] = timer.last # compute values with Timer(name='values', logger=None) as timer: values = self.critic_wg.compute_values(batch) batch = batch.union(values) metrics['timing/values'] = timer.last with Timer(name='adv', logger=None) as timer: # compute scores. Support both model and function-based. # We first compute the scores using reward model. Then, we call reward_fn to combine # the results from reward model and rule-based results. if self.use_rm: # we first compute reward model score reward_tensor = self.rm_wg.compute_rm_score(batch) batch = batch.union(reward_tensor) # we combine with rule-based rm reward_tensor = self.reward_fn(batch) batch.batch['token_level_scores'] = reward_tensor # compute rewards. apply_kl_penalty if available batch, kl_metrics = apply_kl_penalty(batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty) metrics.update(kl_metrics) # compute advantages, executed on the driver process batch = compute_advantage(batch, self.config.algorithm.gamma, self.config.algorithm.lam, adv_estimator=self.config.algorithm.adv_estimator) metrics['timing/adv'] = timer.last # update critic if self.use_critic: with Timer(name='update_critic', logger=None) as timer: critic_output = self.critic_wg.update_critic(batch) metrics['timing/update_critic'] = timer.last critic_output_metrics = reduce_metrics(critic_output.meta_info['metrics']) metrics.update(critic_output_metrics) # implement critic warmup if self.config.trainer.critic_warmup <= global_steps: # update actor with Timer(name='update_actor', logger=None) as timer: actor_output = self.actor_rollout_wg.update_actor(batch) metrics['timing/update_actor'] = timer.last actor_output_metrics = reduce_metrics(actor_output.meta_info['metrics']) metrics.update(actor_output_metrics) # validate if self.val_reward_fn is not None and (global_steps + 1) % self.config.trainer.test_freq == 0: with Timer(name='testing', logger=None) as timer: val_metrics: dict = self._validate() val_metrics = {f'val/{key}': val for key, val in val_metrics.items()} metrics['timing/testing'] = timer.last metrics.update(val_metrics) # collect metrics data_metrics = compute_data_metrics(batch=batch) metrics.update(data_metrics) # TODO: make a canonical logger that supports various backend logger.log(data=metrics, step=global_steps) if self.config.trainer.save_freq > 0 and (global_steps + 1) % self.config.trainer.save_freq == 0: actor_local_path = os.path.join(self.config.trainer.default_local_dir, 'actor', f'global_step_{global_steps}') actor_remote_path = os.path.join(self.config.trainer.default_hdfs_dir, 'actor') self.actor_rollout_wg.save_checkpoint(actor_local_path, actor_remote_path) if self.use_critic: critic_local_path = os.path.join(self.config.trainer.default_local_dir, 'critic', f'global_step_{global_steps}') critic_remote_path = os.path.join(self.config.trainer.default_hdfs_dir, 'critic') self.critic_wg.save_checkpoint(critic_local_path, critic_remote_path) global_steps += 1 # perform validation after training if self.val_reward_fn is not None: val_metrics = self._validate() pprint(f'Final validation metrics: {val_metrics}') ================================================ FILE: docs/workers/sglang_worker.rst ================================================ SGLang Backend ============== Last updated: 05/31/2025. **Authored By SGLang RL Team and listed alphabetically by last name** `Jingyi Chen `_, `Yitong Guan `_, `Zhuobin Huang `_, `Jiajun Li `_, `Ji Li `_, `Shenggui Li `_, `Junrong Lin `_, `Xiang Long `_, `Rui Lu `_, `Jin Pan `_, `Shuai Shi `_, `Yushen Su `_, `Xinyuan Tong `_, `Chendong Wang `_, `Hanchen Zhang `_, `Haoran Wang `_, `Yongan Xiang `_, `Chengxing Xie `_, `Yuhao Yang `_, `Jinwei Yao `_, `Qiaolin Yu `_, `Yuzhen Zhou `_, `Chenyang Zhao `_ Introduction ------------ `SGLang `_ is an open-source state-of-the-art inference service engine, fully adopted by xAI to support all inference needs of Grok during research and serving processes. Currently, verl fully supports using SGLang as the inference engine during the rollout phase. As a rollout engine, SGLang provides the same feature coverage as vLLM., including memory saving and multi-node rollout features. After installing verl and SGLang, simply add ``actor_rollout_ref.rollout.name=sglang`` at startup script to seamlessly switch between the two inference frameworks. In addition, the SGLang team is actively working on supporting features such as Multi-Turn Agentic RL, VLM RLHF, Server-Based RLHF, and Partial Rollout. You can track the related development progress in the `Tracking Roadmap `_. Installation ------------ Please always follow the following command to install SGLang with verl. .. code-block:: bash pip install --upgrade pip # Currently 0.4.8, subject to updates at any time, please refer to the latest version specified in `setup.py` pip install -e ".[sglang]" You can check the following dependencies are in your environment: .. note:: - **PyTorch**: 2.6.0+cu124 - **CUDA**: 12.4 - **flashinfer-python**: 0.2.5+cu124torch2.6 - **SGLang**: 0.4.6.post5 - **sgl-kernel**: 0.1.4 Using SGLang as the Inference Backend for PPO Training on a Single Machine ------------------------------------------------------------------------- We use Qwen/Qwen2-7B-Instruct on the gsm8k dataset for a simple test. 1. Run the following command to prepare the gsm8k dataset: .. code-block:: bash python3 examples/data_preprocess/gsm8k.py 2. Run the following script to conduct a PPO experiment on a single machine with 4 GPUs: .. code-block:: bash export SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK=True PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ critic.model.fsdp_config.param_offload=True \ critic.model.fsdp_config.optimizer_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=console \ trainer.val_before_train=False \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 2>&1 | tee verl_demo.log Why export SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. ``verl`` initializes a ``SGLangRollout`` module during rollout, which is used to evaluate/generate samples. 2. ``SGLangRollout`` will initialize ``Engine``, and further initialize a ``torch.distributed.DeviceMesh``, used to support Tensor Parallel (TP). 3. ``DeviceMesh.init()`` internally checks the free GPU memory of all participating devices. If the difference is too large (more than ~10%), it directly reports an error to avoid initialization failures or deadlocks. Why might there be inconsistent GPU memory? """"""""""""""""""""""""""""""""""""""""""" **1. Ray Distributed Actor loads the model at different times** ``verl`` uses Ray-based multi-process, multi-GPU concurrent training. Each ``WorkerDict`` may be called at different times: .. code-block:: python self.rollout = SGLangRollout(...) Different workers initialize the model at different times → different memory usage. **2. Delayed initialization causes memory bias** Some workers start model loading/inference (e.g., ``generate_sequences()``, ``compute_log_prob()``) earlier than others. Early workers already use up GPU memory → late workers still have empty memory → memory difference appears. **3. SGLang's TP init uses "all-device broadcast", but there's no uniform release timing** Although ``SGLangRollout`` may only involve subset of GPUs, its ``Engine`` initialization calls ``torch.distributed.init_process_group()`` and broadcasts weights, so: - Non-rollout GPUs also join the communication. - Later on, ``DeviceMesh`` init will fail due to "inconsistent memory". **4. Different FSDP/TP loading behaviors also lead to mismatch** If using: .. code-block:: bash actor.fsdp_config.param_offload=True ref.fsdp_config.param_offload=True Then some workers keep params on CPU while others already sharded to GPU → leads to asymmetric memory layout. Using SGLang as the Inference Backend for PPO Training Across Multiple Machines ------------------------------------------------------------------------------ SGLang also supports running verl's RAY-based cross-machine inference in IPv4 and IPv6 scenarios. In the script below, we use TP=16 for cross-machine inference. Suppose we have two interconnected machines: node0 with IP 10.94.16.4 and node1 with IP 10.94.16.5. 1. Start Ray on node0: .. code-block:: bash ray start --head --dashboard-host=0.0.0.0 You will see the following prompt: .. code-block:: bash Usage stats collection is enabled. To disable this, add `--disable-usage-stats` to the command that starts the cluster, or run the following command: `ray disable-usage-stats` before starting the cluster. See https://docs.ray.io/en/master/cluster/usage-stats.html for more details. Local node IP: 10.94.16.4 -------------------- Ray runtime started. -------------------- Next steps To add another node to this Ray cluster, run ray start --address='10.94.16.4:6379' 2. Have node1 join the Ray cluster: Run the following command on node1: .. code-block:: bash ray start --address='10.94.16.4:6379' Run the following command to confirm that the Ray cluster now has two nodes: .. code-block:: bash ray status You can see that the cluster has two nodes with 16 GPUs: .. code-block:: bash ======== Autoscaler status: 2025-04-09 09:25:37.694016 ======== Node status --------------------------------------------------------------- Active: 1 node_ef382ffd687d8f6b060c1b68e63ada7341b936fe5b1901dd04de1027 1 node_1eb4d7d07e793114c23a89d1a41f1f76acf6ef5b35af844a4ee8e4ba Pending: (no pending nodes) Recent failures: (no failures) Resources --------------------------------------------------------------- Usage: 0.0/360.0 CPU 0.0/16.0 GPU 0B/3.39TiB memory 0B/372.53GiB object_store_memory 3. Run the following script to train meta-llama/Llama-3.1-8B-Instruct with TP=16 across 2 machines using 16 GPUs: .. code-block:: bash DATA_DIR=$HOME/data/gsm8k python3 -m verl.trainer.main_ppo \ actor_rollout_ref.rollout.name=sglang \ data.train_files=$DATA_DIR/train.parquet \ data.val_files=$DATA_DIR/test.parquet \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ actor_rollout_ref.model.path=meta-llama/Llama-3.1-8B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=16 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=meta-llama/Llama-3.1-8B-Instruct \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size=16 \ critic.model.fsdp_config.param_offload=True \ critic.model.fsdp_config.optimizer_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.val_before_train=True \ trainer.n_gpus_per_node=8 \ trainer.nnodes=2 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 2>&1 | tee verl_demo.log ================================================ FILE: docs/workers/trtllm_worker.rst ================================================ TensorRT-LLM Backend ==================== Last updated: 12/31/2025. **Authored By TensorRT-LLM Team** Introduction ------------ `TensorRT-LLM `_ is a high-performance LLM inference engine with state-of-the-art optimizations for NVIDIA GPUs. The verl integration of TensorRT-LLM is based on TensorRT-LLM's `Ray orchestrator `_. This integration is in its early stage, with more features and optimizations to come. The TensorRT-LLM rollout engine primarily targets the colocated mode. Instead of relying purely on standard colocated mode, we adopted a mixed design combining aspects of the hybrid engine and colocated mode. Installation ------------ We provide ``docker/Dockerfile.stable.trtllm`` for building a docker image with TensorRT-LLM pre-installed. The verl integration is supported from ``nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6``, and you can choose other TensorRT-LLM versions via ``TRTLLM_BASE_IMAGE`` from the `NGC Catalog `_. Alternatively, refer to the `TensorRT-LLM installation guide `_ for compatible environments if you want to build your own. Install verl with TensorRT-LLM: .. code-block:: bash pip install --upgrade pip pip install -e ".[trtllm]" .. note:: Using the TensorRT-LLM rollout requires setting the following environment variables before launching the Ray cluster. These have been included in all the example scripts: .. code-block:: bash # Clean all SLURM/MPI/PMIx env to avoid PMIx mismatch error. for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done Using TensorRT-LLM as the Rollout Engine for GRPO ------------------------------------------------- We provide the following GRPO recipe scripts for you to test the performance and accuracy curve of TensorRT-LLM as the rollout engine: .. code-block:: bash ## For FSDP training engine bash examples/grpo_trainer/run_qwen2-7b_math_trtllm.sh ## For Megatron-Core training engine bash examples/grpo_trainer/run_qwen2-7b_math_megatron_trtllm.sh Using TensorRT-LLM as the Rollout Engine for DAPO ------------------------------------------------- We provide a DAPO recipe script ``recipe/dapo/test_dapo_7b_math_trtllm.sh``. .. code-block:: bash ## For FSDP training engine bash recipe/dapo/test_dapo_7b_math_trtllm.sh ## For Megatron-Core training engine TRAIN_ENGINE=megatron bash recipe/dapo/test_dapo_7b_math_trtllm.sh ================================================ FILE: examples/cispo_trainer/run_cispo_qwen2_5_0_5b_gsm8k.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet train_files="['$gsm8k_train_path']" test_files="['$gsm8k_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ actor_rollout_ref.actor.policy_loss.loss_mode=cispo \ actor_rollout_ref.actor.clip_ratio_low=10 \ actor_rollout_ref.actor.clip_ratio_high=0.2 \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.model.torch_dtype=bfloat16 \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_cispo_example_gsm8k' \ trainer.experiment_name='qwen2_5_0_5b_cispo' \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=5 \ trainer.test_freq=5 \ trainer.total_epochs=3 $@ ================================================ FILE: examples/data_preprocess/aime2024_multiturn_w_tool.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the DAPO-Math-17k dataset to multiturn format """ import argparse import os import datasets from verl.utils.hdfs_io import copy, makedirs if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/retool_aime2024", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_path = "BytedTsinghua-SIA/AIME-2024" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "default") else: dataset = datasets.load_dataset(data_path, "default") train_dataset = dataset["train"] # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): orig_extra_info = example.pop("extra_info") extra_info = orig_extra_info.copy() extra_info["need_tools_kwargs"] = True extra_info["tools_kwargs"] = { "code_interpreter": { "create_kwargs": { "ground_truth": example["reward_model"]["ground_truth"], }, }, } example["extra_info"] = extra_info return example return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/dapo_multiturn_w_tool.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the DAPO-Math-17k dataset to multiturn format """ import argparse import os import datasets from verl.utils.hdfs_io import copy, makedirs if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/retool_dapo", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_path = "BytedTsinghua-SIA/DAPO-Math-17k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "default") else: dataset = datasets.load_dataset(data_path, "default") train_dataset = dataset["train"] # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): orig_extra_info = example.pop("extra_info") extra_info = orig_extra_info.copy() extra_info["need_tools_kwargs"] = True extra_info["tools_kwargs"] = { "code_interpreter": { "create_kwargs": { "ground_truth": example["reward_model"]["ground_truth"], }, }, } example["extra_info"] = extra_info return example return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/full_hh_rlhf.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ - Preprocess data and split the training set into 75% for training RM and 25% for validting RM. - All the training data is used to train SFT and RL. - Both chosen and rejected is used to train SFT """ import argparse import os import pandas as pd from datasets import load_dataset from tqdm.auto import tqdm from verl.utils.fs import copy, makedirs def generate_sft_dataset(target_hdfs_path_dir, local_dir="~/data/full_hh_rlh/sft", local_dataset_path=None): if local_dataset_path is not None: dataset = load_dataset(local_dataset_path) else: dataset = load_dataset("Dahoas/full-hh-rlhf") output = {"prompt": [], "response": []} for data in tqdm(dataset["train"]): # add chosen output["prompt"].append(data["prompt"]) output["response"].append(data["chosen"]) # add rejection output["prompt"].append(data["prompt"]) output["response"].append(data["rejected"]) df = pd.DataFrame(output) local_dir = os.path.expanduser(local_dir) os.makedirs(local_dir, exist_ok=True) local_path = os.path.join(local_dir, "train.parquet") df.to_parquet(path=local_path) if target_hdfs_path_dir is not None: hdfs_dir = target_hdfs_path_dir + "/" + "train.parquet" makedirs(hdfs_dir) copy(local_path, hdfs_dir) def generate_rm_dataset(target_hdfs_path_dir, local_dir="~/data/full_hh_rlh/rm", local_dataset_path=None): if local_dataset_path is not None: train_dataset = load_dataset(local_dataset_path, split="train[:75%]") test_dataset = load_dataset(local_dataset_path, split="train[-25%:]") else: train_dataset = load_dataset("Dahoas/full-hh-rlhf", split="train[:75%]") test_dataset = load_dataset("Dahoas/full-hh-rlhf", split="train[-25%:]") local_dir = os.path.expanduser(local_dir) os.makedirs(local_dir, exist_ok=True) for dataset, name in zip([train_dataset, test_dataset], ["train", "test"], strict=True): output = {"prompt": [], "chosen": [], "rejected": []} for data in tqdm(dataset): # add chosen output["prompt"].append(data["prompt"]) output["chosen"].append(data["chosen"]) output["rejected"].append(data["rejected"]) df = pd.DataFrame(output) local_path = os.path.join(local_dir, name + ".parquet") df.to_parquet(path=local_path) if target_hdfs_path_dir is not None: hdfs_dir = target_hdfs_path_dir + "/" + name + ".parquet" makedirs(hdfs_dir) copy(local_path, hdfs_dir) def generate_rl_dataset(target_hdfs_path_dir, local_dir="~/data/full_hh_rlhf/rl", local_dataset_path=None): if local_dataset_path is not None: dataset = load_dataset(local_dataset_path) else: dataset = load_dataset("Dahoas/full-hh-rlhf") train_dataset = dataset["train"] data_source = "Dahoas/full-hh-rlhf" # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): prompt = example.pop("prompt") response = example.pop("response") data = { "data_source": data_source, "prompt": [{"role": "user", "content": prompt}], "ability": "alignment", "reward_model": { "style": "model", "ground_truth": response, # should not be used }, "extra_info": {"split": split, "index": idx}, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) local_dir = os.path.expanduser(local_dir) local_path = os.path.join(local_dir, "train.parquet") train_dataset.to_parquet(local_path) if target_hdfs_path_dir is not None: hdfs_dir = target_hdfs_path_dir + "/" + "train.parquet" makedirs(hdfs_dir) copy(local_path, hdfs_dir) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--split", type=str, choices=["sft", "rm", "rl"], required=True) parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", type=str, required=False, default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", type=str, default="~/data/full_hh_rlhf", help="The save directory for the preprocessed dataset.", ) args = parser.parse_args() local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir if args.split == "sft": generate_sft_dataset(args.hdfs_dir, os.path.join(local_save_dir, args.split), args.local_dataset_path) elif args.split == "rm": generate_rm_dataset(args.hdfs_dir, os.path.join(local_save_dir, args.split), args.local_dataset_path) elif args.split == "rl": generate_rl_dataset(args.hdfs_dir, os.path.join(local_save_dir, args.split), args.local_dataset_path) else: raise NotImplementedError ================================================ FILE: examples/data_preprocess/geo3k.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the Geometry3k dataset to parquet format """ import argparse import os import datasets from verl.utils.hdfs_io import copy, makedirs if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None) parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/geo3k", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "hiyouga/geometry3k" if local_dataset_path is not None: dataset = datasets.load_dataset( local_dataset_path, ) else: dataset = datasets.load_dataset( data_source, ) train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = ( r"You FIRST think about the reasoning process as an internal monologue and then provide the final answer. " r"The reasoning process MUST BE enclosed within tags. " r"The final answer MUST BE put in \boxed{}." ) # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): problem = example.pop("problem") prompt = problem + " " + instruction_following answer = example.pop("answer") images = example.pop("images") data = { "data_source": data_source, "prompt": [ { "role": "user", "content": prompt, } ], "images": images, "ability": "math", "reward_model": {"style": "rule", "ground_truth": answer}, "extra_info": { "split": split, "index": idx, "answer": answer, "question": problem, }, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True, num_proc=8) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True, num_proc=8) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/geo3k_multiturn_w_tool.py ================================================ # Copyright 2023-2025 SGLang Team # Copyright Amazon.com, Inc. or its affiliates. # Copyright 2025 Reallm Labs Ltd. or its affiliates # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the Geometry3k dataset to parquet format """ import argparse import os import datasets from verl.utils.hdfs_io import copy, makedirs if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/geo3k_multiturn_w_tool", help="The save directory for the preprocessed dataset.", ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "hiyouga/geometry3k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path) else: dataset = datasets.load_dataset(data_source) train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = ( r"You FIRST think about the reasoning process as an internal monologue and then provide the final answer. " r"The reasoning process MUST BE enclosed within tags. " r"The final answer MUST BE put in \boxed{}." ) # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): problem = example.pop("problem") prompt = problem + " " + instruction_following answer = example.pop("answer") images = example.pop("images") data = { "data_source": data_source, "prompt": [ { "role": "system", "content": ( "You are a math expert. You are given a question and you need to solve it step by step. " "Reasoning step by step before any tool call. " "You should use the `calc_geo3k_reward` tool after step by step solving the question, " "before generate final answer at least once and refine your answer if necessary. " ), }, { "role": "user", "content": prompt, }, ], "images": images, "ability": "math", "reward_model": {"style": "rule", "ground_truth": answer}, "extra_info": { "split": split, "index": idx, "answer": answer, "question": problem, "need_tools_kwargs": True, "tools_kwargs": { "calc_geo3k_reward": { "create_kwargs": {"ground_truth": answer}, # "execute_kwargs": {}, # "calc_reward_kwargs": {}, # "release_kwargs": {}, }, }, }, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True, num_proc=8) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True, num_proc=8) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/gsm8k.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the GSM8k dataset to parquet format """ import argparse import os import re import datasets from verl.utils.hdfs_io import copy, makedirs def extract_solution(solution_str): solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) assert solution is not None final_solution = solution.group(0) final_solution = final_solution.split("#### ")[1].replace(",", "") return final_solution if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/gsm8k", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "openai/gsm8k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "main") else: dataset = datasets.load_dataset(data_source, "main") train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = 'Let\'s think step by step and output the final answer after "####".' # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question_raw = example.pop("question") question = question_raw + " " + instruction_following answer_raw = example.pop("answer") solution = extract_solution(answer_raw) data = { "data_source": data_source, "prompt": [ { "role": "user", "content": question, } ], "ability": "math", "reward_model": {"style": "rule", "ground_truth": solution}, "extra_info": { "split": split, "index": idx, "answer": answer_raw, "question": question_raw, }, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/gsm8k_multiturn_sft.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the GSM8k dataset to parquet format """ import argparse import os import re import datasets from verl.utils.hdfs_io import copy, makedirs def extract_solution(solution_str): solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) assert solution is not None final_solution = solution.group(0) final_solution = final_solution.split("#### ")[1].replace(",", "") return final_solution if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/gsm8k_sft", help="The save directory for the preprocessed dataset." ) parser.add_argument("--hdfs_dir", default=None) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "openai/gsm8k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "main") else: dataset = datasets.load_dataset(data_source, "main") train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = 'Let\'s think step by step and output the final answer after "####".' # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question_raw = example.pop("question") question = question_raw + " " + instruction_following answer_raw = example.pop("answer") data = { "messages": [ { "role": "user", "content": question, }, { "role": "assistant", "content": answer_raw, }, ], } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir local_save_dir = os.path.expanduser(local_save_dir) train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/gsm8k_multiturn_w_interaction.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the GSM8k dataset to parquet format """ import argparse import os import re import datasets from verl.utils.hdfs_io import copy, makedirs def extract_solution(solution_str): solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) assert solution is not None final_solution = solution.group(0) final_solution = final_solution.split("#### ")[1].replace(",", "") return final_solution if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/gsm8k", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "openai/gsm8k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "main") else: dataset = datasets.load_dataset(data_source, "main") train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = "Let's think step by step and output the final answer after `####`." # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question_raw = example.pop("question") question = question_raw + " " + instruction_following answer_raw = example.pop("answer") solution = extract_solution(answer_raw) data = { "data_source": data_source, "agent_name": "tool_agent", "prompt": [ { "role": "system", "content": ( "You are a math expert. You are given a question and you need to solve it step by step. " "You should rethinking carefully if user point out your answer is wrong. " "Put your final answer in the format of `#### `." ), }, { "role": "user", "content": question, }, ], "ability": "math", "reward_model": {"style": "rule", "ground_truth": solution}, "extra_info": { "split": split, "index": idx, "answer": answer_raw, "question": question_raw, "interaction_kwargs": { "name": "gsm8k", "query": question, "ground_truth": solution, }, }, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/gsm8k_multiturn_w_tool.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the GSM8k dataset to parquet format """ import argparse import os import re import datasets from verl.utils.hdfs_io import copy, makedirs def extract_solution(solution_str): solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) assert solution is not None final_solution = solution.group(0) final_solution = final_solution.split("#### ")[1].replace(",", "") return final_solution if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/gsm8k", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "openai/gsm8k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "main") else: dataset = datasets.load_dataset(data_source, "main") train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = "Let's think step by step and output the final answer after `####`." # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question_raw = example.pop("question") question = question_raw + " " + instruction_following answer_raw = example.pop("answer") solution = extract_solution(answer_raw) data = { "data_source": data_source, "prompt": [ { "role": "system", "content": ( "You are a math expert. You are given a question and you need to solve it step by step. " "Reasoning step by step before any tool call. " "You should use the `calc_gsm8k_reward` tool after step by step solving the question, " "before generate final answer at least once and refine your answer if necessary. " "Put your final answer in the format of `#### `." ), }, { "role": "user", "content": question, }, ], "ability": "math", "reward_model": {"style": "rule", "ground_truth": solution}, "extra_info": { "split": split, "index": idx, "answer": answer_raw, "question": question_raw, "need_tools_kwargs": True, "tools_kwargs": { "calc_gsm8k_reward": { "create_kwargs": {"ground_truth": solution}, # "execute_kwargs": {}, # "calc_reward_kwargs": {}, # "release_kwargs": {}, }, }, "interaction_kwargs": { "query": question, "ground_truth": solution, }, }, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/gsm8k_tool_agent_loop.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the GSM8k dataset to parquet format """ import argparse import os import re import datasets from verl.utils.hdfs_io import copy, makedirs def extract_solution(solution_str): solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) assert solution is not None final_solution = solution.group(0) final_solution = final_solution.split("#### ")[1].replace(",", "") return final_solution if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/gsm8k", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "openai/gsm8k" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path, "main") else: dataset = datasets.load_dataset(data_source, "main") train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = "Let's think step by step and output the final answer after `####`." # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question_raw = example.pop("question") question = question_raw + " " + instruction_following answer_raw = example.pop("answer") solution = extract_solution(answer_raw) data = { "data_source": data_source, "agent_name": "tool_agent", "prompt": [ { "role": "system", "content": ( "You are a math expert. You are given a question and you need to solve it step by step. " "Reasoning step by step before any tool call. " "You should use the `calc_gsm8k_reward` tool after step by step solving the question, " "before generate final answer at least once and refine your answer if necessary. " "Put your final answer in the format of `#### `." ), }, { "role": "user", "content": question, }, ], "ability": "math", "reward_model": {"style": "rule", "ground_truth": solution}, "extra_info": { "split": split, "index": idx, "answer": answer_raw, "question": question_raw, "need_tools_kwargs": True, "tools_kwargs": { "calc_gsm8k_reward": { "create_kwargs": {"ground_truth": solution}, # "execute_kwargs": {}, # "calc_reward_kwargs": {}, # "release_kwargs": {}, }, }, "interaction_kwargs": { "query": question, "ground_truth": solution, }, }, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/hellaswag.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess Hellaswag dataset. """ import argparse import os import re import datasets from verl.utils.hdfs_io import copy, makedirs def preprocess(text): text = text.strip() # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag. text = text.replace(" [title]", ". ") text = re.sub("\\[.*?\\]", "", text) text = text.replace(" ", " ") return text if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/hellaswag", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "Rowan/hellaswag" if local_dataset_path is not None: dataset = datasets.load_dataset(local_dataset_path) else: dataset = datasets.load_dataset(data_source, trust_remote_code=True) train_dataset = dataset["train"] val_dataset = dataset["validation"] test_dataset = dataset["test"] instruction = "Please complete the following sentence.\n" def make_map_fn(split): def process_fn(doc, idx): ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize() query = preprocess(doc["activity_label"] + ": " + ctx) choices = [preprocess(ending) for ending in doc["endings"]] gold = int(doc["label"]) data = { "data_source": data_source, "prompt": [{"role": "user", "content": query}], "ability": "nlp", "reward_model": { "style": "model", "eval": "multiple_choice", # using loglikelihood "ground_truth": gold, "choices": choices, }, "extra_info": {"split": split, "index": idx}, } return data return process_fn # filter data that doesn't have a label train_dataset = train_dataset.filter(lambda x: len(x["label"]) > 0) val_dataset = val_dataset.filter(lambda x: len(x["label"]) > 0) test_dataset = test_dataset.filter(lambda x: len(x["label"]) > 0) train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) val_dataset = val_dataset.map(function=make_map_fn("validation"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) val_dataset.to_parquet(os.path.join(local_save_dir, "validation.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/math_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the MATH-lighteval dataset to parquet format """ import argparse import json import os import datasets from verl.utils.hdfs_io import copy, makedirs from verl.utils.reward_score.math_reward import last_boxed_only_string, remove_boxed def extract_solution(solution_str): return remove_boxed(last_boxed_only_string(solution_str)) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None) parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/math", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() local_dataset_path = args.local_dataset_path # 'lighteval/MATH' is no longer available on huggingface. # Use mirror repo: DigitalLearningGmbH/MATH-lighteval data_source = "DigitalLearningGmbH/MATH-lighteval" print(f"Loading the {data_source} dataset from huggingface...", flush=True) if local_dataset_path is not None: dataset = datasets.load_dataset( local_dataset_path, ) else: dataset = datasets.load_dataset( data_source, ) train_dataset = dataset["train"] test_dataset = dataset["test"] instruction_following = "Let's think step by step and output the final answer within \\boxed{}." # add a row to each data item that represents a unique id def make_map_fn(split): def process_fn(example, idx): question = example.pop("problem") question = question + " " + instruction_following answer = example.pop("solution") solution = extract_solution(answer) data = { "data_source": data_source, "prompt": [{"role": "user", "content": question}], "ability": "math", "reward_model": {"style": "rule", "ground_truth": solution}, "extra_info": {"split": split, "index": idx}, } return data return process_fn train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True) test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True) local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir local_dir = os.path.expanduser(local_save_dir) hdfs_dir = args.hdfs_dir train_dataset.to_parquet(os.path.join(local_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_dir, "test.parquet")) # Save one example as JSON for reference example = train_dataset[0] with open(os.path.join(local_dir, "train_example.json"), "w") as f: json.dump(example, f, indent=2) example = test_dataset[0] with open(os.path.join(local_dir, "test_example.json"), "w") as f: json.dump(example, f, indent=2) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/multiturn.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Create a simple multi-turn dataset for testing """ import argparse import os import pandas as pd def main(): parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default="~/data/multiturn") parser.add_argument("--hdfs_dir", default=None) args = parser.parse_args() # Create example conversations conversations = [] # Conversation 1 conversations.append( { "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is the capital of France?"}, {"role": "assistant", "content": "The capital of France is Paris."}, {"role": "user", "content": "And what about Germany?"}, {"role": "assistant", "content": "The capital of Germany is Berlin."}, ] } ) # Conversation 2 conversations.append( { "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Can you explain quantum computing?"}, { "role": "assistant", "content": "Quantum computing is a type of computing that uses quantum-mechanical phenomena, " "such as superposition and entanglement, to perform operations on data.", }, {"role": "user", "content": "How is it different from classical computing?"}, { "role": "assistant", "content": "Classical computing uses bits that are either 0 or 1, while quantum computing uses " "quantum bits or qubits that can exist in multiple states simultaneously due to superposition.", }, ] } ) # Conversation 3 conversations.append( { "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a simple Python function to calculate factorial."}, { "role": "assistant", "content": ( "```python\ndef factorial(n):\n if n == 0 or n == 1:\n return 1\n else:\n " "return n * factorial(n-1)\n```\n\nThis is a recursive function to calculate the " "factorial of a number." ), }, {"role": "user", "content": "Can you make it iterative instead?"}, { "role": "assistant", "content": ( "```python\ndef factorial(n):\n result = 1\n for i in range(1, n+1):\n " "result *= i\n return result\n```\n\nThis is an iterative version of the factorial function." ), }, ] } ) # Create train and test datasets train_data = conversations[:2] # First 2 conversations for training test_data = conversations[2:] # Last conversation for testing # Create output directory local_dir = os.path.expanduser(args.local_dir) os.makedirs(local_dir, exist_ok=True) # Save to parquet files train_df = pd.DataFrame(train_data) test_df = pd.DataFrame(test_data) train_df.to_parquet(os.path.join(local_dir, "train.parquet")) test_df.to_parquet(os.path.join(local_dir, "test.parquet")) # Handle HDFS if specified if args.hdfs_dir is not None: try: from verl.utils.hdfs_io import copy, makedirs makedirs(args.hdfs_dir) copy(src=local_dir, dst=args.hdfs_dir) except ImportError: print("Warning: HDFS support not available. Skipping HDFS copy.") # Print statistics print(f"Train dataset size: {len(train_df)}") print(f"Test dataset size: {len(test_df)}") print(f"Data saved to {local_dir}") if __name__ == "__main__": main() ================================================ FILE: examples/data_preprocess/pokemon.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates """ Preprocess the llamafactory/pokemon-gpt4o-captions dataset to parquet format """ import argparse import os import datasets from verl.utils.hdfs_io import copy, makedirs if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default=None) parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.") parser.add_argument( "--local_save_dir", default="~/data/pokemon-gpt4o-captions", help="The save directory for the preprocessed dataset.", ) args = parser.parse_args() local_dataset_path = args.local_dataset_path data_source = "llamafactory/pokemon-gpt4o-captions" if local_dataset_path is not None: dataset = datasets.load_dataset( local_dataset_path, ) else: dataset = datasets.load_dataset( data_source, ) def map_fn(row: dict): messages = [] conversation = row.pop("conversations") for conv in conversation: if conv["from"] == "gpt": role = "assistant" elif conv["from"] == "human": role = "user" else: raise ValueError(f"Unknown role: {conv['from']}") messages.append( { "role": role, "content": conv["value"], } ) row["messages"] = messages return row dataset = dataset["train"].map(map_fn, num_proc=16) dataset = dataset.train_test_split(test_size=0.1) train_dataset = dataset["train"] test_dataset = dataset["test"] hdfs_dir = args.hdfs_dir local_save_dir = args.local_dir if local_save_dir is not None: print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.") else: local_save_dir = args.local_save_dir train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet")) test_dataset.to_parquet(os.path.join(local_save_dir, "test.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_save_dir, dst=hdfs_dir) ================================================ FILE: examples/data_preprocess/preprocess_search_r1_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import logging import os import tempfile import pandas as pd from huggingface_hub import hf_hub_download from huggingface_hub.utils import EntryNotFoundError from verl.utils.hdfs_io import copy, makedirs # Setup logging logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) # Configuration constants DEFAULT_SYSTEM_CONTENT = "You are a helpful and harmless assistant." DEFAULT_USER_CONTENT_PREFIX = ( "Answer the given question. You must conduct reasoning inside and " "first every time you get new information. After reasoning, if you find you lack " "some knowledge, you can call a search engine by query " "and it will return the top searched results between and " ". You can search as many times as your want. If you find no " "further external knowledge needed, you can directly provide the answer inside " " and , without detailed illustrations. For example, " " Beijing . Question: " ) def process_single_row(row, current_split_name, row_index): """ Process a single row of data for SearchR1-like format. Args: row: DataFrame row containing the original data current_split_name: Name of the current split (train/test) row_index: Index of the row in the DataFrame Returns: pd.Series: Processed row data in the required format """ question = row.get("question", "") # Build prompt structure user_content = user_content_prefix.rstrip("\n") + question prompt = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}] # Extract ground truth from reward_model or fallback to golden_answers reward_model_data = row.get("reward_model") if isinstance(reward_model_data, dict) and "ground_truth" in reward_model_data: ground_truth = reward_model_data.get("ground_truth") else: ground_truth = row.get("golden_answers", []) # Process data source data_source_tagged = "searchR1_" + str(row.get("data_source", "")) # Build tools kwargs structure tools_kwargs = { "search": { "create_kwargs": {"ground_truth": ground_truth, "question": question, "data_source": data_source_tagged} } } # Build complete extra_info structure extra_info = { "index": row_index, "need_tools_kwargs": True, "question": question, "split": current_split_name, "tools_kwargs": tools_kwargs, } return pd.Series( { "data_source": data_source_tagged, "prompt": prompt, "ability": row.get("ability"), "reward_model": reward_model_data, "extra_info": extra_info, "metadata": row.get("metadata"), } ) def main(): local_save_dir = os.path.expanduser(args.local_dir) os.makedirs(local_save_dir, exist_ok=True) processed_files = [] # Download and process files using temporary directory with tempfile.TemporaryDirectory() as tmp_download_dir: for split in ["train", "test"]: parquet_filename = f"{split}.parquet" logger.info(f"Processing {split} split...") try: # Download Parquet file from HuggingFace logger.info(f"Downloading {parquet_filename} from {args.hf_repo_id}") local_parquet_filepath = hf_hub_download( repo_id=args.hf_repo_id, filename=parquet_filename, repo_type="dataset", local_dir=tmp_download_dir, local_dir_use_symlinks=False, ) # Load and process Parquet file df_raw = pd.read_parquet(local_parquet_filepath) logger.info(f"Loaded {len(df_raw)} rows from {parquet_filename}") def apply_process_row(row, split_name=split): return process_single_row(row, current_split_name=split_name, row_index=row.name) df_processed = df_raw.apply(apply_process_row, axis=1) # Save processed DataFrame output_file_path = os.path.join(local_save_dir, f"{split}.parquet") df_processed.to_parquet(output_file_path, index=False) logger.info(f"Saved {len(df_processed)} processed rows to {output_file_path}") processed_files.append(output_file_path) except EntryNotFoundError: logger.warning(f"{parquet_filename} not found in repository {args.hf_repo_id}") except Exception as e: logger.error(f"Error processing {split} split: {e}") if not processed_files: logger.warning("No data was processed or saved") return logger.info(f"Successfully processed {len(processed_files)} files to {local_save_dir}") # Copy to HDFS if specified if args.hdfs_dir: try: makedirs(args.hdfs_dir) copy(src=local_save_dir, dst=args.hdfs_dir) logger.info(f"Successfully copied files to HDFS: {args.hdfs_dir}") except Exception as e: logger.error(f"Error copying files to HDFS: {e}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Download Search-R1 from HuggingFace, process, and save to Parquet.") parser.add_argument( "--hf_repo_id", default="PeterJinGo/nq_hotpotqa_train", help="HuggingFace dataset repository ID." ) parser.add_argument( "--local_dir", default="~/data/searchR1_processed_direct", help="Local directory to save the processed Parquet files.", ) parser.add_argument("--hdfs_dir", default=None, help="Optional HDFS directory to copy the Parquet files to.") args = parser.parse_args() # System and user content configuration system_content = DEFAULT_SYSTEM_CONTENT user_content_prefix = DEFAULT_USER_CONTENT_PREFIX main() ================================================ FILE: examples/dppo_trainer/dppo.md ================================================ # Divergence Proximal Policy Optimization (DPPO)
## Rethinking the Trust Region in LLM Reinforcement Learning [![Paper](https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white )](https://arxiv.org/pdf/2602.04879) [![Github](https://img.shields.io/badge/Stable_RL-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white)](https://github.com/sail-sg/Stable-RL) [![Twitter](https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=twitter&logoColor=white)](https://x.com/QPHutu/status/2019435642539897303)
## ✨Getting started 1. Prepare the datasets by running [prepare_dapo_data.sh](https://github.com/verl-project/verl-recipe/blob/3490a22a0a3adeb7e4787fe70b1060b642efbae4/dapo/prepare_dapo_data.sh): ```bash bash prepare_dapo_data.sh # This downloads the datasets to ${HOME}/verl/data by default ``` 2. Prepare the model: ```bash hf download Qwen/Qwen3-30B-A3B-Base --local-dir ${HOME}/verl/models/Qwen3-30B-A3B-Base ``` 3. Run the script: ```bash # run DPPO-Binary-KL LOSS_MODE=dppo_kl bash examples/dppo_trainer/run_qwen30b_dppo.sh # run DPPO-Binary-TV LOSS_MODE=dppo_tv bash examples/dppo_trainer/run_qwen30b_dppo.sh # run GRPO baseline LOSS_MODE=vanilla CLIP_LOW=0.2 CLIP_HIGH=0.2 bash examples/dppo_trainer/run_qwen30b_dppo.sh # or GRPO with clip higher LOSS_MODE=vanilla CLIP_LOW=0.2 CLIP_HIGH=0.28 bash examples/dppo_trainer/run_qwen30b_dppo.sh ``` ## 📖Introduction
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Comparison of **PPO** and the proposed **DPPO** (the Binary-TV variant). **(Left)** The surrogate objective and corresponding masks for PPO and DPPO. PPO (and variants like GRPO) employs a heuristic mask based on the probability ratio. In contrast, DPPO utilizes a more principled mask based on a direct approximation of policy divergence (e.g., Total Variation), ensuring updates stay within a theoretically grounded trust region. **(Right)** Experimental results on the AIME24 using Qwen3-30B-A3B-Base. DPPO significantly outperforms GRPO baselines, achieving superior training stability and final performance even without rollout routing replay (R3).
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DPPO variants achieve stable training while controlling the training-inference mismatch at a low level. In contrast, methods without a trust region (PG-IS, CISPO) or with a misspecified one (MiniRL) suffer from growing mismatch and eventual collapse.
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The plots show numerical differences between a training and an inference engine for Qwen3-30B-A3B-Base with identical parameters. **(Left)** The probability ratio (used in PPO) is highly volatile for low-probability tokens. **(Right)** In contrast, the TV divergence is more stable. This highlights a key flaw of PPO's clipping mechanism: it **over-penalizes low-probability tokens**, which can slow down learning; and **under-penalizes high-probability tokens**, which can permit large, destabilizing updates.
issue
The most frequently clipped tokens (by GRPO) are important to the reasoning task! They are dominated by: - numbers, like 1, 4 - mathematical symbols, like +, -, = - reasoning and structural Words: Wait, Thus, Next ## Top-K divergence approximation We only implement the DPPO-Binary-TV/DPPO-Binary-KL here due to their simplicity. For the TopK divergence approximation, please refer to the [the original repo](https://github.com/sail-sg/Stable-RL) for a complete implementation. ## Citation If you find our works useful for your research, please consider citing: ```bibtex @article{qi2026dppo, title={Rethinking the Trust Region in LLM Reinforcement Learning}, author={Qi, Penghui and Zhou, Xiangxin and Liu, Zichen and Pang, Tianyu and Du, Chao and Lin, Min and Lee, Wee Sun}, journal={arXiv preprint arXiv:2602.04879}, year={2026} } ``` ## 🌻Acknowledgement We implement our reinforcement learning algorithm extending from [verl](https://github.com/volcengine/verl). We utilize [vLLM](https://github.com/vllm-project/vllm) and [sglang](https://github.com/sgl-project/sglang) for inference. Our models are trained primarily on [Qwen3 family](https://huggingface.co/collections/Qwen/qwen3). Our training data is built from [DAPO-MATH](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k). Thanks for their great contributions! ================================================ FILE: examples/dppo_trainer/run_qwen30b_dppo.sh ================================================ # run Qwen3-30B-A3B-Base on dapo-math-17k dataset set -x # ================================ DPPO Specific Parameters =========================== # Why from GRPO to DPPO? """ The ratio clipping mechanism in GRPO/PPO is structurally ill-suited due to the large, long-tailed vocabularies inherent to LLMs. It over-penalizes low-probability tokens and under-penalizes high-probability ones, leading to training inefficiency and instability. For example, increasing a rare token’s probability from 1e−5 to 1e−3 generates a massive ratio of 100 that triggers clipping, even though the actual divergence is negligible. Conversely, small ratio changes on high-probability tokens can make catastrophic shifts in probability mass (e.g., a drop from 0.99 to 0.8), yet it often remains unpenalized by the clipping mechanism. DPPO addresses this issue by using a divergence-based clipping mechanism, achieving superior training stability and final performance compared to existing methods. DPPO paper: https://arxiv.org/pdf/2602.04879 """ LOSS_MODE=${LOSS_MODE:-"dppo_tv"} if [[ $LOSS_MODE == "dppo_kl" ]]; then # The KL divergence threshold for DPPO. clip_ratio=0.05 clip_ratio_low=${CLIP_LOW:-0.05} clip_ratio_high=${CLIP_HIGH:-0.05} elif [[ $LOSS_MODE == "dppo_tv" ]]; then # The TV divergence threshold for DPPO. clip_ratio=0.15 clip_ratio_low=${CLIP_LOW:-0.15} clip_ratio_high=${CLIP_HIGH:-0.15} elif [[ $LOSS_MODE == "vanilla" ]]; then # GRPO baseline clip_ratio=0.2 clip_ratio_low=${CLIP_LOW:-0.2} clip_ratio_high=${CLIP_HIGH:-0.28} else echo "Invalid loss mode: $LOSS_MODE" exit 1 fi # Disable dual-clip PPO and TIS for a fair comparison between GRPO and DPPO. clip_ratio_c=10000.0 # ===================================== Algorithm ===================================== adv_estimator=grpo # We recommand directly clipping the ratio/divergence with respect to the original # rollout policy (implemented by bypass_mode=True), instead of the recomputed one. # This can not only save the computation cost, but also improve the training stability # for both GRPO and DPPO by controlling the training-inference mismatch at a low level. # See Section 5.2 in https://arxiv.org/pdf/2602.04879 for more details. bypass_mode=True # We recommand using Dr.GRPO to remove the length and difficulty bias in original GRPO. # See Section 3.1 in https://arxiv.org/pdf/2503.20783 for more details. norm_adv_by_std_in_grpo=False # remove the difficulty bias loss_agg_mode="seq-mean-token-sum-norm" # remove the length bias # reference policy use_kl_in_reward=False kl_coef=0.001 use_kl_loss=False kl_loss_coef=0.001 actor_lr=1e-6 critic_lr=2e-6 gae_gamma=1.0 gae_lam=0.95 critic_warmup=0 # ================================== Data/Model/Config ================================= # Node Info NNODES=${NNODES:-2} # wandb backend=megatron # fsdp, fsdp2, megatron project_name=Qwen3-30B-A3B-Base-dapo-math-17k experiment_name="${backend}-${NNODES}nodes-${LOSS_MODE}-low${clip_ratio_low}-high${clip_ratio_high}" # Paths DATA_ROOT=${DATA_ROOT:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${DATA_ROOT}/ckpts/${project_name}/${experiment_name}"} MODEL_PATH=${MODEL_PATH:-"${DATA_ROOT}/models/Qwen3-30B-A3B-Base"} TRAIN_FILE=${TRAIN_FILE:-"${DATA_ROOT}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${DATA_ROOT}/data/aime-2024.parquet"} actor_model_path=$MODEL_PATH critic_model_path=$MODEL_PATH max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=False overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 train_batch_size=256 ppo_mini_batch_size=32 n_resp_per_prompt=16 n_resp_per_prompt_val=1 # ===================================== Training ====================================== actor_max_token_len_per_gpu=$(((max_prompt_length + max_response_length) * 1)) critic_max_token_len_per_gpu=$(((max_prompt_length + max_response_length) * 1)) # FSDP parallelism config USP_SIZE=4 ACTOR_FSDP_CONFIG=" actor_rollout_ref.actor.fsdp_config.strategy=$backend \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$USP_SIZE" # Megatron parallelism config TP_SIZE=2 CP_SIZE=1 PP_SIZE=1 VPP_SIZE=null EP_SIZE=8 ETP_SIZE=1 ACTOR_MEGATRON_CONFIG=" actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP_SIZE \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP_SIZE \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP_SIZE \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=$VPP_SIZE \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP_SIZE \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP_SIZE \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ actor_rollout_ref.actor.megatron.use_mbridge=True" # Actor model config ACTOR_CONFIG=" actor_rollout_ref.actor.optim.lr=$actor_lr \ actor_rollout_ref.model.path=$actor_model_path \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.clip_ratio=$clip_ratio \ actor_rollout_ref.actor.clip_ratio_low=$clip_ratio_low \ actor_rollout_ref.actor.clip_ratio_high=$clip_ratio_high \ actor_rollout_ref.actor.clip_ratio_c=$clip_ratio_c \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.calculate_entropy=True \ actor_rollout_ref.actor.policy_loss.loss_mode=${LOSS_MODE} \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$actor_max_token_len_per_gpu" # Critic model config CIRITC_CONFIG=" critic.optim.lr=$critic_lr \ critic.model.path=$critic_model_path \ critic.model.use_remove_padding=True \ critic.ppo_max_token_len_per_gpu=$critic_max_token_len_per_gpu \ critic.ulysses_sequence_parallel_size=$USP_SIZE" CRITIC_FSDP_CONFIG="${ACTOR_FSDP_CONFIG//actor_rollout_ref.actor/critic.model}" CRITIC_MEGATRON_CONFIG="${ACTOR_MEGATRON_CONFIG//actor_rollout_ref.actor/critic}" if [[ $backend == "megatron" ]]; then CONFIG_NAME=ppo_megatron_trainer ACTOR_CONFIG="$ACTOR_CONFIG $ACTOR_MEGATRON_CONFIG" if [[ $adv_estimator == "gae" ]]; then CIRITC_CONFIG="$CIRITC_CONFIG $CRITIC_MEGATRON_CONFIG" else CIRITC_CONFIG="" fi else # fsdp, fsdp2 CONFIG_NAME=ppo_trainer ACTOR_CONFIG="$ACTOR_CONFIG $ACTOR_FSDP_CONFIG" if [[ $adv_estimator == "gae" ]]; then CIRITC_CONFIG="$CIRITC_CONFIG $CRITIC_FSDP_CONFIG" else CIRITC_CONFIG="" fi fi # ===================================== Inference ===================================== rollout_name=vllm if [ "$rollout_name" = "vllm" ]; then export VLLM_USE_V1=1 fi infer_tp=4 infer_dp=1 infer_ep=1 gpu_memory_utilization=0.7 ROLLOUT_CONFIG=" actor_rollout_ref.rollout.name=$rollout_name \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.tensor_model_parallel_size=$infer_tp \ actor_rollout_ref.rollout.data_parallel_size=$infer_dp \ actor_rollout_ref.rollout.expert_parallel_size=$infer_ep \ actor_rollout_ref.rollout.gpu_memory_utilization=$gpu_memory_utilization \ actor_rollout_ref.rollout.n=$n_resp_per_prompt \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.rollout.val_kwargs.top_p=1.0 \ actor_rollout_ref.rollout.val_kwargs.top_k=-1 \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.n=$n_resp_per_prompt_val" # ===================================== Reward ===================================== REWARD_CONFIG=" reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length}" python3 -m verl.trainer.main_ppo \ --config-path=./config \ --config-name=$CONFIG_NAME \ algorithm.adv_estimator=$adv_estimator \ algorithm.use_kl_in_reward=$use_kl_in_reward \ algorithm.kl_ctrl.kl_coef=$kl_coef \ algorithm.gamma=$gae_gamma \ algorithm.lam=$gae_lam \ algorithm.rollout_correction.bypass_mode=$bypass_mode \ algorithm.norm_adv_by_std_in_grpo=$norm_adv_by_std_in_grpo \ data.train_files="$TRAIN_FILE" \ data.val_files="$TEST_FILE" \ data.return_raw_chat=True \ data.train_batch_size=$train_batch_size \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=False \ data.filter_overlong_prompts_workers=64 \ data.truncation='error' \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=$critic_warmup \ trainer.logger=['console','wandb'] \ trainer.project_name=$project_name \ trainer.experiment_name=$experiment_name \ trainer.default_local_dir=$CKPTS_DIR \ trainer.n_gpus_per_node=8 \ trainer.nnodes=$NNODES \ trainer.val_before_train=False \ trainer.log_val_generations=100 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=10 \ trainer.total_training_steps=500 \ $ACTOR_CONFIG \ $CIRITC_CONFIG \ $ROLLOUT_CONFIG \ $REWARD_CONFIG ================================================ FILE: examples/fapo_trainer/README.md ================================================

FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning

Project Page Infra Design Resources Paper Code

This example include a runnable and fully reproducible example that demonstrates how to use reward model to optimize a policy. ## Quick start First construct the training and evaluation datasets by: ```bash python examples/fapo_trainer/prepare_data.py --local_dir ${RAY_DATA_HOME}/data/ ``` Or you can directly use the data available [here](https://huggingface.co/datasets/dyyyyyyyy/FAPO-Reasoning-Dataset). To integrate the GRM into the final training, we provide two options: 1. **Colocate Mode:** The reward model is colocated with the trainer and runs synchronously. 2. **Standalone Mode:** A separate resource pool is allocated to deploy the GenRM, which runs asynchronously. The following list the most-relevant parameters in the config file: ```yaml reward: reward_model: model_path: "/path/to/your/reward_model" # your reward model path # whether to enable resource pool for the reward model # True -> Standalone Mode, False -> Colocate Mode enable_resource_pool: True # the number of nodes to deploy the reward model # only effective when enable_resource_pool is True nnodes: 1 # the number of GPUs to deploy the reward model on each node # only effective when enable_resource_pool is True n_gpus_per_node: 8 # inference engine configs, similar to those in rollout configs rollout: # set to True in colocate mode, False in standalone mode free_cache_engine: True # ... (ommitted) # customized reward function, where user should implement the invocation logic # of the specified reward model (both generative and discriminative) custom_reward_function: path: null name: compute_score ``` ![](https://github.com/yyDing1/verl-materials/blob/main/reward_loop.svg) ## Choice 1: Colocate Reward Model ```bash cd verl # Repo root export RAY_ADDRESS="..." # The Ray cluster address to connect to export WORKING_DIR="${PWD}" # The local directory to package to the Ray cluster export NNODES=xxx bash examples/fapo_trainer/run_qwen_7b_rm_colocate.sh # 7b fapo model bash examples/fapo_trainer/run_qwen_32b_rm_colocate.sh # 32b fapo model ``` ## Choice 2: Standalone Reward Model ```bash cd verl # Repo root export RAY_ADDRESS="..." # The Ray cluster address to connect to export WORKING_DIR="${PWD}" # The local directory to package to the Ray cluster export NNODES=xxx # for actor/rollout/trainer export RM_NODES=xxx # for standalone reward model bash examples/fapo_trainer/run_qwen_7b_rm_standalone.sh # 7b fapo model bash examples/fapo_trainer/run_qwen_32b_rm_standalone.sh # 32b fapo model ``` ## Results Compared with baseline (no reward model), FAPO significantly improves the reasoning ability of the model. ![fapo-result](https://fapo-rl.github.io/_astro/intro_main.DKe72RHX_1Us2HB.webp) ## Use discriminative reward model If you would like to use discriminative reward models, the usage is essentially similar to GenRM. You only need to replace the "/v1/chat/completions" endpoint in the custom reward function with the reward model's endpoint. We provide a standard way to compute the DisRM reward score, with the implementation in `RewardLoopWorker::compute_score_disrm`. You can enable this computation method by not specifying a custom reward function. ## More complex reward model scenarios Both GenRM and DisRM can obtain reward scores via HTTP requests in the custom reward function. This allows users to flexibly combine rule-based rewards with reward models to construct more sophisticated reward logic. For more detailed usage instructions and infrastructure design, please refer to the [Reward Loop](https://verl.readthedocs.io/en/latest/advance/reward_loop.html) document. ## Citation If you find our works useful for your research, please consider citing: ```bibtex @article{ding2025fapo, title={FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning}, author={Ding, Yuyang and Zhang, Chi and Li, Juntao and Lin, Haibin and Zhang, Min}, journal={arXiv preprint arXiv:2510.22543}, year={2025} } ``` ================================================ FILE: examples/fapo_trainer/prepare_data.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the dataset to parquet format """ import argparse import os from functools import partial from datasets import concatenate_datasets, load_dataset from verl.utils.hdfs_io import copy, makedirs def example_map_fn(example, idx, process_fn, data_source, ability, split): question, prompt, ground_truth = process_fn(example) data = { "data_source": data_source, "prompt": [{"role": "user", "content": prompt}], "ability": ability, "reward_model": {"style": "rule", "ground_truth": ground_truth}, "extra_info": {"split": split, "index": idx, "question": question}, } return data def build_aime2024_dataset(): def process_aime2024(example): question, ground_truth = example["Problem"], str(example["Answer"]) prompt = question.strip() + "\n\n" + "Please reason step by step, and put your final answer within \\boxed{}." return question, prompt, ground_truth data_source = "Maxwell-Jia/AIME_2024" print(f"Loading the {data_source} dataset from huggingface...", flush=True) dataset = load_dataset(data_source, split="train") map_fn = partial(example_map_fn, process_fn=process_aime2024, data_source="aime24", ability="Math", split="test") dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names) return dataset def build_aime2025_dataset(): def process_aime2025(example): question, ground_truth = example["problem"], str(example["solution"]) prompt = question.strip() + "\n\n" + "Please reason step by step, and put your final answer within \\boxed{}." return question, prompt, ground_truth data_source = "yentinglin/aime_2025" print(f"Loading the {data_source} dataset from huggingface...", flush=True) dataset = load_dataset(data_source, split="train") map_fn = partial(example_map_fn, process_fn=process_aime2025, data_source="aime25", ability="Math", split="test") dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names) return dataset def build_gpqa_diamond_dataset(): import random GPQA_QUERY_TEMPLATE = ( "{Question}\n" "A. {A}\nB. {B}\nC. {C}\nD. {D}\n\n" "Please reason step by step, and put your final answer (only the choice letter) within \\boxed{{}}." ) def process_gpqa_diamond(example): choices = [ example["Incorrect Answer 1"].strip(), example["Incorrect Answer 2"].strip(), example["Incorrect Answer 3"].strip(), ] random.shuffle(choices) gold_index = random.randint(0, 3) choices.insert(gold_index, example["Correct Answer"].strip()) question = example["Question"] query_prompt = GPQA_QUERY_TEMPLATE.format( A=choices[0], B=choices[1], C=choices[2], D=choices[3], Question=question, ) gold_choice = "ABCD"[gold_index] return question, query_prompt, gold_choice data_source = "Idavidrein/gpqa" print(f"Loading the {data_source} dataset from huggingface...", flush=True) dataset = load_dataset(data_source, "gpqa_diamond", split="train") map_fn = partial( example_map_fn, process_fn=process_gpqa_diamond, data_source="gpqa-diamond", ability="General", split="test" ) dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names) return dataset def build_dapo_train_dataset(): def process_dapo(example): question, ground_truth = example["prompt"], example["solution"] prompt = question.strip() + "\n\n" + "Please reason step by step, and put your final answer within \\boxed{}." return question, prompt, ground_truth data_source = "open-r1/DAPO-Math-17k-Processed" print(f"Loading the {data_source} dataset from huggingface...", flush=True) dataset = load_dataset(data_source, "all", split="train") map_fn = partial(example_map_fn, process_fn=process_dapo, data_source="math-dapo", ability="Math", split="train") dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names) return dataset if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--local_dir", default="~/data/genrm") parser.add_argument("--hdfs_dir", default=None) parser.add_argument("--tasks", default="all") args = parser.parse_args() train_dataset = build_dapo_train_dataset() train_dataset = concatenate_datasets([train_dataset for _ in range(20)]) test_datasets = [] # AIME 2024 aime24_dataset = build_aime2024_dataset() test_datasets.extend([aime24_dataset for _ in range(32)]) # AIME 2025 aime25_dataset = build_aime2025_dataset() test_datasets.extend([aime25_dataset for _ in range(32)]) # GPQA Diamond gpqa_dataset = build_gpqa_diamond_dataset() test_datasets.extend([gpqa_dataset for _ in range(4)]) test_dataset = concatenate_datasets(test_datasets) local_dir = args.local_dir hdfs_dir = args.hdfs_dir train_dataset.to_parquet(os.path.join(local_dir, "fapo-train-boxed.parquet")) test_dataset.to_parquet(os.path.join(local_dir, "fapo-test-full-boxed.parquet")) if hdfs_dir is not None: makedirs(hdfs_dir) copy(src=local_dir, dst=hdfs_dir) ================================================ FILE: examples/fapo_trainer/reward_fn.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os import aiohttp from transformers import PreTrainedTokenizer from verl.utils.ray_utils import get_event_loop from verl.utils.reward_score.math_dapo import last_boxed_only_string, normalize_final_answer, remove_boxed logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def verify( solution_str: str, gt: str, ) -> tuple[bool, str]: solution_str = solution_str[-300:] boxed_answer = last_boxed_only_string(solution_str) if boxed_answer is not None: extracted_answer = remove_boxed(boxed_answer) else: extracted_answer = "[INVALID]" pred = normalize_final_answer(extracted_answer) gt = normalize_final_answer(gt) return (pred == gt), pred async def compute_score_baseline( solution_str: str, ground_truth: str, **kwargs, ): loop = get_event_loop() """Compute the reward score for Baseline.""" correct, pred = await loop.run_in_executor(None, lambda: verify(solution_str, ground_truth)) reward_score = 1.0 if correct else -1.0 return {"score": reward_score, "acc": correct, "pred": pred} # FAPO Hyper-parameters FAPO_GENRM_TEMPLATE = ( "The following is a math problem with its ground truth answer, along with an AI solution (split into steps):\n\n" "[Math Problem]\n\n" "{problem}\n\n" "[Ground Truth]\n\n" "{ground_truth}\n\n" "[AI Solution]\n\n" "{solution}\n\n" "Your task is to review and critique the solution step by step. " "Once you identify an error in a step, return the index of the step where the earliest error occurs. " "Otherwise, return the index of -1 (which typically denotes 'not found').\n\n" "Please reason step by step, put your final answer (i.e., the index) in \\boxed{{}}." ) MAX_TOKENS = 16384 FLAWED_REWARD_PENALTY = 1.0 # async def generate_aiohttp(router_address: str, prompt_ids: list[int], sampling_params: dict): # payload = { # "input_ids": prompt_ids, # "sampling_params": sampling_params, # } # url = f"http://{router_address}/generate" # try: # session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=None)) # async with session.post(url, json=payload) as resp: # output = await resp.text() # try: # output = json.loads(output) # return output # except Exception: # logger.error(f"Failed to parse JSON response: {output}") # return {} # finally: # await session.close() async def post_request(router_address: str, payload: dict, endpoint: str, max_retries: int = 5): url = f"http://{router_address}/{endpoint}" last_exception = None for attempt in range(max_retries): try: # It's safer to have a timeout instead of None, which can hang indefinitely. timeout = aiohttp.ClientTimeout(total=None) async with aiohttp.ClientSession(timeout=timeout) as session: async with session.post(url, json=payload) as resp: resp.raise_for_status() return await resp.json() except aiohttp.ClientResponseError as e: # Do not retry on 4xx client errors, but retry on 5xx server errors. if 400 <= e.status < 500: logger.error(f"Request to {url} failed with client error HTTP {e.status}: {e}. Not retrying.") raise last_exception = e logger.warning( f"[Attempt {attempt + 1}/{max_retries}] Request to {url} failed with HTTP {e.status}: {e}. Retrying..." ) except (asyncio.TimeoutError, aiohttp.ClientConnectorError) as e: last_exception = e logger.warning(f"[Attempt {attempt + 1}/{max_retries}] Request to {url} failed: {e}. Retrying...") except Exception as e: last_exception = e logger.warning( f"[Attempt {attempt + 1}/{max_retries}] Request to {url} failed with unexpected error: {e}. Retrying..." ) if attempt < max_retries - 1: # Using exponential backoff is generally better than a fixed sleep. backoff_seconds = 2**attempt await asyncio.sleep(min(backoff_seconds, 30)) logger.error(f"Max retries ({max_retries}) reached for request to {url}.") if last_exception: raise last_exception async def compute_score_fapo( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, reward_router_address: str, reward_model_tokenizer: PreTrainedTokenizer, ): """Compute the reward score for FAPO.""" loop = get_event_loop() question, split = extra_info["question"], extra_info["split"] correct, pred = await loop.run_in_executor(None, lambda: verify(solution_str, ground_truth)) reward_score = 1.0 if correct else -1.0 is_flawed_positive = False # for test set or incorrect solution, directly return the reward score if split == "test" or not correct: return {"score": reward_score, "acc": correct, "pred": pred, "is_flawed_positive": is_flawed_positive} grm_prompt = FAPO_GENRM_TEMPLATE.format( problem=question, ground_truth=ground_truth, solution=solution_str, ) messages = [{"role": "user", "content": grm_prompt}] grm_outputs = await post_request( router_address=reward_router_address, payload={ "messages": messages, "max_tokens": MAX_TOKENS, }, endpoint="v1/chat/completions", ) grm_response = grm_outputs["choices"][0]["message"]["content"] try: err_location = remove_boxed(last_boxed_only_string(grm_response)) is_flawed_positive = int(err_location) != -1 except Exception: is_flawed_positive = False if is_flawed_positive: reward_score -= FLAWED_REWARD_PENALTY return {"score": reward_score, "acc": correct, "pred": pred, "is_flawed_positive": is_flawed_positive} ================================================ FILE: examples/fapo_trainer/run_qwen_7b_rm_colocate.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='FAPO-Reproduce' exp_name='FAPO-7B' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=8 train_prompt_mini_bsz=32 # Ray RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-4} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} GRM_PATH=${GRM_PATH:-"${RAY_DATA_HOME}/models/FAPO-GenRM-4B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/fapo-train-boxed.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/fapo-test-full-boxed.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter sp_size=1 use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=1 fsdp_size=8 ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ --address "${RAY_ADDRESS}" \ --working-dir "${WORKING_DIR}" \ -- python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.return_raw_chat=True \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_model.enable=True \ reward.reward_model.enable_resource_pool=False \ reward.reward_model.model_path=${GRM_PATH} \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.90 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.free_cache_engine=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=True \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ reward.custom_reward_function.path=examples/fapo_trainer/reward_fn.py \ reward.custom_reward_function.name=compute_score_fapo \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=200 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/fapo_trainer/run_qwen_7b_rm_standalone.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='FAPO-Reproduce' exp_name='FAPO-7B' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=8 train_prompt_mini_bsz=32 # Ray RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} RM_NODES=${RM_NODES:-2} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} GRM_PATH=${GRM_PATH:-"${RAY_DATA_HOME}/models/FAPO-GenRM-4B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/fapo-train-boxed.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/fapo-test-full-boxed.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter sp_size=1 use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=1 fsdp_size=8 ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ --address "${RAY_ADDRESS}" \ --working-dir "${WORKING_DIR}" \ -- python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.return_raw_chat=True \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_model.enable=True \ reward.reward_model.enable_resource_pool=True \ reward.reward_model.n_gpus_per_node=8 \ reward.reward_model.nnodes=${RM_NODES} \ reward.reward_model.model_path=${GRM_PATH} \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.90 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.free_cache_engine=False \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=True \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ reward.custom_reward_function.path=examples/fapo_trainer/reward_fn.py \ reward.custom_reward_function.name=compute_score_fapo \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=200 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/gdpo_trainer/run_qwen1_5b_gdpo.sh ================================================ export HCCL_ASYNC_ERROR_HANDLING=0 export DATA_DIR="./dataset/rlla_4k" export BASE_MODEL="/path/to/your/Qwen2.5-1.5B-Instruct" export EXPERIMENT_NAME="qwen2.5-1.5B-GDPO" export CKPT_DIR="./results/gdpo" # Env variables for computing score in rlla.py export REFINEDREWARD=0 export COARSEREWARD=0 export CORRECTMAX1=0 export MAX1STEP30MAX3=0 export SCHEDULEREWARD=0 export SCHEDULELENGTH=0 PROJECT_DIR="$(pwd)" trainer_n_gpus_per_node=8 trainer_nnodes=1 python3 -u -m verl.trainer.main_ppo \ algorithm.adv_estimator=gdpo \ +algorithm.gdpo_reward_keys='["accuracy_reward", "format_reward"]' \ data.train_files=$DATA_DIR/train.parquet \ data.val_files=$DATA_DIR/test.parquet \ data.train_batch_size=32 \ data.val_batch_size=16 \ data.max_prompt_length=2048 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ actor_rollout_ref.model.path=$BASE_MODEL \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=4 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.prompt_length=2048 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ reward.custom_reward_function.path="$PROJECT_DIR/verl/utils/reward_score/rlla.py" \ reward.custom_reward_function.name=compute_score \ reward.reward_manager.name=gdpo \ trainer.critic_warmup=0 \ trainer.logger=['console'] \ trainer.project_name="GDPO-qwen2.5" \ trainer.n_gpus_per_node=$trainer_n_gpus_per_node \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.nnodes=$trainer_nnodes \ trainer.save_freq=20 \ trainer.test_freq=10 \ trainer.default_local_dir=$CKPT_DIR \ trainer.total_epochs=15 \ trainer.val_before_train=False 2>&1 ================================================ FILE: examples/generation/run_deepseek7b_mutli_node.sh ================================================ set -x data_path=$HOME/data/rlhf/gsm8k/test.parquet save_path=$HOME/data/rlhf/math/deepseek_v2_lite_gen_test.parquet model_path=deepseek-ai/deepseek-llm-7b-chat python3 -m verl.trainer.main_generation \ trainer.nnodes=2 \ trainer.n_gpus_per_node=8 \ data.path=$data_path \ data.prompt_key=prompt \ data.n_samples=1 \ data.output_path=$save_path \ model.path=$model_path\ +model.trust_remote_code=True \ rollout.temperature=1.0 \ rollout.top_k=50 \ rollout.top_p=0.7 \ rollout.prompt_length=2048 \ rollout.response_length=1024 \ rollout.tensor_model_parallel_size=16 \ rollout.gpu_memory_utilization=0.8 ================================================ FILE: examples/generation/run_deepseek_v2_lite_math.sh ================================================ set -x data_path=$HOME/data/gsm8k/test.parquet save_path=$HOME/data/gsm8k/deepseek_v2_lite_gen_test.parquet model_path=deepseek-ai/deepseek-llm-7b-chat python3 -m verl.trainer.main_generation \ trainer.nnodes=1 \ trainer.n_gpus_per_node=8 \ data.path=$data_path \ data.prompt_key=prompt \ data.n_samples=1 \ data.output_path=$save_path \ model.path=$model_path \ +model.trust_remote_code=True \ rollout.temperature=1.0 \ rollout.top_k=50 \ rollout.top_p=0.7 \ rollout.prompt_length=2048 \ rollout.response_length=1024 \ rollout.tensor_model_parallel_size=2 \ rollout.gpu_memory_utilization=0.8 ================================================ FILE: examples/gmpo_trainer/README.md ================================================
# Geometric-Mean Policy Optimization
This is the official implementaion of paper [***Geometric-Mean Policy Optimization***](https://arxiv.org/abs/2507.20673).
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## 1. Contents - Geometric-Mean Policy Optimization - [1. Contents](#1-contents) - [2. Introduction](#2-introduction) - [3. Code Usage](#3-code-usage) - [4. Contacts](#4-contacts) - [5. Citation](#5-citation) ## 2. Introduction Group Relative Policy Optimization (GRPO) has significantly enhanced the reasoning capability of large language models by optimizing the arithmetic mean of token-level rewards. Unfortunately, GRPO is observed to suffer from unstable policy updates when facing tokens with outlier importance-weighted rewards, which manifest as extreme importance sampling ratios during training. In this study, we propose Geometric-Mean Policy Optimization (GMPO), with the aim to improve the stability of GRPO through suppressing token reward outliers. Instead of optimizing the arithmetic mean, GMPO maximizes the geometric mean of token-level rewards, which is inherently less sensitive to outliers and maintains a more stable range of importance sampling ratio. GMPO is plug-and-play—simply replacing GRPO's arithmetic mean with the geometric mean of token-level rewards, as the latter is inherently less sensitive to outliers. GMPO is theoretically plausible—analysis reveals that both GMPO and GRPO are weighted forms of the policy gradient while the former enjoys more stable weights, which consequently benefits policy optimization and performance. Experiments on multiple mathematical reasoning benchmarks show that GMPO-7B improves the average Pass@1 of GRPO by up to 4.1%, outperforming many state-of-the-art approaches. ## 3. Code Usage The key configurations are: ``` clip_ratio_low=0.4 clip_ratio_high=0.4 loss_mode=geo_mean ``` We observed that using a large clip ratio during Mixture-of-Experts (MoE) model training often leads to optimization instability. When training MoE models, consider lowering the clip ratio to achieve more stable convergence. To get started quickly, run: ``` bash examples/gmpo_trainer/run_qwen2_5-7b_math.sh ``` GMPO can be combined with other methods such as DAPO (experimental - not fully tested): ``` bash examples/gmpo_trainer/test_dapo_7b_math.sh bash examples/gmpo_trainer/test_dapo_qwen3_30b_math.sh ``` ## 4. Contacts If you have any question about our work or this repository, please don't hesitate to contact us by emails or open an issue under this project. - [zhaoyuzhong20@mails.ucas.ac.cn](zhaoyuzhong20@mails.ucas.ac.cn) - [liuyue171@mails.ucas.ac.cn](liuyue171@mails.ucas.ac.cn) - [lecu@microsoft.com](lecu@microsoft.com) - [wanfang@ucas.ac.cn](wanfang@ucas.ac.cn) ## 5. Citation ``` @article{zhao2025geometric, title={Geometric-mean policy optimization}, author={Zhao, Yuzhong and Liu, Yue and Liu, Junpeng and Chen, Jingye and Wu, Xun and Hao, Yaru and Lv, Tengchao and Huang, Shaohan and Cui, Lei and Ye, Qixiang and others}, journal={arXiv preprint arXiv:2507.20673}, year={2025} } ``` ================================================ FILE: examples/gmpo_trainer/run_qwen2_5-7b_math.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" use_kl_loss=False loss_mode=geo_mean clip_ratio=0.4 save_contents="['model', 'optimizer', 'extra']" export WANDB_MODE=offline save_contents="['hf_model']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-Math-7B \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.checkpoint.save_contents=${save_contents} \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_gmpo_example_gsm8k_math' \ trainer.experiment_name='qwen2_5_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/gmpo_trainer/test_dapo_7b_math.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.4 clip_ratio_high=0.4 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=16 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-8} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter sp_size=4 use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=4 fsdp_size=32 loss_mode=geo_mean # export WANDB_MODE=offline save_contents="['model', 'optimizer', 'extra']" # save_contents="['hf_model']" # reference run wandb: https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/runs/ow47vvon?nw=nwusertongyuxuan361 python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.model.use_remove_padding=True \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.actor.checkpoint.save_contents="${save_contents}" \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=10 \ trainer.total_epochs=10 \ trainer.total_training_steps=200 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/gmpo_trainer/test_dapo_qwen3_30b_math.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen3-30B-A3B-Base-MATH-0527a1' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.4 clip_ratio_high=0.4 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=16 train_prompt_mini_bsz=32 loss_mode=geo_mean # export WANDB_MODE=offline save_contents="['model', 'optimizer', 'extra']" # save_contents="['hf_model']" # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-8} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter sp_size=4 use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=4 fsdp_size=32 python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.actor.checkpoint.save_contents="${save_contents}" \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=10 \ trainer.total_epochs=10 \ trainer.total_training_steps=300 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/gpg_trainer/gpg.md ================================================ # GPG: Group Policy Gradient Group Policy Gradient (GPG) is a minimalist reinforcement learning (RL) method that enhances the reasoning ability of large language models without relying on supervised fine-tuning or complex tricks. GPG revisits traditional policy gradients and directly optimizes the RL objective—no surrogate losses, no KL penalties, no critic, and no reference model. Compared to GRPO, GPG is simpler, more efficient, and achieves better results on many tasks. For more details, please refer to the original paper [GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning ](https://arxiv.org/abs/2504.02546). ## Key Components - Use a corrected advantage function to improve policy gradient accuracy and training efficiency. - By eliminating the critic and reference models, avoiding KL divergence constraints, significantly simplifies the training process compared to Group Relative Policy Optimization (GRPO) ## Configuration To configure GPG within the framework, use the following YAML settings. ```yaml algorithm: adv_estimator: gpg actor_rollout_ref: actor: policy_loss: loss_mode: "gpg" ``` ## Advanced Extensions GPG is a simple and strong baseline for model reasoning. Although it avoids using KL loss in its original form, you can still use KL loss to further improve the performance. ```yaml algorithm: adv_estimator: gpg actor_rollout_ref: actor: use_kl_loss: True # enable kl regularization kl_loss_coef: 0.01 policy_loss: loss_mode: "gpg" ``` ================================================ FILE: examples/gpg_trainer/run_qwen2-7b_math.sh ================================================ set -x # If you are using vllm<=0.6.3, you might need to set the following environment variable to avoid bugs: # export VLLM_ATTENTION_BACKEND=XFORMERS gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gpg \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.policy_loss.loss_mode=gpg \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_gpg_example_gsm8k_math' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/gpg_trainer/run_qwen2-7b_math_megatron.sh ================================================ set -x # If you are using vllm<=0.6.3, you might need to set the following environment variable to avoid bugs: # export VLLM_ATTENTION_BACKEND=XFORMERS export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=gpg \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.policy_loss.loss_mode=gpg \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_gpg_example_gsm8k_math' \ trainer.experiment_name='qwen2_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/README.md ================================================ # Group Relative Policy Optimization (GRPO) In reinforcement learning, classic algorithms like PPO rely on a "critic" model to estimate the value of actions, guiding the learning process. However, training this critic model can be resource-intensive. GRPO simplifies this process by eliminating the need for a separate critic model. Instead, it operates as follows: - Group Sampling: For a given problem, the model generates multiple possible solutions, forming a "group" of outputs. - Reward Assignment: Each solution is evaluated and assigned a reward based on its correctness or quality. - Baseline Calculation: The average reward of the group serves as a baseline. - Policy Update: The model updates its parameters by comparing each solution's reward to the group baseline, reinforcing better-than-average solutions and discouraging worse-than-average ones. This approach reduces computational overhead by avoiding the training of a separate value estimation model, making the learning process more efficient. For more details, refer to the original paper [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://arxiv.org/pdf/2402.03300) ## Key Components - No Value Function (Critic-less): unlike PPO, GRPO does not train a separate value network (critic) - Group Sampling (Grouped Rollouts): instead of evaluating one rollout per input, GRPO generates multiple completions (responses) from the current policy for each prompt. This set of completions is referred to as a group. - Relative Rewards: within each group, completions are scored (e.g., based on correctness), and rewards are normalized relative to the group. ## Configuration Note that all configs containing `micro_batch_size` are used to configure the maximum sample or token count per forward or backward pass to avoid GPU OOMs, whose value should not change algorithmic/convergence behavior. Despite that many configurations start with the `ppo_` prefix, they work across different RL algorithms in verl, as the GRPO training loop is similar to that of PPO (without critic). ![image](https://github.com/user-attachments/assets/16aebad1-0da6-4eb3-806d-54a74e712c2d) - `actor_rollout.ref.rollout.n`: For each prompt, sample n times. Default to 1. For GRPO, please set it to a value larger than 1 for group sampling. - `data.train_batch_size`: The global batch size of prompts used to generate a set of sampled trajectories/rollouts. The number of responses/trajectories is `data.train_batch_size * actor_rollout.ref.rollout.n` - `actor_rollout_ref.actor.ppo_mini_batch_size`: The set of sampled trajectories is split into multiple mini-batches with batch_size=ppo_mini_batch_size for PPO actor updates. The ppo_mini_batch_size is a global size across all workers. - `actor_rollout_ref.actor.ppo_epochs`: Number of epochs for GRPO updates on one set of sampled trajectories for actor - `actor_rollout_ref.actor.clip_ratio`: The GRPO clip range. Default to 0.2 - `algorithm.adv_estimator`: Default is gae. Please set it to grpo instead - `actor_rollout_ref.actor.loss_agg_mode`: Default is "token-mean". Options include "token-mean", "seq-mean-token-sum", "seq-mean-token-mean". The original GRPO paper takes the sample-level loss (seq-mean-token-mean), which may be unstable in long-CoT scenarios. All GRPO example scripts provided in verl uses the default configuration "token-mean" for loss aggregation instead. Instead of adding KL penalty in the reward, GRPO regularizes by directly adding the KL divergence between the trained policy and the reference policy to the loss: - `actor_rollout_ref.actor.use_kl_loss`: To use kl loss in the actor. When used, we are not applying KL in the reward function. Default is False. Please set it to True for GRPO. - `actor_rollout_ref.actor.kl_loss_coef`: The coefficient of kl loss. Default is 0.001. - `actor_rollout_ref.actor.kl_loss_type`: Support kl(k1), abs, mse(k2), low_var_kl(k3) and full. Appending "+" in the end (e.g., 'k1+' and 'k3+') would apply straight through to employ k2 for unbiased gradient estimation, regardless of the kl value estimation (see https://github.com/volcengine/verl/pull/2953#issuecomment-3162113848 for more details). How to calculate the kl divergence between actor and reference policy. See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html ## Advanced Extensions ### DrGRPO The work [Understanding R1-Zero-Like Training: A Critical Perspective](https://arxiv.org/pdf/2503.20783) claims there's optimization bias in GRPO, that leads to artificially longer responses, especially for incorrect outputs. This inefficiency stems from the way GRPO calculates advantages using group-based reward normalization, which can inadvertently favor longer, less accurate responses. Instead, DrGRPO aggregates token-level losses by normalizing with a global constant to eliminate length bias. Configure the following to enable DrGRPO, with all other parameters the same as GRPO's: - `actor_rollout_ref.actor.loss_agg_mode`: "seq-mean-token-sum-norm", which turns off seq-dim averaging - `actor_rollout_ref.actor.loss_scale_factor`: (Optional) Set to a constant integer (e.g., max response length) to ensure consistent normalization throughout training. If not set, uses the current batch's response length. - `actor_rollout_ref.actor.use_kl_loss`: Please set it to False for DrGRPO - `algorithm.norm_adv_by_std_in_grpo`: False, which turns off standard deviation norm ## Reference Example Qwen2.5 GRPO training log and commands: [link](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/qwen2-7b-fsdp2.log) ```bash bash examples/grpo_trainer/run_qwen3-8b.sh ``` For more reference performance, please see https://verl.readthedocs.io/en/latest/algo/baseline.html ================================================ FILE: examples/grpo_trainer/run_deepseek671b_math_megatron_80gb.sh ================================================ set -x # # 0. download HF checkpoint # # remove the `quantization_config` in the `config.json` # # set `num_nextn_predict_layers=0` to disable MTP, which is not currently supported # hf download deepseek-ai/DeepSeek-V3-0324 # no offline dist checkpoint needed, now with mbridge>=0.13.0, we can directly init model from huggingface downloaded fp8 weights # tested on docker://verlai/verl:app-verl0.5-transformers4.55.4-vllm0.10.0-mcore0.13.0-te2.2 LLM="" # 2. run the script gsm8k_train_path=/root/data/gsm8k/train.parquet gsm8k_test_path=/root/data/gsm8k/test.parquet train_files=$gsm8k_train_path test_files=$gsm8k_test_path ALL_OFFLOAD=${ALL_OFFLOAD:-True} COMMON_PARAM_OFFLOAD=${COMMON_PARAM_OFFLOAD:-$ALL_OFFLOAD} COMMON_GRAD_OFFLOAD=${COMMON_GRAD_OFFLOAD:-$ALL_OFFLOAD} COMMON_OPTIMIZER_OFFLOAD=${COMMON_OPTIMIZER_OFFLOAD:-$ALL_OFFLOAD} ACTOR_PARAM_OFFLOAD=${ACTOR_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} ACTOR_GRAD_OFFLOAD=${ACTOR_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} ACTOR_OPTIMIZER_OFFLOAD=${ACTOR_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} REF_PARAM_OFFLOAD=${REF_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} CRITIC_PARAM_OFFLOAD=${CRITIC_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} CRITIC_GRAD_OFFLOAD=${CRITIC_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} CRITIC_OPTIMIZER_OFFLOAD=${CRITIC_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} RM_PARAM_OFFLOAD=${RM_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} # 256 H100(80GB) NODES=32 PP=16 TP=1 EP=16 ETP=1 INFER_TP=32 # consider TP/ETP, and enable recompute if short of memory # full recompute n_resp_per_prompt=4 max_prompt_length=2048 max_response_length=4096 use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 # RAY_ADDRESS='auto' ray job submit --working-dir . -- python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=grpo \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=512 \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$LLM \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.rollout.temperature=1.0 \ actor_rollout_ref.rollout.top_p=1.0 \ actor_rollout_ref.rollout.top_k=-1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$INFER_TP \ trainer.logger='["console","tensorboard"]' \ trainer.project_name='verl_megatron_gsm8k_examples' \ trainer.experiment_name='dsv3-32nodes' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=$NODES \ trainer.save_freq=-1 \ trainer.test_freq=5 \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.actor.megatron.override_transformer_config.attention_backend='fused' \ +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_first_pipeline_stage=4 \ +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=1 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.megatron.param_offload=${ACTOR_PARAM_OFFLOAD} \ actor_rollout_ref.actor.megatron.optimizer_offload=${ACTOR_OPTIMIZER_OFFLOAD} \ actor_rollout_ref.actor.megatron.grad_offload=${ACTOR_GRAD_OFFLOAD} \ actor_rollout_ref.ref.megatron.param_offload=${REF_PARAM_OFFLOAD} \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ actor_rollout_ref.actor.megatron.use_mbridge=True \ trainer.default_local_dir=$CKPT_DIR \ trainer.val_before_train=False \ trainer.total_epochs=100 $@ ================================================ FILE: examples/grpo_trainer/run_deepseek671b_math_megatron_96gb.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail ## !!!!!!!important!!!!!! # 1. set the following environment variables on all your nodes # env_vars: # CUDA_DEVICE_MAX_CONNECTIONS: "1" # NCCL_NVLS_ENABLE: "0" # VLLM_USE_V1: 1 # 2. install mbridge=0.1.13 on all your node with the following command: # pip3 install git+https://github.com/ISEEKYAN/mbridge # 3. remove the `quantization_config` in the DeepSeek-V3's `config.json` and # set `num_nextn_predict_layers=0` to disable MTP, which is not currently supported SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" [ -f "${SCRIPT_DIR}/env.sh" ] && source "${SCRIPT_DIR}/env.sh" adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1204 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=96 n_resp_per_prompt=8 train_prompt_mini_bsz=32 # minimum nodes for DeepSeek-V3: 12 nodes NNODES=${NNODES:-12} RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=$RAY_DATA_HOME/models/DeepSeek-V3-config-verl TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 10 / 10)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) offload=True optim_offload=${OFFLOAD_OPTIM:-True} gen_tp=32 train_tp=${TP:-8} train_pp=${PP:-12} EP=${EP:-8} ETP=1 CP=1 optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.} LAST_LAYER=${LAST_LAYER:-6} project_name='verl-deepseek-v3' exp_name="671B-${NNODES}-pp${train_pp}-tp${train_tp}-ep${EP}-actor-length${actor_ppo_max_token_len}" CKPTS_DIR=$RAY_DATA_HOME/ckpt/${project_name}/${exp_name} python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.rollout.name=vllm \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${optim_offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.megatron.context_parallel_size=${CP} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.nccl_timeout=1200 \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.context_parallel_size=${CP} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=False \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_shared_expert_overlap=False \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_embedding_in_pipeline_split=False \ +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_loss_in_pipeline_split=False \ +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=${LAST_LAYER} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','wandb'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=False \ trainer.test_freq=10 \ trainer.save_freq=100 \ trainer.total_epochs=10 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/grpo_trainer/run_deepseek7b_llm.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=80 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=160 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=160 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_deepseek7b_llm_math.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=40 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='deepseek_llm_7b_function_rm_math' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_deepseek7b_llm_math_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='deepseek_llm_7b_math_megatron' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_deepseek7b_llm_seq_balance.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm_seq_packing' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_glm41v_9b.sh ================================================ set -x ENGINE=${1:-vllm} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=zai-org/GLM-4.1V-9B-Thinking \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='glm41v_9b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_gptoss_20b.sh ================================================ #!/bin/bash cat > get_model.py << EOF import torch from transformers import AutoModelForCausalLM, AutoTokenizer, Mxfp4Config model_id = "openai/gpt-oss-20b" output_dir = "$HOME/models/gpt-oss-20b-bf16" quantization_config = Mxfp4Config(dequantize=True) model_kwargs = dict( attn_implementation="eager", torch_dtype=torch.bfloat16, quantization_config=quantization_config, use_cache=False, device_map="auto", ) model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs) # Patch config with custom attribute before saving model.config.attn_implementation = "eager" model.save_pretrained(output_dir) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.save_pretrained(output_dir) EOF python get_model.py # or you can use lmsys/gpt-oss-20b-bf16 # recommend to use same value for train_batch_size and ppo_mini_batch_size # to avoid MOE training instability # use large value for max_response_length if you want to use reasoning effort high. model_dir=$HOME/models/gpt-oss-20b-bf16 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="$gsm8k_train_path" \ data.val_files="$gsm8k_test_path" \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=8192 \ data.filter_overlong_prompts=True \ data.truncation='error' \ +data.apply_chat_template_kwargs.reasoning_effort=medium \ actor_rollout_ref.model.path=${model_dir} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ +actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend=triton \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='oai_oss_20b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=50 \ trainer.test_freq=10 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_minicpmo2_6.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=128 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=False \ data.truncation='error' \ data.image_key=images \ data.trust_remote_code=True \ data.custom_cls.path=recipe/minicpmo/rl_dataset.py \ data.custom_cls.name=RLHFDataset \ actor_rollout_ref.model.path=openbmb/MiniCPM-o-2_6 \ actor_rollout_ref.model.trust_remote_code=True \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.fsdp_config.use_orig_params=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='minicpmo2_6_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_mistral13b_skyworkrm_hhrlhf.sh ================================================ train_files=data/full_hh_rlhf/rl/train.parquet test_files=data/full_hh_rlhf/rl/train.parquet # no use max_prompt_length=4096 max_response_length=2048 gen_tp=4 n_per_prompt=5 adv_estimator="grpo" project_name=verl_full_hh_rlhf_examples exp_name="grpo_mistral13B-skyworkLlama8b-hhrlhf" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=$adv_estimator \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=512 \ data.prompt_key="prompt" \ data.return_raw_chat=True \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=mistralai/Mistral-Nemo-Instruct-2407 \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=10 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.n=$n_per_prompt \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path=Skywork/Skywork-Reward-Llama-3.1-8B \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=8192 \ reward.reward_model.rollout.response_length=4096 \ algorithm.use_kl_in_reward=False \ trainer.logger='["console","wandb"]' \ trainer.val_before_train=False \ trainer.project_name=$project_name \ trainer.experiment_name=$exp_name \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=10 \ trainer.test_freq=-1 \ trainer.total_epochs=5 $@ ================================================ FILE: examples/grpo_trainer/run_moonlight16b_math_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping HF_MODEL_PATH=moonshotai/Moonlight-16B-A3B DIST_CKPT_PATH=${DIST_CKPT_PATH} train_path=$HOME/data/gsm8k/train.parquet test_path=$HOME/data/gsm8k/test.parquet python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_path" \ data.val_files="$test_path" \ data.train_batch_size=192 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.trust_remote_code=True \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.model.trust_remote_code=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=3 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=4 \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=4 \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=1 \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=3 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=4 \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=4 \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=1 \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='moonlight_megatron_ep' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=3 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_nemotron_nano_v3_megatron.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail ################################################### environment ################################################### ### # 1. use docker image `verlai/verl:vllm015.dev`` and install correct dependencies: # pip install nvidia-modelopt # MAX_JOBS=32 pip install git+https://github.com/Dao-AILab/causal-conv1d.git --no-build-isolation --no-cache-dir # MAX_JOBS=32 pip install git+https://github.com/state-spaces/mamba.git --no-build-isolation --no-cache-dir # pip install --no-deps git+https://github.com/NVIDIA-NeMo/Megatron-Bridge # pip install --no-deps git+https://github.com/NVIDIA/Megatron-LM.git@core_dev_r0.16.0 # unset ROCR_VISIBLE_DEVICES # unset PYTORCH_CUDA_ALLOC_CONF ################################################### quick config ################################################### rollout_mode="async" return_raw_chat="False" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi dtype="bfloat16" project_name='DAPO' exp_name='nano_30b' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 2)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 1)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=32 n_resp_per_prompt=16 train_prompt_mini_bsz=32 # Ray RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-1} # Paths MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 10 / 10)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) offload=True gen_tp=8 train_tp=8 train_pp=1 EP=8 ETP=1 ################################################### start of config ################################################### FP8=( # train # +actor_rollout_ref.actor.megatron.override_transformer_config.fp8="e4m3" # e4m3 or hybrid # +actor_rollout_ref.actor.megatron.override_transformer_config.fp8_recipe="blockwise" # +actor_rollout_ref.actor.optim.override_optimizer_config.fp8_recipe="blockwise" # rollout actor_rollout_ref.actor.megatron.dtype=${dtype} actor_rollout_ref.rollout.dtype=${dtype} # +actor_rollout_ref.rollout.quantization="fp8" ) DATA=( data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" data.prompt_key=prompt data.return_raw_chat=$return_raw_chat data.truncation='left' data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.train_batch_size=${train_prompt_bsz} ) REWARD_MODEL=( +reward_model.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} +reward_model.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} +reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} +reward_model.reward_kwargs.overlong_buffer_cfg.log=False +reward_model.reward_kwargs.max_resp_len=${max_response_length} reward_model.reward_manager=dapo ) PERF_OPT=( +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True actor_rollout_ref.model.use_fused_kernels=False +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ ) ACTOR=( actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} actor_rollout_ref.actor.clip_ratio_c=10.0 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.optim.lr_warmup_steps=10 actor_rollout_ref.actor.optim.weight_decay=0.1 actor_rollout_ref.actor.optim.clip_grad=1.0 actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} actor_rollout_ref.actor.megatron.param_offload=${offload} actor_rollout_ref.actor.megatron.optimizer_offload=${offload} actor_rollout_ref.actor.megatron.grad_offload=${offload} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} actor_rollout_ref.actor.megatron.use_mbridge=True actor_rollout_ref.actor.megatron.vanilla_mbridge=False ) ROLLOUT=( actor_rollout_ref.rollout.name=${rollout_name} actor_rollout_ref.rollout.mode=${rollout_mode} actor_rollout_ref.rollout.gpu_memory_utilization=0.70 actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.enable_chunked_prefill=True actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) actor_rollout_ref.rollout.temperature=${temperature} actor_rollout_ref.rollout.top_p=${top_p} actor_rollout_ref.rollout.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.calculate_log_probs=True actor_rollout_ref.rollout.n=${n_resp_per_prompt} ) TRAINER=( trainer.logger=['console','wandb'] trainer.project_name="${project_name}" trainer.experiment_name="${exp_name}" trainer.n_gpus_per_node=8 trainer.nnodes="${NNODES}" trainer.val_before_train=False trainer.test_freq=10 trainer.save_freq=-1 trainer.total_epochs=10 trainer.default_local_dir="${CKPTS_DIR}" trainer.resume_mode=auto trainer.log_val_generations=10 ) FORWARD_ONLY_SETS=( actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} ) MODEL=( actor_rollout_ref.model.path="${MODEL_PATH}" actor_rollout_ref.model.trust_remote_code=True ) ALGORITHM=( algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} ) ################################################### start script ################################################### python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA[@]}" \ "${ALGORITHM[@]}" \ "${MODEL[@]}" \ "${ROLLOUT[@]}" \ "${ACTOR[@]}" \ "${REWARD_MODEL[@]}" \ "${FP8[@]}" \ "${PERF_OPT[@]}" \ "${TRAINER[@]}" \ "${FORWARD_ONLY_SETS[@]}" \ ================================================ FILE: examples/grpo_trainer/run_qwen2-32b_sglang_fsdp_npu.sh ================================================ #!/bin/bash set -xeuo pipefail mkdir -p logs # Project Configuration project_name='GRPO-Qwen2.5-32B-BASE-SGLang' exp_name='GRPO-Qwen2.5-32B-BASE-FSDP-SGLang' # Necessary env export HCCL_CONNECT_TIMEOUT=1500 export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050 export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050 export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 # If the number of nodes is 16, ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export DISABLE_L2_CACHE=1 export TASK_QUEUE_ENABLE=1 # Node Info NNODES=${NNODES:-2} NPUS_PER_NODE=${NPUS_PER_NODE:-8} # Model Weights Paths MODEL_PATH=Qwen/Qwen2.5-32B RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/datasets/deepscaler/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/datasets/deepscaler/test.parquet"} # Data Configuration max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) # Training Batch Configuration train_prompt_bsz=32 train_prompt_mini_bsz=32 n_resp_per_prompt=8 # Algorithm Configuration adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 # Performance and Memory Management Configuration all_offload=True use_dynamic_bsz=False # SGLang Configuration gen_tp=4 gen_sp=1 gen_dp=1 gen_ep=1 gpu_memory_utilization=0.5 # Data Configuration DATA_CONFIG=( # File Paths data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" # Data Structure data.prompt_key=prompt # Batch and Length Configuration data.train_batch_size=${train_prompt_bsz} data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} # Preprocessing data.filter_overlong_prompts=False data.truncation='left' ) # Model Configuration MODEL_CONFIG=( # Model Path actor_rollout_ref.model.path="${MODEL_PATH}" # Model Processing actor_rollout_ref.model.use_remove_padding=True actor_rollout_ref.model.enable_gradient_checkpointing=True ) # Reinforcement Learning Algorithm Configuration ALGORITHM_CONFIG=( # Advantage Estimation algorithm.adv_estimator=${adv_estimator} # KL Divergence Control algorithm.use_kl_in_reward=${use_kl_in_reward} ) # Actor Model Configuration ACTOR_CONFIG=( # Core Runtime Settings actor_rollout_ref.actor.use_torch_compile=False actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} # Loss Function Configuration actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.entropy_coeff=0 # PPO Training Parameters actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} # Optimizer Settings actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.fsdp_config.param_offload=${all_offload} actor_rollout_ref.actor.fsdp_config.optimizer_offload=${all_offload} ) # Reference Model Configuration REF_CONFIG=( # Core Runtime Settings actor_rollout_ref.ref.use_torch_compile=False # Log Probability Inference actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} # Memory Optimization actor_rollout_ref.ref.fsdp_config.param_offload=${all_offload} ) # Rollout Configuration ROLLOUT_CONFIG=( # Rollout Engine actor_rollout_ref.rollout.name=sglang +actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend="ascend" # Generation Parameters actor_rollout_ref.rollout.n=${n_resp_per_prompt} actor_rollout_ref.rollout.top_p=1.0 actor_rollout_ref.rollout.top_k=-1 actor_rollout_ref.rollout.temperature=1.0 # Log Probability Inference actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} # Memory Management actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.data_parallel_size=${gen_dp} actor_rollout_ref.rollout.expert_parallel_size=${gen_ep} actor_rollout_ref.rollout.enable_chunked_prefill=False actor_rollout_ref.rollout.multi_stage_wake_up=True # Validation Generation actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.top_p=1.0 actor_rollout_ref.rollout.val_kwargs.top_k=-1 actor_rollout_ref.rollout.val_kwargs.temperature=1.0 actor_rollout_ref.nccl_timeout=1800 ) # Trainer Configuration TRAINER_CONFIG=( trainer.logger='["console"]' trainer.project_name="${project_name}" trainer.experiment_name="${exp_name}" trainer.nnodes="${NNODES}" trainer.n_gpus_per_node="${NPUS_PER_NODE}" trainer.total_epochs=5 trainer.val_before_train=False trainer.test_freq=-1 trainer.save_freq=100 trainer.default_local_dir="${CKPTS_DIR}" trainer.critic_warmup=0 ) # Main GRPO Training Command # Add the reward function processing for the DeepScaler dataset here python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_trainer.yaml' \ reward.custom_reward_function.path=recipe/r1_ascend/deepscaler.py \ reward.custom_reward_function.name=compute_score \ "${DATA_CONFIG[@]}" \ "${MODEL_CONFIG[@]}" \ "${ACTOR_CONFIG[@]}" \ "${REF_CONFIG[@]}" \ "${ROLLOUT_CONFIG[@]}" \ "${ALGORITHM_CONFIG[@]}" \ "${TRAINER_CONFIG[@]}" \ "$@" | tee logs/run_qwen2_5-32b_grpo_fsdp_sglang_npu.log ================================================ FILE: examples/grpo_trainer/run_qwen2-7b.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=40 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_math.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_math_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping rollout_mode="async" export VLLM_USE_V1=1 return_raw_chat="True" gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" USE_FUSED_KERNELS=True python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.return_raw_chat=$return_raw_chat \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.model.use_fused_kernels=$USE_FUSED_KERNELS \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=$rollout_mode \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='qwen2_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_math_megatron_lora.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail # Need to install Megatron-Bridge # NOTE: Make sure you use Megatron-Bridge later than 0.2.0 # (Recommend https://github.com/NVIDIA-NeMo/Megatron-Bridge/commit/83a7c1134c562d8c6decd10a1f0a6e6a7a8a3a44 or later) # for proper MoE LoRA support. # For Megatron communication/computation overlapping export CUDA_DEVICE_MAX_CONNECTIONS=1 ############################ Quick Config ############################ rollout_name="vllm" # sglang or vllm project_name='verl_grpo_example_gsm8k_math' exp_name='qwen2_7b_megatron_lora' adv_estimator=grpo max_prompt_length=1024 max_response_length=1024 train_prompt_bsz=128 ############################ Paths ############################ gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" ############################ Parameter Groups ############################ DATA=( data.train_files="$train_files" data.val_files="$test_files" data.max_prompt_length=$max_prompt_length data.max_response_length=$max_response_length data.train_batch_size=$train_prompt_bsz data.filter_overlong_prompts=True data.truncation='error' data.shuffle=False ) MODEL=( actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct actor_rollout_ref.model.lora.rank=256 actor_rollout_ref.model.lora.alpha=512 actor_rollout_ref.model.lora.lora_A_init_method=kaiming # # Optional: Use canonical LoRA # actor_rollout_ref.model.lora.type="canonical_lora" # actor_rollout_ref.model.lora.target_modules='["linear_q","linear_k","linear_v","linear_proj","linear_fc1_up","linear_fc1_gate","linear_fc2"]' # # Optional: Add dropout to LoRA layers # actor_rollout_ref.model.lora.dropout=0.05 # actor_rollout_ref.model.lora.dropout_position=pre ) ACTOR=( actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.ppo_mini_batch_size=16 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 actor_rollout_ref.actor.use_dynamic_bsz=True actor_rollout_ref.actor.megatron.use_mbridge=True actor_rollout_ref.actor.megatron.vanilla_mbridge=False actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=1 actor_rollout_ref.actor.megatron.tensor_model_parallel_size=4 actor_rollout_ref.actor.use_kl_loss=True actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.entropy_coeff=0 +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 ) ROLLOUT=( actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.rollout.tensor_model_parallel_size=2 actor_rollout_ref.rollout.name=$rollout_name actor_rollout_ref.rollout.gpu_memory_utilization=0.6 actor_rollout_ref.rollout.n=4 ) REF=( actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=1 actor_rollout_ref.ref.megatron.tensor_model_parallel_size=4 ) ALGORITHM=( algorithm.adv_estimator=$adv_estimator algorithm.use_kl_in_reward=False ) TRAINER=( trainer.logger='["console","wandb"]' trainer.project_name=$project_name trainer.experiment_name=$exp_name trainer.n_gpus_per_node=8 trainer.nnodes=1 trainer.save_freq=20 trainer.test_freq=5 trainer.total_epochs=15 trainer.val_before_train=False ) ############################ Launch ############################ python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA[@]}" \ "${ALGORITHM[@]}" \ "${MODEL[@]}" \ "${ROLLOUT[@]}" \ "${ACTOR[@]}" \ "${REF[@]}" \ "${TRAINER[@]}" \ "$@" ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_math_megatron_trtllm.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping # Clean all slurm / MPI / PMIx env to avoid pmix mismatch error for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done export RAY_DEDUP_LOGS=0 # ----- # Config # ----- TP=${1:-4} ACTOR_TP=${ACTOR_TP:-4} PROJECT_NAME=${PROJECT_NAME:-"verl_grpo_example_gsm8k_math"} EXP_NAME=megatron-trtllm-qwen2-7b-tp${TP}-8gpus if [ $TP -eq 4 ]; then MAX_BATCH_SIZE=1024 else MAX_BATCH_SIZE=384 fi # ----- # Data # ----- DATADIR=${DATADIR:-$PWD/data} GSM8K_TRAIN_PATH=${DATADIR}/gsm8k/train.parquet GSM8K_TEST_PATH=${DATADIR}/gsm8k/test.parquet MATH_TRAIN_PATH=${DATADIR}/math/train.parquet MATH_TEST_PATH=${DATADIR}/math/test.parquet TRAIN_FILES="['$GSM8K_TRAIN_PATH', '$MATH_TRAIN_PATH']" TEST_FILES="['$GSM8K_TEST_PATH', '$MATH_TEST_PATH']" USE_FUSED_KERNELS=True # ----- # Launch # ----- python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ algorithm.adv_estimator=grpo \ data.train_files="$TRAIN_FILES" \ data.val_files="$TEST_FILES" \ data.return_raw_chat=True \ data.train_batch_size=1024 \ data.max_prompt_length=2048 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.model.use_fused_kernels=$USE_FUSED_KERNELS \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${TP} \ actor_rollout_ref.rollout.name=trtllm \ actor_rollout_ref.rollout.mode="async" \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=${MAX_BATCH_SIZE} \ actor_rollout_ref.rollout.max_num_batched_tokens=32768 \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${ACTOR_TP} \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_timeout_iters=32 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_max_tokens_ratio=0.5 \ actor_rollout_ref.rollout.calculate_log_probs=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name="${PROJECT_NAME}" \ trainer.experiment_name=${EXP_NAME} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.resume_mode=disable \ trainer.total_epochs=15 \ "${@:2}" ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_math_trtllm.sh ================================================ set -x # Clean all slurm / MPI / PMIx env to avoid pmix mismatch error for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done export RAY_DEDUP_LOGS=0 # ----- # Config # ----- TP=${1:-4} PROJECT_NAME=${PROJECT_NAME:-"verl_grpo_example_gsm8k_math"} EXP_NAME=trtllm-qwen2-7b-tp${TP}-8gpus${EXP_NAME_SUFFIX:+"-"}${EXP_NAME_SUFFIX} if [ $TP -eq 4 ]; then MAX_BATCH_SIZE=1024 else MAX_BATCH_SIZE=384 fi # ----- # Data # ----- DATADIR=${DATADIR:-$PWD/data} MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2-7B-Instruct"} GSM8K_TRAIN_PATH=${DATADIR}/gsm8k/train.parquet GSM8K_TEST_PATH=${DATADIR}/gsm8k/test.parquet MATH_TRAIN_PATH=${DATADIR}/math/train.parquet MATH_TEST_PATH=${DATADIR}/math/test.parquet TRAIN_FILES="['$GSM8K_TRAIN_PATH', '$MATH_TRAIN_PATH']" TEST_FILES="['$GSM8K_TEST_PATH', '$MATH_TEST_PATH']" # ----- # Launch # ----- python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ algorithm.rollout_correction.rollout_is_threshold=2.0 \ data.train_files="$TRAIN_FILES" \ data.val_files="$TEST_FILES" \ data.train_batch_size=1024 \ data.max_prompt_length=2048 \ data.max_response_length=1024 \ data.return_raw_chat=True \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.hybrid_engine=True \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${TP} \ actor_rollout_ref.rollout.name=trtllm \ actor_rollout_ref.rollout.mode="async" \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=${MAX_BATCH_SIZE} \ actor_rollout_ref.rollout.max_num_batched_tokens=32768 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_timeout_iters=32 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_max_tokens_ratio=0.5 \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name="${PROJECT_NAME}" \ trainer.experiment_name=${EXP_NAME} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.resume_mode=disable \ trainer.total_epochs=15 \ "${@:2}" ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_seq_balance.sh ================================================ set -x # For async rollout mode, dataset should return raw chat. rollout_mode="async" rollout_name="sglang" # sglang or vllm return_raw_chat="True" if [ "$rollout_name" = "vllm" ]; then export VLLM_USE_V1=1 fi python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.return_raw_chat=$return_raw_chat \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$rollout_name \ actor_rollout_ref.rollout.mode=$rollout_mode \ actor_rollout_ref.rollout.multi_turn.format=hermes \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm_kl1e-3' \ trainer.val_before_train=False \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_seq_balance_math_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" offload=True python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=12000 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='qwen2_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2-7b_sgl_megatron.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=4 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5-32b_grpo_megatron_vllm_npu.sh ================================================ #!/bin/bash set -xeuo pipefail mkdir -p logs # Project Configuration project_name='GRPO-Qwen2.5-32B-BASE-MATH' exp_name='GRPO-Qwen2.5-32B-BASE-Megatron-vLLM' # Node Info NNODES=${NNODES:-1} NPUS_PER_NODE=${NPUS_PER_NODE:-16} # Model Weights Paths MODEL_PATH=Qwen/Qwen2.5-32B MCORE_MODEL_PATH=Qwen/Qwen2.5-32B-dist RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=$RAY_DATA_HOME/dataset/gsm8k/train.parquet TEST_FILE=$RAY_DATA_HOME/dataset/gsm8k/test.parquet # Data Configuration max_prompt_length=$((1024 * 1)) max_response_length=$((1024 * 1)) # Training Batch Configuration train_prompt_bsz=128 train_prompt_mini_bsz=32 n_resp_per_prompt=16 # Algorithm Configuration adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 # Performance and Memory Management Configuration all_offload=True use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 8)) optimizer_offload_fraction=1 # Megatron Configuration train_tp=4 train_ep=1 train_etp=1 train_pp=4 train_cp=1 # vLLM Configuration gen_tp=2 gen_dp=1 gen_ep=1 gpu_memory_utilization=0.8 max_model_len=$((max_prompt_length + max_response_length)) max_num_batched_tokens=$(((max_prompt_length + max_response_length) * 1)) # Data Configuration DATA_CONFIG=( data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" data.prompt_key=prompt data.train_batch_size=${train_prompt_bsz} data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.filter_overlong_prompts=False data.truncation='left' ) # Model Configuration MODEL_CONFIG=( actor_rollout_ref.model.path="${MODEL_PATH}" actor_rollout_ref.model.use_remove_padding=True ) # Algorithm Configuration ALGORITHM_CONFIG=( algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} ) # Actor Model Configuration ACTOR_CONFIG=( actor_rollout_ref.actor.use_torch_compile=False actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.ppo_epochs=1 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.optim.lr=1e-6 +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} actor_rollout_ref.actor.megatron.expert_model_parallel_size=${train_ep} actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${train_etp} actor_rollout_ref.actor.megatron.param_offload=${all_offload} actor_rollout_ref.actor.megatron.optimizer_offload=${all_offload} actor_rollout_ref.actor.megatron.grad_offload=${all_offload} actor_rollout_ref.actor.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} actor_rollout_ref.actor.megatron.use_dist_checkpointing=False +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True ) # Reference Model Configuration REF_CONFIG=( actor_rollout_ref.ref.use_torch_compile=False actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.ref.megatron.context_parallel_size=${train_cp} actor_rollout_ref.ref.megatron.expert_model_parallel_size=${train_ep} actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${train_etp} actor_rollout_ref.ref.megatron.param_offload=${all_offload} actor_rollout_ref.ref.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} actor_rollout_ref.ref.megatron.use_dist_checkpointing=False ) # Rollout Configuration ROLLOUT_CONFIG=( actor_rollout_ref.rollout.name=vllm actor_rollout_ref.rollout.n=${n_resp_per_prompt} actor_rollout_ref.rollout.top_p=1.0 actor_rollout_ref.rollout.top_k=-1 actor_rollout_ref.rollout.temperature=1.0 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} actor_rollout_ref.rollout.max_num_batched_tokens=${max_num_batched_tokens} actor_rollout_ref.rollout.max_model_len=${max_model_len} actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.data_parallel_size=${gen_dp} actor_rollout_ref.rollout.expert_parallel_size=${gen_ep} actor_rollout_ref.rollout.enable_chunked_prefill=True actor_rollout_ref.rollout.enable_prefix_caching=True actor_rollout_ref.rollout.enforce_eager=True actor_rollout_ref.rollout.free_cache_engine=True actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.top_p=1.0 actor_rollout_ref.rollout.val_kwargs.top_k=-1 actor_rollout_ref.rollout.val_kwargs.temperature=1.0 ) # Trainer Configuration TRAINER_CONFIG=( trainer.logger='["console","tensorboard"]' trainer.project_name="${project_name}" trainer.experiment_name="${exp_name}" trainer.nnodes="${NNODES}" trainer.n_gpus_per_node="${NPUS_PER_NODE}" trainer.device='npu' trainer.total_epochs=15 trainer.val_before_train=False trainer.test_freq=-1 trainer.save_freq=-1 trainer.default_local_dir="${CKPTS_DIR}" ) # Main GRPO Training Command python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA_CONFIG[@]}" \ "${MODEL_CONFIG[@]}" \ "${ACTOR_CONFIG[@]}" \ "${REF_CONFIG[@]}" \ "${ROLLOUT_CONFIG[@]}" \ "${ALGORITHM_CONFIG[@]}" \ "${TRAINER_CONFIG[@]}" \ "$@" | tee logs/run_qwen2_5-32b_grpo_megatron_vllm_npu.log ================================================ FILE: examples/grpo_trainer/run_qwen2_5-3b_gsm8k_grpo_lora.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ trainer.val_before_train=False \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=16 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.model.lora_rank=64 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.actor.optim.lr=3e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=16 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=40 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2.5_3b_grpo_lora' \ trainer.n_gpus_per_node=2 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ # actor_rollout_ref.actor.ppo_mini_batch_size=256 \ # data.train_batch_size=1024 \ # trainer.n_gpus_per_node=8 \ # actor_rollout_ref.model.use_shm=True \ ================================================ FILE: examples/grpo_trainer/run_qwen2_5-3b_gsm8k_grpo_lora_from_adapter.sh ================================================ set -x lora_adapter_path=${lora_adapter_path:-/path/saved/lora_adapter} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.lora_adapter_path=${lora_adapter_path} \ actor_rollout_ref.actor.optim.lr=3e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=40 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2.5_3b_grpo_lora' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5-7b_math_megatron_diff_tp.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name='qwen2_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_32b_grpo_npu.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-32B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6\ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_5_32b_function_rm' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=2 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_7b_grpo_discrete_prof_npu.sh ================================================ set -x # profiling configuration PROFILE_STEPS="[2,4]" PROFILE_RANKS_ALL=False DISCRETE=True PROFILE_RANKS="[1,2]" # profiling NPU options SAVE_PATH="$HOME/profile_data" LEVEL="level0" CONTENTS=['npu','cpu'] ANALYSIS=True python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=32 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.optim.lr=5e-8 \ actor_rollout_ref.actor.ppo_mini_batch_size=2 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.profiler.enable=True \ actor_rollout_ref.actor.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.actor.profiler.tool_config.npu.discrete=$DISCRETE \ actor_rollout_ref.actor.profiler.tool_config.npu.contents=$CONTENTS \ actor_rollout_ref.actor.profiler.tool_config.npu.level=$LEVEL \ actor_rollout_ref.actor.profiler.tool_config.npu.analysis=$ANALYSIS \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.ref.profiler.enable=True \ actor_rollout_ref.ref.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.ref.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.ref.profiler.tool_config.npu.discrete=$DISCRETE \ actor_rollout_ref.ref.profiler.tool_config.npu.contents=$CONTENTS \ actor_rollout_ref.ref.profiler.tool_config.npu.level=$LEVEL \ actor_rollout_ref.ref.profiler.tool_config.npu.analysis=$ANALYSIS \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_5_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=5 \ global_profiler.tool=npu \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.save_path=$SAVE_PATH $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_7b_grpo_e2e_prof_npu.sh ================================================ set -x # profiling configuration PROFILE_STEPS="[2,4]" PROFILE_RANKS_ALL=True DISCRETE=False # profiling NPU options SAVE_PATH="$HOME/profile_data" LEVEL="level0" CONTENTS=['npu','cpu'] ANALYSIS=True python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=32 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.actor.optim.lr=5e-8 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.ppo_mini_batch_size=2 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.profiler.enable=True \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.actor.profiler.tool_config.npu.discrete=$DISCRETE \ actor_rollout_ref.actor.profiler.tool_config.npu.contents=$CONTENTS \ actor_rollout_ref.actor.profiler.tool_config.npu.level=$LEVEL \ actor_rollout_ref.actor.profiler.tool_config.npu.analysis=$ANALYSIS \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.ref.profiler.enable=True \ actor_rollout_ref.ref.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.ref.profiler.tool_config.npu.discrete=$DISCRETE \ actor_rollout_ref.ref.profiler.tool_config.npu.contents=$CONTENTS \ actor_rollout_ref.ref.profiler.tool_config.npu.level=$LEVEL \ actor_rollout_ref.ref.profiler.tool_config.npu.analysis=$ANALYSIS \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_5_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=5 \ global_profiler.tool=npu \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.save_path=$SAVE_PATH $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_7b_grpo_npu.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.actor.optim.lr=5e-8 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_5_7b_function_rm' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=5 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b-megatron.sh ================================================ set -x ENGINE=${1:-vllm} export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping HF_MODEL_PATH=Qwen/Qwen2.5-VL-7B-Instruct DIST_CKPT_PATH=${DIST_CKPT_PATH} # convert HF model to megatron format offlinely # python scripts/converter_hf_to_mcore.py --hf_model_path $HF_MODEL_PATH --output_path $DIST_CKPT_PATH # megatron tuning guide: # 1. recommend to offload all states by setting ALL_OFFLOAD=True # 2. enable dynamic batch size by setting actor_rollout_ref.actor.use_dynamic_bsz=True ref.log_prob_use_dynamic_bsz=True rollout.log_prob_use_dynamic_bsz=True # 3. set ppo_max_token_len_per_gpu and log_prob_max_token_len_per_gpu as large as possible for better MFU (limited by GPU memory). assure ppo_max_token_len_per_gpu > max_prompt_length+max_response_length, if sequence length is too long, you can increase the TP/PP size # 4. if memory is very limited, enable full recompute, but the mfu will be 30% lower # full recompute settings: # +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ # +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ # +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ ALL_OFFLOAD=${ALL_OFFLOAD:-True} COMMON_PARAM_OFFLOAD=${COMMON_PARAM_OFFLOAD:-$ALL_OFFLOAD} COMMON_GRAD_OFFLOAD=${COMMON_GRAD_OFFLOAD:-$ALL_OFFLOAD} COMMON_OPTIMIZER_OFFLOAD=${COMMON_OPTIMIZER_OFFLOAD:-$ALL_OFFLOAD} ACTOR_PARAM_OFFLOAD=${ACTOR_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} ACTOR_GRAD_OFFLOAD=${ACTOR_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} ACTOR_OPTIMIZER_OFFLOAD=${ACTOR_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} REF_PARAM_OFFLOAD=${REF_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} train_path=$HOME/data/geo3k/train.parquet test_path=$HOME/data/geo3k/test.parquet python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_path" \ data.val_files="$test_path" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=1 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=5120 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=20480 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=20480 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=1 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ actor_rollout_ref.actor.megatron.param_offload=${ACTOR_PARAM_OFFLOAD} \ actor_rollout_ref.actor.megatron.optimizer_offload=${ACTOR_OPTIMIZER_OFFLOAD} \ actor_rollout_ref.actor.megatron.grad_offload=${ACTOR_GRAD_OFFLOAD} \ actor_rollout_ref.ref.megatron.param_offload=${REF_PARAM_OFFLOAD} \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b-sglang.sh ================================================ set -x # python examples/data_preprocess/geo3k.py --local_dir ~/data/geo3k python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=sglang \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \ actor_rollout_ref.rollout.multi_stage_wake_up=True \ global_profiler.tool=torch_memory \ global_profiler.save_path=./mem_snapshots \ global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries=100000 \ global_profiler.global_tool_config.torch_memory.stack_depth=32 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.mode=async \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b-trtllm.sh ================================================ set -x # Clean all slurm / MPI / PMIx env to avoid pmix mismatch error for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done export RAY_DEDUP_LOGS=0 # ----- # Config # ----- TP=${1:-4} PROJECT_NAME=${PROJECT_NAME:-"verl_grpo_example_gsm8k_math"} EXP_NAME=trtllm-qwen2.5-vl-7b-tp${TP}-8gpus${EXP_NAME_SUFFIX:+"-"}${EXP_NAME_SUFFIX} if [ $TP -eq 4 ]; then MAX_BATCH_SIZE=1024 else MAX_BATCH_SIZE=384 fi # ----- # Data # ----- DATADIR=${DATADIR:-$PWD/data} MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2.5-VL-7B-Instruct"} GEO3K_TRAIN_PATH=${DATADIR}/geo3k/train.parquet GEO3K_TEST_PATH=${DATADIR}/geo3k/test.parquet TRAIN_FILES="['$GEO3K_TRAIN_PATH']" TEST_FILES="['$GEO3K_TEST_PATH']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ algorithm.rollout_correction.rollout_is_threshold=2.0 \ data.train_files="$TRAIN_FILES" \ data.val_files="$TEST_FILES" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.return_raw_chat=True \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.trust_remote_code=True \ actor_rollout_ref.hybrid_engine=True \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.model.trust_remote_code=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.strategy=fsdp2 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ +actor_rollout_ref.model.override_config.attn_implementation=eager \ +actor_rollout_ref.ref.model.override_config.attn_implementation=eager \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${TP} \ actor_rollout_ref.rollout.name=trtllm \ actor_rollout_ref.rollout.mode="async" \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=${MAX_BATCH_SIZE} \ actor_rollout_ref.rollout.max_num_batched_tokens=16384 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_timeout_iters=32 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_max_tokens_ratio=0.5 \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.ref.strategy=fsdp2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console"]' \ trainer.project_name="${PROJECT_NAME}" \ trainer.experiment_name=${EXP_NAME} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=10 \ trainer.test_freq=5 \ trainer.resume_mode=disable \ trainer.total_epochs=10 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b.sh ================================================ set -x ENGINE=${1:-vllm} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b_freeze_vision.sh ================================================ set -x ENGINE=${1:-vllm} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.freeze_vision_tower=True \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b_lora.sh ================================================ set -x ENGINE=${1:-vllm} # If you are using vllm<=0.6.3, you might need to set the following environment variable to avoid bugs: # export VLLM_ATTENTION_BACKEND=XFORMERS python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ actor_rollout_ref.actor.optim.lr=3e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.model.lora_rank=64 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.model.exclude_modules='.*visual.*' \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl-7b_seq_balance.sh ================================================ set -x ENGINE=${1:-vllm} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=6144 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=6144 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl_32b_npu.sh ================================================ set -x ENGINE=${1:-vllm} # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. export USE_OPTIMIZED_MODEL=0 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-32B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_32b_function_rm' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=2 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl_3b_npu.sh ================================================ set -x ENGINE=${1:-vllm} # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. export USE_OPTIMIZED_MODEL=0 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=16 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_3b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl_3b_trtllm.sh ================================================ set -x # Clean all slurm / MPI / PMIx env to avoid pmix mismatch error for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done export RAY_DEDUP_LOGS=0 # ----- # Config # ----- TP=${1:-4} PROJECT_NAME=${PROJECT_NAME:-"verl_grpo_example_gsm8k_math"} EXP_NAME=trtllm-qwen2.5-vl-3b-tp${TP}-8gpus${EXP_NAME_SUFFIX:+"-"}${EXP_NAME_SUFFIX} if [ $TP -eq 4 ]; then MAX_BATCH_SIZE=1024 else MAX_BATCH_SIZE=384 fi # ----- # Data # ----- DATADIR=${DATADIR:-$PWD/data} MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2.5-VL-3B-Instruct"} GSM8K_TRAIN_PATH=${DATADIR}/gsm8k/train.parquet GSM8K_TEST_PATH=${DATADIR}/gsm8k/test.parquet MATH_TRAIN_PATH=${DATADIR}/math/train.parquet MATH_TEST_PATH=${DATADIR}/math/test.parquet TRAIN_FILES="['$GSM8K_TRAIN_PATH', '$MATH_TRAIN_PATH']" TEST_FILES="['$GSM8K_TEST_PATH', '$MATH_TEST_PATH']" # ----- # Launch # ----- python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ algorithm.rollout_correction.rollout_is_threshold=2.0 \ data.train_files="$TRAIN_FILES" \ data.val_files="$TEST_FILES" \ data.train_batch_size=1024 \ data.max_prompt_length=2048 \ data.max_response_length=1024 \ data.return_raw_chat=True \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.hybrid_engine=True \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${TP} \ actor_rollout_ref.rollout.name=trtllm \ actor_rollout_ref.rollout.mode="async" \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=${MAX_BATCH_SIZE} \ actor_rollout_ref.rollout.max_num_batched_tokens=32768 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_timeout_iters=32 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_max_tokens_ratio=0.5 \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name="${PROJECT_NAME}" \ trainer.experiment_name=${EXP_NAME} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.resume_mode=disable \ trainer.total_epochs=15 \ "${@:2}" ================================================ FILE: examples/grpo_trainer/run_qwen2_5_vl_7b_npu.sh ================================================ set -x ENGINE=${1:-vllm} # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. export USE_OPTIMIZED_MODEL=0 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_function_rm' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen3-235b_megatron_96gb.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail ## !!!!!!!important!!!!!! ## set the following environment variables on all your nodes # env_vars: # CUDA_DEVICE_MAX_CONNECTIONS: "1" # NCCL_NVLS_ENABLE: "0" # VLLM_USE_V1: 1 # install mbridge=0.1.13 on all your node with the following command: # pip3 install git+https://github.com/ISEEKYAN/mbridge SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" [ -f "${SCRIPT_DIR}/env.sh" ] && source "${SCRIPT_DIR}/env.sh" adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1204 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 1)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=${TRAIN_BS:-32} n_resp_per_prompt=8 train_prompt_mini_bsz=16 # minimum nodes need for qwen3-235B-A22B NNODES=${NNODES:-4} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=$RAY_DATA_HOME/models/Qwen3-235B-A22B TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 10 / 10)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) offload=True OPTIM_OFFLOAD=${OPTIM_OFFLOAD:-True} gen_tp=8 train_tp=${TP:-4} train_pp=${PP:-8} EP=${EP:-4} ETP=1 CP=1 optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.} last_layer=${LAST_LAYER:-10} project_name='verl-qwen3' exp_name="235B-${NNODES}-pp${train_pp}-tp${train_tp}-ep${EP}-actor-length${actor_ppo_max_token_len}" CKPTS_DIR=$RAY_DATA_HOME/ckpt/${project_name}/${exp_name} # TODO: support cuda graph for rollout by setting the following config # actor_rollout_ref.rollout.cudagraph_capture_sizes=[1,2,4,8,16,32] # actor_rollout_ref.rollout.enforce_eager=False python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${OPTIM_OFFLOAD} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.megatron.context_parallel_size=${CP} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.nccl_timeout=1200 \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.context_parallel_size=${CP} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_loss_in_pipeline_split=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_embedding_in_pipeline_split=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','wandb'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=False \ trainer.test_freq=10 \ trainer.save_freq=100 \ trainer.total_epochs=10 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/grpo_trainer/run_qwen3-30b_dapo_megatron_fp8_trtllm.sh ================================================ set -x # Clean all slurm / MPI / PMIx env to avoid pmix mismatch error for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done export RAY_DEDUP_LOGS=0 # ---------------------- # Config for GB200 node # ---------------------- TP=${INFER_TP:-4} ACTOR_TP=${ACTOR_TP:-4} ACTOR_PP=${ACTOR_PP:-2} ACTOR_VPP=${ACTOR_VPP:-2} ACTOR_EP=${ACTOR_EP:-2} ACTOR_CP=${ACTOR_CP:-1} REF_TP=${REF_TP:-4} REF_PP=${REF_PP:-2} REF_VPP=${REF_VPP:-2} REF_EP=${REF_EP:-2} REF_CP=${REF_CP:-1} GEN_MOE_TP=${GEN_MOE_TP:-2} GEN_MOE_EP=${GEN_MOE_EP:-2} PROJECT_NAME=${PROJECT_NAME:-"Qwen3-30B-A3B-DAPO-GB200"} NNODES=${NNODES:-4} GPUS_PER_NODE=${GPUS_PER_NODE:-4} # MOE backend for TRTLLM when using FP8 quantization: # - Blackwell: use DEEPGEMM # - Hopper: use CUTLASS TRTLLM_MOE_BACKEND=${TRTLLM_MOE_BACKEND:-"DEEPGEMM"} EXP_NAME=qwen3-30b-dapo-megatron-fp8-trtllm-n${NNODES}-tp${TP}-moe-tp${GEN_MOE_TP}-moe-ep${GEN_MOE_EP}${EXP_NAME_SUFFIX:+"-"}${EXP_NAME_SUFFIX} if [ $TP -eq 4 ] || [ $TP -eq 2 ]; then MAX_NUM_SEQS=1024 else MAX_NUM_SEQS=384 fi # ----- # Data # ----- DATA_DIR=${DATA_DIR:-"$PWD"} DAPO_MATH_TRAIN=${DAPO_MATH_TRAIN:-"${DATA_DIR}/data/DAPO-Math-17k/data/dapo-math-17k.parquet"} AIME_VAL=${AIME_VAL:-"${DATA_DIR}/data/AIME-2024/data/aime-2024.parquet"} MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen3-30B-A3B-Base"} # When PP=1, Megatron interleaved schedule is invalid; pass null so PP=1 works (e.g. 2-node) [ "${ACTOR_PP}" -gt 1 ] && ACTOR_VPP_OVERRIDE=${ACTOR_VPP} || ACTOR_VPP_OVERRIDE=null [ "${REF_PP}" -gt 1 ] && REF_VPP_OVERRIDE=${REF_VPP} || REF_VPP_OVERRIDE=null # ----- # Launch # ----- python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ algorithm.adv_estimator=grpo \ algorithm.use_kl_in_reward=False \ algorithm.kl_ctrl.kl_coef=0.0 \ reward_model.reward_manager=dapo \ +reward_model.reward_kwargs.overlong_buffer_cfg.enable=True \ +reward_model.reward_kwargs.overlong_buffer_cfg.len=4096 \ +reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=1.0 \ +reward_model.reward_kwargs.overlong_buffer_cfg.log=False \ +reward_model.reward_kwargs.max_resp_len=8192 \ data.train_files="${DAPO_MATH_TRAIN}" \ data.val_files="${AIME_VAL}" \ data.prompt_key=prompt \ data.return_raw_chat=True \ data.truncation=left \ data.max_prompt_length=2048 \ data.max_response_length=8192 \ data.train_batch_size=512 \ data.filter_overlong_prompts=False \ actor_rollout_ref.hybrid_engine=True \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.optim.lr=1e-5 \ actor_rollout_ref.actor.ppo_mini_batch_size=16 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=30720 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.0 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.loss_agg_mode=token-mean \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP_OVERRIDE} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \ actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \ actor_rollout_ref.actor.megatron.param_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=40960 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP_OVERRIDE} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \ actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \ actor_rollout_ref.rollout.name=trtllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=40960 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=${TP} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.max_num_seqs=${MAX_NUM_SEQS} \ actor_rollout_ref.rollout.max_num_batched_tokens=10240 \ actor_rollout_ref.rollout.max_model_len=10240 \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_timeout_iters=32 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.batch_wait_max_tokens_ratio=0.5 \ +actor_rollout_ref.rollout.engine_kwargs.trtllm.moe_config.backend=${TRTLLM_MOE_BACKEND} \ +actor_rollout_ref.rollout.moe_tensor_parallel_size=${GEN_MOE_TP} \ actor_rollout_ref.rollout.expert_parallel_size=${GEN_MOE_EP} \ +actor_rollout_ref.rollout.quantization=fp8 \ actor_rollout_ref.rollout.prompt_length=2048 \ actor_rollout_ref.rollout.response_length=8192 \ actor_rollout_ref.rollout.temperature=1.0 \ actor_rollout_ref.rollout.top_p=1 \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.top_p=0.7 \ actor_rollout_ref.rollout.val_kwargs.n=1 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name="${PROJECT_NAME}" \ trainer.experiment_name=${EXP_NAME} \ trainer.n_gpus_per_node=${GPUS_PER_NODE} \ trainer.nnodes=${NNODES} \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.resume_mode=auto \ trainer.total_epochs=1000 \ trainer.val_before_train=False \ trainer.log_val_generations=10 \ "${@}" ================================================ FILE: examples/grpo_trainer/run_qwen3-32b_npu.sh ================================================ set -x project_name='GRPO-Qwen3' exp_name='GRPO-Qwen3-32b-npu' gen_tp=4 RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-32B"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=1024 \ data.max_prompt_length=2048 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=4 \ +actor_rollout_ref.actor.fsdp_config.mixed_precision.param_dtype=bf16 \ +actor_rollout_ref.actor.fsdp_config.mixed_precision.reduce_dtype=bf16 \ +actor_rollout_ref.actor.fsdp_config.mixed_precision.buffer_dtype=fp32 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=32768 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes=4 \ trainer.resume_from_path=checkpoints/ \ trainer.save_freq=500 \ trainer.test_freq=50 \ trainer.total_epochs=50 $@ ================================================ FILE: examples/grpo_trainer/run_qwen3-4b_gsm8k_grpo_lora_merge.sh ================================================ set -x # initial "val-core/openai/gsm8k/acc/mean@1":0.378316906747536 # after training: "val-core/openai/gsm8k/acc/mean@1":0.9264594389689158 TIMESTAMP=$(date +%Y%m%d.%H%M%S) project_name=verl_grpo_example_gsm8k experiment_name=qwen3_4b_grpo-lora-merged-${TIMESTAMP} train_dir=outputs/$project_name/$experiment_name/ mkdir -p $train_dir export TENSORBOARD_DIR=$train_dir/tensorboard_log/ export VERL_FILE_LOGGER_PATH=$train_dir/metrics.jsonl max_token_len_per_gpu=24576 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ trainer.val_before_train=True \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=128 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=Qwen/Qwen3-4B \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ +actor_rollout_ref.model.lora.merge=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=64 \ actor_rollout_ref.actor.optim.lr=1.0e-05 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${max_token_len_per_gpu} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.strategy=fsdp2 \ actor_rollout_ref.actor.fsdp_config.model_dtype=bf16 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${max_token_len_per_gpu} \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${max_token_len_per_gpu} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.ref.strategy=fsdp2 \ actor_rollout_ref.ref.fsdp_config.model_dtype=bf16 \ algorithm.use_kl_in_reward=False \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=0 \ trainer.logger='["console","tensorboard","file"]' \ trainer.project_name=$project_name \ trainer.experiment_name=$experiment_name \ trainer.default_local_dir=$train_dir \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 \ 2>&1 | tee $train_dir/train_log.txt ================================================ FILE: examples/grpo_trainer/run_qwen3-8b.sh ================================================ # Tested successfully on the hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.4-flashinfer0.2.2-cxx11abi0 image. # It outperforms the Qwen2 7B base model by two percentage points on the test set of GSM8K. set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen3-8B \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen3_8b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen3-8b_npu.sh ================================================ set -x project_name='GRPO-Qwen3' exp_name='GRPO-Qwen3-8B-npu' gen_tp=2 RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-8B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.default_local_dir=${CKPTS_DIR} \ trainer.resume_mode=auto \ actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \ actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \ ++actor_rollout_ref.actor.entropy_from_logits_with_chunking=True \ ++actor_rollout_ref.ref.entropy_from_logits_with_chunking=True \ ++actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ trainer.val_before_train=True \ trainer.save_freq=5 \ trainer.test_freq=5 \ trainer.total_epochs=15 ================================================ FILE: examples/grpo_trainer/run_qwen3_235b_megatron_npu.sh ================================================ set -xeuo pipefail project_name='GRPO' exp_name='GRPO-qwen3-235b-megatron' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 clip_ratio_low=0.2 clip_ratio_high=0.28 enable_filter_groups=False max_num_gen_batches=32 filter_groups_metric=acc max_prompt_length=$((1024 * 8)) max_response_length=$((1024 * 4)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=128 # must be > n_gpus. need to fix gen_prompt_bsz=$((train_prompt_bsz * 1)) n_resp_per_prompt=16 train_prompt_mini_bsz=128 # mini_bsz * n >= micro_bsz * pp * dp NNODES=8 MODEL_PATH=${WORK_DIR}/Qwen3-235B-A22B MCORE_MODEL_PATH=${WORK_DIR}/Qwen3-235B-A22B-Mcore CKPTS_DIR=".ckpt" TRAIN_FILE=${WORK_DIR}/gsm8k/train.parquet TEST_FILE=${WORK_DIR}/gsm8k/test.parquet val_top_p=0.7 USE_MBRIDGE=False USE_CKPT=True offload=True gen_tp=8 gen_dp=8 rollout_max_num_seqs=64 max_num_batched_tokens=$((1024)) train_tp=4 train_ep=4 train_pp=8 actor_ppo_max_token_len=$((max_prompt_length + max_response_length)) infer_ppo_max_token_len=$((max_prompt_length + max_response_length)) actor_ppo_max_token_len=$((actor_ppo_max_token_len)) infer_ppo_max_token_len=$((infer_ppo_max_token_len)) python3 -m verl.trainer.main_ppo --config-path=config --config-name='ppo_megatron_trainer' \ algorithm.adv_estimator=${adv_estimator} \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ actor_rollout_ref.rollout.free_cache_engine=True \ data.train_batch_size=${train_prompt_bsz} \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.filter_overlong_prompts=False \ data.truncation='error' \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=${train_ep} \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_CKPT \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enable_prefix_caching=True \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_first_pipeline_stage=11 \ +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=11 \ actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.data_parallel_size=${gen_dp} \ actor_rollout_ref.rollout.expert_parallel_size=64 \ actor_rollout_ref.rollout.name=vllm \ +actor_rollout_ref.rollout.enable_expert_parallel=True \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.75 \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.rollout.max_num_batched_tokens=${max_num_batched_tokens} \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=${train_ep} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=$USE_CKPT \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ trainer.critic_warmup=0 \ trainer.logger=['console'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=16 \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=False \ trainer.test_freq=-1 \ trainer.save_freq=100 \ trainer.total_epochs=1 \ trainer.default_local_dir="${CKPTS_DIR}" \ actor_rollout_ref.nccl_timeout=7200 \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True \ +actor_rollout_ref.ref.megatron.override_transformer_config.use_flash_attn=True \ actor_rollout_ref.rollout.enforce_eager=True \ trainer.device=npu \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_capture_sizes="[8, 16, 32, 64, 128]" \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_DECODE_ONLY" 2>&1 | tee "logs/verl_qwen3_235b_sy$(date +%Y%m%d_%H%M).log" ================================================ FILE: examples/grpo_trainer/run_qwen3_4b_grpo_vllm_1k_npu.sh ================================================ set -xeuo pipefail source /usr/local/Ascend/ascend-toolkit/set_env.sh source /usr/local/Ascend/nnal/atb/set_env.sh # 使用v1引擎 export VLLM_USE_V1=1 # 指定vllm 版本 export VLLM_VERSION=0.9.1 # 开启二级流水 export TASK_QUEUE_ENABLE=2 # 开启细绑核 export CPU_AFFINITY_CONF=1 # 使用jemalloc优化内存访问（依赖安装jemalloc） export LD_PRELOAD="/usr/lib/aarch64-linux-gnu/libjemalloc.so.2${LD_PRELOAD:+:$LD_PRELOAD}" # A3 机器单机8卡 trainer_n_gpus_per_node=16 trainer_nnodes=1 trainer_project_name='verl_grpo_example_gsm8k' trainer_experiment_name="qwen3_4b_grpo_8npu}" RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-4B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${trainer_project_name}/${trainer_experiment_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"} export TENSORBOARD_DIR="${RAY_DATA_HOME}/tensorboard_dir/${trainer_project_name}/${trainer_experiment_name}" mkdir -p "${RAY_DATA_HOME}/logs/${trainer_project_name}" LOG_PATH="${RAY_DATA_HOME}/logs/${trainer_project_name}/${trainer_experiment_name}.log" use_dynamic_bsz=True python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=${TRAIN_FILE} \ data.val_files=${TEST_FILE} \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=5e-7 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.entropy_coeff=0.001 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=3000 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=8192 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.ref.use_torch_compile=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.project_name=${trainer_project_name} \ trainer.experiment_name=${trainer_experiment_name} \ trainer.logger=['console','tensorboard'] \ trainer.default_local_dir=${CKPTS_DIR} \ trainer.n_gpus_per_node=$trainer_n_gpus_per_node \ trainer.nnodes=$trainer_nnodes \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 \ trainer.val_before_train=False 2>&1 | tee ${LOG_PATH} ================================================ FILE: examples/grpo_trainer/run_qwen3_5-35b-megatron.sh ================================================ #!/usr/bin/env bash # Qwen3.5-35B-A3B MoE GRPO RL with Megatron (single node, 8 GPUs, geo3k dataset) # # notes on vllm: # by 20260225, the latest vllm nightly does not support qwen3.5 rollout, to use this script, you need to # 1. wait until vllm supports qwen3.5 officially, and build a verl docker with that version of vllm # 2. self build a verl docker image with vllm from source code with qwen3.5 support (main branch 20260225 is OK) # I succeeded in running this script with the main branch of vllm on 20260225, yet there are still some minor issues # the vllm qwen3.5 during initialization, need to be fixed. Also, the cuda_graph is somehow not working, need to be # fixed, either by verl team with supoorts to vllm0.16, or by vllm team. # Requirements: # - 8 GPUs (80GB each, e.g. 1x8 H100/H200) # - Additional packages on top of the base image: # pip install --upgrade transformers # pip install flash-linear-attention # pip install -U git+https://github.com/ISEEKYAN/mbridge.git # - Megatron-LM==0.16.0 # # Qwen3.5 architecture notes: # Qwen3.5 uses Gated Delta Net (GDN) linear attention which currently does # NOT support packed sequences (THD format) in Megatron-LM. Therefore: # - model.use_remove_padding=False (deprecated option, will be removed in the future forces bshd compute format) # - actor.megatron.use_remove_padding=False (forces bshd compute format) # - actor.use_dynamic_bsz=False (required for bshd mode) # # Once Megatron-LM adds THD support for Qwen3.5 GDN, use_remove_padding # can be set to True for better performance. # # Tested parallelism config (8 GPUs / 1 node): # TP=2 PP=1 CP=1 EP=8 ETP=1 GEN_TP=8 # export CUDA_DEVICE_MAX_CONNECTIONS=1 export VLLM_USE_V1=1 export VLLM_ALLREDUCE_USE_SYMM_MEM=0 set -xeuo pipefail ########################### Quick Config ########################### TP=${TP:-2} PP=${PP:-1} CP=${CP:-1} EP=${EP:-8} ETP=${ETP:-1} GEN_TP=${GEN_TP:-8} ALL_OFFLOAD=${ALL_OFFLOAD:-True} rollout_name="vllm" project_name='verl_grpo_qwen3_5_35b_geo3k' exp_name='qwen3_5_35b_megatron' adv_estimator=grpo HF_MODEL_PATH=${HF_MODEL_PATH:-"Qwen3.5-35B-A3B"} train_path=${train_path:-$HOME/data/geo3k/train.parquet} test_path=${test_path:-$HOME/data/geo3k/test.parquet} ########################### Parameter Arrays ########################### DATA=( data.train_files=${train_path} data.val_files=${test_path} data.train_batch_size=32 data.max_prompt_length=1024 data.max_response_length=2048 data.truncation='error' data.filter_overlong_prompts=True ) MODEL=( actor_rollout_ref.model.path=${HF_MODEL_PATH} actor_rollout_ref.model.trust_remote_code=True actor_rollout_ref.model.use_remove_padding=False ) ACTOR=( actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.ppo_mini_batch_size=32 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 actor_rollout_ref.actor.ppo_max_token_len_per_gpu=4096 actor_rollout_ref.actor.use_dynamic_bsz=False actor_rollout_ref.actor.use_kl_loss=True actor_rollout_ref.actor.kl_loss_coef=0.01 actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.megatron.use_mbridge=True actor_rollout_ref.actor.megatron.vanilla_mbridge=True actor_rollout_ref.actor.megatron.use_remove_padding=False actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${TP} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${PP} actor_rollout_ref.actor.megatron.context_parallel_size=${CP} actor_rollout_ref.actor.megatron.expert_model_parallel_size=${EP} actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ETP} actor_rollout_ref.actor.megatron.param_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.optimizer_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.grad_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.dtype=bfloat16 ++actor_rollout_ref.actor.megatron.override_transformer_config.attention_backend=auto +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 +actor_rollout_ref.actor.megatron.override_transformer_config.moe_aux_loss_coeff=0.01 +actor_rollout_ref.actor.megatron.override_transformer_config.moe_z_loss_coeff=0.001 +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True ) ROLLOUT=( actor_rollout_ref.rollout.name=${rollout_name} actor_rollout_ref.rollout.tensor_model_parallel_size=${GEN_TP} actor_rollout_ref.rollout.gpu_memory_utilization=0.6 actor_rollout_ref.rollout.n=5 actor_rollout_ref.rollout.mode=async actor_rollout_ref.rollout.dtype=bfloat16 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=False actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=4096 ) REF=( actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=False actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=4096 actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${TP} actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${PP} actor_rollout_ref.ref.megatron.context_parallel_size=${CP} actor_rollout_ref.ref.megatron.expert_model_parallel_size=${EP} actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${ETP} actor_rollout_ref.ref.megatron.param_offload=${ALL_OFFLOAD} ) ALGORITHM=( algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=False ) TRAINER=( trainer.critic_warmup=0 trainer.logger='["console","wandb"]' trainer.project_name=${project_name} trainer.experiment_name=${exp_name} trainer.n_gpus_per_node=8 trainer.nnodes=1 trainer.save_freq=20 trainer.val_before_train=False trainer.test_freq=5 trainer.total_epochs=15 ) ########################### Launch ########################### python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA[@]}" \ "${ALGORITHM[@]}" \ "${MODEL[@]}" \ "${ROLLOUT[@]}" \ "${ACTOR[@]}" \ "${REF[@]}" \ "${TRAINER[@]}" \ "$@" ================================================ FILE: examples/grpo_trainer/run_qwen3_8b_grpo_sglang_1k_spmd_npu.sh ================================================ set -x export HCCL_CONNECT_TIMEOUT=1500 export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050 export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050 export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 # WORKSPACE_HOME and DATA_HOME support custom path configuration. WORKSPACE_HOME=$pwd DATA_HOME=$pwd sp_size=4 num_npu=4 tp_size=4 train_prompt_bsz=16 train_prompt_mini_bsz=16 max_prompt_length=512 max_response_length=1024 CKPTS_DIR=$WORKSPACE_HOME/logs/ckpt/qwen3_8b model_path=$DATA_HOME/models/Qwen3-8B train_data=$DATA_HOME/datasets/processed_gsm8k/train.parquet valid_data=$DATA_HOME/datasets/processed_gsm8k/test.parquet python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$train_data \ data.val_files=$valid_data \ data.train_batch_size=$train_prompt_bsz \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$train_prompt_mini_bsz \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$tp_size \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \ actor_rollout_ref.rollout.n=5 \ +actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend="ascend" \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.nccl_timeout=1800 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.val_before_train=False \ trainer.project_name='verl_grpo_example_512_1024_gsm8k' \ trainer.experiment_name='qwen3_8b_function_rm' \ trainer.n_gpus_per_node=$num_npu \ trainer.nnodes=1 \ trainer.save_freq=1000 \ trainer.test_freq=10000 \ trainer.total_epochs=5 \ trainer.default_local_dir="${CKPTS_DIR}" \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} $@ ================================================ FILE: examples/grpo_trainer/run_qwen3_8b_grpo_sglang_32k_spmd_npu.sh ================================================ set -x export HCCL_CONNECT_TIMEOUT=1500 export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050 export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050 export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 # WORKSPACE_HOME and DATA_HOME support custom path configuration. WORKSPACE_HOME=$pwd DATA_HOME=$pwd sp_size=4 num_gpu=8 tp_size=4 train_prompt_bsz=16 train_prompt_mini_bsz=16 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 32)) CKPTS_DIR=$WORKSPACE_HOME/logs/ckpt/qwen3_8b model_path=$DATA_HOME/models/Qwen3-8B train_data=$DATA_HOME/datasets/dapo/dapo-math-17k.parquet valid_data=$DATA_HOME/datasets/dapo/aime-2024.parquet python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$train_data \ data.val_files=$valid_data \ data.train_batch_size=$train_prompt_bsz \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=False \ data.truncation='error' \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$train_prompt_mini_bsz \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$tp_size \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \ actor_rollout_ref.rollout.n=5 \ +actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend="ascend" \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.nccl_timeout=3600 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.val_before_train=False \ trainer.project_name='verl_grpo_example_2k_32k' \ trainer.experiment_name='qwen3_8b_function_rm' \ trainer.n_gpus_per_node=$num_gpu \ trainer.nnodes=1 \ trainer.save_freq=1000 \ trainer.test_freq=10000 \ trainer.total_epochs=5 \ trainer.default_local_dir="${CKPTS_DIR}" \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} $@ ================================================ FILE: examples/grpo_trainer/run_qwen3_vl-235b-megatron.sh ================================================ set -x ENGINE=${1:-vllm} export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping export VLLM_ALLREDUCE_USE_SYMM_MEM=0 # for vllm0.11.0 with TP HF_MODEL_PATH=${HF_MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-VL-235B-A22B-Instruct"} GEN_TP=${GEN_TP:-16} CP=${CP:-2} TP=${TP:-4} PP=${PP:-8} EP=${EP:-8} ETP=${ETP:-1} train_path=$HOME/data/geo3k/train.parquet test_path=$HOME/data/geo3k/test.parquet python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_path" \ data.val_files="$test_path" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$GEN_TP \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=4096 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.ref.megatron.param_offload=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_loss_in_pipeline_split=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.account_for_embedding_in_pipeline_split=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen3_vl_235b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=8 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen3_vl-30b-megatron.sh ================================================ set -x ENGINE=${1:-vllm} export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping export VLLM_ALLREDUCE_USE_SYMM_MEM=0 # for vllm0.11.0 with TP HF_MODEL_PATH=${HF_MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-VL-30B-A3B-Instruct"} GEN_TP=${GEN_TP:-4} CP=${CP:-2} TP=${TP:-2} PP=${PP:-1} EP=${EP:-8} ETP=${ETP:-1} train_path=$HOME/data/geo3k/train.parquet test_path=$HOME/data/geo3k/test.parquet python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_path" \ data.val_files="$test_path" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$GEN_TP \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=4096 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.ref.megatron.param_offload=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ # Use aux_loss and z_loss to mitigate expert load imbalance when training MoE models +actor_rollout_ref.actor.megatron.override_transformer_config.moe_aux_loss_coeff=0.01 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_z_loss_coeff=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen3_vl_30b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen3_vl-8b-megatron.sh ================================================ set -x ENGINE=${1:-vllm} export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping # dependency: vllm>=0.11.0, megatron-lm>=0.13, mbridge with qwen3vl_cp branch # environment option1: use a stable container later than docker://verlai/verl:vllm011.dev6 # and install mbridge in it by following the instruction in the container # pip remove mbridge if you have installed it # pip install git+https://github.com/ISEEKYAN/mbridge.git@qwen3vl_cp # for correct mbridge # environment option2: use container docker://verlai/verl:vllm011.dev_qwenvl_cp export VLLM_ALLREDUCE_USE_SYMM_MEM=0 # for vllm0.11.0 with TP HF_MODEL_PATH=${HF_MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-VL-8B-Instruct"} GEN_TP=${GEN_TP:-4} CP=${CP:-2} TP=${TP:-2} PP=${PP:-2} train_path=$HOME/data/geo3k/train.parquet test_path=$HOME/data/geo3k/test.parquet python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_path" \ data.val_files="$test_path" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$GEN_TP \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=4096 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=4096 \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.ref.megatron.param_offload=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen3_vl_8b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/grpo_trainer/run_qwen3_vl-8b_npu.sh ================================================ set -x project_name='GRPO-Qwen3_vl' exp_name='GRPO-Qwen3_vl-8B-npu' gen_tp=1 sp_size=1 ENGINE=${1:-vllm} RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-VL-8B-Instruct"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/geo3k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/geo3k/test.parquet"} # Rollout Correction parameters (sequence-level TIS + geometric RS) rollout_is=sequence rollout_is_threshold=2.0 rollout_is_batch_normalize=true rollout_rs=token_k1 rollout_rs_threshold=0.6_1.6 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.rollout.max_num_batched_tokens=20000 \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.strategy=fsdp2 \ actor_rollout_ref.actor.fsdp_config.fsdp_size=32 \ actor_rollout_ref.actor.fsdp_config.reshard_after_forward=True \ actor_rollout_ref.ref.fsdp_config.reshard_after_forward=True \ actor_rollout_ref.actor.fsdp_config.entropy_checkpointing=True \ actor_rollout_ref.actor.entropy_from_logits_with_chunking=True \ actor_rollout_ref.ref.entropy_from_logits_with_chunking=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.name=$ENGINE \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \ actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \ actor_rollout_ref.model.enable_activation_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$sp_size \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=$sp_size \ actor_rollout_ref.rollout.n=5 \ data.shuffle=False \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes=4 \ trainer.default_local_dir=${CKPTS_DIR} \ trainer.resume_mode=auto \ algorithm.rollout_correction.rollout_is=${rollout_is} \ algorithm.rollout_correction.rollout_is_threshold=${rollout_is_threshold} \ algorithm.rollout_correction.rollout_is_batch_normalize=${rollout_is_batch_normalize} \ algorithm.rollout_correction.rollout_rs=${rollout_rs} \ algorithm.rollout_correction.rollout_rs_threshold=${rollout_rs_threshold} \ actor_rollout_ref.rollout.calculate_log_probs=True \ trainer.val_before_train=True \ trainer.save_freq=5 \ trainer.test_freq=5 \ trainer.total_epochs=15 ================================================ FILE: examples/grpo_trainer/run_qwen3_vl_30b_vllm_fsdp_npu.sh ================================================ set -x project_name='GRPO-Qwen3_vl' exp_name='GRPO-Qwen3_vl-30B-npu' gen_tp=8 sp_size=2 ENGINE=${1:-vllm} RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-VL-30B-A3B-Instruct"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/geo3k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/geo3k/test.parquet"} WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Rollout Correction parameters rollout_is=sequence rollout_is_threshold=2.0 rollout_is_batch_normalize=true rollout_rs=token_k1 rollout_rs_threshold=0.6_1.6 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.strategy=fsdp2 \ actor_rollout_ref.actor.fsdp_config.fsdp_size=32 \ actor_rollout_ref.actor.fsdp_config.reshard_after_forward=True \ actor_rollout_ref.ref.fsdp_config.reshard_after_forward=True \ actor_rollout_ref.actor.fsdp_config.entropy_checkpointing=True \ actor_rollout_ref.actor.entropy_from_logits_with_chunking=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$sp_size \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \ actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.ref.entropy_from_logits_with_chunking=True \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=$sp_size \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.max_num_batched_tokens=20000 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ algorithm.use_kl_in_reward=False \ algorithm.rollout_correction.rollout_is=${rollout_is} \ algorithm.rollout_correction.rollout_is_threshold=${rollout_is_threshold} \ algorithm.rollout_correction.rollout_is_batch_normalize=${rollout_is_batch_normalize} \ algorithm.rollout_correction.rollout_rs=${rollout_rs} \ algorithm.rollout_correction.rollout_rs_threshold=${rollout_rs_threshold} \ actor_rollout_ref.rollout.calculate_log_probs=True \ trainer.critic_warmup=0 \ trainer.logger='["console", "wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=16 \ trainer.nnodes=2 \ trainer.default_local_dir=${CKPTS_DIR} \ trainer.resume_mode=auto \ trainer.val_before_train=True \ trainer.save_freq=5 \ trainer.test_freq=5 \ trainer.total_epochs=15 \ ================================================ FILE: examples/grpo_trainer/run_qwen3moe-30b_grpo_megatron_vllm_npu.sh ================================================ #!/bin/bash set -xeuo pipefail mkdir -p logs # Project Configuration project_name='GRPO-Qwen3-30b-A3B-BASE-MATH' exp_name='GRPO-Qwen3-30B-A3B-BASE-Megatron-vLLM' # Node Info NNODES=${NNODES:-1} NPUS_PER_NODE=${NPUS_PER_NODE:-16} # Model Weights Paths MODEL_PATH=Qwen/Qwen3-30B-A3B-Base MCORE_MODEL_PATH=Qwen/Qwen3-30B-A3B-Base-dist RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=$RAY_DATA_HOME/dataset/gsm8k/train.parquet TEST_FILE=$RAY_DATA_HOME/dataset/gsm8k/test.parquet # Data Configuration max_prompt_length=$((1024 * 1)) max_response_length=$((1024 * 1)) # Training Batch Configuration train_prompt_bsz=128 train_prompt_mini_bsz=32 n_resp_per_prompt=16 # Algorithm Configuration adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 # Performance and Memory Management Configuration all_offload=True use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 4)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 8)) optimizer_offload_fraction=1 # Megatron Configuration train_tp=2 train_ep=8 train_etp=1 train_pp=2 train_cp=1 # vLLM Configuration gen_tp=2 gen_dp=1 gen_ep=1 gpu_memory_utilization=0.8 max_model_len=$((max_prompt_length + max_response_length)) max_num_batched_tokens=$(((max_prompt_length + max_response_length) * 1)) # Data Configuration DATA_CONFIG=( data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" data.prompt_key=prompt data.train_batch_size=${train_prompt_bsz} data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.filter_overlong_prompts=False data.truncation='left' ) # Model Configuration MODEL_CONFIG=( actor_rollout_ref.model.path="${MODEL_PATH}" actor_rollout_ref.model.use_remove_padding=True ) # Algorithm Configuration ALGORITHM_CONFIG=( algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} ) # Actor Model Configuration ACTOR_CONFIG=( actor_rollout_ref.actor.use_torch_compile=False actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.ppo_epochs=1 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.optim.lr=1e-6 +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} actor_rollout_ref.actor.megatron.expert_model_parallel_size=${train_ep} actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${train_etp} actor_rollout_ref.actor.megatron.param_offload=${all_offload} actor_rollout_ref.actor.megatron.optimizer_offload=${all_offload} actor_rollout_ref.actor.megatron.grad_offload=${all_offload} actor_rollout_ref.actor.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} actor_rollout_ref.actor.megatron.use_dist_checkpointing=False +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 ) # Reference Model Configuration REF_CONFIG=( actor_rollout_ref.ref.use_torch_compile=False actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.ref.megatron.context_parallel_size=${train_cp} actor_rollout_ref.ref.megatron.expert_model_parallel_size=${train_ep} actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${train_etp} actor_rollout_ref.ref.megatron.param_offload=${all_offload} actor_rollout_ref.ref.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} actor_rollout_ref.ref.megatron.use_dist_checkpointing=False ) # Rollout Configuration ROLLOUT_CONFIG=( actor_rollout_ref.rollout.name=vllm actor_rollout_ref.rollout.n=${n_resp_per_prompt} actor_rollout_ref.rollout.top_p=1.0 actor_rollout_ref.rollout.top_k=-1 actor_rollout_ref.rollout.temperature=1.0 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} actor_rollout_ref.rollout.max_num_batched_tokens=${max_num_batched_tokens} actor_rollout_ref.rollout.max_model_len=${max_model_len} actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.data_parallel_size=${gen_dp} actor_rollout_ref.rollout.expert_parallel_size=${gen_ep} actor_rollout_ref.rollout.enable_chunked_prefill=True actor_rollout_ref.rollout.enable_prefix_caching=True actor_rollout_ref.rollout.enforce_eager=True actor_rollout_ref.rollout.free_cache_engine=True actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.top_p=1.0 actor_rollout_ref.rollout.val_kwargs.top_k=-1 actor_rollout_ref.rollout.val_kwargs.temperature=1.0 ) # Trainer Configuration TRAINER_CONFIG=( trainer.logger='["console","tensorboard"]' trainer.project_name="${project_name}" trainer.experiment_name="${exp_name}" trainer.nnodes="${NNODES}" trainer.n_gpus_per_node="${NPUS_PER_NODE}" trainer.device='npu' trainer.total_epochs=15 trainer.val_before_train=False trainer.test_freq=-1 trainer.save_freq=-1 trainer.default_local_dir="${CKPTS_DIR}" ) # Main GRPO Training Command python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA_CONFIG[@]}" \ "${MODEL_CONFIG[@]}" \ "${ACTOR_CONFIG[@]}" \ "${REF_CONFIG[@]}" \ "${ROLLOUT_CONFIG[@]}" \ "${ALGORITHM_CONFIG[@]}" \ "${TRAINER_CONFIG[@]}" \ "$@" | tee logs/run_qwen3moe-30b_grpo_megatron_vllm_npu.log ================================================ FILE: examples/grpo_trainer/run_qwen3moe-30b_megatron_96gb.sh ================================================ set -x # tested in NNODES=1~4 * 96G H20 GPU NNODES=${NNODES:-1} NGPUS_PER_NODES=${NGPUS_PER_NODES:-8} project_name='DAPO-Qwen3-30b-MATH' exp_name='DAPO-Qwen3-30b-MATH-megatron' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=16 train_prompt_mini_bsz=128 train_ppo_micro_batch_size_per_gpu=2 infer_ppo_micro_batch_size_per_gpu=2 # Paths MODEL_PATH=Qwen/Qwen3-30B-A3B-Base RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet TEST_FILE="['$aime24_test_path']" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length))) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length))) offload=True optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.} COMMON_PP=${COMMON_PP:-1} COMMON_VPP=${COMMON_VPP:-null} COMMON_CP=${COMMON_CP:-1} COMMON_TP=${COMMON_TP:-1} COMMON_EP=${COMMON_EP:-8} COMMON_ETP=${COMMON_ETP:-1} TRAIN_TP=${TRAIN_TP:-$COMMON_TP} INFER_TP=${INFER_TP:-4} ACTOR_PP=${ACTOR_PP:-$COMMON_PP} ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP} ACTOR_CP=${ACTOR_CP:-$COMMON_CP} ACTOR_TP=${ACTOR_TP:-$TRAIN_TP} ACTOR_EP=${ACTOR_EP:-$COMMON_EP} ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP} ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP} REF_PP=${REF_PP:-$COMMON_PP} REF_VPP=${REF_VPP:-$COMMON_VPP} REF_CP=${REF_CP:-$COMMON_CP} REF_TP=${REF_TP:-$TRAIN_TP} REF_EP=${REF_EP:-$COMMON_EP} REF_ETP=${REF_ETP:-$COMMON_ETP} CRITIC_PP=${CRITIC_PP:-$COMMON_PP} CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP} CRITIC_CP=${CRITIC_CP:-$COMMON_CP} CRITIC_TP=${CRITIC_TP:-$TRAIN_TP} CRITIC_EP=${CRITIC_EP:-$COMMON_EP} CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP} RM_PP=${RM_PP:-$COMMON_PP} RM_VPP=${RM_VPP:-$COMMON_VPP} RM_CP=${RM_CP:-$COMMON_CP} RM_TP=${RM_TP:-$TRAIN_TP} RM_EP=${RM_EP:-$COMMON_EP} RM_ETP=${RM_ETP:-$COMMON_ETP} # install mbridge # pip3 install git+https://github.com/ISEEKYAN/mbridge USE_MBRIDGE=True USE_DIST_CKPT=False python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ +actor_rollout_ref.model.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.model.use_fused_kernels=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.lr_decay_style='constant' \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \ actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \ actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','wandb'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODES}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=False \ trainer.test_freq=10 \ trainer.save_freq=100 \ trainer.total_epochs=10 \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/grpo_trainer/run_qwen3moe-30b_megatron_lora.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail # Need to install Megatron-Bridge # NOTE: Make sure you use Megatron-Bridge later than 0.2.0 # (Recommend https://github.com/NVIDIA-NeMo/Megatron-Bridge/commit/83a7c1134c562d8c6decd10a1f0a6e6a7a8a3a44 or later) # for proper MoE LoRA support. # For Megatron communication/computation overlapping export CUDA_DEVICE_MAX_CONNECTIONS=1 ########################### Quick Config ########################### TP=${TP:-2} PP=${PP:-2} CP=${CP:-2} EP=${EP:-4} ETP=${ETP:-1} ALL_OFFLOAD=${ALL_OFFLOAD:-True} rollout_name="vllm" project_name='verl_grpo_example_gsm8k_math' exp_name='qwen3_30b_a3b_megatron_lora' adv_estimator=grpo gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet ########################### Parameter Arrays ########################### DATA=( data.train_files=${gsm8k_train_path} data.val_files=${gsm8k_test_path} data.train_batch_size=128 data.max_prompt_length=1024 data.max_response_length=1024 data.truncation='error' data.filter_overlong_prompts=True data.shuffle=False ) MODEL=( actor_rollout_ref.model.path=Qwen/Qwen3-30B-A3B-Instruct-2507 actor_rollout_ref.model.use_fused_kernels=True actor_rollout_ref.model.lora.rank=32 actor_rollout_ref.model.lora.alpha=64 actor_rollout_ref.model.lora.lora_A_init_method=kaiming # # Optional: Use canonical LoRA # actor_rollout_ref.model.lora.type="canonical_lora" # actor_rollout_ref.model.lora.target_modules='["linear_q","linear_k","linear_v","linear_proj","linear_fc1_up","linear_fc1_gate","linear_fc2"]' # # Optional: Add dropout to LoRA layers # actor_rollout_ref.model.lora.dropout=0.05 # actor_rollout_ref.model.lora.dropout_position=pre ) ACTOR=( actor_rollout_ref.actor.optim.lr=3e-6 actor_rollout_ref.actor.ppo_mini_batch_size=16 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 actor_rollout_ref.actor.megatron.use_mbridge=True actor_rollout_ref.actor.megatron.vanilla_mbridge=False actor_rollout_ref.actor.use_dynamic_bsz=True actor_rollout_ref.actor.use_kl_loss=True actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${TP} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${PP} actor_rollout_ref.actor.megatron.expert_model_parallel_size=${EP} actor_rollout_ref.actor.megatron.context_parallel_size=${CP} actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ETP} actor_rollout_ref.actor.megatron.param_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.optimizer_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.grad_offload=${ALL_OFFLOAD} +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 ) ROLLOUT=( actor_rollout_ref.rollout.tensor_model_parallel_size=8 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True actor_rollout_ref.rollout.name=${rollout_name} actor_rollout_ref.rollout.gpu_memory_utilization=0.25 actor_rollout_ref.rollout.enforce_eager=True actor_rollout_ref.rollout.free_cache_engine=True actor_rollout_ref.rollout.n=4 ) REF=( actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${TP} actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${PP} actor_rollout_ref.ref.megatron.expert_model_parallel_size=${EP} actor_rollout_ref.ref.megatron.context_parallel_size=${CP} actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${ETP} actor_rollout_ref.ref.megatron.param_offload=${ALL_OFFLOAD} ) ALGORITHM=( algorithm.adv_estimator=${adv_estimator} ) TRAINER=( trainer.critic_warmup=0 trainer.logger='["console","wandb"]' trainer.project_name=${project_name} trainer.experiment_name=${exp_name} trainer.n_gpus_per_node=8 trainer.nnodes=1 trainer.save_freq=20 trainer.test_freq=5 trainer.total_epochs=15 ) ########################### Launch ########################### python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA[@]}" \ "${ALGORITHM[@]}" \ "${MODEL[@]}" \ "${ROLLOUT[@]}" \ "${ACTOR[@]}" \ "${REF[@]}" \ "${TRAINER[@]}" \ "$@" ================================================ FILE: examples/grpo_trainer/run_qwen3moe-30b_megatron_lora_fp16.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail pwd=`pwd` rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi TP=${TP:-2} PP=${PP:-2} CP=${CP:-2} EP=${EP:-4} ETP=${ETP:-1} ALL_OFFLOAD=${ALL_OFFLOAD:-True} optimizer_offload_fraction=1. dtype="float16" # ["bfloat16", "float16"] rollout_name="vllm" project_name='verl_grpo_example_gsm8k_math_fp16' exp_name='qwen3_30b_a3b_megatron_lora' adv_estimator=grpo # Paths MODEL_PATH=$HOME/Qwen/Qwen3-30B-A3B-Instruct-2507 CKPTS_DIR=${pwd}/ckpt/${exp_name} gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet ########################### Parameter Arrays ########################### DATA=( data.train_files=${gsm8k_train_path} data.val_files=${gsm8k_test_path} data.train_batch_size=128 data.max_prompt_length=1024 data.max_response_length=1024 data.truncation='error' data.filter_overlong_prompts=True data.shuffle=False data.return_raw_chat=$return_raw_chat data.filter_overlong_prompts_workers=128 ) MODEL=( actor_rollout_ref.model.path=${MODEL_PATH} actor_rollout_ref.model.lora.rank=16 actor_rollout_ref.model.lora.alpha=32 actor_rollout_ref.model.lora.dtype=${dtype} actor_rollout_ref.model.use_fused_kernels=True ) ACTOR=( actor_rollout_ref.actor.optim.lr=3e-6 actor_rollout_ref.actor.ppo_mini_batch_size=16 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 actor_rollout_ref.actor.megatron.use_mbridge=True actor_rollout_ref.actor.megatron.vanilla_mbridge=False actor_rollout_ref.actor.use_dynamic_bsz=True actor_rollout_ref.actor.use_kl_loss=True actor_rollout_ref.actor.kl_loss_coef=0.001 actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${TP} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${PP} actor_rollout_ref.actor.megatron.expert_model_parallel_size=${EP} actor_rollout_ref.actor.megatron.context_parallel_size=${CP} actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ETP} actor_rollout_ref.actor.megatron.param_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.optimizer_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.grad_offload=${ALL_OFFLOAD} actor_rollout_ref.actor.megatron.dtype=${dtype} +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True +actor_rollout_ref.actor.megatron.override_ddp_config.grad_reduce_in_fp32=True +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=${ALL_OFFLOAD} ) ROLLOUT=( actor_rollout_ref.rollout.tensor_model_parallel_size=8 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True actor_rollout_ref.rollout.name=${rollout_name} actor_rollout_ref.rollout.gpu_memory_utilization=0.5 actor_rollout_ref.rollout.enforce_eager=True actor_rollout_ref.rollout.free_cache_engine=True actor_rollout_ref.rollout.n=4 actor_rollout_ref.rollout.dtype=${dtype} ) REF=( actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True actor_rollout_ref.ref.megatron.dtype=${dtype} actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${TP} actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${PP} actor_rollout_ref.ref.megatron.expert_model_parallel_size=${EP} actor_rollout_ref.ref.megatron.context_parallel_size=${CP} actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${ETP} actor_rollout_ref.ref.megatron.param_offload=${ALL_OFFLOAD} ) ALGORITHM=( algorithm.adv_estimator=${adv_estimator} ) TRAINER=( trainer.critic_warmup=0 trainer.logger='["console","wandb"]' trainer.project_name=${project_name} trainer.experiment_name=${exp_name} trainer.n_gpus_per_node=8 trainer.nnodes=1 trainer.save_freq=20 trainer.test_freq=5 trainer.total_epochs=15 trainer.val_before_train=False trainer.max_actor_ckpt_to_keep=1 trainer.default_local_dir="${CKPTS_DIR}" trainer.log_val_generations=10 ) ########################### Launch ########################### python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA[@]}" \ "${ALGORITHM[@]}" \ "${MODEL[@]}" \ "${ROLLOUT[@]}" \ "${ACTOR[@]}" \ "${REF[@]}" \ "${TRAINER[@]}" \ 2>&1 | tee ${pwd}/log/${exp_name}_$(date +'%Y%m%d_%H%M%S').log ================================================ FILE: examples/grpo_trainer/run_qwen3moe-30b_sglang_megatron_npu.sh ================================================ #!/bin/bash set -xeuo pipefail # Project Configuration project_name='DAPO-Qwen3-30b-A3B-BASE-MATH' exp_name='DAPO-Qwen3-30B-A3B-BASE-Megatron-SGLang' # Necessary env export HCCL_CONNECT_TIMEOUT=1500 export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050 export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050 export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 export DISABLE_L2_CACHE=1 export TASK_QUEUE_ENABLE=1 # Node Info NNODES=${NNODES:-1} NPUS_PER_NODE=${NPUS_PER_NODE:-16} # Model Weights Paths MODEL_PATH=Qwen/Qwen3-30B-A3B MCORE_MODEL_PATH=Qwen/Qwen3-30B-A3B-mcore RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet # Data Length Configuration max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) # Training Batch Configuration train_prompt_bsz=16 train_prompt_mini_bsz=16 n_resp_per_prompt=8 # Algorithm Configuration adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 # Performance and Memory Management Configuration all_offload=True use_dynamic_bsz=False actor_ppo_max_token_len=$(((max_prompt_length + max_response_length))) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length))) # Megatron Parallelism Configuration train_tp=4 train_ep=4 train_etp=4 train_pp=1 train_cp=1 # SGLang Generation Configuration gen_tp=4 gen_dp=1 gen_ep=1 gpu_memory_utilization=0.5 max_model_len=$((max_prompt_length + max_response_length)) max_num_batched_tokens=$(((max_prompt_length + max_response_length) * 1)) # Data Configuration DATA_CONFIG=( # File Paths data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" # Data Structure data.prompt_key=prompt # Batch and Length Configuration data.train_batch_size=${train_prompt_bsz} data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} # Preprocessing data.filter_overlong_prompts=False data.truncation='left' ) # Model Configuration MODEL_CONFIG=( # Model Path actor_rollout_ref.model.path="${MODEL_PATH}" # Model Processing actor_rollout_ref.model.use_remove_padding=True ) # Reinforcement Learning Algorithm Configuration ALGORITHM_CONFIG=( # Advantage Estimation algorithm.adv_estimator=${adv_estimator} # KL Divergence Control algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} ) ACTOR_CONFIG=( # Core Runtime Settings actor_rollout_ref.actor.use_torch_compile=False actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} # Loss Function Configuration actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.entropy_coeff=0 # PPO Training Parameters actor_rollout_ref.actor.ppo_epochs=1 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} # Optimizer Settings actor_rollout_ref.actor.optim.lr=1e-6 # Megatron Parallelism Strategy actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} actor_rollout_ref.actor.megatron.expert_model_parallel_size=${train_ep} actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${train_etp} # Memory Optimization actor_rollout_ref.actor.megatron.param_offload=${all_offload} actor_rollout_ref.actor.megatron.optimizer_offload=${all_offload} actor_rollout_ref.actor.megatron.grad_offload=${all_offload} # Model Weights Management actor_rollout_ref.actor.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} actor_rollout_ref.actor.megatron.use_dist_checkpointing=True actor_rollout_ref.actor.megatron.use_mbridge=False # Transformer Architecture Optimizations +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 ) REF_CONFIG=( # Core Runtime Settings actor_rollout_ref.ref.use_torch_compile=False # Log Probability Inference actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} # Megatron Parallelism Strategy actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} actor_rollout_ref.ref.megatron.context_parallel_size=${train_cp} actor_rollout_ref.ref.megatron.expert_model_parallel_size=${train_ep} actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${train_etp} # Memory Optimization actor_rollout_ref.ref.megatron.param_offload=${all_offload} # Model Weights Management actor_rollout_ref.ref.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} actor_rollout_ref.ref.megatron.use_dist_checkpointing=True actor_rollout_ref.ref.megatron.use_mbridge=False ) ROLLOUT_CONFIG=( # Rollout Engine actor_rollout_ref.rollout.name=sglang +actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend="ascend" # Generation Parameters actor_rollout_ref.rollout.n=${n_resp_per_prompt} actor_rollout_ref.rollout.top_p=1.0 actor_rollout_ref.rollout.top_k=-1 actor_rollout_ref.rollout.temperature=1.0 # Log Probability Inference actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} # Memory Management actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} # Parallelism Strategy actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.data_parallel_size=${gen_dp} actor_rollout_ref.rollout.expert_parallel_size=${gen_ep} +actor_rollout_ref.rollout.engine_kwargs.sglang.enable_dp_attention=False # Performance Optimization +actor_rollout_ref.rollout.engine_kwargs.sglang.chunked_prefill_size=-1 actor_rollout_ref.rollout.enforce_eager=False # Validation Generation actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.top_p=1.0 actor_rollout_ref.rollout.val_kwargs.top_k=-1 actor_rollout_ref.rollout.val_kwargs.temperature=1.0 ) TRAINER_CONFIG=( # Logger Configuration trainer.logger='["console"]' # Project Settings trainer.project_name="${project_name}" trainer.experiment_name="${exp_name}" # Hardware Configuration trainer.nnodes="${NNODES}" trainer.n_gpus_per_node="${NPUS_PER_NODE}" trainer.device='npu' # Training Schedule trainer.total_epochs=15 trainer.val_before_train=False trainer.test_freq=-1 trainer.save_freq=-1 # Checkpoint Directory trainer.default_local_dir="${CKPTS_DIR}" ) # profiling configuration PROF_CONFIG=( global_profiler.tool=npu global_profiler.steps=null global_profiler.save_path=/profpath actor_rollout_ref.actor.profiler.enable=True actor_rollout_ref.actor.profiler.ranks="[0]" actor_rollout_ref.actor.profiler.all_ranks=False actor_rollout_ref.actor.profiler.tool_config.npu.discrete=True actor_rollout_ref.actor.profiler.tool_config.npu.contents=['npu','cpu'] actor_rollout_ref.actor.profiler.tool_config.npu.level=level0 actor_rollout_ref.actor.profiler.tool_config.npu.analysis=True actor_rollout_ref.rollout.profiler.enable=True actor_rollout_ref.rollout.profiler.ranks="[0]" actor_rollout_ref.rollout.profiler.all_ranks=False ) python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ "${DATA_CONFIG[@]}" \ "${MODEL_CONFIG[@]}" \ "${ACTOR_CONFIG[@]}" \ "${REF_CONFIG[@]}" \ "${ROLLOUT_CONFIG[@]}" \ "${ALGORITHM_CONFIG[@]}" \ "${TRAINER_CONFIG[@]}" \ "${PROF_CONFIG[@]}" \ "$@" ================================================ FILE: examples/grpo_trainer/run_qwen3next_80b_fsdp_npu.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name="verl_grpo_qwen3-next-80b" experiment_name="Qwen3_Next_80B_Instruct" # Paths WORK_DIR=${WORK_DIR:-"${HOME}/verl"} MODEL_PATH=${WORK_DIR}/Qwen3-Next-80B-A3B-Instruct TRAIN_FILE=${WORK_DIR}/datasets/dapo-math-17k/dapo-math-17k.parquet TEST_FILE=${WORK_DIR}/datasets/aime/aime-2024.parquet # algorithm adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 clip_ratio_low=0.2 clip_ratio_high=0.28 temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # batch train_batch_size=16 rollout_n=16 ppo_mini_batch_size=8 # length max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 20)) # algorithm learning_rate=1e-6 warmup_steps=0 # enable_filter_groups=True # performance sp_size=8 gen_tp=4 use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) offload=True DATA=( data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" data.train_batch_size=${train_batch_size} data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.truncation='error' ) ACTOR=( actor_rollout_ref.actor.strategy=fsdp2 actor_rollout_ref.nccl_timeout=14400 # fsdp actor_rollout_ref.actor.fsdp_config.use_orig_params=True actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 actor_rollout_ref.actor.fsdp_config.param_offload=${offload} actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} actor_rollout_ref.actor.fsdp_config.forward_prefetch=False actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 +actor_rollout_ref.actor.fsdp_config.mixed_precision.reduce_dtype=bf16 # optimizer actor_rollout_ref.actor.optim.lr=${learning_rate} actor_rollout_ref.actor.optim.lr_warmup_steps=${warmup_steps} # ppo config actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} # entropy actor_rollout_ref.actor.entropy_checkpointing=True actor_rollout_ref.actor.entropy_from_logits_with_chunking=True actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.kl_loss_type=low_var_kl actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} actor_rollout_ref.actor.clip_ratio_c=10.0 actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.actor.use_torch_compile=False ) ROLLOUT=( actor_rollout_ref.rollout.name=vllm actor_rollout_ref.rollout.n=${rollout_n} actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.gpu_memory_utilization=0.8 actor_rollout_ref.rollout.load_format=auto actor_rollout_ref.rollout.enforce_eager=True actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) actor_rollout_ref.rollout.calculate_log_probs=True actor_rollout_ref.rollout.temperature=${temperature} actor_rollout_ref.rollout.top_p=${top_p} actor_rollout_ref.rollout.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} ) REF=( actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} actor_rollout_ref.ref.use_torch_compile=False actor_rollout_ref.ref.fsdp_config.param_offload=${offload} actor_rollout_ref.ref.fsdp_config.optimizer_offload=${offload} actor_rollout_ref.ref.fsdp_config.forward_prefetch=False actor_rollout_ref.ref.entropy_checkpointing=True actor_rollout_ref.ref.entropy_from_logits_with_chunking=True actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} ) TRAINER=( trainer.logger='["console"]' trainer.project_name="${project_name}" trainer.experiment_name="${experiment_name}" trainer.n_gpus_per_node=16 trainer.nnodes=4 trainer.val_before_train=False trainer.save_freq=5 trainer.test_freq=-1 trainer.total_epochs=1 trainer.device=npu ) MODEL=( actor_rollout_ref.model.path=${MODEL_PATH} actor_rollout_ref.model.use_remove_padding=True actor_rollout_ref.model.enable_activation_offload=${offload} ) ALGORITHM=( algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} ) # ========================================================= echo "Starting Training with:" echo "Project: ${project_name}, Exp: ${experiment_name}" echo "Rollout N: ${rollout_n}, Batch Size: ${train_batch_size}, LR: ${learning_rate}" python3 -m verl.trainer.main_ppo \ "${DATA[@]}" \ "${ACTOR[@]}" \ "${ROLLOUT[@]}" \ "${REF[@]}" \ "${TRAINER[@]}" \ "${ALGORITHM[@]}" \ "${MODEL[@]}" \ ================================================ FILE: examples/grpo_trainer/run_seed_oss_36b.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=64 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=ByteDance-Seed/Seed-OSS-36B-Base \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.strategy=fsdp2 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=2 \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.ref.strategy=fsdp2 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console"]' \ trainer.project_name='verl_grpo_seed_oss_36b' \ trainer.experiment_name='seed_oss_36b' \ trainer.val_before_train=False \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/gspo_trainer/run_qwen30b_gspo.sh ================================================ # run Qwen3-30B GSPO with new model engine set -x HDFS_ROOT=${HDFS_ROOT:-$PWD} DATA_ROOT=${DATA_ROOT:-$PWD} # wandb backend=megatron # fsdp, fsdp2, megatron project_name=wuxibin_gspo experiment_name=qwen3-30B-base-grpo-$backend default_local_dir=$DATA_ROOT/checkpoint/$project_name/$experiment_name # ===================================== Algorithm ===================================== adv_estimator=grpo loss_mode=gspo # reference policy use_kl_in_reward=False kl_coef=0.001 use_kl_loss=False kl_loss_coef=0.001 clip_ratio_low=3e-4 clip_ratio_high=4e-4 actor_lr=1e-6 critic_lr=2e-6 gae_gamma=1.0 gae_lam=0.95 critic_warmup=0 # ===================================== Data/Model ===================================== train_files=$DATA_ROOT/dataset/BytedTsinghua-SIA/DAPO-Math-17k/data/dapo-math-17k.parquet test_files=$DATA_ROOT/dataset/aime-2024.parquet actor_model_path=$HDFS_ROOT/model/Qwen3-30B-A3B-Base critic_model_path=$actor_model_path max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 train_batch_size=256 ppo_mini_batch_size=32 n_resp_per_prompt=16 n_resp_per_prompt_val=1 # ===================================== Training ===================================== actor_max_token_len_per_gpu=$(((max_prompt_length + max_response_length) * 3)) critic_max_token_len_per_gpu=$(((max_prompt_length + max_response_length) * 4)) # FSDP parallelism config USP_SIZE=4 ACTOR_FSDP_CONFIG=" actor_rollout_ref.actor.fsdp_config.strategy=$backend \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$USP_SIZE" # Megatron parallelism config TP_SIZE=2 CP_SIZE=1 PP_SIZE=1 VPP_SIZE=null EP_SIZE=8 ETP_SIZE=1 ACTOR_MEGATRON_CONFIG=" actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP_SIZE \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP_SIZE \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP_SIZE \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=$VPP_SIZE \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP_SIZE \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP_SIZE \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ actor_rollout_ref.actor.megatron.use_mbridge=True" # Actor model config ACTOR_CONFIG=" actor_rollout_ref.actor.optim.lr=$actor_lr \ actor_rollout_ref.model.path=$actor_model_path \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.clip_ratio_low=$clip_ratio_low \ actor_rollout_ref.actor.clip_ratio_high=$clip_ratio_high \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$actor_max_token_len_per_gpu" # Critic model config CIRITC_CONFIG=" critic.optim.lr=$critic_lr \ critic.model.path=$critic_model_path \ critic.model.use_remove_padding=True \ critic.ppo_max_token_len_per_gpu=$critic_max_token_len_per_gpu \ critic.ulysses_sequence_parallel_size=$USP_SIZE" CRITIC_FSDP_CONFIG="${ACTOR_FSDP_CONFIG//actor_rollout_ref.actor/critic.model}" CRITIC_MEGATRON_CONFIG="${ACTOR_MEGATRON_CONFIG//actor_rollout_ref.actor/critic}" if [[ $backend == "megatron" ]]; then CONFIG_NAME=ppo_megatron_trainer ACTOR_CONFIG="$ACTOR_CONFIG $ACTOR_MEGATRON_CONFIG" if [[ $adv_estimator == "gae" ]]; then CIRITC_CONFIG="$CIRITC_CONFIG $CRITIC_MEGATRON_CONFIG" else CIRITC_CONFIG="" fi else # fsdp, fsdp2 CONFIG_NAME=ppo_trainer ACTOR_CONFIG="$ACTOR_CONFIG $ACTOR_FSDP_CONFIG" if [[ $adv_estimator == "gae" ]]; then CIRITC_CONFIG="$CIRITC_CONFIG $CRITIC_FSDP_CONFIG" else CIRITC_CONFIG="" fi fi # ===================================== Inference ===================================== rollout_name=vllm if [ "$rollout_name" = "vllm" ]; then export VLLM_USE_V1=1 fi infer_tp=4 infer_dp=1 infer_ep=1 gpu_memory_utilization=0.8 ROLLOUT_CONFIG=" actor_rollout_ref.rollout.name=$rollout_name \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.tensor_model_parallel_size=$infer_tp \ actor_rollout_ref.rollout.data_parallel_size=$infer_dp \ actor_rollout_ref.rollout.expert_parallel_size=$infer_ep \ actor_rollout_ref.rollout.gpu_memory_utilization=$gpu_memory_utilization \ actor_rollout_ref.rollout.n=$n_resp_per_prompt \ actor_rollout_ref.rollout.val_kwargs.top_p=0.7 \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.n=$n_resp_per_prompt_val" # ===================================== Reward ===================================== REWARD_CONFIG=" reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length}" python3 -m verl.trainer.main_ppo \ --config-path=./config \ --config-name=$CONFIG_NAME \ algorithm.adv_estimator=$adv_estimator \ algorithm.use_kl_in_reward=$use_kl_in_reward \ algorithm.kl_ctrl.kl_coef=$kl_coef \ algorithm.gamma=$gae_gamma \ algorithm.lam=$gae_lam \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.return_raw_chat=True \ data.train_batch_size=$train_batch_size \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.filter_overlong_prompts_workers=64 \ data.truncation='error' \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=$critic_warmup \ trainer.logger=['console','wandb'] \ trainer.project_name=$project_name \ trainer.experiment_name=$experiment_name \ trainer.default_local_dir=$default_local_dir \ trainer.n_gpus_per_node=$ARNOLD_WORKER_GPU \ trainer.nnodes=$ARNOLD_WORKER_NUM \ trainer.val_before_train=False \ trainer.log_val_generations=100 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=10 \ trainer.total_training_steps=500 \ $ACTOR_CONFIG \ $CIRITC_CONFIG \ $ROLLOUT_CONFIG \ $REWARD_CONFIG ================================================ FILE: examples/gspo_trainer/run_qwen3_32b_gspo_npu.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail mkdir -p logs ulimit -n 32768 ## Basic Environment Settings export RAY_DEDUP_LOGS=0 export HYDRA_FULL_ERROR=1 export TASK_QUEUE_ENABLE=1 export HCCL_EXEC_TIMEOUT=3600 export HCCL_CONNECT_TIMEOUT=3600 export HCCL_ASYNC_ERROR_HANDLING=0 export CPU_AFFINITY_CONF=1 export VLLM_USE_V1=1 export VLLM_ATTENTION_BACKEND=XFORMERS export VLLM_ASCEND_ENABLE_FLASHCOMM=1 export VLLM_ASCEND_ENABLE_PREFETCH_MLP=1 export VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE=1 export LD_PRELOAD=/usr/local/lib/libjemalloc.so.2 # Project Configuration project_name='GSPO-Qwen3-32B-BASE-MATH' exp_name='GSPO-Qwen3-32B-BASE-Megatron-vLLM' # Node Info NNODES=${NNODES:-4} NPUS_PER_NODE=${NPUS_PER_NODE:-16} # Model Weights Paths MODEL_PATH=Qwen/Qwen3-32B RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # File System Paths TRAIN_FILE=$RAY_DATA_HOME/dataset/dapo-math-17k.parquet TEST_FILE=$RAY_DATA_HOME/dataset/aime-2024.parquet # Ray Configuration WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Data Length Configuration max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) # Training Batch Configuration train_prompt_bsz=256 gen_prompt_bsz=$((train_prompt_bsz * 1)) train_prompt_mini_bsz=64 n_resp_per_prompt=16 # GSPO Loss Configuration adv_estimator=grpo loss_mode=gspo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.0003 clip_ratio_high=0.0004 loss_agg_mode="seq-mean-token-mean" # FSDP Parallelism Configuration actor_strategy=fsdp2 ref_strategy=fsdp2 sp_size=4 fsdp_size=-1 # Performance and Memory Management Configuration offload=True use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) # vLLM Configuration gen_tp=4 gpu_memory_utilization=0.7 max_model_len=$((max_prompt_length + max_response_length)) max_num_batched_tokens=$((max_prompt_length + max_response_length)) # Data Configuration DATA_CONFIG=( data.train_files="${TRAIN_FILE}" data.val_files="${TEST_FILE}" data.prompt_key=prompt data.train_batch_size=${train_prompt_bsz} +data.gen_batch_size=${gen_prompt_bsz} data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.truncation='left' ) # Model Configuration MODEL_CONFIG=( actor_rollout_ref.model.path="${MODEL_PATH}" actor_rollout_ref.model.use_remove_padding=True actor_rollout_ref.model.enable_gradient_checkpointing=True ) # Algorithm Configuration ALGORITHM_CONFIG=( algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} ) # Actor Model Configuration ACTOR_CONFIG=( actor_rollout_ref.actor.use_torch_compile=False actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.actor.strategy=${actor_strategy} actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} actor_rollout_ref.actor.clip_ratio_c=10.0 actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.grad_clip=1.0 actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.optim.lr_warmup_steps=10 actor_rollout_ref.actor.optim.weight_decay=0.1 actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} actor_rollout_ref.actor.fsdp_config.param_offload=${offload} actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} actor_rollout_ref.actor.fsdp_config.forward_prefetch=True actor_rollout_ref.actor.entropy_checkpointing=True actor_rollout_ref.actor.entropy_from_logits_with_chunking=True ) # Reference Model Configuration REF_CONFIG=( actor_rollout_ref.ref.use_torch_compile=False actor_rollout_ref.ref.strategy=${ref_strategy} actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} actor_rollout_ref.ref.fsdp_config.param_offload=${offload} actor_rollout_ref.ref.fsdp_config.forward_prefetch=True actor_rollout_ref.ref.entropy_checkpointing=True actor_rollout_ref.ref.entropy_from_logits_with_chunking=True ) # Rollout Configuration ROLLOUT_CONFIG=( actor_rollout_ref.rollout.name=vllm actor_rollout_ref.rollout.calculate_log_probs=True actor_rollout_ref.rollout.n=${n_resp_per_prompt} actor_rollout_ref.rollout.top_p=1.0 actor_rollout_ref.rollout.top_k=-1 actor_rollout_ref.rollout.temperature=1.0 actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} actor_rollout_ref.rollout.max_num_batched_tokens=${max_num_batched_tokens} actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} actor_rollout_ref.rollout.enable_chunked_prefill=True actor_rollout_ref.rollout.enforce_eager=False actor_rollout_ref.rollout.free_cache_engine=True +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_capture_sizes="[8, 16, 32, 64, 128, 192, 256]" +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_DECODE_ONLY" actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.top_p=0.7 actor_rollout_ref.rollout.val_kwargs.top_k=-1 actor_rollout_ref.rollout.val_kwargs.temperature=1.0 actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 ) # Trainer Configuration TRAINER_CONFIG=( trainer.logger='["console"]' trainer.project_name="${project_name}" trainer.experiment_name="${exp_name}" trainer.nnodes="${NNODES}" trainer.n_gpus_per_node="${NPUS_PER_NODE}" trainer.device='npu' trainer.total_epochs=10 trainer.val_before_train=False trainer.test_freq=-1 trainer.save_freq=100 trainer.default_local_dir="${CKPTS_DIR}" trainer.resume_mode=auto trainer.balance_batch=True ) # Main GSPO Training Command python3 -m verl.trainer.main_ppo \ "${DATA_CONFIG[@]}" \ "${MODEL_CONFIG[@]}" \ "${ACTOR_CONFIG[@]}" \ "${REF_CONFIG[@]}" \ "${ROLLOUT_CONFIG[@]}" \ "${ALGORITHM_CONFIG[@]}" \ "${TRAINER_CONFIG[@]}" \ "$@" | tee logs/run_qwen3_32b_gspo_megatron_vllm_npu.log ================================================ FILE: examples/gspo_trainer/test_gspo_3b_math.sh ================================================ #!/usr/bin/env bash #SBATCH --job-name=rl-gspo-3B #SBATCH --partition=main #SBATCH --nodes=1 # Number of nodes #SBATCH --ntasks-per-node=1 # One task per node #SBATCH --cpus-per-task=128 # cpu-cores per task #SBATCH --gres=gpu:8 #SBATCH --mem=0 #SBATCH --exclusive #SBATCH --time=500:00:00 #SBATCH --output=/rl/logs/Qwen2.5-3B/gspo/math/vllm_%x_%j.out #SBATCH --error=/rl/logs/Qwen2.5-3B/gspo/math/vllm_%x_%j.err set -xeuo pipefail # activate the venv echo "Activating verl environment..." eval "$(conda shell.bash hook)" conda deactivate conda activate verl # can make training faster, depends on your infrastructure export NCCL_IBEXT_DISABLE=1 export NCCL_NVLS_ENABLE=1 export NCCL_IB_HCA=mlx5 export UCX_NET_DEVICES=mlx5_0:1,mlx5_1:1,mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1 # Set how many GPUs we actually have on this node. export GPUS_PER_NODE=8 NNODES=${SLURM_JOB_NUM_NODES} export NNODES export VLLM_ATTENTION_BACKEND=FLASH_ATTN export RAY_LOGGING_LEVEL=DEBUG export HYDRA_FULL_ERROR=1 export WANDB_API_KEY=... # your wandb API key echo "Using $NNODES nodes for training..." # ------------------------------------- Setup xp params --------------------------------------- project_name='RL-GSPO' adv_estimator=grpo loss_mode=gspo loss_agg_mode="seq-mean-token-mean" MODEL_PATH=Qwen/Qwen2.5-3B-Instruct offload=false # it's a small model, offloading will just slow-down training rollout_engine=vllm rollout_mode=async return_raw_chat="True" if [ "$rollout_engine" = "vllm" ]; then export VLLM_USE_V1=1 fi gpu_memory_utilization=0.8 reward_manager=dapo adv_estimator=grpo shuffle_dataset=true first_time_dataset_prep=true # prepare dataset test_freq=10 save_freq=10 total_epochs=10 total_training_steps=500 val_before_train=false use_kl_in_reward=false kl_coef=0.0 use_kl_loss=false kl_loss_coef=0.0 clip_ratio_low=0.0003 # as recommended by the paper, see Sec. 5.1 clip_ratio_high=0.0004 # as recommended by the paper, see Sec. 5.1 train_batch_size=512 ppo_mini_batch_size=128 # maintain 4 mini-batches as recommended by the paper, see Sec. 5.1 ppo_micro_batch_size_per_gpu=8 # setup depending on your GPU memory n_resp_per_prompt=16 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) # dapo reward manager params enable_overlong_buffer=false # true overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Paths and namings SFT_MODEL=$(basename $MODEL_PATH) exp_name="${loss_mode}-epslow-${clip_ratio_low}-epshigh-${clip_ratio_high}-${SFT_MODEL}-RL" CKPTS_DIR=/rl/checkpoints/experimental/4b/${loss_mode}/${exp_name} # Sampling params at rollouts temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter sp_size=1 use_dynamic_bsz=true actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=true gen_tp=1 entropy_checkpointing=true # This enables entropy recomputation specifically for the entropy calculation, lowering memory usage during training. # ------------------------------------- train/val data preparation --------------------------------------- if [ "$first_time_dataset_prep" = true ]; then echo "Preprocessing GSM8K dataset..." python examples/data_preprocess/gsm8k.py --local_save_dir /data/gsm8k/ fi gsm8k_train_path=/data/gsm8k/train.parquet gsm8k_test_path=/data/gsm8k/test.parquet # set the paths train_files="['$gsm8k_train_path']" test_files="['$gsm8k_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=${adv_estimator} \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ data.train_files="${train_files}" \ data.val_files="${test_files}" \ data.shuffle=$shuffle_dataset \ data.prompt_key=prompt \ data.truncation='error' \ data.filter_overlong_prompts=true \ data.return_raw_chat=${return_raw_chat} \ data.train_batch_size=${train_batch_size} \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.model.use_remove_padding=true \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.name=${rollout_engine} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=true \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.05 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=true \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=true \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.entropy_checkpointing=${entropy_checkpointing} \ reward.reward_manager.name=${reward_manager} \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=false \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${GPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=${val_before_train} \ trainer.test_freq=${test_freq} \ trainer.save_freq=${save_freq} \ trainer.total_epochs=${total_epochs} \ trainer.total_training_steps=${total_training_steps} \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=2 \ $@ ================================================ FILE: examples/gspo_trainer/test_gspo_3b_math_slurm.sh ================================================ #!/usr/bin/env bash #SBATCH --job-name=rl-gspo-3B #SBATCH --partition=main #SBATCH --nodes=1 # Number of nodes #SBATCH --ntasks-per-node=1 # One task per node #SBATCH --cpus-per-task=128 # cpu-cores per task #SBATCH --gres=gpu:8 #SBATCH --mem=0 #SBATCH --exclusive #SBATCH --time=500:00:00 #SBATCH --output=/rl/logs/Qwen2.5-3B/gspo/math/vllm_%x_%j.out #SBATCH --error=/rl/logs/Qwen2.5-3B/gspo/math/vllm_%x_%j.err set -xeuo pipefail # activate the venv echo "Activating verl environment..." eval "$(conda shell.bash hook)" conda deactivate conda activate verl # can make training faster, depends on your infrastructure export NCCL_IBEXT_DISABLE=1 export NCCL_NVLS_ENABLE=1 export NCCL_IB_HCA=mlx5 export UCX_NET_DEVICES=mlx5_0:1,mlx5_1:1,mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1 # Set how many GPUs we actually have on this node. export GPUS_PER_NODE=8 NNODES=${SLURM_JOB_NUM_NODES} export NNODES export VLLM_ATTENTION_BACKEND=FLASH_ATTN export RAY_memory_monitor_refresh_ms=0 export RAY_LOGGING_LEVEL=DEBUG export HYDRA_FULL_ERROR=1 export WANDB_API_KEY=... # your wandb API key # Let Ray know how many nodes to expect export RAY_NUM_NODES=$NNODES echo "Using $NNODES nodes for training..." # ------------------------------------- Setup xp params --------------------------------------- project_name='RL-GSPO' adv_estimator=grpo loss_mode=gspo loss_agg_mode="seq-mean-token-mean" MODEL_PATH=Qwen/Qwen2.5-3B-Instruct offload=false # it's a small model, offloading will just slow-down training rollout_engine=vllm rollout_mode=async return_raw_chat="True" if [ "$rollout_engine" = "vllm" ]; then export VLLM_USE_V1=1 fi gpu_memory_utilization=0.8 reward_manager=dapo adv_estimator=grpo shuffle_dataset=true first_time_dataset_prep=true # prepare dataset test_freq=10 save_freq=10 total_epochs=10 total_training_steps=500 val_before_train=false use_kl_in_reward=false kl_coef=0.0 use_kl_loss=false kl_loss_coef=0.0 clip_ratio_low=0.0003 # as recommended by the paper, see Sec. 5.1 clip_ratio_high=0.0004 # as recommended by the paper, see Sec. 5.1 train_batch_size=512 ppo_mini_batch_size=128 # maintain 4 mini-batches as recommended by the paper, see Sec. 5.1 ppo_micro_batch_size_per_gpu=8 # setup depending on your GPU memory n_resp_per_prompt=16 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) # dapo reward manager params enable_overlong_buffer=false # true overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Paths and namings SFT_MODEL=$(basename $MODEL_PATH) exp_name="${loss_mode}-epslow-${clip_ratio_low}-epshigh-${clip_ratio_high}-${SFT_MODEL}-RL" CKPTS_DIR=/rl/checkpoints/experimental/4b/${loss_mode}/${exp_name} # Sampling params at rollouts temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter sp_size=1 use_dynamic_bsz=true actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=true gen_tp=1 entropy_checkpointing=true # This enables entropy recomputation specifically for the entropy calculation, lowering memory usage during training. # ------------------------------------- train/val data preparation --------------------------------------- if [ "$first_time_dataset_prep" = true ]; then echo "Preprocessing GSM8K dataset..." python examples/data_preprocess/gsm8k.py --local_save_dir /data/gsm8k/ fi gsm8k_train_path=/data/gsm8k/train.parquet gsm8k_test_path=/data/gsm8k/test.parquet # set the paths train_files="['$gsm8k_train_path']" test_files="['$gsm8k_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=${adv_estimator} \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ data.train_files="${train_files}" \ data.val_files="${test_files}" \ data.shuffle=$shuffle_dataset \ data.prompt_key=prompt \ data.truncation='error' \ data.filter_overlong_prompts=true \ data.return_raw_chat=${return_raw_chat} \ data.train_batch_size=${train_batch_size} \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.model.use_remove_padding=true \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.name=${rollout_engine} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=true \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.05 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization} \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=true \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=true \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.entropy_checkpointing=${entropy_checkpointing} \ reward.reward_manager.name=${reward_manager} \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=false \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${GPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=${val_before_train} \ trainer.test_freq=${test_freq} \ trainer.save_freq=${save_freq} \ trainer.total_epochs=${total_epochs} \ trainer.total_training_steps=${total_training_steps} \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=2 \ $@ ================================================ FILE: examples/gspo_trainer/test_gspo_qwen30b_a3b_ep.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail export NCCL_DEBUG=WARN # export VERL_LOGGING_LEVEL=DEBUG project_name='DAPO' exp_name='GSPO-Qwen3-30B-A3B-Base-MATH' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=3e-4 clip_ratio_high=4e-4 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" loss_mode=gspo train_prompt_bsz=256 n_resp_per_prompt=16 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths # RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"} # CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} # TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} # TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} MODEL_PATH=$HDFS_ROOT/model/Qwen3-30B-A3B-Base CKPTS_DIR=$DATA_ROOT/checkpoint/${project_name}/${exp_name} TRAIN_FILE=$DATA_ROOT/dataset/BytedTsinghua-SIA/DAPO-Math-17k/data/dapo-math-17k.parquet aime24_test_path=$DATA_ROOT/dataset/aime-2024.parquet TEST_FILE="['$aime24_test_path']" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 1)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True # gen rollout_name=vllm # vllm or sglang if [ "$rollout_name" = "vllm" ]; then export VLLM_USE_V1=1 fi gen_tp=1 gen_dp=4 gen_ep=4 # train train_tp=4 train_pp=1 EP=4 ETP=1 python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.return_raw_chat=True \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.data_parallel_size=${gen_dp} \ actor_rollout_ref.rollout.expert_parallel_size=${gen_ep} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.use_mbridge=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger='["console","wandb"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}-tp${gen_tp}-ep${gen_ep}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=False \ trainer.test_freq=10 \ trainer.save_freq=30 \ trainer.total_epochs=10 \ trainer.total_training_steps=300 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: examples/mtp_trainer/runtime_env.yaml ================================================ working_dir: ./ excludes: - ".git/" env_vars: VLLM_USE_V1: "1" HYDRA_FULL_ERROR: "1" NCCL_NVLS_ENABLE: "0" NCCL_SOCKET_IFNAME: "eth0" TMPDIR: "/tmp" CUDA_HOME: "/usr/local/cuda" CUDA_TMPDIR: "/tmp" CUDA_CACHE_PATH: "/tmp/cuda_cache" # For distributed training, the path must be set on a distributed file system (DFS) to ensure visibility across all nodes. HF_HOME: "/tmp/hf_home_mimo" PYTHONPATH: "/tmp/hf_home_mimo/modules/" ================================================ FILE: examples/mtp_trainer/test_dapo_mimo_7b_with_mtp_math_megatron.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-mimo-7b-rl-megatron' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=128 n_resp_per_prompt=16 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/examples/mtp_trainer/runtime_env.yaml"} NNODES=${NNODES:-16} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/MiMo-7B-RL"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=4 train_tp=2 train_pp=2 train_cp=2 common_params=( actor_rollout_ref.model.mtp.enable=True actor_rollout_ref.model.mtp.enable_train=True actor_rollout_ref.model.mtp.mtp_loss_scaling_factor=0.1 actor_rollout_ref.model.mtp.detach_encoder=True ) python -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.context_parallel_size=${train_cp} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger='["console","tensorboard"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=False \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ actor_rollout_ref.rollout.disable_log_stats=False \ actor_rollout_ref.rollout.prometheus.enable=True \ actor_rollout_ref.rollout.prometheus.port=44398 \ actor_rollout_ref.model.trust_remote_code=True \ data.trust_remote_code=True \ trainer.total_training_steps=400 \ actor_rollout_ref.actor.megatron.use_mbridge=True \ "${common_params[@]}" ================================================ FILE: examples/mtp_trainer/test_dapo_mimo_7b_with_mtp_math_megatron_4_4.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-mimo-7b-rl-megatron' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/examples/mtp_trainer/runtime_env.yaml"} NNODES=${NNODES:-16} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/MiMo-7B-RL"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=False gen_tp=2 train_tp=2 train_pp=1 train_cp=1 train_prompt_bsz=128 n_resp_per_prompt=16 train_prompt_mini_bsz=32 mtp_params=( actor_rollout_ref.actor.megatron.use_mbridge=True actor_rollout_ref.model.mtp.enable=True actor_rollout_ref.model.mtp.enable_train=True actor_rollout_ref.model.mtp.mtp_loss_scaling_factor=0.1 actor_rollout_ref.model.mtp.detach_encoder=True actor_rollout_ref.model.mtp.enable_rollout=True ) fully_async=( data.train_batch_size=0 data.gen_batch_size=1 trainer.test_freq=10 actor_rollout_ref.hybrid_engine=False actor_rollout_ref.rollout.calculate_log_probs=True actor_rollout_ref.actor.optim.lr_decay_steps=51200 rollout.total_rollout_steps=$(((512*100))) trainer.nnodes=1 trainer.n_gpus_per_node=4 rollout.nnodes=1 rollout.n_gpus_per_node=4 async_training.staleness_threshold=0.5 async_training.trigger_parameter_sync_step=4 async_training.require_batches=1 async_training.partial_rollout=True ) python -m verl.experimental.fully_async_policy.fully_async_main \ --config-path=config \ --config-name='fully_async_ppo_megatron_trainer.yaml'\ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.context_parallel_size=${train_cp} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ reward_model.reward_manager=dapo \ +reward_model.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward_model.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward_model.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward_model.reward_kwargs.overlong_buffer_cfg.log=False \ +reward_model.reward_kwargs.max_resp_len=${max_response_length} \ actor_rollout_ref.rollout.disable_log_stats=False \ actor_rollout_ref.rollout.prometheus.enable=True \ actor_rollout_ref.rollout.prometheus.port=44398 \ actor_rollout_ref.model.trust_remote_code=True \ data.trust_remote_code=True \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ trainer.total_epochs=10 \ "${mtp_params[@]}" \ "${fully_async[@]}" ================================================ FILE: examples/otb_trainer/run_qwen2_5-7b.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=optimal_token_baseline \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=128 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_fused_kernels=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.calculate_sum_pi_squared=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.75 \ actor_rollout_ref.rollout.n=8 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_5-7b-otb' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/README.md ================================================ # Proximal Policy Optimization (PPO) Proximal Policy Optimization (PPO) is a family of policy gradient methods for reinforcement learning, proposed by OpenAI in 2017. PPO strikes a balance between simplicity, stability, and performance, making it one of the most widely used algorithms in modern RL applications, including large-scale language model fine-tuning. Traditional policy gradient methods like REINFORCE or Vanilla Policy Gradient suffer from: - High variance and sample inefficiency. - Instability due to large policy updates. PPO addresses this problem using a clipped surrogate objective that avoids overly large updates without requiring second-order derivatives. For more technical details regarding PPO, we suggest reading the introduction in the [OpenAI spinning up tutorial](https://spinningup.openai.com/en/latest/algorithms/ppo.html), and the paper [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347). ## Key Components - Actor-Critic Architecture: PPO requires both an actor model (policy) and a critic model (value function). This differs from other algorithms like GRPO and RLOO that don't require a critic model. - Generalized Advantage Estimation (GAE): PPO uses GAE for computing advantage values, which helps reduce variance in policy gradient estimates while maintaining low bias. - Clipped Surrogate Objective: The core of PPO is implemented through the clipped surrogate objective function that limits policy updates. ## Configuration Note that all configs containing `micro_batch_size` are used to configure the maximum sample or token count per forward or backward pass to avoid GPU OOMs, whose value should not change algorithmic/convergence behavior. Most critic configs are similar to those of actors. Note that the critic model is omitted from the figure below. ![image](https://github.com/user-attachments/assets/16aebad1-0da6-4eb3-806d-54a74e712c2d) - `data.train_batch_size`: The global batch size of prompts used to generate a set of sampled trajectories/rollouts. The number of responses/trajectories is `data.train_batch_size * actor_rollout.ref.rollout.n` - `actor_rollout_ref.actor.ppo_mini_batch_size`: The set of sampled trajectories is split into multiple mini-batches with batch_size=ppo_mini_batch_size for PPO actor updates. The ppo_mini_batch_size is a global size across all workers - `critic.ppo_mini_batch_size`: The set of sampled trajectories is split into multiple mini-batches with batch_size=ppo_mini_batch_size for PPO critic updates. The ppo_mini_batch_size is a global size across all workers - `actor_rollout_ref.actor.clip_ratio`: The PPO clip range. Default to 0.2 - `actor_rollout_ref.actor.ppo_epochs`: Number of epochs for PPO updates on one set of sampled trajectories for actor - `critic.ppo_epochs`: Number of epochs for PPO updates on one set of sampled trajectories for critic. Defaults to `actor_rollout_ref.actor.ppo_epochs` - `algorithm.gamma`: discount factor - `algorithm.lam`: The lambda term that trades off between bias and variance in the GAE estimator - `algorithm.adv_estimator`: Support gae, grpo, reinforce_plus_plus, reinforce_plus_plus_baseline, rloo, rloo_vectorized ## Advanced Extensions ### KL Divergence Control Options to prevent the policy from diverging too far from a reference policy. Two mechanisms are available: KL reward penalty and KL loss. For more technical details, see [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) Options to use KL loss for KL divergence control: - `actor_rollout_ref.actor.use_kl_loss`: to use kl loss in the actor. When used, we are not applying KL in the reward function. Default is False - `actor_rollout_ref.actor.kl_loss_coef`: The coefficient of kl loss. Default is 0.001. - `actor_rollout_ref.actor.kl_loss_type`: Support kl(k1), abs, mse(k2), low_var_kl(k3) and full. Appending "+" in the end (e.g., 'k1+' and 'k3+') would apply straight through to employ k2 for unbiased gradient estimation, regardless of the kl value estimation (see https://github.com/volcengine/verl/pull/2953#issuecomment-3162113848 for more details). How to calculate the kl divergence between actor and reference policy. See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html Options to use KL penalty in the reward: - `algorithm.use_kl_in_reward`: Whether to enable in-reward kl penalty. Default is False. - `algorithm.kl_penalty`: Support kl(k1), abs, mse(k2), low_var_kl(k3) and full. This defines the way to calculate the kl divergence between actor and reference policy. For specific options, refer to `kl_penalty` in core_algos.py. See this blog post for detailed analysis: http://joschu.net/blog/kl-approx.html - `algorithm.kl_ctrl.kl_coef`: The (initial) coefficient of in-reward kl_penalty. Default is 0.001. - `algorithm.kl_ctrl.type`: 'fixed' for FixedKLController and 'adaptive' for AdaptiveKLController. - `algorithm.kl_ctrl.horizon`: See source code of AdaptiveKLController for details. - `algorithm.kl_ctrl.target_kl`: See source code of AdaptiveKLController for details. ### Dual-clip PPO The Dual-Clip PPO introduces a approach by applying a lower bound to the policy ratio when the advantage is less than zero, when multiplied by a large raito, does not exceed a specified lower bound. ![image](https://github.com/user-attachments/assets/fc232181-d8b0-4307-8dd2-4dc0a4c1c139) - `actor_rollout_ref.actor.clip_ratio_c`: lower bound of the value for Dual-clip PPO, defaults to 3.0 ## Reference Example Qwen2.5 training log and commands: [link](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-0.5B-bsz256_2-prompt1024-resp512-0.567.log) ```bash bash run_gemma.sh trainer.n_gpus_per_node=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ trainer.logger=console \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ data.train_batch_size=256 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size=2 \ critic.ppo_micro_batch_size=2 ``` Reference performance with verl v0.2: | Model | Method | Score | Link | |-------------------------------|------------------|-------|------------------------------------------------------------------------------------------------| | Qwen/Qwen2.5-0.5B-Instruct | pretrained model | 36.4 | [Qwen Blog](https://qwenlm.github.io/blog/qwen2.5-llm/) | | Qwen/Qwen2.5-0.5B-Instruct | PPO | 56.7 | [PPO Command and Logs](https://github.com/eric-haibin-lin/verl-data/blob/experiments/gsm8k/Qwen2.5-0.5B-bsz256_2-prompt1024-resp512-0.567.log) | ================================================ FILE: examples/ppo_trainer/run_deepseek7b_llm.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=1 \ trainer.use_legacy_worker_impl=auto \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek7b_llm_modelscope.sh ================================================ set -x VERL_USE_MODELSCOPE=True \ python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek7b_llm_pfppo.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ algorithm.use_pf_ppo=True \ algorithm.pf_ppo.reweight_method=pow \ # ["pow", "max_min", "max_random"] algorithm.pf_ppo.weight_pow=2.0 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.rollout.n=5 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek7b_llm_sandbox_fusion.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ reward.sandbox_fusion.url='https://xxxxxxxxx.apigateway-cn-beijing.volceapi.com/run_code' \ reward.sandbox_fusion.max_concurrent=128 \ reward.reward_manager.name=prime \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/Eurus-2-RL-Data/train.parquet \ data.val_files=$HOME/data/Eurus-2-RL-Data/validation.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_sandbox_fusion' \ trainer.experiment_name='deepseek_llm_7b_function_sandbox_fusion' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek7b_llm_sp2.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=64 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ critic.optim.lr=1e-5 \ critic.ulysses_sequence_parallel_size=2 \ critic.model.use_remove_padding=True \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=64 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm_sp2' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek_full_hh_rlhf.sh ================================================ set -x train_files=$HOME/data/full_hh_rlhf/rl/train.parquet test_files=$HOME/data/full_hh_rlhf/rl/train.parquet # no use python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=512 \ data.max_prompt_length=128 \ data.max_response_length=128 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.ppo_micro_batch_size_per_gpu=4 \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path=deepseek-ai/deepseek-llm-7b-chat \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=4 \ reward.reward_model.rollout.prompt_length=256 \ reward.reward_model.rollout.response_length=128 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_megatron_full_hh_rlhf_examples' \ trainer.experiment_name='deepseek_llm_7b_model_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=100 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek_math_gsm8k_megatron.sh ================================================ set -x # Example runnable on H20 * 8 export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ critic.optim.lr=1e-5 \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_ppo_gsm8k_math_examples' \ trainer.experiment_name='deepseek_llm_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=100 $@ ================================================ FILE: examples/ppo_trainer/run_deepseek_math_gsm8k_megatron_nsys.sh ================================================ set -x # Example runnable on H20 * 8 export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files=${train_files:-"$gsm8k_train_path"} test_files=${test_files:-"$gsm8k_test_path"} # Nsight profiling configuration PROFILE_STEPS="[1]" # or [] or null PROFILE_RANKS_ALL=False # or True PROFILE_RANKS=[0,4] DISCRETE=True # or True python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.profiler.enable=True \ actor_rollout_ref.actor.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ critic.optim.lr=1e-5 \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.ppo_micro_batch_size_per_gpu=4 \ critic.profiler.enable=True \ critic.profiler.ranks=$PROFILE_RANKS \ critic.profiler.all_ranks=$PROFILE_RANKS_ALL \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_ppo_gsm8k_math_examples' \ trainer.experiment_name='deepseek_llm_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=100 \ trainer.total_training_steps=1 \ global_profiler.tool=nsys \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.global_tool_config.nsys.discrete=$DISCRETE $@ ================================================ FILE: examples/ppo_trainer/run_gemma.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=google/gemma-2-2b-it \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=False \ critic.model.path=google/gemma-2-2b-it \ critic.model.enable_gradient_checkpointing=False \ critic.ppo_micro_batch_size_per_gpu=4 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example' \ trainer.experiment_name='gemma2b_function_rm' \ trainer.n_gpus_per_node=2 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=10 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_moonlight16b_a3b_gsm8k_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping # 0. download the model hf download moonshotai/Moonlight-16B-A3B-Instruct # 1. convert the model to mcore format # change the HF_MODEL_PATH and DIST_CKPT_PATH to your own path HF_MODEL_PATH=/data/models/moonshotai/Moonlight-16B-A3B-Instruct DIST_CKPT_PATH=/data/mcore_ckpt/Moonlight-16B-A3B-Instruct python scripts/converter_hf_to_mcore.py --hf_model_path $HF_MODEL_PATH --output_path $DIST_CKPT_PATH # 2. run the script gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet train_files=$gsm8k_train_path test_files=$gsm8k_test_path ALL_OFFLOAD=${ALL_OFFLOAD:-False} COMMON_PARAM_OFFLOAD=${COMMON_PARAM_OFFLOAD:-$ALL_OFFLOAD} COMMON_GRAD_OFFLOAD=${COMMON_GRAD_OFFLOAD:-$ALL_OFFLOAD} COMMON_OPTIMIZER_OFFLOAD=${COMMON_OPTIMIZER_OFFLOAD:-$ALL_OFFLOAD} ACTOR_PARAM_OFFLOAD=${ACTOR_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} ACTOR_GRAD_OFFLOAD=${ACTOR_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} ACTOR_OPTIMIZER_OFFLOAD=${ACTOR_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} REF_PARAM_OFFLOAD=${REF_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} CRITIC_PARAM_OFFLOAD=${CRITIC_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} CRITIC_GRAD_OFFLOAD=${CRITIC_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} CRITIC_OPTIMIZER_OFFLOAD=${CRITIC_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} RM_PARAM_OFFLOAD=${RM_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} NODES=4 PP=2 TP=8 EP=8 ETP=1 VLLM_TP=4 # RAY_ADDRESS='auto' ray job submit --working-dir . -- python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.trust_remote_code=True \ actor_rollout_ref.model.path=$LLM \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ critic.optim.lr=1e-5 \ critic.model.path=$LLM \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_megatron_gsm8k_examples' \ trainer.experiment_name='moonlight_16b_a3b_instruct_1node' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=$NODES \ trainer.save_freq=-1 \ trainer.test_freq=5 \ actor_rollout_ref.model.trust_remote_code=True \ critic.model.trust_remote_code=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=13 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$VLLM_TP \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \ critic.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \ critic.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ critic.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ critic.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.megatron.param_offload=${ACTOR_PARAM_OFFLOAD} \ actor_rollout_ref.actor.megatron.optimizer_offload=${ACTOR_OPTIMIZER_OFFLOAD} \ actor_rollout_ref.actor.megatron.grad_offload=${ACTOR_GRAD_OFFLOAD} \ actor_rollout_ref.ref.megatron.param_offload=${REF_PARAM_OFFLOAD} \ critic.megatron.param_offload=${CRITIC_PARAM_OFFLOAD} \ critic.megatron.optimizer_offload=${CRITIC_OPTIMIZER_OFFLOAD} \ critic.megatron.grad_offload=${CRITIC_GRAD_OFFLOAD} \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \ critic.megatron.use_dist_checkpointing=True \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ critic.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ trainer.val_before_train=False \ trainer.total_epochs=100 $@ ================================================ FILE: examples/ppo_trainer/run_qwen1.5_moe_a2.7b-gsm8k_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping # 0. download the model #hf download Qwen/Qwen1.5-MoE-A2.7B-Chat # 1. convert the model to mcore format # change the HF_MODEL_PATH and DIST_CKPT_PATH to your own path HF_MODEL_PATH=/data/models/Qwen/Qwen1.5-MoE-A2.7B-Chat DIST_CKPT_PATH=/data/mcore_ckpt/Qwen1.5-MoE-A2.7B-Chat python scripts/converter_hf_to_mcore.py --hf_model_path $HF_MODEL_PATH --output_path $DIST_CKPT_PATH # 2. run the script gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet train_files=$gsm8k_train_path test_files=$gsm8k_test_path NODES=4 PP=2 TP=4 CP=1 VLLM_TP=4 # RAY_ADDRESS='auto' ray job submit --working-dir . -- python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.ref.megatron.context_parallel_size=$CP \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.tensor_model_parallel_size=$VLLM_TP \ critic.optim.lr=1e-5 \ critic.model.path=$HF_MODEL_PATH \ critic.ppo_micro_batch_size_per_gpu=4 \ critic.megatron.tensor_model_parallel_size=$TP \ critic.megatron.pipeline_model_parallel_size=$PP \ critic.megatron.context_parallel_size=$CP \ critic.megatron.use_dist_checkpointing=True \ critic.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_megatron_gsm8k_examples' \ trainer.experiment_name='qwen1.5_moe_nochat' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=$NODES \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=100 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_math_gsm8k_megatron.sh ================================================ set -x export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_ppo_gsm8k_math_examples' \ trainer.experiment_name='qwen2_7b_megatron' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=100 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_rm.sh ================================================ # Discliamer: the model used in the script is only for academic purpose. set -x # Data preparation scripts are available in ``examples/data_preprocess``. # Example usage: # # python3 examples/data_preprocess/math_dataset.py --local_dir ~/data/math # python3 examples/data_preprocess/gsm8k.py --local_save_dir ~/data/gsm8k gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" # prepare model ckpt hf download Qwen/Qwen2-7B-Instruct --local-dir $HOME/models/Qwen2-7B-Instruct & hf download sfairXC/FsfairX-LLaMA3-RM-v0.1 --local-dir $HOME/models/FsfairX-LLaMA3-RM-v0.1 & wait python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path="$HOME/models/Qwen2-7B-Instruct" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.optim.lr_warmup_steps_ratio=0.05 \ critic.model.path="$HOME/models/Qwen2-7B-Instruct" \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path="$HOME/models/FsfairX-LLaMA3-RM-v0.1" \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=2048 \ reward.reward_model.rollout.response_length=1024 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example' \ trainer.val_before_train=False \ trainer.experiment_name='Qwen2-7B-Instruct_hybrid_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_rm_reward_loop_colocate.sh ================================================ # download datasets and models # python3 examples/data_preprocess/gsm8k.py # python3 examples/data_preprocess/math_dataset.py # hf download Skywork/Skywork-Reward-V2-Llama-3.2-3B --local-dir $HOME/models/Skywork-Reward-V2-Llama-3.2-3B # hf download Qwen/Qwen2.5-3B-Instruct --local-dir $HOME/models/Qwen2.5-3B-Instruct gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path="$HOME/models/Qwen2.5-3B-Instruct" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.optim.lr_warmup_steps_ratio=0.05 \ critic.model.path="$HOME/models/Qwen2.5-3B-Instruct" \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path="$HOME/models/Skywork-Reward-V2-Llama-3.2-3B" \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.prompt_length=4096 \ reward.reward_model.rollout.response_length=4096 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_test_qwen25_rm' \ trainer.val_before_train=False \ trainer.experiment_name='reward_loop_colocate_reward_model' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_rm_seq_balance.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.ppo_mini_batch_size=512 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=24000 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.model.enable_gradient_checkpointing=True \ critic.use_dynamic_bsz=True \ critic.ppo_max_token_len_per_gpu=98304 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path=sfairXC/FsfairX-LLaMA3-RM-v0.1\ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=8192 \ reward.reward_model.rollout.response_length=4096 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='qwen2-7b_hybrid_rm_bsz8k_p4k_r4k_seq_packing' \ trainer.n_gpus_per_node=8 \ trainer.val_before_train=False \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_rm_seq_balance_fused_kernels.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" FUSED_KERNEL_BACKEND=triton # or 'torch' for torch backend python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.model.fused_kernel_options.impl_backend=$FUSED_KERNEL_BACKEND \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.ppo_mini_batch_size=512 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=24000 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.model.enable_gradient_checkpointing=True \ critic.use_dynamic_bsz=True \ critic.ppo_max_token_len_per_gpu=98304 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path=sfairXC/FsfairX-LLaMA3-RM-v0.1 \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=8192 \ reward.reward_model.rollout.response_length=4096 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='qwen2-7b_hybrid_rm_bsz8k_p4k_r4k_seq_packing_fused_kernel' \ trainer.n_gpus_per_node=8 \ trainer.val_before_train=False \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_rm_seq_balance_nsys.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files=${train_files:-"$gsm8k_train_path"} test_files=${test_files:-"$gsm8k_test_path"} PROFILE_STEPS="[1,2,5]" # or [] or null PROFILE_RANKS_ALL=False # or True PROFILE_RANKS=[0,4] DISCRETE=True # or True python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.ppo_mini_batch_size=512 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=12000 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.profiler.enable=True \ actor_rollout_ref.actor.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=24000 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=2 \ critic.use_dynamic_bsz=True \ critic.ppo_max_token_len_per_gpu=98304 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ critic.profiler.enable=True \ critic.profiler.ranks=$PROFILE_RANKS \ critic.profiler.all_ranks=$PROFILE_RANKS_ALL \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path=sfairXC/FsfairX-LLaMA3-RM-v0.1\ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=8192 \ reward.reward_model.rollout.response_length=4096 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='qwen2-7b_hybrid_rm_bsz8k_p4k_r4k_seq_packing' \ trainer.n_gpus_per_node=8 \ trainer.val_before_train=False \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=15 \ trainer.total_training_steps=6 \ global_profiler.profile_continuous_steps=True \ global_profiler.tool=nsys \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.global_tool_config.nsys.discrete=$DISCRETE $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_seq_balance.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" # For async rollout mode, dataset should return raw chat. rollout_mode="async" return_raw_chat="True" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.return_raw_chat=$return_raw_chat \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.ppo_mini_batch_size=512 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=$rollout_mode \ actor_rollout_ref.rollout.multi_turn.format=hermes \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=24000 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_max_token_len_per_gpu=98304 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='qwen2-7b_function_rm_bsz8k_p4k_r4k_seq_packing' \ trainer.n_gpus_per_node=8 \ trainer.val_before_train=False \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2-7b_sglang_seq_balance.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=4096 \ data.max_prompt_length=4096 \ data.max_response_length=4096 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.ppo_mini_batch_size=512 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=24000 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen2-7B-Instruct \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_max_token_len_per_gpu=98304 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='qwen2-7b_function_rm_bsz8k_p4k_r4k_seq_packing' \ trainer.n_gpus_per_node=8 \ trainer.val_before_train=False \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2.5-32b.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-32B-Instruct \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen2.5-32B-Instruct \ critic.model.enable_gradient_checkpointing=False \ critic.ppo_micro_batch_size_per_gpu=8 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example' \ trainer.experiment_name='Qwen2.5-32B-Instruct_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=4 \ trainer.save_freq=20 \ trainer.test_freq=10 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen2.5-3b_rm_reward_loop_colocate.sh ================================================ # download datasets and models # python3 examples/data_preprocess/gsm8k.py # python3 examples/data_preprocess/math_dataset.py # hf download Skywork/Skywork-Reward-V2-Llama-3.2-3B --local-dir $HOME/models/Skywork-Reward-V2-Llama-3.2-3B # hf download Qwen/Qwen2.5-3B-Instruct --local-dir $HOME/models/Qwen2.5-3B-Instruct gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path="$HOME/models/Qwen2.5-3B-Instruct" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.optim.lr_warmup_steps_ratio=0.05 \ critic.model.path="$HOME/models/Qwen2.5-3B-Instruct" \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=32 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path="$HOME/models/Skywork-Reward-V2-Llama-3.2-3B" \ reward.reward_model.rollout.name=vllm \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=4096 \ reward.reward_model.rollout.response_length=4096 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_test_qwen25_rm' \ trainer.val_before_train=True \ trainer.experiment_name='reward_loop_colocate_reward_model' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ppo_trainer/run_qwen3-8b_npu.sh ================================================ set -x export VLLM_USE_V1=1 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/dapo-math-17k.parquet \ data.val_files=$HOME/data/dapo-math-17k.parquet \ data.train_batch_size=256 \ data.max_prompt_length=2000 \ data.max_response_length=12000 \ data.shuffle=False \ actor_rollout_ref.model.path=Qwen/Qwen3-8B \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.max_num_batched_tokens=14000 \ actor_rollout_ref.rollout.max_num_seqs=64 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path=Qwen/Qwen3-8B \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=1 \ critic.ulysses_sequence_parallel_size=2 \ critic.model.fsdp_config.param_offload=True \ critic.model.fsdp_config.optimizer_offload=True \ critic.use_dynamic_bsz=True \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_example_dapo_math_17k' \ trainer.experiment_name='qwen3_8b_fsdp' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=-1 \ trainer.val_before_train=False \ trainer.max_actor_ckpt_to_keep=1 \ trainer.max_critic_ckpt_to_keep=1 \ trainer.total_training_steps=100 $@ ================================================ FILE: examples/prefix_grouper/README.md ================================================ # PrefixGrouper Examples This directory contains examples for using **PrefixGrouper**, an optimization technique that groups samples by shared prompts to reduce redundant computations in GRPO. ## Introduction > Official Repository: [https://github.com/johncaged/PrefixGrouper](https://github.com/johncaged/PrefixGrouper) ``PrefixGrouper`` is a plug-and-play efficient GRPO training tool that requires minimal modifications to existing codebases to achieve reduced computation, lower device memory consumption, and accelerated training. In current mainstream GRPO training pipelines, policy model training primarily involves copying prefixes (typically questions, multimodal inputs, etc.) `G` times. Consequently, when training data prefixes are sufficiently long (e.g., long-context reasoning, image/long-video inference), redundant computation during training becomes non-negligible. **PrefixGrouper** decomposes the original redundant self-attention operation into prefix self-attention + suffix concat-attention.

## Installation ```bash pip install prefix_grouper ``` ## Limitations - Currently only supports FSDP worker (Megatron worker is not supported yet). - Incompatible with `use_dynamic_bsz=True`. - Incompatible with `use_remove_padding=True` (Flash Attention V2 variable length). - Incompatible with `use_fused_kernels=True`. - Incompatible with Ulysses sequence parallelism (`use_ulysses_sp=True`) and ring-attention. Note: `balance_batch=True` is now supported with group-level balancing, which keeps samples with the same uid together on the same rank. However, this requires `batch_size % (world_size * rollout.n) == 0`. For example, with `world_size=8` and `rollout.n=4`, you need `batch_size` to be a multiple of 32. ## How to Use ### 1. Enable PrefixGrouper in Config Simply set `use_prefix_grouper=True` in your training config: ```yaml actor_rollout_ref: actor: use_prefix_grouper: True model: use_remove_padding: False ``` Optionally enable balance_batch for better load distribution: ```yaml trainer: balance_batch: True # Now supported with group-level balancing ``` ### 2. Run Training Use the provided script `run_qwen3_prefix_grouper.sh` as an example: ```bash bash examples/prefix_grouper/run_qwen3_prefix_grouper.sh ``` ## How It Works When `use_prefix_grouper=True`, verl automatically patches the attention functions in `transformers.modeling_utils.ALL_ATTENTION_FUNCTIONS` to support the `prefix_grouper` parameter. No model code modifications are needed. The patch wraps each attention function to: 1. Extract `prefix_grouper` from kwargs 2. If `prefix_grouper` is None, call original attention 3. If `prefix_grouper` is provided, use PrefixGrouper's optimized attention computation ## Performance **Benchmark Results** (Qwen3-4B, 4×H800, `rollout.n=4`): | Context Length | Metric | PG | No PG | Speedup | |----------------|--------|-----|-------|---------| | **4K** | `old_log_prob` | 1.31s | 1.70s | **1.30x** | | | `update_actor` | 4.80s | 6.07s | **1.26x** | | | `step` | 17.08s | 19.40s | **1.14x** | | **8K** | `old_log_prob` | 1.69s | 2.63s | **1.56x** | | | `update_actor` | 5.98s | 10.18s | **1.70x** | | | `step` | 19.48s | 24.71s | **1.27x** | As context length increases, the speedup becomes more pronounced. ================================================ FILE: examples/prefix_grouper/run_qwen3_prefix_grouper.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen3-8B \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_prefix_grouper=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen3_function_rm_pg' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.balance_batch=True \ trainer.total_epochs=15 $@ ================================================ FILE: examples/ray/tutorial.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "0ddc582b", "metadata": {}, "source": [ "# VeRL Ray API Tutorial" ] }, { "cell_type": "markdown", "id": "71fe3b94", "metadata": {}, "source": [ "## Chapter 1: Ray Basics" ] }, { "cell_type": "code", "execution_count": 144, "id": "1347d381", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 145, "id": "e75b9d44", "metadata": { "tags": [] }, "outputs": [], "source": [ "import warnings\n", "\n", "import ray\n", "import torch\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 146, "id": "2e90ae00", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-11-01 17:27:19,132\tINFO worker.py:1752 -- Started a local Ray instance.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9cc9d2ccbdfb48918c8fd6cd13a0807a", "version_major": 2, "version_minor": 0 }, "text/html": [ "
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Python version:3.9.2
Ray version:2.10.0
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\n" ], "text/plain": [ "RayContext(dashboard_url='', python_version='3.9.2', ray_version='2.10.0', ray_commit='09abba26b5bf2707639bb637c208d062a47b46f6')" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36m(GPUAccumulator pid=224400)\u001b[0m rank 0, value: tensor([1.], device='cuda:0')\n", "\u001b[36m(GPUAccumulator pid=225234)\u001b[0m rank 2, value: tensor([3.], device='cuda:0')\n", "\u001b[36m(GPUAccumulator pid=225607)\u001b[0m rank 0, value: tensor([2.], device='cuda:0')\n", "\u001b[36m(GPUAccumulator pid=226423)\u001b[0m rank 1, value: tensor([3.], device='cuda:0')\n", "\u001b[36m(GPUAccumulator pid=226857)\u001b[0m rank 3, value: tensor([6.], device='cuda:0')\n", "\u001b[36m(GPUAccumulatorDecorator pid=227475)\u001b[0m 10\n", "\u001b[36m(GPUAccumulatorDecorator pid=227475)\u001b[0m rank 0, value: tensor([10.], device='cuda:0')\n", "\u001b[36m(GPUAccumulatorDecorator pid=227655)\u001b[0m rank 1, value: tensor([11.], device='cuda:0')\n" ] } ], "source": [ "# Build a local ray cluster. The head node and worker node are on this machine\n", "ray.init()" ] }, { "cell_type": "markdown", "id": "a127e4e4", "metadata": {}, "source": [ "Implement an Accumulator class." ] }, { "cell_type": "code", "execution_count": 147, "id": "20e7b9a3", "metadata": { "tags": [] }, "outputs": [], "source": [ "@ray.remote\n", "class Accumulator:\n", " def __init__(self):\n", " self.value = 0\n", "\n", " def add(self, x):\n", " self.value += x\n", "\n", " def get_value(self):\n", " return self.value" ] }, { "cell_type": "code", "execution_count": 148, "id": "3b80098c", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Instantiate an accumulator. Accumulator can be viewed as a process, acting as an RPC service.\n", "accumulator = Accumulator.remote()" ] }, { "cell_type": "code", "execution_count": 149, "id": "b14b1009", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "value_ref = accumulator.get_value.remote() # Check the current value. Note that this function returns immediately and does not actually wait for the remote execution to complete.\n", "# Get the value\n", "value = ray.get(value_ref)\n", "print(value)" ] }, { "cell_type": "code", "execution_count": 150, "id": "513a84b3", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "# Accumulate, then check the result.\n", "accumulator.add.remote(10) # Similarly, the 'add' here will return immediately.\n", "new_value = ray.get(accumulator.get_value.remote())\n", "print(new_value)" ] }, { "cell_type": "markdown", "id": "3c332fe0", "metadata": {}, "source": [ "## Chapter 2: Resource Pool and RayWorkerGroup\n", "In the previous example, it was a simple single-process worker. \n", "In this example, we implement a worker with a GPU and form a RayWorkerGroup. Within this RayWorkerGroup, we implement a simple operation of an accumulator." ] }, { "cell_type": "code", "execution_count": 151, "id": "04229afb", "metadata": { "tags": [] }, "outputs": [], "source": [ "from verl.single_controller.base import Worker\n", "from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, merge_resource_pool" ] }, { "cell_type": "code", "execution_count": 152, "id": "0d0dbd58", "metadata": { "tags": [] }, "outputs": [], "source": [ "resource_pool = RayResourcePool([4], use_gpu=True)" ] }, { "cell_type": "code", "execution_count": 153, "id": "68f6838a", "metadata": { "tags": [] }, "outputs": [], "source": [ "@ray.remote\n", "class GPUAccumulator(Worker):\n", " def __init__(self) -> None:\n", " super().__init__()\n", " # The initial value of each rank is the same as the rank\n", " self.value = torch.zeros(size=(1,), device=\"cuda\") + self.rank\n", "\n", " def add(self, x):\n", " self.value += x\n", " print(f\"rank {self.rank}, value: {self.value}\")\n", " return self.value.cpu()" ] }, { "cell_type": "code", "execution_count": 154, "id": "23aad8fe", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[tensor([1.]), tensor([2.]), tensor([3.]), tensor([4.])]\n" ] } ], "source": [ "# Each worker's initial value is its rank, and then each rank's value is incremented by 1, so the values obtained on each rank are [1, 2, 3, 4]\n", "class_with_args = RayClassWithInitArgs(cls=GPUAccumulator)\n", "worker_group = RayWorkerGroup(resource_pool, class_with_args)\n", "print(worker_group.execute_all_sync(\"add\", x=[1, 1, 1, 1]))" ] }, { "cell_type": "markdown", "id": "e6705284", "metadata": {}, "source": [ "The principle of parameter passing: The input parameter is a list of length world_size, where each element in the list is dispatched respectively to each worker in the RayWorkerGroup. \n", "The return parameter is also a list, corresponding to the return value of each worker." ] }, { "cell_type": "markdown", "id": "d25c2412", "metadata": {}, "source": [ "### GPU Resource Sharing" ] }, { "cell_type": "markdown", "id": "f74f6d24", "metadata": {}, "source": [ "RayWorkerGroups mapped to the same resource pool share the GPU. In this example, we implement three resource pools: the first occupies 4 GPUs, the second also occupies 4 GPUs, and the last occupies all 8 GPUs. Among them, the first resource pool reuses the resource pool mentioned above." ] }, { "cell_type": "code", "execution_count": 155, "id": "49f9c06f", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Create a new resource pool and then merge the newly created resource pool with the previous one.\n", "resource_pool_1 = RayResourcePool([4], use_gpu=True, name_prefix=\"a\")\n", "resource_pool_merge = merge_resource_pool(resource_pool, resource_pool_1)" ] }, { "cell_type": "code", "execution_count": 156, "id": "05c2e305", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Establish a RayWorkerGroup on the newly created resource pool.\n", "worker_group_1 = RayWorkerGroup(resource_pool_1, class_with_args)\n", "worker_group_merge = RayWorkerGroup(resource_pool_merge, class_with_args)" ] }, { "cell_type": "code", "execution_count": 157, "id": "6b9b13f4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[tensor([2.]), tensor([3.]), tensor([4.]), tensor([5.])]\n" ] } ], "source": [ "# Run 'add' on the second set of 4 GPUs; the result should be [2, 3, 4, 5].\n", "output_1 = worker_group_1.execute_all_sync(\"add\", x=[2, 2, 2, 2])\n", "print(output_1)" ] }, { "cell_type": "code", "execution_count": 158, "id": "d856d030", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[tensor([3.]), tensor([4.]), tensor([5.]), tensor([6.]), tensor([7.]), tensor([8.]), tensor([9.]), tensor([10.])]\n" ] } ], "source": [ "# Run 'add' on the merged set of 8 GPUs; the result should be [3, 4, 5, 6, 7, 8, 9, 10].\n", "output_merge = worker_group_merge.execute_all_sync(\"add\", x=[3, 3, 3, 3, 3, 3, 3, 3])\n", "print(output_merge)" ] }, { "cell_type": "code", "execution_count": 159, "id": "33a4628c", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 4 8\n" ] } ], "source": [ "print(worker_group.world_size, worker_group_1.world_size, worker_group_merge.world_size)" ] }, { "cell_type": "markdown", "id": "3df19d13", "metadata": {}, "source": [ "## Chapter 3: Data Dispatch, Execution and Collection" ] }, { "cell_type": "markdown", "id": "acb22d9d", "metadata": {}, "source": [ "In the above example, we used the `execute_all_sync` function in the RayWorkerGroup to dispatch data from the driver to each worker. This is very inconvenient for coding. \n", "In this chapter, we use the form of function decorators to allow RayWorkerGroup to directly call functions written in the Worker, and to greatly simplify parameter passing." ] }, { "cell_type": "code", "execution_count": 160, "id": "35237432", "metadata": { "tags": [] }, "outputs": [], "source": [ "from verl.single_controller.base.decorator import Dispatch, Execute, register" ] }, { "cell_type": "code", "execution_count": 161, "id": "88b8ba3b", "metadata": { "tags": [] }, "outputs": [], "source": [ "@ray.remote\n", "class GPUAccumulatorDecorator(Worker):\n", " def __init__(self) -> None:\n", " super().__init__()\n", " # The initial value of each rank is the same as the rank\n", " self.value = torch.zeros(size=(1,), device=\"cuda\") + self.rank\n", "\n", " # map from a single input to all the worker\n", " @register(Dispatch.ONE_TO_ALL)\n", " def add(self, x):\n", " print(x)\n", " self.value = self.value + x\n", " print(f\"rank {self.rank}, value: {self.value}\")\n", " return self.value.cpu()" ] }, { "cell_type": "code", "execution_count": 162, "id": "eddaa043", "metadata": { "tags": [] }, "outputs": [], "source": [ "class_with_args = RayClassWithInitArgs(cls=GPUAccumulatorDecorator)\n", "gpu_accumulator_decorator = RayWorkerGroup(resource_pool_merge, class_with_args)" ] }, { "cell_type": "code", "execution_count": 163, "id": "10087c91", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[tensor([10.]), tensor([11.]), tensor([12.]), tensor([13.]), tensor([14.]), tensor([15.]), tensor([16.]), tensor([17.])]\n" ] } ], "source": [ "# As we can see, 10 is automatically dispatched to each Worker in this RayWorkerGroup.\n", "print(gpu_accumulator_decorator.add(x=10))" ] }, { "cell_type": "markdown", "id": "540ee6ad", "metadata": {}, "source": [ "### Custom Dispatch, Collection\n", "Users can customize `dispatch` and `collection` function. You only need to write the `dispatch_fn` and `collect_fn` functions yourself. We also support executing RPC only on rank_zero, with specific examples provided below." ] }, { "cell_type": "code", "execution_count": 164, "id": "8e041270", "metadata": { "tags": [] }, "outputs": [], "source": [ "from verl.single_controller.base.decorator import Dispatch, collect_all_to_all, register" ] }, { "cell_type": "code", "execution_count": 165, "id": "43b5be31", "metadata": { "tags": [] }, "outputs": [], "source": [ "def two_to_all_dispatch_fn(worker_group, *args, **kwargs):\n", " \"\"\"\n", " Assume the input is a list of 2. Duplicate the input interleaved and pass to each worker.\n", " \"\"\"\n", " for arg in args:\n", " assert len(arg) == 2\n", " for i in range(worker_group.world_size - 2):\n", " arg.append(arg[i % 2])\n", " for k, v in kwargs.items():\n", " assert len(v) == 2\n", " for i in range(worker_group.world_size - 2):\n", " v.append(v[i % 2])\n", " return args, kwargs\n", "\n", "\n", "@ray.remote\n", "class TestActor(Worker):\n", " # TODO: pass *args and **kwargs is bug prone and not very convincing\n", " def __init__(self, x) -> None:\n", " super().__init__()\n", " self._x = x\n", "\n", " def foo(self, y):\n", " return self._x + y\n", "\n", " @register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.RANK_ZERO)\n", " def foo_rank_zero(self, x, y):\n", " return self._x + y + x\n", "\n", " @register(dispatch_mode={\"dispatch_fn\": two_to_all_dispatch_fn, \"collect_fn\": collect_all_to_all})\n", " def foo_custom(self, x, y):\n", " return self._x + y + x" ] }, { "cell_type": "code", "execution_count": 166, "id": "83ec6609", "metadata": { "tags": [] }, "outputs": [], "source": [ "class_with_args = RayClassWithInitArgs(cls=TestActor, x=2)\n", "worker_group = RayWorkerGroup(resource_pool, class_with_args)" ] }, { "cell_type": "code", "execution_count": 167, "id": "62c58d8a", "metadata": { "tags": [] }, "outputs": [], "source": [ "output_ref = worker_group.foo_custom(x=[1, 2], y=[5, 6])\n", "assert output_ref == [8, 10, 8, 10]\n", "\n", "output_ref = worker_group.foo_rank_zero(x=1, y=2)\n", "assert output_ref == 5" ] }, { "cell_type": "code", "execution_count": 168, "id": "14689353", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n" ] } ], "source": [ "print(gpu_accumulator_decorator.world_size)" ] }, { "cell_type": "code", "execution_count": 169, "id": "2c80bbf4", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Shutdown ray cluster\n", "ray.shutdown()" ] }, { "cell_type": "markdown", "id": "a5c8151c", "metadata": {}, "source": [ "## Chapter 4: NVMegatronRayWorkerGroup" ] }, { "cell_type": "markdown", "id": "cd5680e9", "metadata": {}, "source": [ "Due to the Ray issue, we can only support max_colocate_count=1 in RayResourcePool for now. \n", "This means that each GPU can only have one process.\n", "We can support max_colocate > 1 when applying this pull request: https://github.com/ray-project/ray/pull/44385" ] }, { "cell_type": "markdown", "id": "92724419", "metadata": {}, "source": [ "Therefore, we need to restart the ray and initialize a new resource_pool to demonstrate the **NVMegatronRayWorkerGroup**" ] }, { "cell_type": "code", "execution_count": null, "id": "9b038538", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Build a local ray cluster. The head node and worker node are on this machine\n", "ray.init()" ] }, { "cell_type": "markdown", "id": "ebfd8798", "metadata": {}, "source": [ "Finally, we implement a `NVMegatronRayWorkerGroup`, within which we create a Megatron and then run a tensor parallel (tp) split Llama mlp layer. Here, we use a complex dispatch mode, `Megatron_COMPUTE`. This dispatch mode assumes that user passes the data partitioned by DP dimension. The data is dispatched to all tp/pp ranks within the same dp group, and ultimately only collects output data from tp=0 and the last pp. In this way, for users that only write code on the driver, the Megatron behind the RPC becomes transparent." ] }, { "cell_type": "code", "execution_count": 171, "id": "5a032154", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/opt/tiger/Megatron-LM\n", "/opt/tiger/Megatron-LM/megatron/__init__.py\n" ] } ], "source": [ "import sys\n", "\n", "current_pythonpath = os.environ.get(\"PYTHONPATH\", \"\")\n", "\n", "new_path = \"/opt/tiger/Megatron-LM\"\n", "\n", "new_pythonpath = f\"{new_path}:{current_pythonpath}\" if current_pythonpath else new_path\n", "\n", "os.environ[\"PYTHONPATH\"] = new_pythonpath\n", "\n", "print(new_path)\n", "sys.path.append(new_path)\n", "\n", "import megatron\n", "\n", "print(megatron.__file__)" ] }, { "cell_type": "code", "execution_count": 172, "id": "8c84cd5a", "metadata": { "tags": [] }, "outputs": [], "source": [ "from megatron.core import parallel_state as mpu\n", "from omegaconf import OmegaConf\n", "\n", "from verl.single_controller.base.decorator import Dispatch, Execute, register\n", "from verl.single_controller.base.megatron.worker import MegatronWorker\n", "from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup\n", "from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup" ] }, { "cell_type": "code", "execution_count": 173, "id": "1b1debcc", "metadata": { "tags": [] }, "outputs": [], "source": [ "resource_pool = RayResourcePool([4], use_gpu=True, max_colocate_count=1)" ] }, { "cell_type": "code", "execution_count": 174, "id": "bccbe081", "metadata": { "tags": [] }, "outputs": [], "source": [ "@ray.remote\n", "class MLPLayerWorker(MegatronWorker):\n", " def __init__(self):\n", " super().__init__()\n", " rank = int(os.environ[\"LOCAL_RANK\"])\n", " torch.distributed.init_process_group(backend=\"nccl\")\n", " torch.cuda.set_device(rank)\n", "\n", " mpu.initialize_model_parallel(\n", " tensor_model_parallel_size=4,\n", " pipeline_model_parallel_size=1,\n", " virtual_pipeline_model_parallel_size=None,\n", " pipeline_model_parallel_split_rank=None,\n", " use_sharp=False,\n", " context_parallel_size=1,\n", " expert_model_parallel_size=1,\n", " nccl_communicator_config_path=None,\n", " )\n", " from megatron.core import tensor_parallel\n", "\n", " tensor_parallel.model_parallel_cuda_manual_seed(10)\n", "\n", " @register(Dispatch.ONE_TO_ALL)\n", " def init_model(self, config):\n", " from omegaconf import OmegaConf\n", "\n", " from verl.models.llama.megatron.layers import ParallelLlamaMLP\n", " from verl.utils.megatron_utils import init_model_parallel_config\n", "\n", " megatron_config = OmegaConf.create(\n", " {\n", " \"sequence_parallel\": False,\n", " \"param_dtype\": \"fp32\",\n", " \"tensor_model_parallel_size\": mpu.get_tensor_model_parallel_world_size(),\n", " \"pipeline_model_parallel_rank\": mpu.get_pipeline_model_parallel_rank(),\n", " \"pipeline_model_parallel_size\": mpu.get_pipeline_model_parallel_world_size(),\n", " \"virtual_pipeline_model_parallel_rank\": mpu.get_virtual_pipeline_model_parallel_rank(),\n", " \"virtual_pipeline_model_parallel_size\": mpu.get_virtual_pipeline_model_parallel_world_size(),\n", " }\n", " )\n", "\n", " megatron_config = init_model_parallel_config(megatron_config)\n", " self.parallel_layer = ParallelLlamaMLP(config=config, megatron_config=megatron_config)\n", "\n", " @register(Dispatch.ONE_TO_ALL)\n", " def get_weights(self):\n", " output = {}\n", " for key, val in self.parallel_layer.named_parameters():\n", " output[key] = val\n", " return output\n", "\n", " @register(Dispatch.MEGATRON_COMPUTE)\n", " def run_layer(self, x):\n", " x = x.to(\"cuda\")\n", " y = self.parallel_layer(x)\n", " return y" ] }, { "cell_type": "code", "execution_count": 175, "id": "a655271d", "metadata": { "tags": [] }, "outputs": [], "source": [ "layer_cls = RayClassWithInitArgs(cls=MLPLayerWorker)\n", "layer_worker_group = NVMegatronRayWorkerGroup(\n", " resource_pool=resource_pool,\n", " ray_cls_with_init=layer_cls,\n", ")" ] }, { "cell_type": "code", "execution_count": 176, "id": "f105ebee", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 4 1 1\n" ] } ], "source": [ "print(layer_worker_group.world_size, layer_worker_group.tp_size, layer_worker_group.pp_size, layer_worker_group.dp_size)" ] }, { "cell_type": "code", "execution_count": 177, "id": "38655091", "metadata": { "tags": [] }, "outputs": [], "source": [ "ffn_hidden_size = 11008\n", "batch_size = 16\n", "seq_len = 2048\n", "hidden_size = 4096\n", "\n", "config = OmegaConf.create(\n", " {\n", " \"hidden_size\": hidden_size,\n", " \"intermediate_size\": ffn_hidden_size,\n", " \"hidden_act\": \"silu\",\n", " \"pretraining_tp\": 1,\n", " \"tp\": layer_worker_group.tp_size,\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 178, "id": "a026efca", "metadata": { "tags": [] }, "outputs": [], "source": [ "x = torch.rand(size=(seq_len, batch_size, hidden_size), dtype=torch.float32)" ] }, { "cell_type": "code", "execution_count": 179, "id": "f5fcaf13", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[None, None, None, None]" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_worker_group.init_model(config)" ] }, { "cell_type": "code", "execution_count": 180, "id": "3f5cc9b4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2048, 16, 4096])\n" ] } ], "source": [ "output = layer_worker_group.run_layer(\n", " [x]\n", ") # This must be a list of size 1, ensuring that the input equals the data parallel (dp).\n", "print(output[0].shape)" ] }, { "cell_type": "code", "execution_count": 181, "id": "49792210", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Shutdown ray cluster\n", "ray.shutdown()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: examples/reinforce_plus_plus_trainer/run_qwen2-7b_math_rf.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=reinforce_plus_plus \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=3e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=1024 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=mse \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=True \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/reinforce_plus_plus_trainer/run_qwen2-7b_math_rf_baseline.sh ================================================ set -x gsm8k_train_path=$HOME/data/gsm8k/train.parquet gsm8k_test_path=$HOME/data/gsm8k/test.parquet math_train_path=$HOME/data/math/train.parquet math_test_path=$HOME/data/math/test.parquet train_files="['$gsm8k_train_path', '$math_train_path']" test_files="['$gsm8k_test_path', '$math_test_path']" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=reinforce_plus_plus_baseline \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=3e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=1024 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=mse \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=True \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/remax_trainer/run_qwen2.5-3b_seq_balance.sh ================================================ set -x export HF_DATASETS_OFFLINE=1 export TRANSFORMERS_OFFLINE=1 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=remax \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=512 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=30000 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=True \ algorithm.kl_penalty=kl \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_remax_example_gsm8k' \ trainer.experiment_name='qwen2.5_3b_function_rm_kl1e-3' \ trainer.val_before_train=False \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=5 $@ ================================================ FILE: examples/remax_trainer/run_qwen2.5-7b_seq_balance.sh ================================================ set -x export HF_DATASETS_OFFLINE=1 export TRANSFORMERS_OFFLINE=1 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=remax \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=4 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=True \ algorithm.kl_penalty=kl \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_remax_example_gsm8k' \ trainer.experiment_name='qwen2.5_7b_function_rm_kl1e-3' \ trainer.val_before_train=False \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=10 $@ ================================================ FILE: examples/rloo_trainer/run_qwen2-7b.sh ================================================ set -x python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=rloo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=80 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=160 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=160 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=True \ algorithm.kl_penalty=kl \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_rloo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/rollout_correction/run_with_rollout_corr.sh ================================================ #!/usr/bin/env bash # Example: RLOO (REINFORCE Leave-One-Out) with Rollout Correction # This demonstrates self-normalized sequence-level IS with pure policy gradient # # References: # - Rollout Correction Docs: https://github.com/volcengine/verl/blob/main/docs/algo/rollout_corr.md # - Rollout Correction Math: https://github.com/volcengine/verl/blob/main/docs/algo/rollout_corr_math.md set -xeuo pipefail # ============================================================================== # Rollout Correction Configuration (RLOO) # ============================================================================== # Importance Sampling (IS) weights configuration rollout_is="sequence" # Self-normalized sequence-level IS rollout_is_threshold=2.0 # Upper threshold for IS weights rollout_is_batch_normalize="true" # Self-normalization (mean=1.0) # Rejection Sampling (RS) configuration rollout_rs="null" # No rejection sampling for basic RLOO rollout_rs_threshold="null" # RS threshold spec (string or float) # Bypass mode with REINFORCE loss (no PPO clipping) bypass_mode="true" # Skip old_log_prob computation loss_type="reinforce" # REINFORCE with explicit IS weights (alternative: "ppo_clip") # ============================================================================== # Model and Data Configuration # ============================================================================== MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2.5-7B"} TRAIN_FILE=${TRAIN_FILE:-"data/train.parquet"} TEST_FILE=${TEST_FILE:-"data/test.parquet"} max_prompt_length=2048 max_response_length=4096 # ============================================================================== # Training Configuration # ============================================================================== train_batch_size=128 ppo_mini_batch_size=32 ppo_epochs=1 learning_rate=5e-7 # ============================================================================== # Algorithm Configuration (RLOO) # ============================================================================== adv_estimator=rloo # RLOO advantage estimator gamma=1.0 # ============================================================================== # Launch Training # ============================================================================== python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_batch_size} \ data.truncation='left' \ algorithm.adv_estimator=${adv_estimator} \ algorithm.gamma=${gamma} \ algorithm.rollout_correction.rollout_is=${rollout_is} \ algorithm.rollout_correction.rollout_is_threshold=${rollout_is_threshold} \ algorithm.rollout_correction.rollout_is_batch_normalize=${rollout_is_batch_normalize} \ algorithm.rollout_correction.rollout_rs=${rollout_rs} \ algorithm.rollout_correction.rollout_rs_threshold=${rollout_rs_threshold} \ algorithm.rollout_correction.bypass_mode=${bypass_mode} \ algorithm.rollout_correction.loss_type=${loss_type} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=${learning_rate} \ actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.ppo_epochs=${ppo_epochs} \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.name=vllm \ trainer.logger='["console","wandb"]' \ trainer.project_name="rollout_corr_rloo_example" \ trainer.experiment_name="rloo_seq_is_pure" \ trainer.total_epochs=10 echo "Training completed!" echo "" echo "RLOO Configuration:" echo " - Algorithm: RLOO (REINFORCE Leave-One-Out)" echo " - Advantage estimator: ${adv_estimator}" echo " - IS mode: ${rollout_is} (self-normalized: ${rollout_is_batch_normalize})" echo " - IS threshold: ${rollout_is_threshold}" echo " - Bypass mode: ${bypass_mode}, loss_type: ${loss_type}" echo "" echo "Monitor these key metrics in wandb:" echo " - rollout_corr/rollout_is_mean (should be ~1.0 before batch norm)" echo " - rollout_corr/rollout_is_batch_norm_factor (normalization factor applied)" echo " - rollout_corr/rollout_is_eff_sample_size (should be >0.5)" ================================================ FILE: examples/rollout_correction/run_with_rollout_corr_multi_rs.sh ================================================ #!/usr/bin/env bash # Example: PPO-clip with Rollout Correction using multiple RS criteria # Demonstrates chaining token-level and sequence-level rejection sampling # (token_k1 + seq_max_k2) alongside optional IS metrics. # # References: # - Rollout Correction Docs: https://github.com/volcengine/verl/blob/main/docs/algo/rollout_corr.md # - Rollout Correction Math: https://github.com/volcengine/verl/blob/main/docs/algo/rollout_corr_math.md set -xeuo pipefail # ============================================================================== # Rollout Correction Configuration (PPO-clip + multi RS) # ============================================================================== # Importance Sampling (IS) weights configuration rollout_is="token" # Token-level IS for metrics/analysis rollout_is_threshold=2.0 # Upper threshold for IS weights rollout_is_batch_normalize="false" # Keep raw truncated weights # Rejection Sampling (RS) configuration (multi-criteria) # - token_k1 keeps per-token ratios inside [lower, upper] # - seq_max_k2 rejects sequences with extreme chi-square spikes rollout_rs="token_k1,seq_max_k2" rollout_rs_threshold="0.6_1.6,2.5" # Bypass PPO mode (reuse rollout_log_prob) bypass_mode="true" loss_type="ppo_clip" # ============================================================================== # Model and Data Configuration # ============================================================================== MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2.5-7B"} TRAIN_FILE=${TRAIN_FILE:-"data/train.parquet"} TEST_FILE=${TEST_FILE:-"data/test.parquet"} max_prompt_length=2048 max_response_length=4096 # ============================================================================== # Training Configuration # ============================================================================== train_batch_size=128 ppo_mini_batch_size=32 ppo_epochs=1 learning_rate=3e-6 # ============================================================================== # Algorithm Configuration # ============================================================================== adv_estimator=grpo gamma=1.0 # ============================================================================== # Launch Training # ============================================================================== python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_batch_size} \ data.truncation='left' \ algorithm.adv_estimator=${adv_estimator} \ algorithm.gamma=${gamma} \ algorithm.rollout_correction.rollout_is=${rollout_is} \ algorithm.rollout_correction.rollout_is_threshold=${rollout_is_threshold} \ algorithm.rollout_correction.rollout_is_batch_normalize=${rollout_is_batch_normalize} \ algorithm.rollout_correction.rollout_rs=\'${rollout_rs}\' \ algorithm.rollout_correction.rollout_rs_threshold=\'${rollout_rs_threshold}\' \ algorithm.rollout_correction.bypass_mode=${bypass_mode} \ algorithm.rollout_correction.loss_type=${loss_type} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=${learning_rate} \ actor_rollout_ref.actor.ppo_mini_batch_size=${ppo_mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.ppo_epochs=${ppo_epochs} \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.name=vllm \ trainer.logger='["console","wandb"]' \ trainer.project_name="rollout_corr_multi_rs_example" \ trainer.experiment_name="ppo_clip_multi_rs" \ trainer.total_epochs=5 echo "Training completed!" echo "" echo "Multi-RS Configuration:" echo " - rollout_is: ${rollout_is} (threshold=${rollout_is_threshold}, batch_norm=${rollout_is_batch_normalize})" echo " - rollout_rs: ${rollout_rs}" echo " - rollout_rs_threshold: ${rollout_rs_threshold}" echo " - bypass_mode: ${bypass_mode}, loss_type: ${loss_type}" echo "" echo "Track these metrics in wandb:" echo " - rollout_corr/rollout_rs_token_k1_mean" echo " - rollout_corr/rollout_rs_seq_max_k2_mean" echo " - rollout_corr/rollout_rs_masked_fraction" ================================================ FILE: examples/router_replay/README.md ================================================ # Router Replay Router Replay is an advanced routing replay functionality within the Verl framework designed for Mixture of Experts (MoE) models. It enables deterministic training by recording and replaying routing decisions, ensuring consistent model behavior across training runs. ## Key Features ### Multiple Operating Modes - **`disabled`**: Router replay functionality is completely disabled - **`R2`**: Standard router replay mode for recording and replaying routing decisions - **`R3`**: Rollout-specific router replay mode optimized for reinforcement learning workflows ### Core Capabilities - **Seamless Integration**: Works with reinforcement learning pipelines including PPO - **Distributed Training Support**: Compatible with multi-GPU and multi-node training environments - **Flexible Configuration**: Easy to configure via YAML files or command-line parameters ## Configuration ### RouterReplayConfig Parameters ```yaml router_replay: mode: "disabled" # Available options: disabled, R2, R3 record_file: null # Path for recording routing decisions replay_file: null # Path for replaying recorded decisions ``` ## Quick Start Guide ### Enabling R2 Mode #### Configuration File Method Add the following to your training configuration: ```yaml actor: router_replay: mode: "R2" ``` #### Command Line Method Enable R2 mode via command-line parameters: ```bash actor_rollout_ref.actor.router_replay.mode="R2" ``` ### Enabling R3 Mode #### Configuration File Method Configure both actor and rollout settings: ```yaml # Actor configuration router_replay: mode: "R3" # Rollout configuration enable_rollout_routing_replay: True ``` #### Command Line Method Enable R3 mode via command-line parameters: ```bash actor_rollout_ref.actor.router_replay.mode="R3" actor_rollout_ref.rollout.enable_rollout_routing_replay=True ``` R3 mode requires the rollout backend to support returning router selection results. Currently, this functionality is being tested based on the vllm implementation at https://github.com/vllm-project/vllm/pull/28284 as well as bug fix at https://github.com/vllm-project/vllm/pull/33013 and SGLang implementation at https://github.com/sgl-project/sglang/commit/bed301a5acaa9577c9aa706468bdf242f6a43051. ================================================ FILE: examples/router_replay/run_qwen30_a3b_megatron_sglang.sh ================================================ set -x NODES=6 # R2: enable routing replay # R3: enable rollout routing replay # If enabling R3, please set actor_rollout_ref.rollout.enable_rollout_routing_replay=True # R3 example is based on SGLang related commit https://github.com/sgl-project/sglang/commit/bed301a5acaa9577c9aa706468bdf242f6a43051 ROUTING_REPLAY_MODE="R3" if [ "$ROUTING_REPLAY_MODE" = "R3" ]; then ENABLE_ROLLOUT_ROUTING_REPLAY=True else ENABLE_ROLLOUT_ROUTING_REPLAY=False fi DIST_CKPT_PATH="" HF_MODEL_PATH="" TRAIN_DATA_PATH="" TEST_DATA_PATH="" export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping PP=6 VPP=None TP=1 EP=8 ETP=1 SGLANG_INFER_TP=4 offload=True gpu_memory_utilization=0.65 bs=3 micro_bs=3 use_dynamic_bsz=False max_prompt_length=512 max_response_length=512 ppo_mini_batch_size=3 actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) USE_LEGACY_WORKER_IMPL="enable" # disable, enable if [ "$USE_LEGACY_WORKER_IMPL" = "disable" ]; then ROUTING_REPLAY_MODE_ARG="actor_rollout_ref.actor.megatron.router_replay.mode=${ROUTING_REPLAY_MODE}" remove_padding=True else ROUTING_REPLAY_MODE_ARG="actor_rollout_ref.actor.router_replay.mode=${ROUTING_REPLAY_MODE}" remove_padding=False fi exper_name=Node${NODES}_bs${bs}_${PP}${TP}${EP}${ETP}_${SGLANG_INFER_TP}_minbs${ppo_mini_batch_size}_micro_bs${micro_bs} python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ algorithm.adv_estimator=grpo \ data.train_files=$TRAIN_DATA_PATH \ data.val_files=$TEST_DATA_PATH \ data.train_batch_size=$bs \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.model.use_remove_padding=${remove_padding} \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ ${ROUTING_REPLAY_MODE_ARG} \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=False \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=$micro_bs \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=$micro_bs \ actor_rollout_ref.rollout.tensor_model_parallel_size=$SGLANG_INFER_TP \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.enable_rollout_routing_replay=True \ actor_rollout_ref.rollout.skip_tokenizer_init=True \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.rollout.gpu_memory_utilization=$gpu_memory_utilization \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.enable_rollout_routing_replay=${ENABLE_ROLLOUT_ROUTING_REPLAY} \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=$micro_bs \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=['console'] \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name="$exper_name" \ trainer.nnodes=$NODES \ trainer.n_gpus_per_node=8 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_training_steps=50000 \ trainer.balance_batch=False \ trainer.use_legacy_worker_impl=${USE_LEGACY_WORKER_IMPL} \ trainer.val_before_train=False 2>&1 ================================================ FILE: examples/router_replay/run_qwen30_a3b_megatron_vllm.sh ================================================ set -x NODES=1 # R2: enable routing replay # R3: enable rollout routing replay # If enabling R3, please set actor_rollout_ref.rollout.enable_rollout_routing_replay=True # R3 example is based on vllm related pr: # - https://github.com/vllm-project/vllm/pull/28284 # - https://github.com/vllm-project/vllm/pull/33013 ROUTING_REPLAY_MODE="R3" if [ "$ROUTING_REPLAY_MODE" = "R3" ]; then ENABLE_ROLLOUT_ROUTING_REPLAY=True else ENABLE_ROLLOUT_ROUTING_REPLAY=False fi DIST_CKPT_PATH="" HF_MODEL_PATH="" TRAIN_DATA_PATH="" TEST_DATA_PATH="" export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping PP=1 VPP=None TP=2 EP=8 ETP=1 VLLM_INFER_TP=2 offload=True gpu_memory_utilization=0.65 bs=8 micro_bs=3 use_dynamic_bsz=True max_prompt_length=1024 max_response_length=1024 ppo_mini_batch_size=8 actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) USE_LEGACY_WORKER_IMPL="disable" # disable, enable if [ "$USE_LEGACY_WORKER_IMPL" = "disable" ]; then ROUTING_REPLAY_MODE_ARG="actor_rollout_ref.actor.megatron.router_replay.mode=${ROUTING_REPLAY_MODE}" remove_padding=True else ROUTING_REPLAY_MODE_ARG="actor_rollout_ref.actor.router_replay.mode=${ROUTING_REPLAY_MODE}" remove_padding=False fi exper_name=Node${NODES}_bs${bs}_${PP}${TP}${EP}${ETP}_${VLLM_INFER_TP}_minbs${ppo_mini_batch_size}_micro_bs${micro_bs} python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ algorithm.adv_estimator=grpo \ data.train_files=$TRAIN_DATA_PATH \ data.val_files=$TEST_DATA_PATH \ data.train_batch_size=$bs \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.model.use_remove_padding=$remove_padding \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ ${ROUTING_REPLAY_MODE_ARG} \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type=flex \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=$micro_bs \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=$micro_bs \ actor_rollout_ref.rollout.tensor_model_parallel_size=$VLLM_INFER_TP \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.rollout.gpu_memory_utilization=$gpu_memory_utilization \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.enable_rollout_routing_replay=${ENABLE_ROLLOUT_ROUTING_REPLAY} \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=$micro_bs \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$TP \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$EP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$ETP \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=['console'] \ trainer.project_name='verl_grpo_example_gsm8k_math' \ trainer.experiment_name="$exper_name" \ trainer.nnodes=$NODES \ trainer.n_gpus_per_node=8 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.total_training_steps=50000 \ trainer.balance_batch=False \ trainer.use_legacy_worker_impl=${USE_LEGACY_WORKER_IMPL} \ trainer.val_before_train=False 2>&1 ================================================ FILE: examples/sapo_trainer/run_qwen30b_sapo.sh ================================================ #!/bin/bash #SBATCH --job-name=sapo-30B #SBATCH --partition=main #SBATCH --nodes=1 # Number of nodes #SBATCH --ntasks-per-node=1 # One task per node #SBATCH --cpus-per-task=128 # cpu-cores per task (>1 if multi-threaded tasks) #SBATCH --gres=gpu:8 #SBATCH --gpus-per-node=8 #SBATCH --mem=0 #SBATCH --exclusive #SBATCH --time=500:00:00 #SBATCH --output=logs/sapo/30B/frugal_math/%x_%j.out #SBATCH --error=logs/sapo/30B/frugal_math/%x_%j.err # This script runs the training of RL on multi-nodes. It does resume automatically from latest checkpoint if the run crashes. # Example run with Qwen3-30B SAPO with new model engine set -x export WANDB_API_KEY=YOUR_WANDB_API_KEY_HERE ENV_NAME=verl_0_6_1 # Ensure Python can import the top-level verl package even when the script is relocated by Slurm if [[ -n "$SLURM_SUBMIT_DIR" && -d "$SLURM_SUBMIT_DIR" ]]; then cd "$SLURM_SUBMIT_DIR" SCRIPT_SOURCE_DIR="$SLURM_SUBMIT_DIR" else SCRIPT_SOURCE_DIR=$(cd -- "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd) fi REPO_ROOT=$(cd -- "$SCRIPT_SOURCE_DIR/../.." >/dev/null 2>&1 && pwd) VERL_REPO_ROOT="$REPO_ROOT" add_repo_to_pythonpath() { if [[ -z "$PYTHONPATH" ]]; then export PYTHONPATH="$VERL_REPO_ROOT" else case ":$PYTHONPATH:" in *":$VERL_REPO_ROOT:"*) ;; *) export PYTHONPATH="$VERL_REPO_ROOT:$PYTHONPATH" ;; esac fi } add_repo_to_pythonpath # can make training faster depending on clusters export NCCL_IBEXT_DISABLE=1 export NCCL_NVLS_ENABLE=1 export NCCL_IB_HCA=mlx5 export UCX_NET_DEVICES=mlx5_0:1,mlx5_1:1,mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1 # Determine how many nodes were allocated. NNODES=${SLURM_JOB_NUM_NODES} export NNODES # Determine how many GPUs we actually have on the master node. # Carefull! Assumes all nodes have same number of GPUs! # SLURM sets SLURM_GPUS_PER_NODE only when #SBATCH --gpus-per-node is used, not with --gres. # uncomment below line to manually set number of gpus per node if not using --gpus-per-node # export SLURM_GPUS_PER_NODE=8 # SLURM_GPUS_PER_NODE=${SLURM_GPUS_PER_NODE:-$(nvidia-smi -L | wc -l)} # 8 # export SLURM_GPUS_PER_NODE echo "SLURM_GPUS_PER_NODE: $SLURM_GPUS_PER_NODE" # Set DATA_ROOT to current working directory if not set DATA_ROOT=${DATA_ROOT:-$PWD} echo "DATA_ROOT: $DATA_ROOT" # wandb logging backend=fsdp # fsdp, fsdp2, megatron project_name=RL4LLM # experiment_name=qwen3-30B-base-sapo-$backend experiment_name=qwen3-30B-base-vanilla-$backend default_local_dir=$DATA_ROOT/checkpoint/$project_name/$experiment_name # ===================================== Algorithm ===================================== adv_estimator=grpo loss_mode=sapo # explicitly specify sapo! default is vanilla and is not compatible with SAPO. It uses clipping instead of smoothing. # reference policy use_kl_in_reward=False kl_coef=0.001 use_kl_loss=False kl_loss_coef=0.001 # Positive and negative tau for smoothing function in SAPO (https://arxiv.org/pdf/2511.20347) # default values used in the paper with Qwen3-30B-A3B-Base # clipping is not used in SAPO! tau_pos=1.0 tau_neg=1.05 actor_lr=1e-6 critic_lr=2e-6 gae_gamma=1.0 gae_lam=0.95 critic_warmup=0 # ===================================== Data/Model ===================================== first_time_dataset_prep=true HF_DATA_PATH="BytedTsinghua-SIA/DAPO-Math-17k" STAGE="stage-1" if [ "$first_time_dataset_prep" = true ]; then echo "Preparing training dataset..." python $VERL_REPO_ROOT/examples/data_preprocess/dapo_multiturn_w_tool.py \ --local_save_dir $DATA_ROOT/dataset/dapo/ echo "Training dataset prepared." echo "Preparing testing dataset..." python $VERL_REPO_ROOT/examples/data_preprocess/aime2024_multiturn_w_tool.py \ --local_save_dir $DATA_ROOT/dataset/test/aime_24/ echo "Testing dataset prepared." echo "Dataset preparation completed." fi train_files=$DATA_ROOT/dataset/dapo/train.parquet test_files=$DATA_ROOT/dataset/test/aime_24/train.parquet actor_model_path=Qwen/Qwen3-30B-A3B-Base critic_model_path=$actor_model_path max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 train_batch_size=256 ppo_mini_batch_size=32 n_resp_per_prompt=16 n_resp_per_prompt_val=1 # ===================================== Training ===================================== actor_max_token_len_per_gpu=$(((max_prompt_length + max_response_length) * 3)) critic_max_token_len_per_gpu=$(((max_prompt_length + max_response_length) * 4)) enable_gradient_checkpointing=True param_offload=False optimizer_offload=False VAL_BEFORE_TRAIN=False SAVE_FREQ=-1 # we do not save! TEST_FREQ=10 TOTAL_EPOCHS=10 TOTAL_TRAINING_STEPS=2000 # FSDP parallelism config USP_SIZE=4 ACTOR_FSDP_CONFIG=" actor_rollout_ref.actor.fsdp_config.strategy=$backend \ actor_rollout_ref.actor.fsdp_config.param_offload=$param_offload \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=$optimizer_offload \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$USP_SIZE" # Megatron parallelism config TP_SIZE=1 CP_SIZE=1 PP_SIZE=1 VPP_SIZE=null EP_SIZE=8 ETP_SIZE=1 ACTOR_MEGATRON_CONFIG=" actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP_SIZE \ actor_rollout_ref.actor.megatron.context_parallel_size=$CP_SIZE \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP_SIZE \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=$VPP_SIZE \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$EP_SIZE \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ETP_SIZE \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \ +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ actor_rollout_ref.actor.megatron.use_mbridge=True" # Actor model config ACTOR_CONFIG=" actor_rollout_ref.actor.optim.lr=$actor_lr \ actor_rollout_ref.model.path=$actor_model_path \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=${enable_gradient_checkpointing} \ actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.tau_pos=$tau_pos \ actor_rollout_ref.actor.tau_neg=$tau_neg \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$actor_max_token_len_per_gpu" # Critic model config CIRITC_CONFIG=" critic.optim.lr=$critic_lr \ critic.model.path=$critic_model_path \ critic.model.use_remove_padding=True \ critic.ppo_max_token_len_per_gpu=$critic_max_token_len_per_gpu \ critic.ulysses_sequence_parallel_size=$USP_SIZE" CRITIC_FSDP_CONFIG="${ACTOR_FSDP_CONFIG//actor_rollout_ref.actor/critic.model}" CRITIC_MEGATRON_CONFIG="${ACTOR_MEGATRON_CONFIG//actor_rollout_ref.actor/critic}" if [[ $backend == "megatron" ]]; then CONFIG_NAME=ppo_megatron_trainer ACTOR_CONFIG="$ACTOR_CONFIG $ACTOR_MEGATRON_CONFIG" if [[ $adv_estimator == "gae" ]]; then CIRITC_CONFIG="$CIRITC_CONFIG $CRITIC_MEGATRON_CONFIG" else CIRITC_CONFIG="" fi else # fsdp, fsdp2 CONFIG_NAME=ppo_trainer ACTOR_CONFIG="$ACTOR_CONFIG $ACTOR_FSDP_CONFIG" if [[ $adv_estimator == "gae" ]]; then CIRITC_CONFIG="$CIRITC_CONFIG $CRITIC_FSDP_CONFIG" else CIRITC_CONFIG="" fi fi # ===================================== Inference ===================================== rollout_engine=vllm infer_tp=4 infer_dp=1 infer_ep=1 gpu_memory_utilization=0.8 ROLLOUT_CONFIG=" actor_rollout_ref.rollout.name=$rollout_engine \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.tensor_model_parallel_size=$infer_tp \ actor_rollout_ref.rollout.data_parallel_size=$infer_dp \ actor_rollout_ref.rollout.expert_parallel_size=$infer_ep \ actor_rollout_ref.rollout.gpu_memory_utilization=$gpu_memory_utilization \ actor_rollout_ref.rollout.n=$n_resp_per_prompt \ actor_rollout_ref.rollout.val_kwargs.top_p=0.7 \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.n=$n_resp_per_prompt_val" # ===================================== Reward ===================================== REWARD_CONFIG=" reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length}" # ============================= Prepare RAY on Slurm =============================== # we should activate it before we start ray to avoid errors echo "Activating $ENV_NAME environment..." eval "$(conda shell.bash hook)" conda deactivate conda activate "$ENV_NAME" add_repo_to_pythonpath export VLLM_ATTENTION_BACKEND=FLASH_ATTN export RAY_memory_monitor_refresh_ms=0 export RAY_LOGGING_LEVEL=DEBUG export HYDRA_FULL_ERROR=1 # Let Ray know how many nodes to expect export RAY_NUM_NODES=$NNODES # Get head node and its IP nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST") nodes_array=($nodes) head_node=${nodes_array[0]} head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address) # Convert to IPv4 if needed if [[ "$head_node_ip" == *" "* ]]; then IFS=' ' read -ra ADDR <<<"$head_node_ip" if [[ ${#ADDR[0]} -gt 16 ]]; then head_node_ip=${ADDR[1]} else head_node_ip=${ADDR[0]} fi echo "IPV6 address detected. Using IPV4: $head_node_ip" fi port=6379 ip_head=$head_node_ip:$port export MASTER_ADDR=$head_node_ip export MASTER_PORT=$port export ip_head echo "Starting Ray HEAD at $head_node ($ip_head)" until nvidia-smi > /dev/null 2>&1; do echo "Waiting for GPU visibility..." sleep 2 done srun --nodes=1 --ntasks=1 -w "$head_node" \ ray start --head --node-ip-address="$head_node_ip" --port=$port \ --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & sleep 10 worker_num=$((SLURM_JOB_NUM_NODES - 1)) for ((i = 1; i <= worker_num; i++)); do node_i=${nodes_array[$i]} echo "Starting WORKER $i at $node_i" until nvidia-smi > /dev/null 2>&1; do echo "Waiting for GPU visibility..." sleep 2 done srun --nodes=1 --ntasks=1 -w "$node_i" \ ray start --address "$ip_head" --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & sleep 5 done # Final launch barrier sleep 10 # ================================= Launch Training ================================ echo "Using $SLURM_NNODES nodes for training..." echo "==== Confirming Ray sees all GPUs ====" python -c "import ray; ray.init(address='auto'); print(ray.cluster_resources())" echo "==== Done checking resources ====" # we should activate it before we start ray to avoid errors echo "Activating $ENV_NAME environment..." eval "$(conda shell.bash hook)" conda deactivate conda activate "$ENV_NAME" add_repo_to_pythonpath srun --overlap --nodes=${NNODES} --ntasks=1 -w "$head_node"\ python -m verl.trainer.main_ppo \ --config-path=./config \ --config-name=$CONFIG_NAME \ algorithm.adv_estimator=$adv_estimator \ algorithm.use_kl_in_reward=$use_kl_in_reward \ algorithm.kl_ctrl.kl_coef=$kl_coef \ algorithm.gamma=$gae_gamma \ algorithm.lam=$gae_lam \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.return_raw_chat=True \ data.train_batch_size=$train_batch_size \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.filter_overlong_prompts_workers=64 \ data.truncation='error' \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=$critic_warmup \ trainer.logger=['console','wandb'] \ trainer.project_name=$project_name \ trainer.experiment_name=$experiment_name \ trainer.default_local_dir=$default_local_dir \ trainer.n_gpus_per_node=$SLURM_GPUS_PER_NODE \ trainer.nnodes=$NNODES \ trainer.val_before_train=$VAL_BEFORE_TRAIN \ trainer.log_val_generations=100 \ trainer.save_freq=$SAVE_FREQ \ trainer.test_freq=$TEST_FREQ \ trainer.total_epochs=$TOTAL_EPOCHS \ trainer.total_training_steps=$TOTAL_TRAINING_STEPS \ $ACTOR_CONFIG \ $CIRITC_CONFIG \ $ROLLOUT_CONFIG \ $REWARD_CONFIG ================================================ FILE: examples/sapo_trainer/run_qwen3_8b_sapo_npu.sh ================================================ set -euxo pipefail ulimit -n 32768 ## Basic Environment Settings export RAY_DEDUP_LOGS=0 export HYDRA_FULL_ERROR=1 export TASK_QUEUE_ENABLE=1 export HCCL_EXEC_TIMEOUT=3600 export HCCL_CONNECT_TIMEOUT=3600 export HCCL_ASYNC_ERROR_HANDLING=0 export CPU_AFFINITY_CONF=1 export VLLM_USE_V1=1 project_name='SAPO-Qwen3' exp_name='SAPO-Qwen3-8B-npu' gen_tp=2 RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/Qwen3-8B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/dataset/dapo_processed/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/dataset/aime-24_processed/train.parquet"} # reference policy use_kl_in_reward=False kl_coef=0.001 use_kl_loss=False kl_loss_coef=0.001 # ------Algorithm settings------- # Positive and negative tau for smoothing function in SAPO (https://arxiv.org/pdf/2511.20347) # default values used in the paper with Qwen3-30B-A3B-Base # clipping is not used in SAPO! loss_mode=sapo # explicitly specify sapo! default is vanilla and is not compatible with SAPO. It uses clipping instead of smoothing. tau_pos=1.0 tau_neg=1.05 gae_gamma=1.0 gae_lam=0.95 python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ algorithm.use_kl_in_reward=$use_kl_in_reward \ algorithm.kl_ctrl.kl_coef=$kl_coef \ algorithm.gamma=$gae_gamma \ algorithm.lam=$gae_lam \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.filter_overlong_prompts_workers=64 \ data.truncation='error' \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \ actor_rollout_ref.actor.tau_pos=$tau_pos \ actor_rollout_ref.actor.tau_neg=$tau_neg \ actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.policy_loss.loss_mode=${loss_mode} \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ trainer.critic_warmup=0 \ trainer.logger='["console"]' \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.default_local_dir=${CKPTS_DIR} \ trainer.resume_mode=auto \ actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \ actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \ actor_rollout_ref.actor.entropy_from_logits_with_chunking=True \ actor_rollout_ref.ref.entropy_from_logits_with_chunking=True \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 ================================================ FILE: examples/sft/gsm8k/run_deepseek_6b7.sh ================================================ set -x if [ "$#" -lt 2 ]; then echo "Usage: run_deepseek_6b7.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=4 \ optim.lr=1e-4 \ engine=fsdp \ model.path=deepseek-ai/deepseek-coder-6.7b-instruct \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-deepseek-coder-6.7b-instruct \ trainer.total_epochs=4 \ trainer.logger='["console","wandb"]' $@ ================================================ FILE: examples/sft/gsm8k/run_gemma_2b.sh ================================================ # Tested with 2 & 4 GPUs set -x if [ "$#" -lt 2 ]; then echo "Usage: run_gemma_2b.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=4 \ model.path=google/gemma-2b-it \ optim.lr=1e-4 \ engine=fsdp \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-gemma-2b-it \ trainer.total_epochs=2 \ trainer.logger='["console","wandb"]' $@ ================================================ FILE: examples/sft/gsm8k/run_gemma_7b.sh ================================================ set -x if [ "$#" -lt 2 ]; then echo "Usage: run_gemma_7b.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=4 \ optim.lr=1e-4 \ engine=fsdp \ model.path=google/gemma-1.1-7b-it \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-gemma-1.1-7b-it \ trainer.total_epochs=4 \ trainer.logger='["console","wandb"]' $@ ================================================ FILE: examples/sft/gsm8k/run_mimo_megatron_mtp.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail NUM_GPUS=${NUM_GPUS:-8} SP_SIZE=${SP_SIZE:-1} TP_SIZE=${TP_SIZE:-1} PP_SIZE=${PP_SIZE:-1} VPP_SIZE=${VPP_SIZE:-null} CP_SIZE=${CP_SIZE:-1} PAD_MODE=${PAD_MODE:-no_padding} USE_REMOVE_PADDING=${USE_REMOVE_PADDING:-False} LR="1e-5" MINLR="1e-6" export VERL_SFT_LOGGING_LEVEL=INFO backend=${BACKEND:-megatron} TENSORBOARD_DIR=~/tensorboard MASTER_ADDR=${MASTER_ADDR:-localhost} MASTER_PORT=${MASTER_PORT:-29500} NNODES=${NNODES:-1} RANK=${RANK:-0} ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"} # Note the default MultiturnSFT Dataset requires all the sys/user/assistant in 'data.message_key' DATASET_DIR=${DATASET_DIR:-~/dataset/rl/gsm8k} TRAIN_FILES=${DATASET_DIR}/train.parquet VAL_FILES=${DATASET_DIR}/eval.parquet project_name=verl_sft_test RESUME_MODE=disable MODEL_PATH="XiaomiMiMo/MiMo-7B-RL" ckpts_home=${ckpts_home:-~/verl/test/gsm8k-sft-${backend}} # currently relies on these two commits that is not on master PYPATH=$HOME/pythonpath mkdir -p $PYPATH && cd $PYPATH [ -d Megatron-LM ] || git clone https://github.com/NVIDIA/Megatron-LM -b dev && (cd Megatron-LM; git checkout 23e092f41ec8bc659020e401ddac9576c1cfed7e) [ -d mbridge ] || git clone https://github.com/ArronHZG/mbridge -b feature/verl_mtp && (cd mbridge; git checkout 6bf2d45a15dc4fb52d2f0c38ff546bee33447d10) cd - export PYTHONPATH=$PYTHONPATH:$PYPATH/mbridge:$PYPATH/Megatron-LM MEGATRON_ENGINE_CONFIG="\ engine=${backend} \ optim=${backend} \ optim.lr=${LR} \ optim.min_lr=${MINLR} \ optim.lr_warmup_steps=10 \ optim.weight_decay=0.1 \ optim.betas='[0.9,0.95]' \ optim.clip_grad=1.0 \ optim.lr_warmup_init=0 \ optim.lr_decay_style=cosine \ engine.override_transformer_config.recompute_method=uniform \ engine.override_transformer_config.recompute_granularity=full \ engine.override_transformer_config.recompute_num_layers=1 \ engine.use_dist_checkpointing=False \ engine.tensor_model_parallel_size=${TP_SIZE} \ engine.pipeline_model_parallel_size=${PP_SIZE} \ engine.virtual_pipeline_model_parallel_size=${VPP_SIZE} \ engine.context_parallel_size=${CP_SIZE} \ engine.use_mbridge=True \ " ENGINE_CONFIG="$MEGATRON_ENGINE_CONFIG" echo "Using megatron engine" exp_name=gsm8k-${backend}-tp${TP_SIZE}-pp${PP_SIZE}-vpp${VPP_SIZE}-cp${CP_SIZE}-lr-${MINLR}-${LR} mkdir -p "${ckpts_home}" $COMMAND \ data.train_files="${TRAIN_FILES}" \ data.val_files="${TRAIN_FILES}" \ data.train_batch_size=64 \ data.micro_batch_size_per_gpu=2 \ data.pad_mode=${PAD_MODE} \ data.truncation=error \ data.max_length=1024 \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=2048 \ data.messages_key=prompt \ data.num_workers=0 \ model.path=$MODEL_PATH \ model.use_remove_padding=${USE_REMOVE_PADDING} \ model.trust_remote_code=True \ model.mtp.enable=True \ ${ENGINE_CONFIG} \ trainer.test_freq=after_each_epoch \ trainer.save_freq=-1 \ trainer.logger="['console']" \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.total_epochs=1 \ trainer.default_local_dir="${ckpts_home}" \ trainer.resume_mode=${RESUME_MODE} ================================================ FILE: examples/sft/gsm8k/run_nemotron_nano_v3.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail ################################################### environment ################################################### ### # 1. use docker image `verlai/verl:vllm015.dev`` and install correct dependencies: # pip install nvidia-modelopt # MAX_JOBS=32 pip install git+https://github.com/Dao-AILab/causal-conv1d.git --no-build-isolation --no-cache-dir # MAX_JOBS=32 pip install git+https://github.com/state-spaces/mamba.git --no-build-isolation --no-cache-dir # pip install --no-deps git+https://github.com/NVIDIA-NeMo/Megatron-Bridge # pip install --no-deps git+https://github.com/NVIDIA/Megatron-LM.git@core_dev_r0.16.0 # unset ROCR_VISIBLE_DEVICES # unset PYTORCH_CUDA_ALLOC_CONF ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"} TRAIN_FILES=$HOME/data/gsm8k/train.parquet VAL_FILES=$HOME/data/gsm8k/eval.parquet backend=${BACKEND:-megatron} project_name=verl_sft_gsm8k RESUME_MODE=auto MODEL_NAME=${MODEL_NAME:-NVIDIA-Nemotron-3-Nano-30B-A3B-BF16} TP_SIZE=${TP_SIZE:-8} PP_SIZE=${PP_SIZE:-1} VPP_SIZE=${VPP_SIZE:-null} CP_SIZE=${CP_SIZE:-1} EP_SIZE=${EP_SIZE:-8} ETP_SIZE=${ETP_SIZE:-1} PAD_MODE=${PAD_MODE:-no_padding} USE_REMOVE_PADDING=${USE_REMOVE_PADDING:-True} DTYPE=${DTYPE:-"bfloat16"} MEGATRON_ENGINE_CONFIG=( engine=${backend} optim=${backend} optim.lr=2e-5 optim.lr_warmup_steps=5 optim.weight_decay=0.1 optim.betas="[0.9,0.95]" optim.clip_grad=1.0 optim.lr_warmup_init=0 optim.lr_decay_style=cosine optim.min_lr=2e-6 +optim.override_optimizer_config.optimizer_offload_fraction=1 +optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True +optim.override_optimizer_config.use_precision_aware_optimizer=True +optim.override_optimizer_config.optimizer_cpu_offload=True engine.tensor_model_parallel_size=${TP_SIZE} engine.pipeline_model_parallel_size=${PP_SIZE} engine.virtual_pipeline_model_parallel_size=${VPP_SIZE} engine.context_parallel_size=${CP_SIZE} engine.use_mbridge=True engine.dtype=${DTYPE} engine.vanilla_mbridge=False engine.expert_model_parallel_size=${EP_SIZE} engine.expert_tensor_parallel_size=${ETP_SIZE} engine.override_transformer_config.attention_backend=auto +engine.override_transformer_config.recompute_method=uniform +engine.override_transformer_config.recompute_granularity=full +engine.override_transformer_config.recompute_num_layers=1 ) ENGINE_CONFIG="${MEGATRON_ENGINE_CONFIG[@]}" echo "Using megatron engine" exp_name=${MODEL_NAME}-${backend}-tp${TP_SIZE}-pp${PP_SIZE}-vpp${VPP_SIZE}-cp${CP_SIZE}-megatron-20260210 torchrun --nnodes=1 --nproc_per_node=8 ${ENTRYPOINT} \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.train_batch_size=96 \ data.max_length=2048 \ data.pad_mode=${PAD_MODE} \ data.truncation=error \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=2048 \ data.messages_key=messages \ data.ignore_input_ids_mismatch=True \ model.path=$MODEL_PATH \ model.use_remove_padding=${USE_REMOVE_PADDING} \ model.trust_remote_code=True \ ${ENGINE_CONFIG} \ trainer.test_freq=-1 \ trainer.save_freq=500 \ trainer.logger=['console','wandb'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.total_epochs=1 \ trainer.default_local_dir="${ckpts_home}" \ trainer.resume_mode=${RESUME_MODE} \ trainer.max_ckpt_to_keep=10 \ checkpoint.save_contents=[model,optimizer,extra] ================================================ FILE: examples/sft/gsm8k/run_qwen3_30b_automodel.sh ================================================ # Requires: Automodel, transformers>=5.3.0, torchao # MoE also requires: grouped_gemm (github.com/fanshiqing/grouped_gemm v1.1.4) set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen3_30b_automodel.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/hellaswag_sft/hellaswag_sft.parquet \ data.val_files=$HOME/data/hellaswag_sft/hellaswag_sft.parquet \ data.train_batch_size=512 \ data.max_length=2048 \ data.truncation=left \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=8192 \ data.messages_key=messages \ data.ignore_input_ids_mismatch=True \ data.train_max_samples=-1 \ data.val_max_samples=1024 \ model=hf_model \ model.path=Qwen/Qwen3-30B-A3B-Base \ model.trust_remote_code=True \ model.use_remove_padding=True \ engine=automodel \ engine.distributed_strategy=fsdp2 \ engine.tp_size=1 \ engine.pp_size=1 \ engine.cp_size=1 \ engine.ep_size=8 \ engine.backend_config.dispatcher=deepep \ engine.backend_config.attn=te \ engine.backend_config.linear=te \ engine.backend_config.rms_norm=torch_fp32 \ engine.backend_config.enable_fsdp_optimizations=True \ engine.backend_config.experts=torch_mm \ engine.activation_checkpointing=True \ engine.model_dtype=bf16 \ engine.attn_implementation=te \ engine.use_torch_compile=False \ optim=automodel \ optim.optimizer=FusedAdam \ optim.optimizer_impl=transformer_engine.pytorch.optimizers.fused_adam \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.1 \ optim.weight_decay=0 \ optim.betas='[0.9,0.95]' \ optim.clip_grad=1.0 \ optim.init_lr_ratio=0.1 \ optim.min_lr_ratio=0.01 \ optim.lr_scheduler_type=cosine \ optim.master_weights=true \ optim.store_param_remainders=true \ optim.exp_avg_dtype=bf16 \ optim.exp_avg_sq_dtype=bf16 \ trainer.default_local_dir=$save_path \ trainer.project_name=hellaswag-sft \ trainer.experiment_name=hellaswag-sft-qwen3-30b-automodel \ trainer.total_epochs=2 \ trainer.total_training_steps=100 \ trainer.save_freq=-1 \ trainer.test_freq=10 \ trainer.logger=console \ trainer.seed=1111 \ trainer.nnodes=1 \ trainer.resume_mode=disable $@ ================================================ FILE: examples/sft/gsm8k/run_qwen3_5_megatron.sh ================================================ #!/usr/bin/env bash # Qwen3.5-397B-A17B SFT with Megatron backend + mbridge # # Requirements: # - 128+ GPUs (80GB each, e.g. 16x8 H100/H200) # - Docker: verlai/verl:vllm015 (or equivalent) # - Additional packages on top of the base image: # pip install --upgrade transformers # pip install flash-linear-attention # pip install -U git+https://github.com/ISEEKYAN/mbridge.git # - Megatron-LM==0.16.0 # # Qwen3.5 architecture notes: # Qwen3.5 uses Gated Delta Net (GDN) linear attention which currently does # NOT support packed sequences (THD format) in Megatron-LM. Therefore: # - engine.use_remove_padding=False (forces bshd compute format) # - data.use_dynamic_bsz=False (required for bshd mode) # # Once https://github.com/NVIDIA/Megatron-LM/pull/2644 is merged, THD # format will be supported and engine.use_remove_padding can be set to True # for better performance. # # Tested parallelism config (128 GPUs / 16 nodes): # TP=2 PP=4 EP=32 CP=1 set -xeuo pipefail # ============================================================ # Distributed # ============================================================ NUM_GPUS=${NUM_GPUS:-8} MASTER_ADDR=${MASTER_ADDR:-localhost} MASTER_PORT=${MASTER_PORT:-29500} NNODES=${NNODES:-16} NODE_RANK=${NODE_RANK:-0} # ============================================================ # Data # ============================================================ DATASET_DIR=${DATASET_DIR:-~/dataset} TRAIN_FILES=${TRAIN_FILES:-${DATASET_DIR}/train.parquet} # ============================================================ # Model # ============================================================ MODEL_PATH=${MODEL_PATH:-Qwen/Qwen3.5-397B-A17B} # ============================================================ # Parallelism # ============================================================ TP_SIZE=${TP_SIZE:-2} PP_SIZE=${PP_SIZE:-4} VPP_SIZE=${VPP_SIZE:-null} CP_SIZE=${CP_SIZE:-1} EP_SIZE=${EP_SIZE:-32} ETP_SIZE=${ETP_SIZE:-1} # ============================================================ # Training # ============================================================ TRAIN_BATCH_SIZE=${TRAIN_BATCH_SIZE:-128} MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-2} MAX_LENGTH=${MAX_LENGTH:-2048} LR=${LR:-2e-5} MIN_LR=${MIN_LR:-2e-6} DTYPE=${DTYPE:-bfloat16} BACKEND=megatron RESUME_MODE=${RESUME_MODE:-disable} project_name=verl_sft_qwen3_5 exp_name=qwen3_5-${BACKEND}-tp${TP_SIZE}-pp${PP_SIZE}-cp${CP_SIZE}-ep${EP_SIZE} ckpts_home=${ckpts_home:-~/verl/checkpoints/${project_name}/${exp_name}} mkdir -p "${ckpts_home}" # ============================================================ # Engine config # ============================================================ # Key Qwen3.5 settings: # engine.use_remove_padding=False - GDN requires bshd format (no THD) # engine.vanilla_mbridge=True - use mbridge (not megatron-bridge) ENGINE_CONFIG="\ engine=${BACKEND} \ optim=${BACKEND} \ optim.lr=${LR} \ optim.min_lr=${MIN_LR} \ optim.lr_warmup_steps=10 \ optim.weight_decay=0.1 \ optim.betas='[0.9,0.95]' \ optim.clip_grad=1.0 \ optim.lr_warmup_init=0 \ optim.lr_decay_style=cosine \ +optim.override_optimizer_config.optimizer_offload_fraction=1 \ +optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +optim.override_optimizer_config.use_precision_aware_optimizer=True \ +optim.override_optimizer_config.optimizer_cpu_offload=True \ engine.tensor_model_parallel_size=${TP_SIZE} \ engine.pipeline_model_parallel_size=${PP_SIZE} \ engine.virtual_pipeline_model_parallel_size=${VPP_SIZE} \ engine.context_parallel_size=${CP_SIZE} \ engine.expert_model_parallel_size=${EP_SIZE} \ engine.expert_tensor_parallel_size=${ETP_SIZE} \ engine.use_mbridge=True \ engine.vanilla_mbridge=True \ engine.dtype=${DTYPE} \ engine.use_remove_padding=False \ engine.override_transformer_config.attention_backend=auto \ +engine.override_transformer_config.recompute_method=uniform \ +engine.override_transformer_config.recompute_granularity=full \ +engine.override_transformer_config.recompute_num_layers=1" # ============================================================ # Launch # ============================================================ torchrun \ --nproc_per_node=${NUM_GPUS} \ --nnodes=${NNODES} \ --node_rank=${NODE_RANK} \ --master_addr=${MASTER_ADDR} \ --master_port=${MASTER_PORT} \ -m verl.trainer.sft_trainer \ data.train_files="${TRAIN_FILES}" \ data.train_batch_size=${TRAIN_BATCH_SIZE} \ data.micro_batch_size_per_gpu=${MICRO_BATCH_SIZE} \ data.max_length=${MAX_LENGTH} \ data.pad_mode=no_padding \ data.truncation=error \ data.use_dynamic_bsz=False \ data.max_token_len_per_gpu=${MAX_LENGTH} \ data.messages_key=messages \ model.path=${MODEL_PATH} \ model.use_remove_padding=False \ model.trust_remote_code=True \ ${ENGINE_CONFIG} \ trainer.test_freq=-1 \ trainer.save_freq=500 \ trainer.logger="['console']" \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.total_epochs=1 \ trainer.default_local_dir="${ckpts_home}" \ trainer.resume_mode=${RESUME_MODE} ================================================ FILE: examples/sft/gsm8k/run_qwen3_8b_sft_peft_sp2_npu.sh ================================================ set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen3_8b_sft_peft_sp2_npu.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.micro_batch_size_per_gpu=64 \ optim.lr=1e-4 \ engine=fsdp \ engine.ulysses_sequence_parallel_size=2 \ model.path=Qwen/Qwen3-8B \ model.use_remove_padding=true \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-qwen3-8b-instruct \ trainer.logger=console \ trainer.total_epochs=2 $@ \ model.lora_rank=32 \ model.lora_alpha=16 \ model.target_modules=all-linear ================================================ FILE: examples/sft/gsm8k/run_qwen_05_automodel.sh ================================================ # Requires: Automodel, transformers>=5.3.0, torchao # MoE also requires: grouped_gemm (github.com/fanshiqing/grouped_gemm v1.1.4) set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen_05_automodel.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k_sft/train.parquet \ data.val_files=$HOME/data/gsm8k_sft/test.parquet \ data.train_batch_size=128 \ data.pad_mode=no_padding \ data.truncation=error \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=2048 \ data.messages_key=messages \ data.ignore_input_ids_mismatch=True \ model=hf_model \ model.path=Qwen/Qwen2.5-0.5B-Instruct \ model.use_remove_padding=True \ engine=automodel \ engine.distributed_strategy=fsdp2 \ engine.tp_size=1 \ engine.pp_size=1 \ engine.cp_size=1 \ engine.ep_size=1 \ engine.use_torch_compile=False \ optim=automodel \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas='[0.9,0.95]' \ optim.clip_grad=1.0 \ optim.init_lr_ratio=0 \ optim.min_lr_ratio=0.1 \ optim.lr_scheduler_type=cosine \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-qwen-2.5-0.5b-automodel \ trainer.total_epochs=2 \ trainer.test_freq=-1 \ trainer.save_freq=-1 \ trainer.logger=console \ trainer.seed=1111 \ trainer.resume_mode=disable $@ ================================================ FILE: examples/sft/gsm8k/run_qwen_05_peft.sh ================================================ # Tested with 2 & 4 GPUs set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen_05_peft.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.micro_batch_size_per_gpu=4 \ optim.lr=1e-4 \ engine=fsdp \ model.path=Qwen/Qwen2.5-0.5B-Instruct \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-qwen-2.5-0.5b-instruct \ trainer.logger=console \ trainer.total_epochs=1 $@ \ model.lora_rank=32\ model.lora_alpha=16 \ model.target_modules=all-linear # Or you can do this: # model.target_modules=[q_proj,v_proj] \ ================================================ FILE: examples/sft/gsm8k/run_qwen_05_sp2.sh ================================================ set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen_05_sp2.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=4 \ optim.lr=1e-4 \ engine=fsdp \ engine.ulysses_sequence_parallel_size=2 \ model.path=Qwen/Qwen2.5-0.5B-Instruct \ model.use_remove_padding=true \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-qwen-2.5-0.5b-instruct-sp2 \ trainer.logger=console \ trainer.total_training_steps=1 $@ ================================================ FILE: examples/sft/gsm8k/run_qwen_05_sp2_liger.sh ================================================ set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen_05_sp2.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=4 \ optim.lr=1e-4 \ engine=fsdp \ engine.ulysses_sequence_parallel_size=2 \ model.path=Qwen/Qwen2.5-0.5B-Instruct \ model.use_liger=True \ model.use_remove_padding=true \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-qwen-2.5-0.5b-instruct-sp2-liger \ trainer.logger=console $@ ================================================ FILE: examples/sft/gsm8k/run_seed_oss_36b_sft.sh ================================================ set -x if [ "$#" -lt 2 ]; then echo "Usage: run_seed_oss_36b_sft.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.micro_batch_size_per_gpu=4 \ optim.lr=1e-4 \ engine=fsdp \ engine.ulysses_sequence_parallel_size=2 \ model.path=ByteDance-Seed/Seed-OSS-36B-Base \ model.use_remove_padding=true \ trainer.default_local_dir=$save_path \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-seed-oss-36b \ trainer.logger=console \ trainer.total_training_steps=1 $@ ================================================ FILE: examples/sft/multiturn/run_qwen_05_sp2.sh ================================================ #!/bin/bash set -x if [ "$#" -lt 2 ]; then echo "Usage: run_qwen_05_sp2.sh [other_configs...]" exit 1 fi nproc_per_node=$1 save_path=$2 # Shift the arguments so $@ refers to the rest shift 2 torchrun --nnodes=1 --nproc_per_node=$nproc_per_node \ -m verl.trainer.sft_trainer \ data.train_files=$HOME/data/multiturn/train.parquet \ data.val_files=$HOME/data/multiturn/test.parquet \ data.messages_key=messages \ data.micro_batch_size_per_gpu=4 \ model.path=Qwen/Qwen2.5-0.5B-Instruct \ model.use_remove_padding=true \ engine.ulysses_sequence_parallel_size=2 \ trainer.default_local_dir=$save_path \ trainer.project_name=multiturn-sft \ trainer.experiment_name=multiturn-sft-qwen-2.5-0.5b-instruct-sp2 \ trainer.logger=console \ trainer.total_training_steps=1 $@ ================================================ FILE: examples/sft/vlm/run_qwen3_vl_2b.sh ================================================ #!/usr/bin/env bash # python examples/data_preprocess/pokemon.py set -xeuo pipefail HDFS_ROOT=${HDFS_ROOT:-$PWD} DATA_ROOT=${DATA_ROOT:-$PWD} ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"} TRAIN_FILES=${HOME}/data/pokemon-gpt4o-captions/train.parquet backend=${BACKEND:-fsdp} project_name=verl_sft_test RESUME_MODE=auto MODEL_ID=${HDFS_ROOT}/model/Qwen3-VL-2B-Instruct # MODEL_ID=${HDFS_ROOT}/model/Qwen3-VL-30B-A3B-Instruct SP_SIZE=${SP_SIZE:-2} FSDP_SIZE=${FSDP_SIZE:--1} FSDP_STRATEGY=${FSDP_STRATEGY:-"fsdp2"} TP_SIZE=${TP_SIZE:-2} PP_SIZE=${PP_SIZE:-2} VPP_SIZE=${VPP_SIZE:-null} CP_SIZE=${CP_SIZE:-1} PAD_MODE=${PAD_MODE:-no_padding} USE_REMOVE_PADDING=${USE_REMOVE_PADDING:-True} FSDP_ENGINE_CONFIG="\ engine=${backend} \ optim=${backend} \ optim.lr=2e-5 \ optim.lr_warmup_steps_ratio=0.01 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.min_lr_ratio=0.1 \ optim.warmup_style=cosine \ engine.ulysses_sequence_parallel_size=${SP_SIZE} \ engine.strategy=${FSDP_STRATEGY} \ engine.fsdp_size=${FSDP_SIZE}" MEGATRON_ENGINE_CONFIG="\ engine=${backend} \ optim=${backend} \ optim.lr=2e-5 \ optim.lr_warmup_steps_ratio=0.01 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.lr_warmup_init=0 \ optim.lr_decay_style=cosine \ optim.min_lr=2e-6 \ engine.tensor_model_parallel_size=${TP_SIZE} \ engine.pipeline_model_parallel_size=${PP_SIZE} \ engine.virtual_pipeline_model_parallel_size=${VPP_SIZE} \ engine.context_parallel_size=${CP_SIZE} \ engine.use_mbridge=True \ engine.vanilla_mbridge=True" if [ "$backend" = "fsdp" ]; then ENGINE_CONFIG="$FSDP_ENGINE_CONFIG" echo "Using fsdp engine" exp_name=pokemon-qwen3-2b-${backend}-${FSDP_STRATEGY}-sp${SP_SIZE}-fsdp-1202a1 else ENGINE_CONFIG="$MEGATRON_ENGINE_CONFIG" echo "Using megatron engine" exp_name=pokemon-qwen3-2b-${backend}-tp${TP_SIZE}-pp${PP_SIZE}-vpp${VPP_SIZE}-cp${CP_SIZE}-megatron-1202a1 fi CKPT_HOME=${CKPT_HOME:-$HOME/open_verl/sft/${project_name}/${exp_name}} mkdir -p "${CKPT_HOME}" torchrun --standalone --nnodes=1 --nproc-per-node=${NUM_TRAINERS:-8} \ ${ENTRYPOINT} \ data.train_files="${TRAIN_FILES}" \ data.train_batch_size=96 \ data.max_length=2048 \ data.pad_mode=${PAD_MODE} \ data.truncation=error \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=65536 \ model.path=$MODEL_ID \ model.use_remove_padding=${USE_REMOVE_PADDING} \ ${ENGINE_CONFIG} \ trainer.test_freq=-1 \ trainer.save_freq=4000 \ trainer.logger=['console','wandb'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.total_epochs=10 \ trainer.default_local_dir="${CKPT_HOME}" \ trainer.resume_mode=${RESUME_MODE} \ trainer.max_ckpt_to_keep=5 \ checkpoint.save_contents=[model,optimizer,extra] ================================================ FILE: examples/sglang_multiturn/README.md ================================================ # Multi-Turn Rollout Example (GSM8K) This example demonstrates how to perform **multi-turn rollout** using SGLang with a tool-calling capable model (e.g., Qwen2.5-3B) on the GSM8K dataset. ## Usage ### Step 1: Download GSM8K Dataset ```bash cd examples/data_preprocess python3 gsm8k_multiturn_w_tool.py ``` This will download and preprocess the GSM8K dataset into ~/data/gsm8k/. ### Step 2: Run Multi-Turn Rollout If you have 8 GPUs Use the standard 8-GPU script: ```bash cd your_verl_root_dir bash examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn.sh ``` If you have only 4 GPUs Use the fallback 4-GPU script: ```bash cd your_verl_root_dir bash examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn_4xgpu.sh ``` ## Notes - The rollout supports multi-turn conversations with tool-calling capabilities. - Current tools are used for GSM8K answer evaluation. - Future versions may extend to search and code interpreter tools. ================================================ FILE: examples/sglang_multiturn/config/geo3k_multiturn_grpo.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 2048 max_response_length: 2048 train_batch_size: 256 return_raw_chat: True return_multi_modal_inputs: False actor_rollout_ref: hybrid_engine: True model: custom_chat_template: "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{%- if tools %}{{- '<|im_start|>system\\n' }}{%- if messages[0]['role'] == 'system' %}{{- messages[0]['content'] }}{%- else %}{{- 'You are a helpful assistant.' }}{%- endif %}{{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}{%- for tool in tools %}{{- \"\\n\" }}{{- tool | tojson }}{%- endfor %}{{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}{% for message in messages %}{% if message['role'] != 'system' or loop.first == false %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{%- elif message.role == \"assistant\" %}{{- '<|im_start|>' + message.role }}{%- if message.content %}{{- '\\n' + message.content }}{%- endif %}{%- for tool_call in message.tool_calls %}{%- if tool_call.function is defined %}{%- set tool_call = tool_call.function %}{%- endif %}{{- '\\n\\n{\"name\": \"' }}{{- tool_call.name }}{{- '\", \"arguments\": ' }}{{- tool_call.arguments | tojson }}{{- '}\\n' }}{%- endfor %}{{- '<|im_end|>\\n' }}{%- elif message.role == \"tool\" %}{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}{{- '<|im_start|>user' }}{%- endif %}{{- '\\n\\n' }}{% if message['content'] is string %}{{ message.content }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'text' or 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{- '\\n' }}{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}{{- '<|im_end|>\\n' }}{%- endif %}{%- endif %}{% endif %}{% endfor %}{%- else %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{%- elif message.role == \"assistant\" %}{{- '<|im_start|>' + message.role }}{%- if message.content %}{{- '\\n' + message.content }}{%- endif %}{%- for tool_call in message.tool_calls %}{%- if tool_call.function is defined %}{%- set tool_call = tool_call.function %}{%- endif %}{{- '\\n\\n{\"name\": \"' }}{{- tool_call.name }}{{- '\", \"arguments\": ' }}{{- tool_call.arguments | tojson }}{{- '}\\n' }}{%- endfor %}{{- '<|im_end|>\\n' }}{%- elif message.role == \"tool\" %}{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}{{- '<|im_start|>user' }}{%- endif %}{{- '\\n\\n' }}{% if message['content'] is string %}{{ message.content }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'text' or 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{- '\\n' }}{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}{{- '<|im_end|>\\n' }}{%- endif %}{%- endif %}{% endfor %}{%- endif %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}" rollout: name: sglang multi_turn: enable: True max_assistant_turns: 5 # tool_config_path: "./config/tool_config/gsm8k_tool_config.yaml" ================================================ FILE: examples/sglang_multiturn/config/geo3k_multiturn_megatron_grpo.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_megatron_trainer - _self_ data: max_prompt_length: 2048 max_response_length: 2048 train_batch_size: 256 return_raw_chat: True return_multi_modal_inputs: False actor_rollout_ref: hybrid_engine: True model: custom_chat_template: "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{%- if tools %}{{- '<|im_start|>system\\n' }}{%- if messages[0]['role'] == 'system' %}{{- messages[0]['content'] }}{%- else %}{{- 'You are a helpful assistant.' }}{%- endif %}{{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}{%- for tool in tools %}{{- \"\\n\" }}{{- tool | tojson }}{%- endfor %}{{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}{% for message in messages %}{% if message['role'] != 'system' or loop.first == false %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{%- elif message.role == \"assistant\" %}{{- '<|im_start|>' + message.role }}{%- if message.content %}{{- '\\n' + message.content }}{%- endif %}{%- for tool_call in message.tool_calls %}{%- if tool_call.function is defined %}{%- set tool_call = tool_call.function %}{%- endif %}{{- '\\n\\n{\"name\": \"' }}{{- tool_call.name }}{{- '\", \"arguments\": ' }}{{- tool_call.arguments | tojson }}{{- '}\\n' }}{%- endfor %}{{- '<|im_end|>\\n' }}{%- elif message.role == \"tool\" %}{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}{{- '<|im_start|>user' }}{%- endif %}{{- '\\n\\n' }}{% if message['content'] is string %}{{ message.content }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'text' or 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{- '\\n' }}{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}{{- '<|im_end|>\\n' }}{%- endif %}{%- endif %}{% endif %}{% endfor %}{%- else %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{%- elif message.role == \"assistant\" %}{{- '<|im_start|>' + message.role }}{%- if message.content %}{{- '\\n' + message.content }}{%- endif %}{%- for tool_call in message.tool_calls %}{%- if tool_call.function is defined %}{%- set tool_call = tool_call.function %}{%- endif %}{{- '\\n\\n{\"name\": \"' }}{{- tool_call.name }}{{- '\", \"arguments\": ' }}{{- tool_call.arguments | tojson }}{{- '}\\n' }}{%- endfor %}{{- '<|im_end|>\\n' }}{%- elif message.role == \"tool\" %}{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}{{- '<|im_start|>user' }}{%- endif %}{{- '\\n\\n' }}{% if message['content'] is string %}{{ message.content }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'text' or 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{- '\\n' }}{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}{{- '<|im_end|>\\n' }}{%- endif %}{%- endif %}{% endfor %}{%- endif %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}" rollout: name: sglang multi_turn: enable: True max_assistant_turns: 5 # tool_config_path: "./config/tool_config/gsm8k_tool_config.yaml" ================================================ FILE: examples/sglang_multiturn/config/gsm8k_multiturn_grpo.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_assistant_turns: 5 ================================================ FILE: examples/sglang_multiturn/config/gsm8k_multiturn_grpo_server.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_assistant_turns: 5 sglang_rollout_mode: server server: timeout: 60 max_attempts: 3 retry_delay: 2 max_connections: 1000 max_start_wait_time: 300.0 ================================================ FILE: examples/sglang_multiturn/config/gsm8k_multiturn_grpo_w_interaction.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_user_turns: 5 ================================================ FILE: examples/sglang_multiturn/config/gsm8k_multiturn_megatron_grpo.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_megatron_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_assistant_turns: 5 ================================================ FILE: examples/sglang_multiturn/config/interaction_config/gsm8k_interaction_config.yaml ================================================ interaction: - name: "gsm8k" class_name: "verl.interactions.gsm8k_interaction.Gsm8kInteraction" config: {} ================================================ FILE: examples/sglang_multiturn/config/retool_multiturn_grpo.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_assistant_turns: 5 tool_config_path: "./config/tool_config/sandbox_fusion_tool_config.yaml" ================================================ FILE: examples/sglang_multiturn/config/search_multiturn_grpo.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True shuffle: False actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_assistant_turns: 2 format: qwen ================================================ FILE: examples/sglang_multiturn/config/search_multiturn_grpo_one_step_off.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ data: max_prompt_length: 1024 max_response_length: 1024 train_batch_size: 256 return_raw_chat: True shuffle: False actor_rollout_ref: hybrid_engine: True rollout: name: sglang multi_turn: enable: True max_assistant_turns: 2 format: qwen ================================================ FILE: examples/sglang_multiturn/config/tool_config/geo3k_tool_config.yaml ================================================ tools: - class_name: "verl.tools.geo3k_tool.Geo3kTool" config: type: native tool_schema: type: "function" function: name: "calc_geo3k_reward" description: "A tool for calculating the reward of geo3k. (1.0 if parsed answer is correct, 0.0 if parsed answer is incorrect or not correctly parsed)" parameters: type: "object" properties: answer: type: "string" description: "The model's answer to the geo3k problem, must be a digits" required: ["answer"] ================================================ FILE: examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml ================================================ tools: - class_name: "verl.tools.gsm8k_tool.Gsm8kTool" config: type: native tool_schema: type: "function" function: name: "calc_gsm8k_reward" description: "A tool for calculating the reward of gsm8k. (1.0 if parsed answer is correct, 0.0 if parsed answer is incorrect or not correctly parsed)" parameters: type: "object" properties: answer: type: "string" description: "The model's answer to the GSM8K math problem, must be a digits" required: ["answer"] ================================================ FILE: examples/sglang_multiturn/config/tool_config/mcp_server.json ================================================ { "mcpServers": { "Tavily Expert": { "url": "your_tavily_expert_url", "auth_token": "your_tavily_api_token" } } } ================================================ FILE: examples/sglang_multiturn/config/tool_config/mcp_tool_config.yaml ================================================ tools: - class_name: verl.tools.mcp_search_tool.MCPSearchTool config: rate_limit: 120 timeout: 120 type: mcp mcp: mcp_servers_config_path: ./mcp_server.json # optional tool_selected_list: - tavily_search_tool ================================================ FILE: examples/sglang_multiturn/config/tool_config/sandbox_fusion_tool_config.yaml ================================================ tools: - class_name: "verl.tools.sandbox_fusion_tools.SandboxFusionTool" config: sandbox_fusion_url: "https://xxx.apigateway-cn-beijing.volceapi.com/run_code" num_workers: 10 enable_global_rate_limit: true rate_limit: 10 default_timeout: 30 default_language: "python" memory_limit_mb: 1024 type: native tool_schema: type: "function" function: name: "code_interpreter" description: "A tool for executing code." parameters: type: "object" properties: code: type: "string" description: "The code to execute." required: ["code"] ================================================ FILE: examples/sglang_multiturn/config/tool_config/search_tool_config.yaml ================================================ tools: - class_name: verl.tools.search_tool.SearchTool config: retrieval_service_url: http://127.0.0.1:8000/retrieve num_workers: 120 rate_limit: 120 timeout: 30 type: native tool_schema: type: function function: name: search description: Searches the web for relevant information based on the given query. parameters: type: object properties: query_list: type: array item: type: string description: A list of fully-formed semantic queries. The tool will return search results for each query. required: - query_list ================================================ FILE: examples/sglang_multiturn/geo3k/run_qwen2.5-3b_geo3k_multiturn.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='geo3k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=2048 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='geo3k_async_rl' \ trainer.experiment_name='qwen2.5-3b_function_rm-geo3k-sgl-multi-w-tool-verify-n16' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ data.train_files=$HOME/data/geo3k_multiturn_w_tool/train.parquet \ data.val_files=$HOME/data/geo3k_multiturn_w_tool/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/geo3k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/geo3k/run_qwen2.5-3b_geo3k_multiturn_4xgpu.sh ================================================ # run on 4xH100 # make sure your current working directory is the root of the project set -x export HYDRA_FULL_ERROR=1 ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='geo3k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=2048 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='geo3k_async_rl' \ trainer.experiment_name='qwen2.5-3b_function_rm-geo3k-async-sgl-multi-w-tool-verify-n16-4cards' \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.total_epochs=15 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=8192 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=8192 \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=8192 \ critic.ppo_max_token_len_per_gpu=8192 \ critic.forward_max_token_len_per_gpu=8192 \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/geo3k_tool_config.yaml" \ $@ ================================================ FILE: examples/sglang_multiturn/geo3k/run_qwen2.5-3b_megatron_geo3k_multiturn.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project # this is a verification training script, the parallel setting should be tuned to your model set -x export PYTHONUNBUFFERED=1 export RAY_DEDUP_LOGS=0 export RUST_BACKTRACE=1 export HYDRA_FULL_ERROR=1 export CUDA_DEVICE_MAX_CONNECTIONS=1 ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='geo3k_multiturn_megatron_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=2048 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.context_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.megatron.seed=42 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.context_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='geo3k_async_rl' \ trainer.experiment_name='qwen2.5-3b_function_rm-geo3k-sgl-multi-w-tool-n8-mcore-v2505201745_seed42' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ data.train_files=$HOME/data/geo3k_multiturn_w_tool/train.parquet \ data.val_files=$HOME/data/geo3k_multiturn_w_tool/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/geo3k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/gsm8k_toolcall_shaping/gsm8k_toolcall_shaping.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations from typing import Any, Optional from verl.utils.reward_score.gsm8k import compute_score as gsm8k_compute_score def toolcall_shaping_reward( data_source: Optional[str], solution_str: str, ground_truth: str, extra_info: Optional[dict[str, Any]] = None, *, method: str = "strict", format_score: float = 0.1, score: float = 1.0, shaping_reward: float = 0.1, trigger_substring: str = "", **kwargs, ) -> float: """ GSM8K reward + tool-call shaping reward (trajectory-level). """ base = gsm8k_compute_score(solution_str, ground_truth, method, format_score, score) bonus = shaping_reward if (trigger_substring and trigger_substring in solution_str) else 0.0 return float(base + bonus) # Optional: keep a default name for convenience in verl config (default is compute_score) [web:59][web:65] def compute_score( data_source: Optional[str], solution_str: str, ground_truth: str, extra_info: Optional[dict[str, Any]] = None, **kwargs, ) -> float: return toolcall_shaping_reward( data_source=data_source, solution_str=solution_str, ground_truth=ground_truth, extra_info=extra_info, **kwargs, ) ================================================ FILE: examples/sglang_multiturn/gsm8k_toolcall_shaping/run_gsm8k_grpo_toolcall_shaping.sh ================================================ # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.sampler.class_name="RandomCurriculumSampler" \ data.sampler.class_path="pkg://tests.utils.dataset.test_create_rl_sampler_on_cpu" \ data.dataloader_num_workers=0 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.train_batch_size=256 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen0.5b_gsm8k_toolcall_shaping' \ reward.custom_reward_function.path="$PROJECT_DIR/examples/sglang_multiturn/gsm8k_toolcall_shaping/gsm8k_toolcall_shaping.py" \ reward.custom_reward_function.name=compute_score \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=20 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen0.5b_gsm8k_multiturn_curriculum.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.sampler.class_name="RandomCurriculumSampler" \ data.sampler.class_path="pkg://tests.utils.dataset.test_create_rl_sampler_on_cpu" \ data.dataloader_num_workers=0 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.train_batch_size=256 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen3-4b_function_rm-gsm8k-sgl-multi-w-tool-verify-n16' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-0.5b_gsm8k_multiturn_w_interaction.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" TRAIN_BATCH_SIZE=${TRAIN_BATCH_SIZE:-512} MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-8} OFFLOAD=${OFFLOAD:-False} python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo_w_interaction' \ algorithm.adv_estimator=grpo \ data.train_batch_size=$TRAIN_BATCH_SIZE \ data.max_prompt_length=1024 \ data.max_response_length=$((1024 * 3)) \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.enable_activation_offload=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=$TRAIN_BATCH_SIZE \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=$MICRO_BATCH_SIZE \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.fsdp_config.param_offload=$OFFLOAD \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=$OFFLOAD \ actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=$MICRO_BATCH_SIZE \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=$MICRO_BATCH_SIZE \ actor_rollout_ref.ref.fsdp_config.param_offload=$OFFLOAD \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen2.5-0.5b_function_rm-gsm8k-sgl-multi-w-interaction-n8' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ data.train_files=$HOME/data/gsm8k_verl_sgl_multi_turn_w_interaction/train.parquet \ data.val_files=$HOME/data/gsm8k_verl_sgl_multi_turn_w_interaction/test.parquet \ actor_rollout_ref.rollout.multi_turn.interaction_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/interaction_config/gsm8k_interaction_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" function now() { date '+%d-%H-%M' } EXPERIMENT_NAME="qwen2.5-3b_baseline_$(now)" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ global_profiler.tool=torch_memory \ global_profiler.save_path=./mem_snapshots \ global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries=100000 \ global_profiler.global_tool_config.torch_memory.stack_depth=32 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \ actor_rollout_ref.rollout.multi_stage_wake_up=True \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.over_sample_rate=0.1 \ actor_rollout_ref.rollout.mode=async \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='multi-turn-grpo-qwen2.5-3b-sglang' \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.val_before_train=True \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn_4xgpu.sh ================================================ # run on 4xH100 # make sure your current working directory is the root of the project set -x export HYDRA_FULL_ERROR=1 ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen2.5-3b_function_rm-gsm8k-async-sgl-multi-w-tool-verify-n16-4cards' \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.total_epochs=15 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=8192 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=8192 \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=8192 \ critic.ppo_max_token_len_per_gpu=8192 \ critic.forward_max_token_len_per_gpu=8192 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ actor_rollout_ref.rollout.multi_turn.interaction_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/interaction_config/gsm8k_interaction_config.yaml" \ actor_rollout_ref.rollout.multi_turn.max_user_turns=1 \ $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn_4xgpu_server.sh ================================================ # run on 4xH100 # make sure your current working directory is the root of the project set -x export HYDRA_FULL_ERROR=1 ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo_server' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console", "wandb"]' \ trainer.project_name='gsm8k_async_rl_server' \ trainer.experiment_name='qwen2.5-3b_function_rm-gsm8k-async-sgl-multi-w-tool-verify-n16-4cards' \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.total_epochs=15 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=8192 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=8192 \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=8192 \ critic.ppo_max_token_len_per_gpu=8192 \ critic.forward_max_token_len_per_gpu=8192 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ actor_rollout_ref.rollout.multi_turn.interaction_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/interaction_config/gsm8k_interaction_config.yaml" \ actor_rollout_ref.rollout.multi_turn.max_user_turns=1 \ $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn_server.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" function now() { date '+%d-%H-%M' } EXPERIMENT_NAME="qwen2.5-3b_baseline_$(now)" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo_server' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \ actor_rollout_ref.rollout.multi_stage_wake_up=True \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.over_sample_rate=0 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='multi-turn-grpo-qwen2.5-3b-sglang' \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.val_before_train=True \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_multiturn_vllm_fsdp.sh ================================================ # run on Ascend 910 # make sure your current working directory is the root of the project set -x ulimit -n 65535 #set vllm v1 env export VLLM_USE_V1=1 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" TRAIN_BATCH_SIZE=32 MICRO_BATCH_SIZE=8 python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ actor_rollout_ref.rollout.name=vllm \ algorithm.adv_estimator=grpo \ data.train_batch_size=${TRAIN_BATCH_SIZE} \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path="Qwen/Qwen2.5-3B-Instruct" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=${TRAIN_BATCH_SIZE} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${MICRO_BATCH_SIZE} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${MICRO_BATCH_SIZE} \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9\ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${MICRO_BATCH_SIZE} \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen2.5-3b_function_rm-gsm8k-sgl-multi-w-tool-verify-n16' \ trainer.n_gpus_per_node=16 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.logger='["console"]' \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ trainer.total_epochs=15 \ actor_rollout_ref.rollout.trace.token2text=False \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.multi_turn.enable=true \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ actor_rollout_ref.rollout.free_cache_engine=True ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_tool_agent_mlflow.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.rollout.trace.backend=mlflow \ actor_rollout_ref.rollout.trace.token2text=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","mlflow"]' \ trainer.project_name='gsm8k_tool-agent' \ trainer.experiment_name='qwen2.5-3b_function_rm-gsm8k-sgl-tool-agent-verify-n16' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.total_training_steps=2 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen2.5-3b_megatron_gsm8k_multiturn.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project # this is a verification training script, the parallel setting should be tuned to your model set -x export PYTHONUNBUFFERED=1 export RAY_DEDUP_LOGS=0 export RUST_BACKTRACE=1 export HYDRA_FULL_ERROR=1 export CUDA_DEVICE_MAX_CONNECTIONS=1 ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_megatron_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=/user/longxiang1/models/Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.context_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.megatron.seed=42 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.context_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen2.5-3b_function_rm-gsm8k-sgl-multi-w-tool-n8-mcore-v2505201745_seed42' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ data.train_files=/user/longxiang1/data/gsm8k_verl_sgl_multi_turn_preprocessed_v2/train.parquet \ data.val_files=/user/longxiang1/data/gsm8k_verl_sgl_multi_turn_preprocessed_v2/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen3-4b_gsm8k_multiturn.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen3-4B \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.rollout.over_sample_rate=0.1 \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name='qwen3-4b_function_rm-gsm8k-sgl-multi-w-tool-verify-n16' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.total_epochs=15 $@ ================================================ FILE: examples/sglang_multiturn/run_qwen3_4b_dapo_multiturn.sh ================================================ set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" pip install --upgrade "huggingface-hub>=0.34.0" hf download \ BytedTsinghua-SIA/DAPO-Math-17k \ --repo-type dataset \ --local-dir $HOME/data/BytedTsinghua-SIA/DAPO-Math-17k hf download \ Maxwell-Jia/AIME_2024 \ --repo-type dataset \ --local-dir $HOME/data/Maxwell-Jia/AIME_2024 # Note: # 1. # a sandbox fusion server is needed to run the code interpreter tool. # docker run -it -p 8080:8080 volcengine/sandbox-fusion:server-20250609 # 2. # The model located at font-info/qwen3-4b-sft-SGLang-RL (https://huggingface.co/font-info/qwen3-4b-sft-SGLang-RL) # is a fine-tuned version provided by the SGLang RL team. Without supervised fine-tuning (SFT) # on the Retool dataset, Dapo training will not converge. # If you still wish to perform SFT from scratch, follow the steps below: # Step 1: Download the SFT dataset #hf download JoeYing/ReTool-SFT --repo-type dataset --local-dir ./ReTool-SFT # Step 2: Preprocess the data for SFT #python3 recipe/retool/retool_sft_preprocess.py # Step 3: Run SFT training #bash recipe/retool/run_qwen2-32b_sft.sh # having trouble setup? see https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/multi-turn/release_log/latest_sglang.md for more details. python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ algorithm.use_kl_in_reward=False \ algorithm.kl_ctrl.kl_coef=0.0 \ data.train_files=$HOME/data/BytedTsinghua-SIA/DAPO-Math-17k \ data.val_files=$HOME/data/Maxwell-Jia/AIME_2024 \ data.return_raw_chat=True \ data.train_batch_size=32 \ data.max_prompt_length=2048 \ data.max_response_length=16384 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.custom_cls.path=$PROJECT_DIR/recipe/retool/retool.py \ data.custom_cls.name=CustomRLHFDataset \ reward.custom_reward_function.path=$PROJECT_DIR/recipe/retool/retool.py \ reward.custom_reward_function.name=compute_score \ actor_rollout_ref.model.path=font-info/qwen3-4b-sft-SGLang-RL \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.0 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=32768 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \ actor_rollout_ref.rollout.multi_stage_wake_up=True \ actor_rollout_ref.rollout.multi_turn.enable=True \ actor_rollout_ref.rollout.multi_turn.max_user_turns=16 \ actor_rollout_ref.rollout.multi_turn.max_assistant_turns=16 \ actor_rollout_ref.rollout.multi_turn.tool_config_path=$PROJECT_DIR/recipe/retool/sandbox_fusion_tool_config.yaml \ actor_rollout_ref.rollout.multi_turn.format=hermes \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.val_kwargs.top_p=0.6 \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.n=30 \ trainer.logger=['console','wandb'] \ trainer.project_name=sglang-dapo-multiturn \ trainer.experiment_name=qwen3_4b_sft_dapo_multiturn \ trainer.n_gpus_per_node=8 \ trainer.log_val_generations=20 \ trainer.val_before_train=True \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=20 \ trainer.total_epochs=15 \ $@ ================================================ FILE: examples/sglang_multiturn/search_r1_like/local_dense_retriever/download.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 Search-R1 Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/PeterGriffinJin/Search-R1/blob/main/scripts/download.py import argparse from huggingface_hub import hf_hub_download parser = argparse.ArgumentParser(description="Download files from a Hugging Face dataset repository.") parser.add_argument("--repo_id", type=str, default="PeterJinGo/wiki-18-e5-index", help="Hugging Face repository ID") parser.add_argument("--save_path", type=str, required=True, help="Local directory to save files") args = parser.parse_args() repo_id = "PeterJinGo/wiki-18-e5-index" for file in ["part_aa", "part_ab"]: hf_hub_download( repo_id=repo_id, filename=file, # e.g., "e5_Flat.index" repo_type="dataset", local_dir=args.save_path, ) repo_id = "PeterJinGo/wiki-18-corpus" hf_hub_download( repo_id=repo_id, filename="wiki-18.jsonl.gz", repo_type="dataset", local_dir=args.save_path, ) ================================================ FILE: examples/sglang_multiturn/search_r1_like/local_dense_retriever/retrieval_server.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 Search-R1 Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/PeterGriffinJin/Search-R1/blob/main/search_r1/search/retrieval_server.py import argparse import json import warnings from typing import Optional import datasets import faiss import numpy as np import torch import uvicorn from fastapi import FastAPI from pydantic import BaseModel from tqdm import tqdm from transformers import AutoModel, AutoTokenizer def load_corpus(corpus_path: str): corpus = datasets.load_dataset("json", data_files=corpus_path, split="train", num_proc=4) return corpus def load_docs(corpus, doc_idxs): results = [corpus[int(idx)] for idx in doc_idxs] return results def load_model(model_path: str, use_fp16: bool = False): model = AutoModel.from_pretrained(model_path, trust_remote_code=True) model.eval() model.cuda() if use_fp16: model = model.half() tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True, trust_remote_code=True) return model, tokenizer def pooling(pooler_output, last_hidden_state, attention_mask=None, pooling_method="mean"): if pooling_method == "mean": last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] elif pooling_method == "cls": return last_hidden_state[:, 0] elif pooling_method == "pooler": return pooler_output else: raise NotImplementedError("Pooling method not implemented!") class Encoder: def __init__(self, model_name, model_path, pooling_method, max_length, use_fp16): self.model_name = model_name self.model_path = model_path self.pooling_method = pooling_method self.max_length = max_length self.use_fp16 = use_fp16 self.model, self.tokenizer = load_model(model_path=model_path, use_fp16=use_fp16) self.model.eval() @torch.no_grad() def encode(self, query_list: list[str], is_query=True) -> np.ndarray: # processing query for different encoders if isinstance(query_list, str): query_list = [query_list] if "e5" in self.model_name.lower(): if is_query: query_list = [f"query: {query}" for query in query_list] else: query_list = [f"passage: {query}" for query in query_list] if "bge" in self.model_name.lower(): if is_query: query_list = [ f"Represent this sentence for searching relevant passages: {query}" for query in query_list ] inputs = self.tokenizer( query_list, max_length=self.max_length, padding=True, truncation=True, return_tensors="pt" ) inputs = {k: v.cuda() for k, v in inputs.items()} if "T5" in type(self.model).__name__: # T5-based retrieval model decoder_input_ids = torch.zeros((inputs["input_ids"].shape[0], 1), dtype=torch.long).to( inputs["input_ids"].device ) output = self.model(**inputs, decoder_input_ids=decoder_input_ids, return_dict=True) query_emb = output.last_hidden_state[:, 0, :] else: output = self.model(**inputs, return_dict=True) query_emb = pooling( output.pooler_output, output.last_hidden_state, inputs["attention_mask"], self.pooling_method ) if "dpr" not in self.model_name.lower(): query_emb = torch.nn.functional.normalize(query_emb, dim=-1) query_emb = query_emb.detach().cpu().numpy() query_emb = query_emb.astype(np.float32, order="C") del inputs, output torch.cuda.empty_cache() return query_emb class BaseRetriever: def __init__(self, config): self.config = config self.retrieval_method = config.retrieval_method self.topk = config.retrieval_topk self.index_path = config.index_path self.corpus_path = config.corpus_path def _search(self, query: str, num: int, return_score: bool): raise NotImplementedError def _batch_search(self, query_list: list[str], num: int, return_score: bool): raise NotImplementedError def search(self, query: str, num: int = None, return_score: bool = False): return self._search(query, num, return_score) def batch_search(self, query_list: list[str], num: int = None, return_score: bool = False): return self._batch_search(query_list, num, return_score) class BM25Retriever(BaseRetriever): def __init__(self, config): super().__init__(config) from pyserini.search.lucene import LuceneSearcher self.searcher = LuceneSearcher(self.index_path) self.contain_doc = self._check_contain_doc() if not self.contain_doc: self.corpus = load_corpus(self.corpus_path) self.max_process_num = 8 def _check_contain_doc(self): return self.searcher.doc(0).raw() is not None def _search(self, query: str, num: int = None, return_score: bool = False): if num is None: num = self.topk hits = self.searcher.search(query, num) if len(hits) < 1: if return_score: return [], [] else: return [] scores = [hit.score for hit in hits] if len(hits) < num: warnings.warn("Not enough documents retrieved!", stacklevel=2) else: hits = hits[:num] if self.contain_doc: all_contents = [json.loads(self.searcher.doc(hit.docid).raw())["contents"] for hit in hits] results = [ { "title": content.split("\n")[0].strip('"'), "text": "\n".join(content.split("\n")[1:]), "contents": content, } for content in all_contents ] else: results = load_docs(self.corpus, [hit.docid for hit in hits]) if return_score: return results, scores else: return results def _batch_search(self, query_list: list[str], num: int = None, return_score: bool = False): results = [] scores = [] for query in query_list: item_result, item_score = self._search(query, num, True) results.append(item_result) scores.append(item_score) if return_score: return results, scores else: return results class DenseRetriever(BaseRetriever): def __init__(self, config): super().__init__(config) self.index = faiss.read_index(self.index_path) if config.faiss_gpu: co = faiss.GpuMultipleClonerOptions() co.useFloat16 = True co.shard = True self.index = faiss.index_cpu_to_all_gpus(self.index, co=co) self.corpus = load_corpus(self.corpus_path) self.encoder = Encoder( model_name=self.retrieval_method, model_path=config.retrieval_model_path, pooling_method=config.retrieval_pooling_method, max_length=config.retrieval_query_max_length, use_fp16=config.retrieval_use_fp16, ) self.topk = config.retrieval_topk self.batch_size = config.retrieval_batch_size def _search(self, query: str, num: int = None, return_score: bool = False): if num is None: num = self.topk query_emb = self.encoder.encode(query) scores, idxs = self.index.search(query_emb, k=num) idxs = idxs[0] scores = scores[0] results = load_docs(self.corpus, idxs) if return_score: return results, scores.tolist() else: return results def _batch_search(self, query_list: list[str], num: int = None, return_score: bool = False): if isinstance(query_list, str): query_list = [query_list] if num is None: num = self.topk results = [] scores = [] for start_idx in tqdm(range(0, len(query_list), self.batch_size), desc="Retrieval process: "): query_batch = query_list[start_idx : start_idx + self.batch_size] batch_emb = self.encoder.encode(query_batch) batch_scores, batch_idxs = self.index.search(batch_emb, k=num) batch_scores = batch_scores.tolist() batch_idxs = batch_idxs.tolist() # load_docs is not vectorized, but is a python list approach flat_idxs = sum(batch_idxs, []) batch_results = load_docs(self.corpus, flat_idxs) # chunk them back batch_results = [batch_results[i * num : (i + 1) * num] for i in range(len(batch_idxs))] results.extend(batch_results) scores.extend(batch_scores) del batch_emb, batch_scores, batch_idxs, query_batch, flat_idxs, batch_results torch.cuda.empty_cache() if return_score: return results, scores else: return results def get_retriever(config): if config.retrieval_method == "bm25": return BM25Retriever(config) else: return DenseRetriever(config) ##################################### # FastAPI server below ##################################### class Config: """ Minimal config class (simulating your argparse) Replace this with your real arguments or load them dynamically. """ def __init__( self, retrieval_method: str = "bm25", retrieval_topk: int = 10, index_path: str = "./index/bm25", corpus_path: str = "./data/corpus.jsonl", dataset_path: str = "./data", data_split: str = "train", faiss_gpu: bool = True, retrieval_model_path: str = "./model", retrieval_pooling_method: str = "mean", retrieval_query_max_length: int = 256, retrieval_use_fp16: bool = False, retrieval_batch_size: int = 128, ): self.retrieval_method = retrieval_method self.retrieval_topk = retrieval_topk self.index_path = index_path self.corpus_path = corpus_path self.dataset_path = dataset_path self.data_split = data_split self.faiss_gpu = faiss_gpu self.retrieval_model_path = retrieval_model_path self.retrieval_pooling_method = retrieval_pooling_method self.retrieval_query_max_length = retrieval_query_max_length self.retrieval_use_fp16 = retrieval_use_fp16 self.retrieval_batch_size = retrieval_batch_size class QueryRequest(BaseModel): queries: list[str] topk: Optional[int] = None return_scores: bool = False app = FastAPI() @app.post("/retrieve") def retrieve_endpoint(request: QueryRequest): """ Endpoint that accepts queries and performs retrieval. Input format: { "queries": ["What is Python?", "Tell me about neural networks."], "topk": 3, "return_scores": true } Output format (when return_scores=True，similarity scores are returned): { "result": [ [ # Results for each query { {"document": doc, "score": score} }, # ... more documents ], # ... results for other queries ] } """ if not request.topk: request.topk = config.retrieval_topk # fallback to default # Perform batch retrieval results, scores = retriever.batch_search( query_list=request.queries, num=request.topk, return_score=request.return_scores ) # Format response resp = [] for i, single_result in enumerate(results): if request.return_scores: # If scores are returned, combine them with results combined = [] for doc, score in zip(single_result, scores[i], strict=True): combined.append({"document": doc, "score": score}) resp.append(combined) else: resp.append(single_result) return {"result": resp} if __name__ == "__main__": parser = argparse.ArgumentParser(description="Launch the local faiss retriever.") parser.add_argument( "--index_path", type=str, default="/home/peterjin/mnt/index/wiki-18/e5_Flat.index", help="Corpus indexing file." ) parser.add_argument( "--corpus_path", type=str, default="/home/peterjin/mnt/data/retrieval-corpus/wiki-18.jsonl", help="Local corpus file.", ) parser.add_argument("--topk", type=int, default=3, help="Number of retrieved passages for one query.") parser.add_argument("--retriever_name", type=str, default="e5", help="Name of the retriever model.") parser.add_argument( "--retriever_model", type=str, default="intfloat/e5-base-v2", help="Path of the retriever model." ) parser.add_argument("--faiss_gpu", action="store_true", help="Use GPU for computation") args = parser.parse_args() # 1) Build a config (could also parse from arguments). # In real usage, you'd parse your CLI arguments or environment variables. config = Config( retrieval_method=args.retriever_name, # or "dense" index_path=args.index_path, corpus_path=args.corpus_path, retrieval_topk=args.topk, faiss_gpu=args.faiss_gpu, retrieval_model_path=args.retriever_model, retrieval_pooling_method="mean", retrieval_query_max_length=256, retrieval_use_fp16=True, retrieval_batch_size=512, ) # 2) Instantiate a global retriever so it is loaded once and reused. retriever = get_retriever(config) # 3) Launch the server. By default, it listens on http://127.0.0.1:8000 uvicorn.run(app, host="0.0.0.0", port=8000) ================================================ FILE: examples/sglang_multiturn/search_r1_like/run_qwen2.5-3b_instruct_search_multiturn.sh ================================================ # run on 8xH20 # make sure your current working directory is the root of the project set -x ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" TRAIN_DATA="$HOME/data/searchR1_processed_direct/train.parquet" VAL_DATA="$HOME/data/searchR1_processed_direct/test.parquet" TOOL_CONFIG="$CONFIG_PATH/tool_config/search_tool_config.yaml" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='search_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=512 \ data.val_batch_size=256 \ data.max_prompt_length=4096 \ data.max_response_length=3000 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.285 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.max_model_len=15000 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.multi_turn.max_assistant_turns=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.val_before_train=False \ trainer.logger='["console","wandb"]' \ trainer.project_name='search_r1_like_async_rl' \ trainer.experiment_name='qwen2.5-3b-instruct_function_rm-search-async-sgl-multi-w-searchtool-verify-n16' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=100 \ trainer.test_freq=50 \ data.train_files="$TRAIN_DATA" \ data.val_files="$VAL_DATA" \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$TOOL_CONFIG" \ trainer.total_epochs=1 $@ ================================================ FILE: examples/skypilot/README.md ================================================ # verl with SkyPilot Run verl reinforcement learning training jobs on Kubernetes clusters or cloud platforms with GPU nodes using [SkyPilot](https://github.com/skypilot-org/skypilot). ## Installation and Configuration ### Step 1: Install SkyPilot Choose the installation based on your target platform: ```bash # For Kubernetes only pip install "skypilot[kubernetes]" # For AWS pip install "skypilot[aws]" # For Google Cloud Platform pip install "skypilot[gcp]" # For Azure pip install "skypilot[azure]" # For multiple platforms pip install "skypilot[kubernetes,aws,gcp,azure]" ``` ### Step 2: Configure Your Platform See https://docs.skypilot.co/en/latest/getting-started/installation.html ### Step 3: Set Up Environment Variables Export necessary API keys for experiment tracking: ```bash # For Weights & Biases tracking export WANDB_API_KEY="your-wandb-api-key" # For HuggingFace gated models (if needed) export HF_TOKEN="your-huggingface-token" ``` ## Examples ### PPO Training ```bash sky launch -c verl-ppo verl-ppo.yaml --secret WANDB_API_KEY -y ``` Runs PPO training on GSM8K dataset using Qwen2.5-0.5B-Instruct model across 2 nodes with H100 GPUs. Based on examples in [`../ppo_trainer/`](../ppo_trainer/). ### GRPO Training ```bash sky launch -c verl-grpo verl-grpo.yaml --secret WANDB_API_KEY -y ``` Runs GRPO (Group Relative Policy Optimization) training on MATH dataset using Qwen2.5-7B-Instruct model. Memory-optimized configuration for 2 nodes. Based on examples in [`../grpo_trainer/`](../grpo_trainer/). ### Multi-turn Tool Usage Training ```bash sky launch -c verl-multiturn verl-multiturn-tools.yaml --secret WANDB_API_KEY --secret HF_TOKEN -y ``` Single-node training with 8xH100 GPUs for multi-turn tool usage with Qwen2.5-3B-Instruct. Includes tool and interaction configurations for GSM8K. Based on examples in [`../sglang_multiturn/`](../sglang_multiturn/) but uses vLLM instead of sglang. ## Configuration The example YAML files are pre-configured with: - **Infrastructure**: Kubernetes clusters (`infra: k8s`) - can be changed to `infra: aws` or `infra: gcp`, etc. - **Docker Image**: verl's official Docker image with CUDA 12.6 support - **Setup**: Automatically clones and installs verl from source - **Datasets**: Downloads required datasets during setup phase - **Ray Cluster**: Configures distributed training across nodes - **Logging**: Supports Weights & Biases via `--secret WANDB_API_KEY` - **Models**: Supports gated HuggingFace models via `--secret HF_TOKEN` ## Launch Command Options - `-c `: Cluster name for managing the job - `--secret KEY`: Pass secrets for API keys (can be used multiple times) - `-y`: Skip confirmation prompt ## Monitoring Your Jobs ### Check cluster status ```bash sky status ``` ### View logs ```bash sky logs verl-ppo # View logs for the PPO job ``` ### SSH into head node ```bash ssh verl-ppo ``` ### Access Ray dashboard ```bash sky status --endpoint 8265 verl-ppo # Get dashboard URL ``` ### Stop a cluster ```bash sky down verl-ppo ``` ================================================ FILE: examples/skypilot/verl-grpo.yaml ================================================ resources: infra: k8s accelerators: H100:1 memory: 128+ image_id: docker:verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.0-fa2.7.4 ports: 8265 num_nodes: 2 secrets: WANDB_API_KEY: setup: | rm -rf verl git clone https://github.com/volcengine/verl.git cd verl pip3 install -v -e .[vllm] pip3 install flashinfer-python echo "Downloading Math dataset..." mkdir -p ~/data/math python3 "$(pwd)/examples/data_preprocess/math_dataset.py" --local_dir ~/data/math echo "Math dataset download completed" run: | HEAD_IP=$(echo "$SKYPILOT_NODE_IPS" | head -n1) NUM_NODES=$SKYPILOT_NUM_NODES NUM_GPUS_PER_NODE=$SKYPILOT_NUM_GPUS_PER_NODE if [ "$SKYPILOT_NODE_RANK" == "0" ]; then echo "Starting Ray head node..." ps aux | grep ray | grep 6379 &> /dev/null || ray start --head --disable-usage-stats \ --port=6379 \ --dashboard-host=0.0.0.0 \ --dashboard-port=8265 # Wait for all worker nodes to join retry_count=0 max_retries=30 while [ $retry_count -lt $max_retries ]; do connected_nodes=$(ray status 2>/dev/null | grep -c "node_" || echo "0") echo "Connected nodes: $connected_nodes/$NUM_NODES (attempt $((retry_count+1))/$max_retries)" if [ "$connected_nodes" -ge "$NUM_NODES" ]; then echo "All nodes connected to Ray cluster" break fi retry_count=$((retry_count+1)) sleep 10 done python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/math/train.parquet \ data.val_files=$HOME/data/math/test.parquet \ data.train_batch_size=32 \ data.max_prompt_length=256 \ data.max_response_length=256 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen2.5-7B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=16 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.ppo_epochs=1 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.rollout.n=1 \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=2048 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=[console,wandb] \ trainer.project_name=verl_math_grpo_demo \ trainer.experiment_name=qwen25_7b_grpo \ trainer.n_gpus_per_node=$NUM_GPUS_PER_NODE \ trainer.nnodes=$NUM_NODES \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 else sleep 15 echo "Starting Ray worker node..." ps aux | grep ray | grep $HEAD_IP:6379 &> /dev/null || ray start --address $HEAD_IP:6379 --disable-usage-stats sleep 10 fi echo "Node setup and Ray start script finished for rank $SKYPILOT_NODE_RANK." ================================================ FILE: examples/skypilot/verl-multiturn-tools.yaml ================================================ resources: infra: k8s accelerators: H100:8 memory: 128+ image_id: docker:verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.0-fa2.7.4 ports: 8265 num_nodes: 1 secrets: WANDB_API_KEY: HF_TOKEN: # in case you're using gated models from the HF hub setup: | rm -rf verl git clone https://github.com/volcengine/verl.git cd verl pip3 install -v -e .[vllm] pip3 install flashinfer-python pip install "transformers<4.54.0" # https://github.com/vllm-project/vllm-ascend/issues/2046 # Download GSM8K dataset for multiturn tool training echo "Downloading GSM8K dataset..." mkdir -p ~/data/gsm8k python3 "$(pwd)/examples/data_preprocess/gsm8k.py" --local_dir ~/data/gsm8k echo "GSM8K dataset download completed" run: | NUM_GPUS_PER_NODE=$SKYPILOT_NUM_GPUS_PER_NODE PROJECT_DIR="$(pwd)/verl" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" # Single node setup - no worker coordination needed echo "Starting Ray head node..." ps aux | grep ray | grep 6379 &> /dev/null || ray start --head --disable-usage-stats \ --port=6379 \ --dashboard-host=0.0.0.0 \ --dashboard-port=8265 cd verl python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=512 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=512 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=64 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=16 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=64 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=[console,wandb] \ trainer.project_name=verl_multiturn_tools \ trainer.experiment_name=qwen25_7b_gsm8k_multiturn_tools \ trainer.n_gpus_per_node=$NUM_GPUS_PER_NODE \ trainer.nnodes=1 \ trainer.save_freq=10 \ trainer.test_freq=5 \ trainer.total_epochs=10 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=8192 \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=8192 \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=8192 \ critic.ppo_max_token_len_per_gpu=8192 \ critic.forward_max_token_len_per_gpu=8192 \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ actor_rollout_ref.rollout.multi_turn.interaction_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/interaction_config/gsm8k_interaction_config.yaml" \ actor_rollout_ref.rollout.multi_turn.max_user_turns=1 echo "Node setup and Ray start script finished for rank $SKYPILOT_NODE_RANK." ================================================ FILE: examples/skypilot/verl-ppo.yaml ================================================ resources: infra: k8s accelerators: H100:1 memory: 128+ image_id: docker:verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.0-fa2.7.4 ports: 8265 num_nodes: 2 secrets: WANDB_API_KEY: setup: | rm -rf verl git clone https://github.com/volcengine/verl.git cd verl pip3 install -v -e .[vllm] pip3 install flashinfer-python # Download GSM8K dataset - alternative approach echo "Downloading GSM8K dataset..." mkdir -p ~/data/gsm8k # Check if the script exists and use absolute path if [ -f "$(pwd)/examples/data_preprocess/gsm8k.py" ]; then python3 "$(pwd)/examples/data_preprocess/gsm8k.py" --local_dir ~/data/gsm8k else echo "Warning: gsm8k.py script not found, skipping dataset download" # You might want to download the dataset manually or use a different approach fi echo "GSM8K dataset download completed" run: | # Get the Head node's IP and total number of nodes HEAD_IP=$(echo "$SKYPILOT_NODE_IPS" | head -n1) NUM_NODES=$SKYPILOT_NUM_NODES # login wandb # python3 -c "import wandb; wandb.login(relogin=True, key='$WANDB_API_KEY')" if [ "$SKYPILOT_NODE_RANK" == "0" ]; then # Head node starts Ray Head echo "Starting Ray head node..." ps aux | grep ray | grep 6379 &> /dev/null || ray start --head --disable-usage-stats \ --port=6379 \ --dashboard-host=0.0.0.0 \ --dashboard-port=8265 # Wait for all worker nodes to join the cluster with better checking echo "Waiting for all nodes to join Ray cluster..." retry_count=0 max_retries=30 while [ $retry_count -lt $max_retries ]; do connected_nodes=$(ray status 2>/dev/null | grep -c "node_" || echo "0") echo "Connected nodes: $connected_nodes/$NUM_NODES (attempt $((retry_count+1))/$max_retries)" if [ "$connected_nodes" -ge "$NUM_NODES" ]; then echo "All nodes connected to Ray cluster" break fi retry_count=$((retry_count+1)) sleep 10 done if [ $retry_count -eq $max_retries ]; then echo "WARNING: Not all nodes connected to Ray cluster after $max_retries attempts" echo "Current Ray status:" ray status fi python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=[console,wandb] \ trainer.val_before_train=False \ trainer.default_hdfs_dir=null \ trainer.n_gpus_per_node=1 \ trainer.nnodes=2 \ trainer.save_freq=20 \ trainer.test_freq=20 \ trainer.total_epochs=2 \ trainer.project_name=verl_examples \ trainer.experiment_name=experiment_name_gsm8k else # Wait for Ray Head to start sleep 15 # Worker node starts Ray Worker echo "Starting Ray worker node..." ps aux | grep ray | grep $HEAD_IP:6379 &> /dev/null || ray start --address $HEAD_IP:6379 --disable-usage-stats sleep 10 fi echo "Node setup and Ray start script finished for rank $SKYPILOT_NODE_RANK." ================================================ FILE: examples/slurm/ray_on_slurm.slurm ================================================ #!/bin/bash #SBATCH --job-name=verl-ray-on-slurm #SBATCH --nodes=2 #SBATCH --ntasks-per-node=1 #SBATCH --mem=200G #SBATCH --partition=your-partition #SBATCH --time=01:00:00 #SBATCH --account=your-account #SBATCH --gpus-per-node=4 #SBATCH --cpus-per-task=64 #SBATCH --output=slurm-%j.out #SBATCH --error=slurm-%j.err # load necessary modules # replace these information with your own verl_workdir=/path/to/verl train_files=/path/to/gsm8k/train.parquet val_files=/path/to/gsm8k/test.parquet apptainer_image_path=/path/to/verl-ngc.sif # replace these information with your own # Getting the node names nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST") nodes_array=("$nodes") head_node=${nodes_array[0]} head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address) # if we detect a space character in the head node IP, we'll # convert it to an ipv4 address. This step is optional. if [[ "$head_node_ip" == *" "* ]]; then IFS=' ' read -ra ADDR <<<"$head_node_ip" if [[ ${#ADDR[0]} -gt 16 ]]; then head_node_ip=${ADDR[1]} else head_node_ip=${ADDR[0]} fi echo "IPV6 address detected. We split the IPV4 address as $head_node_ip" fi port=6379 ip_head=$head_node_ip:$port export ip_head echo "IP Head: $ip_head" # make sure we set environment variables before Ray initialization printenv echo "Starting HEAD at $head_node" srun --nodes=1 --ntasks=1 -w "$head_node" \ apptainer run --nv --bind $verl_workdir $apptainer_image_path \ ray start --head --node-ip-address="$head_node_ip" --port=$port \ --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & # optional, though may be useful in certain versions of Ray < 1.0. sleep 10 # number of nodes other than the head node worker_num=$((SLURM_JOB_NUM_NODES - 1)) for ((i = 1; i <= worker_num; i++)); do node_i=${nodes_array[$i]} echo "Starting WORKER $i at $node_i" srun --nodes=1 --ntasks=1 -w "$node_i" \ apptainer run --nv --bind $verl_workdir $apptainer_image_path \ ray start --address "$ip_head" --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block & sleep 5 done PYTHONUNBUFFERED=1 srun --overlap --nodes=1 --ntasks=1 -w "$head_node" \ apptainer run --nv --bind $verl_workdir $apptainer_image_path \ python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$train_files \ data.val_files=$val_files \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.use_kl_in_reward=False \ trainer.logger=console \ trainer.val_before_train=False \ trainer.n_gpus_per_node="${SLURM_GPUS_PER_NODE}" \ trainer.nnodes="${SLURM_NNODES}" \ trainer.save_freq=10 \ trainer.test_freq=10 \ trainer.total_epochs=15 2>&1 | tee verl_demo_slurm.log ================================================ FILE: examples/split_placement/README.md ================================================ # Split Placement Example Here we introduce how to run the naive implementation of the split placement of PPO algorithm. We will release the complete version of flexible placement in the near future. For quickstart, you can only follow Step 2 to modify the code and then follow Step 4 to execute the split placement example. ### Step 1: Placing the models to different GPUs Specify the placement and resource allocation. In the example, we place the actor and reference in the first half of the GPUs while map the critic and reward model (if any) to the second half of the GPUs. ```python actor_rollout_ref_pool_id = 'actor_rollout_ref_pool' critic_pool_id = 'critic_pool' if config.trainer.nnodes // 2 == 0 and config.trainer.n_gpus_per_node // 2 > 0: resource_pool_spec = { actor_rollout_ref_pool_id: [config.trainer.n_gpus_per_node // 2] * config.trainer.nnodes, critic_pool_id: [config.trainer.n_gpus_per_node // 2] * config.trainer.nnodes, } else: resource_pool_spec = { actor_rollout_ref_pool_id: [config.trainer.n_gpus_per_node] * (config.trainer.nnodes // 2), critic_pool_id: [config.trainer.n_gpus_per_node] * (config.trainer.nnodes // 2), } print(f'resource_pool_spec: {resource_pool_spec}') mapping = { Role.ActorRollout: actor_rollout_ref_pool_id, Role.Critic: critic_pool_id, Role.RefPolicy: actor_rollout_ref_pool_id, } mapping[Role.RewardModel] = critic_pool_id ``` ### Step 2: Make the models executed asynchronously Based on the model placement, we need to make the models executed asynchronously. To do so, you need to turn off the `blocking` flag (i.e., `blocking=False`) in our decorator of some model operations. For example, we hope the actor update and critic update can be executed in parallel, then we need to make the following modification in `fsdp_workers.py` ``` @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO, blocking=False) def update_actor(self, data: DataProto): ... @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO, blocking=False) def update_critic(self, data: DataProto): ... ``` We can also parallelize the computation of `ref_log_prob` and `values` and `rewards` in the split placement. For simplicity of the tutorial, we don't do this in this example. ### Step 3: Execute these operation in parallel in the single controller process To implement the parallel execution of the actor and critic update, the only thing we need to modify in the `ray_trainer.py` is to `get` the concurrent `futures` on the single controller process. ```python critic_output = critic_output.get() actor_output = actor_output.get() ``` ### Step 4: Run the split placement example ``` bash run_deepseek7b_llm.sh ``` ================================================ FILE: examples/split_placement/config/ppo_trainer_split.yaml ================================================ # the ppo trainer split config will override default ppo_trainer.yaml hydra: searchpath: - file://../../verl/trainer/config defaults: - ppo_trainer - _self_ data: tokenizer: null train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 # set to -1 to use full dataset val_max_samples: -1 # set to -1 to use full dataset prompt_key: prompt max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null # DEPRECATED: Validation datasets are sent to inference engines as a whole batch, which will schedule the memory themselves return_raw_input_ids: False # This should be set to true when the tokenizer between policy and rm differs return_raw_chat: False return_full_prompt: False shuffle: True seed: 42 actor_rollout_ref: hybrid_engine: True model: path: ~/models/deepseek-llm-7b-chat external_lib: null override_config: { } enable_gradient_checkpointing: True use_remove_padding: False actor: strategy: fsdp # This is for backward-compatibility ppo_mini_batch_size: 256 ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: False ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length} grad_clip: 1.0 clip_ratio: 0.2 entropy_coeff: 0.0 use_kl_loss: False # True for GRPO kl_loss_coef: 0.001 # for grpo kl_loss_type: low_var_kl # for grpo ppo_epochs: 1 shuffle: False ulysses_sequence_parallel_size: 1 # sp size optim: lr: 1e-6 lr_warmup_steps: -1 # Prioritized. Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine lr_scheduler_type: constant # select from constant/cosine total_training_steps: -1 # must be override by program fsdp_config: wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 param_offload: False optimizer_offload: False fsdp_size: -1 ref: fsdp_config: param_offload: False wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size rollout: name: vllm temperature: 1.0 top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1 prompt_length: ${data.max_prompt_length} # not use for opensource response_length: ${data.max_response_length} # for vllm rollout dtype: bfloat16 # should align with FSDP gpu_memory_utilization: 0.5 ignore_eos: False enforce_eager: True free_cache_engine: True load_format: dummy_dtensor tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_num_seqs: 1024 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} disable_log_stats: True enable_chunked_prefill: True # could get higher throughput # for hf rollout do_sample: True # number of responses (i.e. num sample times) n: 1 # > 1 for grpo critic: strategy: fsdp optim: lr: 1e-5 lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine lr_scheduler_type: constant # select from constant/cosine total_training_steps: -1 # must be override by program model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${actor_rollout_ref.model.path} override_config: { } external_lib: ${actor_rollout_ref.model.external_lib} enable_gradient_checkpointing: True use_remove_padding: False fsdp_config: param_offload: False optimizer_offload: False wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 fsdp_size: -1 ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size} ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null forward_micro_batch_size: ${critic.ppo_micro_batch_size} forward_micro_batch_size_per_gpu: ${critic.ppo_micro_batch_size_per_gpu} use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} ppo_max_token_len_per_gpu: 32768 # (${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}) * 2 forward_max_token_len_per_gpu: ${critic.ppo_max_token_len_per_gpu} ulysses_sequence_parallel_size: 1 # sp size ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs} shuffle: ${actor_rollout_ref.actor.shuffle} grad_clip: 1.0 cliprange_value: 0.5 reward_model: enable: False strategy: fsdp model: input_tokenizer: ${actor_rollout_ref.model.path} # set this to null if the chat template is identical path: ~/models/FsfairX-LLaMA3-RM-v0.1 external_lib: ${actor_rollout_ref.model.external_lib} use_remove_padding: False fsdp_config: min_num_params: 0 param_offload: False fsdp_size: -1 micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu micro_batch_size_per_gpu: null # set a number max_length: null ulysses_sequence_parallel_size: 1 # sp size use_dynamic_bsz: ${critic.use_dynamic_bsz} forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu} reward_manager: naive algorithm: gamma: 1.0 lam: 1.0 adv_estimator: gae use_kl_in_reward: False kl_penalty: kl # how to estimate kl divergence kl_ctrl: type: fixed kl_coef: 0.001 trainer: total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: [ 'console', 'wandb' ] log_val_generations: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 # auto: find the last ckpt to resume. If can't find, start from scratch resume_mode: auto # or disable or resume_path if resume_from_path is set resume_from_path: null test_freq: -1 critic_warmup: 0 default_hdfs_dir: null default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} ray_kwargs: ray_init: num_cpus: null # `None` means using all CPUs, which might cause hang if limited in systems like SLURM. Please set to a number allowed then. ================================================ FILE: examples/split_placement/main_ppo_split.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Note that we don't combine the main with ray_trainer as ray_trainer is used by other main. """ import hydra import ray import torch from omegaconf import OmegaConf from split_monkey_patch import fit from verl import DataProto from verl.trainer.ppo.ray_trainer import RayPPOTrainer from verl.trainer.ppo.utils import need_reference_policy from verl.utils.reward_score import gsm8k, math_reward def _select_rm_score_fn(data_source): if data_source == "openai/gsm8k": return gsm8k.compute_score elif data_source == "lighteval/MATH": return math_reward.compute_score else: raise NotImplementedError class RewardManager: def __init__(self, tokenizer, num_examine) -> None: self.tokenizer = tokenizer self.num_examine = num_examine # the number of batches of decoded responses to print to the console def __call__(self, data: DataProto, return_dict: bool = False): """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn if "rm_scores" in data.batch.keys(): return data.batch["rm_scores"] reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) already_print_data_sources = {} for i in range(len(data)): data_item = data[i] # DataProtoItem prompt_ids = data_item.batch["prompts"] prompt_length = prompt_ids.shape[-1] valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum() valid_prompt_ids = prompt_ids[-valid_prompt_length:] response_ids = data_item.batch["responses"] valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode sequences = torch.cat((valid_prompt_ids, valid_response_ids)) sequences_str = self.tokenizer.decode(sequences) ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] # select rm_score data_source = data_item.non_tensor_batch["data_source"] compute_score_fn = _select_rm_score_fn(data_source) score = compute_score_fn(solution_str=sequences_str, ground_truth=ground_truth) reward_tensor[i, valid_response_length - 1] = score if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 print(sequences_str) if return_dict: return {"reward_tensor": reward_tensor} else: return reward_tensor @hydra.main(config_path="config", config_name="ppo_trainer_split", version_base=None) def main(config): if not ray.is_initialized(): # this is for local ray cluster default_runtime_env = {"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}} ray_init_kwargs = config.ray_kwargs.get("ray_init", {}) runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {}) runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs) ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env}) print(f"ray init kwargs: {ray_init_kwargs}") ray.init(**OmegaConf.to_container(ray_init_kwargs)) ray.get(main_task.remote(config)) @ray.remote def main_task(config): # print initial config from pprint import pprint from omegaconf import OmegaConf from verl.utils.fs import copy_to_local pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) # download the checkpoint from hdfs local_path = copy_to_local(config.actor_rollout_ref.model.path) # instantiate tokenizer from verl.utils import hf_tokenizer tokenizer = hf_tokenizer(local_path) # define worker classes if config.actor_rollout_ref.actor.strategy in {"fsdp", "fsdp2"}: assert config.critic.strategy in {"fsdp", "fsdp2"} from verl.single_controller.ray import RayWorkerGroup from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker ray_worker_group_cls = RayWorkerGroup elif config.actor_rollout_ref.actor.strategy == "megatron": assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.single_controller.ray import RayWorkerGroup from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker ray_worker_group_cls = RayWorkerGroup else: raise NotImplementedError from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role role_worker_mapping = { Role.ActorRollout: ray.remote(ActorRolloutRefWorker), Role.Critic: ray.remote(CriticWorker), } # NOTE: initialze two resource pool actor_rollout_ref_pool_id = "actor_rollout_ref_pool" critic_pool_id = "critic_pool" if config.trainer.nnodes // 2 == 0 and config.trainer.n_gpus_per_node // 2 > 0: resource_pool_spec = { actor_rollout_ref_pool_id: [config.trainer.n_gpus_per_node // 2] * config.trainer.nnodes, critic_pool_id: [config.trainer.n_gpus_per_node // 2] * config.trainer.nnodes, } else: resource_pool_spec = { actor_rollout_ref_pool_id: [config.trainer.n_gpus_per_node] * (config.trainer.nnodes // 2), critic_pool_id: [config.trainer.n_gpus_per_node] * (config.trainer.nnodes // 2), } print(f"resource_pool_spec: {resource_pool_spec}") mapping = { Role.ActorRollout: actor_rollout_ref_pool_id, Role.Critic: critic_pool_id, } # use reference model if need_reference_policy(config): role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker) mapping[Role.RefPolicy] = actor_rollout_ref_pool_id reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0) # Note that we always use function-based RM for validation val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1) resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) RayPPOTrainer.fit = fit trainer = RayPPOTrainer( config=config, tokenizer=tokenizer, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, reward_fn=reward_fn, val_reward_fn=val_reward_fn, ) trainer.init_workers() trainer.fit() if __name__ == "__main__": main() ================================================ FILE: examples/split_placement/run_deepseek7b_llm.sh ================================================ set -x python3 main_ppo_split.py \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=deepseek-ai/deepseek-llm-7b-chat \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ critic.optim.lr=1e-5 \ critic.model.path=deepseek-ai/deepseek-llm-7b-chat \ critic.model.enable_gradient_checkpointing=False \ critic.ppo_micro_batch_size_per_gpu=8 \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_example_gsm8k' \ trainer.experiment_name='deepseek_llm_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/split_placement/split_monkey_patch.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ An naive implementation of split placment example """ import uuid from copy import deepcopy from pprint import pprint import numpy as np import torch from verl import DataProto from verl.trainer.ppo.ray_trainer import ( AdvantageEstimator, apply_kl_penalty, compute_advantage, compute_data_metrics, compute_timing_metrics, marked_timer, ) from verl.trainer.ppo.reward import compute_reward from verl.utils.metric import reduce_metrics def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ from omegaconf import OmegaConf from verl.utils.tracking import Tracking logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) self.global_steps = 0 # load checkpoint before doing anything self._load_checkpoint() # perform validation before training # currently, we only support validation using the reward_function. if self.val_reward_fn is not None and self.config.trainer.get("val_before_train", True): val_metrics = self._validate() pprint(f"Initial validation metrics: {val_metrics}") logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get("val_only", False): return # we start from step 1 self.global_steps += 1 last_val_metrics = None for epoch in range(self.config.trainer.total_epochs): for batch_dict in self.train_dataloader: metrics = {} timing_raw = {} batch: DataProto = DataProto.from_single_dict(batch_dict) # pop those keys for generation gen_batch = batch.pop(batch_keys=["input_ids", "attention_mask", "position_ids"]) is_last_step = self.global_steps >= self.total_training_steps with marked_timer("step", timing_raw): # generate a batch with marked_timer("gen", timing_raw): gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch) timing_raw.update(gen_batch_output.meta_info["timing"]) gen_batch_output.meta_info.pop("timing", None) if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX: with marked_timer("gen_max", timing_raw): gen_baseline_batch = deepcopy(gen_batch) gen_baseline_batch.meta_info["do_sample"] = False gen_baseline_output = self.actor_rollout_wg.generate_sequences(gen_baseline_batch) batch = batch.union(gen_baseline_output) # compute reward model score on batch rm_scores = None if self.use_rm and "rm_scores" not in batch.batch.keys(): rm_scores = self.rm_wg.compute_rm_score(batch) batch = batch.union(rm_scores) reward_baseline_tensor, _ = compute_reward(batch, self.reward_fn) reward_baseline_tensor = reward_baseline_tensor.sum(dim=-1) keys_to_pop = set(gen_baseline_output.batch.keys()) if rm_scores is not None: keys_to_pop.update(rm_scores.batch.keys()) batch.pop(batch_keys=list(keys_to_pop)) batch.batch["reward_baselines"] = reward_baseline_tensor del rm_scores, gen_baseline_batch, gen_baseline_output batch.non_tensor_batch["uid"] = np.array( [str(uuid.uuid4()) for _ in range(len(batch.batch))], dtype=object ) # repeat to align with repeated responses in rollout batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) batch = batch.union(gen_batch_output) # Balance the number of valid tokens across DP ranks. # NOTE: This usually changes the order of data in the `batch`, # which won't affect the advantage calculation (since it's based on uid), # but might affect the loss calculation (due to the change of mini-batching). # TODO: Decouple the DP balancing and mini-batching. self._balance_batch(batch, metrics=metrics) # compute global_valid tokens batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist() # recompute old_log_probs with marked_timer("old_log_prob", timing_raw): old_log_prob = self.actor_rollout_wg.compute_log_prob(batch) batch = batch.union(old_log_prob) if self.use_reference_policy: # compute reference log_prob with marked_timer("ref", timing_raw): ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch) batch = batch.union(ref_log_prob) # compute values if self.use_critic: with marked_timer("values", timing_raw): values = self.critic_wg.compute_values(batch) batch = batch.union(values) with marked_timer("adv", timing_raw): # compute scores. Support both model and function-based. # We first compute the scores using reward model. Then, we call reward_fn to combine # the results from reward model and rule-based results. if self.use_rm and "rm_scores" not in batch.batch.keys(): # we first compute reward model score reward_tensor = self.rm_wg.compute_rm_score(batch) batch = batch.union(reward_tensor) # we combine with rule-based rm reward_tensor, _ = compute_reward(batch, self.reward_fn) batch.batch["token_level_scores"] = reward_tensor # compute rewards. apply_kl_penalty if available if self.config.algorithm.use_kl_in_reward: batch, kl_metrics = apply_kl_penalty( batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty ) metrics.update(kl_metrics) else: batch.batch["token_level_rewards"] = batch.batch["token_level_scores"] # compute advantages, executed on the driver process norm_adv_by_std_in_grpo = self.config.algorithm.get("norm_adv_by_std_in_grpo", True) batch = compute_advantage( batch, adv_estimator=self.config.algorithm.adv_estimator, gamma=self.config.algorithm.gamma, lam=self.config.algorithm.lam, num_repeat=self.config.actor_rollout_ref.rollout.n, norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo, config=self.config.algorithm, ) # implement critic warmup if self.config.trainer.critic_warmup <= self.global_steps: # update actor with marked_timer("update_actor_call", timing_raw): actor_output = self.actor_rollout_wg.update_actor(batch) else: actor_output = None # update critic if self.use_critic: with marked_timer("update_critic_call", timing_raw): critic_output = self.critic_wg.update_critic(batch) # NOTE: make sure you set blocking=False in update_actor and update_crtic in the worker class with marked_timer("update_actor_critic", timing_raw): critic_output = critic_output.get() critic_output_metrics = reduce_metrics(critic_output.meta_info["metrics"]) metrics.update(critic_output_metrics) if actor_output is not None: actor_output = actor_output.get() actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"]) metrics.update(actor_output_metrics) # validate if ( self.val_reward_fn is not None and self.config.trainer.test_freq > 0 and (is_last_step or self.global_steps % self.config.trainer.test_freq == 0) ): with marked_timer("testing", timing_raw): val_metrics: dict = self._validate() if is_last_step: last_val_metrics = val_metrics metrics.update(val_metrics) if self.config.trainer.save_freq > 0 and ( is_last_step or self.global_steps % self.config.trainer.save_freq == 0 ): with marked_timer("save_checkpoint", timing_raw): self._save_checkpoint() # collect metrics metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic)) metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw)) # TODO: make a canonical logger that supports various backend logger.log(data=metrics, step=self.global_steps) if self.global_steps >= self.total_training_steps: pprint(f"Final validation metrics: {last_val_metrics}") return self.global_steps += 1 ================================================ FILE: examples/tuning/0.5b/qwen2-0.5b_grpo-lora_1_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=4 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-0.5b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=0.5b-${NOW} MODEL_PATH=Qwen/Qwen2.5-0.5B-Instruct set -x nproc_per_gpu=1 nnodes=1 ngpu_per_node=1 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ trainer.val_before_train=False \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.1 \ actor_rollout_ref.rollout.n=1 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/1.5b/qwen2-1.5b_grpo-lora_1_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-1.5b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=1.5b-${NOW} MODEL_PATH=Qwen/Qwen2.5-1.5B-Instruct set -x nproc_per_gpu=128 nnodes=1 ngpu_per_node=1 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.1 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/14b/qwen2-14b_grpo-lora_2_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-14b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=14b-${NOW} MODEL_PATH=Qwen/Qwen2.5-14B-Instruct set -x nproc_per_gpu=58 # 32√ → 64× → 48√ → 56√ → 60× → 58√ → 59× nnodes=1 ngpu_per_node=2 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.25 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=2 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/14b/qwen2_14b_grpo_4_h800_fsdp_vllm.sh ================================================ set -x gsm8k_train_path=$HOME/data/rlhf/gsm8k/train.parquet gsm8k_test_path=$HOME/data/rlhf/math/test.parquet model_path=Qwen/Qwen2.5-Coder-14B-Instruct train_files="['$gsm8k_train_path']" test_files="['$gsm8k_test_path']" PYTHONPATH=/opt/tiger/open_verl python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_14b_function_rm' \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ ================================================ FILE: examples/tuning/32b/qwen2-32b_grpo-lora_4_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-32b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=32b-${NOW} MODEL_PATH=Qwen/Qwen2.5-32B-Instruct set -x nproc_per_gpu=45 # 32√ → 64× → 48× → 40√ → 44√ → 46× → 45× nnodes=1 ngpu_per_node=4 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=4 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/32b/qwen2_32B_grpo_8_h20_megatron_vllm.sh ================================================ set -x # we need this to avoid fragmentation of GPU memory export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 gsm8k_train_path=$HOME/data/rlhf/gsm8k/train.parquet gsm8k_test_path=$HOME/data/rlhf/math/test.parquet train_files="['$gsm8k_train_path']" test_files="['$gsm8k_test_path']" model_path=Qwen/Qwen2.5-32B python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=512 \ data.max_prompt_length=2048 \ data.max_response_length=6144 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=8 \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.megatron.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='megatron_vllm_qwen2_32b' \ trainer.experiment_name='qwen2_32b_grpo_8_h20' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/tuning/3b/qwen2-3b_grpo-lora_1_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-3b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=3b-${NOW} MODEL_PATH=Qwen/Qwen2.5-3B-Instruct set -x nproc_per_gpu=62 nnodes=1 ngpu_per_node=1 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.1 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/70b/qwen2-70b_grpo_32_h20_fsdp_vllm.sh ================================================ set -x gsm8k_train_path=$HOME/data/rlhf/gsm8k/train.parquet gsm8k_val_path=$HOME/data/rlhf/math/test.parquet model_path=Qwen/Qwen2-72B-Instruct python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$data_path \ data.val_files=$gsm8k_val_path \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=16 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='Qwen2_72B_Instruct' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=4 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ ================================================ FILE: examples/tuning/70b/qwen2-70b_grpo_32_h800_fsdp_vllm.sh ================================================ set -x #### important: vllm version must be >= 0.8.3 gsm8k_train_path=$HOME/data/rlhf/gsm8k/train.parquet gsm8k_val_path=$HOME/data/rlhf/math/test.parquet model_path=Qwen/Qwen2-72B-Instruct python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$gsm8k_train_path \ data.val_files=$gsm8k_val_path \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=16 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='Qwen2_72B_Instruct' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=4 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ ================================================ FILE: examples/tuning/70b/qwen2-72b_grpo-lora_8_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-72b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=72b-${NOW} MODEL_PATH=Qwen/Qwen2.5-72B-Instruct set -x nproc_per_gpu=22 # 16√ → 32× → 24× → 20√ → 22√ → 23× nnodes=1 ngpu_per_node=8 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/7b/qwen2-7b_grpo-lora_1_h100_fsdp_vllm.sh ================================================ # -*- coding: utf-8 -*- export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NOW=$(date +%Y%m%d) export WANDB_DIR=gsm8k-grpo-lora-qwen2.5-7b-${NOW} export WANDB_PROJECT=${WANDB_DIR} export WANDB_EXP=7b-${NOW} MODEL_PATH=Qwen/Qwen2.5-7B-Instruct set -x nproc_per_gpu=16 # 64√ → 128× → 96√ → 112× → 104× → 100√ → 102× → 101× nnodes=1 ngpu_per_node=1 total_procs=$(( nproc_per_gpu * nnodes * ngpu_per_node )) mini_batch_size=$(( total_procs )) python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=data/gsm8k/train.parquet \ data.val_files=data/gsm8k/test.parquet \ data.train_batch_size=${total_procs} \ data.val_batch_size=${total_procs} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=$MODEL_PATH \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.model.lora_rank=32 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.model.target_modules=all-linear \ actor_rollout_ref.actor.optim.lr=3e-5 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.ppo_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.2 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.max_num_seqs=512 \ actor_rollout_ref.rollout.max_model_len=1536 \ actor_rollout_ref.rollout.max_num_batched_tokens=1536 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size=${mini_batch_size} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \ actor_rollout_ref.actor.entropy_coeff=0.001 \ algorithm.kl_ctrl.kl_coef=0.001 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name=${WANDB_PROJECT} \ trainer.experiment_name=${WANDB_EXP} \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ 2>&1 | tee ${WANDB_PROJECT}.log ================================================ FILE: examples/tuning/7b/qwen2-7b_grpo_2_h800_fsdp_vllm.sh ================================================ set -x gsm8k_train_path=$HOME/data/rlhf/gsm8k/train.parquet gsm8k_test_path=$HOME/data/rlhf/math/test.parquet model_path=Qwen/Qwen2-7B-Instruct train_files="['$gsm8k_train_path']" test_files="['$gsm8k_test_path']" PYTHONPATH=/opt/tiger/open_verl python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=1024 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=2 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ================================================ FILE: examples/tutorial/agent_loop_get_started/agent_loop_tutorial.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Train ReAct agent with code sandbox\n", "\n", "In this tutorial, we will demonstrate how to train a [ReAct](https://arxiv.org/abs/2210.03629) agent to solve math problem with code sandbox.\n", "\n", "The agent works as follows:\n", "1. Given a math problem, the agent first query LLM to generate response and tool calls, which are python code to be executed in sandbox.\n", "2. If there is a tool call, the agent execute the python code in code sandbox.\n", "3. After code execution, the agent get the result from sandbox and append to chat history.\n", "4. The agent query LLM again until no tool call or max context length reached.\n", "\n", "\n", "
\n", " \"ReAct\"\n", "
\n", " source: LangGraph\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Prerequisite\n", "\n", "To run the examples in this notebook, you need to install the verl package first.\n", "```bash\n", "git clone https://github.com/volcengine/verl\n", "cd verl\n", "pip install -e .\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-10-16 23:20:11,956\tINFO worker.py:2004 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", "/usr/local/lib/python3.12/dist-packages/ray/_private/worker.py:2052: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0\n", " warnings.warn(\n" ] } ], "source": [ "import asyncio\n", "import sys\n", "import tempfile\n", "import os\n", "import socket\n", "import json\n", "\n", "import requests\n", "import ray\n", "import fastapi\n", "import uvicorn\n", "from starlette.requests import Request\n", "from starlette.responses import JSONResponse\n", "from pprint import pprint\n", "\n", "import verl\n", "\n", "ray.init()\n", "verl_config_dir = os.path.join(os.path.dirname(verl.__file__), \"trainer/config\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For demo purpose, we will use Qwen/Qwen3-1.7B as the LLM. First, let's download required model and dataset used in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pyarrow.parquet as pq\n", "from huggingface_hub import snapshot_download\n", "\n", "snapshot_download(\n", " repo_id=\"verl-team/lighteval-MATH-preprocessed\",\n", " repo_type=\"dataset\",\n", " local_dir=os.path.expanduser(\"~/verl-team/lighteval-MATH-preprocessed\"),\n", ")\n", "snapshot_download(\n", " repo_id=\"Qwen/Qwen3-1.7B\",\n", " repo_type=\"model\",\n", " local_dir=os.path.expanduser(\"~/Qwen/Qwen3-1.7B\"),\n", ")\n", "\n", "model_path = os.path.expanduser(\"~/Qwen/Qwen3-1.7B\")\n", "train_file = os.path.expanduser(\"~/verl-team/lighteval-MATH-preprocessed/train.parquet\")\n", "test_file = os.path.expanduser(\"~/verl-team/lighteval-MATH-preprocessed/test.parquet\")\n", "\n", "test = pq.read_table(test_file)\n", "test_file = os.path.expanduser(\"~/verl-team/lighteval-MATH-preprocessed/test_100.parquet\")\n", "pq.write_table(test[:100], test_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "verl support both vllm and sglang rollout server for high performance inference. This tutorial has been tested on both vllm and sglang, you can choose either of them to run the tutorial." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rollout_name = \"???\" # vllm or sglang" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Basic tool call\n", "For beginning, let's see how we can do basic tool call in verl with example from [Transformer tool use](https://huggingface.co/docs/transformers/main/chat_extras#tool-use). To use tool in verl, we need to define a tool class that inherits from `BaseTool`, and implement the following methods:\n", "- `get_openai_tool_schema`: return the schema of the tool in `OpenAIFunctionToolSchema` format.\n", "- `execute`: execute the tool with the given parameters, and return the result in `ToolResponse` format." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"type\": \"function\",\n", " \"function\": {\n", " \"name\": \"get_current_temperature\",\n", " \"description\": \"Get current temperature at a location.\",\n", " \"parameters\": {\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"location\": {\n", " \"type\": \"string\",\n", " \"description\": \"The location to get the temperature for, in the format \\\"City, State, Country\\\".\"\n", " },\n", " \"unit\": {\n", " \"type\": \"string\",\n", " \"description\": \"The unit to return the temperature in. Defaults to \\\"celsius\\\".\",\n", " \"enum\": [\n", " \"celsius\",\n", " \"fahrenheit\"\n", " ]\n", " }\n", " },\n", " \"required\": [\n", " \"location\"\n", " ]\n", " }\n", " }\n", "}\n" ] } ], "source": [ "from transformers.utils import get_json_schema\n", "from verl.tools.base_tool import BaseTool, OpenAIFunctionToolSchema, ToolResponse\n", "\n", "\n", "class WeatherTool(BaseTool):\n", " def get_current_temperature(self, location: str, unit: str = \"celsius\"):\n", " \"\"\"Get current temperature at a location.\n", "\n", " Args:\n", " location: The location to get the temperature for, in the format \"City, State, Country\".\n", " unit: The unit to return the temperature in. Defaults to \"celsius\". (choices: [\"celsius\", \"fahrenheit\"])\n", "\n", " Returns:\n", " the temperature, the location, and the unit in a dict\n", " \"\"\"\n", " return {\n", " \"temperature\": 26.1,\n", " \"location\": location,\n", " \"unit\": unit,\n", " }\n", "\n", " def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema:\n", " schema = get_json_schema(self.get_current_temperature)\n", " return OpenAIFunctionToolSchema(**schema)\n", "\n", " async def execute(self, instance_id: str, parameters: dict, **kwargs) -> tuple[ToolResponse, float, dict]:\n", " try:\n", " result = self.get_current_temperature(**parameters)\n", " return ToolResponse(text=json.dumps(result)), 0, {}\n", " except Exception as e:\n", " return ToolResponse(text=str(e)), 0, {}\n", "\n", "\n", "weather_tool = WeatherTool(config={}, tool_schema=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's launch a standalone rollout server without hybrid engine (which is more heavy to start) to test the basic tool call." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from hydra import compose, initialize_config_dir\n", "from verl.workers.rollout.replica import get_rollout_replica_class\n", "\n", "with initialize_config_dir(config_dir=verl_config_dir):\n", " config = compose(\n", " config_name=\"ppo_trainer\",\n", " overrides=[\n", " \"actor_rollout_ref.rollout.name=\" + rollout_name,\n", " \"actor_rollout_ref.rollout.mode=async\",\n", " \"actor_rollout_ref.rollout.tensor_model_parallel_size=1\",\n", " \"actor_rollout_ref.model.path=\" + model_path,\n", " \"actor_rollout_ref.rollout.response_length=4096\",\n", " \"actor_rollout_ref.rollout.skip_tokenizer_init=False\",\n", " \"+actor_rollout_ref.rollout.engine_kwargs.vllm.enable_auto_tool_choice=True\",\n", " \"+actor_rollout_ref.rollout.engine_kwargs.vllm.tool_call_parser=hermes\",\n", " \"+actor_rollout_ref.rollout.engine_kwargs.sglang.tool_call_parser=qwen25\",\n", " ],\n", " )\n", "\n", "rollout_server_class = get_rollout_replica_class(config.actor_rollout_ref.rollout.name)\n", "rollout_server = rollout_server_class(\n", " replica_rank=0,\n", " config=config.actor_rollout_ref.rollout,\n", " model_config=config.actor_rollout_ref.model,\n", ")\n", "\n", "await rollout_server.init_standalone()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can query LLM with openai client. Note that we need to pass the tool schema to server to guide LLM generating tool calls. We can see that the LLM correctly generates a tool call to get the temperature in Paris." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'content': \"Hey, what's the temperature in Paris right now?\", 'role': 'user'},\n", " {'role': 'assistant',\n", " 'tool_calls': [{'function': {'arguments': '{\"location\": \"Paris, France\"}',\n", " 'name': 'get_current_temperature'},\n", " 'id': 'call_b10bdde504a0411690e96b55',\n", " 'index': -1,\n", " 'type': 'function'}]}]\n" ] } ], "source": [ "from openai import AsyncOpenAI\n", "\n", "client = AsyncOpenAI(\n", " api_key=\"dummy\",\n", " base_url=f\"http://{rollout_server._server_address}/v1\",\n", ")\n", "\n", "messages = [{\"role\": \"user\", \"content\": \"Hey, what's the temperature in Paris right now?\"}]\n", "completion = await client.chat.completions.create(\n", " model=config.actor_rollout_ref.model.path,\n", " messages=messages,\n", " tools=[weather_tool.tool_schema.model_dump(exclude_unset=True, exclude_none=True)],\n", " extra_body={\n", " \"chat_template_kwargs\": {\"enable_thinking\": False},\n", " },\n", ")\n", "\n", "message = completion.choices[0].message.model_dump(exclude_unset=True, exclude_none=True)\n", "messages.append(message)\n", "pprint(messages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can execute the tool call with arguments generated by LLM and get the temperature in Paris." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "text='{\"temperature\": 26.1, \"location\": \"Paris, France\", \"unit\": \"celsius\"}' image=None video=None\n" ] } ], "source": [ "args = json.loads(message[\"tool_calls\"][0][\"function\"][\"arguments\"])\n", "tool_response, _, _ = await weather_tool.execute(\"\", args)\n", "print(tool_response)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can add the tool response to chat history and query LLM again. With the tool response, LLM can generate a final response to the user." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'content': \"Hey, what's the temperature in Paris right now?\", 'role': 'user'},\n", " {'role': 'assistant',\n", " 'tool_calls': [{'function': {'arguments': '{\"location\": \"Paris, France\"}',\n", " 'name': 'get_current_temperature'},\n", " 'id': 'call_b10bdde504a0411690e96b55',\n", " 'index': -1,\n", " 'type': 'function'}]},\n", " {'content': '{\"temperature\": 26.1, \"location\": \"Paris, France\", \"unit\": '\n", " '\"celsius\"}',\n", " 'role': 'tool'},\n", " {'content': 'The current temperature in Paris is 26.1°C.',\n", " 'role': 'assistant'}]\n" ] } ], "source": [ "messages.append({\"role\": \"tool\", \"content\": tool_response.text})\n", "completion = await client.chat.completions.create(\n", " model=config.actor_rollout_ref.model.path,\n", " messages=messages,\n", " tools=[weather_tool.tool_schema.model_dump(exclude_unset=True, exclude_none=True)],\n", " extra_body={\n", " \"chat_template_kwargs\": {\"enable_thinking\": False},\n", " },\n", ")\n", "\n", "message = completion.choices[0].message.model_dump(exclude_unset=True, exclude_none=True)\n", "messages.append(message)\n", "pprint(messages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Advanced tool call with code sandbox\n", "\n", "Now, let's see a more realistic example of tool call with code sandbox, which is widely used in real-world applications.\n", "\n", "### 2.1 Implement a naive code sandbox\n", "\n", "To execute python code snippet generated by LLM, we need a code sandbox environment. In this tutorial, we will implement a very naive code sandbox, which is\n", "a FastAPI http server with `/run_code` endpoint. The server works as follows:\n", "1. Receive a http request, write the python code snippet to a temp file.\n", "2. Spawn a subprocess to execute the code, and get stdout and stderr of the subprocess.\n", "3. Return the stdout and stderr of the subprocess as http response.\n", "\n", "> 🚨 **WARNING:** This naive code sandbox is for demonstration purpose only, do not use it in production. Please use docker/kata container for stronger isolation and security restriction." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "@ray.remote(num_cpus=1)\n", "class Sandbox:\n", " \"\"\"Sandbox to execute python code.\"\"\"\n", "\n", " def __init__(self):\n", " self.address = ray._private.services.get_node_ip_address()\n", " self.port = self._get_free_port()\n", " asyncio.create_task(self._start_fastapi_server())\n", "\n", " async def code_execution(self, request: Request):\n", " request_json = await request.json()\n", " code = request_json[\"code\"]\n", " # print(f\"execute code:\\n{code}\")\n", "\n", " _, temp_file = tempfile.mkstemp(suffix=\".py\", prefix=\"temp_code\", dir=None, text=True)\n", " with open(temp_file, \"w\") as f:\n", " f.write(code)\n", "\n", " try:\n", " process = await asyncio.create_subprocess_exec(\n", " sys.executable, temp_file, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE\n", " )\n", "\n", " stdout, stderr = await process.communicate()\n", "\n", " response = {\n", " \"status\": \"Success\" if process.returncode == 0 else \"Failed\",\n", " \"run_result\": {\n", " \"status\": \"Finished\",\n", " \"stdout\": stdout.decode(),\n", " \"stderr\": stderr.decode(),\n", " \"return_code\": process.returncode,\n", " },\n", " }\n", " return JSONResponse(content=response)\n", " finally:\n", " try:\n", " os.unlink(temp_file)\n", " except Exception:\n", " pass\n", "\n", " def _get_free_port(self):\n", " with socket.socket() as sock:\n", " sock.bind((\"\", 0))\n", " return sock.getsockname()[1]\n", "\n", " async def _start_fastapi_server(self):\n", " app = fastapi.FastAPI()\n", " app.router.add_api_route(\"/run_code\", self.code_execution, methods=[\"POST\"])\n", "\n", " config = uvicorn.Config(app, host=[\"::\", \"0.0.0.0\"], port=self.port, log_level=\"warning\")\n", " server = uvicorn.Server(config)\n", " await server.serve()\n", "\n", " async def get_server_address(self) -> str:\n", " \"\"\"Get FastAPI server address.\"\"\"\n", " return f\"{self.address}:{self.port}\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "sandbox = Sandbox.remote()\n", "sandbox_address = ray.get(sandbox.get_server_address.remote())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Define sandbox tool\n", "\n", "As shown in the previous section, we also defined a tool for the code sandbox. In the `execute` method, we send the code snippet to code sandbox by http request and get the output." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"type\": \"function\",\n", " \"function\": {\n", " \"name\": \"code_interpreter\",\n", " \"description\": \"Execute the code in the sandbox.\",\n", " \"parameters\": {\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"code\": {\n", " \"type\": \"string\",\n", " \"description\": \"The code to be executed.\"\n", " }\n", " },\n", " \"required\": [\n", " \"code\"\n", " ]\n", " }\n", " }\n", "}\n" ] } ], "source": [ "import re\n", "import aiohttp\n", "\n", "\n", "class SandboxTool(BaseTool):\n", " def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema):\n", " super().__init__(config, tool_schema)\n", " # Different model may use different code pattern, e.g. python, py, etc.\n", " self.code_pattern = re.compile(r\"```py(.*?)```\", re.DOTALL)\n", "\n", " async def code_interpreter(self, code: str) -> str:\n", " \"\"\"Execute the code in the sandbox.\n", "\n", " Args:\n", " code: The code to be executed.\n", "\n", " Returns:\n", " str: The output of the code execution.\n", " \"\"\"\n", " async with aiohttp.ClientSession() as session:\n", " async with session.post(\n", " self.config.get(\"sandbox_fusion_url\"),\n", " json={\"code\": code},\n", " ) as resp:\n", " resp.raise_for_status()\n", " result = await resp.json()\n", " stdout, stderr = result[\"run_result\"][\"stdout\"], result[\"run_result\"][\"stderr\"]\n", " return stdout + stderr\n", "\n", " def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema:\n", " schema = get_json_schema(self.code_interpreter)\n", " return OpenAIFunctionToolSchema(**schema)\n", "\n", " async def execute(self, instance_id: str, parameters: dict, **kwargs) -> tuple[str, float, dict]:\n", " code = parameters[\"code\"]\n", " matches = self.code_pattern.findall(code)\n", " if matches:\n", " code = matches[0].strip()\n", "\n", " # NOTE: Some script may not explicitly print result, we need to add a print statement to the end of the script.\n", " # More better way is to SFT the model to make it print result by default, we skip SFT stage in this tutorial.\n", " lines = code.split(\"\\n\")\n", " for i, line in reversed(list(enumerate(lines))):\n", " if line == \"\":\n", " continue\n", " if not lines[i].startswith(\"print\"):\n", " lines[i] = f\"print({line})\"\n", " break\n", " code = \"\\n\".join(lines)\n", "\n", " result = await self.code_interpreter(code)\n", " return ToolResponse(text=result), 0.0, {}\n", "\n", "\n", "sandbox_tool = SandboxTool(config={\"sandbox_fusion_url\": f\"http://{sandbox_address}/run_code\"}, tool_schema=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's try to execute a valid code and check the response with stdout." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(ToolResponse(text='sqrt(3)\\n', image=None, video=None), 0.0, {})\n" ] } ], "source": [ "code = \"\"\"```py\n", "import sympy\n", "\n", "print(sympy.sqrt(3))\n", "```\"\"\"\n", "\n", "print(await sandbox_tool.execute(instance_id=\"\", parameters={\"code\": code}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let's try to execute an invalid code and check the response with stderr. The error message is important to inform LLM to fix code in next generation." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(ToolResponse(text='Traceback (most recent call last):\\n File \"/tmp/temp_code3e2f638_.py\", line 2, in \\n print(sympy.sqrt(3))\\n ^^^^^\\nNameError: name \\'sympy\\' is not defined\\n', image=None, video=None), 0.0, {})\n" ] } ], "source": [ "code_invalid = \"\"\"\n", "print(sympy.sqrt(3))\n", "\"\"\"\n", "\n", "print(await sandbox_tool.execute(instance_id=\"\", parameters={\"code\": code_invalid}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Test sandbox tool\n", "\n", "Now, we can test sandbox tool with real math problem. In this tutorial, we will use the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset, which consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ebd09c8816b140a59a879e5a5e218950", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"parquet\", data_files=test_file)[\"train\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For debug purpose, we can implement ReAct agent as a simple loop. For RL training, there are more subtle issue and corner case to deal with, we provide a built-in ReAct agent loop which will be discussed in next section." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No tool calls, finish_reason: stop\n" ] } ], "source": [ "messages = dataset[\"prompt\"][0]\n", "\n", "while True:\n", " # 1. Chat with the model\n", " completion = await client.chat.completions.create(\n", " model=config.actor_rollout_ref.model.path,\n", " messages=messages,\n", " tools=[sandbox_tool.tool_schema.model_dump(exclude_unset=True, exclude_none=True)],\n", " extra_body={\n", " \"chat_template_kwargs\": {\"enable_thinking\": False},\n", " },\n", " )\n", "\n", " message = completion.choices[0].message.model_dump(exclude_unset=True, exclude_none=True)\n", " messages.append(message)\n", "\n", " # 2. Call tools\n", " finish_reason = completion.choices[0].finish_reason\n", " if finish_reason != \"tool_calls\":\n", " print(f\"No tool calls, finish_reason: {finish_reason}\")\n", " break\n", "\n", " try:\n", " tool_calls = completion.choices[0].message.tool_calls[0]\n", " args = json.loads(tool_calls.function.arguments)\n", " result, _, _ = await sandbox_tool.execute(\"\", args)\n", " except Exception as e:\n", " print(f\"Error: {e}\")\n", "\n", " # 3. Add tool response to messages\n", " messages.append(\n", " {\n", " \"role\": \"tool\",\n", " \"content\": result.text,\n", " }\n", " )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'content': \"How many vertical asymptotes does the graph of $y=\\\\frac{2}{x^2+x-6}$ have? Let's think step by step and output the final answer within \\\\boxed{}.\",\n", " 'role': 'user'},\n", " {'content': \"To determine the number of vertical asymptotes for the function $ y = \\\\frac{2}{x^2 + x - 6} $, we need to find the values of $ x $ where the denominator equals zero, as these points are where the function is undefined and potentially where it has vertical asymptotes.\\n\\nThe denominator is $ x^2 + x - 6 $. To find the vertical asymptotes, we need to solve the equation:\\n\\n$$ x^2 + x - 6 = 0 $$\\n\\nThis is a quadratic equation, and we can solve it using the quadratic formula:\\n\\n$$ x = \\\\frac{-b \\\\pm \\\\sqrt{b^2 - 4ac}}{2a} $$\\n\\nwhere $ a = 1 $, $ b = 1 $, and $ c = -6 $. Let's solve this equation to find the values of $ x $ where the denominator is zero, which will give us the vertical asymptotes.\",\n", " 'role': 'assistant',\n", " 'tool_calls': [{'id': 'call_4d873672ff8445159e4e5e45',\n", " 'function': {'arguments': '{\"code\": \"from sympy import symbols, solve\\\\nx = symbols(\\'x\\')\\\\nroots = solve(x**2 + x - 6, x)\\\\nroots\"}',\n", " 'name': 'code_interpreter'},\n", " 'type': 'function',\n", " 'index': -1}]},\n", " {'role': 'tool', 'content': '[-3, 2]\\n'},\n", " {'content': 'The roots of the equation $ x^2 + x - 6 = 0 $ are $ x = -3 $ and $ x = 2 $. These are the values of $ x $ where the denominator is zero, which means the function $ y = \\\\frac{2}{x^2 + x - 6} $ is undefined at these points. \\n\\nSince the denominator is zero at these values, the function has vertical asymptotes at $ x = -3 $ and $ x = 2 $. Therefore, the graph of the function has two vertical asymptotes.\\n\\nThe final answer is $\\\\boxed{2}$.',\n", " 'role': 'assistant'}]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "messages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the ReAct agent properly query LLM, execute sandbox tool call, finally generate the answer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. End-to-end training with tool agent loop\n", "\n", "After tool has been implemented and tested, we can do end-to-end RL training to tune the model to properly use the tool. To simplify agentic RL training, verl provide [Agent Loop](https://verl.readthedocs.io/en/latest/advance/agent_loop.html) abstraction, which allow user to define custom agent loop:\n", "- Search agent\n", "- Math agent\n", "- SWE agent\n", "- GUI agent\n", "- ...\n", "\n", "For ease of use, verl provide two pre-defined agent loop:\n", "- SingleTurnAgentLoop: single-turn conversation without tool calling\n", "- ToolAgentLoop: multi-turn conversation with tool calling, interaction\n", "\n", "To use ToolAgentLoop, user only need to provide tools configuration in json/yaml file. In the configuration file, user should specify following fields for each tool:\n", "- class_name: fully qualified class name of the tool used to dynamically load the custom tool class\n", "- config: key-word arguments used to initialize the tool instance\n", "\n", "Let's dump our sandbox tool configuration to a json file:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-10-16 23:07:16,868\tINFO worker.py:2004 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n" ] } ], "source": [ "ray.shutdown()\n", "\n", "sandbox = Sandbox.remote()\n", "sandbox_address = ray.get(sandbox.get_server_address.remote())\n", "\n", "tool_config = {\n", " \"tools\": [\n", " {\n", " \"class_name\": \"sandbox.SandboxTool\",\n", " \"config\": {\n", " \"type\": \"native\",\n", " \"sandbox_fusion_url\": f\"http://{sandbox_address}/run_code\",\n", " },\n", " },\n", " ],\n", "}\n", "\n", "tool_config_path = \"tool_config.json\"\n", "with open(tool_config_path, \"w\") as f:\n", " json.dump(tool_config, f)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_174199/3963810189.py:3: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", " with initialize_config_dir(config_dir=verl_config_dir):\n" ] } ], "source": [ "from hydra import compose, initialize_config_dir\n", "\n", "with initialize_config_dir(config_dir=verl_config_dir):\n", " config = compose(\n", " config_name=\"ppo_trainer\",\n", " overrides=[\n", " \"algorithm.adv_estimator=grpo\",\n", " \"data.train_files=\" + train_file,\n", " \"data.val_files=\" + test_file,\n", " \"data.return_raw_chat=True\",\n", " \"data.train_batch_size=32\",\n", " \"data.max_prompt_length=1024\",\n", " \"data.max_response_length=1024\",\n", " \"+data.apply_chat_template_kwargs.enable_thinking=False\",\n", " # actor related\n", " \"actor_rollout_ref.model.path=\" + model_path,\n", " \"actor_rollout_ref.actor.ppo_mini_batch_size=8\",\n", " \"actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8\",\n", " \"actor_rollout_ref.actor.fsdp_config.param_offload=True\",\n", " \"actor_rollout_ref.actor.fsdp_config.optimizer_offload=True\",\n", " # rollout related\n", " \"actor_rollout_ref.rollout.name=\" + rollout_name,\n", " \"actor_rollout_ref.rollout.mode=async\",\n", " \"actor_rollout_ref.rollout.tensor_model_parallel_size=1\",\n", " \"actor_rollout_ref.rollout.n=8\",\n", " \"actor_rollout_ref.rollout.multi_turn.tool_config_path=\" + tool_config_path,\n", " \"actor_rollout_ref.rollout.agent.default_agent_loop=tool_agent\",\n", " \"actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8\",\n", " # trainer related\n", " \"trainer.val_before_train=True\",\n", " \"trainer.log_val_generations=10\",\n", " \"trainer.n_gpus_per_node=8\",\n", " \"trainer.test_freq=-1\",\n", " \"trainer.total_training_steps=5\",\n", " \"trainer.logger=['console','tensorboard', 'wandb']\",\n", " \"trainer.project_name=verl\",\n", " \"trainer.experiment_name=\" + os.path.basename(model_path),\n", " ],\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from verl.trainer.main_ppo import main\n", "\n", "main(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For demo purpose, we only train 5 steps, you can verify the training process by checking wandb metrics:\n", "- num_turns: min/max/mean chat conversation turns in each step.\n", "- critic rewards: min/max/mean critic rewards in each step.\n", "\n", "For more realistic agentic RL training, please refer to our recipe:\n", "- [retool](https://github.com/volcengine/verl-recipe/tree/main/retool): implementation of paper [ReTool: Reinforcement Learning for Strategic Tool Use in LLMs](https://arxiv.org/abs/2504.11536)\n", "- [collabllm](https://github.com/volcengine/verl-recipe/tree/main/collabllm): implementation of paper [CollabLLM: From Passive Responders to Active Collaborators](https://arxiv.org/pdf/2502.00640)\n", "- [deepeyes](https://github.com/volcengine/verl-recipe/tree/main/deepeyes): implementation of paper [DeepEyes: Incentivizing \"Thinking with Images\" via Reinforcement Learning](https://arxiv.org/abs/2505.14362)" ] } ], "metadata": { "fileId": "398ea641-8a51-4a0b-b64e-6b7cd6b72164", "filePath": "/opt/tiger/open_verl/examples/agent_loop_tutorial.ipynb", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/tutorial/agent_loop_get_started/sandbox.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re import aiohttp from transformers.utils import get_json_schema from verl.tools.base_tool import BaseTool, OpenAIFunctionToolSchema, ToolResponse class SandboxTool(BaseTool): def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): super().__init__(config, tool_schema) # Different model may use different code pattern, e.g. python, py, etc. self.code_pattern = re.compile(r"```py(.*?)```", re.DOTALL) async def code_interpreter(self, code: str) -> str: """Execute the code in the sandbox. Args: code: The code to be executed. Returns: str: The output of the code execution. """ async with aiohttp.ClientSession() as session: async with session.post( self.config.get("sandbox_fusion_url"), json={"code": code}, ) as resp: resp.raise_for_status() result = await resp.json() stdout, stderr = result["run_result"]["stdout"], result["run_result"]["stderr"] return stdout + stderr def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: schema = get_json_schema(self.code_interpreter) return OpenAIFunctionToolSchema(**schema) async def execute(self, instance_id: str, parameters: dict, **kwargs) -> tuple[str, float, dict]: code = parameters["code"] matches = self.code_pattern.findall(code) if matches: code = matches[0].strip() # NOTE: Some script may not explicitly print result, we need to add a print statement to the end of the script. # More better way is to SFT the model to make it print result by default, we skip SFT stage in this tutorial. lines = code.split("\n") for i, line in reversed(list(enumerate(lines))): if line == "": continue if not lines[i].startswith("print"): lines[i] = f"print({line})" break code = "\n".join(lines) result = await self.code_interpreter(code) return ToolResponse(text=result), 0.0, {} ================================================ FILE: pyproject.toml ================================================ # ------------------------------- # build-system # ------------------------------- [build-system] requires = [ "setuptools>=61.0", "wheel" ] build-backend = "setuptools.build_meta" # ------------------------------- # project (PEP 621 metadata) # ------------------------------- [project] name = "verl" # We'll mark the version as "dynamic" because it's read from the file "verl/version/version" # (PEP 621 calls this "dynamic version"). # The actual version is specified in the [tool.setuptools.dynamic] section below. dynamic = ["version", "dependencies", "optional-dependencies", "authors", "urls"] description = "verl: Volcano Engine Reinforcement Learning for LLM" license = {text = "Apache-2.0"} # Changed from file to text format readme = {file = "README.md", content-type = "text/markdown"} requires-python = ">=3.10" # ------------------------------- # tool.ruff - Linting configuration # ------------------------------- [tool.ruff] # Note: While the formatter will attempt to format lines such that they remain within the line-length, # it isn't a hard upper bound, and formatted lines may exceed the line-length. line-length = 120 exclude = ["scripts/legacy_model_merger.py"] [tool.ruff.lint] isort = {known-first-party = ["verl"]} # c.f. https://github.com/vllm-project/vllm/blob/ce8d6b75fc0586045df75ee1568a5b5f9957251b/pyproject.toml select = [ # pycodestyle "E", # Pyflakes "F", # pyupgrade "UP", # flake8-bugbear "B", # isort "I", "G", ] ignore = [ # star imports "F405", "F403", # lambda expression assignment "E731", # Loop control variable not used within loop body "B007", # f-string format "UP032", # `.log()` statement uses f-string "G004", # X | None for type annotations "UP045", # deprecated import "UP035", ] # ------------------------------- # tool.mypy - typechecking config # ------------------------------- [tool.mypy] pretty = true ignore_missing_imports = true explicit_package_bases = true follow_imports = "skip" # Blanket silence ignore_errors = true [[tool.mypy.overrides]] module = [ "verl.trainer.config.algorithm", "verl.trainer.ppo.core_algos", "verl.trainer.ppo.reward", "verl.workers.reward_manager", "verl.workers.reward_manager.*", ] ignore_errors = false # ------------------------------- # tool.setuptools - Additional config # ------------------------------- [tool.setuptools] # True means `setuptools` will attempt to include all relevant files in package_data automatically. # This corresponds to `include_package_data=True` in setup.py. include-package-data = true # We read the version from a file in 'verl/version/version' [tool.setuptools.dynamic] version = {file = "verl/version/version"} # If you need to mimic `package_dir={'': '.'}`: [tool.setuptools.package-dir] "" = "." # If you need to include specific non-Python data (like YAML files or version file): # This is the rough equivalent of package_data={'': ['version/*'], 'verl': ['trainer/config/*.yaml']} [tool.setuptools.package-data] verl = [ "version/*", "trainer/config/*.yaml", "trainer/config/*/*.yaml", "experimental/*/config/*.yaml", ] ================================================ FILE: requirements-cuda.txt ================================================ flash-attn ================================================ FILE: requirements-npu.txt ================================================ # requirements.txt records the full set of dependencies for development accelerate codetiming datasets dill hydra-core numpy<2.0.0 pandas peft>=0.15.2 pyarrow>=15.0.0 pybind11 pylatexenc tensordict>=0.8.0,<=0.10.0,!=0.9.0 ray[default] wandb mathruler torchdata einops qwen_vl_utils hf_transfer triton-ascend==3.2.0 ================================================ FILE: requirements-test.txt ================================================ pytest pre-commit py-spy pytest-asyncio pytest-rerunfailures ================================================ FILE: requirements.txt ================================================ # requirements.txt records the full set of dependencies for development accelerate codetiming datasets dill hydra-core liger-kernel numpy<2.0.0 pandas peft pyarrow>=19.0.0 pybind11 pylatexenc pre-commit ray[default] tensordict>=0.8.0,<=0.10.0,!=0.9.0 torchdata transformers # vllm==0.8.4 wandb packaging>=20.0 uvicorn fastapi latex2sympy2_extended math_verify tensorboard ================================================ FILE: requirements_sglang.txt ================================================ # requirements.txt records the full set of dependencies for development accelerate codetiming datasets dill flash-attn hydra-core numpy<2.0.0 pandas peft pyarrow>=19.0.0 pybind11 pylatexenc ray[default]>=2.10 tensordict>=0.8.0,<=0.10.0,!=0.9.0 torchdata torchvision transformers wandb sglang[all]==0.5.2 huggingface_hub ================================================ FILE: scripts/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: scripts/converter_hf_to_mcore.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import warnings from contextlib import contextmanager from importlib.metadata import version from typing import Any, Callable, ContextManager, Optional import numpy as np import torch import torch.distributed as dist try: # NPU patch import mindspeed.megatron_adaptor # noqa: F401 from mindspeed.megatron_adaptor import repatch except ImportError: repatch = None pass from accelerate import init_empty_weights from megatron.core import dist_checkpointing from megatron.core import parallel_state as mpu from megatron.core.dist_checkpointing.mapping import ShardedTensor from megatron.core.dist_checkpointing.serialization import StrictHandling from megatron.core.models.gpt.gpt_model import ModelType from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from packaging.version import Version from transformers import AutoConfig from verl.model_merger.megatron_model_merger import get_dynamic_pipeline_shards from verl.models.mcore import hf_to_mcore_config from verl.utils.device import get_device_name, get_torch_device from verl.utils.megatron_utils import get_model def _init_args(): """ Examples: 1. single rank conversion for any model: > python converter_hf_to_mcore.py --hf_model_path %{hf_model} --output_path ${output_path} 2. distributed conversion for DeepseekV3 671B: > torchrun --nproc_per_node 1 --nnodes 4 --node_rank ${RANK} converter_hf_to_mcore.py \ --hf_model_path %{hf_model} --output_path ${output_path} """ parser = argparse.ArgumentParser() parser.add_argument("--hf_model_path", type=str, required=True, help="The path for the huggingface model") parser.add_argument("--output_path", type=str, required=True, help="The path for the output mcore model") parser.add_argument("--pp_size", type=int, default=1, help="pipeline model parallel size") parser.add_argument("--ep_size", type=int, default=1, help="expert model parallel size") parser.add_argument("--use_cpu_initialization", action="store_true", help="Whether to use cpu initialization") parser.add_argument("--test", action="store_true", help="Whether to test the conversion") parser.add_argument("--trust_remote_code", action="store_true", help="Whether to trust remote code") args = parser.parse_args() return args def test_conversion(megatron_model_provider, tfconfig, output_path, model): ########### test ########### # load model model_test = get_model( model_provider_func=megatron_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True, transformer_config=tfconfig, ) ref_state_dict = model_test[0].module.sharded_state_dict() dist_checkpointing.load(ref_state_dict, output_path, strict=StrictHandling.ASSUME_OK_UNEXPECTED) dut_state_dict = model[0].module.state_dict() for name in dut_state_dict.keys(): if dut_state_dict[name] is None: print(f"[Warning] {name} is none in dut_state_dict") continue dut_data = dut_state_dict[name].data if name in ref_state_dict: ref_data = ref_state_dict[name] if isinstance(ref_data, ShardedTensor): ref_data = ref_data.data.view(ref_data.local_shape) else: ref_data = ref_data.data assert dut_data.shape == ref_data.shape, f"{name=} {dut_data.shape=} {ref_data.shape=}" assert (dut_data == ref_data).all(), f"{name} is not equal" print(f"{name} is equal") else: print(f"[Warning] {name} is not in ref_state_dict") for name in ref_state_dict.keys(): if ref_state_dict[name] is None: print(f"[Warning] {name} is none in ref_state_dict") continue ref_data = ref_state_dict[name] if isinstance(ref_data, ShardedTensor): ref_data = ref_data.data.view(ref_data.local_shape) else: ref_data = ref_data.data if name in dut_state_dict: dut_data = dut_state_dict[name].data assert dut_data.shape == ref_data.shape, f"{name=} {dut_data.shape=} {ref_data.shape=}" assert (dut_data == ref_data).all(), f"{name} is not equal" print(f"{name} is equal") else: print(f"[Warning] {name} is not in dut_state_dict") print("Conversion test passed!") @torch.inference_mode() def convert_checkpoint_from_transformers_to_megatron( hf_model, model, hf_config, layer_start_end: Optional[tuple[int, int]] = None ): if layer_start_end is None: layer_start_end = (0, len(model.decoder.layers)) layer_start, layer_end = layer_start_end pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() ep_rank = mpu.get_expert_model_parallel_rank() ep_size = mpu.get_expert_model_parallel_world_size() numel = 0 num_attention_heads = hf_config.num_attention_heads num_key_value_heads = hf_config.num_key_value_heads hidden_dim = hf_config.hidden_size head_dim = getattr(hf_config, "head_dim", hidden_dim // num_attention_heads) if num_attention_heads != num_key_value_heads: print("[WARNING] Converting GQA model") has_qkv_bias = getattr(hf_config, "qkv_bias", False) or getattr(hf_config, "attention_bias", False) has_share_expert = getattr(hf_config, "shared_expert_intermediate_size", None) if pp_rank == 0: numel += safe_copy(hf_model.model.embed_tokens.weight, model.embedding.word_embeddings.weight) assert len(model.decoder.layers) == (layer_end - layer_start), ( f"Expected {len(model.decoder.layers)} layers, but got {layer_end - layer_start}" ) for layer_idx, (layer, hf_layer) in enumerate( zip(model.decoder.layers, hf_model.model.layers[layer_start:layer_end], strict=True) ): global_layer_idx = layer_idx + layer_start numel_cur = numel numel += safe_copy(hf_layer.input_layernorm.weight, layer.self_attention.linear_qkv.layer_norm_weight) q = hf_layer.self_attn.q_proj.weight.view( [num_key_value_heads, head_dim * num_attention_heads // num_key_value_heads, -1] ) k = hf_layer.self_attn.k_proj.weight.view([num_key_value_heads, head_dim, -1]) v = hf_layer.self_attn.v_proj.weight.view([num_key_value_heads, head_dim, -1]) qkv = torch.cat([q, k, v], dim=1).view(-1, hidden_dim).contiguous() numel += safe_copy(qkv, layer.self_attention.linear_qkv.weight) if has_qkv_bias: q_bias = hf_layer.self_attn.q_proj.bias.view([num_key_value_heads, -1]) k_bias = hf_layer.self_attn.k_proj.bias.view([num_key_value_heads, -1]) v_bias = hf_layer.self_attn.v_proj.bias.view([num_key_value_heads, -1]) qkv_bias = torch.cat([q_bias, k_bias, v_bias], dim=1).view(-1).contiguous() numel += safe_copy(qkv_bias, layer.self_attention.linear_qkv.bias) if hasattr(hf_layer.self_attn, "q_norm"): numel += safe_copy(hf_layer.self_attn.q_norm.weight.data, layer.self_attention.q_layernorm.weight) numel += safe_copy(hf_layer.self_attn.k_norm.weight.data, layer.self_attention.k_layernorm.weight) numel += safe_copy(hf_layer.self_attn.o_proj.weight, layer.self_attention.linear_proj.weight) numel += safe_copy(hf_layer.post_attention_layernorm.weight, layer.pre_mlp_layernorm.weight) numel += safe_copy(hf_layer.mlp.gate.weight, layer.mlp.router.weight) for idx, hf_expert in enumerate(hf_layer.mlp.experts): num_experts = len(hf_layer.mlp.experts) num_local_experts = num_experts // ep_size expert_idx_start = ep_rank * num_local_experts expert_idx_end = (ep_rank + 1) * num_local_experts if idx < expert_idx_start or idx >= expert_idx_end: continue local_expert_idx = idx - expert_idx_start fc1_weight = torch.cat([hf_expert.gate_proj.weight, hf_expert.up_proj.weight]) numel += safe_copy(fc1_weight, layer.mlp.experts.linear_fc1._parameters[f"weight{local_expert_idx}"]) numel += safe_copy( hf_expert.down_proj.weight, layer.mlp.experts.linear_fc2._parameters[f"weight{local_expert_idx}"] ) if has_share_expert: numel += safe_copy(hf_layer.mlp.shared_expert_gate.weight, layer.mlp.shared_experts.gate_weight) shared_fc1_weight = torch.cat( [hf_layer.mlp.shared_expert.gate_proj.weight, hf_layer.mlp.shared_expert.up_proj.weight] ) numel += safe_copy(shared_fc1_weight, layer.mlp.shared_experts.linear_fc1.weight) numel += safe_copy(hf_layer.mlp.shared_expert.down_proj.weight, layer.mlp.shared_experts.linear_fc2.weight) print(f"{pp_rank=} {global_layer_idx=} {layer_idx=} {numel=} numel this layer={numel - numel_cur}") if pp_rank == pp_size - 1: numel += safe_copy(hf_model.model.norm.weight, model.decoder.final_layernorm.weight) numel += safe_copy(hf_model.lm_head.weight, model.output_layer.weight) return numel def safe_copy( src_tensor: torch.Tensor, dst_tensor: torch.Tensor, skip_dtype_assert: bool = False, ): if not skip_dtype_assert: if src_tensor.dtype != dst_tensor.dtype: raise ValueError(f"Get source dtype {src_tensor.dtype}, but target dtype {dst_tensor.dtype}") assert src_tensor.shape == dst_tensor.shape dst_tensor.data.copy_(src_tensor.data) return src_tensor.numel() @torch.inference_mode() def convert_checkpoint_from_transformers_to_megatron_qwen2_5_vl(hfmodel, mgmodel, hf_config): mgmodel = mgmodel.bfloat16() hfmodel = hfmodel.bfloat16() num_attention_heads = hf_config.num_attention_heads num_query_groups = hf_config.num_key_value_heads hidden_size = hf_config.hidden_size head_dim = hidden_size // num_attention_heads # 1. vision model if Version(version("transformers")) < Version("4.52.0"): print("Using transformers < 4.52 API to load vision model") hfvision = hfmodel.visual else: hfvision = hfmodel.model.visual mgvision = mgmodel.vision_model vision_hidden_size = mgvision.config.hidden_size vision_num_query_groups = mgvision.config.num_query_groups vision_head_dim = vision_hidden_size // mgvision.config.num_attention_heads copied_numel = 0 safe_copy(hfvision.rotary_pos_emb.inv_freq, mgvision.rotary_pos_emb.inv_freq) copied_numel += safe_copy(hfvision.patch_embed.proj.weight, mgvision.patch_embed.proj.weight) for hfblock, mgblock in zip(hfvision.blocks, mgvision.decoder.layers, strict=True): # norm1 --> linear_qkv.norm copied_numel += safe_copy(hfblock.norm1.weight, mgblock.self_attention.linear_qkv.layer_norm_weight) # norm2 --> mlp.linear_fc1.norm copied_numel += safe_copy(hfblock.norm2.weight, mgblock.mlp.linear_fc1.layer_norm_weight) # qkv --> self_attention.linear_qkv converted_weight = ( hfblock.attn.qkv.weight.view(3, vision_num_query_groups, -1, vision_head_dim, vision_hidden_size) .transpose(0, 1) .flatten(1, 2) .reshape(-1, vision_hidden_size) .contiguous() ) copied_numel += safe_copy(converted_weight, mgblock.self_attention.linear_qkv.weight) converted_bias = ( hfblock.attn.qkv.bias.view(3, vision_num_query_groups, -1) .transpose(0, 1) .flatten(1, 2) .view(-1) .contiguous() ) copied_numel += safe_copy(converted_bias, mgblock.self_attention.linear_qkv.bias) # proj --> self_attention.linear_proj copied_numel += safe_copy(hfblock.attn.proj.weight, mgblock.self_attention.linear_proj.weight) copied_numel += safe_copy(hfblock.attn.proj.bias, mgblock.self_attention.linear_proj.bias) # mlp --> mlp: gate fc1_weight = torch.cat([hfblock.mlp.gate_proj.weight, hfblock.mlp.up_proj.weight]) fc1_bias = torch.cat([hfblock.mlp.gate_proj.bias, hfblock.mlp.up_proj.bias]) copied_numel += safe_copy(fc1_weight, mgblock.mlp.linear_fc1.weight) copied_numel += safe_copy(fc1_bias, mgblock.mlp.linear_fc1.bias) copied_numel += safe_copy(hfblock.mlp.down_proj.weight, mgblock.mlp.linear_fc2.weight) copied_numel += safe_copy(hfblock.mlp.down_proj.bias, mgblock.mlp.linear_fc2.bias) # 2. vision projector hfprojector = hfvision.merger mgprojector = mgvision.projection copied_numel += safe_copy(hfprojector.ln_q.weight, mgvision.decoder.final_layernorm.weight) copied_numel += safe_copy(hfprojector.mlp[0].weight, mgprojector.encoder.linear_fc1.weight) copied_numel += safe_copy(hfprojector.mlp[0].bias, mgprojector.encoder.linear_fc1.bias) copied_numel += safe_copy(hfprojector.mlp[2].weight, mgprojector.encoder.linear_fc2.weight) copied_numel += safe_copy(hfprojector.mlp[2].bias, mgprojector.encoder.linear_fc2.bias) n_params = sum([t.numel() for t in hfvision.state_dict().values()]) assert n_params == copied_numel, f"n_params={n_params} != copied_numel={copied_numel}" # 3. llm [just Qwen2] if Version(version("transformers")) < Version("4.52.0"): print("Using transformers < 4.52 API to load llm") hfllm = hfmodel.model else: hfllm = hfmodel.model.language_model mgllm = mgmodel.language_model copied_numel = 0 copied_numel += safe_copy(hfllm.embed_tokens.weight, mgllm.embedding.word_embeddings.weight) layermaps = zip(mgllm.decoder.layers, hfllm.layers, strict=True) for mglayer, hflayer in layermaps: copied_numel += safe_copy(hflayer.input_layernorm.weight, mglayer.self_attention.linear_qkv.layer_norm_weight) q_proj_weight = hflayer.self_attn.q_proj.weight.view(num_query_groups, -1, head_dim, hidden_size) k_proj_weight = hflayer.self_attn.k_proj.weight.view(num_query_groups, -1, head_dim, hidden_size) v_proj_weight = hflayer.self_attn.v_proj.weight.view(num_query_groups, -1, head_dim, hidden_size) qkv_proj = torch.cat([q_proj_weight, k_proj_weight, v_proj_weight], dim=1).view(-1, hidden_size).contiguous() copied_numel += safe_copy(qkv_proj, mglayer.self_attention.linear_qkv.weight) q_proj_bias = hflayer.self_attn.q_proj.bias.view(num_query_groups, -1) k_proj_bias = hflayer.self_attn.k_proj.bias.view(num_query_groups, -1) v_proj_bias = hflayer.self_attn.v_proj.bias.view(num_query_groups, -1) qkv_bias = torch.cat([q_proj_bias, k_proj_bias, v_proj_bias], dim=1).view(-1).contiguous() copied_numel += safe_copy(qkv_bias, mglayer.self_attention.linear_qkv.bias) copied_numel += safe_copy(hflayer.self_attn.o_proj.weight, mglayer.self_attention.linear_proj.weight) fc1_weight = torch.cat([hflayer.mlp.gate_proj.weight, hflayer.mlp.up_proj.weight]) copied_numel += safe_copy(fc1_weight, mglayer.mlp.linear_fc1.weight) copied_numel += safe_copy(hflayer.mlp.down_proj.weight, mglayer.mlp.linear_fc2.weight) copied_numel += safe_copy(hflayer.post_attention_layernorm.weight, mglayer.mlp.linear_fc1.layer_norm_weight) copied_numel += safe_copy(hfllm.norm.weight, mgllm.decoder.final_layernorm.weight) if not hf_config.tie_word_embeddings: safe_copy(hfmodel.lm_head.weight, mgllm.output_layer.weight) n_params = sum([t.numel() for t in hfllm.state_dict().values()]) assert n_params == copied_numel, f"n_params={n_params} != copied_numel={copied_numel}" @torch.inference_mode() def convert_checkpoint_from_transformers_to_megatron_dpskv3( hf_model, model, hf_config, tfconfig, layer_start_end: Optional[tuple[int, int]] = None, ): warnings.warn("MTP model is not supported yet", stacklevel=2) if layer_start_end is None: layer_start_end = (0, len(model.decoder.layers)) layer_start, layer_end = layer_start_end numel: int = 0 pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() ep_rank = mpu.get_expert_model_parallel_rank() ep_size = mpu.get_expert_model_parallel_world_size() if pp_rank == 0: numel += safe_copy(hf_model.model.embed_tokens.weight, model.embedding.word_embeddings.weight) assert len(model.decoder.layers) == (layer_end - layer_start), ( f"Expected {len(model.decoder.layers)} layers, but got {layer_end - layer_start}" ) for layer_idx, (layer, hf_layer) in enumerate( zip(model.decoder.layers, hf_model.model.layers[layer_start:layer_end], strict=True) ): global_layer_idx = layer_idx + layer_start numel_cur: int = numel numel += safe_copy(hf_layer.input_layernorm.weight, layer.input_layernorm.weight) if hf_config.q_lora_rank is None: numel += safe_copy(hf_layer.self_attn.q_proj.weight, layer.self_attention.linear_q_proj.weight) else: numel += safe_copy(hf_layer.self_attn.q_a_proj.weight, layer.self_attention.linear_q_down_proj.weight) numel += safe_copy(hf_layer.self_attn.q_b_proj.weight, layer.self_attention.linear_q_up_proj.weight) numel += safe_copy( hf_layer.self_attn.q_a_layernorm.weight, layer.self_attention.linear_q_up_proj.layer_norm_weight ) numel += safe_copy( hf_layer.self_attn.kv_a_proj_with_mqa.weight, layer.self_attention.linear_kv_down_proj.weight ) numel += safe_copy(hf_layer.self_attn.kv_b_proj.weight, layer.self_attention.linear_kv_up_proj.weight) numel += safe_copy( hf_layer.self_attn.kv_a_layernorm.weight, layer.self_attention.linear_kv_up_proj.layer_norm_weight ) numel += safe_copy(hf_layer.self_attn.o_proj.weight, layer.self_attention.linear_proj.weight) if not hasattr(layer.mlp, "router"): numel += safe_copy(hf_layer.post_attention_layernorm.weight, layer.mlp.linear_fc1.layer_norm_weight) numel += safe_copy( torch.cat([hf_layer.mlp.gate_proj.weight, hf_layer.mlp.up_proj.weight]), layer.mlp.linear_fc1.weight ) numel += safe_copy(hf_layer.mlp.down_proj.weight, layer.mlp.linear_fc2.weight) else: numel += safe_copy(hf_layer.mlp.gate.weight, layer.mlp.router.weight) # NOTE: the e_score_correction_bias in mcore model will be initialized with bfloat16 and \ # recover to fp32 in the first forward. There is always a diff in the bias between two models (~0.3%) numel += safe_copy( hf_layer.mlp.gate.e_score_correction_bias, layer.mlp.router.expert_bias, skip_dtype_assert=True ) if tfconfig.moe_grouped_gemm: for i, hf_expert in enumerate(hf_layer.mlp.experts): num_experts = len(hf_layer.mlp.experts) num_local_experts = num_experts // ep_size expert_idx_start = ep_rank * num_local_experts expert_idx_end = (ep_rank + 1) * num_local_experts if i < expert_idx_start or i >= expert_idx_end: continue local_expert_idx = i - expert_idx_start fc1_weight = torch.cat([hf_expert.gate_proj.weight, hf_expert.up_proj.weight]) linear_fc1_weighti = getattr(layer.mlp.experts.linear_fc1, "weight" + str(local_expert_idx)) numel += safe_copy(fc1_weight, linear_fc1_weighti) linear_fc2_weighti = getattr(layer.mlp.experts.linear_fc2, "weight" + str(local_expert_idx)) numel_w2 = safe_copy(hf_expert.down_proj.weight, linear_fc2_weighti) numel += numel_w2 else: for i, hf_expert in enumerate(hf_layer.mlp.experts): expert = layer.mlp.experts.local_experts[i] fc1_weight = torch.cat([hf_expert.gate_proj.weight, hf_expert.up_proj.weight]) numel += safe_copy(fc1_weight, expert.linear_fc1.weight) numel += safe_copy(hf_expert.down_proj.weight, expert.linear_fc2.weight) numel += safe_copy(hf_layer.post_attention_layernorm.weight, layer.pre_mlp_layernorm.weight) shared_fc1_weight = torch.cat( [hf_layer.mlp.shared_experts.gate_proj.weight, hf_layer.mlp.shared_experts.up_proj.weight] ) numel += safe_copy(shared_fc1_weight, layer.mlp.shared_experts.linear_fc1.weight) numel += safe_copy(hf_layer.mlp.shared_experts.down_proj.weight, layer.mlp.shared_experts.linear_fc2.weight) print(f"{pp_rank=} {global_layer_idx=} {layer_idx=} {numel=} numel this layer={numel - numel_cur}") numel_hf_one_layer = sum([i.numel() for i in hf_layer.state_dict().values()]) if hasattr(layer.mlp, "router"): numel_hf_one_layer -= numel_w2 * 3 * len(hf_layer.mlp.experts) // ep_size * (ep_size - 1) assert numel - numel_cur == numel_hf_one_layer, "numel mismatch" if pp_rank == pp_size - 1: numel += safe_copy(hf_model.model.norm.weight, model.decoder.final_layernorm.weight) if not hf_config.tie_word_embeddings: numel += safe_copy(hf_model.lm_head.weight, model.output_layer.weight) print(f"{pp_rank=} {numel=}") return numel @contextmanager def noop_context() -> Any: yield def support_distributed_convert(hf_config: AutoConfig) -> bool: for arch in ["DeepseekV3ForCausalLM", "Qwen3MoeForCausalLM", "Qwen2MoeForCausalLM"]: if arch in hf_config.architectures: return True return False def convert_hf_to_mcore( hf_model_path, output_path, pp_size=1, ep_size=1, use_cpu_initialization=False, test=False, trust_remote_code=False ): os.makedirs(output_path, exist_ok=True) if len(os.listdir(output_path)) > 0 and not test: print(f"Output path {output_path} is not empty, skipping conversion") return # init torch distributed and mpu if "WORLD_SIZE" not in os.environ: os.environ["RANK"] = "0" os.environ["WORLD_SIZE"] = "1" os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" torch.distributed.init_process_group("nccl") local_rank = os.getenv("LOCAL_RANK", 0) world_size = dist.get_world_size() get_torch_device().set_device(f"{get_device_name()}:{local_rank}") if ep_size * pp_size != world_size: pp_size = world_size print(f"pp_size is set to {pp_size}") mpu.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=pp_size, virtual_pipeline_model_parallel_size=None, context_parallel_size=1, expert_model_parallel_size=ep_size, ) model_parallel_cuda_manual_seed(0) # init hf config hf_config = AutoConfig.from_pretrained(hf_model_path, trust_remote_code=trust_remote_code) print(hf_config, flush=True) if repatch: if hf_config.architectures[0] == "DeepseekV3ForCausalLM": config_repatch = dict(multi_head_latent_attention=True) repatch(config_repatch) if world_size > 1 and not support_distributed_convert(hf_config): raise NotImplementedError(f"distributed conversion is not supported for {hf_config.architectures} yet.") pipeline_shards = get_dynamic_pipeline_shards(hf_config.num_hidden_layers, pp_size) print(f"Pipeline shards: {pipeline_shards}", flush=True) tfconfig = hf_to_mcore_config( hf_config, torch.bfloat16, num_layers_in_first_pipeline_stage=pipeline_shards[0] if len(pipeline_shards) > 1 else None, num_layers_in_last_pipeline_stage=pipeline_shards[-1] if len(pipeline_shards) > 2 else None, ) tfconfig.use_cpu_initialization = use_cpu_initialization tie_word_embeddings = getattr(hf_config, "tie_word_embeddings", False) # init megatron model def megatron_model_provider(pre_process, post_process): from verl.models.mcore import init_mcore_model parallel_model = init_mcore_model( tfconfig, hf_config, pre_process, post_process, share_embeddings_and_output_weights=tie_word_embeddings, value=False, ) return parallel_model context: Callable[..., ContextManager] = init_empty_weights if use_cpu_initialization else noop_context with context(): model = get_model( model_provider_func=megatron_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=False, transformer_config=tfconfig, ) if use_cpu_initialization: # convert meta device to empty tensor so it can use `copy_` function model[0].module = model[0].module.to_empty(device="cpu") with warnings.catch_warnings(): warnings.simplefilter("ignore") from transformers import AutoModelForCausalLM, AutoModelForImageTextToText # init hf model if "Qwen2_5_VLForConditionalGeneration" in hf_config.architectures: hf_model = AutoModelForImageTextToText.from_pretrained( hf_model_path, torch_dtype=torch.bfloat16, trust_remote_code=trust_remote_code ) else: hf_model = AutoModelForCausalLM.from_pretrained( hf_model_path, torch_dtype=torch.bfloat16, trust_remote_code=trust_remote_code ) hf_state_dict = hf_model.state_dict() pp_rank = mpu.get_pipeline_model_parallel_rank() # distributed convert if world_size > 1 and support_distributed_convert(hf_config): pipeline_cumsum = np.cumsum(pipeline_shards) layer_start = 0 if pp_rank == 0 else pipeline_cumsum[pp_rank - 1] layer_end = pipeline_cumsum[pp_rank] if "DeepseekV3ForCausalLM" in hf_config.architectures: numel_partial: int = convert_checkpoint_from_transformers_to_megatron_dpskv3( hf_model, model[0].module, hf_config, tfconfig=tfconfig, layer_start_end=(layer_start, layer_end) ) elif "Qwen3MoeForCausalLM" in hf_config.architectures or "Qwen2MoeForCausalLM" in hf_config.architectures: numel_partial: int = convert_checkpoint_from_transformers_to_megatron( hf_model, model[0].module, hf_config, layer_start_end=(layer_start, layer_end) ) else: raise NotImplementedError(f"Distributed conversion is not supported for {hf_config.architectures} yet.") numel_tensor = torch.tensor([numel_partial]).to(get_device_name()) dist.all_reduce(numel_tensor, op=dist.ReduceOp.SUM) numel = int(numel_tensor.cpu().item()) print(f"total numel={numel} vs {hf_model.num_parameters()=}") if numel != hf_model.num_parameters(): warnings.warn(f"numel mismatch: {numel=} != {hf_model.num_parameters()=}", stacklevel=1) # load hf state dict to megatron model elif "Qwen2MoeForCausalLM" in hf_config.architectures: convert_checkpoint_from_transformers_to_megatron(hf_model, model[0].module, hf_config) elif "Qwen2_5_VLForConditionalGeneration" in hf_config.architectures: convert_checkpoint_from_transformers_to_megatron_qwen2_5_vl(hf_model, model[0].module, hf_config) elif "DeepseekV3ForCausalLM" in hf_config.architectures: convert_checkpoint_from_transformers_to_megatron_dpskv3(hf_model, model[0].module, hf_config, tfconfig=tfconfig) elif "Qwen3MoeForCausalLM" in hf_config.architectures: convert_checkpoint_from_transformers_to_megatron(hf_model, model[0].module, hf_config) else: assert not use_cpu_initialization, "use_cpu_initialization is only supported for MoE model" from verl.models.mcore.loader import load_state_dict_to_megatron_gptmodel load_state_dict_to_megatron_gptmodel( state_dict=hf_state_dict, wrapped_models=model, config=hf_config, params_dtype=torch.bfloat16, is_value_model=False, ) megatron_state_dict = model[0].module.sharded_state_dict() del hf_state_dict, hf_model # save megatron model if len(os.listdir(output_path)) == 0: dist_checkpointing.save(megatron_state_dict, output_path, sharded_strategy=None, async_sharded_save=False) if test: test_conversion(megatron_model_provider, tfconfig, output_path, model) if __name__ == "__main__": args = _init_args() convert_hf_to_mcore( args.hf_model_path, args.output_path, args.pp_size, args.ep_size, args.use_cpu_initialization, args.test, args.trust_remote_code, ) ================================================ FILE: scripts/diagnose.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Diagnose script for checking OS/hardware/python/pip/verl/network. The output of this script can be a very good hint to issue/problem. """ import os import platform import socket import subprocess import sys import time import psutil try: from urllib.parse import urlparse from urllib.request import urlopen except ImportError: from urllib2 import urlopen from urlparse import urlparse import argparse import importlib.metadata import torch URLS = { "PYPI": "https://pypi.python.org/pypi/pip", } REGIONAL_URLS = { "cn": { "PYPI(douban)": "https://pypi.douban.com/", "Conda(tsinghua)": "https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/", } } def test_connection(name, url, timeout=10): """Simple connection test""" urlinfo = urlparse(url) start = time.time() try: socket.gethostbyname(urlinfo.netloc) except Exception as e: print("Error resolving DNS for {}: {}, {}".format(name, url, e)) return dns_elapsed = time.time() - start start = time.time() try: _ = urlopen(url, timeout=timeout) except Exception as e: print("Error open {}: {}, {}, DNS finished in {} sec.".format(name, url, e, dns_elapsed)) return load_elapsed = time.time() - start print("Timing for {}: {}, DNS: {:.4f} sec, LOAD: {:.4f} sec.".format(name, url, dns_elapsed, load_elapsed)) def check_python(): print("----------Python Info----------") print("Version :", platform.python_version()) print("Compiler :", platform.python_compiler()) print("Build :", platform.python_build()) print("Arch :", platform.architecture()) def check_pip(): print("------------Pip Info-----------") try: import pip print("Version :", pip.__version__) print("Directory :", os.path.dirname(pip.__file__)) except ImportError: print("No corresponding pip install for current python.") def _get_current_git_commit(): try: result = subprocess.run(["git", "rev-parse", "HEAD"], capture_output=True, text=True, check=True) return result.stdout.strip() except subprocess.CalledProcessError as e: print(f"Error running git command: {e.stderr.strip()}") return None except FileNotFoundError: print("Did not find command: git") return None def check_verl(): print("----------verl Info-----------") try: sys.path.insert(0, os.getcwd()) import verl print("Version :", verl.__version__) verl_dir = os.path.dirname(verl.__file__) print("Directory :", verl_dir) try: commit_hash = _get_current_git_commit() print("Commit Hash :", commit_hash) except AttributeError: print("Commit hash not found. ") except ImportError as e: print(f"No verl installed: {e}") except Exception as e: import traceback if not isinstance(e, IOError): print("An error occurred trying to import verl.") print("This is very likely due to missing or incompatible library files.") print(traceback.format_exc()) def check_os(): print("----------Platform Info----------") print("Platform :", platform.platform()) print("system :", platform.system()) print("node :", platform.node()) print("release :", platform.release()) print("version :", platform.version()) def check_hardware(): print("----------Hardware Info----------") print("machine :", platform.machine()) print("processor :", platform.processor()) if sys.platform.startswith("darwin"): pipe = subprocess.Popen(("sysctl", "-a"), stdout=subprocess.PIPE) output = pipe.communicate()[0] for line in output.split(b"\n"): if b"brand_string" in line or b"features" in line: print(line.strip()) elif sys.platform.startswith("linux"): subprocess.call(["lscpu"]) elif sys.platform.startswith("win32"): subprocess.call(["wmic", "cpu", "get", "name"]) def check_network(args): print("----------Network Test----------") if args.timeout > 0: print("Setting timeout: {}".format(args.timeout)) socket.setdefaulttimeout(10) for region in args.region.strip().split(","): r = region.strip().lower() if not r: continue if r in REGIONAL_URLS: URLS.update(REGIONAL_URLS[r]) else: import warnings warnings.warn("Region {} do not need specific test, please refer to global sites.".format(r), stacklevel=2) for name, url in URLS.items(): test_connection(name, url, args.timeout) def check_environment(): print("----------Environment----------") for k, v in os.environ.items(): if k.startswith("VERL_") or k.startswith("OMP_") or k.startswith("KMP_") or k == "CC" or k == "CXX": print('{}="{}"'.format(k, v)) def check_pip_package_versions(): packages = ["vllm", "sglang", "ray", "torch"] for package in packages: try: version = importlib.metadata.version(package) print(f"{package}\t : {version}") except importlib.metadata.PackageNotFoundError: print(f"{package}\t : not found.") def check_cuda_versions(): if torch.cuda.is_available(): try: cuda_runtime_version = torch.version.cuda print(f"CUDA Runtime : {cuda_runtime_version}") import subprocess nvcc_output = subprocess.check_output(["nvcc", "--version"]).decode("utf-8") cuda_compiler_version = next((line for line in nvcc_output.splitlines() if "release" in line), None) if cuda_compiler_version: print(f"CUDA Compiler : {cuda_compiler_version.strip()}") else: print("Could not determine CUDA compiler version.") except FileNotFoundError as e: print(f"CUDA compiler : Not found: {e}") except Exception as e: print(f"An error occurred while checking CUDA versions: {e}") else: print("CUDA is not available.") def _get_cpu_memory(): """ Get the total CPU memory capacity in GB. """ memory = psutil.virtual_memory() return memory.total / (1024**3) def _get_gpu_info(): """ Get GPU type, GPU memory, and GPU count using nvidia-smi command. """ try: result = subprocess.run( ["nvidia-smi", "--query-gpu=gpu_name,memory.total", "--format=csv,noheader,nounits"], capture_output=True, text=True, check=True, ) gpu_lines = result.stdout.strip().split("\n") gpu_count = len(gpu_lines) gpu_info = [] for line in gpu_lines: gpu_name, gpu_memory = line.split(", ") gpu_info.append( { "type": gpu_name, "memory": float(gpu_memory) / 1024, # Convert to GB } ) return gpu_count, gpu_info except (subprocess.CalledProcessError, FileNotFoundError): print("Failed to execute nvidia-smi command.") return 0, [] def _get_system_info(): """ Get CPU memory capacity, GPU type, GPU memory, and GPU count. """ cpu_memory = _get_cpu_memory() gpu_count, gpu_info = _get_gpu_info() return {"cpu_memory": cpu_memory, "gpu_count": gpu_count, "gpu_info": gpu_info} def check_system_info(): print("----------System Info----------") system_info = _get_system_info() print(f"CPU Memory\t: {system_info['cpu_memory']:.2f} GB") print(f"GPU Count\t: {system_info['gpu_count']}") for i, gpu in enumerate(system_info["gpu_info"]): print(f"GPU {i + 1}\tType : {gpu['type']}") print(f"GPU {i + 1}\tMemory : {gpu['memory']:.2f} GB") def parse_args(): """Parse arguments.""" parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description="Diagnose script for checking the current system.", ) choices = ["python", "pip", "verl", "system", "os", "environment"] for choice in choices: parser.add_argument("--" + choice, default=1, type=int, help="Diagnose {}.".format(choice)) parser.add_argument("--network", default=0, type=int, help="Diagnose network.") parser.add_argument("--hardware", default=0, type=int, help="Diagnose hardware.") parser.add_argument( "--region", default="", type=str, help="Additional sites in which region(s) to test. \ Specify 'cn' for example to test mirror sites in China.", ) parser.add_argument("--timeout", default=10, type=int, help="Connection test timeout threshold, 0 to disable.") args = parser.parse_args() return args if __name__ == "__main__": args = parse_args() if args.python: check_python() if args.pip: check_pip() check_pip_package_versions() if args.verl: check_verl() if args.os: check_os() if args.hardware: check_hardware() if args.network: check_network(args) if args.environment: check_environment() check_cuda_versions() if args.system: check_system_info() ================================================ FILE: scripts/generate_trainer_config.sh ================================================ #!/usr/bin/env bash set -euox pipefail # Define config specifications: "config_name:output_file:config_arg" CONFIG_SPECS=( "ppo_trainer:_generated_ppo_trainer.yaml:" "ppo_megatron_trainer:_generated_ppo_megatron_trainer.yaml:--config-name=ppo_megatron_trainer.yaml" "ppo_trainer:_generated_ppo_veomni_trainer.yaml:model_engine=veomni" "ppo_trainer:_generated_ppo_torchtitan_trainer.yaml:model_engine=torchtitan" ) generate_config() { local config_name="$1" local output_file="$2" local config_arg="$3" local target_cfg="verl/trainer/config/${output_file}" local tmp_header=$(mktemp) local tmp_cfg=$(mktemp) echo "# This reference configration yaml is automatically generated via 'scripts/generate_trainer_config.sh'" > "$tmp_header" echo "# in which it invokes 'python3 scripts/print_cfg.py --cfg job ${config_arg}' to flatten the 'verl/trainer/config/${config_name}.yaml' config fields into a single file." >> "$tmp_header" echo "# Do not modify this file directly." >> "$tmp_header" echo "# The file is usually only for reference and never used." >> "$tmp_header" echo "" >> "$tmp_header" python3 scripts/print_cfg.py --cfg job ${config_arg} > "$tmp_cfg" cat "$tmp_header" > "$target_cfg" sed -n '/^actor_rollout_ref/,$p' "$tmp_cfg" >> "$target_cfg" rm "$tmp_cfg" "$tmp_header" echo "Generated: $target_cfg" } for spec in "${CONFIG_SPECS[@]}"; do IFS=':' read -r config_name output_file config_arg <<< "$spec" generate_config "$config_name" "$output_file" "$config_arg" done for spec in "${CONFIG_SPECS[@]}"; do IFS=':' read -r config_name output_file config_arg <<< "$spec" target_cfg="verl/trainer/config/${output_file}" if ! git diff --exit-code -- "$target_cfg" >/dev/null; then echo "✖ $target_cfg is out of date. Please regenerate via 'scripts/generate_trainer_config.sh' and commit the changes." exit 1 fi done echo "All good" exit 0 ================================================ FILE: scripts/init_random_model.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script override a model with custom config and random weights, mainly for create small models for debugging purposes. Usage: python scripts/init_random_model.py \ --hf_model_path \ --new_config_path \ --output_path """ import argparse import json import os import warnings from typing import Any from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, PretrainedConfig def _init_args(): parser = argparse.ArgumentParser() parser.add_argument("--hf_model_path", type=str, required=True, help="The path for the huggingface model") parser.add_argument("--new_config_path", type=str, required=True, help="The path for the new config file") parser.add_argument("--output_path", type=str, required=True, help="The path for the output random model") parser.add_argument( "--trust_remote_code", action="store_true", help="Whether to trust remote code when loading HF model. Disabled by default for security.", ) args = parser.parse_args() return args def check_output_path(output_path: str): if os.path.exists(output_path): warnings.warn(f"Output path '{output_path}' already exists. Will do nothing.", stacklevel=2) exit() else: os.makedirs(output_path, exist_ok=True) print(f"Output path '{output_path}' created.") def check_configs(original_config: dict[str, Any], new_config: dict[str, Any]) -> bool: """ Check if the original config and new config are compatible. This is a placeholder function; actual implementation may vary based on requirements. """ # Example check: ensure 'model_type' is the same if new_config.get("model_type", None) is not None and original_config.get("model_type") != new_config.get( "model_type" ): raise RuntimeError("Model types do not match.") for key in new_config: if key not in original_config: warnings.warn( f"Key '{key}' in new config does not exist in original config, may not take effect.", stacklevel=2 ) def init_random_model(hf_model_path, new_config_path, output_path, trust_remote_code: bool = False): config = AutoConfig.from_pretrained(hf_model_path, trust_remote_code=trust_remote_code) tokenizer = AutoTokenizer.from_pretrained(hf_model_path, trust_remote_code=trust_remote_code) config_dict = PretrainedConfig.get_config_dict(hf_model_path)[0] print(config_dict) with open(new_config_path) as f: new_config_dict = json.load(f) check_configs(config_dict, new_config_dict) config_dict.update(new_config_dict) new_confg = config.from_dict(config_dict) print(f"new_config: {new_confg}") if trust_remote_code: model = AutoModelForCausalLM.from_pretrained( hf_model_path, config=new_confg, trust_remote_code=trust_remote_code, torch_dtype=new_confg.torch_dtype ) else: model = AutoModelForCausalLM.from_config(new_confg, torch_dtype=new_confg.torch_dtype) model.save_pretrained(output_path) tokenizer.save_pretrained(output_path) new_confg.save_pretrained(output_path) print(f"Random model initialized and saved to {output_path}") if __name__ == "__main__": args = _init_args() check_output_path(args.output_path) init_random_model( hf_model_path=args.hf_model_path, new_config_path=args.new_config_path, output_path=args.output_path, trust_remote_code=args.trust_remote_code, ) ================================================ FILE: scripts/install_sglang_mcore_npu.sh ================================================ #!/bin/bash set -e NPU_DEVICE=${NPU_DEVICE:=A3} USE_MEGATRON=${USE_MEGATRON:-1} export MAX_JOBS=32 echo "1. install SGLang from source" git clone -b v0.5.8 https://github.com/sgl-project/sglang.git cd sglang mv python/pyproject_other.toml python/pyproject.toml pip install -e python[srt_npu] cd .. echo "2. install torch & torch_npu & triton_ascend & other basic packages" pip install torch==2.7.1 torch_npu==2.7.1.post2 torchvision==0.22.1 pip install pybind11 click==8.2.1 mbridge "numpy<2.0.0" cachetools echo "3. install sgl-kernel-npu form source, detailed readme in https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/deep_ep/README.md" git clone https://github.com/sgl-project/sgl-kernel-npu.git cd sgl-kernel-npu git checkout 46b73de sed -i '101s/^/# /' build.sh if [ "$NPU_DEVICE" = "A3" ]; then bash build.sh fi if [ "$NPU_DEVICE" = "A2" ]; then bash build.sh -a deepep2 fi pip install output/torch_memory_saver*.whl pip install output/sgl_kernel_npu*.whl pip install output/deep_ep*.whl cd "$(pip show deep-ep | grep -E '^Location:' | awk '{print $2}')" && ln -s deep_ep/deep_ep_cpp*.so && cd - python -c "import deep_ep; print(deep_ep.__path__)" cd .. # install sgl-kernel-npu from release whl # if [ "$NPU_DEVICE" = "A3" ]; then # wget https://github.com/sgl-project/sgl-kernel-npu/releases/download/2026.01.21/sgl-kernel-npu_2026.01.21_8.5.0_a3.zip # fi # if [ "$NPU_DEVICE" = "A2" ]; then # wget https://github.com/sgl-project/sgl-kernel-npu/releases/download/2026.01.21/sgl-kernel-npu_2026.01.21_8.5.0_910b.zip # fi # unzip sgl-kernel-npu*.zip # pip install output/torch_memory_saver*.whl # pip install output/sgl_kernel_npu*.whl # pip install output/deep_ep*.whl if [ $USE_MEGATRON -eq 1 ]; then echo "4. install Megatron and MindSpeed" git clone -b 2.3.0_core_r0.12.1 https://gitcode.com/Ascend/MindSpeed.git pip install -e MindSpeed pip install git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.12.1 fi echo "5. May need to uninstall timm & triton" pip uninstall -y timm triton echo "Successfully installed all packages" ================================================ FILE: scripts/install_vllm_sglang_mcore.sh ================================================ #!/bin/bash USE_MEGATRON=${USE_MEGATRON:-1} USE_SGLANG=${USE_SGLANG:-1} export MAX_JOBS=32 echo "1. install inference frameworks and pytorch they need" if [ $USE_SGLANG -eq 1 ]; then pip install "sglang[all]==0.5.2" --no-cache-dir && pip install torch-memory-saver --no-cache-dir fi pip install --no-cache-dir "vllm==0.11.0" echo "2. install basic packages" pip install "transformers[hf_xet]>=4.51.0" accelerate datasets peft hf-transfer \ "numpy<2.0.0" "pyarrow>=15.0.0" pandas "tensordict>=0.8.0,<=0.10.0,!=0.9.0" torchdata \ ray[default] codetiming hydra-core pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler \ pytest py-spy pre-commit ruff tensorboard echo "pyext is lack of maintainace and cannot work with python 3.12." echo "if you need it for prime code rewarding, please install using patched fork:" echo "pip install git+https://github.com/ShaohonChen/PyExt.git@py311support" pip install "nvidia-ml-py>=12.560.30" "fastapi[standard]>=0.115.0" "optree>=0.13.0" "pydantic>=2.9" "grpcio>=1.62.1" echo "3. install FlashAttention and FlashInfer" # Install flash-attn-2.8.1 (cxx11abi=False) wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.1/flash_attn-2.8.1+cu12torch2.8cxx11abiFALSE-cp312-cp312-linux_x86_64.whl && \ pip install --no-cache-dir flash_attn-2.8.1+cu12torch2.8cxx11abiFALSE-cp312-cp312-linux_x86_64.whl pip install --no-cache-dir flashinfer-python==0.3.1 if [ $USE_MEGATRON -eq 1 ]; then echo "4. install TransformerEngine and Megatron" echo "Notice that TransformerEngine installation can take very long time, please be patient" pip install "onnxscript==0.3.1" NVTE_FRAMEWORK=pytorch pip3 install --no-deps git+https://github.com/NVIDIA/TransformerEngine.git@v2.6 pip3 install --no-deps git+https://github.com/NVIDIA/Megatron-LM.git@core_v0.13.1 fi echo "5. May need to fix opencv" pip install opencv-python pip install opencv-fixer && \ python -c "from opencv_fixer import AutoFix; AutoFix()" if [ $USE_MEGATRON -eq 1 ]; then echo "6. Install cudnn python package (avoid being overridden)" pip install nvidia-cudnn-cu12==9.10.2.21 fi echo "Successfully installed all packages" ================================================ FILE: scripts/legacy_model_merger.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script is used to merge huggingface model and test verl checkpoints from FSDP and Megatron backends. To merge FSDP checkpoints: ```sh python scripts/legacy_model_merger.py merge \ --backend fsdp \ --local_dir checkpoints/verl_fsdp_gsm8k_examples/qwen2_5_0b5_fsdp_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model ``` To merge Megatron checkpoints: ```sh python scripts/legacy_model_merger.py merge \ --backend megatron \ --tie-word-embedding \ --local_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model ``` For more details, please refer to documentation: https://verl.readthedocs.io/en/latest/advance/checkpoint.html#convert-fsdp-and-megatron-checkpoints-to-huggingface-format-model """ import argparse import os import re import warnings from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass, field from pathlib import Path from typing import Optional, Union import numpy as np import torch from accelerate import init_empty_weights from safetensors.torch import load_file from torch.distributed._tensor import Placement, Shard from transformers import ( AutoConfig, AutoModelForCausalLM, AutoModelForTokenClassification, GenerationConfig, PretrainedConfig, ) try: # for torch 2.5+ from torch.distributed.tensor import DTensor except ImportError: from torch.distributed._tensor import DTensor from tqdm import tqdm from verl.utils import hf_processor, hf_tokenizer from verl.utils.transformers_compat import get_auto_model_for_vision2seq AutoModelForVision2Seq = get_auto_model_for_vision2seq() @dataclass class ModelMergerConfig: operation: str # 'merge' or 'test' backend: str local_dir: str hf_model_config_path: str target_dir: Optional[str] = "tmp" hf_upload_path: Optional[str] = None private: bool = False test_hf_dir: Optional[str] = None tie_word_embedding: bool = False is_value_model: bool = False hf_model_path: Optional[str] = None hf_upload: bool = field(init=False) def __post_init__(self): self.hf_upload = self.operation == "merge" and bool(self.hf_upload_path) if self.operation == "test": self.target_dir = None self.hf_upload_path = None self.private = False class BaseModelMerger(ABC): def __init__(self, config: ModelMergerConfig): self.config = config self.hf_model_config_path = config.hf_model_config_path if config.hf_model_path: print( "Warning: --hf_model_path is deprecated and will be removed in a future version. Currently verl will save huggingface model configuration files into checkpoint directories. Therefore, there is no need to provide --hf_model_path. " ) self.hf_model_config_path = config.hf_model_path # Auto-detect huggingface subdirectory if it exists huggingface_subdir = os.path.join(self.hf_model_config_path, "huggingface") if os.path.isdir(huggingface_subdir): self.hf_model_config_path = huggingface_subdir self.model_config = AutoConfig.from_pretrained(self.hf_model_config_path) def get_transformers_auto_model_class(self): # Handle case where architectures might be None or empty if self.model_config.architectures is None or len(self.model_config.architectures) == 0: # Try to infer from model_type if architectures is missing model_type = getattr(self.model_config, 'model_type', '').lower() if 'vision' in model_type or 'vl' in model_type: return AutoModelForVision2Seq elif 'causal' in model_type or 'gpt' in model_type or 'llama' in model_type or 'qwen' in model_type: return AutoModelForCausalLM else: raise NotImplementedError( f"Cannot determine model class: architectures is None and model_type '{model_type}' is not recognized" ) architecture = self.model_config.architectures[0] if "ForTokenClassification" in architecture: return AutoModelForTokenClassification elif "ForCausalLM" in architecture: return AutoModelForCausalLM elif "ForConditionalGeneration" in architecture: return AutoModelForVision2Seq raise NotImplementedError(f"Unknown architecture {self.model_config.architectures}") def patch_model_generation_config(self, model): """ The generation_config created from model config may be different to the pretrained model, this may lead to error when generating: https://github.com/volcengine/verl/issues/1246 This function patch the generation_config created from model config to the pretrained model. """ if model.can_generate(): try: model.generation_config = GenerationConfig.from_pretrained(self.hf_model_config_path) except OSError: print( f"Warning: Generation config file not found in {self.hf_model_config_path}, using a generation config created from the model config." ) return model def save_lora_adapter(self, state_dict: dict[str, torch.Tensor]): """ Save lora adapter to safetensors. Returns: lora_path: str, the path to the lora adapter. None if no lora adapter found. Note: This function change the 'state_dict' in place. """ lora_params_names = [name for name in state_dict.keys() if "lora_" in name] if len(lora_params_names) == 0: return None import json from typing import OrderedDict import peft from safetensors.torch import save_file lora_params = OrderedDict() target_modules = set() lora_key = None for name in lora_params_names: lora_key = name.replace(".default.weight", ".weight") target_modules.add(lora_key.split(".")[-3]) lora_params[lora_key] = state_dict.pop(name) lora_rank = min(lora_params[lora_key].shape[0], lora_params[lora_key].shape[1]) peft_dict = { "r": lora_rank, "lora_alpha": 0, # lora_alpha is not set. An error should be raised to inform the user to set it manually. "target_modules": list(target_modules), } peft_config = peft.LoraConfig(**peft_dict).to_dict() peft_config["task_type"] = peft_config["task_type"].value if peft_config["task_type"] else None peft_config["peft_type"] = peft_config["peft_type"].value if peft_config["peft_type"] else None peft_config["target_modules"] = list(peft_config["target_modules"]) lora_path = os.path.join(self.config.target_dir, "lora_adapter") os.makedirs(lora_path, exist_ok=True) with open(os.path.join(lora_path, "adapter_config.json"), "w", encoding="utf-8") as f: json.dump(peft_config, f, ensure_ascii=False, indent=4) save_file(lora_params, os.path.join(lora_path, "adapter_model.safetensors")) for name in list(state_dict.keys()): key = ( name.replace("base_model.model.", "") .replace(".base_layer.weight", ".weight") .replace(".base_layer.bias", ".bias") ) state_dict[key] = state_dict.pop(name) return lora_path def save_hf_model_and_tokenizer(self, state_dict: dict[str, torch.Tensor]): auto_model_class = self.get_transformers_auto_model_class() with init_empty_weights(): model = auto_model_class.from_config(self.model_config, torch_dtype=torch.bfloat16) model.to_empty(device="cpu") model = self.patch_model_generation_config(model) lora_path = self.save_lora_adapter(state_dict) if lora_path: print(f"Saving lora adapter to {lora_path}") print(f"Saving model to {self.config.target_dir}") model.save_pretrained(self.config.target_dir, state_dict=state_dict) del state_dict del model processor = hf_processor(self.hf_model_config_path) try: tokenizer = hf_tokenizer(self.hf_model_config_path) except Exception as e: warnings.warn(f"Failed to create tokenizer: {e}. This may affect tokenizer saving", stacklevel=1) tokenizer = None if processor is not None: print(f"Saving processor to {self.config.target_dir}") processor.save_pretrained(self.config.target_dir) if tokenizer is not None: print(f"Saving tokenizer to {self.config.target_dir}") tokenizer.save_pretrained(self.config.target_dir) def upload_to_huggingface(self): from huggingface_hub import HfApi api = HfApi() api.create_repo(repo_id=self.config.hf_upload_path, private=self.config.private, exist_ok=True) api.upload_folder(folder_path=self.config.target_dir, repo_id=self.config.hf_upload_path, repo_type="model") @abstractmethod def merge_and_save(self): raise NotImplementedError("Subclasses should implement this method") class FSDPModelMerger(BaseModelMerger): def _get_world_size(self) -> int: """Extracts the FSDP world_size from checkpoint filenames (e.g., 'model_world_size_8_rank_0.pt').""" for filename in os.listdir(self.config.local_dir): match = re.match(r"model_world_size_(\d+)_rank_0\.pt", filename) if match: return int(match.group(1)) raise FileNotFoundError( f"Could not determine world size. No file matching 'model_world_size_(\\d+)_rank_0.pt' found in {self.config.local_dir}" ) def _load_rank_zero_state_dict(self, world_size: int) -> dict: return torch.load( Path(self.config.local_dir) / f"model_world_size_{world_size}_rank_0.pt", map_location="cpu", weights_only=False, ) def _extract_device_mesh_info(self, state_dict: dict, world_size: int) -> tuple[np.ndarray, tuple[str, ...]]: """ Retrieves sharding information (device_mesh, mesh_dim_names) from a DTensor in the state_dict. If no DTensor is found, infers a simple FSDP mesh based on world_size. """ pivot_key = sorted(list(state_dict.keys()))[0] weight = state_dict[pivot_key] if isinstance(weight, DTensor): # get sharding info device_mesh = weight.device_mesh mesh = device_mesh.mesh mesh_dim_names = device_mesh.mesh_dim_names else: # for non-DTensor mesh = np.array([world_size], dtype=np.int64) mesh_dim_names = ("fsdp",) return mesh, mesh_dim_names def _calculate_shard_configuration( self, mesh: np.ndarray, mesh_dim_names: tuple[str, ...] ) -> tuple[int, tuple[int, ...]]: """Calculates the total number of shards and the shape of the device mesh.""" assert mesh_dim_names in (("fsdp",), ("ddp", "fsdp")), f"Unsupported mesh_dim_names {mesh_dim_names}" if "tp" in mesh_dim_names: # TODO: "tp" is not supported yet due to the above assert total_shards = mesh.shape[-1] * mesh.shape[-2] mesh_shape = (mesh.shape[-2], mesh.shape[-1]) else: total_shards = mesh.shape[-1] mesh_shape = (mesh.shape[-1],) return total_shards, mesh_shape def _merge_by_placement(self, tensors: list[torch.Tensor], placement: Placement) -> torch.Tensor: """Merges a list of tensors based on their DTensor placement""" if placement.is_replicate(): return tensors[0] elif placement.is_partial(): raise NotImplementedError("Partial placement is not supported yet") elif placement.is_shard(): return torch.cat(tensors, dim=placement.dim).contiguous() raise NotImplementedError(f"Unsupported placement: {placement}") def _load_and_merge_state_dicts( self, world_size: int, total_shards: int, mesh_shape: tuple[int, ...], mesh_dim_names: tuple[str, ...] ) -> dict[str, torch.Tensor]: model_state_dict_lst = [None] * total_shards def process_one_shard(rank: int, model_state_dict_lst: list): model_path = Path(self.config.local_dir) / f"model_world_size_{world_size}_rank_{rank}.pt" state_dict = torch.load(model_path, map_location="cpu", weights_only=False) model_state_dict_lst[rank] = state_dict return state_dict with ThreadPoolExecutor(max_workers=min(32, os.cpu_count())) as executor: futures = [executor.submit(process_one_shard, rank, model_state_dict_lst) for rank in range(total_shards)] for future in tqdm(futures, desc=f"Loading {total_shards} FSDP shards", total=total_shards): future.result() # Merge state dicts from all shards state_dict = {} param_placements: dict[str, list] = {} for key in set(model_state_dict_lst[0].keys()): state_dict[key] = [] for model_state_shard in model_state_dict_lst: # add tensor shard in order of rank to state_dict[key] tensor = model_state_shard.pop(key) if isinstance(tensor, DTensor): state_dict[key].append(tensor._local_tensor.bfloat16()) placements = tuple(tensor.placements) # replicated placement at dp dimension can be discarded if mesh_dim_names[0] in ("dp", "ddp"): placements = placements[1:] if key not in param_placements: param_placements[key] = placements else: assert param_placements[key] == placements else: state_dict[key].append(tensor.bfloat16()) del model_state_dict_lst # Merge tensors for key in sorted(state_dict): if not isinstance(state_dict[key], list): print(f"No need to merge key {key}") continue if key in param_placements: # merge shards placements: tuple[Shard] = param_placements[key] if len(mesh_shape) == 1: # 1-D list, FSDP without TP assert len(placements) == 1 shards = state_dict[key] state_dict[key] = self._merge_by_placement(shards, placements[0]) else: # 2-D list, FSDP + TP raise NotImplementedError("FSDP + TP is not supported yet") else: state_dict[key] = torch.cat(state_dict[key], dim=0) return state_dict def merge_and_save(self): world_size = self._get_world_size() rank_zero_state_dict = self._load_rank_zero_state_dict(world_size) mesh, mesh_dim_names = self._extract_device_mesh_info(rank_zero_state_dict, world_size) print(f"Got device mesh {mesh}, mesh_dim_names {mesh_dim_names}") total_shards, mesh_shape = self._calculate_shard_configuration(mesh, mesh_dim_names) print(f"Processing model shards with {total_shards} {mesh_shape} in total") merged_state_dict = self._load_and_merge_state_dicts(world_size, total_shards, mesh_shape, mesh_dim_names) if self.config.operation == "test": if not self.config.test_hf_dir: raise ValueError("test_hf_dir must be provided for test operation") self._test_state_dict(merged_state_dict) elif self.config.operation == "merge": self.save_hf_model_and_tokenizer(merged_state_dict) if self.config.hf_upload: self.upload_to_huggingface() else: raise ValueError(f"Unknown operation: {self.config.operation}") def _test_state_dict(self, state_dict: dict[str, torch.Tensor]): auto_model_class = self.get_transformers_auto_model_class() hf_model = auto_model_class.from_pretrained(self.config.test_hf_dir, torch_dtype=torch.bfloat16) hf_state_dict = hf_model.state_dict() del hf_model hf_model_keys = set(hf_state_dict.keys()) collected_keys = set(state_dict.keys()) missing_keys = hf_model_keys - collected_keys assert len(missing_keys) == 0, f"Missing keys in collected state dict: {list(sorted(missing_keys))}" extra_keys = collected_keys - hf_model_keys assert len(extra_keys) == 0, f"Extra keys in collected state dict: {list(sorted(extra_keys))}" for key in hf_model_keys: hf_shape = hf_state_dict[key].shape collected_shape = state_dict[key].shape assert hf_shape == collected_shape, ( f"Shape mismatch for key '{key}': original {hf_shape} vs collected {collected_shape}" ) hf_dtype = hf_state_dict[key].dtype collected_dtype = state_dict[key].dtype assert hf_dtype == collected_dtype, ( f"Dtype mismatch for key '{key}': original {hf_dtype} vs collected {collected_dtype}" ) torch.testing.assert_close(hf_state_dict[key], state_dict[key], atol=1e-6, rtol=1e-6) print("FSDP checks passed: The merged state_dict matches the hf model saved by FSDPCheckpointManager.") class MegatronModelMerger(BaseModelMerger): def __init__(self, config: ModelMergerConfig): from verl.utils.megatron_utils import get_hf_config_and_tokenizer_checkpoint_path config.hf_model_config_path = get_hf_config_and_tokenizer_checkpoint_path(config.local_dir) super().__init__(config) self.params_mapping = { # megatron core gpt model name, huggingface model name # NOTICE: It's a little bit tricky, when 2 keys have the same prefix, we need to make sure the longer key within the containing relationship is processed first. "embedding.word_embeddings": "model.embed_tokens", # attn "self_attention.linear_qkv.layer_norm_weight": "input_layernorm.weight", "self_attention.linear_qkv.layer_norm_bias": "input_layernorm.bias", "self_attention.linear_qkv": "self_attn.qkv_proj", "self_attention.q_layernorm": "self_attn.q_norm", "self_attention.k_layernorm": "self_attn.k_norm", "self_attention.linear_proj": "self_attn.o_proj", # mla "self_attention.linear_q_proj": "self_attn.q_proj", "self_attention.linear_q_down_proj": "self_attn.q_a_proj", "self_attention.linear_q_up_proj.layer_norm_weight": "self_attn.q_a_layernorm.weight", "self_attention.linear_q_up_proj": "self_attn.q_b_proj", "self_attention.linear_kv_down_proj": "self_attn.kv_a_proj_with_mqa", "self_attention.linear_kv_up_proj.layer_norm_weight": "self_attn.kv_a_layernorm.weight", "self_attention.linear_kv_up_proj": "self_attn.kv_b_proj", # mlp "pre_mlp_layernorm": "post_attention_layernorm", "mlp.linear_fc1.layer_norm_weight": "post_attention_layernorm.weight", "mlp.linear_fc1.layer_norm_bias": "post_attention_layernorm.bias", "mlp.linear_fc1": "mlp.gate_up_proj", "mlp.linear_fc2": "mlp.down_proj", # moe "mlp.router.expert_bias": "mlp.gate.e_score_correction_bias", "mlp.router": "mlp.gate", "mlp.shared_experts.linear_fc1": "mlp.shared_experts.gate_up_proj", "mlp.shared_experts.linear_fc2": "mlp.shared_experts.down_proj", "linear_fc1": "gate_up_proj", "linear_fc2": "down_proj", # output "final_layernorm": "norm", "output_layer": "lm_head", } def _get_tp_pp_rank_from_sharded_dir(self, sharded_dir: str) -> tuple[int, int]: tp_rank = pp_rank = None rank_list = sharded_dir.split("_")[2:] if re.match(r"mp_rank_(\d\d)_(\d\d\d)", sharded_dir): tp_rank = int(rank_list[0]) pp_rank = int(rank_list[1]) elif re.match(r"mp_rank_(\d\d)", sharded_dir): tp_rank = int(rank_list[0]) pp_rank = 0 assert tp_rank is not None and pp_rank is not None, f"Invalid sharded dir {sharded_dir}" return tp_rank, pp_rank def _check_megatron_checkpoint_path(self, model_path: str) -> tuple[list[str], int, int]: """ Validates the Megatron checkpoint structure (presence of 'model.pt' in sharded directories). Determines TP and PP sizes from directory names. """ tp_size = 0 pp_size = 0 sharded_dirs = sorted(os.listdir(model_path)) for sharded_dir in sharded_dirs: assert "model.pt" in os.listdir(Path(model_path) / sharded_dir), f"model.pt not found in {sharded_dir}" tp_rank, pp_rank = self._get_tp_pp_rank_from_sharded_dir(sharded_dir) tp_size = max(tp_size, tp_rank + 1) pp_size = max(pp_size, pp_rank + 1) return sharded_dirs, tp_size, pp_size def _merge_across_tp( self, key: str, tp_data: list[torch.Tensor], config: PretrainedConfig, tp_size: int, is_value_model: bool = False, ) -> Union[torch.Tensor, list[torch.Tensor]]: if "linear_fc1.weight" in key: # if the tensor is gate and proj gate_lst = [] up_lst = [] for infer_param in tp_data: gate, up = infer_param.chunk(2) gate_lst.append(gate) up_lst.append(up) gate = torch.cat(gate_lst, dim=0) up = torch.cat(up_lst, dim=0) return [gate, up] elif "self_attention.linear_qkv." in key and "layer_norm" not in key: # if the tensor is qkv, for each param on tp, split into q, k, v # concat q, k, v separately. q_lst = [] k_lst = [] v_lst = [] assert config.num_attention_heads % config.num_key_value_heads == 0 num_q_per_kv = config.num_attention_heads // config.num_key_value_heads assert tp_data[0].shape[0] % (num_q_per_kv + 2) == 0 kv_size_per_tp = tp_data[0].shape[0] // (num_q_per_kv + 2) split_size = [kv_size_per_tp * num_q_per_kv, kv_size_per_tp, kv_size_per_tp] for infer_param in tp_data: num_query_groups_per_partition = config.num_key_value_heads // tp_size for chunk in infer_param.chunk(num_query_groups_per_partition): split_size = [ kv_size_per_tp * num_q_per_kv // num_query_groups_per_partition, kv_size_per_tp // num_query_groups_per_partition, kv_size_per_tp // num_query_groups_per_partition, ] q, k, v = chunk.split(split_size) q_lst.append(q) k_lst.append(k) v_lst.append(v) q = torch.cat(q_lst, dim=0) k = torch.cat(k_lst, dim=0) v = torch.cat(v_lst, dim=0) return [q, k, v] elif "layer_norm" in key or "layernorm" in key or "router" in key or ("output_layer" in key and is_value_model): return tp_data[0] else: dim = 0 if "linear_fc2.weight" in key or "self_attention.linear_proj" in key: dim = 1 return torch.cat(tp_data, dim=dim) def _load_state_dicts( self, model_ckpt_path: str, sharded_dirs: list[str], tp_size: int, pp_size: int ) -> list[list[dict]]: model_state_dict_lst = [[None for _ in range(tp_size)] for _ in range(pp_size)] def _process_one_megatron_shard(sharded_dir: str): model_file_path = Path(model_ckpt_path) / sharded_dir / "model.pt" state_dict = torch.load(model_file_path, map_location="cpu", weights_only=False) tp_rank, pp_rank = self._get_tp_pp_rank_from_sharded_dir(sharded_dir) model_state_dict_lst[pp_rank][tp_rank] = state_dict with ThreadPoolExecutor(max_workers=min(32, os.cpu_count())) as executor: futures = [executor.submit(_process_one_megatron_shard, sharded_dir) for sharded_dir in sharded_dirs] for future in tqdm(futures, desc=f"Loading {len(sharded_dirs)} Megatron shards", total=len(sharded_dirs)): future.result() return model_state_dict_lst def _check_megatron_state_key(self, key: str) -> bool: """ Checks if the key is a valid Megatron state key. Now the model merger only supports keys that start with "decoder/embedding/output_layer" in TransformerLayer. Shall not use key starts with "model." """ if key.startswith("model."): raise ValueError( f"Invalid key {key} in Megatron state_dict. Expected keys to start with 'decoder/embedding/output_layer' in TransformerLayer." ) skip_checking_keys = ["embedding.word_embeddings", "output_layer"] for skip_key in skip_checking_keys: if skip_key in key: print(f"skip checking key {key}") return # Exclude extra state keys if not key.startswith("decoder"): raise ValueError( f"Invalid key {key} in Megatron state_dict. Expected keys to start with 'decoder' in TransformerLayer." ) def _merge_state_dicts( self, model_state_dict_lst: list[list[dict]], tp_size: int, pp_size: int ) -> dict[str, torch.Tensor]: state_dict = {} vpp_size = len(model_state_dict_lst[0][0]) layers_cum = 0 for vpp_rank in range(vpp_size): for pp_rank in range(pp_size): layers_handled = 0 keys = model_state_dict_lst[pp_rank][0][vpp_rank].keys() for key in keys: if "extra_state" in key: continue if self.config.tie_word_embedding and ("output_layer" in key): print("skip lm_head and reward_head loading because of tie_word_embeddings") continue self._check_megatron_state_key(key) hf_name = self._replace_name(key, self.params_mapping) assert hf_name is not None, f"Failed to convert layer name [{key}] from megatron to huggingface." if "model.layers." in hf_name: local_layer_no = int(hf_name.split(".")[2]) layers_handled = max(local_layer_no, layers_handled) global_layer_no = local_layer_no + layers_cum new_key_list = hf_name.split(".") new_key_list[2] = str(global_layer_no) hf_name = ".".join(new_key_list) else: warnings.warn(f"hf_name {hf_name} will not be fixed with layer number", stacklevel=2) tp_data = [model_state_dict_lst[pp_rank][tp_rank][vpp_rank][key] for tp_rank in range(tp_size)] merged = self._merge_across_tp(key, tp_data, self.model_config, tp_size, self.config.is_value_model) if not isinstance(merged, list): state_dict[hf_name] = merged elif len(merged) == 3: # split qkv for n, d in zip(["q", "k", "v"], merged): state_dict[hf_name.replace("qkv", n)] = d elif len(merged) == 2: # split gate up state_dict[hf_name.replace("gate_up", "gate")] = merged[0] state_dict[hf_name.replace("gate_up", "up")] = merged[1] print( f"converted {key} to {hf_name} with shape {merged.shape if isinstance(merged, torch.Tensor) else [t.shape for t in merged]}" ) layers_cum += layers_handled + 1 # zero based return state_dict def merge_and_save(self): from verl.utils.megatron_utils import get_model_checkpoint_path model_ckpt_path = get_model_checkpoint_path(self.config.local_dir) sharded_dirs, tp_size, pp_size = self._check_megatron_checkpoint_path(model_ckpt_path) print(f"sharded_dirs: {sharded_dirs}, tp_size: {tp_size}, pp_size: {pp_size}, mp_size: {len(sharded_dirs)}") model_state_dict_lst = self._load_state_dicts(model_ckpt_path, sharded_dirs, tp_size, pp_size) merged_state_dict = self._merge_state_dicts(model_state_dict_lst, tp_size, pp_size) del model_state_dict_lst if self.config.operation == "test": if not self.config.test_hf_dir: raise ValueError("test_hf_dir must be provided for test operation") self._test_state_dict(merged_state_dict) elif self.config.operation == "merge": self.save_hf_model_and_tokenizer(merged_state_dict) if self.config.hf_upload: self.upload_to_huggingface() else: raise ValueError(f"Unknown operation: {self.config.operation}") def _test_state_dict(self, state_dict: dict[str, torch.Tensor]): """ Compares the merged Megatron state_dict against a reference safetensors model. Applies necessary name mappings from Megatron to Hugging Face conventions using _replace_name. """ ref_state_dict = load_file(Path(self.config.test_hf_dir) / "model.safetensors") for name, loaded_weight in state_dict.items(): # name = self._replace_name(original_name, self.params_mapping) if not name or name.endswith(".bias") and name not in ref_state_dict: continue if "rotary_emb.inv_freq" in name: continue if self.config.tie_word_embedding and "lm_head.weight" in name: continue if name not in ref_state_dict: raise RuntimeError(f"key: {name} not exist in state_dict") param = ref_state_dict[name] assert loaded_weight.dtype == param.dtype torch.testing.assert_close(loaded_weight, param, atol=1e-2, rtol=5e-2) def _replace_name(self, megatron_name: str, name_mapping: dict[str, str]) -> str: for m_name, v_name in name_mapping.items(): if m_name not in megatron_name: continue megatron_name = megatron_name.replace("decoder", "model") param_name = megatron_name.replace(m_name, v_name) return param_name return None # Return None if no mapping found def main(): parser = argparse.ArgumentParser(description="verl model merger") subparsers = parser.add_subparsers(dest="operation", required=True, help="Specify 'merge' or 'test' operation.") base_op_parser = argparse.ArgumentParser(add_help=False) base_op_parser.add_argument( "--backend", type=str, required=True, choices=["fsdp", "megatron"], help="The backend of the model" ) base_op_parser.add_argument("--local_dir", type=str, required=True, help="Path to the saved model checkpoints") base_op_parser.add_argument( "--hf_model_path", type=str, default=None, help="(Deprecated) Path to the original Hugging Face model for config.", ) base_op_parser.add_argument( "--tie-word-embedding", action="store_true", help="Whether to tie word embedding weights (currently only Megatron supported)", ) base_op_parser.add_argument( "--is-value-model", action="store_true", help="Whether the model is a value model (currently only Megatron supported)", ) merge_parser = subparsers.add_parser("merge", parents=[base_op_parser], help="Merge model checkpoints and save.") merge_parser.add_argument( "--target_dir", default="tmp", type=str, help="Directory to save the merged huggingface model" ) merge_parser.add_argument( "--hf_upload_path", default=None, type=str, help="Hugging Face repository ID to upload the model" ) merge_parser.add_argument( "--private", action="store_true", help="Whether to upload the model to a private Hugging Face repository" ) test_parser = subparsers.add_parser( "test", parents=[base_op_parser], help="Test merged model against a reference Hugging Face model" ) test_parser.add_argument( "--test_hf_dir", type=str, required=True, help="Path to the reference Hugging Face model directory for testing" ) args = parser.parse_args() common_config_args = { "operation": args.operation, "backend": args.backend, "tie_word_embedding": args.tie_word_embedding, "is_value_model": args.is_value_model, "local_dir": args.local_dir, "hf_model_path": args.hf_model_path, "hf_model_config_path": args.local_dir, } if args.operation == "merge": config = ModelMergerConfig( **common_config_args, target_dir=args.target_dir, hf_upload_path=args.hf_upload_path, private=args.private, test_hf_dir=None, ) os.makedirs(config.target_dir, exist_ok=True) elif args.operation == "test": config = ModelMergerConfig( **common_config_args, test_hf_dir=args.test_hf_dir, # the following args are not used by test operation target_dir=None, hf_upload_path=None, private=False, ) else: raise NotImplementedError(f"Unknown operation: {args.operation}") if config.backend == "fsdp": merger = FSDPModelMerger(config) elif config.backend == "megatron": merger = MegatronModelMerger(config) else: raise NotImplementedError(f"Unknown backend: {config.backend}") merger.merge_and_save() if __name__ == "__main__": main() ================================================ FILE: scripts/megatron_merge_lora.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from pprint import pprint import hydra import ray import torch from omegaconf import OmegaConf from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.megatron_utils import get_hf_model_checkpoint_path, load_megatron_model_to_gpu from verl.workers.megatron_workers import ActorRolloutRefWorker os.environ["NCCL_DEBUG"] = "WARN" os.environ["TOKENIZERS_PARALLELISM"] = "true" class CustomSaveWorker(ActorRolloutRefWorker): @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_merged_weights(self, hf_ckpt_path): import os if self._is_offload_param: load_megatron_model_to_gpu(self.actor_module) torch.distributed.barrier() print(f"[Rank {os.environ.get('RANK', '?')}] Saving weights to {hf_ckpt_path}...") if self.vanilla_bridge: self.bridge.save_weights( self.actor_module, hf_ckpt_path, distributed_filesystem=True, memory_efficient=True ) else: self.bridge.save_hf_weights(self.actor_module, hf_ckpt_path) return True @hydra.main(config_path="../verl/trainer/config", config_name="ppo_megatron_trainer", version_base=None) def main(config): assert config.actor_rollout_ref.model.lora.adapter_path is not None, "adapter_path must be specified" if ( config.actor_rollout_ref.actor.optim.lr_decay_steps is None or config.actor_rollout_ref.actor.optim.lr_decay_steps < 1 ): # set to bypass OptimizerParamScheduler checks config.actor_rollout_ref.actor.optim.lr_decay_steps = 100000 run_merge(config) def run_merge(config) -> None: if not ray.is_initialized(): # this is for local ray cluster default_runtime_env = {"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}} ray_init_kwargs = config.ray_kwargs.get("ray_init", {}) runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {}) runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs) ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env}) print(f"ray init kwargs: {ray_init_kwargs}") ray.init(**OmegaConf.to_container(ray_init_kwargs)) ray.get(main_task.remote(config)) @ray.remote(num_cpus=1) def main_task(config): pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) ray_cls_with_init = RayClassWithInitArgs( cls=ray.remote(CustomSaveWorker), config=config.actor_rollout_ref, role="actor" ) resource_pool = RayResourcePool(process_on_nodes=[config.trainer.n_gpus_per_node] * config.trainer.nnodes) worker = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=config.trainer.device, ) worker.init_model() adapter_path = config.actor_rollout_ref.model.lora.adapter_path hf_ckpt_path = get_hf_model_checkpoint_path(os.path.dirname(adapter_path)) worker.save_merged_weights(hf_ckpt_path) if __name__ == "__main__": """ Use the same config as your training script, besides **specifying the adapter_path**. For example, your training script starts with: `python3 -m verl.trainer.main_ppo --config-name=ppo_megatron_trainer ...` Now replace it with `python3 ./scripts/megatron_merge_lora.py --config-name=ppo_megatron_trainer ...` """ main() ================================================ FILE: scripts/print_cfg.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. try: import hydra except ImportError as e: raise ImportError("Please install hydra-core via 'pip install hydra-core' and retry.") from e @hydra.main(config_path="../verl/trainer/config", config_name="ppo_trainer", version_base=None) def main(config): """Main entry point for PPO training with Hydra configuration management. Args: config_dict: Hydra configuration dictionary containing training parameters. """ print(config) from verl.utils.config import omega_conf_to_dataclass profiler_config = omega_conf_to_dataclass(config.critic.profiler) print(profiler_config) if __name__ == "__main__": main() ================================================ FILE: scripts/rollout_viewer.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import re import traceback from pathlib import Path from typing import Annotated, Optional import aiofiles try: import ujson as json except ImportError: import json import typer from rich.highlighter import ReprHighlighter from rich.markdown import Markdown from rich.table import Table from rich.text import Text from textual import on from textual.app import App, ComposeResult from textual.containers import Horizontal, Vertical, VerticalScroll from textual.widgets import Input, ProgressBar, Select, SelectionList, Static INDEX_KEY = "__IDX" FILE_SUFFIX = ".jsonl" def check_textual_version(): # check if textual version is equal to 0.52.1 import textual from packaging.version import Version if Version(textual.__version__) != Version("0.52.1"): raise ImportError(f"Textual version {textual.__version__} is not supported, please pip install textual==0.52.1") check_textual_version() async def load_path(p: Path, data: dict, mask_strs: str, idx: int, pbar): samples = [] async with aiofiles.open(p, encoding="utf-8") as f: async for line in f: d = json.loads(line) for k in d: if isinstance(d[k], str): if mask_strs: d[k] = re.sub(rf"{mask_strs}", "*", d[k]) else: d[k] = json.dumps(d[k], ensure_ascii=False, indent=4) d[INDEX_KEY] = len(samples) samples.append(d) data[idx] = {"samples": samples} print(f"path {p} loaded") pbar.advance(1) async def load_dir(path: Path, data: dict[int, dict], pbar, mask_strs: str = ""): paths = list(path.glob(f"*{FILE_SUFFIX}")) paths = sorted(paths, key=lambda x: int(x.stem)) tasks = [load_path(p, data, mask_strs, i, pbar) for i, p in enumerate(paths)] await asyncio.gather(*tasks) class Highlighter(ReprHighlighter): highlights = ReprHighlighter.highlights + [ r"(?P[][\<\>{}()\|（）【】\[\]=`])", r"\<\|(?P[\w\W]*?)\|\>", ] def center_word_with_equals_exactly(word: str, total_length: int, char: str = "=") -> str: if len(word) > total_length: return word padding = total_length - len(word) left_pad = (padding) // 2 right_pad = (padding + 1) // 2 return char * left_pad + " " + word + " " + char * right_pad def highlight_keyword(content: str, keyword: Optional[str]): if not keyword: return Text(content) text = Text() parts = content.split(keyword) for i, part in enumerate(parts): text.append(part, style=None) if i < len(parts) - 1: # text.append(keyword, style=Style(color="#d154d1", bgcolor="yellow", bold=True)) text.append(keyword, style="on #8f51b5") return text help_doc = """ ⌨️ keybinds： - `f/esc`: find/cancel - `tab/←/→`: change focus - `j/k`: page down/up - `g/G`: scroll home/end - `n/N`: next sample/step - `p/P`: previous sample/step - `s`: switch display mode - plain text - rich table """ class JsonLineViewer(App): BINDINGS = [ ("left", "focus_previous", "Focus Previous"), ("right", "focus_next", "Focus Next"), ("s", "swith_render", "switch render"), # control ("n", "next_sample", "Next Sample"), ("N", "next_step", "Next Step"), ("p", "previous_sample", "Previous Sample"), ("P", "previous_step", "Previous Step"), # search ("f", "toggle_search", "find"), ("enter", "next_search", "find next"), ("escape", "cancel_search", "cancel find"), # scroll ("j", "page_down", "page down"), ("k", "page_up", "page up"), ("g", "page_home", "page home"), ("G", "page_end", "page end"), ] CSS = """ Select:focus > SelectCurrent { border: tall #8f51b5; } Select.-expanded > SelectCurrent { border: tall #8f51b5; } #select-container { width: 15%; height: 100%; align: center top; } #search-container { height: 10%; align: center top; } #search-box { width: 50%; } #reqid-box { width: 50%; } """ def __init__(self, step_num: int, data: dict[int, dict], pbar): super().__init__() self.step_num = step_num self.data = data self.render_table = False self.selected_step_index = 0 self.selected_sample_index = 0 self.pbar = pbar self.matches = [] self.current_match_index = 0 self.highlighter = Highlighter() first_samples = data[list(data.keys())[0]]["samples"] # Prepare the initial field filter list (all keys from the first sample) self.filter_fields = [(f, f, True) for f in first_samples[0].keys()] # Internal set used for fast membership checks when we add new fields on the fly. # We keep it here so that when new columns appear in later steps (e.g. `request_id`), # they can be added to the UI automatically without restarting the viewer. self._field_set: set[str] = set(first_samples[0].keys()) self.sample_num = len(first_samples) def compose(self) -> ComposeResult: with Horizontal(id="search-container"): yield Input(placeholder="find something...", id="search-box") yield Input(placeholder="request id...", id="reqid-box") with Vertical(id="search-container2"): yield self.pbar yield Static("", id="search-status") with Horizontal(): with Vertical(id="select-container"): yield Static("\n") yield Static( renderable=Markdown( help_doc, ), markup=False, ) yield Static("\n") yield Select( id="step-select", value=0, prompt="select step", options=[("step: 1", 0)], allow_blank=False, ) yield Select( id="sample-select", value=0, prompt="select sample", options=[("sample: 1", 0)], allow_blank=False, ) yield Select( id="sample-sort", value=0, prompt="排序", options=[ ("sort", 0), ("score asc", 1), ("score desc", 2), ], allow_blank=False, ) yield SelectionList[int](("Select ALL", 1, True), id="fields-select-all") with VerticalScroll(id="scroll-view2"): yield SelectionList[str](*self.filter_fields, id="fields-select") with VerticalScroll(id="scroll-view"): yield Static(id="content", markup=False) async def on_mount(self) -> None: self.step_select = self.query_one("#step-select", Select) self.sample_select = self.query_one("#sample-select", Select) self.sample_sort = self.query_one("#sample-sort", Select) self.content_display = self.query_one("#content", Static) self.search_box = self.query_one("#search-box", Input) self.reqid_box = self.query_one("#reqid-box", Input) self.scroll_view = self.query_one("#scroll-view", VerticalScroll) self.search_status = self.query_one("#search-status", Static) self.fields_select = self.query_one("#fields-select", SelectionList) self.fields_select.border_title = "field filter" if self.data: self.step_select.set_options([(f"step: {i + 1}", i) for i in range(self.step_num)]) self.sample_select.set_options([(f"sample: {i + 1}", i) for i in range(self.sample_num)]) self.step_select.focus() await self.update_content() def update_result_options(self, offset: int = 0, sort_desc: Optional[bool] = None): options = [] if isinstance(self.selected_step_index, int) and self.selected_step_index < len(self.data): if self.sample_num is None or sort_desc is not None: samples = self.data[self.selected_step_index].get("samples", []) if not samples: self.selected_sample_index = offset return if sort_desc is not None: samples = sorted( samples, key=lambda x: x.get("score", x.get("score_1", 0)), reverse=sort_desc, ) options = [(f"sample: {r[INDEX_KEY] + 1}", r[INDEX_KEY]) for r in samples] self.sample_select.set_options(options) self.sample_num = len(samples) if sort_desc is not None and options: self.selected_sample_index = options[0][1] else: self.selected_sample_index = offset async def update_content(self, search_keyword: Optional[str] = None): content = "" try: samples = self.data[self.selected_step_index].get("samples", []) content_dict_full = samples[self.selected_sample_index] # Dynamically track any NEW keys that appear and add them to the field filter. self._update_fields_select(content_dict_full.keys()) # Apply field selection filter (only show selected fields) content_dict = {k: v for k, v in content_dict_full.items() if k in self.fields_select.selected} if self.render_table: content = Table("key", "value", show_lines=True) for k in content_dict: v = content_dict[k] v = f"{v}" content.add_row( k, self.highlighter(highlight_keyword(v, search_keyword)), ) else: text = Text() for k in content_dict: v = content_dict[k] s = center_word_with_equals_exactly(k, 64) + f"\n{v}\n" text.append(highlight_keyword(s, search_keyword)) content = self.highlighter(text) except KeyError: content = f"Loading data asynchronously, progress: {len(self.data)}/{self.step_num} step" except Exception: content = self.highlighter(traceback.format_exc()) self.content_display.update(content) # --------------------------------------------------------------------- # Request-ID jump logic # --------------------------------------------------------------------- @on(Input.Submitted, "#reqid-box") async def on_reqid_submitted(self, event: Input.Submitted) -> None: """Jump to the sample that has a matching `request_id`.""" req_id_raw = event.value.strip() # Remove hyphens so search is tolerant to different id formats req_id = req_id_raw.replace("-", "") if not req_id: return found = False for step_idx, step_data in self.data.items(): for sample in step_data.get("samples", []): sample_id = str(sample.get("request_id", "")) if sample_id.replace("-", "") == req_id: # Update selected indices self.selected_step_index = step_idx self.step_select.value = step_idx # Ensure sample list is updated and select sample self.update_result_options(offset=sample[INDEX_KEY]) self.selected_sample_index = sample[INDEX_KEY] self.sample_select.value = sample[INDEX_KEY] await self._clear_search() await self.update_content() found = True break if found: break if not found: self.search_status.update(Text(f"request_id '{req_id_raw}' not found", style="bold red")) else: # Keep the typed id in the input box so users see what was searched. pass # --------------------------------------------------------------------- # Helper: add new fields to SelectionList on-the-fly # --------------------------------------------------------------------- def _update_fields_select(self, keys): """Add any unseen *keys* to the field-selection widget so they can be toggled. The viewer is often launched with only the first step loaded. Later steps may introduce new columns (e.g. `request_id`). This helper ensures those fields become visible without requiring a restart. """ # Ensure we have the widget (only after on_mount) if not hasattr(self, "fields_select"): return for k in keys: if k not in self._field_set: self._field_set.add(k) try: # By default, new fields are selected so they appear immediately. self.fields_select.add_option(k, k, selected=True) except Exception: # Fallback for older textual versions where signature is different. self.fields_select.add_option((k, k, True)) @on(Select.Changed, "#step-select") async def step_changed(self, event): self.selected_step_index = event.value self.update_result_options() await self.update_content() @on(Select.Changed, "#sample-select") async def sample_changed(self, event): self.selected_sample_index = event.value await self._clear_search() await self.update_content() @on(Select.Changed, "#sample-sort") async def sort_changed(self, event): v = event.value self.update_result_options(sort_desc=None if v == 0 else False if v == 1 else True) await self.update_content() @on(SelectionList.SelectedChanged, "#fields-select") async def fields_changed(self, event): await self.update_content() @on(SelectionList.SelectedChanged, "#fields-select-all") async def fields_all_changed(self, event): s = self.query_one("#fields-select-all", SelectionList) if s.selected: self.fields_select.select_all() else: self.fields_select.deselect_all() def action_focus_previous(self): self.screen.focus_previous() def action_focus_next(self): self.screen.focus_next() async def action_next_step(self) -> None: self.selected_step_index += 1 if self.selected_step_index >= self.step_num: self.selected_step_index = 0 self.step_select.value = self.selected_step_index self.update_result_options() await self.update_content() async def action_next_sample(self) -> None: self.selected_sample_index += 1 if not self.sample_num or self.selected_sample_index >= self.sample_num: self.selected_sample_index = 0 self.sample_select.value = self.selected_sample_index await self._clear_search() await self.update_content() async def action_previous_step(self) -> None: self.selected_step_index -= 1 if self.selected_step_index < 0: self.selected_step_index = self.step_num - 1 self.step_select.value = self.selected_step_index self.update_result_options() await self.update_content() async def action_previous_sample(self) -> None: self.selected_sample_index -= 1 if self.selected_sample_index < 0: self.selected_sample_index = self.sample_num - 1 self.sample_select.value = self.selected_sample_index await self._clear_search() await self.update_content() async def action_swith_render(self): self.render_table = not self.render_table await self.update_content() def action_toggle_search(self) -> None: self.search_box.focus() async def action_cancel_search(self) -> None: self.search_box.value = "" await self._clear_search() await self.update_content() async def _clear_search(self): self.matches = [] self.search_status.update("") self.current_match_index = 0 @on(Input.Submitted, "#search-box") async def on_search_submitted(self, event: Input.Submitted) -> None: self.matches = [] self.current_match_index = 0 if event.value: await self.update_content(event.value) renderable = self.content_display.render() if isinstance(renderable, Table): return assert isinstance(renderable, Text) console = self.content_display._console lines = renderable.wrap(console, self.scroll_view.container_size.width) line_idx_recorded = set() for line_idx, line in enumerate(lines): if line_idx in line_idx_recorded: continue if event.value in line: self.matches.append( { "line": line_idx, "word": event.value, } ) line_idx_recorded.add(line_idx) self.scroll_view.focus() await self.action_next_search() async def action_next_search(self) -> None: if not self.matches or self.current_match_index >= len(self.matches): return target_line = self.matches[self.current_match_index]["line"] self.scroll_view.scroll_to(x=0, y=target_line * 1, animate=False) self.current_match_index = (self.current_match_index + 1) % len(self.matches) self.search_status.update( Text( f"Find ：{self.current_match_index + 1}/{len(self.matches)}", style="bold on #8f51b5", ) ) def action_page_up(self): self.scroll_view.scroll_page_up(animate=False) def action_page_down(self): self.scroll_view.scroll_page_down(animate=False) def action_page_home(self): self.scroll_view.scroll_home(animate=False) def action_page_end(self): self.scroll_view.scroll_end(animate=False) async def _run(path: Path, mask_str: str): assert path.exists(), f"{path} not exist" paths = list(path.glob(f"*{FILE_SUFFIX}")) paths = sorted(paths, key=lambda x: int(x.stem)) if not paths: raise ValueError(f"no available reward dump files under f{path}") print(f"get jsonl file nums: {len(paths)}") pbar = ProgressBar(total=len(paths), name="data load progress") data = {} await load_path(paths[0], data, mask_str, 0, pbar) app = JsonLineViewer(step_num=len(paths), data=data, pbar=pbar) await asyncio.gather(load_dir(path, data, pbar, mask_str), app.run_async()) app = typer.Typer() @app.command(help="launch TUI APP") def run( rollout_data_dir: Path, mask_str: Annotated[str, typer.Option(help="string that will be masked to *")] = r"<\|image_pad\|>|<\|imgpad\|>", ): loop = asyncio.get_event_loop() loop.run_until_complete(_run(rollout_data_dir, mask_str)) if __name__ == "__main__": app() ================================================ FILE: scripts/veomni/moe_merge.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Merge individual MoE expert weights into stacked tensors for efficient loading. This script takes a HuggingFace checkpoint with individual expert weights (e.g., model.layers.{i}.mlp.experts.{j}.gate_proj.weight) and merges them into stacked tensors (e.g., model.layers.{i}.mlp.experts.gate_proj) for faster loading and better memory efficiency in VeOmni. The merging process: 1. Loads individual expert weights from the HF checkpoint 2. Stacks them into single tensors for each projection type 3. Handles all three projection types: gate_proj, up_proj, down_proj 4. Supports both Qwen3-MoE (num_experts) and DeepSeek (n_routed_experts) formats 5. Handles models with initial dense layers (first_k_dense_replace) Usage: python moe_merge.py --raw_hf_path --merge_hf_path """ import os from argparse import ArgumentParser from dataclasses import dataclass from glob import glob from typing import Generator import torch from safetensors.torch import safe_open from tqdm import tqdm from transformers import AutoConfig from veomni.models import build_tokenizer, save_model_weights @dataclass class StateDictIterator: filepath: str def __iter__(self) -> Generator[tuple[str, "torch.Tensor"], None, None]: if self.filepath.endswith(".safetensors"): with safe_open(self.filepath, framework="pt", device="cpu") as f: for key in f.keys(): yield key, f.get_tensor(key) else: state_dict = torch.load(self.filepath, map_location="cpu", weights_only=True, mmap=True) for key in state_dict.keys(): yield key, state_dict[key] def main(raw_hf_path, merge_hf_path): torch.set_default_dtype(torch.bfloat16) os.makedirs(merge_hf_path, exist_ok=True) config = AutoConfig.from_pretrained(raw_hf_path) tokenizer = build_tokenizer(raw_hf_path) safetensor_files = list(glob(os.path.join(raw_hf_path, "*.safetensors"))) safetensor_files.sort() state_dict_iterators = [StateDictIterator(shard_file) for shard_file in safetensor_files] new_state_dict = {} for state_dict_iterator in tqdm(state_dict_iterators, desc="Loading checkpoint shards"): for name, tensor in state_dict_iterator: new_state_dict[name] = tensor.cpu() print(new_state_dict.keys()) if hasattr(config, "num_experts"): # qwen3moe num_experts = config.num_experts elif hasattr(config, "n_routed_experts"): # deepseek num_experts = config.n_routed_experts else: raise RuntimeError("could not find how many experts to assign") num_hidden_layers = config.num_hidden_layers if hasattr(config, "first_k_dense_replace"): # deepseek first k dense layer moe_layer_start_idx = config.first_k_dense_replace else: # moe layer only in the model moe_layer_start_idx = 0 for i in range(moe_layer_start_idx, num_hidden_layers): gate_proj = [] for j in range(num_experts): gate_proj.append(new_state_dict.pop(f"model.layers.{i}.mlp.experts.{j}.gate_proj.weight")) new_state_dict[f"model.layers.{i}.mlp.experts.gate_proj"] = torch.stack(gate_proj) up_proj = [] for j in range(num_experts): up_proj.append(new_state_dict.pop(f"model.layers.{i}.mlp.experts.{j}.up_proj.weight")) new_state_dict[f"model.layers.{i}.mlp.experts.up_proj"] = torch.stack(up_proj) down_proj = [] for j in range(num_experts): down_proj.append(new_state_dict.pop(f"model.layers.{i}.mlp.experts.{j}.down_proj.weight")) new_state_dict[f"model.layers.{i}.mlp.experts.down_proj"] = torch.stack(down_proj) model_assets = [config, tokenizer] save_model_weights(merge_hf_path, new_state_dict, model_assets=model_assets) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--raw_hf_path", type=str, required=True) parser.add_argument("--merge_hf_path", type=str, required=True) args = parser.parse_args() main(args.raw_hf_path, args.merge_hf_path) ================================================ FILE: scripts/veomni/moe_split.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Reverse process of moe_merge.py - splits merged MoE expert weights back to individual experts. This script takes a HF checkpoint that has been processed by moe_merge.py (where expert weights are stacked into single tensors) and splits them back to the original format with individual expert weights. The process reverses the merging by: 1. Loading stacked tensors like model.layers.{i}.mlp.experts.gate_proj 2. Unstacking them back to individual experts model.layers.{i}.mlp.experts.{j}.gate_proj.weight 3. Handling all three projection types: gate_proj, up_proj, down_proj Usage: python moe_split.py --merge_hf_path --split_hf_path """ import os from argparse import ArgumentParser from dataclasses import dataclass from glob import glob from typing import Generator import torch from safetensors.torch import safe_open from tqdm import tqdm from transformers import AutoConfig from veomni.models import build_tokenizer, save_model_weights @dataclass class StateDictIterator: filepath: str def __iter__(self) -> Generator[tuple[str, "torch.Tensor"], None, None]: if self.filepath.endswith(".safetensors"): with safe_open(self.filepath, framework="pt", device="cpu") as f: for key in f.keys(): yield key, f.get_tensor(key) else: state_dict = torch.load(self.filepath, map_location="cpu", weights_only=True, mmap=True) for key in state_dict.keys(): yield key, state_dict[key] def main(merge_hf_path, split_hf_path): torch.set_default_dtype(torch.bfloat16) os.makedirs(split_hf_path, exist_ok=True) config = AutoConfig.from_pretrained(merge_hf_path) tokenizer = build_tokenizer(merge_hf_path) safetensor_files = list(glob(os.path.join(merge_hf_path, "*.safetensors"))) safetensor_files.sort() state_dict_iterators = [StateDictIterator(shard_file) for shard_file in safetensor_files] new_state_dict = {} for state_dict_iterator in tqdm(state_dict_iterators, desc="Loading checkpoint shards"): for name, tensor in state_dict_iterator: new_state_dict[name] = tensor.cpu() num_experts = config.num_experts num_hidden_layers = config.num_hidden_layers for i in range(num_hidden_layers): print(f"Converting layer {i}") for proj_name in ["gate_proj", "up_proj", "down_proj"]: stacked_key = f"model.layers.{i}.mlp.experts.{proj_name}" if stacked_key in new_state_dict: stacked_tensor = new_state_dict.pop(stacked_key) for j in range(num_experts): expert_key = f"model.layers.{i}.mlp.experts.{j}.{proj_name}.weight" new_state_dict[expert_key] = stacked_tensor[j] model_assets = [config, tokenizer] print("Saving to safetensors") save_model_weights(split_hf_path, new_state_dict, model_assets=model_assets) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--merge_hf_path", type=str, required=True) parser.add_argument("--split_hf_path", type=str, required=True) args = parser.parse_args() main(args.merge_hf_path, args.split_hf_path) ================================================ FILE: setup.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # setup.py is the fallback installation script when pyproject.toml does not work import os from pathlib import Path from setuptools import find_packages, setup version_folder = os.path.dirname(os.path.join(os.path.abspath(__file__))) with open(os.path.join(version_folder, "verl/version/version")) as f: __version__ = f.read().strip() install_requires = [ "accelerate", "codetiming", "datasets", "dill", "hydra-core", "numpy<2.0.0", "pandas", "peft", "pyarrow>=19.0.0", "pybind11", "pylatexenc", "ray[default]>=2.41.0", "torchdata", "tensordict>=0.8.0,<=0.10.0,!=0.9.0", "transformers", "wandb", "packaging>=20.0", "tensorboard", ] TEST_REQUIRES = ["pytest", "pre-commit", "py-spy", "pytest-asyncio", "pytest-rerunfailures"] PRIME_REQUIRES = ["pyext"] GEO_REQUIRES = ["mathruler", "torchvision", "qwen_vl_utils"] GPU_REQUIRES = ["liger-kernel", "flash-attn"] MATH_REQUIRES = ["math-verify"] # Add math-verify as an optional dependency VLLM_REQUIRES = ["tensordict>=0.8.0,<=0.10.0,!=0.9.0", "vllm>=0.8.5,<=0.12.0"] TRTLLM_REQUIRES = ["tensorrt-llm>=1.2.0rc6"] SGLANG_REQUIRES = [ "tensordict>=0.8.0,<=0.10.0,!=0.9.0", "sglang[srt,openai]==0.5.6", "torch==2.9.1", ] TRL_REQUIRES = ["trl<=0.9.6"] MCORE_REQUIRES = ["mbridge"] extras_require = { "test": TEST_REQUIRES, "prime": PRIME_REQUIRES, "geo": GEO_REQUIRES, "gpu": GPU_REQUIRES, "math": MATH_REQUIRES, "vllm": VLLM_REQUIRES, "sglang": SGLANG_REQUIRES, "trl": TRL_REQUIRES, "mcore": MCORE_REQUIRES, "trtllm": TRTLLM_REQUIRES, } this_directory = Path(__file__).parent long_description = (this_directory / "README.md").read_text() setup( name="verl", version=__version__, package_dir={"": "."}, packages=find_packages(where="."), url="https://github.com/volcengine/verl", license="Apache 2.0", author="Bytedance - Seed - MLSys", author_email="zhangchi.usc1992@bytedance.com, gmsheng@connect.hku.hk", description="verl: Volcano Engine Reinforcement Learning for LLM", install_requires=install_requires, extras_require=extras_require, package_data={ "": ["version/*"], "verl": [ "trainer/config/*.yaml", "trainer/config/*/*.yaml", "experimental/*/config/*.yaml", ], }, include_package_data=True, long_description=long_description, long_description_content_type="text/markdown", ) ================================================ FILE: tests/README.md ================================================ # Tests layout Each folder under tests/ corresponds to a test category for a sub-namespace in verl. For instance: - `tests/trainer` for testing functionality related to `verl/trainer` - `tests/models` for testing functionality related to `verl/models` - ... There are a few folders with `special_` prefix, created for special purposes: - `special_distributed`: unit tests that must run with multiple GPUs - `special_e2e`: end-to-end tests with training/generation scripts - `special_npu`: tests for NPUs - `special_sanity`: a suite of quick sanity tests - `special_standalone`: a set of test that are designed to run in dedicated environments Accelerators for tests - By default tests are run with GPU available, except for the ones under `special_npu`, and any test script whose name ends with `on_cpu.py`. - For test scripts with `on_cpu.py` name suffix would be tested on CPU resources in linux environment. # Workflow layout All CI tests are configured by yaml files in `.github/workflows/`. Here's an overview of all test configs: 1. A list of always triggered CPU sanity tests: `check-pr-title.yml`, `secrets_scan.yml`, `check-pr-title,yml`, `pre-commit.yml`, `doc.yml` 2. Some heavy multi-GPU unit tests, such as `model.yml`, `vllm.yml`, `sgl.yml` 3. End-to-end tests: `e2e_*.yml` 4. Unit tests - `cpu_unit_tests.yml`, run pytest on all scripts with file name pattern `tests/**/test_*_on_cpu.py` - `gpu_unit_tests.yml`, run pytest on all scripts with file without the `on_cpu.py` suffix. - Since cpu/gpu unit tests by default runs all tests under `tests`, please make sure tests are manually excluded in them when - new workflow yaml is added to `.github/workflows` - new tests are added to workflow mentioned in 2. ================================================ FILE: tests/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/checkpoint_engine/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/checkpoint_engine/test_correctness_on_gpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest import ray from tests.checkpoint_engine.test_utils import create_rollout_worker_group, create_trainer_worker_group from verl.checkpoint_engine import CheckpointEngineManager from verl.single_controller.ray.base import ( RayResourcePool, split_resource_pool, ) from verl.utils.device import get_device_name from verl.utils.ray_utils import auto_await from verl.workers.config import CheckpointEngineConfig, HFModelConfig, RolloutConfig @pytest.mark.asyncio @pytest.mark.parametrize("rebuild_group", [False, True]) @pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)]) @auto_await async def test_nccl_checkpoint_engine( rebuild_group, num_trainer, num_rollout, num_nodes=1, num_gpus_per_node=8, check_allclose=True, model_path="~/models/Qwen/Qwen3-8B-Base", ): model_path = os.path.expanduser(model_path) ray.init( runtime_env={ "env_vars": { "UCX_TLS": "rc,tcp,cuda", "UCX_MAX_RNDV_RAILS": "4", "UCX_LOG_LEVEL": "INFO", "VERL_LOGGING_LEVEL": "DEBUG", } } ) # initialize config checkpoint_engine_config = CheckpointEngineConfig( backend="nccl", engine_kwargs={"nccl": {"rebuild_group": rebuild_group}} ) model_config = HFModelConfig(path=model_path, use_remove_padding=True) rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config) # create trainer and rollout worker group resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3) trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout]) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose) # create checkpoint engine manager checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas) for _ in range(3): await checkpoint_manager.update_weights() rollout.check_weights() ray.shutdown() @pytest.mark.skip(reason="temporary skip since our ci environment is not ready") @pytest.mark.asyncio @pytest.mark.parametrize("device", ["cuda", "cpu"]) @pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)]) @auto_await async def test_nixl_checkpoint_engine( num_trainer, num_rollout, device, num_nodes=1, num_gpus_per_node=8, check_allclose=True, model_path="~/models/Qwen/Qwen3-8B-Base", ): model_path = os.path.expanduser(model_path) ray.init( runtime_env={ "env_vars": { # TODO: it's pretty hard to set these environment variables right, please consult # with your network admin. Maybe auto adjust UCX_* according to NCCL_IB_*? "UCX_TLS": "rc,ud,cuda", # "UCX_IB_GID_INDEX": "3", # NCCL_IB_GID_INDEX # "UCX_IB_DEVICES": "mlx5_1:1,mlx5_2:1,mlx5_3:1", # NCCL_IB_HCA "UCX_RC_TIMEOUT": "30s", # NCCL_IB_TIMEOUT "UCX_RC_RETRY_COUNT": "7", # NCCL_IB_RETRY_COUNT "UCX_KEEPALIVE_INTERVAL": "1s", "UCX_KEEPALIVE_NUM_EPS": "10", "UCX_MAX_RNDV_RAILS": "4", "UCX_IB_ROCE_REACHABILITY_MODE": "all", "UCX_LOG_LEVEL": "INFO", "VERL_LOGGING_LEVEL": "DEBUG", } } ) # initialize config checkpoint_engine_config = CheckpointEngineConfig(backend="nixl", engine_kwargs={"nixl": {"device": device}}) model_config = HFModelConfig(path=model_path, use_remove_padding=True) rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config) # create trainer and rollout worker group resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3) trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout]) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose) # create checkpoint engine manager checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas) for _ in range(3): await checkpoint_manager.update_weights() rollout.check_weights() ray.shutdown() @pytest.mark.skip(reason="temporary skip since our ci environment is not ready") @pytest.mark.asyncio @pytest.mark.parametrize("rebuild_group", [False]) @pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)]) @auto_await async def test_kimi_checkpoint_engine( rebuild_group, num_trainer, num_rollout, num_nodes=1, num_gpus_per_node=8, check_allclose=True, model_path="~/models/Qwen/Qwen3-8B-Base", ): model_path = os.path.expanduser(model_path) ray.init( runtime_env={ "env_vars": { "NCCL_IB_HCA": "mlx5", "VERL_LOGGING_LEVEL": "DEBUG", } } ) # initialize config checkpoint_engine_config = CheckpointEngineConfig( backend="kimi_ckpt_engine", engine_kwargs={"kimi_ckpt_engine": {"rebuild_group": rebuild_group}} ) model_config = HFModelConfig(path=model_path, use_remove_padding=True) rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config) # create trainer and rollout worker group resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3) resource_pool.get_placement_groups(device_name=get_device_name()) trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout]) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose) # create checkpoint engine manager checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas) for _ in range(3): await checkpoint_manager.update_weights() rollout.check_weights() ray.shutdown() if __name__ == "__main__": test_nccl_checkpoint_engine( rebuild_group=False, num_trainer=2, num_rollout=30, num_nodes=4, num_gpus_per_node=8, check_allclose=False, model_path=os.environ["HDFS_ROOT"] + "/model/Qwen3-30B-A3B-Base", ) ================================================ FILE: tests/checkpoint_engine/test_correctness_on_npu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest import ray from tests.checkpoint_engine.test_utils import create_rollout_worker_group, create_trainer_worker_group from verl.checkpoint_engine import CheckpointEngineManager from verl.single_controller.ray.base import ( RayResourcePool, split_resource_pool, ) from verl.utils.device import get_device_name from verl.utils.ray_utils import auto_await from verl.workers.config import CheckpointEngineConfig, HFModelConfig, RolloutConfig @pytest.mark.asyncio @pytest.mark.parametrize("rebuild_group", [False]) @pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)]) @auto_await async def test_hccl_checkpoint_engine( rebuild_group, num_trainer, num_rollout, num_nodes=1, num_gpus_per_node=8, check_allclose=True, model_path="~/models/Qwen/Qwen3-8B-Base", ): model_path = os.path.expanduser(model_path) ray.init( runtime_env={ "env_vars": { "HCCL_CONNECT_TIMEOUT": "1500", "HCCL_HOST_SOCKET_PORT_RANGE": "60000-60050", "HCCL_NPU_SOCKET_PORT_RANGE": "61000-61050", "VERL_LOGGING_LEVEL": "DEBUG", } } ) # initialize config checkpoint_engine_config = CheckpointEngineConfig( backend="nccl", engine_kwargs={"nccl": {"rebuild_group": rebuild_group}} ) model_config = HFModelConfig(path=model_path, use_remove_padding=True) rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config) # create trainer and rollout worker group resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3) resource_pool.get_placement_groups(device_name=get_device_name()) trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout]) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose) # create checkpoint engine manager checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas) for _ in range(3): await checkpoint_manager.update_weights() rollout.check_weights() ray.shutdown() @pytest.mark.skip(reason="temporary skip since our ci environment is not ready") @pytest.mark.asyncio @pytest.mark.parametrize("rebuild_group", [False]) @pytest.mark.parametrize("num_trainer, num_rollout", [(4, 28)]) async def test_kimi_checkpoint_engine( rebuild_group, num_trainer, num_rollout, num_nodes=2, num_gpus_per_node=16, check_allclose=True, model_path="~/models/Qwen/Qwen3-32B", ): model_path = os.path.expanduser(model_path) ray.init( runtime_env={ "env_vars": { "HCCL_CONNECT_TIMEOUT": "1500", "VERL_LOGGING_LEVEL": "DEBUG", } } ) # initialize config checkpoint_engine_config = CheckpointEngineConfig( backend="kimi_ckpt_engine", engine_kwargs={"kimi_ckpt_engine": {"rebuild_group": rebuild_group}} ) model_config = HFModelConfig(path=model_path, use_remove_padding=True) rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config) # create trainer and rollout worker group resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3) resource_pool.get_placement_groups(device_name=get_device_name()) trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout]) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose) # create checkpoint engine manager checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas) for _ in range(3): await checkpoint_manager.update_weights() rollout.check_weights() ray.shutdown() @pytest.mark.skip(reason="temporary skip since our ci environment is not ready") @pytest.mark.asyncio @pytest.mark.parametrize("device", ["npu"]) @pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)]) async def test_mooncake_checkpoint_engine( rebuild_group, num_trainer, num_rollout, device, num_nodes=1, num_gpus_per_node=8, check_allclose=True, model_path="~/models/Qwen/Qwen3-8B-Base", ): model_path = os.path.expanduser(model_path) ray.init( runtime_env={ "env_vars": { "ASCEND_USE_SHORT_CONNECTION": "1", "VERL_LOGGING_LEVEL": "DEBUG", } } ) # initialize config checkpoint_engine_config = CheckpointEngineConfig( backend="mooncake", engine_kwargs={"mooncake": {"device": device, "rebuild_group": rebuild_group}} ) model_config = HFModelConfig(path=model_path, use_remove_padding=True) rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config) # create trainer and rollout worker group resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3) resource_pool.get_placement_groups(device_name=get_device_name()) trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout]) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose) # create checkpoint engine manager checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas) for _ in range(3): await checkpoint_manager.update_weights() rollout.check_weights() ray.shutdown() if __name__ == "__main__": test_hccl_checkpoint_engine( rebuild_group=False, num_trainer=2, num_rollout=6, num_nodes=1, num_gpus_per_node=8, check_allclose=False, model_path=os.environ["HDFS_ROOT"] + "/model/Qwen3-30B-A3B-Base", ) ================================================ FILE: tests/checkpoint_engine/test_special_server_adapter.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import pytest import ray from omegaconf import DictConfig from transformers import PreTrainedTokenizer from tests.checkpoint_engine.test_utils import create_trainer_worker_group from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.agent_loop.agent_loop import AgentLoopManager, AsyncLLMServerManager from verl.experimental.fully_async_policy.agent_loop.agent_loop import FullyAsyncLLMServerManager from verl.single_controller.ray import ( RayResourcePool, ) from verl.utils.config import omega_conf_to_dataclass from verl.workers.config import CheckpointEngineConfig, HFModelConfig @pytest.fixture def init_config() -> DictConfig: from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose( config_name="ppo_trainer", overrides=[ "+async_training.partial_rollout=True", ], ) config.actor_rollout_ref.model.path = os.path.expanduser("~/models/Qwen/Qwen3-VL-2B-Instruct") config.actor_rollout_ref.rollout.name = os.environ["ROLLOUT_NAME"] config.actor_rollout_ref.rollout.max_num_seqs = 256 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.checkpoint_engine.backend = "nccl" config.actor_rollout_ref.rollout.nnodes = 1 config.trainer.n_gpus_per_node = 4 config.trainer.nnodes = 1 return config async def _run_update_weights_with_global_steps_none( server_manager: AsyncLLMServerManager, checkpoint_manager: CheckpointEngineManager, tokenizer: PreTrainedTokenizer, ): await checkpoint_manager.update_weights(global_steps=None) prompt = [{"role": "user", "content": "How to make a sandwich?"}] prompt_ids = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, tokenize=True) output = await server_manager.generate( request_id="test_0", prompt_ids=prompt_ids, sampling_params={ "temperature": 1.0, "logprobs": True, }, ) assert output.stop_reason not in ("aborted", "abort"), ( f"output.stop_reason is {output.stop_reason}, expected not abort" ) assert output.extra_fields["global_steps"] is None, ( f"output.extra_fields['global_steps'] is {output.extra_fields['global_steps']}, expected None" ) print("========== [update_weights with global_steps=None] ==========") print("[RESPONSE]", tokenizer.decode(output.token_ids, skip_special_tokens=True)) async def _run_server_manager_without_resume( initial_steps: int, train_steps: int, server_manager: AsyncLLMServerManager, checkpoint_manager: CheckpointEngineManager, prompts: list[list[dict]], tokenizer: PreTrainedTokenizer, ): for global_steps in range(initial_steps, initial_steps + train_steps): tasks = [] for i, prompt in enumerate(prompts): prompt_ids = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, tokenize=True) tasks.append( asyncio.create_task( server_manager.generate( request_id=f"test_{global_steps}_{i}", prompt_ids=prompt_ids, sampling_params={ "temperature": 1.0, "logprobs": True, }, ) ) ) # wait a while and update weights to interrupt the generation await asyncio.sleep(2) await checkpoint_manager.update_weights(global_steps=global_steps) outputs = await asyncio.gather(*tasks) expected_steps = global_steps - 1 for output in outputs: global_steps = output.extra_fields["global_steps"] assert output.stop_reason in ("aborted", "abort"), ( f"output.stop_reason is {output.stop_reason}, expected in abort" ) assert global_steps == expected_steps, f"output.global_steps is {global_steps}, expected {expected_steps}" print(f"========== [{initial_steps=}, {train_steps=}] ==========") print("[RESPONSE]", tokenizer.decode(outputs[0].token_ids, skip_special_tokens=True)) async def _run_server_manager_with_resume( initial_steps: int, train_steps: int, server_manager: FullyAsyncLLMServerManager, checkpoint_manager: CheckpointEngineManager, prompts: list[list[dict]], tokenizer: PreTrainedTokenizer, ): # 1. rollout generate responses tasks = [] for i, prompt in enumerate(prompts): prompt_ids = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, tokenize=True) tasks.append( asyncio.create_task( server_manager.generate( request_id=f"test_{initial_steps}_{i}", prompt_ids=prompt_ids, sampling_params={ "temperature": 1.0, "logprobs": True, }, ) ) ) # 2. trainer update weights to rollout multiple times for global_steps in range(initial_steps, initial_steps + train_steps): # wait a while and update weights to interrupt the generation await asyncio.sleep(2) await checkpoint_manager.update_weights(global_steps=global_steps) # 3. wait for rollout generate responses finished outputs = await asyncio.gather(*tasks) expected_min_steps = initial_steps - 1 for output in outputs: min_global_steps = output.extra_fields["min_global_steps"] max_global_steps = output.extra_fields["max_global_steps"] assert min_global_steps == expected_min_steps, ( f"output.min_global_steps is {min_global_steps}, expected {expected_min_steps}" ) assert max_global_steps > expected_min_steps, ( f"output.max_global_steps is {max_global_steps}, expected > {expected_min_steps}" ) assert output.stop_reason not in ("aborted", "abort"), ( f"output.stop_reason is {output.stop_reason}, expected not abort" ) print(f"========== [{initial_steps=}, {train_steps=}] ==========") print("[RESPONSE]", tokenizer.decode(outputs[0].token_ids, skip_special_tokens=True)) @pytest.mark.asyncio async def test_server_adapter(init_config): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", "VLLM_DISABLE_COMPILE_CACHE": "1", } } ) # 1. create trainer worker group model_config: HFModelConfig = omega_conf_to_dataclass(init_config.actor_rollout_ref.model) checkpoint_engine_config: CheckpointEngineConfig = omega_conf_to_dataclass( init_config.actor_rollout_ref.rollout.checkpoint_engine ) trainer_pool = RayResourcePool(process_on_nodes=[init_config.trainer.n_gpus_per_node], max_colocate_count=3) trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config) trainer.reset() # 2. create standalone rollout with AgentLoopManager agent_loop_manager = await AgentLoopManager.create(config=init_config) servers = list( zip( agent_loop_manager.server_addresses, [server._server_handle for server in agent_loop_manager.rollout_replicas], strict=True, ) ) load_balancer_handle = agent_loop_manager.global_load_balancer # 3. create checkpoint engine manager checkpoint_manager = CheckpointEngineManager( config=checkpoint_engine_config, trainer=trainer, replicas=agent_loop_manager.rollout_replicas ) n = 4 prompts = [ [{"role": "user", "content": "Please write an article about the history of China, at least 1000 words."}], [{"role": "user", "content": "Please write an article about the history of America, at least 1000 words."}], [{"role": "user", "content": "Please write an article about the geography of China, at least 1000 words."}], [{"role": "user", "content": "Please write an article about the geography of America, at least 1000 words."}], ] * n server_manager = AsyncLLMServerManager( config=init_config, servers=servers, load_balancer_handle=load_balancer_handle ) # 4. test update_weights with global_steps=None await _run_update_weights_with_global_steps_none( server_manager=server_manager, checkpoint_manager=checkpoint_manager, tokenizer=model_config.tokenizer, ) # 5. test AsyncLLMServerManager without partial rollout resume await checkpoint_manager.update_weights(global_steps=0) await _run_server_manager_without_resume( initial_steps=1, train_steps=3, server_manager=server_manager, checkpoint_manager=checkpoint_manager, prompts=prompts, tokenizer=model_config.tokenizer, ) # 6. test FullyAsyncLLMServerManager with partial rollout resume server_manager = FullyAsyncLLMServerManager( config=init_config, servers=servers, load_balancer_handle=load_balancer_handle ) await _run_server_manager_with_resume( initial_steps=4, train_steps=3, server_manager=server_manager, checkpoint_manager=checkpoint_manager, prompts=prompts, tokenizer=model_config.tokenizer, ) ray.shutdown() ================================================ FILE: tests/checkpoint_engine/test_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio from typing import Generator import ray import torch from transformers import AutoModelForCausalLM from verl.checkpoint_engine import CheckpointEngineRegistry, CheckpointEngineWorker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.device import get_device_name from verl.utils.fs import copy_to_local from verl.workers.config import CheckpointEngineConfig, FSDPEngineConfig, HFModelConfig, RolloutConfig from verl.workers.engine_workers import TrainingWorker, TrainingWorkerConfig from verl.workers.rollout import BaseRollout, RolloutReplica class TrainingWorkerTest(TrainingWorker): def __init__(self, config: TrainingWorkerConfig, checkpoint_engine_config: CheckpointEngineConfig) -> None: super().__init__(config) backend = checkpoint_engine_config.backend bucket_size = checkpoint_engine_config.update_weights_bucket_megabytes << 20 engine_kwargs = checkpoint_engine_config.engine_kwargs.get(backend, {}) if torch.distributed.get_rank() == 0: engine_kwargs["is_master"] = True self.checkpoint_engine = CheckpointEngineRegistry.new(backend, bucket_size=bucket_size, **engine_kwargs) @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) async def update_weights(self, global_steps: int = None): per_tensor_param, _ = self.engine.get_per_tensor_param() await self.checkpoint_engine.send_weights(per_tensor_param) @register(dispatch_mode=Dispatch.DP_COMPUTE, blocking=False) def execute_checkpoint_engine(self, method: str, *args, **kwargs): return getattr(self.checkpoint_engine, method)(*args, **kwargs) class MockServerAdapter(BaseRollout): def __init__(self, config: RolloutConfig, model_config: HFModelConfig, check_allclose: bool = True): super().__init__(config, model_config, device_mesh=None) self.check_allclose = check_allclose self.model = None self.received_weights: dict[str, torch.Tensor] = {} async def resume(self, tags: list[str]): raise NotImplementedError() async def release(self): raise NotImplementedError() async def update_weights( self, weights: Generator[tuple[str, torch.Tensor], None, None], **kwargs, ): async for name, weight in weights: weight = weight.clone() if self.check_allclose: self.received_weights[name] = weight.clone() def check_weights(self): if not self.check_allclose: return if self.model is None: local_path = copy_to_local(self.model_config.path) self.model = AutoModelForCausalLM.from_pretrained(local_path, torch_dtype=torch.bfloat16, device_map="cpu") for name, weight in self.model.state_dict().items(): assert name in self.received_weights, f"weight {name} not received" received = self.received_weights[name] assert torch.allclose(weight.to(received.device), received), f"weight {name} not equal" self.received_weights.clear() class MockReplica(RolloutReplica): async def init_hybrid(self, worker_group: RayWorkerGroup): """Init hybrid rollout server, rollout engine and training engine(fsdp/megatron) fused in same process. Args: worker_group: RayWorkerGroup, fused workers where training engine(fsdp/megatron) have been initialized. """ self.workers = worker_group.workers[ self.world_size * self.replica_rank : self.world_size * (self.replica_rank + 1) ] def get_ray_class_with_init_args(self) -> RayClassWithInitArgs: """Get rollout worker actor class for colocated and standalone mode.""" raise NotImplementedError async def launch_servers(self): """Launch http server in each node.""" raise NotImplementedError class CheckpointEngineWorkerTest(CheckpointEngineWorker): def __init__( self, rollout_config: RolloutConfig, model_config: HFModelConfig, check_allclose: bool = True, *args, **kwargs ) -> None: server_adapter = MockServerAdapter(rollout_config, model_config, check_allclose) super().__init__(rollout_config, model_config, server_adapter, *args, **kwargs) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def check_weights(self): self.server_adapter.check_weights() def create_trainer_worker_group( resource_pool: RayResourcePool, model_config: HFModelConfig, checkpoint_engine_config: CheckpointEngineConfig ) -> RayWorkerGroup: engine_config = FSDPEngineConfig(forward_only=True, fsdp_size=resource_pool.world_size, strategy="fsdp") trainer_config = TrainingWorkerConfig( model_type="language_model", model_config=model_config, engine_config=engine_config, ) ray_cls_with_init = RayClassWithInitArgs( cls=ray.remote(TrainingWorkerTest), config=trainer_config, checkpoint_engine_config=checkpoint_engine_config, ) ray_cls_with_init.update_options( { "runtime_env": { "env_vars": { "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True", } } } ) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=get_device_name()) return wg async def create_rollout_worker_group( resource_pool: RayResourcePool, model_config: HFModelConfig, rollout_config: RolloutConfig, check_allclose: bool = True, ) -> tuple[RayWorkerGroup, list[MockReplica]]: # create rollout worker group ray_cls_with_init = RayClassWithInitArgs( cls=ray.remote(CheckpointEngineWorkerTest), model_config=model_config, rollout_config=rollout_config, check_allclose=check_allclose, ) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=get_device_name()) # create rollout replicas rollout_world_size = ( rollout_config.tensor_model_parallel_size * rollout_config.data_parallel_size * rollout_config.pipeline_model_parallel_size ) num_replicas = wg.world_size // rollout_world_size replicas = [] for replica_rank in range(num_replicas): replica = MockReplica( replica_rank=replica_rank, config=rollout_config, model_config=model_config, ) replicas.append(replica) await asyncio.gather(*[replica.init_hybrid(wg) for replica in replicas]) return wg, replicas ================================================ FILE: tests/experimental/agent_loop/agent_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray from omegaconf import DictConfig from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.agent_loop import AgentLoopManager from verl.experimental.reward_loop import RewardLoopManager from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup from verl.single_controller.ray.base import create_colocated_worker_cls from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role from verl.utils import omega_conf_to_dataclass from verl.workers.fsdp_workers import AsyncActorRolloutRefWorker def init_agent_loop_manager(config: DictConfig) -> AgentLoopManager | RayWorkerGroup: # =========================== 1. Create hybrid ActorRollout workers =========================== actor_rollout_cls = AsyncActorRolloutRefWorker role_worker_mapping = { Role.ActorRollout: ray.remote(actor_rollout_cls), } global_pool_id = "global_pool" resource_pool_spec = { global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes, } mapping = { Role.ActorRollout: global_pool_id, } if config.reward.reward_model.enable_resource_pool: mapping[Role.RewardModel] = "reward_pool" if config.reward.reward_model.n_gpus_per_node <= 0: raise ValueError("config.reward.reward_model.n_gpus_per_node must be greater than 0") if config.reward.reward_model.nnodes <= 0: raise ValueError("config.reward.reward_model.nnodes must be greater than 0") reward_pool = [config.reward.reward_model.n_gpus_per_node] * config.reward.reward_model.nnodes resource_pool_spec["reward_pool"] = reward_pool resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) resource_pool_manager.create_resource_pool() resource_pool_to_cls = {pool: {} for pool in resource_pool_manager.resource_pool_dict.values()} # create actor and rollout resource_pool = resource_pool_manager.get_resource_pool(Role.ActorRollout) actor_rollout_cls = RayClassWithInitArgs( cls=role_worker_mapping[Role.ActorRollout], config=config.actor_rollout_ref, role="actor_rollout" ) resource_pool_to_cls[resource_pool]["actor_rollout"] = actor_rollout_cls all_wg = {} for resource_pool, class_dict in resource_pool_to_cls.items(): worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) actor_rollout_wg = all_wg["actor_rollout"] actor_rollout_wg.init_model() if config.actor_rollout_ref.rollout.mode == "sync": raise ValueError("Agent loop tests require async rollout mode. Please set rollout.mode=async.") # =========================== 2. Create AgentLoopManager =========================== rm_resource_pool = ( resource_pool_manager.get_resource_pool(Role.RewardModel) if config.reward.reward_model.enable else None ) reward_loop_manager = RewardLoopManager( config=config, rm_resource_pool=rm_resource_pool, ) agent_loop_manager = AgentLoopManager.create( config=config, worker_group=actor_rollout_wg, reward_loop_worker_handles=reward_loop_manager.reward_loop_workers, ) checkpoint_manager = CheckpointEngineManager( config=omega_conf_to_dataclass(config.actor_rollout_ref.rollout.checkpoint_engine), trainer=actor_rollout_wg, replicas=agent_loop_manager.rollout_replicas, ) checkpoint_manager.sleep_replicas() checkpoint_manager.update_weights() return agent_loop_manager ================================================ FILE: tests/experimental/agent_loop/qwen_vl_tool_chat_template.jinja2 ================================================ {% set image_count = namespace(value=0) %} {% set video_count = namespace(value=0) %} {%- if tools %} {{- '<|im_start|>system\n' }} {%- if messages[0]['role'] == 'system' %} {%- if messages[0]['content'] is string %} {{- messages[0]['content'] }} {%- else %} {{- messages[0]['content'][0]['text'] }} {%- endif %} {%- else %} {{- 'You are a helpful assistant.' }} {%- endif %} {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} {%- for tool in tools %} {{- "\n" }} {{- tool | tojson }} {%- endfor %} {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} {% for message in messages %} {% if message['role'] != 'system' or loop.first == false %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} <|im_start|>{{ message['role'] }} {% if message['content'] is string %} {{ message['content'] }}<|im_end|> {% else %} {% for content in message['content'] %} {% if content['type'] == 'image' or 'image' in content or 'image_url' in content %} {% set image_count.value = image_count.value + 1 %} {% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|> {% elif content['type'] == 'video' or 'video' in content %} {% set video_count.value = video_count.value + 1 %} {% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|> {% elif 'text' in content %} {{ content['text'] }} {% endif %} {% endfor %}<|im_end|> {% endif %} {%- elif message.role == "assistant" %} {{- '<|im_start|>' + message.role }} {%- if message.content %} {{- '\n' + message.content }} {%- endif %} {%- for tool_call in message.tool_calls %} {%- if tool_call.function is defined %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '\n\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {{- tool_call.arguments | tojson }} {{- '}\n' }} {%- endfor %} {{- '<|im_end|>\n' }} {%- elif message.role == "tool" %} {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} {{- '<|im_start|>user' }} {%- endif %} {{- '\n\n' }} {% if message['content'] is string %} {{ message.content }} {% else %} {% for content in message['content'] %} {% if content['type'] == 'image' or 'image' in content or 'image_url' in content %} {% set image_count.value = image_count.value + 1 %} {% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|> {% elif content['type'] == 'video' or 'video' in content %} {% set video_count.value = video_count.value + 1 %} {% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|> {% elif content['type'] == 'text' or 'text' in content %} {{ content['text'] }} {% endif %} {% endfor %} {% endif %} {{- '\n' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<|im_end|>\n' }} {%- endif %} {%- endif %} {% endif %} {% endfor %} {%- else %} {% for message in messages %} {% if loop.first and message['role'] != 'system' %} <|im_start|>system You are a helpful assistant.<|im_end|> {% endif %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} <|im_start|>{{ message['role'] }} {% if message['content'] is string %} {{ message['content'] }}<|im_end|> {% else %} {% for content in message['content'] %} {% if content['type'] == 'image' or 'image' in content or 'image_url' in content %} {% set image_count.value = image_count.value + 1 %} {% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|> {% elif content['type'] == 'video' or 'video' in content %} {% set video_count.value = video_count.value + 1 %} {% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|> {% elif 'text' in content %} {{ content['text'] }} {% endif %} {% endfor %}<|im_end|> {% endif %} {%- elif message.role == "assistant" %} {{- '<|im_start|>' + message.role }} {%- if message.content %} {{- '\n' + message.content }} {%- endif %} {%- for tool_call in message.tool_calls %} {%- if tool_call.function is defined %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '\n\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {{- tool_call.arguments | tojson }} {{- '}\n' }} {%- endfor %} {{- '<|im_end|>\n' }} {%- elif message.role == "tool" %} {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} {{- '<|im_start|>user' }} {%- endif %} {{- '\n\n' }} {% if message['content'] is string %} {{ message.content }} {% else %} {% for content in message['content'] %} {% if content['type'] == 'image' or 'image' in content or 'image_url' in content %} {% set image_count.value = image_count.value + 1 %} {% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|> {% elif content['type'] == 'video' or 'video' in content %} {% set video_count.value = video_count.value + 1 %} {% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|> {% elif content['type'] == 'text' or 'text' in content %} {{ content['text'] }} {% endif %} {% endfor %} {% endif %} {{- '\n' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<|im_end|>\n' }} {%- endif %} {%- endif %} {% endfor %} {%- endif %} {% if add_generation_prompt %} <|im_start|>assistant {% endif %} ================================================ FILE: tests/experimental/agent_loop/test_agent_loop_extra_fields_schema_on_cpu.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import warnings from typing import Any, Optional import numpy as np import pytest import torch from omegaconf import OmegaConf from verl.experimental.agent_loop.agent_loop import ( AgentLoopMetrics, AgentLoopOutput, AgentLoopWorker, DictConfigWrap, _InternalAgentLoopOutput, ) from verl.experimental.agent_loop.single_turn_agent_loop import SingleTurnAgentLoop from verl.utils.dataset.rl_dataset import RLHFDataset from verl.workers.rollout.replica import TokenOutput class _FakeServerManager: async def generate( self, request_id: str, *, prompt_ids: list[int], sampling_params: dict[str, Any], image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, ) -> TokenOutput: del request_id, sampling_params, image_data, video_data # Return a short, deterministic "generation" for testing. return TokenOutput(token_ids=prompt_ids[-1:] + [11, 12, 13], log_probs=[0.0, 0.0, 0.0, 0.0]) async def generate_for_partial( self, request_id: str, *, prompt_ids: list[int], sampling_params: dict[str, Any], image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, ) -> tuple[list[int], list[float], bool]: del request_id, sampling_params, image_data, video_data # Return a short partial generation and "not cancelled". response_ids = prompt_ids[-1:] + [21, 22] response_logprobs = [0.0] * len(response_ids) return response_ids, response_logprobs, False class _FakeTokenizer: padding_side = "right" def apply_chat_template( self, messages: list[dict[str, Any]], *, tools: Optional[list[dict]] = None, add_generation_prompt: bool = True, tokenize: bool = True, **kwargs, ) -> list[int]: del messages, tools, add_generation_prompt, tokenize, kwargs # Minimal tokenization: return a small prompt. return [101, 102] def pad( self, encoded_inputs: dict[str, list[int]], *, padding: str, max_length: int, return_tensors: str, return_attention_mask: bool, ) -> dict[str, torch.Tensor]: del padding, return_tensors input_ids = encoded_inputs["input_ids"] if len(input_ids) > max_length: if self.padding_side == "left": input_ids = input_ids[-max_length:] else: input_ids = input_ids[:max_length] pad_len = max_length - len(input_ids) if self.padding_side == "left": padded_ids = [0] * pad_len + input_ids attention_mask = [0] * pad_len + [1] * len(input_ids) else: padded_ids = input_ids + [0] * pad_len attention_mask = [1] * len(input_ids) + [0] * pad_len output = {"input_ids": torch.tensor([padded_ids], dtype=torch.long)} if return_attention_mask: output["attention_mask"] = torch.tensor([attention_mask], dtype=torch.long) return output def decode(self, ids: list[int] | torch.Tensor, skip_special_tokens: bool = True) -> str: del ids, skip_special_tokens return "" def _pad_1d(ids: list[int], *, length: int, pad_id: int = 0) -> list[int]: if len(ids) > length: return ids[:length] return ids + [pad_id] * (length - len(ids)) def _to_internal( *, output_prompt_ids: list[int], output_response_ids: list[int], output_response_mask: list[int], metrics: AgentLoopMetrics, extra_fields: dict[str, Any], num_turns: int, prompt_len: int, response_len: int, ) -> _InternalAgentLoopOutput: prompt_ids = _pad_1d(output_prompt_ids, length=prompt_len, pad_id=0) response_ids = _pad_1d(output_response_ids, length=response_len, pad_id=0) response_mask = _pad_1d(output_response_mask, length=response_len, pad_id=0) seq_len = prompt_len + response_len attention_mask = _pad_1d([1] * len(output_prompt_ids), length=prompt_len, pad_id=0) + _pad_1d( [1] * len(output_response_ids), length=response_len, pad_id=0, ) input_ids = prompt_ids + response_ids position_ids = list(range(seq_len)) def t(x: list[int]) -> torch.Tensor: return torch.tensor([x], dtype=torch.long) return _InternalAgentLoopOutput( prompt_ids=t(prompt_ids), response_ids=t(response_ids), response_mask=t(response_mask), attention_mask=t(attention_mask), input_ids=t(input_ids), position_ids=t(position_ids), response_logprobs=None, routed_experts=None, multi_modal_inputs=None, multi_modal_data=None, reward_score=None, num_turns=num_turns, metrics=metrics, extra_fields=extra_fields, ) @pytest.mark.asyncio async def test_agent_loop_extra_fields_schema_stable_for_training_concat_on_cpu(): # Minimal config surface used by the agent loops. config = OmegaConf.create( { "actor_rollout_ref": { "rollout": {"prompt_length": 16, "response_length": 16, "multi_turn": {"tool_config_path": None}}, "model": {}, }, "data": { "tool_config_path": None, "apply_chat_template_kwargs": {}, }, } ) server_manager = _FakeServerManager() tokenizer = _FakeTokenizer() processor = None trainer_config = DictConfigWrap(config) data_config = DictConfigWrap(config.data) single_turn = SingleTurnAgentLoop( trainer_config=trainer_config, server_manager=server_manager, tokenizer=tokenizer, processor=processor, dataset_cls=RLHFDataset, data_config=data_config, ) raw_prompt = [{"role": "user", "content": "hi"}] sampling_params: dict[str, Any] = {} out = await single_turn.run(sampling_params=sampling_params, raw_prompt=raw_prompt) # Agent loop outputs should always contain these fields with consistent types. assert out.extra_fields["turn_scores"] == [] assert out.extra_fields["tool_rewards"] == [] internal_a = _to_internal( output_prompt_ids=out.prompt_ids, output_response_ids=out.response_ids, output_response_mask=out.response_mask, metrics=out.metrics, extra_fields=out.extra_fields, num_turns=out.num_turns, prompt_len=len(out.prompt_ids), response_len=len(out.response_ids), ) # Mimic two "worker chunks" and concatenate as in training. dummy_worker = type("_DummyWorker", (), {"reward_loop_worker_handles": None})() merged = AgentLoopWorker._postprocess( dummy_worker, inputs=[internal_a], input_non_tensor_batch={ "index": np.array([0], dtype=object), "agent_name": np.array(["single_turn_agent"], dtype=object), }, ) # Stable schema: present regardless of which loop produced a sample. stable_keys = ( "turn_scores", "tool_rewards", "min_global_steps", "max_global_steps", "extras", ) for key in stable_keys: assert key in merged.non_tensor_batch, f"missing key in merged batch: {key}" assert merged.non_tensor_batch[key].shape == (1,), ( f"invalid shape for {key}: {merged.non_tensor_batch[key].shape}" ) # And the list-typed fields are actually lists (not missing / scalar). assert merged.non_tensor_batch["turn_scores"][0] == [] assert merged.non_tensor_batch["tool_rewards"][0] == [] @pytest.mark.asyncio async def test_agent_loop_postprocess_accepts_read_only_routed_experts_on_cpu(): class _DummyWorker: _compute_multi_modal_inputs = AgentLoopWorker._compute_multi_modal_inputs _compute_position_ids = AgentLoopWorker._compute_position_ids _compute_score = AgentLoopWorker._compute_score def __init__(self): self.tokenizer = _FakeTokenizer() self.rollout_config = OmegaConf.create({"prompt_length": 4, "response_length": 4}) self.processor = None self.reward_loop_worker_handles = None routed_experts = np.arange(8, dtype=np.int64).reshape(4, 2, 1) routed_experts.setflags(write=False) assert not routed_experts.flags.writeable output = AgentLoopOutput( prompt_ids=[101, 102], response_ids=[11, 12], response_mask=[1, 1], routed_experts=routed_experts, metrics=AgentLoopMetrics(), extra_fields={}, ) with warnings.catch_warnings(): warnings.filterwarnings( "error", message="The given NumPy array is not writable.*", category=UserWarning, ) internal = await AgentLoopWorker._agent_loop_postprocess( _DummyWorker(), output, raw_prompt=[{"role": "user", "content": "hi"}], ) expected = torch.tensor(routed_experts.copy()).unsqueeze(0) assert internal.routed_experts is not None assert internal.routed_experts.shape == (1, 8, 2, 1) torch.testing.assert_close(internal.routed_experts[:, 2:6], expected) assert torch.count_nonzero(internal.routed_experts[:, :2]) == 0 assert torch.count_nonzero(internal.routed_experts[:, 6:]) == 0 ================================================ FILE: tests/experimental/agent_loop/test_basic_agent_loop.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from typing import Any import numpy as np import pytest import ray from omegaconf import DictConfig from transformers.utils import get_json_schema from tests.experimental.agent_loop.agent_utils import init_agent_loop_manager from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.agent_loop.agent_loop import GlobalRequestLoadBalancer, get_trajectory_info from verl.protocol import DataProto from verl.tools.base_tool import BaseTool, OpenAIFunctionToolSchema from verl.tools.schemas import ToolResponse from verl.utils import hf_tokenizer from verl.utils.config import omega_conf_to_dataclass from verl.workers.config import CheckpointEngineConfig @pytest.fixture def init_config() -> DictConfig: from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose( config_name="ppo_trainer", overrides=[ "actor_rollout_ref.actor.use_dynamic_bsz=true", # test sleep/wake_up with fsdp offload "actor_rollout_ref.actor.fsdp_config.param_offload=True", "actor_rollout_ref.actor.fsdp_config.optimizer_offload=True", "reward.reward_manager.name=dapo", "+reward.reward_kwargs.overlong_buffer_cfg.enable=False", "+reward.reward_kwargs.overlong_buffer_cfg.len=3072", "+reward.reward_kwargs.max_resp_len=4096", ], ) model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") config.actor_rollout_ref.model.path = model_path config.actor_rollout_ref.rollout.name = os.environ["ROLLOUT_NAME"] config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.enforce_eager = True config.actor_rollout_ref.rollout.prompt_length = 4096 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.n = 4 config.actor_rollout_ref.rollout.agent.num_workers = 2 config.actor_rollout_ref.rollout.skip_tokenizer_init = True return config def test_single_turn(init_config): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) agent_loop_manager = init_agent_loop_manager(init_config) raw_prompts = [ [ { "role": "user", "content": "Let's play a role playing game. Your name is Alice, your favorite color is blue.", } ], [{"role": "user", "content": "Let's play a role playing game. Your name is Bob, your favorite color is red."}], ] batch = DataProto( non_tensor_batch={ "raw_prompt": np.array(raw_prompts), "agent_name": np.array(["single_turn_agent"] * len(raw_prompts)), "data_source": np.array(["openai/gsm8k"] * len(raw_prompts)), "reward_model": np.array([{"style": "rule", "ground_truth": "1.0"}] * len(raw_prompts)), }, ) n = init_config.actor_rollout_ref.rollout.n batch = batch.repeat(n) result = agent_loop_manager.generate_sequences(prompts=batch) assert len(result) == len(raw_prompts) * n # check result seq_len = result.batch["prompts"].size(1) + result.batch["responses"].size(1) assert result.batch["input_ids"].size(1) == seq_len assert result.batch["attention_mask"].size(1) == seq_len assert result.batch["position_ids"].size(1) == seq_len if init_config.actor_rollout_ref.rollout.calculate_log_probs: assert result.batch["rollout_log_probs"].size(1) == result.batch["responses"].size(1) # check compute score assert result.batch["rm_scores"].shape == result.batch["responses"].shape reward_tensor = result.batch["rm_scores"] reward_extra_keys = result.meta_info.get("reward_extra_keys", []) reward_extra_info = {key: result.non_tensor_batch[key] for key in reward_extra_keys} assert reward_tensor.shape == result.batch["responses"].shape assert "acc" in reward_extra_info, f"reward_extra_info {reward_extra_info} should contain 'acc'" assert reward_extra_info["acc"].shape == (len(result),), f"invalid acc: {reward_extra_info['acc']}" # check turns num_turns = result.non_tensor_batch["__num_turns__"] assert np.all(num_turns == 2) print("Test passed!") ray.shutdown() class WeatherTool(BaseTool): def get_current_temperature(self, location: str, unit: str = "celsius"): """Get current temperature at a location. Args: location: The location to get the temperature for, in the format "City, State, Country". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, and the unit in a dict """ print(f"[DEBUG] get_current_temperature: {location}, {unit}") return { "temperature": 26.1, "location": location, "unit": unit, } def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: schema = get_json_schema(self.get_current_temperature) return OpenAIFunctionToolSchema(**schema) async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: try: result = self.get_current_temperature(**parameters) return ToolResponse(text=json.dumps(result)), 0, {} except Exception as e: return ToolResponse(text=str(e)), 0, {} class WeatherToolWithData(BaseTool): def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: schema = get_json_schema(self.get_temperature_date) return OpenAIFunctionToolSchema(**schema) def get_temperature_date(self, location: str, date: str, unit: str = "celsius"): """Get temperature at a location and date. Args: location: The location to get the temperature for, in the format "City, State, Country". date: The date to get the temperature for, in the format "Year-Month-Day". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, the date and the unit in a dict """ print(f"[DEBUG] get_temperature_date: {location}, {date}, {unit}") return { "temperature": 25.9, "location": location, "date": date, "unit": unit, } async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: try: result = self.get_temperature_date(**parameters) return ToolResponse(text=json.dumps(result)), 0, {} except Exception as e: return ToolResponse(text=str(e)), 0, {} def test_tool_agent(init_config): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } }, ignore_reinit_error=True, ) # =========================== 1. Init rollout manager =========================== tool_config = { "tools": [ { "class_name": "tests.experimental.agent_loop.test_basic_agent_loop.WeatherTool", "config": {"type": "native"}, }, { "class_name": "tests.experimental.agent_loop.test_basic_agent_loop.WeatherToolWithData", "config": {"type": "native"}, }, ] } tool_config_path = "/tmp/tool_config.json" with open(tool_config_path, "w") as f: json.dump(tool_config, f) n = 2 init_config.actor_rollout_ref.rollout.n = n init_config.actor_rollout_ref.rollout.multi_turn.tool_config_path = tool_config_path init_config.actor_rollout_ref.rollout.multi_turn.max_parallel_calls = 2 init_config.actor_rollout_ref.rollout.calculate_log_probs = True agent_loop_manager = init_agent_loop_manager(init_config) # =========================== 2. Generate sequences =========================== raw_prompts = [ [ {"role": "user", "content": "How are you?"}, ], [ {"role": "user", "content": "What's the temperature in Los Angeles now?"}, ], [ {"role": "user", "content": "What's the temperature in New York now?"}, ], [ { "role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\n\n" "Current Date: 2024-09-30", }, {"role": "user", "content": "What's the temperature in San Francisco now? How about tomorrow?"}, ], ] batch = DataProto( non_tensor_batch={ "raw_prompt": np.array([np.array(prompt) for prompt in raw_prompts], dtype=object), "agent_name": np.array(["tool_agent"] * len(raw_prompts)), "data_source": np.array(["openai/gsm8k"] * len(raw_prompts)), "reward_model": np.array([{"style": "rule", "ground_truth": "1.0"}] * len(raw_prompts)), }, ) batch = batch.repeat(n) result = agent_loop_manager.generate_sequences(prompts=batch) assert len(result) == len(raw_prompts) * n # Check turns num_turns = result.non_tensor_batch["__num_turns__"] print(f"num_turns: {num_turns}") for i in range(len(num_turns)): if i // n == 0: # [user, assistant] assert num_turns[i] == 2 else: # [user, assistant, tool, assistant] assert num_turns[i] == 4 # Check response_mask tokenizer = hf_tokenizer(init_config.actor_rollout_ref.model.path) responses = result.batch["responses"] response_mask = result.batch["response_mask"] attention_mask = result.batch["attention_mask"] assert result.batch["rm_scores"].size(1) == responses.size(1) assert responses.size() == response_mask.size(), f"{responses.size()} != {response_mask.size()}" assert result.batch["rollout_log_probs"].size(1) == result.batch["responses"].size(1) response_length = response_mask.size(1) for i in range(len(responses)): # response with tool response valid_tokens = responses[i][attention_mask[i][-response_length:].bool()] response_with_obs = tokenizer.decode(valid_tokens) # response without tool response valid_tokens = responses[i][response_mask[i].bool()] response_without_obs = tokenizer.decode(valid_tokens) assert "" not in response_without_obs, ( f"found in response: {response_without_obs}" ) assert "" not in response_without_obs, ( f"found in response: {response_without_obs}" ) print("=========================") print(response_with_obs) print("---") print(response_without_obs) print("Test passed!") ray.shutdown() def test_tool_agent_with_interaction(init_config): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) # =========================== 1. Init rollout manager =========================== tool_config = { "tools": [ { "class_name": "tests.experimental.agent_loop.test_basic_agent_loop.WeatherTool", "config": {"type": "native"}, }, { "class_name": "tests.experimental.agent_loop.test_basic_agent_loop.WeatherToolWithData", "config": {"type": "native"}, }, ] } tool_config_path = "/tmp/tool_config.json" with open(tool_config_path, "w") as f: json.dump(tool_config, f) interaction_config = { "interaction": [ {"name": "weather", "class_name": "verl.interactions.weather_interaction.WeatherInteraction", "config": {}} ] } interaction_config_path = "/tmp/interaction_config.json" with open(interaction_config_path, "w") as f: json.dump(interaction_config, f) n = 2 init_config.actor_rollout_ref.rollout.n = n init_config.actor_rollout_ref.rollout.multi_turn.tool_config_path = tool_config_path init_config.actor_rollout_ref.rollout.multi_turn.interaction_config_path = interaction_config_path init_config.actor_rollout_ref.rollout.multi_turn.max_parallel_calls = 2 agent_loop_manager = init_agent_loop_manager(init_config) checkpoint_engine_config = omega_conf_to_dataclass( init_config.actor_rollout_ref.rollout.checkpoint_engine, CheckpointEngineConfig ) checkpoint_manager = CheckpointEngineManager( config=checkpoint_engine_config, trainer=agent_loop_manager.worker_group, replicas=agent_loop_manager.rollout_replicas, ) checkpoint_manager.sleep_replicas() checkpoint_manager.update_weights() # =========================== 2. Generate sequences =========================== raw_prompts = [ [ {"role": "user", "content": "How are you?"}, ], [ {"role": "user", "content": "What's the temperature in Los Angeles now?"}, ], [ {"role": "user", "content": "What's the temperature in New York now?"}, ], [ { "role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\n\n" "Current Date: 2024-09-30", }, {"role": "user", "content": "What's the temperature in San Francisco now? How about tomorrow?"}, ], ] batch = DataProto( non_tensor_batch={ "raw_prompt": np.array([np.array(prompt) for prompt in raw_prompts], dtype=object), "agent_name": np.array(["tool_agent"] * len(raw_prompts)), "data_source": np.array(["openai/gsm8k"] * len(raw_prompts)), "reward_model": np.array([{"style": "rule", "ground_truth": "1.0"}] * len(raw_prompts)), "extra_info": np.array( [ {"interaction_kwargs": {"name": "weather"}}, {"interaction_kwargs": {"name": "weather"}}, {"interaction_kwargs": {"name": "weather"}}, {"interaction_kwargs": {"name": "weather"}}, ] ), }, ) batch = batch.repeat(n) result = agent_loop_manager.generate_sequences(prompts=batch) assert len(result) == len(raw_prompts) * n # Check turns num_turns = result.non_tensor_batch["__num_turns__"] print(f"num_turns: {num_turns}") for i in range(len(num_turns)): if i // n == 0: # [user, assistant, user] assert num_turns[i] == 3 else: # [user, assistant, tool, assistant, user] assert num_turns[i] == 5 # Check response_mask tokenizer = hf_tokenizer(init_config.actor_rollout_ref.model.path) responses = result.batch["responses"] response_mask = result.batch["response_mask"] attention_mask = result.batch["attention_mask"] assert responses.size() == response_mask.size(), f"{responses.size()} != {response_mask.size()}" response_length = response_mask.size(1) for i in range(len(responses)): # response with tool response valid_tokens = responses[i][attention_mask[i][-response_length:].bool()] response_with_obs = tokenizer.decode(valid_tokens) # response without tool response valid_tokens = responses[i][response_mask[i].bool()] response_without_obs = tokenizer.decode(valid_tokens) assert "\udb82\udc89" not in response_without_obs, f"found \udb82\udc89 in response: {response_without_obs}" assert "\udb82\udc8a" not in response_without_obs, f"found \udb82\udc8a in response: {response_without_obs}" print("=========================") print(response_with_obs) print("---") print(response_without_obs) print("Test passed!") ray.shutdown() @pytest.mark.asyncio async def test_get_trajectory_info(): """Tests the get_trajectory_info method.""" # Initialize the class to set up class-level attributes step = 10 index = [1, 1, 3, 3] expected_info = [ {"step": step, "sample_index": 1, "rollout_n": 0, "validate": False}, {"step": step, "sample_index": 1, "rollout_n": 1, "validate": False}, {"step": step, "sample_index": 3, "rollout_n": 0, "validate": False}, {"step": step, "sample_index": 3, "rollout_n": 1, "validate": False}, ] trajectory_info = await get_trajectory_info(step, index, validate=False) assert trajectory_info == expected_info # ────────────────────────────────────────────────────────────────────── # GlobalRequestLoadBalancer unit tests (lightweight, no GPU required) # ────────────────────────────────────────────────────────────────────── @pytest.fixture(scope="module") def ray_for_lb(): ray.init(ignore_reinit_error=True) yield ray.shutdown() class TestLoadBalancerRouting: """Least-loaded selection.""" def test_distributes_across_servers(self, ray_for_lb): lb = GlobalRequestLoadBalancer.remote(server_actor_ids=["s0", "s1", "s2"]) servers = [ray.get(lb.acquire_server.remote(request_id=f"r{i}")) for i in range(3)] assert sorted(servers) == ["s0", "s1", "s2"] def test_new_requests_route_to_least_loaded(self, ray_for_lb): lb = GlobalRequestLoadBalancer.remote(server_actor_ids=["s0", "s1", "s2"]) # Load s0 with 3 inflight requests ray.get(lb.acquire_server.remote(request_id="a")) # -> s0 ray.get(lb.acquire_server.remote(request_id="a")) # sticky -> s0 ray.get(lb.acquire_server.remote(request_id="a")) # sticky -> s0 # Load s1 with 1 inflight request ray.get(lb.acquire_server.remote(request_id="b")) # -> s1 # s2 has 0 inflight, so next new request must go to s2 s_new = ray.get(lb.acquire_server.remote(request_id="d")) assert s_new == "s2" def test_release_rebalances(self, ray_for_lb): lb = GlobalRequestLoadBalancer.remote(server_actor_ids=["s0", "s1"]) s0 = ray.get(lb.acquire_server.remote(request_id="r0")) s1 = ray.get(lb.acquire_server.remote(request_id="r1")) assert s0 != s1 ray.get(lb.release_server.remote(server_id=s0)) ray.get(lb.release_server.remote(server_id=s1)) s2 = ray.get(lb.acquire_server.remote(request_id="r2")) s3 = ray.get(lb.acquire_server.remote(request_id="r3")) assert s2 != s3 def test_release_invalid_server_raises(self, ray_for_lb): lb = GlobalRequestLoadBalancer.remote(server_actor_ids=["s0", "s1"]) with pytest.raises(ray.exceptions.RayTaskError, match="Invalid server_id") as excinfo: ray.get(lb.release_server.remote(server_id="nonexistent")) assert "Invalid server_id" in str(excinfo.value) def test_release_without_inflight_raises(self, ray_for_lb): lb = GlobalRequestLoadBalancer.remote(server_actor_ids=["s0", "s1"]) with pytest.raises(ray.exceptions.RayTaskError, match="no inflight") as excinfo: ray.get(lb.release_server.remote(server_id="s1")) assert "no inflight" in str(excinfo.value) class TestLoadBalancerStickySession: """Request-level sticky session.""" def test_same_request_id_same_server(self, ray_for_lb): lb = GlobalRequestLoadBalancer.remote(server_actor_ids=["s0", "s1", "s2", "s3"]) s0 = ray.get(lb.acquire_server.remote(request_id="conv-abc")) ray.get(lb.release_server.remote(server_id=s0)) s1 = ray.get(lb.acquire_server.remote(request_id="conv-abc")) assert s0 == s1 ================================================ FILE: tests/experimental/agent_loop/test_gpt_oss_tool_parser.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from transformers import AutoTokenizer from verl.experimental.agent_loop.tool_parser import GptOssToolParser @pytest.mark.asyncio @pytest.mark.skip(reason="local test only") async def test_gpt_oss_tool_parser(): example_text = """ <|start|>assistant<|channel|>commentary to=functions.get_current_weather \ <|constrain|>json<|message|>{"location": "Tokyo"}<|call|> <|start|>functions.get_current_weather to=assistant<|channel|>commentary<|message|>\ { "temperature": 20, "sunny": true }<|end|>""" tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b") response_ids = tokenizer.encode(example_text) tool_parser = GptOssToolParser(tokenizer) _, function_calls = await tool_parser.extract_tool_calls(response_ids) assert len(function_calls) == 1 assert function_calls[0].name == "get_current_weather" assert function_calls[0].arguments == '{"location": "Tokyo"}' ================================================ FILE: tests/experimental/agent_loop/test_multi_modal.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from typing import Any import numpy as np import pytest import ray from omegaconf import DictConfig from PIL import Image from transformers.utils import get_json_schema from tests.experimental.agent_loop.agent_utils import init_agent_loop_manager from verl.protocol import DataProto from verl.tools.base_tool import BaseTool, OpenAIFunctionToolSchema from verl.tools.schemas import ToolResponse from verl.utils import hf_tokenizer def parse_multi_modal_type(messages: list[dict]) -> str: message = messages[-1] if isinstance(message["content"], str): return "text" for content in message["content"]: if content["type"] == "image": return "image" elif content["type"] == "video": return "video" return "text" @pytest.fixture def init_config() -> DictConfig: from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose( config_name="ppo_trainer", overrides=[ "actor_rollout_ref.actor.use_dynamic_bsz=true", # test sleep/wake_up with fsdp offload "actor_rollout_ref.actor.fsdp_config.param_offload=True", "actor_rollout_ref.actor.fsdp_config.optimizer_offload=True", ], ) model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-VL-3B-Instruct") config.actor_rollout_ref.model.path = model_path config.actor_rollout_ref.rollout.name = os.environ["ROLLOUT_NAME"] config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.enforce_eager = True config.actor_rollout_ref.rollout.prompt_length = 10240 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.n = 4 config.actor_rollout_ref.rollout.agent.num_workers = 2 config.actor_rollout_ref.rollout.skip_tokenizer_init = True return config class ImageGeneratorTool(BaseTool): def generate_image(self, description: str, size: str = "256x256"): """Generate a simple image based on description. Args: description: The description of the image to generate. size: The size of the image. Defaults to "256x256". (choices: ["256x256", "512x512"]) Returns: A generated image """ print(f"[DEBUG] generate_image: {description}, {size}") # Create a simple colored image for testing width, height = map(int, size.split("x")) # Create different colors based on description if "red" in description.lower(): color = (255, 0, 0) elif "blue" in description.lower(): color = (0, 0, 255) elif "green" in description.lower(): color = (0, 255, 0) else: color = (128, 128, 128) # gray # Create image image = Image.new("RGB", (width, height), color) # Add some pattern to make it more interesting for i in range(0, width, 50): for j in range(0, height, 50): # Add white squares in a grid pattern for x in range(i, min(i + 20, width)): for y in range(j, min(j + 20, height)): image.putpixel((x, y), (255, 255, 255)) return image def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: schema = get_json_schema(self.generate_image) return OpenAIFunctionToolSchema(**schema) async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: try: image = self.generate_image(**parameters) # Return the PIL Image directly - the framework should handle the conversion return ToolResponse(image=[image]), 0, {} except Exception as e: return ToolResponse(text=str(e)), 0, {} @pytest.mark.flaky(reruns=3) def test_multimodal_tool_agent(init_config): """Test agent loop with multimodal tool that returns images using Qwen VL model.""" ray.shutdown() ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } }, ignore_reinit_error=True, ) # Add custom chat template to enable tool calling support (same as recipe/deepeyes) template_path = os.path.join(os.path.dirname(__file__), "qwen_vl_tool_chat_template.jinja2") with open(template_path, encoding="utf-8") as f: custom_chat_template = f.read() init_config.actor_rollout_ref.model.custom_chat_template = custom_chat_template # =========================== 1. Init rollout manager with image tool =========================== tool_config = { "tools": [ { "class_name": "tests.experimental.agent_loop.test_multi_modal.ImageGeneratorTool", "config": {"type": "native"}, }, ] } tool_config_path = "/tmp/multimodal_tool_config.json" with open(tool_config_path, "w") as f: json.dump(tool_config, f) n = 2 init_config.actor_rollout_ref.rollout.n = n init_config.actor_rollout_ref.rollout.multi_turn.tool_config_path = tool_config_path init_config.actor_rollout_ref.rollout.multi_turn.max_parallel_calls = 1 init_config.actor_rollout_ref.rollout.multi_turn.max_user_turns = 1 agent_loop_manager = init_agent_loop_manager(init_config) # =========================== 2. Generate sequences with multimodal prompts =========================== raw_prompts = [ [ {"role": "user", "content": "How are you?"}, ], [ { "role": "user", "content": [ { "type": "video", "video": os.path.expanduser("~/models/hf_data/test-videos/space_woaudio.mp4"), "min_pixels": 4 * 32 * 32, "max_pixels": 256 * 32 * 32, "total_pixels": 4096 * 32 * 32, }, { "type": "text", "text": "Describe this video. Then you must call the " "image generator tool to generate a green image for me.", }, ], }, ], [ {"role": "user", "content": "Please generate a red image for me."}, ], [ {"role": "user", "content": "Can you create a blue picture with size 512x512?"}, ], [ { "role": "system", "content": ( "You are Qwen VL, created by Alibaba Cloud. You are a helpful " "assistant that can generate and analyze images." ), }, {"role": "user", "content": "Generate a green landscape image and describe what you see in it."}, ], ] batch = DataProto( non_tensor_batch={ "raw_prompt": np.array([np.array(prompt) for prompt in raw_prompts], dtype=object), "agent_name": np.array(["tool_agent"] * len(raw_prompts)), "data_source": np.array(["openai/gsm8k"] * len(raw_prompts)), "reward_model": np.array([{"style": "rule", "ground_truth": "1.0"}] * len(raw_prompts)), }, ) batch = batch.repeat(n) result = agent_loop_manager.generate_sequences(prompts=batch) assert len(result) == len(raw_prompts) * n # Check turns num_turns = result.non_tensor_batch["__num_turns__"] multi_modal_inputs = result.non_tensor_batch["multi_modal_inputs"] print(f"num_turns: {num_turns}") for i in range(len(num_turns)): multi_modal_type = parse_multi_modal_type(raw_prompts[i // n]) if multi_modal_type == "video": assert "pixel_values_videos" in multi_modal_inputs[i], f"Sample {i} should have pixel_values_videos" assert "video_grid_thw" in multi_modal_inputs[i], f"Sample {i} should have video_grid_thw" if i // n == 0: # First prompt: "How are you?" - should have 2 turns [user, assistant] assert num_turns[i] == 2, f"Expected 2 turns but got {num_turns[i]} for sample {i}" elif i // n == 1: # TODO: prompt with video not generate tool call as expected assert num_turns[i] == 2 or num_turns[i] == 4, ( f"Expected 2 or 4 turns but got {num_turns[i]} for sample {i}" ) else: # Tool-calling prompts should have 4 turns [user, assistant, tool, assistant] assert num_turns[i] == 4, f"Expected 4 turns but got {num_turns[i]} for sample {i}" assert "pixel_values" in multi_modal_inputs[i], f"Sample {i} should have pixel_values" assert "image_grid_thw" in multi_modal_inputs[i], f"Sample {i} should have image_grid_thw" # Check that images were properly returned in the tool responses tokenizer = hf_tokenizer(init_config.actor_rollout_ref.model.path) responses = result.batch["responses"] response_mask = result.batch["response_mask"] attention_mask = result.batch["attention_mask"] assert responses.size() == response_mask.size(), f"{responses.size()} != {response_mask.size()}" response_length = response_mask.size(1) image_found_count = 0 for i in range(len(responses)): # response with tool response (including images) valid_tokens = responses[i][attention_mask[i][-response_length:].bool()] response_with_obs = tokenizer.decode(valid_tokens) # response without tool response valid_tokens = responses[i][response_mask[i].bool()] response_without_obs = tokenizer.decode(valid_tokens) # Check that tool responses were properly masked out from training assert "" not in response_without_obs, ( f"found in response: {response_without_obs}" ) assert "" not in response_without_obs, ( f"found in response: {response_without_obs}" ) # Check that images were included in the full response if "" in response_with_obs or "image" in response_with_obs.lower(): image_found_count += 1 print("=========================") print("Response with tool observations:") print(response_with_obs) print("---") print("Response without tool observations:") print(response_without_obs) # Verify that tool-calling responses contained image-related content print(f"Found {image_found_count} responses with image content out of {len(responses)}") # We should have at least some image content from the tool-calling prompts # Note: First prompt might not use tools, so we don't expect 100% image content expected_tool_calls = sum(1 for i in range(len(num_turns)) if num_turns[i] == 4) assert image_found_count >= 0, ( f"No image-related content found, but expected at least some from {expected_tool_calls} tool calls" ) print("Multimodal tool test passed!") ray.shutdown() def test_multimodal_single_turn_agent(init_config): """Test single turn agent loop with multimodal inputs using Qwen VL model.""" ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } }, ignore_reinit_error=True, ) # =========================== 1. Init rollout manager =========================== n = 2 init_config.actor_rollout_ref.rollout.n = n init_config.actor_rollout_ref.rollout.multi_turn.max_parallel_calls = 1 init_config.actor_rollout_ref.rollout.multi_turn.max_user_turns = 1 agent_loop_manager = init_agent_loop_manager(init_config) # =========================== 2. Generate sequences with multimodal prompts =========================== # Create a simple test image test_image = Image.new("RGB", (256, 256), (100, 150, 200)) test_image2 = Image.new("RGB", (512, 512), (100, 150, 200)) raw_prompts = [ # text [ {"role": "user", "content": "Hello, how are you?"}, ], # image [ { "role": "user", "content": [ {"type": "image", "image": test_image}, {"type": "text", "text": "What color is this image?"}, ], }, ], # system + image [ { "role": "system", "content": "You are Qwen VL, created by Alibaba Cloud. You are a helpful assistant.", }, { "role": "user", "content": [ {"type": "image", "image": test_image2}, {"type": "text", "text": "Describe this image in detail."}, ], }, ], # video [ { "role": "user", "content": [ { "type": "video", "video": os.path.expanduser("~/models/hf_data/test-videos/space_woaudio.mp4"), "min_pixels": 4 * 32 * 32, "max_pixels": 256 * 32 * 32, "total_pixels": 4096 * 32 * 32, }, {"type": "text", "text": "Describe this video."}, ], }, ], ] batch = DataProto( non_tensor_batch={ "raw_prompt": np.array([np.array(prompt) for prompt in raw_prompts], dtype=object), "agent_name": np.array(["single_turn_agent"] * len(raw_prompts)), "data_source": np.array(["openai/gsm8k"] * len(raw_prompts)), "reward_model": np.array([{"style": "rule", "ground_truth": "1.0"}] * len(raw_prompts)), }, ) batch = batch.repeat(n) result = agent_loop_manager.generate_sequences(prompts=batch) assert len(result) == len(raw_prompts) * n # Check turns - all should be single turn (2: user + assistant) num_turns = result.non_tensor_batch["__num_turns__"] print(f"num_turns: {num_turns}") for i in range(len(num_turns)): assert num_turns[i] == 2, f"Expected 2 turns but got {num_turns[i]} for sample {i}" # Verify responses tokenizer = hf_tokenizer(init_config.actor_rollout_ref.model.path) prompts = result.batch["prompts"] responses = result.batch["responses"] response_mask = result.batch["response_mask"] input_ids = result.batch["input_ids"] position_ids = result.batch["position_ids"] multi_modal_inputs = result.non_tensor_batch["multi_modal_inputs"] assert responses.size() == response_mask.size(), f"{responses.size()} != {response_mask.size()}" assert position_ids.size() == (input_ids.size(0), 4, input_ids.size(1)) # (batch_size, 4, seq_len) # Check for image pads in prompts image_pad_count = 0 for i in range(len(prompts)): prompt_ids = prompts[i][prompts[i] != tokenizer.pad_token_id].tolist() prompt_text = tokenizer.decode(prompt_ids) # Check if this sample should have image pads (samples with index 1 and 2 in each repeat have images) sample_idx = i // n has_image_pad = "<|image_pad|>" in prompt_text or "<|vision_start|>" in prompt_text print("=========================") print(f"Sample {i} (original prompt index: {sample_idx}):") print(f"Prompt length: {len(prompt_ids)} tokens") print(f"Has image_pad: {has_image_pad}") # Check multi-modal type multi_modal_type = parse_multi_modal_type(raw_prompts[sample_idx]) if multi_modal_type == "text": assert len(multi_modal_inputs[i]) == 0, f"Sample {i} should not have multi-modal inputs" elif multi_modal_type == "image": assert "pixel_values" in multi_modal_inputs[i], f"Sample {i} should have pixel_values" assert "image_grid_thw" in multi_modal_inputs[i], f"Sample {i} should have image_grid_thw" else: assert "pixel_values_videos" in multi_modal_inputs[i], f"Sample {i} should have pixel_values_videos" assert "video_grid_thw" in multi_modal_inputs[i], f"Sample {i} should have video_grid_thw" # Show first 200 chars of prompt print(f"Prompt text (first 200 chars): {prompt_text[:200]}...") for i in range(len(responses)): valid_tokens = responses[i][response_mask[i].bool()] response_text = tokenizer.decode(valid_tokens) print(f"Sample {i} response: {response_text[:100]}...") # Verify that we found image pads in multimodal samples expected_multimodal_samples = 2 * n # 2 prompts with images, repeated n times print(f"\nFound {image_pad_count} samples with image_pad out of {expected_multimodal_samples} expected") print("Single turn multimodal test passed!") ray.shutdown() ================================================ FILE: tests/experimental/agent_loop/test_standalone_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import pytest import ray from omegaconf import DictConfig from openai import AsyncOpenAI, OpenAI from tests.experimental.agent_loop.agent_utils import init_agent_loop_manager from verl.checkpoint_engine import CheckpointEngineManager from verl.utils import omega_conf_to_dataclass from verl.workers.rollout.replica import get_rollout_replica_class @pytest.fixture def init_config() -> DictConfig: from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose(config_name="ppo_trainer") config.trainer.n_gpus_per_node = 4 config.trainer.nnodes = 2 config.actor_rollout_ref.actor.use_dynamic_bsz = True config.actor_rollout_ref.model.path = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") config.actor_rollout_ref.rollout.name = os.environ["ROLLOUT_NAME"] config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.skip_tokenizer_init = False return config @pytest.mark.asyncio @pytest.mark.parametrize("tp_size", [2, 4]) async def test_standalone_rollout(init_config, tp_size): """Test standalone rollout single node and multi nodes.""" ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", "NCCL_P2P_DISABLE": "1", # disable p2p in L20 } } ) init_config.actor_rollout_ref.rollout.tensor_model_parallel_size = tp_size num_replicas = (init_config.trainer.n_gpus_per_node * init_config.trainer.nnodes) // tp_size rollout_config = init_config.actor_rollout_ref.rollout model_config = init_config.actor_rollout_ref.model # create standalone rollout server rollout_server_class = get_rollout_replica_class(init_config.actor_rollout_ref.rollout.name) rollout_servers = [ rollout_server_class( replica_rank=replica_rank, config=rollout_config, model_config=model_config, gpus_per_node=init_config.trainer.n_gpus_per_node, ) for replica_rank in range(num_replicas) ] await asyncio.gather(*[server.init_standalone() for server in rollout_servers]) server_handles = [server._server_handle for server in rollout_servers] server_addresses = [server._server_address for server in rollout_servers] assert len(server_handles) == num_replicas assert len(server_addresses) == num_replicas os.environ.pop("HTTPS_PROXY", None) os.environ.pop("HTTP_PROXY", None) os.environ.pop("NO_PROXY", None) client = AsyncOpenAI( api_key="123-abc", base_url=f"http://{server_addresses[0]}/v1", ) completion = await client.chat.completions.create( model=init_config.actor_rollout_ref.model.path, messages=[{"role": "user", "content": "What can you do?"}], ) print(completion.choices[0].message.content) ray.shutdown() @pytest.mark.skip(reason="local test only") def test_hybrid_rollout_with_ep(init_config): """Test hybrid rollout with expert parallelism, DP=2, TP=4, EP=8.""" ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) model_path = os.path.expanduser("~/models/Qwen/Qwen3-30B-A3B-Instruct-2507") init_config.actor_rollout_ref.model.path = model_path # parallelism config init_config.actor_rollout_ref.rollout.tensor_model_parallel_size = 2 init_config.actor_rollout_ref.rollout.data_parallel_size = 4 init_config.actor_rollout_ref.rollout.expert_parallel_size = 8 # 1. init hybrid worker: FSDP+rollout # - build FSDP model and optimizer # - offload FSDP model and optimizer, build rollout # - sleep rollout and load FSDP model and optimizer agent_loop_manager = init_agent_loop_manager(init_config) checkpoint_manager = CheckpointEngineManager( config=omega_conf_to_dataclass(init_config.actor_rollout_ref.rollout.checkpoint_engine), trainer=agent_loop_manager.worker_group, replicas=agent_loop_manager.rollout_replicas, ) checkpoint_manager.sleep_replicas() checkpoint_manager.update_weights() # 3. test async openai call server_address = agent_loop_manager.server_addresses[0] client = OpenAI( api_key="123-abc", base_url=f"http://{server_address}/v1", ) smapling_params = { "temperature": 1.0, "top_p": 1.0, "max_tokens": 512, } response = client.chat.completions.create( model=model_path, messages=[{"role": "user", "content": "What can you do?"}], **smapling_params, ) completion = response.choices[0].message.content print(f"response: {completion}") print("Test passed!") ray.shutdown() ================================================ FILE: tests/experimental/reward_loop/reward_fn.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import aiohttp from openai.types.chat import ChatCompletion from transformers import PreTrainedTokenizer GRM_PROMPT_TEMPLATE = """ You are given a problem and a proposed solution. Problem: {problem} Solution: {solution} Please evaluate how well the solution addresses the problem. Give a score from 1 to 10, where: - 1 means the solution is completely irrelevant or incorrect. - 5 means the solution is partially correct but incomplete or not well reasoned. - 10 means the solution is fully correct, well-reasoned, and directly solves the problem. Only output the score as a single number (integer). """.strip() async def chat_complete(router_address: str, chat_complete_request: dict): url = f"http://{router_address}/v1/chat/completions" try: timeout = aiohttp.ClientTimeout(total=None) session = aiohttp.ClientSession(timeout=timeout) async with session.post(url, json=chat_complete_request) as resp: output = await resp.text() output = json.loads(output) return ChatCompletion(**output) except Exception as e: raise e finally: await session.close() async def compute_score_gsm8k( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, reward_router_address: str, reward_model_tokenizer: PreTrainedTokenizer, ): """Compute the reward score.""" grm_prompt = GRM_PROMPT_TEMPLATE.format(problem=extra_info["question"], solution=solution_str) messages = [{"role": "user", "content": grm_prompt}] sampling_params = {"temperature": 0.7, "top_p": 0.8, "max_tokens": 4096} model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") chat_complete_request = { "messages": messages, "model": model_name, **sampling_params, } result = await chat_complete( router_address=reward_router_address, chat_complete_request=chat_complete_request, ) grm_response = result.choices[0].message.content try: score = int(grm_response.split("\n\n")[-1].strip()) except Exception: score = 0 return {"score": score, "acc": score == 10, "genrm_response": grm_response} def compute_score_math_verify( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs, ): """Compute the reward score.""" from verl.utils.reward_score.math_verify import compute_score return compute_score( model_output=solution_str, ground_truth=ground_truth, ) ================================================ FILE: tests/experimental/reward_loop/test_agent_reward_loop_colocate.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray from hydra import compose, initialize_config_dir from torchdata.stateful_dataloader import StatefulDataLoader from transformers import AutoTokenizer from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.agent_loop import AgentLoopManager from verl.experimental.reward_loop import RewardLoopManager from verl.protocol import DataProto from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup from verl.trainer.main_ppo import create_rl_sampler from verl.trainer.ppo.ray_trainer import ResourcePoolManager from verl.utils import omega_conf_to_dataclass from verl.utils.dataset.rl_dataset import RLHFDataset, collate_fn from verl.utils.device import get_device_name from verl.workers.fsdp_workers import ActorRolloutRefWorker, AsyncActorRolloutRefWorker def test_agent_reward_loop_standalone(): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose(config_name="ppo_trainer") rollout_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") reward_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") # actor_rollout_ref config config.data.return_raw_chat = True config.data.max_prompt_length = 1024 config.data.max_response_length = 4096 config.actor_rollout_ref.model.path = rollout_model_path config.actor_rollout_ref.actor.use_dynamic_bsz = True config.actor_rollout_ref.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = 2 config.actor_rollout_ref.rollout.gpu_memory_utilization = 0.8 config.actor_rollout_ref.rollout.enforce_eager = True config.actor_rollout_ref.rollout.prompt_length = 1024 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.skip_tokenizer_init = True config.trainer.n_gpus_per_node = 8 config.trainer.nnodes = 1 config.reward.reward_manager.name = "dapo" config.reward.reward_model.enable = True config.reward.reward_model.enable_resource_pool = False config.reward.reward_model.n_gpus_per_node = 8 config.reward.reward_model.model_path = reward_model_path config.reward.reward_model.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.reward.reward_model.rollout.gpu_memory_utilization = 0.8 config.reward.reward_model.rollout.tensor_model_parallel_size = 2 config.reward.reward_model.rollout.skip_tokenizer_init = False config.reward.reward_model.rollout.prompt_length = 5120 config.reward.reward_model.rollout.response_length = 4096 config.reward.custom_reward_function.path = "tests/experimental/reward_loop/reward_fn.py" config.reward.custom_reward_function.name = "compute_score_gsm8k" # 1. init reward model manager actor_rollout_cls = ( AsyncActorRolloutRefWorker if config.actor_rollout_ref.rollout.mode == "async" else ActorRolloutRefWorker ) global_pool_id = "global_pool" resource_pool_spec = { global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes, } resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=None) resource_pool_manager.create_resource_pool() resource_pool = resource_pool_manager.resource_pool_dict[global_pool_id] actor_rollout_cls = RayClassWithInitArgs( cls=ray.remote(actor_rollout_cls), config=config.actor_rollout_ref, role="actor_rollout" ) actor_rollout_wg = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=actor_rollout_cls, device_name=get_device_name() ) actor_rollout_wg.init_model() agent_loop_manager = AgentLoopManager.create( config=config, worker_group=actor_rollout_wg, ) # sleep rollout replicas checkpoint_manager = CheckpointEngineManager( config=omega_conf_to_dataclass(config.actor_rollout_ref.rollout.checkpoint_engine), trainer=actor_rollout_wg, replicas=agent_loop_manager.rollout_replicas, ) checkpoint_manager.sleep_replicas() reward_loop_manager = RewardLoopManager(config, rm_resource_pool=resource_pool) # 2. init test data local_folder = os.path.expanduser("~/data/gsm8k/") data_files = [os.path.join(local_folder, "train.parquet")] tokenizer = AutoTokenizer.from_pretrained(rollout_model_path) dataset = RLHFDataset( data_files=data_files, tokenizer=tokenizer, config=config.data, processor=None, ) batch_size = 64 sampler = create_rl_sampler(config.data, dataset) dataloader = StatefulDataLoader( dataset=dataset, batch_size=batch_size, num_workers=config.data.dataloader_num_workers, drop_last=True, collate_fn=collate_fn, sampler=sampler, ) # 3. generate responses batch_dict = next(iter(dataloader)) batch = DataProto.from_single_dict(batch_dict) def _get_gen_batch(batch: DataProto) -> DataProto: reward_keys = set({"data_source", "reward_model", "extra_info", "uid"}) & batch.non_tensor_batch.keys() # pop those keys for generation batch_keys_to_pop = [] non_tensor_batch_keys_to_pop = set(batch.non_tensor_batch.keys()) - reward_keys gen_batch = batch.pop( batch_keys=batch_keys_to_pop, non_tensor_batch_keys=list(non_tensor_batch_keys_to_pop), ) # For agent loop, we need reward model keys to compute score. gen_batch.non_tensor_batch.update(batch.non_tensor_batch) return gen_batch # wake up rollout replicas via update_weight checkpoint_manager.update_weights() gen_batch = _get_gen_batch(batch) gen_batch = agent_loop_manager.generate_sequences(gen_batch) checkpoint_manager.sleep_replicas() batch = batch.union(gen_batch) rm_outputs = reward_loop_manager.compute_rm_score(batch) for output in rm_outputs[:5]: print(output.non_tensor_batch) print("done") ray.shutdown() ================================================ FILE: tests/experimental/reward_loop/test_agent_reward_loop_standalone.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray from hydra import compose, initialize_config_dir from torchdata.stateful_dataloader import StatefulDataLoader from verl.experimental.agent_loop import AgentLoopManager from verl.experimental.reward_loop import RewardLoopManager from verl.protocol import DataProto from verl.trainer.main_ppo import create_rl_sampler from verl.utils import hf_tokenizer from verl.utils.dataset.rl_dataset import RLHFDataset, collate_fn def test_agent_reward_loop_standalone(): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose(config_name="ppo_trainer") rollout_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") reward_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") # actor_rollout_ref config config.data.return_raw_chat = True config.data.max_prompt_length = 1024 config.data.max_response_length = 4096 config.actor_rollout_ref.model.path = rollout_model_path config.actor_rollout_ref.actor.use_dynamic_bsz = True config.actor_rollout_ref.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = 2 config.actor_rollout_ref.rollout.gpu_memory_utilization = 0.9 config.actor_rollout_ref.rollout.enforce_eager = True config.actor_rollout_ref.rollout.prompt_length = 1024 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.skip_tokenizer_init = True config.actor_rollout_ref.rollout.nnodes = 1 config.trainer.n_gpus_per_node = 4 config.trainer.nnodes = 1 config.reward.reward_manager.name = "dapo" config.reward.reward_model.enable = True config.reward.reward_model.enable_resource_pool = True config.reward.reward_model.n_gpus_per_node = 4 config.reward.reward_model.nnodes = 1 config.reward.reward_model.model_path = reward_model_path config.reward.reward_model.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.reward.reward_model.rollout.gpu_memory_utilization = 0.9 config.reward.reward_model.rollout.tensor_model_parallel_size = 2 config.reward.reward_model.rollout.skip_tokenizer_init = False config.reward.reward_model.rollout.prompt_length = 5120 config.reward.reward_model.rollout.response_length = 4096 config.reward.custom_reward_function.path = "tests/experimental/reward_loop/reward_fn.py" config.reward.custom_reward_function.name = "compute_score_gsm8k" # 1. init reward model manager reward_loop_manager = RewardLoopManager(config) agent_loop_manager = AgentLoopManager.create( config=config, reward_loop_worker_handles=reward_loop_manager.reward_loop_workers, ) # 2. init test data local_folder = os.path.expanduser("~/data/gsm8k/") data_files = [os.path.join(local_folder, "train.parquet")] tokenizer = hf_tokenizer(rollout_model_path) dataset = RLHFDataset( data_files=data_files, tokenizer=tokenizer, config=config.data, processor=None, ) batch_size = 64 sampler = create_rl_sampler(config.data, dataset) dataloader = StatefulDataLoader( dataset=dataset, batch_size=batch_size, num_workers=config.data.dataloader_num_workers, drop_last=True, collate_fn=collate_fn, sampler=sampler, ) # 3. generate responses batch_dict = next(iter(dataloader)) batch = DataProto.from_single_dict(batch_dict) # standalone reward model should wake up for agent_reward_loop gen_batch = agent_loop_manager.generate_sequences(prompts=batch) rm_scores = gen_batch.batch["rm_scores"] sample_scores = rm_scores.sum(dim=1) print(sample_scores) ray.shutdown() ================================================ FILE: tests/experimental/reward_loop/test_async_token_bucket_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import time import pytest from verl.experimental.reward_loop.reward_manager.limited import AsyncTokenBucket class TestAsyncTokenBucket: """Unit tests for AsyncTokenBucket rate limiter.""" @pytest.mark.asyncio async def test_basic_acquire(self): """Test basic token acquisition.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) # Should be able to acquire tokens immediately when bucket is full start = time.time() await bucket.acquire(5.0) elapsed = time.time() - start assert elapsed < 0.1, "Initial acquire should be immediate" assert bucket.tokens == pytest.approx(5.0, abs=0.1) @pytest.mark.asyncio async def test_refill_mechanism(self): """Test that tokens refill over time.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) # Consume all tokens await bucket.acquire(10.0) assert bucket.tokens == pytest.approx(0.0, abs=0.1) # Wait for refill (should get ~5 tokens in 0.5 seconds at 10 tokens/sec) await asyncio.sleep(0.5) # Try to acquire 4 tokens (should succeed without waiting) start = time.time() await bucket.acquire(4.0) elapsed = time.time() - start assert elapsed < 0.1, "Acquire should be quick after refill" @pytest.mark.asyncio async def test_waiting_for_tokens(self): """Test that acquire waits when insufficient tokens available.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) # Consume all tokens await bucket.acquire(10.0) # Try to acquire more tokens (should wait ~0.5 seconds for 5 tokens) start = time.time() await bucket.acquire(5.0) elapsed = time.time() - start # Should wait approximately 0.5 seconds (5 tokens / 10 tokens per second) assert 0.4 < elapsed < 0.7, f"Expected ~0.5s wait, got {elapsed:.3f}s" @pytest.mark.asyncio async def test_max_tokens_cap(self): """Test that tokens don't exceed max_tokens capacity.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=5.0) # Wait for potential overflow await asyncio.sleep(1.0) # Tokens should be capped at max_tokens await bucket.acquire(1.0) # After 1 second at 10 tokens/sec, should have max_tokens (5.0) # After acquiring 1, should have 4.0 remaining assert bucket.tokens <= 5.0, "Tokens should not exceed max_tokens" @pytest.mark.asyncio async def test_fractional_tokens(self): """Test acquiring fractional tokens.""" bucket = AsyncTokenBucket(rate_limit=100.0, max_tokens=100.0) # Acquire fractional amounts await bucket.acquire(0.5) await bucket.acquire(1.5) await bucket.acquire(2.3) assert bucket.tokens == pytest.approx(100.0 - 0.5 - 1.5 - 2.3, abs=0.1) @pytest.mark.asyncio async def test_concurrent_acquires(self): """Test multiple concurrent acquire operations.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) async def acquire_task(num_tokens: float, task_id: int): await bucket.acquire(num_tokens) return task_id # Launch 5 concurrent tasks, each acquiring 3 tokens (15 total) # Bucket only has 10, so some will need to wait start = time.time() tasks = [acquire_task(3.0, i) for i in range(5)] results = await asyncio.gather(*tasks) elapsed = time.time() - start # Should take at least 0.5 seconds to refill 5 tokens # (15 needed - 10 available) / 10 tokens per second = 0.5 seconds assert elapsed >= 0.4, f"Expected >=0.4s for concurrent acquires, got {elapsed:.3f}s" assert len(results) == 5, "All tasks should complete" @pytest.mark.asyncio async def test_high_rate_limit(self): """Test with high rate limit (simulating high-throughput scenarios).""" bucket = AsyncTokenBucket(rate_limit=1000.0, max_tokens=1000.0) # Rapidly acquire tokens start = time.time() for _ in range(100): await bucket.acquire(10.0) # 1000 tokens total elapsed = time.time() - start # Should complete in approximately 1 second assert elapsed < 1.5, f"High rate limit test took too long: {elapsed:.3f}s" @pytest.mark.asyncio async def test_zero_initial_state(self): """Test that bucket starts with full tokens.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) assert bucket.tokens == 10.0, "Bucket should start full" assert bucket.last_update is None, "last_update should be None initially" # After first acquire, last_update should be set await bucket.acquire(1.0) assert bucket.last_update is not None, "last_update should be set after acquire" @pytest.mark.asyncio async def test_rate_limit_accuracy(self): """Test rate limit accuracy over time.""" rate = 50.0 # 50 tokens per second bucket = AsyncTokenBucket(rate_limit=rate, max_tokens=rate) # Consume all tokens and measure refill time for 25 tokens await bucket.acquire(50.0) start = time.time() await bucket.acquire(25.0) elapsed = time.time() - start expected_time = 25.0 / rate # 0.5 seconds # Allow 20% margin for timing inaccuracy assert abs(elapsed - expected_time) < expected_time * 0.2, f"Expected ~{expected_time:.3f}s, got {elapsed:.3f}s" @pytest.mark.asyncio async def test_sequential_acquires(self): """Test sequential acquire operations.""" bucket = AsyncTokenBucket(rate_limit=20.0, max_tokens=20.0) # Sequential acquires without waiting await bucket.acquire(5.0) await bucket.acquire(5.0) await bucket.acquire(5.0) await bucket.acquire(5.0) # Bucket should be empty assert bucket.tokens == pytest.approx(0.0, abs=0.1) # Next acquire should wait start = time.time() await bucket.acquire(10.0) elapsed = time.time() - start assert elapsed >= 0.4, "Should wait for token refill" @pytest.mark.asyncio async def test_default_max_tokens(self): """Test that max_tokens defaults to rate_limit.""" bucket = AsyncTokenBucket(rate_limit=15.0) assert bucket.max_tokens == 15.0, "max_tokens should default to rate_limit" assert bucket.tokens == 15.0, "Initial tokens should equal max_tokens" @pytest.mark.asyncio async def test_single_token_acquire(self): """Test default acquire of 1 token.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) await bucket.acquire() # Default num_tokens=1.0 assert bucket.tokens == pytest.approx(9.0, abs=0.1) @pytest.mark.asyncio async def test_large_token_acquire(self): """Test acquiring more tokens than bucket capacity.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) # Try to acquire 50 tokens (5x capacity) start = time.time() await bucket.acquire(50.0) elapsed = time.time() - start # Should wait for: (50 - 10) / 10 = 4 seconds assert 3.5 < elapsed < 5.0, f"Expected ~4s wait for large acquire, got {elapsed:.3f}s" @pytest.mark.asyncio async def test_thread_safety_with_lock(self): """Test that lock prevents race conditions.""" bucket = AsyncTokenBucket(rate_limit=100.0, max_tokens=100.0) results = [] async def acquire_and_record(): await bucket.acquire(10.0) results.append(1) # Launch many concurrent tasks tasks = [acquire_and_record() for _ in range(10)] await asyncio.gather(*tasks) # All tasks should complete assert len(results) == 10, "All tasks should complete successfully" # Bucket should have consumed exactly 100 tokens assert bucket.tokens == pytest.approx(0.0, abs=0.5) @pytest.mark.asyncio async def test_multiple_wait_cycles(self): """Test multiple wait cycles in the acquire loop.""" bucket = AsyncTokenBucket(rate_limit=10.0, max_tokens=10.0) # Consume all tokens await bucket.acquire(10.0) # Acquire tokens that require multiple refill cycles start = time.time() await bucket.acquire(15.0) elapsed = time.time() - start # Should wait for 15 tokens / 10 tokens per second = 1.5 seconds assert 1.3 < elapsed < 1.8, f"Expected ~1.5s for multiple refill cycles, got {elapsed:.3f}s" @pytest.mark.asyncio async def test_rapid_small_acquires(self): """Test many rapid small acquisitions.""" bucket = AsyncTokenBucket(rate_limit=100.0, max_tokens=100.0) start = time.time() for _ in range(50): await bucket.acquire(2.0) # 100 tokens total elapsed = time.time() - start # Should complete quickly since we're within capacity assert elapsed < 0.5, f"Rapid small acquires took too long: {elapsed:.3f}s" if __name__ == "__main__": pytest.main([__file__, "-v"]) ================================================ FILE: tests/experimental/reward_loop/test_math_verify.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray from hydra import compose, initialize_config_dir from torchdata.stateful_dataloader import StatefulDataLoader from transformers import AutoTokenizer from tests.experimental.agent_loop.agent_utils import init_agent_loop_manager from verl.protocol import DataProto from verl.trainer.main_ppo import create_rl_sampler from verl.utils.dataset.rl_dataset import RLHFDataset, collate_fn def test_agent_reward_loop_standalone(): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose(config_name="ppo_trainer") rollout_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") # actor_rollout_ref config config.data.return_raw_chat = True config.data.max_prompt_length = 1024 config.data.max_response_length = 4096 config.actor_rollout_ref.model.path = rollout_model_path config.actor_rollout_ref.actor.use_dynamic_bsz = True config.actor_rollout_ref.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = 2 config.actor_rollout_ref.rollout.gpu_memory_utilization = 0.9 config.actor_rollout_ref.rollout.enforce_eager = True config.actor_rollout_ref.rollout.prompt_length = 2048 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.skip_tokenizer_init = True config.trainer.n_gpus_per_node = 8 config.trainer.nnodes = 1 config.reward.reward_manager.name = "remote" config.reward.num_workers = 2 config.reward.custom_reward_function.path = "tests/experimental/reward_loop/reward_fn.py" config.reward.custom_reward_function.name = "compute_score_math_verify" # 1. init reward model manager agent_loop_manager = init_agent_loop_manager(config) # 2. init test data local_folder = os.path.expanduser("~/data/math/") data_files = [os.path.join(local_folder, "train.parquet")] tokenizer = AutoTokenizer.from_pretrained(rollout_model_path) dataset = RLHFDataset( data_files=data_files, tokenizer=tokenizer, config=config.data, processor=None, ) batch_size = 64 sampler = create_rl_sampler(config.data, dataset) dataloader = StatefulDataLoader( dataset=dataset, batch_size=batch_size, num_workers=config.data.dataloader_num_workers, drop_last=True, collate_fn=collate_fn, sampler=sampler, ) # 3. generate responses batch_dict = next(iter(dataloader)) batch = DataProto.from_single_dict(batch_dict) gen_batch = agent_loop_manager.generate_sequences(prompts=batch) rm_scores = gen_batch.batch["rm_scores"] accuracy = rm_scores.sum(dim=-1).mean() print(accuracy) ray.shutdown() ================================================ FILE: tests/experimental/reward_loop/test_rate_limited_reward_manager_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os.path import time import pytest import torch from omegaconf import DictConfig from transformers import AutoTokenizer from verl import DataProto from verl.experimental.reward_loop.reward_manager.limited import RateLimitedRewardManager # Mock API reward functions for testing class MockAPICounter: """Shared counter to track API calls across tests.""" def __init__(self): self.call_count = 0 self.call_times = [] self.lock = asyncio.Lock() async def record_call(self): async with self.lock: self.call_count += 1 self.call_times.append(time.time()) def reset(self): self.call_count = 0 self.call_times.clear() def get_rate_per_second(self, window_start: float = None): """Calculate API call rate over a time window.""" if window_start is None: if not self.call_times: return 0.0 window_start = self.call_times[0] if not self.call_times: return 0.0 window_end = self.call_times[-1] duration = window_end - window_start if duration <= 0: return 0.0 calls_in_window = sum(1 for t in self.call_times if t >= window_start) return calls_in_window / duration # Global counter instance api_counter = MockAPICounter() def mock_sync_reward_function( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs ) -> float: """Synchronous mock reward function that simulates API call.""" # Simulate API processing time time.sleep(0.01) # Simple scoring logic score = 1.0 if solution_str.strip() == ground_truth.strip() else 0.0 return score async def mock_async_reward_function( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs ) -> float: """Asynchronous mock reward function that simulates API call.""" # Record API call for rate tracking await api_counter.record_call() # Simulate async API call (e.g., HTTP request) await asyncio.sleep(0.01) # Simple scoring logic score = 1.0 if solution_str.strip() == ground_truth.strip() else 0.0 return score async def mock_slow_api_function( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs ) -> float: """Slow mock API function for timeout testing.""" await asyncio.sleep(2.0) # Simulate slow API return 0.5 async def mock_failing_api_function( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs ) -> float: """Mock API function that raises an exception.""" await api_counter.record_call() raise ValueError("Simulated API error") async def mock_dict_result_function( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs ) -> dict: """Mock API function that returns dict result.""" await api_counter.record_call() await asyncio.sleep(0.01) correct = solution_str.strip() == ground_truth.strip() return {"score": 1.0 if correct else 0.0, "correct": correct, "reasoning": "Mock reasoning"} def create_test_data_proto(tokenizer, response_text: str, ground_truth: str, data_source: str = "test"): """Helper to create DataProto for testing.""" response_ids = tokenizer.encode(response_text, add_special_tokens=False) response_tensor = torch.tensor([response_ids], dtype=torch.long) attention_mask = torch.ones_like(response_tensor) data = DataProto.from_dict( { "responses": response_tensor, "attention_mask": attention_mask, } ) # Wrap non-tensor values in lists to match batch dimension data.non_tensor_batch = {"data_source": [data_source], "reward_model": [{"ground_truth": ground_truth}]} return data class TestRateLimitedRewardManager: """Integration tests for RateLimitedRewardManager with mock API functions.""" @pytest.fixture(autouse=True) def setup_and_teardown(self): """Reset global state before each test.""" api_counter.reset() # Reset class state RateLimitedRewardManager._class_initialized = False RateLimitedRewardManager._semaphore = None RateLimitedRewardManager._rpm_limiter = None RateLimitedRewardManager._tpm_limiter = None yield # Cleanup api_counter.reset() @pytest.fixture def tokenizer(self): """Load a simple tokenizer for testing.""" return AutoTokenizer.from_pretrained(os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct")) @pytest.mark.asyncio async def test_basic_reward_computation(self, tokenizer): """Test basic reward computation without rate limiting.""" config = DictConfig({"reward": {"max_concurrent": 10, "timeout": 10.0}}) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) # Create test data data = create_test_data_proto(tokenizer, "correct answer", "correct answer") # Compute reward result = await manager.run_single(data) assert "reward_score" in result assert result["reward_score"] == 1.0 assert api_counter.call_count == 1 @pytest.mark.asyncio async def test_rpm_rate_limiting(self, tokenizer): """Test request per minute (RPM) rate limiting.""" # Set RPM limit to 60 (1 request per second) config = DictConfig( { "reward": { "max_concurrent": 10, "max_rpm": 60, # 1 request per second "timeout": 10.0, } } ) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) # Create test data data = create_test_data_proto(tokenizer, "answer", "answer") # Make 3 requests - should be rate limited start_time = time.time() results = [] for _ in range(3): result = await manager.run_single(data) results.append(result) elapsed = time.time() - start_time # Should take at least ~2 seconds for 3 requests at 1 req/sec assert elapsed >= 1.8, f"RPM limiting failed: {elapsed:.3f}s for 3 requests" assert all(r["reward_score"] == 1.0 for r in results) assert api_counter.call_count == 3 @pytest.mark.asyncio async def test_tpm_rate_limiting(self, tokenizer): """Test tokens per minute (TPM) rate limiting.""" # Set TPM limit to 6000 (100 tokens per second) # With 2000 tokens per request, that's 0.05 req/sec or 20 seconds per request config = DictConfig( { "reward": { "max_concurrent": 10, "max_tpm": 6000, # 100 tokens per second "estimated_tokens_per_request": 2000, # Each request = 2000 tokens "timeout": 30.0, } } ) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) data = create_test_data_proto(tokenizer, "answer", "answer") # Make 2 requests start_time = time.time() result1 = await manager.run_single(data) result2 = await manager.run_single(data) elapsed = time.time() - start_time # First request: consumes 2000 tokens (immediate) # Second request: needs 2000 tokens, waits for refill # Wait time: 2000 tokens / 100 tokens per second = 20 seconds assert elapsed >= 18.0, f"TPM limiting failed: {elapsed:.3f}s for 2 requests" assert result1["reward_score"] == 1.0 assert result2["reward_score"] == 1.0 @pytest.mark.asyncio async def test_concurrency_limiting(self, tokenizer): """Test concurrent request limiting.""" config = DictConfig( { "reward": { "max_concurrent": 2, # Only 2 concurrent requests "timeout": 10.0, } } ) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) data = create_test_data_proto(tokenizer, "answer", "answer") # Launch 5 concurrent requests start_time = time.time() tasks = [manager.run_single(data) for _ in range(5)] results = await asyncio.gather(*tasks) elapsed = time.time() - start_time # All should succeed assert len(results) == 5 assert all(r["reward_score"] == 1.0 for r in results) # With concurrency=2 and 0.01s per request, should take at least 0.03s # (3 batches: 2+2+1) assert elapsed >= 0.02, f"Concurrency limiting may not be working: {elapsed:.3f}s" @pytest.mark.asyncio async def test_timeout_handling(self, tokenizer): """Test timeout handling for slow API.""" config = DictConfig( { "reward": { "max_concurrent": 10, "timeout": 0.5, # 500ms timeout } } ) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_slow_api_function) data = create_test_data_proto(tokenizer, "answer", "answer") # Should timeout and return 0.0 result = await manager.run_single(data) assert result["reward_score"] == 0.0 assert result["reward_extra_info"].get("timeout") is True assert result["reward_extra_info"].get("acc") == 0.0 @pytest.mark.asyncio async def test_error_handling(self, tokenizer): """Test error handling for failing API.""" config = DictConfig({"reward": {"max_concurrent": 10, "timeout": 10.0}}) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_failing_api_function) data = create_test_data_proto(tokenizer, "answer", "answer") # Should catch exception and return 0.0 result = await manager.run_single(data) assert result["reward_score"] == 0.0 assert "error" in result["reward_extra_info"] assert "Simulated API error" in result["reward_extra_info"]["error"] assert result["reward_extra_info"].get("acc") == 0.0 assert api_counter.call_count == 1 @pytest.mark.asyncio async def test_dict_result_format(self, tokenizer): """Test handling of dict return format from reward function.""" config = DictConfig({"reward": {"max_concurrent": 10, "timeout": 10.0}}) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_dict_result_function) data = create_test_data_proto(tokenizer, "correct", "correct") result = await manager.run_single(data) assert result["reward_score"] == 1.0 assert result["reward_extra_info"]["score"] == 1.0 assert result["reward_extra_info"]["correct"] is True assert result["reward_extra_info"]["reasoning"] == "Mock reasoning" @pytest.mark.asyncio async def test_sync_reward_function(self, tokenizer): """Test that synchronous reward functions work correctly.""" config = DictConfig({"reward": {"max_concurrent": 10, "timeout": 10.0}}) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_sync_reward_function) data = create_test_data_proto(tokenizer, "answer", "answer") result = await manager.run_single(data) assert result["reward_score"] == 1.0 assert manager.is_async_reward_score is False @pytest.mark.asyncio async def test_combined_rate_limits(self, tokenizer): """Test all three rate limiting layers together.""" config = DictConfig( { "reward": { "max_concurrent": 2, "max_rpm": 120, # 2 requests per second "max_tpm": 12000, # 200 tokens per second "estimated_tokens_per_request": 100, # 0.5 seconds per request "timeout": 10.0, } } ) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) data = create_test_data_proto(tokenizer, "answer", "answer") # Make 6 requests to exceed burst capacity (RPM bucket starts with 2 tokens) start_time = time.time() tasks = [manager.run_single(data) for _ in range(6)] results = await asyncio.gather(*tasks) elapsed = time.time() - start_time # Bucket starts with 2 RPM tokens and 200 TPM tokens # First 2 requests: use burst capacity (2 RPM tokens, 200 TPM tokens) # Next 4 requests: need 4 RPM tokens (wait 2 seconds) and 400 TPM tokens (wait 2 seconds) # Limiting factor: RPM at 2 seconds assert elapsed >= 1.8, f"Combined rate limiting: {elapsed:.3f}s" assert all(r["reward_score"] == 1.0 for r in results) assert api_counter.call_count == 6 @pytest.mark.asyncio async def test_correct_vs_incorrect_answers(self, tokenizer): """Test scoring of correct vs incorrect answers.""" config = DictConfig({"reward": {"max_concurrent": 10, "timeout": 10.0}}) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) # Test correct answer data_correct = create_test_data_proto(tokenizer, "right answer", "right answer") result_correct = await manager.run_single(data_correct) # Test incorrect answer data_incorrect = create_test_data_proto(tokenizer, "wrong answer", "right answer") result_incorrect = await manager.run_single(data_incorrect) assert result_correct["reward_score"] == 1.0 assert result_incorrect["reward_score"] == 0.0 @pytest.mark.asyncio async def test_high_throughput(self, tokenizer): """Test high throughput with many concurrent requests.""" config = DictConfig( { "reward": { "max_concurrent": 20, "max_rpm": 6000, # 100 requests per second "timeout": 10.0, } } ) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager(config=config, tokenizer=tokenizer, compute_score=mock_async_reward_function) data = create_test_data_proto(tokenizer, "answer", "answer") # Launch 200 concurrent requests (more than burst capacity of 100) start_time = time.time() tasks = [manager.run_single(data) for _ in range(200)] results = await asyncio.gather(*tasks) elapsed = time.time() - start_time assert len(results) == 200 assert all(r["reward_score"] == 1.0 for r in results) # Bucket starts with 100 tokens (burst capacity) # First 100 requests: use burst capacity instantly # Next 100 requests: need to wait for refill at 100 tokens/sec = 1 second minimum # Total time should be at least 1 second assert elapsed >= 0.9, f"Should take at least 0.9s for rate limiting, took {elapsed:.3f}s" # Calculate actual rate over the time window actual_rate = api_counter.call_count / elapsed # Average rate should not significantly exceed 100 req/sec # Allow some burst overhead due to initial capacity assert actual_rate <= 200, f"Rate limiting failed: {actual_rate:.1f} req/sec (max 200)" @pytest.mark.asyncio async def test_class_initialization_once(self, tokenizer): """Test that class initialization only happens once.""" config = DictConfig({"reward": {"max_concurrent": 5, "timeout": 10.0}}) # Initialize multiple times RateLimitedRewardManager.init_class(config, tokenizer) first_semaphore = RateLimitedRewardManager._semaphore RateLimitedRewardManager.init_class(config, tokenizer) second_semaphore = RateLimitedRewardManager._semaphore # Should be the same object assert first_semaphore is second_semaphore @pytest.mark.asyncio async def test_extra_info_handling(self, tokenizer): """Test that extra_info is properly passed to reward function.""" received_extra_info = {} async def mock_reward_with_extra_info( data_source: str, solution_str: str, ground_truth: str, extra_info: dict, **kwargs ): received_extra_info.update(extra_info) return 1.0 config = DictConfig({"reward": {"max_concurrent": 10, "timeout": 10.0}}) RateLimitedRewardManager.init_class(config, tokenizer) manager = RateLimitedRewardManager( config=config, tokenizer=tokenizer, compute_score=mock_reward_with_extra_info ) data = create_test_data_proto(tokenizer, "answer", "answer") data.non_tensor_batch["extra_info"] = [{"custom_field": "test_value"}] await manager.run_single(data) assert "custom_field" in received_extra_info assert received_extra_info["custom_field"] == "test_value" if __name__ == "__main__": pytest.main([__file__, "-v", "-s"]) ================================================ FILE: tests/experimental/reward_loop/test_reward_model_disrm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray import torch from hydra import compose, initialize_config_dir from verl.experimental.reward_loop import RewardLoopManager from verl.protocol import DataProto from verl.utils import hf_tokenizer from verl.utils.model import compute_position_id_with_mask from verl.utils.tokenizer import normalize_token_ids def create_data_samples(tokenizer) -> DataProto: convs = [ [ { "role": "user", "content": "What is the range of the numeric output of a sigmoid node in a neural network?", }, {"role": "assistant", "content": "Between -1 and 1."}, ], [ { "role": "user", "content": "What is the range of the numeric output of a sigmoid node in a neural network?", }, {"role": "assistant", "content": "Between 0 and 1."}, ], [ {"role": "user", "content": "What is the capital of Australia?"}, { "role": "assistant", "content": "Canberra is the capital city of Australia.", }, ], [ {"role": "user", "content": "What is the capital of Australia?"}, { "role": "assistant", "content": "Sydney is the capital of Australia.", }, ], ] raw_prompt = [conv[:1] for conv in convs] data_source = ["gsm8k"] * len(convs) reward_info = [{"ground_truth": "Not Used"}] * len(convs) extra_info = [{"question": conv[0]["content"]} for conv in convs] prompt_length, response_length = 1024, 4096 pad_token_id = tokenizer.pad_token_id prompts, responses, input_ids, attention_masks = [], [], [], [] for conv in convs: prompt_tokens = normalize_token_ids(tokenizer.apply_chat_template(conv[:1], tokenize=True)) response_tokens = normalize_token_ids(tokenizer.apply_chat_template(conv, tokenize=True))[len(prompt_tokens) :] padded_prompt = [pad_token_id] * (prompt_length - len(prompt_tokens)) + prompt_tokens padded_response = response_tokens + [pad_token_id] * (response_length - len(response_tokens)) attention_mask = ( [0] * (prompt_length - len(prompt_tokens)) + [1] * len(prompt_tokens) + [1] * len(response_tokens) + [0] * (response_length - len(response_tokens)) ) prompts.append(torch.tensor(padded_prompt)) responses.append(torch.tensor(padded_response)) input_ids.append(torch.tensor(padded_prompt + padded_response)) attention_masks.append(torch.tensor(attention_mask)) prompts = torch.stack(prompts) responses = torch.stack(responses) input_ids = torch.stack(input_ids) attention_masks = torch.stack(attention_masks) position_ids = compute_position_id_with_mask(attention_masks) data = DataProto.from_dict( tensors={ "prompts": prompts, "responses": responses, "input_ids": input_ids, "attention_mask": attention_masks, "position_ids": position_ids, }, non_tensors={ "data_source": data_source, "reward_model": reward_info, "raw_prompt": raw_prompt, "extra_info": extra_info, }, ) return data, convs def test_reward_model_manager(): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose(config_name="ppo_trainer") rollout_model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") reward_model_name = os.path.expanduser("~/models/Skywork/Skywork-Reward-V2-Llama-3.2-1B") config.actor_rollout_ref.model.path = rollout_model_name config.reward.num_workers = 1 config.reward.reward_manager.name = "dapo" config.reward.reward_model.enable = True config.reward.reward_model.enable_resource_pool = True config.reward.reward_model.n_gpus_per_node = 8 config.reward.reward_model.nnodes = 1 config.reward.reward_model.model_path = reward_model_name config.reward.reward_model.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.reward.reward_model.rollout.gpu_memory_utilization = 0.9 config.reward.reward_model.rollout.tensor_model_parallel_size = 2 config.reward.reward_model.rollout.skip_tokenizer_init = False config.reward.reward_model.rollout.prompt_length = 2048 config.reward.reward_model.rollout.response_length = 4096 # 1. init reward model manager reward_loop_manager = RewardLoopManager(config) # 2. init test data rollout_tokenizer = hf_tokenizer(rollout_model_name) data, convs = create_data_samples(rollout_tokenizer) # 3. generate responses outputs = reward_loop_manager.compute_rm_score(data) for idx, (conv, output) in enumerate(zip(convs, outputs, strict=True)): print(f"Problem {idx}:\n{conv[0]['content']}\n") print(f"AI Solution {idx}:\n{conv[1]['content']}\n") print(f"DisRM Score {idx}:\n{output.batch['rm_scores'].sum(dim=-1).item()}\n") print("=" * 50 + "\n") ray.shutdown() ================================================ FILE: tests/experimental/reward_loop/test_reward_model_genrm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray import torch from hydra import compose, initialize_config_dir from verl.experimental.reward_loop import RewardLoopManager from verl.protocol import DataProto from verl.utils import hf_tokenizer from verl.utils.model import compute_position_id_with_mask from verl.utils.tokenizer import normalize_token_ids def create_data_samples(tokenizer) -> DataProto: convs = [ [ { "role": "user", "content": "What is the range of the numeric output of a sigmoid node in a neural network?", }, {"role": "assistant", "content": "Between -1 and 1."}, ], [ { "role": "user", "content": "What is the range of the numeric output of a sigmoid node in a neural network?", }, {"role": "assistant", "content": "Between 0 and 1."}, ], [ {"role": "user", "content": "What is the capital of Australia?"}, { "role": "assistant", "content": "Canberra is the capital city of Australia.", }, ], [ {"role": "user", "content": "What is the capital of Australia?"}, { "role": "assistant", "content": "Sydney is the capital of Australia.", }, ], ] raw_prompt = [conv[:1] for conv in convs] data_source = ["gsm8k"] * len(convs) reward_info = [{"ground_truth": "Not Used"}] * len(convs) extra_info = [{"question": conv[0]["content"]} for conv in convs] prompt_length, response_length = 1024, 4096 pad_token_id = tokenizer.pad_token_id prompts, responses, input_ids, attention_masks = [], [], [], [] for conv in convs: prompt_tokens = normalize_token_ids(tokenizer.apply_chat_template(conv[:1], tokenize=True)) response_tokens = normalize_token_ids(tokenizer.apply_chat_template(conv, tokenize=True))[len(prompt_tokens) :] padded_prompt = [pad_token_id] * (prompt_length - len(prompt_tokens)) + prompt_tokens padded_response = response_tokens + [pad_token_id] * (response_length - len(response_tokens)) attention_mask = ( [0] * (prompt_length - len(prompt_tokens)) + [1] * len(prompt_tokens) + [1] * len(response_tokens) + [0] * (response_length - len(response_tokens)) ) prompts.append(torch.tensor(padded_prompt)) responses.append(torch.tensor(padded_response)) input_ids.append(torch.tensor(padded_prompt + padded_response)) attention_masks.append(torch.tensor(attention_mask)) prompts = torch.stack(prompts) responses = torch.stack(responses) input_ids = torch.stack(input_ids) attention_masks = torch.stack(attention_masks) position_ids = compute_position_id_with_mask(attention_masks) data = DataProto.from_dict( tensors={ "prompts": prompts, "responses": responses, "input_ids": input_ids, "attention_mask": attention_masks, "position_ids": position_ids, }, non_tensors={ "data_source": data_source, "reward_model": reward_info, "raw_prompt": raw_prompt, "extra_info": extra_info, }, ) return data, convs def test_reward_model_manager(): ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } } ) with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose(config_name="ppo_trainer") rollout_model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") reward_model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") config.actor_rollout_ref.model.path = rollout_model_name config.reward.custom_reward_function.path = "tests/experimental/reward_loop/reward_fn.py" config.reward.custom_reward_function.name = "compute_score_gsm8k" config.reward.num_workers = 1 config.reward.reward_manager.name = "dapo" config.reward.reward_model.enable = True config.reward.reward_model.enable_resource_pool = True config.reward.reward_model.n_gpus_per_node = 8 config.reward.reward_model.nnodes = 1 config.reward.reward_model.model_path = reward_model_name config.reward.reward_model.rollout.name = os.getenv("ROLLOUT_NAME", "vllm") config.reward.reward_model.rollout.gpu_memory_utilization = 0.9 config.reward.reward_model.rollout.tensor_model_parallel_size = 2 config.reward.reward_model.rollout.skip_tokenizer_init = False config.reward.reward_model.rollout.prompt_length = 2048 config.reward.reward_model.rollout.response_length = 4096 # 1. init reward model manager reward_loop_manager = RewardLoopManager(config) # 2. init test data rollout_tokenizer = hf_tokenizer(rollout_model_name) data, convs = create_data_samples(rollout_tokenizer) # 3. generate responses outputs = reward_loop_manager.compute_rm_score(data) for idx, (conv, output) in enumerate(zip(convs, outputs, strict=True)): print(f"Problem {idx}:\n{conv[0]['content']}\n") print(f"AI Solution {idx}:\n{conv[1]['content']}\n") print(f"GRM Response {idx}:\n{output.non_tensor_batch['genrm_response']}\n") print("=" * 50 + "\n") ray.shutdown() ================================================ FILE: tests/experimental/vla/test_sim_envs.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import numpy as np import pytest from omegaconf import OmegaConf # @pytest.mark.parametrize("simulator_type", ["libero", "isaac"]) @pytest.mark.parametrize("simulator_type", ["isaac"]) def test_sim_env_creation_and_step(simulator_type): num_envs = 8 actions = np.array( [ [5.59112417e-01, 8.06460073e-02, 1.36817226e-02, -4.64279854e-04, -1.72158767e-02, -6.57548380e-04, -1], [2.12711899e-03, -3.13366604e-01, 3.41386353e-04, -4.64279854e-04, -8.76528812e-03, -6.57548380e-04, -1], [7.38182960e-02, -4.64548351e-02, -6.63602950e-02, -4.64279854e-04, -2.32520114e-02, -6.57548380e-04, -1], [7.38182960e-02, -1.60845593e-01, 3.41386353e-04, -4.64279854e-04, 1.05503430e-02, -6.57548380e-04, -1], [7.38182960e-02, -3.95982152e-01, -7.97006313e-02, -5.10713711e-03, 3.22804279e-02, -6.57548380e-04, -1], [2.41859427e-02, -3.64206941e-01, -6.63602950e-02, -4.64279854e-04, 1.05503430e-02, -6.57548380e-04, -1], [4.62447664e-02, -5.16727952e-01, -7.97006313e-02, -4.64279854e-04, 1.05503430e-02, 8.73740975e-03, -1], [4.62447664e-02, -5.73923331e-01, 3.41386353e-04, -4.64279854e-04, 6.92866212e-03, -6.57548380e-04, -1], ] ) cfg = OmegaConf.create( { "max_episode_steps": 512, "only_eval": False, "reward_coef": 1.0, "init_params": { "camera_names": ["agentview"], }, "video_cfg": { "save_video": True, "video_base_dir": "/tmp/test_sim_env_creation_and_step", }, "task_suite_name": "libero_10", "num_envs": num_envs, "num_group": 1, "group_size": num_envs, "seed": 0, }, ) sim_env = None if simulator_type == "isaac": from verl.experimental.vla.envs.isaac_env.isaac_env import IsaacEnv sim_env = IsaacEnv(cfg, rank=0, world_size=1) elif simulator_type == "libero": from verl.experimental.vla.envs.libero_env.libero_env import LiberoEnv sim_env = LiberoEnv(cfg, rank=0, world_size=1) else: raise ValueError(f"simulator_type {simulator_type} is not supported") video_count = 0 for i in [0]: # The first call to step with actions=None will reset the environment step = 0 sim_env.reset_envs_to_state_ids([0] * num_envs, [i] * num_envs) for action in actions: obs_venv, reward_venv, terminated_venv, truncated_venv, info_venv = sim_env.step( np.array([action] * num_envs) ) assert isinstance(obs_venv, dict) assert reward_venv.shape == (num_envs,) assert terminated_venv.shape == (num_envs,) assert truncated_venv.shape == (num_envs,) assert isinstance(info_venv, dict) if terminated_venv.any() or truncated_venv.any(): break step += 1 sim_env.flush_video(video_sub_dir=f"task_{i}") assert os.path.exists(os.path.join(cfg.video_cfg.video_base_dir, f"rank_0/task_{i}/{video_count}.mp4")) os.remove(os.path.join(cfg.video_cfg.video_base_dir, f"rank_0/task_{i}/{video_count}.mp4")) video_count += 1 print("test passed") sim_env.close() if __name__ == "__main__": unittest.main() ================================================ FILE: tests/interactions/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/interactions/test_gsm8k_interaction.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from unittest.mock import patch import pytest from verl.interactions.gsm8k_interaction import Gsm8kInteraction class TestGsm8kInteraction: """Test cases for Gsm8kInteraction class.""" def setup_method(self): """Set up test environment before each test method.""" self.config = {"name": "gsm8k"} self.interaction = Gsm8kInteraction(self.config) def test_init(self): """Test Gsm8kInteraction initialization.""" assert self.interaction._instance_dict == {} assert self.interaction.config == self.config assert self.interaction.name == "gsm8k" @pytest.mark.asyncio async def test_start_interaction_with_instance_id(self): """Test start_interaction with provided instance_id.""" instance_id = "test_instance" ground_truth = "42" result_id = await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) assert result_id == instance_id assert instance_id in self.interaction._instance_dict assert self.interaction._instance_dict[instance_id]["response"] == "" assert self.interaction._instance_dict[instance_id]["ground_truth"] == ground_truth assert self.interaction._instance_dict[instance_id]["reward"] == 0.0 @pytest.mark.asyncio async def test_start_interaction_without_instance_id(self): """Test start_interaction without provided instance_id (auto-generated).""" ground_truth = "42" result_id = await self.interaction.start_interaction(ground_truth=ground_truth) assert result_id is not None assert len(result_id) == 36 # UUID4 length assert result_id in self.interaction._instance_dict assert self.interaction._instance_dict[result_id]["ground_truth"] == ground_truth @pytest.mark.asyncio async def test_start_interaction_without_ground_truth(self): """Test start_interaction without ground_truth parameter.""" instance_id = "test_instance" result_id = await self.interaction.start_interaction(instance_id=instance_id) assert result_id == instance_id assert self.interaction._instance_dict[instance_id]["ground_truth"] is None @pytest.mark.asyncio async def test_generate_response_correct_answer_with_prefix(self): """Test generate_response with correct answer already having #### prefix.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [{"role": "assistant", "content": "#### 42"}] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=1.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is True assert response == "Your response is correct!" assert reward == 1.0 assert metadata == {} assert self.interaction._instance_dict[instance_id]["response"] == "#### 42" @pytest.mark.asyncio async def test_generate_response_correct_answer_without_prefix(self): """Test generate_response with correct answer missing #### prefix.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [{"role": "assistant", "content": "42"}] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=1.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is True assert response == "Your response is correct!" assert reward == 1.0 assert self.interaction._instance_dict[instance_id]["response"] == "42" @pytest.mark.asyncio async def test_generate_response_incorrect_answer(self): """Test generate_response with incorrect answer.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [{"role": "assistant", "content": "24"}] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=0.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is False assert response == "Your response is incorrect! You need to reflect on your answer and try again." assert reward == 0.0 assert self.interaction._instance_dict[instance_id]["response"] == "24" @pytest.mark.asyncio async def test_generate_response_multiple_messages(self): """Test generate_response with multiple messages (should use last assistant message).""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [ {"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "### 4"}, {"role": "user", "content": "What is 40+2?"}, {"role": "assistant", "content": "#### 42"}, ] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=1.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is True assert response == "Your response is correct!" assert self.interaction._instance_dict[instance_id]["response"] == "#### 42" @pytest.mark.asyncio async def test_generate_response_no_assistant_message(self): """Test generate_response with no assistant messages.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [{"role": "user", "content": "Hello!"}] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=0.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is False assert self.interaction._instance_dict[instance_id]["response"] == "" @pytest.mark.asyncio async def test_calculate_score_direct_call(self): """Test calculate_score method directly.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) # Set a response self.interaction._instance_dict[instance_id]["response"] = "#### 42" with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=1.0) as mock_compute: score = await self.interaction.calculate_score(instance_id) assert score == 1.0 mock_compute.assert_called_once_with("#### 42", "42", method="strict", format_score=0.0, score=1.0) @pytest.mark.asyncio async def test_calculate_score_with_kwargs(self): """Test calculate_score method with additional kwargs.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) # Set a response self.interaction._instance_dict[instance_id]["response"] = "#### 24" with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=0.0) as mock_compute: score = await self.interaction.calculate_score(instance_id, extra_param="test") assert score == 0.0 mock_compute.assert_called_once_with("#### 24", "42", method="strict", format_score=0.0, score=1.0) @pytest.mark.asyncio async def test_finalize_interaction(self): """Test finalize_interaction method.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) assert instance_id in self.interaction._instance_dict await self.interaction.finalize_interaction(instance_id) assert instance_id not in self.interaction._instance_dict @pytest.mark.asyncio async def test_finalize_interaction_with_kwargs(self): """Test finalize_interaction method with additional kwargs.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) assert instance_id in self.interaction._instance_dict await self.interaction.finalize_interaction(instance_id, extra_param="test") assert instance_id not in self.interaction._instance_dict @pytest.mark.asyncio async def test_finalize_nonexistent_interaction(self): """Test finalize_interaction with non-existent instance_id.""" instance_id = "nonexistent_instance" # This should raise KeyError with pytest.raises(KeyError): await self.interaction.finalize_interaction(instance_id) @pytest.mark.asyncio async def test_full_interaction_workflow_correct(self): """Test complete interaction workflow with correct answer.""" ground_truth = "42" # Start interaction instance_id = await self.interaction.start_interaction(ground_truth=ground_truth) # Generate response with correct answer messages = [{"role": "assistant", "content": "42"}] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=1.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is True assert reward == 1.0 # Finalize interaction await self.interaction.finalize_interaction(instance_id) assert instance_id not in self.interaction._instance_dict @pytest.mark.asyncio async def test_full_interaction_workflow_incorrect(self): """Test complete interaction workflow with incorrect answer.""" ground_truth = "42" # Start interaction instance_id = await self.interaction.start_interaction(ground_truth=ground_truth) # Generate response with incorrect answer messages = [{"role": "assistant", "content": "24"}] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=0.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is False assert reward == 0.0 # Continue with another attempt messages.append({"role": "user", "content": response}) messages.append({"role": "assistant", "content": "42"}) with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=1.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is True assert reward == 1.0 # Finalize interaction await self.interaction.finalize_interaction(instance_id) assert instance_id not in self.interaction._instance_dict @pytest.mark.asyncio async def test_multiple_concurrent_interactions(self): """Test multiple concurrent interaction instances.""" ground_truth_1 = "42" ground_truth_2 = "24" # Start multiple interactions instance_id_1 = await self.interaction.start_interaction(ground_truth=ground_truth_1) instance_id_2 = await self.interaction.start_interaction(ground_truth=ground_truth_2) assert len(self.interaction._instance_dict) == 2 assert instance_id_1 in self.interaction._instance_dict assert instance_id_2 in self.interaction._instance_dict # Test responses for both instances messages_1 = [{"role": "assistant", "content": "42"}] messages_2 = [{"role": "assistant", "content": "24"}] with patch("verl.utils.reward_score.gsm8k.compute_score", side_effect=[1.0, 1.0]): should_terminate_1, _, reward_1, _ = await self.interaction.generate_response(instance_id_1, messages_1) should_terminate_2, _, reward_2, _ = await self.interaction.generate_response(instance_id_2, messages_2) assert should_terminate_1 is True assert should_terminate_2 is True assert reward_1 == 1.0 assert reward_2 == 1.0 # Finalize both interactions await self.interaction.finalize_interaction(instance_id_1) await self.interaction.finalize_interaction(instance_id_2) assert len(self.interaction._instance_dict) == 0 @pytest.mark.asyncio async def test_edge_case_empty_messages(self): """Test edge case with empty messages list.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=0.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is False assert reward == 0.0 assert self.interaction._instance_dict[instance_id]["response"] == "" @pytest.mark.asyncio async def test_edge_case_message_without_content(self): """Test edge case with message without content field.""" instance_id = "test_instance" ground_truth = "42" # Setup instance await self.interaction.start_interaction(instance_id=instance_id, ground_truth=ground_truth) messages = [ {"role": "assistant"} # Missing content field ] with patch("verl.utils.reward_score.gsm8k.compute_score", return_value=0.0): should_terminate, response, reward, metadata = await self.interaction.generate_response( instance_id, messages ) assert should_terminate is False assert reward == 0.0 assert self.interaction._instance_dict[instance_id]["response"] is None def test_inheritance_from_base_interaction(self): """Test that Gsm8kInteraction properly inherits from BaseInteraction.""" from verl.interactions.base import BaseInteraction assert isinstance(self.interaction, BaseInteraction) # Test that all required methods are implemented assert hasattr(self.interaction, "start_interaction") assert hasattr(self.interaction, "generate_response") assert hasattr(self.interaction, "calculate_score") assert hasattr(self.interaction, "finalize_interaction") # Test that methods are callable assert callable(self.interaction.start_interaction) assert callable(self.interaction.generate_response) assert callable(self.interaction.calculate_score) assert callable(self.interaction.finalize_interaction) def test_name_attribute_initialization(self): """Test name attribute initialization with different configs.""" # Test with explicit name in config config_with_name = {"name": "custom_gsm8k"} interaction_with_name = Gsm8kInteraction(config_with_name) assert interaction_with_name.name == "custom_gsm8k" # Test with default name when not provided in config config_without_name = {} interaction_without_name = Gsm8kInteraction(config_without_name) assert interaction_without_name.name == "interaction_agent" # Default from BaseInteraction # Test that name is accessible as attribute assert hasattr(self.interaction, "name") assert self.interaction.name == "gsm8k" ================================================ FILE: tests/interactions/test_interaction_registry.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import tempfile import pytest from omegaconf import OmegaConf from verl.interactions.base import BaseInteraction from verl.interactions.gsm8k_interaction import Gsm8kInteraction from verl.interactions.utils.interaction_registry import ( get_interaction_class, initialize_interactions_from_config, ) class TestInteractionRegistry: def test_get_interaction_class(self): """Test getting interaction class by name.""" # Test getting base interaction class base_cls = get_interaction_class("verl.interactions.base.BaseInteraction") assert base_cls == BaseInteraction # Test getting gsm8k interaction class gsm8k_cls = get_interaction_class("verl.interactions.gsm8k_interaction.Gsm8kInteraction") assert gsm8k_cls == Gsm8kInteraction def test_initialize_single_interaction_from_config(self): """Test initializing single interaction from config.""" # Create temporary config file config_content = { "interaction": [ { "name": "test_gsm8k", "class_name": "verl.interactions.gsm8k_interaction.Gsm8kInteraction", "config": {}, } ] } with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: interaction_map = initialize_interactions_from_config(temp_config_path) # Check that interaction was created assert len(interaction_map) == 1 assert "test_gsm8k" in interaction_map assert isinstance(interaction_map["test_gsm8k"], Gsm8kInteraction) assert interaction_map["test_gsm8k"].name == "test_gsm8k" finally: os.unlink(temp_config_path) def test_initialize_multiple_interactions_from_config(self): """Test initializing multiple interactions from config.""" config_content = { "interaction": [ { "name": "gsm8k_solver", "class_name": "verl.interactions.gsm8k_interaction.Gsm8kInteraction", "config": {}, }, { "name": "base_agent", "class_name": "verl.interactions.base.BaseInteraction", "config": {"custom_param": "test_value"}, }, ] } with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: interaction_map = initialize_interactions_from_config(temp_config_path) # Check that both interactions were created assert len(interaction_map) == 2 assert "gsm8k_solver" in interaction_map assert "base_agent" in interaction_map # Check types assert isinstance(interaction_map["gsm8k_solver"], Gsm8kInteraction) assert isinstance(interaction_map["base_agent"], BaseInteraction) # Check names were injected assert interaction_map["gsm8k_solver"].name == "gsm8k_solver" assert interaction_map["base_agent"].name == "base_agent" # Check custom config was passed assert interaction_map["base_agent"].config.get("custom_param") == "test_value" finally: os.unlink(temp_config_path) def test_initialize_interaction_without_explicit_name(self): """Test that interaction name is derived from class name when not specified.""" config_content = { "interaction": [{"class_name": "verl.interactions.gsm8k_interaction.Gsm8kInteraction", "config": {}}] } with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: interaction_map = initialize_interactions_from_config(temp_config_path) # Check that interaction name was derived from class name assert len(interaction_map) == 1 assert "gsm8k" in interaction_map # Should be "gsm8k" after removing "interaction" suffix assert isinstance(interaction_map["gsm8k"], Gsm8kInteraction) assert interaction_map["gsm8k"].name == "gsm8k" finally: os.unlink(temp_config_path) def test_initialize_empty_config(self): """Test initializing from empty config.""" config_content = {"interaction": []} with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: interaction_map = initialize_interactions_from_config(temp_config_path) assert len(interaction_map) == 0 finally: os.unlink(temp_config_path) def test_invalid_class_name(self): """Test handling of invalid class name.""" config_content = { "interaction": [{"name": "invalid", "class_name": "invalid.module.InvalidClass", "config": {}}] } with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: with pytest.raises(ModuleNotFoundError): initialize_interactions_from_config(temp_config_path) finally: os.unlink(temp_config_path) def test_duplicate_interaction_names(self): """Test handling of duplicate interaction names.""" config_content = { "interaction": [ {"name": "duplicate", "class_name": "verl.interactions.base.BaseInteraction", "config": {}}, { "name": "duplicate", "class_name": "verl.interactions.gsm8k_interaction.Gsm8kInteraction", "config": {}, }, ] } with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: with pytest.raises(ValueError, match="Duplicate interaction name 'duplicate' found"): initialize_interactions_from_config(temp_config_path) finally: os.unlink(temp_config_path) def test_auto_name_generation_edge_cases(self): """Test automatic name generation for various class name patterns.""" config_content = { "interaction": [ {"class_name": "verl.interactions.base.BaseInteraction", "config": {}}, {"class_name": "verl.interactions.gsm8k_interaction.Gsm8kInteraction", "config": {}}, ] } with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: OmegaConf.save(config_content, f.name) temp_config_path = f.name try: interaction_map = initialize_interactions_from_config(temp_config_path) # Check that names were generated correctly assert len(interaction_map) == 2 assert "base" in interaction_map # BaseInteraction -> base assert "gsm8k" in interaction_map # Gsm8kInteraction -> gsm8k finally: os.unlink(temp_config_path) ================================================ FILE: tests/kill_github_tests.sh ================================================ #!/bin/bash if [ "$#" -ne 1 ]; then echo "Usage: $0 YOUR_GITHUB_TOKEN" echo "Please provide exactly one input argument for your github token." exit 1 fi # Set your GitHub repository details OWNER="volcengine" REPO="verl" TOKEN=$1 # API URL for workflow runs API_URL="https://api.github.com/repos/$OWNER/$REPO/actions/runs?status=queued" # Check required commands command -v jq >/dev/null 2>&1 || { echo "jq is required but not installed. Aborting."; exit 1; } # Get queued workflow runs response=$(curl -s -H "Authorization: token $TOKEN" -H "Accept: application/vnd.github.v3+json" "$API_URL") # Run this for debugging # echo $response # Extract run IDs queued_run_ids=$(echo "$response" | jq -r '.workflow_runs[] | .id') if [ -z "$queued_run_ids" ]; then echo "No queued workflow runs found." exit 0 fi # Cancel each queued run for run_id in $queued_run_ids; do echo "Cancelling run $run_id" cancel_url="https://api.github.com/repos/$OWNER/$REPO/actions/runs/$run_id/cancel" curl -s -X POST -H "Authorization: token $TOKEN" -H "Accept: application/vnd.github.v3+json" "$cancel_url" done echo "Cancelled all queued workflow runs." ================================================ FILE: tests/models/test_engine.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os os.environ["NCCL_DEBUG"] = "WARN" from functools import partial import numpy as np import pytest import ray import torch import torch.distributed as dist import torch.multiprocessing as mp from transformers import ( AutoConfig, AutoModelForCausalLM, AutoModelForTokenClassification, AutoTokenizer, Qwen3Config, Qwen3MoeConfig, ) from verl import DataProto from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.trainer.config import CheckpointConfig from verl.utils import tensordict_utils as tu from verl.utils.model import compute_position_id_with_mask, create_random_mask from verl.utils.torch_functional import logprobs_from_logits_naive from verl.workers.config import ( ActorConfig, CriticConfig, FSDPEngineConfig, FSDPOptimizerConfig, HFModelConfig, McoreEngineConfig, McoreOptimizerConfig, ) from verl.workers.engine_workers import TrainingWorker, TrainingWorkerConfig from verl.workers.utils.losses import ppo_loss, sft_loss, value_loss from verl.workers.utils.padding import left_right_2_no_padding, no_padding_2_padding def get_test_language_model(device_count): if device_count == 1: model = "~/models/HuggingFaceTB/SmolLM2-135M-Instruct" else: model = "~/models/Qwen/Qwen2.5-0.5B" model = os.path.expanduser(model) return model def create_training_config(model_type, strategy, device_count, model): if device_count == 1: tp = pp = cp = fsdp_size = 1 else: tp = pp = cp = 2 fsdp_size = 4 path = os.path.expanduser(model) model_config = HFModelConfig(path=path, use_remove_padding=True) kwargs = dict( param_offload=True, optimizer_offload=True, grad_offload=True, use_dynamic_bsz=True, use_remove_padding=True, max_token_len_per_gpu=500, infer_max_token_len_per_gpu=1000, ) if strategy == "megatron": engine_config = McoreEngineConfig( forward_only=False, use_mbridge=True, tensor_model_parallel_size=tp, pipeline_model_parallel_size=pp, context_parallel_size=cp, **kwargs, ) optimizer_config = McoreOptimizerConfig(lr_decay_steps=10) elif strategy in ["fsdp", "fsdp2"]: engine_config = FSDPEngineConfig( forward_only=False, fsdp_size=fsdp_size, strategy=strategy, ulysses_sequence_parallel_size=cp, **kwargs ) optimizer_config = FSDPOptimizerConfig() else: raise NotImplementedError(f"strategy {strategy} is not supported") config = TrainingWorkerConfig( model_type=model_type, model_config=model_config, engine_config=engine_config, optimizer_config=optimizer_config, checkpoint_config=None, ) return config @pytest.mark.parametrize("strategy", ["fsdp", "fsdp2", "megatron"]) def test_actor_engine(strategy): ray.init() device_count = torch.cuda.device_count() config = create_training_config( model_type="language_model", strategy=strategy, device_count=device_count, model=get_test_language_model(device_count), ) ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(TrainingWorker), config=config) resource_pool = RayResourcePool(process_on_nodes=[device_count]) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init) # init model wg.reset() sft_loss_ = partial(sft_loss, config=config) wg.set_loss_fn(sft_loss_) batch_size = 8 seqlen = 32 response_length = seqlen // 2 torch.manual_seed(1) np.random.seed(1) input_ids = torch.randint(0, config.model_config.hf_config.vocab_size, (batch_size, seqlen)) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_valid_token=0.8, max_ratio_of_left_padding=0.2, min_ratio_of_valid_token=0.6 ) position_ids = compute_position_id_with_mask(attention_mask) global_token_num = torch.sum(attention_mask, dim=-1).tolist() print(input_ids.float().mean(), attention_mask.float().mean()) responses = input_ids[:, response_length:] response_mask = attention_mask[:, response_length:] assert torch.all(response_mask[:, 0] == 1) data = DataProto.from_single_dict( { "input_ids": input_ids, "prompts": input_ids[:, :response_length], "attention_mask": attention_mask, "position_ids": position_ids, "responses": responses, "response_mask": response_mask, }, meta_info={"temperature": 1.0, "global_token_num": global_token_num, "compute_loss": False}, ) data_td = data.to_tensordict() data_td = left_right_2_no_padding(data_td) # eval output = wg.infer_batch(data_td) output = output.get() logprobs_unpad = tu.get(output, "log_probs").cpu() logprobs = no_padding_2_padding(logprobs_unpad, data_td) output = DataProto.from_single_dict({"old_log_probs": logprobs}) # load hf model and compare results with hf model path = config.model_config.path hf_model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16) hf_output = hf_model(input_ids, attention_mask=attention_mask) hf_logprobs = logprobs_from_logits_naive( hf_output.logits[:, -response_length - 1 : -1, :].float(), input_ids[:, -response_length:] ) hf_logprobs_mean = torch.mean(hf_logprobs * response_mask) mcore_logprobs_mean = torch.mean(output.batch["old_log_probs"] * response_mask) torch.testing.assert_close(hf_logprobs_mean, mcore_logprobs_mean, atol=1e-3, rtol=1e-2) data = data.union(output) # TODO: sft_loss_ is not compatible with ActorWorker until we replace DataProto with torch.jagged TensorDict # wg.set_loss_fn(sft_loss_) # train for one step # metrics = wg.update_actor(data) # print(metrics) # add ppo data data.batch["advantages"] = torch.rand_like(responses, dtype=torch.float32) data.batch["ref_log_prob"] = torch.rand_like(responses, dtype=torch.float32) # construct actor config actor_config = ActorConfig(strategy=strategy, rollout_n=1, ppo_micro_batch_size_per_gpu=-1) # set ppo loss ppo_loss_ = partial(ppo_loss, config=actor_config) wg.set_loss_fn(ppo_loss_) # update again data_td = data.to_tensordict() data_td = left_right_2_no_padding(data_td) # auto load/offload tu.assign_non_tensor(data_td, global_batch_size=data_td.shape[0]) ppo_metrics = wg.train_batch(data_td) ppo_metrics = ppo_metrics.get() ppo_metrics = tu.get(ppo_metrics, "metrics") print(ppo_metrics) # test manual load/offload tu.assign_non_tensor(data_td, disable_auto_offload=True) wg.to("device") ppo_metrics = wg.train_batch(data_td) ppo_metrics = ppo_metrics.get() ppo_metrics = tu.get(ppo_metrics, "metrics") print(ppo_metrics) wg.to("cpu") ray.shutdown() def create_value_model(language_model_path, output_path): config = AutoConfig.from_pretrained(language_model_path) config.num_labels = 1 config.classifier_dropout = 0 config.tie_word_embeddings = False model = AutoModelForTokenClassification.from_config(config) tokenizer = AutoTokenizer.from_pretrained(os.path.expanduser(language_model_path)) assert model.config.num_labels == 1 path = os.path.expanduser(output_path) model.save_pretrained(path) tokenizer.save_pretrained(path) config.save_pretrained(path) return path @pytest.mark.parametrize("strategy", ["fsdp", "fsdp2"]) def test_critic_engine(strategy): device_count = torch.cuda.device_count() value_model_path = os.path.expanduser("~/models/test_model") language_model_path = get_test_language_model(device_count=device_count) create_value_model(language_model_path, value_model_path) torch.manual_seed(1) np.random.seed(1) ray.init() config = create_training_config( model_type="value_model", strategy=strategy, device_count=device_count, model=value_model_path ) ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(TrainingWorker), config=config) resource_pool = RayResourcePool(process_on_nodes=[device_count]) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init) # init model wg.reset() batch_size = 8 seqlen = 32 response_length = seqlen // 2 input_ids = torch.randint(0, config.model_config.hf_config.vocab_size, (batch_size, seqlen)) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_valid_token=0.8, max_ratio_of_left_padding=0.2, min_ratio_of_valid_token=0.6 ) position_ids = compute_position_id_with_mask(attention_mask) global_token_num = torch.sum(attention_mask, dim=-1).tolist() print(input_ids.float().mean(), attention_mask.float().mean()) responses = input_ids[:, response_length:] response_mask = attention_mask[:, response_length:] assert torch.all(response_mask[:, 0] == 1) data = DataProto.from_single_dict( { "input_ids": input_ids, "prompts": input_ids[:, :response_length], "attention_mask": attention_mask, "position_ids": position_ids, "responses": responses, "response_mask": response_mask, }, meta_info={"temperature": 1.0, "global_token_num": global_token_num, "compute_loss": False}, ) data_td = data.to_tensordict() data_td = left_right_2_no_padding(data_td) # eval output = wg.infer_batch(data_td) output = output.get() values_unpad = tu.get(output, "values").float().cpu() values = no_padding_2_padding(values_unpad, data_td) output = DataProto.from_single_dict({"values": values}) # load hf model and compare results with hf model with torch.device("cuda"), torch.autocast(device_type="cuda", dtype=torch.bfloat16): hf_model = AutoModelForTokenClassification.from_pretrained( value_model_path, torch_dtype=torch.float32, attn_implementation="flash_attention_2" ) hf_output = hf_model(input_ids.cuda(), attention_mask=attention_mask.cuda()) hf_values = hf_output.logits[:, -response_length - 1 : -1, :].float().squeeze(-1).cpu() hf_values_mean = torch.mean(hf_values * response_mask) engine_values = torch.mean(output.batch["values"] * response_mask) torch.testing.assert_close(hf_values_mean, engine_values, atol=1e-2, rtol=1e-2) data = data.union(output) # add ppo data data.batch["returns"] = torch.rand_like(responses, dtype=torch.float32) # update again # create critic config critic_config = CriticConfig( strategy=strategy, rollout_n=1, ppo_micro_batch_size_per_gpu=-1, model_config=config.model_config ) value_loss_ = partial(value_loss, config=critic_config) wg.set_loss_fn(value_loss_) # update again data_td = data.to_tensordict() data_td = left_right_2_no_padding(data_td) # auto load/offload tu.assign_non_tensor(data_td, global_batch_size=data_td.shape[0]) ppo_metrics = wg.train_batch(data_td) ppo_metrics = ppo_metrics.get() ppo_metrics = tu.get(ppo_metrics, "metrics") print(ppo_metrics) ray.shutdown() def create_actor_model(tmp_path, config): model = AutoModelForCausalLM.from_config(config) path = os.path.join(tmp_path, "test_model") model.save_pretrained(path) config.save_pretrained(path) return path def _worker(rank: int, world_size: int, rendezvous_file: str, strategy: str, model_path: str): torch.cuda.set_device(rank) dist.init_process_group( backend="nccl", init_method=f"file://{rendezvous_file}", rank=rank, world_size=world_size, ) ref_model_config = AutoConfig.from_pretrained(model_path) with torch.device("meta"): ref_model = AutoModelForCausalLM.from_config(ref_model_config) from verl.workers.engine import BaseEngine, EngineRegistry # construct configs model_config = HFModelConfig(path=model_path, load_tokenizer=False) if strategy == "megatron": engine_config = McoreEngineConfig( forward_only=False, use_mbridge=True, tensor_model_parallel_size=2, pipeline_model_parallel_size=2, context_parallel_size=1, ) optimizer_config = McoreOptimizerConfig(lr_decay_steps=10) elif strategy in ["fsdp", "fsdp2"]: engine_config = FSDPEngineConfig( forward_only=False, fsdp_size=4, strategy=strategy, ulysses_sequence_parallel_size=2 ) optimizer_config = FSDPOptimizerConfig() else: raise NotImplementedError(f"strategy {strategy} is not supported") checkpoint_config = CheckpointConfig() # build model engine engine: BaseEngine = EngineRegistry.new( model_type="language_model", backend=engine_config.strategy, model_config=model_config, engine_config=engine_config, optimizer_config=optimizer_config, checkpoint_config=checkpoint_config, ) engine.initialize() # get per tensor parameter per_tensor_params, _ = engine.get_per_tensor_param() ref_state_dict = ref_model.state_dict() # load ground truth and compare for key, value in per_tensor_params: assert key in ref_state_dict, f"{key} not in ref_state_dict" assert value.shape == ref_state_dict[key].shape, ( f"{key} shape not equal, {value.shape} != {ref_state_dict[key].shape}" ) if rank == 0: print(key, value.shape) dist.barrier() dist.destroy_process_group() @pytest.mark.parametrize("world_size", [8]) @pytest.mark.parametrize("config", [Qwen3Config(num_hidden_layers=2), Qwen3MoeConfig(num_hidden_layers=2)]) @pytest.mark.parametrize("strategy", ["megatron", "fsdp", "fsdp2"]) def test_per_tensor_generator(world_size, tmp_path, config, strategy): rendezvous_file = str(tmp_path / "rdzv_mask") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) # create a model model_path = create_actor_model(tmp_path, config) # spawn workers mp.spawn( fn=_worker, args=(world_size, rendezvous_file, strategy, model_path), nprocs=world_size, join=True, ) ================================================ FILE: tests/models/test_tiled_mlp_accuracy.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test script to verify TiledMLP accuracy by comparing logits and gradients between regular MLP and TiledMLP under FSDP2. Run with: torchrun --nproc_per_node=2 tests/test_tiled_mlp_accuracy.py """ import torch import torch.distributed as dist from torch.distributed.device_mesh import init_device_mesh from torch.distributed.fsdp import fully_shard def setup_distributed(): dist.init_process_group(backend="nccl") rank = dist.get_rank() world_size = dist.get_world_size() torch.cuda.set_device(rank) return rank, world_size def create_model(model_name="Qwen/Qwen3-1.7B", num_layers=2): """Load a Qwen3-1.7B model with only 2 layers from pretrained weights.""" from transformers import AutoConfig, AutoModelForCausalLM config = AutoConfig.from_pretrained(model_name, trust_remote_code=True) config.num_hidden_layers = num_layers model = AutoModelForCausalLM.from_pretrained( model_name, config=config, torch_dtype=torch.bfloat16, trust_remote_code=True, attn_implementation="flash_attention_2", ) return model def apply_fsdp2(model, device_mesh): """Apply FSDP2 sharding to model.""" for layer in model.model.layers: fully_shard(layer, mesh=device_mesh) fully_shard(model, mesh=device_mesh) return model def run_forward_backward(model, input_ids, labels): """Run forward and backward pass, return logits and gradients.""" model.zero_grad() outputs = model(input_ids=input_ids, labels=labels) logits = outputs.logits.clone().detach() loss = outputs.loss loss.backward() # Collect MLP gradients gradients = {} for name, param in model.named_parameters(): if "mlp" in name and param.grad is not None: gradients[name] = param.grad.clone().detach() return logits, gradients, loss.item() def compare_results(logits1, grads1, logits2, grads2, rank): """Compare logits and gradients between two runs.""" # Compare logits logits_diff = (logits1 - logits2).abs() logits_max_diff = logits_diff.max().item() logits_mean_diff = logits_diff.mean().item() # Compare gradients (only for params that exist on this rank due to FSDP sharding) all_pass = True grad_results = [] for name in sorted(grads1.keys()): if name in grads2: g1, g2 = grads1[name], grads2[name] diff = (g1 - g2).abs() max_diff = diff.max().item() mean_diff = diff.mean().item() # Check if within tolerance (1e-2 for bf16) passed = max_diff < 1e-2 if not passed: all_pass = False grad_results.append((name, max_diff, mean_diff, passed)) # Only print on rank 0 to avoid duplicate output if rank == 0: print("\n=== Comparison Results ===") print("\nLogits:") print(f" Max diff: {logits_max_diff:.2e}") print(f" Mean diff: {logits_mean_diff:.2e}") print("\nMLP Parameter Gradients:") if grad_results: for name, max_diff, mean_diff, passed in grad_results: status = "✓" if passed else "✗" print(f" {name}: max={max_diff:.2e}, mean={mean_diff:.2e} {status}") else: print(" (Gradients sharded to other ranks under FSDP2)") return all_pass def main(): rank, world_size = setup_distributed() device_mesh = init_device_mesh("cuda", (world_size,)) model_name = "Qwen/Qwen3-1.7B" num_layers = 2 if rank == 0: print(f"Running TiledMLP accuracy test with {world_size} GPUs") print(f"Model: {model_name} ({num_layers} layers, from pretrained)") dist.barrier() # ========== Create Model 1: WITHOUT TiledMLP ========== if rank == 0: print("\n" + "=" * 60) print("Creating Model 1 (without TiledMLP)") print("=" * 60) model1 = create_model(model_name, num_layers) model1 = apply_fsdp2(model1, device_mesh) model1 = model1.cuda() # Create deterministic input torch.manual_seed(42) batch_size, seq_len = 2, 256 vocab_size = model1.config.vocab_size input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device="cuda") labels = input_ids.clone() # ========== Run Model 1: WITHOUT TiledMLP ========== if rank == 0: print("\n" + "=" * 60) print("Running forward/backward on Model 1 (without TiledMLP)") print("=" * 60) logits1, grads1, loss1 = run_forward_backward(model1, input_ids, labels) if rank == 0: print(f"Loss: {loss1:.4f}") # Free model1 memory before creating model2 del model1 torch.cuda.empty_cache() dist.barrier() # ========== Create Model 2, apply TiledMLP patch, then FSDP2 ========== if rank == 0: print("\n" + "=" * 60) print("Creating Model 2 (with TiledMLP, patch before FSDP2)") print("=" * 60) model2 = create_model(model_name, num_layers) # Apply TiledMLP patch AFTER model instantiation but BEFORE FSDP2 wrap if rank == 0: print("Applying TiledMLP monkey patch before FSDP2...") from verl.models.transformers.tiled_mlp import apply_tiled_mlp_monkey_patch apply_tiled_mlp_monkey_patch(num_shards=4, model_type="qwen3") model2 = apply_fsdp2(model2, device_mesh) model2 = model2.cuda() dist.barrier() # ========== Run Model 2: WITH TiledMLP ========== if rank == 0: print("\n" + "=" * 60) print("Running forward/backward on Model 2 (with TiledMLP)") print("=" * 60) logits2, grads2, loss2 = run_forward_backward(model2, input_ids, labels) if rank == 0: print(f"Loss: {loss2:.4f}") dist.barrier() # ========== Compare Results ========== all_pass = compare_results(logits1, grads1, logits2, grads2, rank) dist.barrier() if rank == 0: print("\n" + "=" * 60) print("SUMMARY") print("=" * 60) print(f"Loss diff: {abs(loss1 - loss2):.2e}") print(f"All gradient checks: {'PASS' if all_pass else 'FAIL'}") # Cleanup del model2 torch.cuda.empty_cache() dist.destroy_process_group() if __name__ == "__main__": main() ================================================ FILE: tests/models/test_transformer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from transformers import ( ApertusConfig, AutoModelForCausalLM, AutoModelForTokenClassification, GemmaConfig, LlamaConfig, MistralConfig, Qwen2Config, ) from verl.utils.device import get_device_name if get_device_name() == "cuda": from flash_attn.bert_padding import index_first_axis, pad_input, rearrange, unpad_input elif get_device_name() == "npu": from verl.utils.attention_utils import index_first_axis, pad_input, rearrange, unpad_input from verl.utils.model import compute_position_id_with_mask, create_random_mask from verl.utils.torch_functional import log_probs_from_logits_all_rmpad, masked_mean # TODO(sgm): add more models for test # we only need one scale for each model test_configs = [ LlamaConfig(num_hidden_layers=1), MistralConfig(num_hidden_layers=1), GemmaConfig(num_hidden_layers=1), Qwen2Config(num_hidden_layers=1), ApertusConfig(num_hidden_layers=1), ] def test_hf_casual_models(): batch_size = 4 seqlen = 128 response_length = 127 for config in test_configs: # config = AutoConfig.from_pretrained(test_case) with torch.device(get_device_name()): model = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model = model.to(device=get_device_name()) input_ids = torch.randint(low=0, high=config.vocab_size, size=(batch_size, seqlen), device=get_device_name()) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0.1, max_ratio_of_valid_token=0.8, min_ratio_of_valid_token=0.5, ) position_ids = compute_position_id_with_mask( attention_mask ) # TODO(sgm): we can construct the position_ids_rmpad here input_ids_rmpad, indices, *_ = unpad_input( input_ids.unsqueeze(-1), attention_mask ) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary position_ids_rmpad = index_first_axis( rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices ).transpose(0, 1) # input with input_ids_rmpad and postition_ids to enable flash attention varlen logits_rmpad = model( input_ids_rmpad, position_ids=position_ids_rmpad, use_cache=False ).logits # (1, total_nnz, vocab_size) origin_logits = model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, use_cache=False ).logits origin_logits_rmpad, origin_logits_indices, *_ = unpad_input(origin_logits, attention_mask) logits_rmpad = logits_rmpad.squeeze(0) log_probs = log_probs_from_logits_all_rmpad( input_ids_rmpad=input_ids_rmpad, logits_rmpad=logits_rmpad, indices=indices, batch_size=batch_size, seqlen=seqlen, response_length=response_length, ) # (batch, seqlen) origin_log_probs = log_probs_from_logits_all_rmpad( input_ids_rmpad=input_ids_rmpad, logits_rmpad=origin_logits_rmpad, indices=origin_logits_indices, batch_size=batch_size, seqlen=seqlen, response_length=response_length, ) # (batch, seqlen) torch.testing.assert_close( masked_mean(log_probs, attention_mask[:, -response_length - 1 : -1]), masked_mean(origin_log_probs, attention_mask[:, -response_length - 1 : -1]), atol=1e-2, rtol=1e-5, ) print("Check pass") def test_hf_value_models(): batch_size = 4 seqlen = 128 for config in test_configs: # config = AutoConfig.from_pretrained(test_case) config.num_labels = 1 config.classifier_dropout = 0 config.hidden_dropout = 0 with torch.device(get_device_name()): model = AutoModelForTokenClassification.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model = model.to(device=get_device_name()) input_ids = torch.randint(low=0, high=config.vocab_size, size=(batch_size, seqlen), device=get_device_name()) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0.1, max_ratio_of_valid_token=0.8, min_ratio_of_valid_token=0.5, ) position_ids = compute_position_id_with_mask( attention_mask ) # TODO(sgm): we can construct the position_ids_rmpad here input_ids_rmpad, indices, *_ = unpad_input( input_ids.unsqueeze(-1), attention_mask ) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary position_ids_rmpad = index_first_axis( rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices ).transpose(0, 1) origin_logits = model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, use_cache=False ).logits # input with input_ids_rmpad and postition_ids to enable flash attention varlen rmpad_logits = model( input_ids_rmpad, position_ids=position_ids_rmpad, use_cache=False ).logits # (1, total_nnz, 1) rmpad_logits = rmpad_logits.squeeze(0) pad_logits = pad_input(rmpad_logits, indices, batch_size, seqlen=seqlen) torch.testing.assert_close( masked_mean(pad_logits, attention_mask[:, :, None]), masked_mean(origin_logits, attention_mask[:, :, None]), atol=1e-2, rtol=1e-5, ) print("Value model check pass") def test_attn_implementation_override(): """Test that attn_implementation override config is properly respected.""" # Test case 1: Test the actual extraction logic (no network required) test_cases = [ ({}, "flash_attention_2"), # Default case ({"attn_implementation": "eager"}, "eager"), # Override case ({"attn_implementation": "sdpa"}, "sdpa"), # Another override ({"other_config": "value"}, "flash_attention_2"), # No attn_implementation key ] for override_config, expected in test_cases: actual = override_config.get("attn_implementation", "flash_attention_2") assert actual == expected, f"Expected {expected}, got {actual} for config {override_config}" # Test case 2: Test with local config creation (simulate FSDP worker behavior) # Test default behavior override_config_default = {} attn_implementation_default = override_config_default.get("attn_implementation", "flash_attention_2") assert attn_implementation_default == "flash_attention_2" # Test override behavior override_config_eager = {"attn_implementation": "eager"} attn_implementation_eager = override_config_eager.get("attn_implementation", "flash_attention_2") assert attn_implementation_eager == "eager" # Test that we can create a config with specific attn_implementation config_with_eager = LlamaConfig(num_hidden_layers=1, _attn_implementation="eager") assert config_with_eager._attn_implementation == "eager" config_with_flash = LlamaConfig(num_hidden_layers=1, _attn_implementation="flash_attention_2") assert config_with_flash._attn_implementation == "flash_attention_2" print("✓ All attn_implementation override config tests passed") def test_fsdp_worker_attn_implementation_integration(): """Test integration of attn_implementation with FSDP worker logic.""" # Mock the FSDP worker configuration scenario mock_override_config = {"attn_implementation": "eager"} # Test the exact logic used in FSDP workers attn_implementation = mock_override_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "eager" # Test with empty config (should default) mock_override_config_empty = {} attn_implementation_default = mock_override_config_empty.get("attn_implementation", "flash_attention_2") assert attn_implementation_default == "flash_attention_2" # Test that the parameter would be passed correctly to both AutoConfig and Model expected_calls = [ ("AutoConfig.from_pretrained", {"attn_implementation": attn_implementation}), ("AutoModel.from_pretrained", {"attn_implementation": attn_implementation}), ] # Verify the parameter extraction works as expected for call_name, expected_params in expected_calls: assert expected_params["attn_implementation"] == "eager" print("✓ FSDP worker integration test passed") if __name__ == "__main__": test_hf_casual_models() test_hf_value_models() test_attn_implementation_override() test_fsdp_worker_attn_implementation_integration() ================================================ FILE: tests/models/test_transformers_ulysses.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import copy from dataclasses import dataclass import pytest import torch import torch.distributed import transformers from packaging import version from torch.distributed import init_device_mesh from transformers import AutoModelForCausalLM, LlamaConfig, PretrainedConfig, Qwen2Config from verl.models.transformers.monkey_patch import apply_monkey_patch from verl.protocol import DataProto from verl.utils.device import get_device_name, get_torch_device from verl.utils.distributed import initialize_global_process_group from verl.utils.model import compute_position_id_with_mask, create_random_mask from verl.utils.ulysses import ( gather_outputs_and_unpad, get_ulysses_sequence_parallel_world_size, set_ulysses_sequence_parallel_group, ulysses_pad_and_slice_inputs, ) from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager if get_device_name() == "cuda": from flash_attn.bert_padding import index_first_axis, rearrange, unpad_input elif get_device_name() == "npu": from verl.utils.attention_utils import index_first_axis, rearrange, unpad_input # TODO(sgm): add more models for test # we only need one scale for each model @dataclass class SequenceParallelConfig: config: PretrainedConfig sp_size: int is_valid: bool def test_configs(): configs = [ SequenceParallelConfig( LlamaConfig(num_hidden_layers=2, num_attention_heads=32, num_key_value_heads=32), sp_size=8, is_valid=True ), SequenceParallelConfig( Qwen2Config(num_hidden_layers=2, num_attention_heads=28, num_key_value_heads=4, hidden_size=3584), sp_size=4, is_valid=True, ), SequenceParallelConfig( Qwen2Config(num_hidden_layers=2, num_attention_heads=28, num_key_value_heads=4, hidden_size=3584), sp_size=8, is_valid=False, ), SequenceParallelConfig( Qwen2Config(num_hidden_layers=2, num_attention_heads=32, num_key_value_heads=4), sp_size=4, is_valid=True ), SequenceParallelConfig( Qwen2Config(num_hidden_layers=2, num_attention_heads=32, num_key_value_heads=4), sp_size=8, is_valid=True ), ] if version.parse(transformers.__version__) >= version.parse("4.56.0"): from transformers import ApertusConfig configs.append( SequenceParallelConfig( ApertusConfig(num_hidden_layers=2, num_attention_heads=32, num_key_value_heads=32, hidden_size=4096), sp_size=8, is_valid=True, ) ) return configs def sync_model_parameters_global(layer): # synchronize weights for p in layer.parameters(): torch.distributed.broadcast(tensor=p.data, src=0) @pytest.mark.parametrize("test_config", test_configs()) def test_hf_casual_fwd_bwd(test_config): if not torch.distributed.is_initialized(): initialize_global_process_group() context = contextlib.nullcontext() if test_config.is_valid else pytest.raises(AssertionError) with context: world_size = torch.distributed.get_world_size() _hf_casual_fwd_bwd(test_config.config, test_config.sp_size, world_size // test_config.sp_size) # TODO: seems not work, will cause `socketStartConnect: Connect to xxx failed : Software caused connection abort` # torch.distributed.destroy_process_group() def _hf_casual_fwd(config, sp_size, dp_size): assert get_torch_device().device_count() >= 2, "need at least 2 gpus for test" ulysses_device_mesh = init_device_mesh( device_type=get_device_name(), mesh_shape=(dp_size, sp_size), mesh_dim_names=("dp", "sp") ) sharding_manager = FSDPUlyssesShardingManager(ulysses_device_mesh) batch_size = 1 seqlen = 128 # response_length = 127 # patch before load with torch.device(get_device_name()): model = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) apply_monkey_patch(model, sp_size) model = model.to(device=get_device_name()) sync_model_parameters_global(model) # different rank will generate different input_ids following fsdp input_ids = torch.randint(low=0, high=config.vocab_size, size=(batch_size, seqlen), device=get_device_name()) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0, max_ratio_of_valid_token=0.9, min_ratio_of_valid_token=0.8 ) position_ids = compute_position_id_with_mask( attention_mask ) # TODO(sgm): we can construct the position_ids_rmpad here model_inputs = { "input_ids": input_ids.to(get_device_name()), "attention_mask": attention_mask.to(get_device_name()), "position_ids": position_ids.int().to(get_device_name()), } model_inputs = DataProto.from_dict(model_inputs) # 1. perform ulysses forward with sharding_manager: model_inputs = sharding_manager.preprocess_data(model_inputs) input_ids = model_inputs.batch["input_ids"] attention_mask = model_inputs.batch["attention_mask"] position_ids = model_inputs.batch["position_ids"] input_ids_rmpad, indices, *_ = unpad_input( input_ids.unsqueeze(-1), attention_mask ) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary position_ids_rmpad = index_first_axis( rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices ).transpose(0, 1) # slice input tensor for ulysses # input_ids are padded and sliced # postition_ids are only padded but not sliced input_ids_rmpad_sliced, position_ids_rmpad_padded, pad_size = ulysses_pad_and_slice_inputs( input_ids_rmpad, position_ids_rmpad, sp_size=get_ulysses_sequence_parallel_world_size() ) # input with input_ids_rmpad and postition_ids to enable flash attention varlen logits_split_in_seq = model( input_ids_rmpad_sliced, position_ids=position_ids_rmpad_padded, use_cache=False ).logits # (1, total_nnz/n, vocab_size) # all_gather output logits_full = gather_outputs_and_unpad(logits_split_in_seq, gather_dim=1, unpad_dim=1, padding_size=pad_size) # 2. perform normal forward set_ulysses_sequence_parallel_group(None) logits_rmpad_local = model( input_ids_rmpad, position_ids=position_ids_rmpad, use_cache=False ).logits # (1, total_nnz, vocab_size) mean_local = logits_rmpad_local.mean() mean_full = logits_full.mean() torch.testing.assert_close(mean_local, mean_full, rtol=1e-2, atol=1e-5) def _hf_casual_fwd_bwd(config, sp_size, dp_size): assert get_torch_device().device_count() >= 2, "need at least 2 gpus for test" ulysses_device_mesh = init_device_mesh( device_type=get_device_name(), mesh_shape=(dp_size, sp_size), mesh_dim_names=("dp", "sp") ) sharding_manager = FSDPUlyssesShardingManager(ulysses_device_mesh) batch_size = 1 seqlen = 128 # response_length = 127 # patch before load with torch.device(get_device_name()): model = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) apply_monkey_patch(model, sp_size) model = model.to(device=get_device_name()) sync_model_parameters_global(model) # different rank will generate different input_ids following fsdp input_ids = torch.randint(low=0, high=config.vocab_size, size=(batch_size, seqlen), device=get_device_name()) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0, max_ratio_of_valid_token=0.9, min_ratio_of_valid_token=0.8 ) position_ids = compute_position_id_with_mask( attention_mask ) # TODO(sgm): we can construct the position_ids_rmpad here model_inputs = { "input_ids": input_ids.to(get_device_name()), "attention_mask": attention_mask.to(get_device_name()), "position_ids": position_ids.int().to(get_device_name()), } model_inputs = DataProto.from_dict(model_inputs) # 1. perform ulysses forward with sharding_manager: model_inputs = sharding_manager.preprocess_data(model_inputs) input_ids = model_inputs.batch["input_ids"] attention_mask = model_inputs.batch["attention_mask"] position_ids = model_inputs.batch["position_ids"] input_ids_rmpad, indices, *_ = unpad_input( input_ids.unsqueeze(-1), attention_mask ) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary position_ids_rmpad = index_first_axis( rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices ).transpose(0, 1) # slice input tensor for ulysses # input_ids are padded and sliced # postition_ids are only padded but not sliced input_ids_rmpad_sliced, position_ids_rmpad_padded, pad_size = ulysses_pad_and_slice_inputs( input_ids_rmpad, position_ids_rmpad, sp_size=get_ulysses_sequence_parallel_world_size() ) # input with input_ids_rmpad and postition_ids to enable flash attention varlen logits_split_in_seq = model( input_ids_rmpad_sliced, position_ids=position_ids_rmpad_padded, use_cache=False ).logits # (1, total_nnz/n, vocab_size) # all_gather output logits_full = gather_outputs_and_unpad(logits_split_in_seq, gather_dim=1, unpad_dim=1, padding_size=pad_size) # 2. perform normal forward set_ulysses_sequence_parallel_group(None) input_ids_full = copy.deepcopy(input_ids_rmpad) position_ids_full = copy.deepcopy(position_ids_rmpad) model_no_sp = copy.deepcopy(model) logits_rmpad_local = model_no_sp( input_ids_full, position_ids=position_ids_full, use_cache=False ).logits # (1, total_nnz, vocab_size) mean_local = logits_rmpad_local.mean() mean_full = logits_full.mean() mean_full.backward() mean_local.backward() # 3. check the gradients grad = model.model.layers[0].self_attn.q_proj.weight.grad grad_full = model_no_sp.model.layers[0].self_attn.q_proj.weight.grad torch.testing.assert_close(mean_local, mean_full, rtol=1e-2, atol=3e-5) # The check should be less strict because the gradient is not an averaged value. torch.testing.assert_close(grad, grad_full, rtol=1e-2, atol=1e-3) if __name__ == "__main__": pytest.main([__file__, "-svv"]) ================================================ FILE: tests/single_controller/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/single_controller/base/test_decorator.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import verl.single_controller.base.decorator as decorator_module from verl.single_controller.base.decorator import ( DISPATCH_MODE_FN_REGISTRY, Dispatch, _check_dispatch_mode, get_predefined_dispatch_fn, register_dispatch_mode, update_dispatch_mode, ) @pytest.fixture def reset_dispatch_registry(): # Store original state original_registry = DISPATCH_MODE_FN_REGISTRY.copy() yield # Reset registry after test decorator_module.DISPATCH_MODE_FN_REGISTRY.clear() decorator_module.DISPATCH_MODE_FN_REGISTRY.update(original_registry) def test_register_new_dispatch_mode(reset_dispatch_registry): # Test registration def dummy_dispatch(worker_group, *args, **kwargs): return args, kwargs def dummy_collect(worker_group, output): return output register_dispatch_mode("TEST_MODE", dummy_dispatch, dummy_collect) # Verify enum extension _check_dispatch_mode(Dispatch.TEST_MODE) # Verify registry update assert get_predefined_dispatch_fn(Dispatch.TEST_MODE) == { "dispatch_fn": dummy_dispatch, "collect_fn": dummy_collect, } # Clean up Dispatch.remove("TEST_MODE") def test_update_existing_dispatch_mode(reset_dispatch_registry): # Store original implementation original_mode = Dispatch.ONE_TO_ALL # New implementations def new_dispatch(worker_group, *args, **kwargs): return args, kwargs def new_collect(worker_group, output): return output # Test update= update_dispatch_mode(original_mode, new_dispatch, new_collect) # Verify update assert get_predefined_dispatch_fn(original_mode)["dispatch_fn"] == new_dispatch assert get_predefined_dispatch_fn(original_mode)["collect_fn"] == new_collect ================================================ FILE: tests/single_controller/check_worker_alive/main.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys import time import ray from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.base.worker import Worker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup @ray.remote class TestActor(Worker): def __init__(self) -> None: super().__init__() @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) def foo(self, wait_time): time.sleep(wait_time) sys.exit(1) if __name__ == "__main__": wait_time = int(os.getenv("WAIT_TIME", "10")) ray.init() # test single-node-no-partition print("test single-node-no-partition") resource_pool = RayResourcePool([2], use_gpu=False) class_with_args = RayClassWithInitArgs(cls=TestActor) print("create worker group") wg = RayWorkerGroup(resource_pool, class_with_args, name_prefix="test") wg.start_worker_aliveness_check(1) time.sleep(1) print(time.time(), "start foo") _ = wg.foo(wait_time) print("foo started") print( time.time(), f"wait 6x wait time {wait_time * 6} to let signal returned to process but still not exceed process wait time", ) time.sleep(wait_time * 6) ray.shutdown() ================================================ FILE: tests/single_controller/detached_worker/README.md ================================================ # Detached Worker ## How to run (Only on a single node) - Start a local ray cluster: ```bash ray start --head --port=6379 ``` - Run the server ```bash python3 server.py ``` - On another terminal, Run the client ```bash python3 client.py ``` ================================================ FILE: tests/single_controller/detached_worker/client.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ In client, we can get the server handler and send RPC request """ import ray import torch from server import Trainer from tensordict import TensorDict from verl import DataProto from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup def compute_position_id_with_mask(mask): return torch.clip(torch.cumsum(mask, dim=-1) - 1, min=0, max=None) if __name__ == "__main__": ray.init(address="auto", namespace="verl") # get the worker group using names worker_names = ["trainerTrainer_0:0", "trainerTrainer_0:1"] cls_with_init_args = RayClassWithInitArgs(cls=Trainer) worker_group = RayWorkerGroup.from_detached(worker_names=worker_names, ray_cls_with_init=cls_with_init_args) batch_size = 16 sequence_length = 1024 # give Trainer some data to train input_ids = torch.randint(low=0, high=256, size=(batch_size, sequence_length), dtype=torch.int64, device="cuda") attention_mask = torch.ones_like(input_ids) position_ids = compute_position_id_with_mask(attention_mask) data = DataProto( batch=TensorDict( {"input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids}, batch_size=batch_size, ), meta_info={}, ) output = worker_group.train_model(data) print(output) ================================================ FILE: tests/single_controller/detached_worker/run.sh ================================================ #!/bin/bash ray start --head --port=6379 python3 server.py python3 client.py ray stop --force ================================================ FILE: tests/single_controller/detached_worker/server.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Server starts a Trainer. Client sends data to the server to train. """ import os os.environ["MEGATRON_USE_CUDA_TIMER"] = "0" os.environ["MEGATRON_START_PROCESS_TIMER"] = "False" os.environ["NCCL_DEBUG"] = "WARN" import ray import torch from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core.models.gpt.gpt_model import ModelType from omegaconf import OmegaConf from tensordict import TensorDict from torch import nn from transformers import LlamaConfig from verl import DataProto from verl.models.llama.megatron import ParallelLlamaForCausalLMRmPadPP from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.megatron.optimizer import get_megatron_optimizer, init_megatron_optim_config from verl.utils.megatron_utils import get_model, mcore_model_parallel_config @ray.remote class Trainer(Worker): def __init__(self): super().__init__() if not torch.distributed.is_initialized(): rank = int(os.environ["LOCAL_RANK"]) torch.distributed.init_process_group(backend="nccl") torch.cuda.set_device(rank) mpu.initialize_model_parallel( tensor_model_parallel_size=2, pipeline_model_parallel_size=1, virtual_pipeline_model_parallel_size=None, use_sharp=False, context_parallel_size=1, expert_model_parallel_size=1, nccl_communicator_config_path=None, ) tensor_parallel.model_parallel_cuda_manual_seed(10) is_collect = ( mpu.get_tensor_model_parallel_rank() == 0 and mpu.get_pipeline_model_parallel_rank() == mpu.get_pipeline_model_parallel_world_size() - 1 and mpu.get_context_parallel_rank() == 0 ) self._register_dispatch_collect_info( mesh_name="train", dp_rank=mpu.get_data_parallel_rank(), is_collect=is_collect ) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): actor_model_config = LlamaConfig( vocab_size=256, hidden_size=2048, intermediate_size=5504, num_hidden_layers=24, num_attention_heads=16, num_key_value_heads=16, ) megatron_config = mcore_model_parallel_config(sequence_parallel=True, params_dtype=torch.bfloat16) self.megatron_config = megatron_config def megatron_actor_model_provider(pre_process, post_process): # vpp is not supported yet because it will hang for some reason. Need debugging # this_megatron_config = copy.deepcopy(megatron_config) # this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank parallel_model = ParallelLlamaForCausalLMRmPadPP( config=actor_model_config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process, ) parallel_model.cuda() return parallel_model actor_module = get_model( model_provider_func=megatron_actor_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True, ) actor_module = nn.ModuleList(actor_module) optim_config = OmegaConf.create({"lr": 1e-6, "clip_grad": 1.0}) optim_config = init_megatron_optim_config(optim_config) self.optimizer_config = optim_config actor_optimizer = get_megatron_optimizer(model=actor_module, config=optim_config) self.model = actor_module[0] self.optimizer = actor_optimizer @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train")) def train_model(self, data: DataProto) -> DataProto: input_ids = data.batch["input_ids"] attention_mask = data.batch["attention_mask"] position_ids = data.batch["position_ids"] self.optimizer.zero_grad() self.model.zero_grad_buffer( zero_buffer=(not self.optimizer_config.use_distributed_optimizer) ) # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm # update for 1 iteration output = self.model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids).logits output.mean().backward() update_successful, grad_norm, num_zeros_in_grad = self.optimizer.step( self.megatron_config, self.megatron_config.timers ) return DataProto(batch=TensorDict({"loss": output.detach()}, batch_size=output.shape[0])) if __name__ == "__main__": ray.init(address="auto", namespace="verl") resource_pool = RayResourcePool(process_on_nodes=[2], detached=True) cls_with_init_args = RayClassWithInitArgs(cls=Trainer) worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=cls_with_init_args, name_prefix="trainer", detached=True, ) worker_group.init_model() worker_names = worker_group.worker_names print(worker_names) ================================================ FILE: tests/single_controller/test_auto_padding_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import ray import torch from verl import DataProto from verl.protocol import DataProtoConfig from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup # or set env var VERL_AUTO_PADDING = "1" / "true" DataProtoConfig.auto_padding = True @ray.remote class Actor(Worker): def __init__(self) -> None: super().__init__() @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def add(self, data: DataProto): data.batch["a"] += self.rank return data def test_auto_padding(): ray.init(num_cpus=100) chunk_size = 4 actor_cls = RayClassWithInitArgs(cls=Actor) resource_pool = RayResourcePool(process_on_nodes=[chunk_size], use_gpu=False) actor_wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=actor_cls) # test locally first for test_size in range(4, 20): local_data = DataProto.from_dict({"a": torch.zeros(test_size)}, {"na": np.zeros(test_size, dtype=object)}) # print(f"before padding, local_data = {local_data}") padding_size = (chunk_size - (test_size % chunk_size)) if (test_size % chunk_size > 0) else 0 local_data.padding(padding_size) # print(f"after padding, local_data = {local_data}") assert len(local_data) == len(local_data) + len(local_data) % chunk_size, ( f"expecting padded length to be {len(local_data) + len(local_data) % chunk_size}, but got {len(local_data)}" ) chunked = local_data.chunk(chunk_size) assert len(chunked) == chunk_size, f"during test_size = {test_size}, expecting {chunk_size}, got {chunked}" for dp in chunked: assert len(dp) == test_size // chunk_size + bool(test_size % chunk_size), ( f"test size = {test_size}, expecting dp to be length of " f"{test_size // chunk_size + bool(test_size % chunk_size)}, but got {len(dp)}: {dp} {chunked}" ) # test with RayWorkerGroup method decorated as dispatch_mode=Dispatch.DP_COMPUTE_PROTO data = DataProto.from_dict({"a": torch.zeros(10)}, {"na": np.array([str(i) for i in range(10)], dtype=object)}) output = actor_wg.add(data) print(output.batch["a"]) assert len(output) == 10, "Failed in args split and padding." data = DataProto.from_dict({"a": torch.zeros(10)}, {"na": np.array([str(i) for i in range(10)], dtype=object)}) output = actor_wg.add(data=data) print(output.batch["a"]) assert len(output) == 10, "Failed in kwargs split and padding." data = DataProto.from_dict({"a": torch.zeros(1)}, {"na": np.array([str(i) for i in range(1)], dtype=object)}) output = actor_wg.add(data) print(output.batch["a"]) assert len(output) == 1, "Failed in args split and padding." data = DataProto.from_dict({"a": torch.zeros(1)}, {"na": np.array([str(i) for i in range(1)], dtype=object)}) output = actor_wg.add(data=data) print(output.batch["a"]) assert len(output) == 1, "Failed in kwargs split and padding." data = DataProto.from_dict({"a": torch.zeros(8)}, {"na": np.array([str(i) for i in range(8)], dtype=object)}) output = actor_wg.add(data) print(output.batch["a"]) assert len(output) == 8, "Failed in args split and padding." data = DataProto.from_dict({"a": torch.zeros(8)}, {"na": np.array([str(i) for i in range(8)], dtype=object)}) output = actor_wg.add(data=data) print(output.batch["a"]) assert len(output) == 8, "Failed in kwargs split and padding." # test data proto specific config DataProtoConfig.auto_padding = False data = DataProto.from_dict( {"a": torch.zeros(10)}, {"na": np.array([str(i) for i in range(10)], dtype=object)}, auto_padding=True ) output = actor_wg.add(data) print(output.batch["a"]) assert len(output) == 10, "Failed in args split and padding." data = DataProto.from_dict( {"a": torch.zeros(10)}, {"na": np.array([str(i) for i in range(10)], dtype=object)}, auto_padding=True ) output = actor_wg.add(data=data) print(output.batch["a"]) assert len(output) == 10, "Failed in kwargs split and padding." data = DataProto.from_single_dict( {"a": torch.zeros(1), "na": np.array([str(i) for i in range(1)], dtype=object)}, auto_padding=True ) output = actor_wg.add(data) print(output.batch["a"]) assert len(output) == 1, "Failed in args split and padding." data = DataProto.from_single_dict( {"a": torch.zeros(1), "na": np.array([str(i) for i in range(1)], dtype=object)}, auto_padding=True ) output = actor_wg.add(data=data) print(output.batch["a"]) assert len(output) == 1, "Failed in kwargs split and padding." data = DataProto.from_single_dict({"a": torch.zeros(8), "na": np.array([str(i) for i in range(8)], dtype=object)}) output = actor_wg.add(data) print(output.batch["a"]) assert len(output) == 8, "Failed in args split and padding." data = DataProto.from_single_dict({"a": torch.zeros(8), "na": np.array([str(i) for i in range(8)], dtype=object)}) output = actor_wg.add(data=data) print(output.batch["a"]) assert len(output) == 8, "Failed in kwargs split and padding." ray.shutdown() if __name__ == "__main__": test_auto_padding() ================================================ FILE: tests/single_controller/test_colocated_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray.base import ( RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, create_colocated_worker_cls, ) from verl.utils.device import get_device_name @ray.remote class Actor(Worker): def __init__(self) -> None: super().__init__() @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def add(self, data: DataProto): data.batch["a"] += self.rank return data @ray.remote class Critic(Worker): def __init__(self, config) -> None: super().__init__() self.config = config @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) async def sub(self, data: DataProto): data.batch["a"] -= self.config["b"] return data def test_colocated_workers(): ray.init() import torch data = DataProto.from_dict({"a": torch.zeros(10)}) # create separate workers on the same resource pool actor_cls = RayClassWithInitArgs(cls=Actor) critic_cls = RayClassWithInitArgs(cls=Critic, config={"b": 10}) resource_pool = RayResourcePool(process_on_nodes=[2]) actor_wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=actor_cls, device_name=get_device_name()) critic_wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=critic_cls, device_name=get_device_name()) expected_actor_output = actor_wg.add(data) expected_critic_output = critic_wg.sub(data) # create colocated workers cls_dict = {"actor": actor_cls, "critic": critic_cls} ray_cls_with_init = create_colocated_worker_cls(cls_dict) wg_dict = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=get_device_name() ) spawn_wg = wg_dict.spawn(prefix_set=cls_dict.keys()) colocated_actor_wg = spawn_wg["actor"] colocated_critic_wg = spawn_wg["critic"] actor_output = colocated_actor_wg.add(data) critic_output = colocated_critic_wg.sub(data) torch.testing.assert_close(expected_actor_output.batch, actor_output.batch, atol=0, rtol=0) torch.testing.assert_close(expected_critic_output.batch, critic_output.batch, atol=0, rtol=0) ray.shutdown() ================================================ FILE: tests/single_controller/test_colocated_workers_fused.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray.base import ( RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, create_colocated_worker_cls_fused, ) from verl.utils.device import get_device_name @ray.remote class Actor(Worker): def __init__(self) -> None: super().__init__() @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def add(self, data: DataProto): data.batch["a"] += self.rank return data @ray.remote class Critic(Worker): def __init__(self, config) -> None: super().__init__() self.config = config @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def sub(self, data: DataProto): data.batch["a"] -= self.config["b"] return data def test_colocated_workers_fused(): ray.init() import torch data = DataProto.from_dict({"a": torch.zeros(10)}) # create separate workers on the same resource pool actor_cls = RayClassWithInitArgs(cls=Actor) critic_cls = RayClassWithInitArgs(cls=Critic, config={"b": 10}) resource_pool = RayResourcePool(process_on_nodes=[2]) actor_wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=actor_cls, device_name=get_device_name()) critic_wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=critic_cls, device_name=get_device_name()) expected_actor_output = actor_wg.add(data) expected_critic_output = critic_wg.sub(data) # create colocated workers cls_dict = {"actor": actor_cls, "critic": critic_cls} ray_cls_with_init = create_colocated_worker_cls_fused(cls_dict) wg_dict = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=get_device_name() ) spawn_wg = wg_dict.spawn(prefix_set=cls_dict.keys()) colocated_actor_wg = spawn_wg["actor"] colocated_critic_wg = spawn_wg["critic"] actor_output = colocated_actor_wg.add(data) critic_output = colocated_critic_wg.sub(data) torch.testing.assert_close(expected_actor_output.batch, actor_output.batch, atol=0, rtol=0) torch.testing.assert_close(expected_critic_output.batch, critic_output.batch, atol=0, rtol=0) ray.shutdown() ================================================ FILE: tests/single_controller/test_data_transfer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ In this test, we instantiate a data parallel worker with 8 GPUs """ import ray import tensordict import torch from codetiming import Timer from packaging import version from torch import distributed as dist from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.device import get_device_name from verl.utils.ray_utils import parallel_put @ray.remote class DummyWorker(Worker): def __init__(self): super().__init__() dist.init_process_group() @register(dispatch_mode=Dispatch.DP_COMPUTE, blocking=False) def do_nothing(self, data): for key in data.batch.keys(): data.batch[key] += 1 if version.parse(tensordict.__version__) >= version.parse("0.5.0"): data.batch = data.batch.consolidate() return data def test_data_transfer(): ray.init() # construct resource pool resource_pool = RayResourcePool([8]) cls_with_init = RayClassWithInitArgs(cls=DummyWorker) # construct worker group wg = RayWorkerGroup(resource_pool, cls_with_init, device_name=get_device_name()) # this is real dataset size batch_size = 4096 seqlen = 32768 data_dict = {} for i in range(2): data_dict[str(i)] = torch.randint(0, 10000, (batch_size, seqlen)) data = DataProto.from_dict(tensors=data_dict) print(data) # we manually split data here and send to each worker data_list = data.chunk(wg.world_size) for i in range(wg.world_size): # consolidate is necessary if version.parse(tensordict.__version__) >= version.parse("0.5.0"): data_list[i].batch = data_list[i].batch.consolidate() with Timer(name="ray.pickle", initial_text=True): for i in range(wg.world_size): ray.cloudpickle.pickle.dumps(data_list[i]) with Timer(name="raw.pickle", initial_text=True): import pickle for i in range(wg.world_size): pickle.dumps(data_list[i]) # we put in advance with Timer(name="put", initial_text=True): # takes around 40 seconds data_list_ref = parallel_put(data_list) # for i in range(wg.world_size): # data_list[i] = ray.put(data_list[i]) with Timer(name="launch", initial_text=True): output_ref = wg.do_nothing(data_list_ref) with Timer(name="get", initial_text=True): # takes around 40 seconds output_lst = ray.get(output_ref) for input_data, output_data in zip(data_list, output_lst, strict=True): for key in input_data.batch.keys(): assert torch.all(torch.eq(input_data.batch[key] + 1, output_data.batch[key])), ( input_data.batch[key], output_data.batch[key], key, ) ray.shutdown() ================================================ FILE: tests/single_controller/test_decorator_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import time import pytest import ray import torch from tensordict import TensorDict from verl.protocol import DataProto, DataProtoFuture from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.single_controller.base.worker import Worker from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils import tensordict_utils as tu # Pytest fixture for Ray setup/teardown @pytest.fixture def ray_init_shutdown(): ray.init(num_cpus=100) yield ray.shutdown() # Define a simple worker for testing @ray.remote class DecoratorTestWorker(Worker): def __init__(self, initial_value=0): super().__init__() self.value = initial_value # Simulate some setup if needed time.sleep(0.1) # Ensure worker init completes self._register_dispatch_collect_info(mesh_name="train", dp_rank=self.rank, is_collect=True) # Test method for synchronous DP compute (default behavior) @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def dp_compute(self, data: DataProto) -> DataProto: time.sleep(0.1) # Simulate work rank_value = torch.tensor(self.rank, device=data.batch["input"].device, dtype=data.batch["input"].dtype) data.batch["output"] = data.batch["input"] + self.value + rank_value return data # Test async def method with DP compute (default behavior) @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO, blocking=False) async def async_dp_compute(self, data: DataProto) -> DataProto: # Simulate async work await asyncio.sleep(0.1) # Simulate async work rank_value = torch.tensor(self.rank, device=data.batch["input"].device, dtype=data.batch["input"].dtype) data.batch["output_async"] = data.batch["input"] * 2 + self.value + rank_value return data @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train"), blocking=False) def dp_compute_td(self, data: TensorDict) -> TensorDict: # note that we have to call contiguous so that we can modify data in plac data = tu.contiguous(data) rank_value = torch.tensor(self.rank, device=data["input"].device, dtype=data["input"].dtype) data["output"] = data["input"] + self.value + rank_value position_ids = data.pop("position_ids") position_ids._ragged_idx = 2 for i, position_id in enumerate(position_ids.unbind(dim=0)): assert (position_id == torch.arange(4 + rank_value * 2 + i).expand(position_id.shape)).all() return data # Test function for synchronous DP compute def test_decorator_dp_compute(ray_init_shutdown): """ Tests the default behavior of a synchronous decorated method with DP_COMPUTE_PROTO. Verifies the result correctness. """ num_workers = 2 resource_pool = RayResourcePool([num_workers], use_gpu=False, max_colocate_count=1) # Use CPU for simplicity cls_with_args = RayClassWithInitArgs(cls=DecoratorTestWorker, initial_value=10) worker_group = RayWorkerGroup( resource_pool, cls_with_args, name_prefix=f"decorator_test_sync_dp_{int(time.time())}" ) # Prepare input data (size 4, for 2 workers) input_tensor = torch.arange(4, dtype=torch.float32) data = DataProto(batch=TensorDict({"input": input_tensor}, batch_size=[4])) # Call the decorated method output = worker_group.dp_compute(data) # Assert the result correctness assert isinstance(output, DataProto), "Expected DataProto result" assert "output" in output.batch.keys() assert len(output) == len(data), "Output length should match input length" # Expected output calculation for DP_COMPUTE_PROTO with 2 workers # Worker 0 gets data[0:2], Worker 1 gets data[2:4] # Worker 0 adds initial_value(10) + rank(0) = 10 # Worker 1 adds initial_value(10) + rank(1) = 11 expected_output_part1 = torch.tensor([0, 1], dtype=torch.float32) + 10 + 0 expected_output_part2 = torch.tensor([2, 3], dtype=torch.float32) + 10 + 1 expected_output = torch.cat([expected_output_part1, expected_output_part2]) torch.testing.assert_close(output.batch["output"], expected_output, msg="Sync DP compute output data mismatch") # Test function for async def method with DP compute def test_decorator_async_function(ray_init_shutdown): """ Tests the decorator with an `async def` method using DP_COMPUTE_PROTO. Verifies that the call returns a future and the result is correct after .get(). """ num_workers = 2 resource_pool = RayResourcePool([num_workers], use_gpu=False, max_colocate_count=1) cls_with_args = RayClassWithInitArgs(cls=DecoratorTestWorker, initial_value=5) worker_group = RayWorkerGroup( resource_pool, cls_with_args, name_prefix=f"decorator_test_async_dp_{int(time.time())}" ) # Prepare input data (size 4, for 2 workers) input_tensor = torch.arange(4, dtype=torch.float32) data = DataProto(batch=TensorDict({"input": input_tensor}, batch_size=[4])) # Call the async decorated method - this should return a future future_output: DataProtoFuture = worker_group.async_dp_compute(data) # Assert that the call returned a future assert isinstance(future_output, DataProtoFuture), "Expected DataProtoFuture for async def call" # Get the result (this should block) result_data = future_output.get() # Assert the result correctness assert isinstance(result_data, DataProto) assert "output_async" in result_data.batch.keys() assert len(result_data) == len(data), "Output length should match input length" # Expected output calculation for DP_COMPUTE_PROTO with 2 workers # Worker 0 gets data[0:2], Worker 1 gets data[2:4] # Worker 0 calculates: input * 2 + initial_value(5) + rank(0) # Worker 1 calculates: input * 2 + initial_value(5) + rank(1) expected_output_part1 = (torch.tensor([0, 1], dtype=torch.float32) * 2) + 5 + 0 expected_output_part2 = (torch.tensor([2, 3], dtype=torch.float32) * 2) + 5 + 1 expected_output = torch.cat([expected_output_part1, expected_output_part2]) torch.testing.assert_close( result_data.batch["output_async"], expected_output, msg="Async DP compute output data mismatch" ) def test_decorator_dp_compute_td(ray_init_shutdown): num_workers = 2 resource_pool = RayResourcePool([num_workers], use_gpu=False, max_colocate_count=1) # Use CPU for simplicity cls_with_args = RayClassWithInitArgs(cls=DecoratorTestWorker, initial_value=10) worker_group = RayWorkerGroup( resource_pool, cls_with_args, name_prefix=f"decorator_test_sync_dp_{int(time.time())}" ) # Prepare input data (size 4, for 2 workers) input_tensor = torch.arange(4, dtype=torch.float32) position_ids = torch.nested.as_nested_tensor( [ torch.arange(4).expand(4, 4).contiguous(), torch.arange(5).expand(4, 5).contiguous(), torch.arange(6).expand(4, 6).contiguous(), torch.arange(7).expand(4, 7).contiguous(), ], layout=torch.jagged, ) data = TensorDict({"input": input_tensor, "position_ids": position_ids}, batch_size=[4]) # Call the decorated method output = worker_group.dp_compute_td(data) output = output.get() # Assert the result correctness assert isinstance(output, TensorDict), "Expected DataProto result" assert "output" in output.keys() assert len(output) == len(data), "Output length should match input length" # Expected output calculation for DP_COMPUTE_PROTO with 2 workers # Worker 0 gets data[0:2], Worker 1 gets data[2:4] # Worker 0 adds initial_value(10) + rank(0) = 10 # Worker 1 adds initial_value(10) + rank(1) = 11 expected_output_part1 = torch.tensor([0, 1], dtype=torch.float32) + 10 + 0 expected_output_part2 = torch.tensor([2, 3], dtype=torch.float32) + 10 + 1 expected_output = torch.cat([expected_output_part1, expected_output_part2]) torch.testing.assert_close(output["output"], expected_output, msg="Sync DP compute output data mismatch") ================================================ FILE: tests/single_controller/test_device_mesh_register.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import ray import torch from tensordict import TensorDict import verl.utils.tensordict_utils as tu from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import make_nd_compute_dataproto_dispatch_fn, register from verl.utils.device import get_device_name, get_nccl_backend @ray.remote class TestActor(Worker): def __init__(self): super().__init__() import torch.distributed torch.distributed.init_process_group(backend=get_nccl_backend()) self.infer_device_mesh = torch.distributed.device_mesh.init_device_mesh( device_type=get_device_name(), mesh_shape=[2, 4], mesh_dim_names=["dp", "tp"] ) self.train_device_mesh = torch.distributed.device_mesh.init_device_mesh( device_type=get_device_name(), mesh_shape=[2, 2, 2], mesh_dim_names=["pp", "dp", "tp"] ) self._register_dispatch_collect_info( "infer", dp_rank=self.infer_device_mesh["dp"].get_local_rank(), is_collect=self.infer_device_mesh["tp"].get_local_rank() == 0, ) self._register_dispatch_collect_info( "train", dp_rank=self.train_device_mesh["dp"].get_local_rank(), is_collect=self.train_device_mesh["tp"].get_local_rank() == 0 and self.train_device_mesh["pp"].get_local_rank() == 1, ) @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="infer")) def generate_data_proto(self, data: DataProto): tp_rank = self.infer_device_mesh["tp"].get_local_rank() dp_rank = self.infer_device_mesh["dp"].get_local_rank() data.batch["a"] += (tp_rank + 1) * dp_rank return data @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="infer")) def generate_tensordict(self, data: TensorDict): tp_rank = self.infer_device_mesh["tp"].get_local_rank() dp_rank = self.infer_device_mesh["dp"].get_local_rank() data["a"] += (tp_rank + 1) * dp_rank return data @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train")) def train_data_proto(self, data: DataProto): tp_rank = self.train_device_mesh["tp"].get_local_rank() dp_rank = self.train_device_mesh["dp"].get_local_rank() pp_rank = self.train_device_mesh["pp"].get_local_rank() data.batch["a"] += (tp_rank + 1) * (dp_rank + 2) * (pp_rank + 3) # tp rank 0, pp rank 1, dp rank 0, output data added: 8 + 3 = 11 # tp rank 0, pp rank 1, dp rank 1, output data added: 12 + 4 = 16 return data @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train")) def train_tensordict(self, data: TensorDict): tp_rank = self.train_device_mesh["tp"].get_local_rank() dp_rank = self.train_device_mesh["dp"].get_local_rank() pp_rank = self.train_device_mesh["pp"].get_local_rank() data["a"] += (tp_rank + 1) * (dp_rank + 2) * (pp_rank + 3) # tp rank 0, pp rank 1, dp rank 0, output data added: 8 + 3 = 11 # tp rank 0, pp rank 1, dp rank 1, output data added: 12 + 4 = 16 return data @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="infer")) def generate_nested_tensor(self, data: TensorDict): tp_rank = self.infer_device_mesh["tp"].get_local_rank() dp_rank = self.infer_device_mesh["dp"].get_local_rank() assert data.shape[0] == 8 data["input_ids"] += tp_rank + dp_rank print(data) return data def test_dist_global_info_wg(): # create a worker group with size 8 # register a infer dist info with tp=4, dp=2 # register a train dist info with tp=2, dp=2, pp=2 # test the correctness of data dispatch and computation from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup ray.init() ray_cls = RayClassWithInitArgs(TestActor) resource_pool = RayResourcePool(process_on_nodes=[8]) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls, device_name=get_device_name()) infer_input_data_proto = DataProto.from_single_dict(data={"a": torch.tensor([1, 2])}) infer_output_data_proto = wg.generate_data_proto(infer_input_data_proto) assert wg._dispatch_info["infer"] == [0, 0, 0, 0, 1, 1, 1, 1] assert torch.all(torch.eq(infer_output_data_proto.batch["a"], torch.tensor([1, 3]))) infer_input_tensordict = infer_input_data_proto.to_tensordict() infer_output_tensordict = wg.generate_tensordict(infer_input_tensordict) assert torch.all(torch.eq(infer_output_tensordict["a"], torch.tensor([1, 3]))) train_input_data_proto = DataProto.from_single_dict(data={"a": torch.tensor([3, 4])}) train_output_data_proto = wg.train_data_proto(train_input_data_proto) assert wg._dispatch_info["train"] == [0, 0, 1, 1, 0, 0, 1, 1] assert torch.all(torch.eq(train_output_data_proto.batch["a"], torch.tensor([11, 16]))) train_input_tensordict = train_input_data_proto.to_tensordict() train_output_tensordict = wg.train_tensordict(train_input_tensordict) assert torch.all(torch.eq(train_output_tensordict["a"], torch.tensor([11, 16]))) # create a batch size of input_ids input_ids = [ torch.randint(low=0, high=128, size=(np.random.randint(low=1, high=10, dtype=np.int64),)) for _ in range(16) ] input_ids = torch.nested.as_nested_tensor(input_ids, layout=torch.jagged) data = tu.get_tensordict(tensor_dict={"input_ids": input_ids}) output = wg.generate_nested_tensor(data) input_ids_chunked = list(input_ids.chunk(2)) print(input_ids_chunked) input_ids_chunked[0] += 0 input_ids_chunked[1] += 1 expected = tu.concat_nested_tensors(input_ids_chunked) assert torch.all(torch.eq(output["input_ids"].values(), expected.values())) ray.shutdown() if __name__ == "__main__": test_dist_global_info_wg() ================================================ FILE: tests/single_controller/test_driverfunc_to_worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray import torch from tensordict import TensorDict from verl import DataProto from verl.single_controller.base.worker import Worker from verl.single_controller.ray import RayWorkerGroup from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool from verl.utils.device import get_device_name os.environ["RAY_DEDUP_LOGS"] = "0" os.environ["NCCL_DEBUG"] = "WARN" @ray.remote class ModelActor(Worker): def __init__(self): pass class HackSelf: def __init__(self): pass def get_aux_metrics(self, test_proto): sequence_ids = test_proto.batch["sequence_ids"] decode_count = [] for i in range(sequence_ids.size(0)): decode_count.append(len(sequence_ids[i].tolist())) ret_proto = DataProto( batch=TensorDict( {"sequence_ids": sequence_ids, "decode_count": torch.tensor(decode_count)}, batch_size=sequence_ids.size(0) ) ) return ret_proto def test(): # construct model ray.init() # create 2 workers, each hold a GPU resource_pool = RayResourcePool([2], use_gpu=True, name_prefix="a") class_with_args = RayClassWithInitArgs(cls=ModelActor) shard_wg = RayWorkerGroup(resource_pool, class_with_args, device_name=get_device_name()) test_bs = 8 test_proto = DataProto( TensorDict( { "sequence_ids": torch.ones([test_bs, 2048], dtype=torch.int64), }, batch_size=test_bs, ), meta_info={"query_length": 1536}, ) # Sharding among different ranks ret_proto1 = shard_wg.execute_with_func_generator(get_aux_metrics, test_proto) # compare execute on driver hs = HackSelf() ret_proto2 = get_aux_metrics(hs, test_proto) torch.testing.assert_close(ret_proto1.batch["decode_count"], ret_proto2.batch["decode_count"]) ray.shutdown() ================================================ FILE: tests/single_controller/test_fused_workers_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray.base import ( RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, create_colocated_worker_raw_cls, ) @ray.remote class Actor(Worker): def __init__(self) -> None: super().__init__() @register(dispatch_mode=Dispatch.ONE_TO_ALL) def add(self, x): x += self.rank return x @ray.remote class Critic(Worker): def __init__(self, val) -> None: super().__init__() self.val = val @register(dispatch_mode=Dispatch.ALL_TO_ALL) def sub(self, x): x -= self.val return x actor_cls = RayClassWithInitArgs(cls=Actor) critic_cls = RayClassWithInitArgs(cls=Critic, val=10) cls_dict = {"actor": actor_cls, "critic": critic_cls} FusedBaseClass = create_colocated_worker_raw_cls(cls_dict) @ray.remote class HybridWorker(FusedBaseClass): @register(dispatch_mode=Dispatch.ONE_TO_ALL) def foo(self, x): return self.critic.sub(self.actor.add(x)) def test_fused_workers(): ray.init(num_cpus=100) # create separate workers on the same resource pool process_on_nodes = [2] resource_pool = RayResourcePool(process_on_nodes=process_on_nodes, use_gpu=False) # create colocated workers hybrid_cls_with_init = RayClassWithInitArgs(cls=HybridWorker) hybrid_cls_with_init.fused_worker_used = True fused_wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=hybrid_cls_with_init) fused_wg.fuse(cls_dict.keys()) x = fused_wg.actor.add(0.1) print(x) y = fused_wg.critic.sub(x) print(y) z = fused_wg.foo(0.1) print(z) for i, j in zip(y, z, strict=True): assert i == j ray.shutdown() if __name__ == "__main__": test_fused_workers() ================================================ FILE: tests/single_controller/test_get_set_dispatch_collect_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from verl.single_controller.base import Worker def test_get_set_dispatch_collect_cpu(): os.environ["RANK"] = "0" os.environ["LOCAL_RANK"] = "0" os.environ["WORLD_SIZE"] = "2" os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12345" ref = Worker() ref._register_dispatch_collect_info(mesh_name="actor", dp_rank=0, is_collect=True) actor = Worker() actor._register_dispatch_collect_info(mesh_name="actor", dp_rank=1, is_collect=False) actor_rollout_ref = Worker() actor_rollout_ref.set_dispatch_collect(mesh_name="ref", **ref.get_dispatch_collect()) actor_rollout_ref.set_dispatch_collect(mesh_name="actor", **actor.get_dispatch_collect()) assert actor_rollout_ref._query_dispatch_info("ref") == 0 assert actor_rollout_ref._query_collect_info("ref") assert actor_rollout_ref._query_dispatch_info("actor") == 1 assert not actor_rollout_ref._query_collect_info("actor") # test conflict mesh_name actor2 = Worker() actor2._register_dispatch_collect_info(mesh_name="actor", dp_rank=1, is_collect=False) with pytest.raises(AssertionError): actor_rollout_ref.set_dispatch_collect(mesh_name="actor", **actor2.get_dispatch_collect()) ================================================ FILE: tests/single_controller/test_high_level_scheduling_api.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gc import time import ray from verl.single_controller.base.worker import Worker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, merge_resource_pool from verl.utils.device import get_device_name @ray.remote class TestActor(Worker): # TODO: pass *args and **kwargs is bug prone and not very convincing def __init__(self, cuda_visible_devices=None) -> None: super().__init__(cuda_visible_devices) def get_node_id(self): return ray.get_runtime_context().get_node_id() def test(): ray.init() # test single-node-no-partition print("test single-node-no-partition") resource_pool = RayResourcePool([8], use_gpu=True) class_with_args = RayClassWithInitArgs(cls=TestActor) print("create actor worker group") actor_wg = RayWorkerGroup( resource_pool, class_with_args, name_prefix="high_level_api_actor", device_name=get_device_name() ) print("create critic worker group") critic_wg = RayWorkerGroup( resource_pool, class_with_args, name_prefix="hight_level_api_critic", device_name=get_device_name() ) print("create ref worker group") ref_wg = RayWorkerGroup( resource_pool, class_with_args, name_prefix="high_level_api_ref", device_name=get_device_name() ) assert actor_wg.execute_all_sync("get_cuda_visible_devices") == [str(i) for i in range(8)] assert critic_wg.execute_all_sync("get_cuda_visible_devices") == [str(i) for i in range(8)] assert ref_wg.execute_all_sync("get_cuda_visible_devices") == [str(i) for i in range(8)] del actor_wg del critic_wg del ref_wg gc.collect() # make sure ray actors are deleted [ray.util.remove_placement_group(pg) for pg in resource_pool.get_placement_groups()] print("wait 5s to remove placemeng_group") time.sleep(5) # test single-node-multi-partition print("test single-node-multi-partition") rm_resource_pool = RayResourcePool([4], use_gpu=True, name_prefix="rm") ref_resource_pool = RayResourcePool([4], use_gpu=True, name_prefix="ref") total_resource_pool = merge_resource_pool(rm_resource_pool, ref_resource_pool) assert rm_resource_pool.world_size == 4 assert ref_resource_pool.world_size == 4 assert total_resource_pool.world_size == 8 actor_wg = RayWorkerGroup( total_resource_pool, class_with_args, name_prefix="high_level_api_actor", device_name=get_device_name() ) critic_wg = RayWorkerGroup( total_resource_pool, class_with_args, name_prefix="high_level_api_critic", device_name=get_device_name() ) ref_wg = RayWorkerGroup( ref_resource_pool, class_with_args, name_prefix="high_level_api_ref", device_name=get_device_name() ) assert actor_wg.execute_all_sync("get_cuda_visible_devices") == [str(i) for i in range(8)] assert critic_wg.execute_all_sync("get_cuda_visible_devices") == [str(i) for i in range(8)] assert ref_wg.execute_all_sync("get_cuda_visible_devices") == [str(i) for i in range(4, 8)] ray.shutdown() ================================================ FILE: tests/single_controller/test_nested_worker.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.base.worker import Worker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.device import get_device_name class TestActor(Worker): # TODO: pass *args and **kwargs is bug prone and not very convincing def __init__(self, x) -> None: super().__init__() self.a = x @register(dispatch_mode=Dispatch.ONE_TO_ALL) def get(self): return self.a + self.rank class TestHighLevelActor(Worker): def __init__(self, x=None) -> None: super().__init__() self.test_actor = TestActor(x=x) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def get(self): return self.test_actor.get() def test_nested_worker(): ray.init(num_cpus=100) # create 4 workers, each hold a GPU resource_pool = RayResourcePool([4], use_gpu=True) class_with_args = RayClassWithInitArgs(cls=ray.remote(TestActor), x=2) worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=class_with_args, name_prefix="worker_group_basic", device_name=get_device_name(), ) output = worker_group.get() assert output == [2, 3, 4, 5] class_with_args = RayClassWithInitArgs(cls=ray.remote(TestHighLevelActor), x=2) high_level_worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=class_with_args, name_prefix="worker_group_basic_2", device_name=get_device_name(), ) output_1 = high_level_worker_group.get() assert output_1 == [2, 3, 4, 5] ray.shutdown() ================================================ FILE: tests/single_controller/test_ray_collectives.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test for using ray collective group. Suppose we Actor and Rollout. Actor contains 4 workers and Rollout contains 2 workers. We established a Worker to Rollout relationship by using collective groups Actor: rank 0, 1 - Rollout rank 0 Rollout rank 2, 3 - Rollout rank 1 Then, we initiate 4 p2p comms from actor to rollout """ import ray import ray.util.collective as collective import torch from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup @ray.remote class Actor(Worker): @register(Dispatch.ONE_TO_ALL) def init(self): remote_rank = self.rank // 2 self.group_name = f"A{self.rank}_R{remote_rank}" collective.init_collective_group(world_size=2, rank=0, backend="nccl", group_name=self.group_name) @register(Dispatch.ONE_TO_ALL, blocking=False) def send_tensors(self): tensor = torch.ones(size=(4,), dtype=torch.float32, device="cuda") * self.rank collective.send(tensor=tensor, dst_rank=1, group_name=self.group_name) @ray.remote class Rollout(Worker): @register(Dispatch.ONE_TO_ALL) def init(self): self.remote_first_rank = self.rank * 2 self.remote_second_rank = self.remote_first_rank + 1 self.first_group_name = f"A{self.remote_first_rank}_R{self.rank}" self.second_group_name = f"A{self.remote_second_rank}_R{self.rank}" collective.init_collective_group(world_size=2, rank=1, backend="nccl", group_name=self.first_group_name) collective.init_collective_group(world_size=2, rank=1, backend="nccl", group_name=self.second_group_name) @register(Dispatch.ONE_TO_ALL, blocking=False) def receive_tensors(self): self.tensor1 = torch.randn(size=(4,), dtype=torch.float32, device="cuda") self.tensor2 = torch.randn(size=(4,), dtype=torch.float32, device="cuda") collective.recv(self.tensor1, src_rank=0, group_name=self.first_group_name) collective.recv(self.tensor2, src_rank=0, group_name=self.second_group_name) @register(Dispatch.ONE_TO_ALL) def get_tensors(self): return {f"src_{self.remote_first_rank}": self.tensor1, f"src_{self.remote_second_rank}": self.tensor2} def test_ray_collective_group(): ray.init() actor_resource_pool = RayResourcePool([4]) rollout_resource_pool = RayResourcePool([2]) actor_cls = RayClassWithInitArgs(cls=Actor) rollout_cls = RayClassWithInitArgs(cls=Rollout) actor_wg = RayWorkerGroup( resource_pool=actor_resource_pool, ray_cls_with_init=actor_cls, name_prefix="collective_group_actor" ) rollout_wg = RayWorkerGroup( resource_pool=rollout_resource_pool, ray_cls_with_init=rollout_cls, name_prefix="collective_group_rollout" ) actor_wg.init() rollout_wg.init() out1 = actor_wg.send_tensors() out2 = rollout_wg.receive_tensors() # block to wait ray.get(out1) ray.get(out2) output = rollout_wg.get_tensors() rollout_0_output = output[0] rollout_1_output = output[1] output = rollout_0_output | rollout_1_output print(output) for i in range(4): assert torch.sum(output[f"src_{i}"]).item() == 4 * i ray.shutdown() if __name__ == "__main__": test_ray_collective_group() ================================================ FILE: tests/single_controller/test_ray_local_envs_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ e2e test verl.single_controller.ray """ import os import ray from verl.single_controller.base.worker import Worker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup @ray.remote class TestActor(Worker): def __init__(self) -> None: super().__init__() def getenv(self, key): val = os.getenv(key, f"{key} not set") return val def test_basics(): ray.init(num_cpus=100) # create 4 workers, each hold a GPU resource_pool = RayResourcePool([4], use_gpu=False) class_with_args = RayClassWithInitArgs(cls=TestActor) worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=class_with_args, name_prefix="worker_group_basic" ) output = worker_group.execute_all_sync("getenv", key="RAY_LOCAL_WORLD_SIZE") assert output == ["4", "4", "4", "4"] ray.shutdown() def test_customized_worker_env(): ray.init(num_cpus=100) # create 4 workers, each hold a GPU resource_pool = RayResourcePool([4], use_gpu=False) class_with_args = RayClassWithInitArgs(cls=TestActor) worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=class_with_args, name_prefix="worker_group_customized", worker_env={ "test_key": "test_value", # new key will be appended }, ) output = worker_group.execute_all_sync("getenv", key="test_key") assert output == ["test_value", "test_value", "test_value", "test_value"] try: worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=class_with_args, name_prefix="worker_group_error", worker_env={ "WORLD_SIZE": "100", # override system env will result in error }, ) except ValueError as e: assert "WORLD_SIZE" in str(e) else: raise ValueError("test failed") ray.shutdown() if __name__ == "__main__": test_basics() test_customized_worker_env() ================================================ FILE: tests/single_controller/test_ray_utils_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import ray from verl.utils.ray_utils import parallel_put # Initialize Ray for testing if not already done globally @pytest.fixture() def init_ray(): ray.init(num_cpus=4) yield ray.shutdown() def test_parallel_put_basic(init_ray): data = [1, "hello", {"a": 2}, [3, 4]] refs = parallel_put(data) assert len(refs) == len(data) retrieved_data = [ray.get(ref) for ref in refs] assert retrieved_data == data def test_parallel_put_empty(init_ray): data = [] with pytest.raises(AssertionError): _ = parallel_put(data) def test_parallel_put_workers(init_ray): data = list(range(20)) # Test with specific number of workers refs = parallel_put(data, max_workers=4) assert len(refs) == len(data) retrieved_data = [ray.get(ref) for ref in refs] assert retrieved_data == data # Test with default workers (should cap) refs_default = parallel_put(data) assert len(refs_default) == len(data) retrieved_data_default = [ray.get(ref) for ref in refs_default] assert retrieved_data_default == data ================================================ FILE: tests/single_controller/test_rvdz.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray @ray.remote class TestWorker: def __init__(self, rank, world_size, group_name): self.rank = rank self.world_size = world_size self.group_name = group_name self.communicator = None def init(self): from verl.utils.rendezvous.ray_backend import create_nccl_communicator_in_ray self.communicator = create_nccl_communicator_in_ray(self.rank, self.world_size, self.group_name) def test(self): if self.communicator is None: return None return self.communicator.rank_id() def test_rvdz(): ray.init() group_name = "test_group" world_size = 2 workers = [TestWorker.options(num_gpus=1).remote(rank, world_size, group_name) for rank in range(world_size)] ray.get([worker.init.remote() for worker in workers]) ranks = ray.get([worker.test.remote() for worker in workers]) assert ranks == [0, 1], f"expecting [0, 1], got {ranks}" ray.shutdown() ================================================ FILE: tests/single_controller/test_split_resource_pool.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import ray import torch from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray.base import ( RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, split_resource_pool, ) from verl.utils.device import get_device_name, get_nccl_backend @ray.remote class Actor(Worker): def __init__(self, worker_id) -> None: super().__init__() self.worker_id = worker_id self.temp_tensor = torch.rand(4096, 4096).to(get_device_name()) if not torch.distributed.is_initialized(): rank = int(os.environ.get("RANK", 0)) world_size = int(os.environ.get("WORLD_SIZE", 1)) torch.distributed.init_process_group(backend=get_nccl_backend(), world_size=world_size, rank=rank) @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def add(self, data: DataProto): data.batch["a"] += self.rank + self.worker_id return data def test_split_resource_pool_with_split_size(): ray.init() # assume we have 2 nodes, with 4 GPUs each global_resource_pool = RayResourcePool(process_on_nodes=[4, 4]) global_resource_pool.get_placement_groups(device_name=get_device_name()) # first 4 gpus for actor_1, last 4 gpus for actor_2 actor_1_resource_pool, actor_2_resource_pool = split_resource_pool(resource_pool=global_resource_pool, split_size=4) actor_cls_1 = RayClassWithInitArgs(cls=Actor, worker_id=0) actor_cls_2 = RayClassWithInitArgs(cls=Actor, worker_id=100) actor_worker_1 = RayWorkerGroup( resource_pool=actor_1_resource_pool, ray_cls_with_init=actor_cls_1, device_name=get_device_name() ) actor_worker_2 = RayWorkerGroup( resource_pool=actor_2_resource_pool, ray_cls_with_init=actor_cls_2, device_name=get_device_name() ) assert actor_worker_1.world_size == 4 assert actor_worker_2.world_size == 4 data = DataProto.from_dict({"a": torch.zeros(8)}) actor_output_1 = actor_worker_1.add(data) actor_output_2 = actor_worker_2.add(data) assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1, 2, 2, 3, 3] assert actor_output_2.batch["a"].tolist() == [100, 100, 101, 101, 102, 102, 103, 103] ray.shutdown() def test_split_resource_pool_with_split_size_list(): ray.init() # assume we have 4 nodes, with 2 GPUs each global_resource_pool = RayResourcePool(process_on_nodes=[2, 2, 2, 2]) global_resource_pool.get_placement_groups(device_name=get_device_name()) # first 2 gpus for actor_1, last 6 gpus for actor_2 actor_1_resource_pool, actor_2_resource_pool = split_resource_pool( resource_pool=global_resource_pool, split_size=[2, 6], ) actor_cls_1 = RayClassWithInitArgs(cls=Actor, worker_id=0) actor_cls_2 = RayClassWithInitArgs(cls=Actor, worker_id=100) actor_worker_1 = RayWorkerGroup( resource_pool=actor_1_resource_pool, ray_cls_with_init=actor_cls_1, device_name=get_device_name() ) actor_worker_2 = RayWorkerGroup( resource_pool=actor_2_resource_pool, ray_cls_with_init=actor_cls_2, device_name=get_device_name() ) assert actor_worker_1.world_size == 2 assert actor_worker_2.world_size == 6 data_1 = DataProto.from_dict({"a": torch.zeros(4)}) data_2 = DataProto.from_dict({"a": torch.zeros(6)}) actor_output_1 = actor_worker_1.add(data_1) actor_output_2 = actor_worker_2.add(data_2) print(actor_output_1.batch["a"].tolist()) print(actor_output_2.batch["a"].tolist()) assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1] assert actor_output_2.batch["a"].tolist() == [100, 101, 102, 103, 104, 105] ray.shutdown() def test_split_resource_pool_with_split_size_list_cross_nodes(): ray.init() # assume we have 4 nodes, with 2 GPUs each global_resource_pool = RayResourcePool(process_on_nodes=[4, 4]) global_resource_pool.get_placement_groups(device_name=get_device_name()) # first 2 gpus for actor_1, last 6 gpus for actor_2 actor_1_resource_pool, actor_2_resource_pool = split_resource_pool( resource_pool=global_resource_pool, split_size=[2, 6], ) actor_cls_1 = RayClassWithInitArgs(cls=Actor, worker_id=0) actor_cls_2 = RayClassWithInitArgs(cls=Actor, worker_id=100) actor_worker_1 = RayWorkerGroup( resource_pool=actor_1_resource_pool, ray_cls_with_init=actor_cls_1, device_name=get_device_name() ) actor_worker_2 = RayWorkerGroup( resource_pool=actor_2_resource_pool, ray_cls_with_init=actor_cls_2, device_name=get_device_name() ) assert actor_worker_1.world_size == 2 assert actor_worker_2.world_size == 6 data_1 = DataProto.from_dict({"a": torch.zeros(4)}) data_2 = DataProto.from_dict({"a": torch.zeros(6)}) actor_output_1 = actor_worker_1.add(data_1) actor_output_2 = actor_worker_2.add(data_2) print(actor_output_1.batch["a"].tolist()) print(actor_output_2.batch["a"].tolist()) assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1] assert actor_output_2.batch["a"].tolist() == [100, 101, 102, 103, 104, 105] ray.shutdown() def test_split_resource_pool_with_split_twice(): ray.init() # assume we have 4 nodes, with 2 GPUs each global_resource_pool = RayResourcePool(process_on_nodes=[2, 2, 2, 2]) global_resource_pool.get_placement_groups(device_name=get_device_name()) # actors with [2, 1, 1, 1, 1, 2] (split twice) rp_1, rp_2, rp_3 = split_resource_pool( resource_pool=global_resource_pool, split_size=[2, 4, 2], ) rp_2_1, rp_2_2, rp_2_3, rp_2_4 = split_resource_pool( resource_pool=rp_2, split_size=1, ) fp_list = [rp_1, rp_2_1, rp_2_2, rp_2_3, rp_2_4, rp_3] correct_world_size = [2, 1, 1, 1, 1, 2] correct_output = [ [0.0, 0.0, 1.0, 1.0], # 2 worker [100.0, 100.0, 100.0, 100.0], # 1 worker [200.0, 200.0, 200.0, 200.0], # 1 worker [300.0, 300.0, 300.0, 300.0], # 1 worker [400.0, 400.0, 400.0, 400.0], # 1 worker [500.0, 500.0, 501.0, 501.0], # 2 worker ] for idx, rp in enumerate(fp_list): actor_cls = RayClassWithInitArgs(cls=Actor, worker_id=idx * 100) actor_worker = RayWorkerGroup(resource_pool=rp, ray_cls_with_init=actor_cls, device_name=get_device_name()) data = DataProto.from_dict({"a": torch.zeros(4)}) actor_output = actor_worker.add(data) assert actor_worker.world_size == correct_world_size[idx] assert actor_output.batch["a"].tolist() == correct_output[idx] ray.shutdown() ================================================ FILE: tests/single_controller/test_worker_group_basics.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ e2e test verl.single_controller.ray """ import ray import torch from verl.single_controller.base.decorator import Dispatch, Execute, collect_all_to_all, register from verl.single_controller.base.worker import Worker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.device import get_device_name def two_to_all_dispatch_fn(worker_group, *args, **kwargs): """ Assume the input is a list of 2. Duplicate the input interleaved and pass to each worker. """ for arg in args: assert len(arg) == 2 for i in range(worker_group.world_size - 2): arg.append(arg[i % 2]) for k, v in kwargs.items(): assert len(v) == 2 for i in range(worker_group.world_size - 2): v.append(v[i % 2]) return args, kwargs def get_ray_remote_options() -> str: """Function that gets the torch.device based on the current machine. This currently only supports CPU, CUDA, NPU. Returns: device """ if get_device_name() == "cuda": return dict(num_gpus=0.1) elif get_device_name() == "npu": return dict(resources={"NPU": 0.1}) return dict(num_cpus=0.1) @ray.remote class TestActor(Worker): # TODO: pass *args and **kwargs is bug prone and not very convincing def __init__(self, x) -> None: super().__init__() self._x = x def foo(self, y): return self._x + y @register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.RANK_ZERO) def foo_rank_zero(self, x, y): return self._x + y + x @register(Dispatch.ONE_TO_ALL, blocking=False) def foo_one_to_all(self, x, y): return self._x + y + x @register(Dispatch.ALL_TO_ALL, blocking=False) def foo_all_to_all(self, x, y): return self._x + y + x @register(dispatch_mode={"dispatch_fn": two_to_all_dispatch_fn, "collect_fn": collect_all_to_all}) def foo_custom(self, x, y): return self._x + y + x @ray.remote(**get_ray_remote_options()) def remote_call_wg(worker_names): class_with_args = RayClassWithInitArgs(cls=TestActor, x=2) worker_group = RayWorkerGroup.from_detached( worker_names=worker_names, ray_cls_with_init=class_with_args, name_prefix=None ) print(worker_group.worker_names) output_ref = worker_group.foo_custom(x=[1, 2], y=[5, 6]) assert output_ref == [8, 10, 8, 10] output_ref = worker_group.foo_rank_zero(x=1, y=2) assert output_ref == 5 return worker_group.worker_names def add_one(data): data = data.to(get_device_name()) data += 1 data = data.to("cpu") return data def test_basics(): ray.init(num_cpus=100) # create 4 workers, each hold a GPU resource_pool = RayResourcePool([4], use_gpu=True) class_with_args = RayClassWithInitArgs(cls=TestActor, x=2) worker_group = RayWorkerGroup( resource_pool=resource_pool, ray_cls_with_init=class_with_args, name_prefix="worker_group_basic", device_name=get_device_name(), ) print(worker_group.worker_names) # this will wait for all the results output = worker_group.execute_all_sync("foo", y=3) assert output == [5, 5, 5, 5] # this is a list of object reference. It won't block. output_ref = worker_group.execute_all_async("foo", y=4) print(output_ref) assert ray.get(output_ref) == [6, 6, 6, 6] output_ref = worker_group.foo_one_to_all(x=1, y=2) assert ray.get(output_ref) == [5, 5, 5, 5] output_ref = worker_group.foo_all_to_all(x=[1, 2, 3, 4], y=[5, 6, 7, 8]) assert ray.get(output_ref) == [8, 10, 12, 14] print(ray.get(remote_call_wg.remote(worker_group.worker_names))) output = worker_group.execute_func_rank_zero(add_one, torch.ones(2, 2)) torch.testing.assert_close(output, torch.ones(2, 2) + 1) ray.shutdown() if __name__ == "__main__": test_basics() ================================================ FILE: tests/single_controller/test_worker_group_torch.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os os.environ["RAY_DEDUP_LOGS"] = "0" os.environ["NCCL_DEBUG"] = "WARN" import ray import torch import torch.distributed from verl.single_controller.base.worker import Worker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup from verl.utils.device import get_device_name @ray.remote class TestAllGatherActor(Worker): def __init__(self, size) -> None: super().__init__() self.size = size def init(self): torch.distributed.init_process_group() self.tensor = torch.zeros(size=(self.size,), dtype=torch.int64, device=get_device_name()) self.tensor += self.rank def all_gather(self): world_size = self._world_size output = torch.zeros( size=(self.tensor.shape[0] * world_size,), dtype=self.tensor.dtype, device=self.tensor.device ) torch.distributed.all_gather_into_tensor(output, self.tensor, async_op=False) return output @ray.remote class TestAllGatherActorV2(Worker): def __init__(self, size) -> None: super().__init__() self.size = size torch.distributed.init_process_group() self.tensor = torch.zeros(size=(self.size,), dtype=torch.int64, device=get_device_name()) self.tensor += self.rank def all_gather(self): world_size = self._world_size output = torch.zeros( size=(self.tensor.shape[0] * world_size,), dtype=self.tensor.dtype, device=self.tensor.device ) torch.distributed.all_gather_into_tensor(output, self.tensor, async_op=False) return output def test_all_gather_torch(): """ In this test, we instantiate 4 GPUs in a group and test the all_gather """ ray.init() # create 4 workers, each hold a GPU resource_pool = RayResourcePool([4], use_gpu=True) class_with_args = RayClassWithInitArgs(cls=TestAllGatherActor, size=2) worker_group = RayWorkerGroup( resource_pool, class_with_args, name_prefix="worker_group_torch", device_name=get_device_name() ) worker_group.execute_all_sync("init") output = worker_group.execute_all_sync("all_gather") for i in range(1, len(output)): assert torch.all(output[i] == output[0]) output = output[0].cpu() print(output) assert torch.all(output == torch.tensor([0, 0, 1, 1, 2, 2, 3, 3], dtype=torch.int64)) ray.shutdown() def test_all_gather_torch_v2(): """ In this test, we instantiate 4 GPUs in a group and test the all_gather """ ray.init() # create 4 workers, each hold a GPU resource_pool = RayResourcePool([4], use_gpu=True) class_with_args = RayClassWithInitArgs(cls=TestAllGatherActorV2, size=2) worker_group = RayWorkerGroup( resource_pool, class_with_args, name_prefix="worker_group_torch", device_name=get_device_name() ) output = worker_group.execute_all_sync("all_gather") for i in range(1, len(output)): assert torch.all(output[i] == output[0]) output = output[0].cpu() print(output) assert torch.all(output == torch.tensor([0, 0, 1, 1, 2, 2, 3, 3], dtype=torch.int64)) ray.shutdown() ================================================ FILE: tests/special_distributed/README.md ================================================ This folder is reserved for unit tests (instead of end-to-end tests) that require multiple GPUs. ================================================ FILE: tests/special_distributed/run_all.sh ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #!/usr/bin/env bash set -e -x torchrun --nproc-per-node=4 --standalone tests/special_distributed/test_tensor_dict.py torchrun --nproc-per-node=4 --standalone tests/special_distributed/test_torch_functional.py ================================================ FILE: tests/special_distributed/test_fsdp_ckpt.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil import tempfile import torch import torch.distributed from torch.distributed import init_device_mesh from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import MixedPrecision, ShardingStrategy from transformers import AutoModelForCausalLM, AutoTokenizer, Qwen2Config from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.utils.device import get_device_name, get_torch_device from verl.utils.distributed import initialize_global_process_group from verl.utils.fsdp_utils import MixedPrecisionPolicy, apply_fsdp2 def create_random_input_ids(batch_size, seq_len, vocab_size): if get_device_name() == "cuda": from flash_attn.bert_padding import unpad_input elif get_device_name() == "npu": from verl.utils.attention_utils import unpad_input from verl.utils.model import compute_position_id_with_mask, create_random_mask input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=get_device_name()) attention_mask = create_random_mask( input_ids, max_ratio_of_left_padding=0.1, min_ratio_of_valid_token=0.5, max_ratio_of_valid_token=0.7 ) position_ids = compute_position_id_with_mask(attention_mask) input_ids = unpad_input(input_ids.unsqueeze(-1), attention_mask)[0].transpose(0, 1) position_ids = unpad_input(position_ids.unsqueeze(-1), attention_mask)[0].transpose(0, 1) return input_ids, position_ids def test_fsdp_ckpt(strategy="fsdp"): assert get_torch_device().device_count() >= 2, "need at least 2 gpus for test" local_rank, rank, world_size = initialize_global_process_group() device_mesh = init_device_mesh(get_device_name(), mesh_shape=(world_size,), mesh_dim_names=("dp",)) model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") config = Qwen2Config(num_hidden_layers=1) with torch.device(get_device_name()): model = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model = model.to(device=get_device_name()) # Wrap model with FSDP if strategy == "fsdp": mixed_precision = MixedPrecision( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32 ) model = FSDP( model, use_orig_params=False, device_id=get_torch_device().current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, device_mesh=device_mesh, ) else: mp_policy = MixedPrecisionPolicy( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, cast_forward_inputs=True ) fsdp_kwargs = { "mesh": device_mesh, "mp_policy": mp_policy, } apply_fsdp2(model, fsdp_kwargs, {}) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9) # Create checkpoint manager tokenizer = AutoTokenizer.from_pretrained(model_name) checkpoint_manager = FSDPCheckpointManager( model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, tokenizer=tokenizer ) # Generate sample input batch_size = 10 seq_len = 1024 vocab_size = config.vocab_size # First input for initial update input_ids1, position_ids1 = create_random_input_ids(batch_size, seq_len, vocab_size) # Second input for verification input_ids2, position_ids2 = create_random_input_ids(batch_size, seq_len, vocab_size) # Step 1: Initial update and save checkpoint outputs1 = model(input_ids=input_ids1, position_ids=position_ids1) loss1 = outputs1.logits.mean() loss1.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Save checkpoint after first update temp_dir = tempfile.mkdtemp() checkpoint_path = os.path.join(temp_dir, "checkpoint") checkpoint_manager.save_checkpoint(local_path=checkpoint_path, hdfs_path=None, global_step=0) saved_state_dict = model.state_dict() # Step 2: Second update and forward pass outputs2 = model(input_ids=input_ids2, position_ids=position_ids2) loss2 = outputs2.logits.mean() loss2.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Record logits after second update with torch.no_grad(): logits_before_load = model(input_ids=input_ids2, position_ids=position_ids2).logits # Step 3: Load checkpoint and repeat second update checkpoint_manager.load_checkpoint(checkpoint_path) loaded_state_dict = model.state_dict() for key in loaded_state_dict: assert key in saved_state_dict, f"Key {key} not found in saved state dict" torch.testing.assert_close(loaded_state_dict[key], saved_state_dict[key], atol=0.0, rtol=0.0) # Repeat the second update with same input outputs3 = model(input_ids=input_ids2, position_ids=position_ids2) loss3 = outputs3.logits.mean() loss3.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Record logits after loaded checkpoint and update with torch.no_grad(): logits_after_load = model(input_ids=input_ids2, position_ids=position_ids2).logits # Step 4: Verify outputs match torch.testing.assert_close(logits_before_load, logits_after_load, atol=0.0, rtol=0.0) print("Checkpoint save/load test passed!") # Cleanup shutil.rmtree(temp_dir) torch.distributed.barrier() torch.distributed.destroy_process_group() if __name__ == "__main__": strategy = os.environ.get("STRATEGY", "fsdp") os.environ["FLASH_ATTENTION_DETERMINISTIC"] = "1" test_fsdp_ckpt(strategy=strategy) ================================================ FILE: tests/special_distributed/test_mcore_config_converter.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import megatron.core.parallel_state as mpu import torch from megatron.core.transformer import MLATransformerConfig, TransformerConfig from transformers import AutoConfig, PretrainedConfig from verl.models.mcore import hf_to_mcore_config from verl.utils.distributed import destroy_global_process_group, initialize_global_process_group TEST_MODELS = [ "Qwen/Qwen2.5-7B", # Qwen2 dense "Qwen/Qwen3-8B", # Qwen3 dense "deepseek-ai/deepseek-coder-1.3b-instruct", # deepseek dense "Qwen/Qwen2-57B-A14B", # Qwen2 moe "Qwen/Qwen3-30B-A3B", # Qwen3 moe # "mistralai/Mixtral-8x7B-v0.1", # Mixtral # require authentication "deepseek-ai/DeepSeek-V3-Base", # Deepseek V3 ] def check_config_converter_results(tf_config: TransformerConfig | MLATransformerConfig, hf_config: PretrainedConfig): assert tf_config.num_layers == hf_config.num_hidden_layers, ( f"Number of layers mismatch: {tf_config.num_layers} != {hf_config.num_hidden_layers}" ) assert tf_config.hidden_size == hf_config.hidden_size, ( f"Hidden size mismatch: {tf_config.hidden_size} != {hf_config.hidden_size}" ) assert tf_config.num_attention_heads == hf_config.num_attention_heads, ( f"Number of attention heads mismatch: {tf_config.num_attention_heads} != {hf_config.num_attention_heads}" ) assert tf_config.num_query_groups == hf_config.num_key_value_heads, ( f"Number of query groups mismatch: {tf_config.num_query_groups} != {hf_config.num_key_value_heads}" ) assert tf_config.ffn_hidden_size == hf_config.intermediate_size, ( f"FFN hidden size mismatch: {tf_config.ffn_hidden_size} != {hf_config.intermediate_size}" ) assert tf_config.attention_dropout == hf_config.attention_dropout, ( f"Attention dropout mismatch: {tf_config.attention_dropout} != {hf_config.attention_dropout}" ) assert tf_config.hidden_dropout == getattr(hf_config, "hidden_dropout", 0.0), ( f"Hidden dropout mismatch: {tf_config.hidden_dropout} != {getattr(hf_config, 'hidden_dropout', 0.0)}" ) if getattr(hf_config, "head_dim", None) is not None: assert tf_config.kv_channels == getattr(hf_config, "head_dim", None), ( f"Head dim mismatch: {tf_config.kv_channels} != {getattr(hf_config, 'head_dim', None)}" ) assert tf_config.layernorm_epsilon == hf_config.rms_norm_eps, ( f"Layernorm epsilon mismatch: {tf_config.layernorm_epsilon} != {hf_config.rms_norm_eps}" ) def modify_hf_config(name: str, hf_config: PretrainedConfig): if name == "deepseek-ai/DeepSeek-V3-Base": hf_config.num_nextn_predict_layers = 0 hf_config.quantization_config = None return hf_config def test_mcore_config_converter(): """ Test the conversion of Hugging Face model configurations to MCore configurations. """ local_rank, rank, world_size = initialize_global_process_group() mpu.initialize_model_parallel( tensor_model_parallel_size=2, pipeline_model_parallel_size=2, virtual_pipeline_model_parallel_size=None, use_sharp=False, context_parallel_size=2, expert_model_parallel_size=1, expert_tensor_parallel_size=None, nccl_communicator_config_path=None, ) for model_name in TEST_MODELS: print(f"testing {model_name}") hf_config = AutoConfig.from_pretrained(os.path.expanduser(f"~/models/configs/{model_name}/config.json")) hf_config = modify_hf_config(model_name, hf_config) tf_config = hf_to_mcore_config(hf_config, torch.bfloat16) check_config_converter_results(tf_config, hf_config) destroy_global_process_group() if __name__ == "__main__": test_mcore_config_converter() ================================================ FILE: tests/special_distributed/test_tensor_dict.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os os.environ["NCCL_DEBUG"] = "WARN" import numpy as np import torch import torch.distributed from verl.protocol import DataProto, all_gather_data_proto from verl.utils.device import get_device_name from verl.utils.distributed import initialize_global_process_group def test_all_gather_data_proto(): device_mesh = torch.distributed.device_mesh.init_device_mesh( get_device_name(), mesh_shape=[2, 2], mesh_dim_names=["dp", "tp"] ) global_rank = torch.distributed.get_rank() obs = torch.tensor([[1 * global_rank, 2 * global_rank + 1], [3 * global_rank, 4 * global_rank + 1]]) labels = ["a", "b"] if global_rank % 2 == 0 else ["b", "a"] labels = np.array(labels, dtype=object) data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) all_gather_data_proto(data=data, process_group=device_mesh.get_group("dp")) if global_rank == 0: expected_obs = torch.tensor([[0, 1], [0, 1], [2, 5], [6, 9]], device=get_device_name()) expected_labels = ["a", "b", "a", "b"] elif global_rank == 1: expected_obs = torch.tensor([[1, 3], [3, 5], [3, 7], [9, 13]], device=get_device_name()) expected_labels = ["b", "a", "b", "a"] elif global_rank == 2: expected_obs = torch.tensor([[0, 1], [0, 1], [2, 5], [6, 9]], device=get_device_name()) expected_labels = ["a", "b", "a", "b"] elif global_rank == 3: expected_obs = torch.tensor([[1, 3], [3, 5], [3, 7], [9, 13]], device=get_device_name()) expected_labels = ["b", "a", "b", "a"] torch.testing.assert_close(data.batch["obs"], expected_obs, atol=0, rtol=0) assert (data.non_tensor_batch["labels"] == expected_labels).all() assert data.meta_info == {"info": "test_info"} def test_vocab_parallel_entropy(): from megatron.core import parallel_state as mpu from verl.utils.megatron.tensor_parallel import vocab_parallel_entropy from verl.utils.profiler import log_gpu_memory_usage from verl.utils.torch_functional import entropy_from_logits mpu.initialize_model_parallel( tensor_model_parallel_size=2, pipeline_model_parallel_size=1, virtual_pipeline_model_parallel_size=None ) batch_size = 2 seqlen = 128 vocab_size = 155136 logits = torch.randn(batch_size * seqlen, vocab_size, device=get_device_name(), requires_grad=True) target = torch.randint( low=0, high=vocab_size, size=(batch_size * seqlen,), device=get_device_name(), dtype=torch.int64 ) # broadcast across tp torch.distributed.broadcast( logits, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group() ) torch.distributed.broadcast( target, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group() ) tp_rank = mpu.get_tensor_model_parallel_rank() vocab_size_per_tp = vocab_size // mpu.get_tensor_model_parallel_world_size() # get the local logits of each tp vocab_parallel_logits = ( logits.clone().detach()[:, tp_rank * vocab_size_per_tp : (tp_rank + 1) * vocab_size_per_tp].requires_grad_() ) logits.grad = None vocab_parallel_logits.grad = None log_gpu_memory_usage("begin") output_entropy = vocab_parallel_entropy(vocab_parallel_logits) log_gpu_memory_usage("after forward") grad_output = torch.randn_like(output_entropy) output_entropy.backward(grad_output) log_gpu_memory_usage("after backward") target_entropy = entropy_from_logits(logits) torch.testing.assert_close(output_entropy, target_entropy) target_entropy.backward(grad_output) torch.testing.assert_close( logits.grad[:, tp_rank * vocab_size_per_tp : (tp_rank + 1) * vocab_size_per_tp], vocab_parallel_logits.grad ) # make sure logits is not altered torch.testing.assert_close( logits[:, tp_rank * vocab_size_per_tp : (tp_rank + 1) * vocab_size_per_tp], vocab_parallel_logits ) if mpu.get_tensor_model_parallel_rank() == 0: print("test_vocab_parallel_entropy passes") mpu.destroy_model_parallel() if __name__ == "__main__": local_rank, rank, world_size = initialize_global_process_group() test_all_gather_data_proto() test_vocab_parallel_entropy() ================================================ FILE: tests/special_distributed/test_torch_functional.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import torch from verl.utils.torch_functional import allgather_dict_into_dict if __name__ == "__main__": torch.distributed.init_process_group(backend="gloo") local_rank = int(os.environ["LOCAL_RANK"]) rank = int(os.environ["RANK"]) world_size = int(os.environ["WORLD_SIZE"]) metrics_dict = {"loss": [0 + rank, 1 + rank, 2 + rank], "grad_norm": rank} result = allgather_dict_into_dict(data=metrics_dict, group=None) assert result["loss"] == [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5]] assert result["grad_norm"] == [0, 1, 2, 3] print(result) ================================================ FILE: tests/special_e2e/README.md ================================================ This folder is reserved for end-to-end tests that typically require multiple GPUs. ================================================ FILE: tests/special_e2e/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/special_e2e/check_custom_rwd_fn.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse def check_congratulations_in_file(output_file): with open(output_file) as f: output = f.read() success_message = "Congratulations!!! You have called my_reward_function successfully!!!" assert success_message in output, f"Success message of my_reward_function not found in {output_file}" print("Check passes") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--output_file", required=True, type=str) args = parser.parse_args() check_congratulations_in_file(args.output_file) ================================================ FILE: tests/special_e2e/check_results.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import numpy as np def extract_reward_from_line(line): # TODO: this function needs error handling try: key_vals = line.split(" - ") for key_val in key_vals: key, val = key_val.split(":") if key == "critic/rewards/mean": reward = float(val) return reward return -np.inf except Exception: return -np.inf if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--output_file", required=True, type=str) parser.add_argument("--target", type=float, default=0.2, help="target reward score") args = parser.parse_args() with open(args.output_file) as f: output = f.read().split("\n") best_reward = -np.inf for line in output: if line.startswith("step"): reward = extract_reward_from_line(line) if reward > best_reward: best_reward = reward print(f"Best reward is {best_reward}") assert best_reward > args.target, f"Best reward must be greater than {args.target}. best_reward: {best_reward}" print("Check passes") ================================================ FILE: tests/special_e2e/envs/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .digit_completion import DigitCompletion __all__ = ["DigitCompletion"] ================================================ FILE: tests/special_e2e/envs/digit_completion/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from transformers import AutoTokenizer, LlamaConfig from .task import DigitCompletion, generate_ground_truth_response from .tokenizer import CharTokenizer AutoTokenizer.register(LlamaConfig, CharTokenizer, exist_ok=True) __all__ = ["DigitCompletion", "generate_ground_truth_response", "CharTokenizer"] ================================================ FILE: tests/special_e2e/envs/digit_completion/task.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Task and environment definition for digit completion.""" import numpy as np class DigitCompletion: """ The implementation of a simple digit completion task. The prompt is a sequence of numbers with fixed difference. The task is to complete the next N numbers. If the max number is reached, the next number should be modulo with max number. For example, - prompt = [1, 2, 3] - N = 5 - max_number = 6 the response should be [4, 5, 6, 7%6, 8%6] = [4, 5, 6, 0, 1] Note that the tokenizer is char-level to increase the difficulty. """ def __init__(self, max_number: int, max_diff: int, max_num_in_response: int, seed=0): """ Args: max_number: the maximum number allowed in the arithmetic sequence max_diff: the maximum diff. The actual common diff will be sampled from [0, max_diff] max_num_in_response: the maximum number in the response """ super().__init__() self.max_number = max_number self.max_diff = max_diff self.max_num_in_response = max_num_in_response assert self.max_num_in_response < 10 assert self.max_number > 0 assert self.max_diff > 0 self.max_number_length = len(str(max_number)) # {num1},{num2}:{max_num_in_response},{max_number} self._prompt_length = self.max_number_length * 2 + 4 + self.max_number_length # no negative is allowed self.np_rng = np.random.default_rng(seed=seed) def __str__(self): return ( f"Prompt length: {self.prompt_length}. Response length: {self.response_length}, " f"Max number: {self.max_number}. Max diff: {self.max_diff}, " f"Max number in response: {self.max_num_in_response}" ) def get_state(self): return {"rng": self.np_rng} def set_state(self, state): assert "rng" in state, "rng must be inside state" self.np_rng = state["rng"] @property def prompt_length(self): return self._prompt_length @property def response_length(self): # number length + comma length + [EOS] # The actual number times 1.5 to allow 'U' return (self.max_num_in_response * self.max_number_length + (self.max_num_in_response - 1) + 1) * 2 def add(self, a, b): return (a + b) % self.max_number def get_all_prompts(self): all_prompts = [] for first_num in range(self.max_number + 1): for diff in range(0, self.max_diff + 1): second_num = self.add(first_num, diff) for num_to_complete in range(self.max_num_in_response + 1): prompt = str(first_num) + "," + str(second_num) + f":{self.max_number},{num_to_complete}" all_prompts.append(prompt) return all_prompts def sample_str_prompts(self): # step 1: sample initial numbers first_num = self.np_rng.integers(self.max_number + 1) diff = self.np_rng.integers(self.max_diff + 1) second_num = self.add(first_num, diff) num_to_complete = self.np_rng.integers(self.max_num_in_response + 1) prompt = str(first_num) + "," + str(second_num) + f":{self.max_number},{num_to_complete}" return prompt def sample_batch_str_prompts(self, batch_size): str_prompts = [] for _ in range(batch_size): str_prompts.append(self.sample_str_prompts()) return str_prompts def compute_attention_mask(prompts, pad_token_id): mask = np.ones_like(prompts) mask[prompts == pad_token_id] = 0 return mask def compute_position_id_with_mask(mask): return np.clip(np.cumsum(mask, axis=-1) - 1, a_min=0, a_max=None) def generate_ground_truth_response(prompt: str): """Generate ground truth response given a prompt.""" num, info = prompt.split(":") num1, num2 = num.split(",") max_number, num_to_gen = info.split(",") num1 = int(num1) num2 = int(num2) max_number = int(max_number) num_to_gen = int(num_to_gen) diff = (num2 - num1) % max_number results = [] last_num = num2 for _ in range(num_to_gen): curr = (last_num + diff) % max_number results.append(str(curr)) last_num = curr response = ",".join(results) return response def compute_reward(prompt: str, response: str, sequence_reward=1.0): """We compute dense reward here so that we can directly train RL without SFT""" response_length = len(response) ground_truth_response = generate_ground_truth_response(prompt) per_token_reward = sequence_reward / (len(ground_truth_response) + 1) # including [EOS] # pad reward = np.zeros(response_length, dtype=np.float32) # this assumes that each char is a token # assign reward until mismatches ground_truth_idx = 0 for i in range(response_length): if ground_truth_idx == len(ground_truth_response): break ground_truth_response_token = ground_truth_response[ground_truth_idx] response_token = response[i] if ground_truth_response_token == response_token: reward[i] = per_token_reward ground_truth_idx += 1 else: # no matches break return reward, {"ground_truth_response": ground_truth_response} if __name__ == "__main__": task = DigitCompletion(max_number=20, max_diff=3, max_num_in_response=5) print(task.sample_str_prompts()) prompt = "7,8:20,0" response = "" print(compute_reward(prompt, response)) prompt = "7,8:20,0" response = "E000" print(compute_reward(prompt, response)) prompt = "9,10:20,2" response = "11,12,13" print(compute_reward(prompt, response)) ================================================ FILE: tests/special_e2e/envs/digit_completion/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Copied from https://github.com/dariush-bahrami/character-tokenizer/blob/master/charactertokenizer/core.py CharacterTokenzier for Hugging Face Transformers. This is heavily inspired from CanineTokenizer in transformers package. """ import json import os from pathlib import Path from typing import Optional, Sequence from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer class CharTokenizer(PreTrainedTokenizer): def __init__(self, characters: Sequence[str], model_max_length: int, chat_template, **kwargs): """Character tokenizer for Hugging Face transformers. Args: characters (Sequence[str]): List of desired characters. Any character which is not included in this list will be replaced by a special token called [UNK] with id=6. Following are list of all of the special tokens with their corresponding ids: "[CLS]": 0 "[SEP]": 1 "[BOS]": 2 "[MASK]": 3 "[PAD]": 4 "[RESERVED]": 5 "[UNK]": 6 an id (starting at 7) will be assigned to each character. model_max_length (int): Model maximum sequence length. """ eos_token_str = "E" sep_token_str = "S" pad_token_str = "P" unk_token_str = "U" self.characters = characters self.model_max_length = model_max_length eos_token = AddedToken(eos_token_str, lstrip=False, rstrip=False) sep_token = AddedToken(sep_token_str, lstrip=False, rstrip=False) pad_token = AddedToken(pad_token_str, lstrip=False, rstrip=False) unk_token = AddedToken(unk_token_str, lstrip=False, rstrip=False) self._vocab_str_to_int = { sep_token_str: 0, eos_token_str: 1, pad_token_str: 2, unk_token_str: 3, **{ch: i + 4 for i, ch in enumerate(characters)}, } self._vocab_int_to_str = {v: k for k, v in self._vocab_str_to_int.items()} super().__init__( eos_token=eos_token, sep_token=sep_token, pad_token=pad_token, unk_token=unk_token, add_prefix_space=False, model_max_length=model_max_length, **kwargs, ) self.chat_template = chat_template @property def vocab_size(self) -> int: return len(self._vocab_str_to_int) def get_vocab(self): return self._vocab_str_to_int def _tokenize(self, text: str) -> list[str]: return list(text) def _convert_token_to_id(self, token: str) -> int: return self._vocab_str_to_int.get(token, self._vocab_str_to_int["U"]) def _convert_id_to_token(self, index: int) -> str: return self._vocab_int_to_str[index] def convert_tokens_to_string(self, tokens): return "".join(tokens) def build_inputs_with_special_tokens( self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None ) -> list[int]: sep = [self.sep_token_id] cls = [self.cls_token_id] result = cls + token_ids_0 + sep if token_ids_1 is not None: result += token_ids_1 + sep return result def get_special_tokens_mask( self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False, ) -> list[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True, ) result = [1] + ([0] * len(token_ids_0)) + [1] if token_ids_1 is not None: result += ([0] * len(token_ids_1)) + [1] return result def get_config(self) -> dict: return { "char_ords": [ord(ch) for ch in self.characters], "model_max_length": self.model_max_length, "chat_template": self.chat_template, } @classmethod def from_config(cls, config: dict): cfg = {} cfg["characters"] = [chr(i) for i in config["char_ords"]] cfg["model_max_length"] = config["model_max_length"] cfg["chat_template"] = config["chat_template"] return cls(**cfg) def save_pretrained(self, save_directory: str | os.PathLike, **kwargs): cfg_file = Path(save_directory) / "tokenizer_config.json" cfg = self.get_config() with open(cfg_file, "w") as f: json.dump(cfg, f, indent=4) @classmethod def from_pretrained(cls, save_directory: str | os.PathLike, **kwargs): cfg_file = Path(save_directory) / "tokenizer_config.json" with open(cfg_file) as f: cfg = json.load(f) return cls.from_config(cfg) ================================================ FILE: tests/special_e2e/generation/run_gen_qwen05.sh ================================================ #!/usr/bin/env bash # Tested with 1 & 4 GPUs set -xeuo pipefail MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} NGPUS_PER_NODE=${NGPUS_PER_NODE:-4} OUTPUT_PATH=${OUTPUT_PATH:-$HOME/data/gen/qwen_05_gen_test.parquet} GEN_TP=${GEN_TP:-2} # Default tensor parallel size to 2 python3 -m verl.trainer.main_generation \ trainer.nnodes=1 \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ data.path="${HOME}/data/gsm8k/test.parquet" \ data.prompt_key=prompt \ data.n_samples=1 \ data.output_path="${OUTPUT_PATH}" \ model.path="${MODEL_ID}" \ +model.trust_remote_code=True \ rollout.temperature=1.0 \ rollout.top_k=50 \ rollout.top_p=0.7 \ rollout.prompt_length=2048 \ rollout.response_length=1024 \ rollout.tensor_model_parallel_size="${GEN_TP}" \ rollout.gpu_memory_utilization=0.8 ================================================ FILE: tests/special_e2e/generation/run_gen_qwen05_server.sh ================================================ #!/usr/bin/env bash # Tested with 1 & 4 GPUs set -xeuo pipefail MODEL_ID=${MODEL_ID:-$HOME/models/Qwen/Qwen2.5-0.5B-Instruct} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} OUTPUT_PATH=${OUTPUT_PATH:-$HOME/data/gen/qwen_05_gen_test.parquet} GEN_TP=${GEN_TP:-2} # Default tensor parallel size to 2 python3 -m verl.trainer.main_generation_server \ trainer.nnodes=1 \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ actor_rollout_ref.model.path="${MODEL_ID}" \ actor_rollout_ref.model.trust_remote_code=True \ actor_rollout_ref.rollout.temperature=1.0 \ actor_rollout_ref.rollout.top_k=50 \ actor_rollout_ref.rollout.top_p=0.7 \ actor_rollout_ref.rollout.prompt_length=2048 \ actor_rollout_ref.rollout.response_length=1024 \ actor_rollout_ref.rollout.tensor_model_parallel_size="${GEN_TP}" \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.n=4 \ data.train_files="${HOME}/data/gsm8k/test.parquet" \ data.prompt_key=prompt \ +data.output_path="${OUTPUT_PATH}" \ ================================================ FILE: tests/special_e2e/ppo_trainer/expert_parallel/qwen2moe_minimal.json ================================================ { "num_hidden_layers": 2, "max_window_layers": 2 } ================================================ FILE: tests/special_e2e/ppo_trainer/expert_parallel/qwen3moe_minimal.json ================================================ { "num_hidden_layers": 2, "max_window_layers": 2 } ================================================ FILE: tests/special_e2e/ppo_trainer/run_function_reward.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail NUM_GPUS=${NUM_GPUS:-8} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" TRAIN_FILES=${TRAIN_FILES:-$HOME/data/gsm8k/train.parquet} VAL_FILES=${VAL_FILES:-$HOME/data/gsm8k/test.parquet} MAX_PROMPT_LEN=${MAX_PROMPT_LEN:-512} MAX_RESPONSE_LEN=${MAX_RESPONSE_LEN:-512} ENGINE=${ENGINE:-vllm} if [ "$ENGINE" = "vllm" ]; then export VLLM_USE_V1=1 fi ROLLOUT_MODE="async" RETURN_RAW_CHAT="True" SKIP_TOKENIZER_INIT="True" GPU_MEMORY_UTILIZATION=${GPU_MEMORY_UTILIZATION:-0.7} ACTOR_FSDP_PARAM_OFFLOAD=${ACTOR_FSDP_PARAM_OFFLOAD:-False} ACTOR_FSDP_OPTIMIZER_OFFLOAD=${ACTOR_FSDP_OPTIMIZER_OFFLOAD:-False} REF_FSDP_PARAM_OFFLOAD=${REF_FSDP_PARAM_OFFLOAD:-True} RM_PAD=${RM_PAD:-True} FUSED_KERNELS=${FUSED_KERNELS:-False} FUSED_KERNEL_BACKEND=${FUSED_KERNEL_BACKEND:-torch} # or 'triton' for triton backend ADV_ESTIMATOR=${ADV_ESTIMATOR:-gae} LOSS_MODE=${LOSS_MODE:-vanilla} USE_KL=${USE_KL:-False} CUSTOM_REWARD_FN=${CUSTOM_REWARD_FN:-False} ENABLE_CHUNKED_PREFILL=${ENABLE_CHUNKED_PREFILL:-True} # For vLLM VLM placeholder issue: https://github.com/vllm-project/vllm/issues/15185 STRATEGY=${STRATEGY:-fsdp} # LoRA config LORA_RANK=${LORA_RANK:-0} LORA_ALPHA=${LORA_ALPHA:-${LORA_RANK}} LORA_TARGET=${LORA_TARGET:-"all-linear"} LORA_EXCLUDE=${LORA_EXCLUDE:-"DONT_EXCLUDE"} USE_SHM=${USE_SHM:-False} LOAD_FORMAT=${LOAD_FORMAT:-dummy} LAYERED_SUMMON=${LAYERED_SUMMON:-False} # Validation VAL_BEFORE_TRAIN=${VAL_BEFORE_TRAIN:-False} TEST_FREQ=${TEST_FREQ:--1} # Save & Resume RESUME_MODE=${RESUME_MODE:-disable} SAVE_FREQ=${SAVE_FREQ:--1} TOTAL_TRAIN_STEPS=${TOTAL_TRAIN_STEPS:-1} # whether to save hf_model SAVE_HF_MODEL=${SAVE_HF_MODEL:-False} FSDP_SIZE=${FSDP_SIZE:--1} SP_SIZE=${SP_SIZE:-1} if [ "${SAVE_HF_MODEL}" = "True" ]; then CHECKPOINT_CONTENTS="['model','hf_model','optimizer','extra']" else CHECKPOINT_CONTENTS="['model','optimizer','extra']" fi train_traj_micro_bsz_per_gpu=2 # b n_resp_per_prompt=4 # g train_traj_micro_bsz=$((train_traj_micro_bsz_per_gpu * NUM_GPUS)) # b * n train_traj_mini_bsz=$((train_traj_micro_bsz * 2)) # 2 * b * n train_prompt_mini_bsz=$((train_traj_mini_bsz * n_resp_per_prompt)) # 2 * b * n / g train_prompt_bsz=$((train_prompt_mini_bsz * 2)) # 4 * b * n / g reward_fn_name=null reward_fn_file_path=null output_file="$(pwd)/output.txt" if [ "${CUSTOM_REWARD_FN}" = "True" ]; then reward_fn_name="my_reward_function" reward_fn_file_path="$(pwd)/my_reward_function.py" rm -rf "${reward_fn_file_path}" cat < "$reward_fn_file_path" def ${reward_fn_name}(data_source, solution_str, ground_truth, extra_info=None): print(f"Congratulations!!! You have called ${reward_fn_name} successfully!!!") return 0.1 EOF rm -rf "${output_file}" fi exp_name="${VERL_EXP_NAME:-$(basename "${MODEL_ID,,}")-function-reward-minimal}" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator="${ADV_ESTIMATOR}" \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.train_batch_size="${train_prompt_bsz}" \ data.max_prompt_length="${MAX_PROMPT_LEN}" \ data.max_response_length="${MAX_RESPONSE_LEN}" \ data.return_raw_chat=${RETURN_RAW_CHAT} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.use_shm=${USE_SHM} \ actor_rollout_ref.model.lora_rank=${LORA_RANK} \ actor_rollout_ref.model.lora_alpha=${LORA_ALPHA} \ actor_rollout_ref.model.target_modules=${LORA_TARGET} \ actor_rollout_ref.model.exclude_modules=${LORA_EXCLUDE} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding="${RM_PAD}" \ actor_rollout_ref.model.use_fused_kernels=${FUSED_KERNELS} \ actor_rollout_ref.model.fused_kernel_options.impl_backend=${FUSED_KERNEL_BACKEND} \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.actor.strategy=${STRATEGY} \ actor_rollout_ref.actor.fsdp_config.param_offload=${ACTOR_FSDP_PARAM_OFFLOAD} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${ACTOR_FSDP_OPTIMIZER_OFFLOAD} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${FSDP_SIZE} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size="${SP_SIZE}" \ actor_rollout_ref.actor.checkpoint.save_contents=${CHECKPOINT_CONTENTS} \ actor_rollout_ref.actor.use_kl_loss="${USE_KL}" \ actor_rollout_ref.actor.policy_loss.loss_mode="${LOSS_MODE}" \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name="${ENGINE}" \ actor_rollout_ref.rollout.mode="${ROLLOUT_MODE}" \ actor_rollout_ref.rollout.load_format=${LOAD_FORMAT} \ actor_rollout_ref.rollout.layered_summon=${LAYERED_SUMMON} \ actor_rollout_ref.rollout.skip_tokenizer_init="${SKIP_TOKENIZER_INIT}" \ actor_rollout_ref.rollout.gpu_memory_utilization="${GPU_MEMORY_UTILIZATION}" \ actor_rollout_ref.rollout.enable_chunked_prefill="${ENABLE_CHUNKED_PREFILL}" \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.ref.fsdp_config.param_offload="${REF_FSDP_PARAM_OFFLOAD}" \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding="${RM_PAD}" \ critic.model.path="${MODEL_PATH}" \ critic.model.enable_gradient_checkpointing=False \ critic.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.custom_reward_function.path="${reward_fn_file_path}"\ reward.custom_reward_function.name="${reward_fn_name}"\ algorithm.use_kl_in_reward="${USE_KL}" \ algorithm.kl_penalty=kl \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl-test' \ trainer.experiment_name="${exp_name}" \ trainer.nnodes=1 \ trainer.n_gpus_per_node="${NUM_GPUS}" \ trainer.val_before_train="${VAL_BEFORE_TRAIN}" \ trainer.test_freq="${TEST_FREQ}" \ trainer.save_freq="${SAVE_FREQ}" \ trainer.resume_mode="${RESUME_MODE}" \ trainer.total_epochs=2 \ trainer.device=cuda \ trainer.total_training_steps="${TOTAL_TRAIN_STEPS}" $@ \ | tee "${output_file}" if [ "${CUSTOM_REWARD_FN}" = "True" ]; then python3 tests/special_e2e/check_custom_rwd_fn.py --output_file="${output_file}" check_exit_code=$? rm -rf "${reward_fn_file_path}" rm -rf "${output_file}" # Return the exit code of check_custom_rwd_fn.py if it fails if [ $check_exit_code -ne 0 ]; then exit $check_exit_code fi fi ================================================ FILE: tests/special_e2e/ppo_trainer/run_model_reward.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail NUM_GPUS=${NUM_GPUS:-8} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" TRAIN_FILES=${TRAIN_FILES:-$HOME/data/gsm8k/train.parquet} VAL_FILES=${VAL_FILES:-$HOME/data/gsm8k/test.parquet} RM_PAD=${RM_PAD:-True} FUSED_KERNELS=${FUSED_KERNELS:-False} FUSED_KERNEL_BACKEND=${FUSED_KERNEL_BACKEND:-torch} # or 'triton' for triton backend SP_SIZE=${SP_SIZE:-1} SEQ_BALANCE=${SEQ_BALANCE:-False} LIGER=${LIGER:-False} # Validation VAL_BEFORE_TRAIN=${VAL_BEFORE_TRAIN:-False} TEST_FREQ=${TEST_FREQ:--1} # Save & Resume RESUME_MODE=${RESUME_MODE:-disable} SAVE_FREQ=${SAVE_FREQ:--1} TOTAL_TRAIN_STEPS=${TOTAL_TRAIN_STEPS:-1} train_traj_micro_bsz_per_gpu=2 # b n_resp_per_prompt=4 # g train_traj_micro_bsz=$((train_traj_micro_bsz_per_gpu * NUM_GPUS)) # b * n train_traj_mini_bsz=$((train_traj_micro_bsz * 2)) # 2 * b * n train_prompt_mini_bsz=$((train_traj_mini_bsz * n_resp_per_prompt)) # 2 * b * n / g train_prompt_bsz=$((train_prompt_mini_bsz * 2)) # 4 * b * n / g train_max_token_num_per_gpu=32768 infer_max_token_num_per_gpu=32768 exp_name="$(basename "${MODEL_ID,,}")-model-reward-minimal" python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.train_batch_size=${train_prompt_bsz} \ data.max_prompt_length=512 \ data.max_response_length=512 \ data.return_raw_chat=True \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.use_liger="${LIGER}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding="${RM_PAD}" \ actor_rollout_ref.model.use_fused_kernels=${FUSED_KERNELS} \ actor_rollout_ref.model.fused_kernel_options.impl_backend=${FUSED_KERNEL_BACKEND} \ actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.use_dynamic_bsz="${SEQ_BALANCE}" \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${train_max_token_num_per_gpu} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size="${SP_SIZE}" \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_max_token_num_per_gpu} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_max_token_num_per_gpu} \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ critic.optim.lr=1e-5 \ critic.ulysses_sequence_parallel_size="${SP_SIZE}" \ critic.model.use_remove_padding="${RM_PAD}" \ critic.optim.lr_warmup_steps_ratio=0.05 \ critic.model.path="${MODEL_PATH}" \ critic.model.enable_gradient_checkpointing=False \ critic.use_dynamic_bsz="${SEQ_BALANCE}" \ critic.ppo_max_token_len_per_gpu=${train_max_token_num_per_gpu} \ critic.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ critic.model.fsdp_config.param_offload=False \ critic.model.fsdp_config.optimizer_offload=False \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path="${MODEL_PATH}" \ reward.reward_model.rollout.gpu_memory_utilization=0.8 \ reward.reward_model.rollout.tensor_model_parallel_size=1 \ reward.reward_model.rollout.prompt_length=1024 \ reward.reward_model.rollout.response_length=512 \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl-test' \ trainer.experiment_name="${exp_name}" \ trainer.nnodes=1 \ trainer.n_gpus_per_node="${NUM_GPUS}" \ trainer.val_before_train="${VAL_BEFORE_TRAIN}" \ trainer.test_freq="${VAL_BEFORE_TRAIN}" \ trainer.save_freq="${SAVE_FREQ}" \ trainer.resume_mode="${RESUME_MODE}" \ trainer.total_epochs=2 \ trainer.total_training_steps="${TOTAL_TRAIN_STEPS}" $@ ================================================ FILE: tests/special_e2e/ppo_trainer/run_single_gpu.sh ================================================ PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=console \ trainer.val_before_train=False \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ actor_rollout_ref.rollout.name=hf \ trainer.total_training_steps=2 ================================================ FILE: tests/special_e2e/ppo_trainer/run_single_gpu_with_engine.sh ================================================ PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=256 \ data.max_prompt_length=512 \ data.max_response_length=256 \ actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ critic.optim.lr=1e-5 \ critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.logger=['console'] \ trainer.val_before_train=False \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ actor_rollout_ref.rollout.name=hf \ trainer.use_legacy_worker_impl=disable \ trainer.total_training_steps=2 ================================================ FILE: tests/special_e2e/run_dapo.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail NUM_GPUS=${NUM_GPUS:-8} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" adv_estimator=grpo kl_coef=0.0 use_kl_in_reward=False use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=1024 max_response_length=2048 enable_overlong_buffer=True overlong_buffer_len=128 overlong_penalty_factor=1.0 loss_agg_mode="token-mean" enable_filter_groups=True filter_groups_metric=seq_reward max_num_gen_batches=10 train_traj_micro_bsz_per_gpu=2 # b n_resp_per_prompt=4 # g train_traj_micro_bsz=$((train_traj_micro_bsz_per_gpu * NUM_GPUS)) # b * n train_traj_mini_bsz=$((train_traj_micro_bsz * 2)) # 2 * b * n train_prompt_mini_bsz=$((train_traj_mini_bsz * n_resp_per_prompt)) # 2 * b * n / g train_prompt_bsz=$((train_prompt_mini_bsz * 2)) # 4 * b * n / g gen_prompt_bsz=$((train_prompt_bsz * 4)) exp_name="$(basename "${MODEL_ID,,}")-dapo-minimal" python3 -m recipe.dapo.main_dapo \ data.train_files="${HOME}/data/gsm8k/train.parquet" \ data.val_files="${HOME}/data/gsm8k/test.parquet" \ reward.reward_manager.name=dapo \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ reward.overlong_buffer.enable=${enable_overlong_buffer} \ reward.overlong_buffer.len=${overlong_buffer_len} \ reward.overlong_buffer.penalty_factor=${overlong_penalty_factor} \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ algorithm.filter_groups.enable=${enable_filter_groups} \ algorithm.filter_groups.metric=${filter_groups_metric} \ algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_fused_kernels=True \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ trainer.logger=console \ trainer.project_name='verl-test' \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=${NUM_GPUS} \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.total_epochs=2 \ trainer.resume_mode=disable \ trainer.val_before_train=False \ trainer.total_training_steps=1 $@ ================================================ FILE: tests/special_e2e/run_fully_async_policy.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail # Test script for fully_async_policy E2E regression testing # This script runs fully async PPO training with both FSDP2 and Megatron backends # to ensure the asynchronous training mechanism works correctly NUM_GPUS=${NUM_GPUS:-8} ACTOR_STRATEGY=${ACTOR_STRATEGY:-"fsdp2"} # fsdp2 or megatron # Download model if not exists MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} # hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=1024 max_response_length=2048 enable_overlong_buffer=True overlong_buffer_len=128 overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Temperature parameters temperature=1.0 top_p=1.0 top_k=-1 val_top_p=0.7 # Fully async specific parameters n_gpus_rollout=4 n_gpus_training=4 train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=16 total_rollout_steps=$(((128))) test_freq=-1 staleness_threshold=0.5 trigger_parameter_sync_step=4 partial_rollout=True use_trainer_do_validate=False exp_name="$(basename "${MODEL_ID,,}")-fully-async-policy-${ACTOR_STRATEGY}-minimal" echo "Running fully_async_policy with ${ACTOR_STRATEGY} strategy" echo "Total GPUs: ${NUM_GPUS}, Rollout GPUs: ${n_gpus_rollout}, Training GPUs: ${n_gpus_training}" # Common parameters for both FSDP2 and Megatron common_params=( data.train_files="${HOME}/data/gsm8k/train.parquet" data.val_files="${HOME}/data/gsm8k/test.parquet" data.prompt_key=prompt data.truncation='left' data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.train_batch_size=${train_prompt_bsz} data.gen_batch_size=${gen_prompt_bsz} data.return_raw_chat=${return_raw_chat} actor_rollout_ref.rollout.n=${n_resp_per_prompt} actor_rollout_ref.rollout.calculate_log_probs=True algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} actor_rollout_ref.hybrid_engine=False actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} actor_rollout_ref.actor.clip_ratio_c=10.0 actor_rollout_ref.model.path="${MODEL_PATH}" actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.optim.lr_warmup_steps=-1 actor_rollout_ref.actor.optim.weight_decay=0.1 actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} actor_rollout_ref.rollout.gpu_memory_utilization=0.80 actor_rollout_ref.rollout.temperature=${temperature} actor_rollout_ref.rollout.top_p=${top_p} actor_rollout_ref.rollout.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.enable_chunked_prefill=True actor_rollout_ref.rollout.name=${rollout_name} actor_rollout_ref.rollout.mode=${rollout_mode} actor_rollout_ref.rollout.disable_log_stats=False reward.reward_manager.name=dapo +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} +reward.reward_kwargs.overlong_buffer_cfg.log=False +reward.reward_kwargs.max_resp_len=${max_response_length} trainer.logger=['console'] trainer.project_name='verl-test-fully-async' trainer.experiment_name="${exp_name}" trainer.val_before_train=True trainer.save_freq=-1 trainer.resume_mode=disable trainer.nnodes=1 trainer.n_gpus_per_node=${n_gpus_training} trainer.log_val_generations=10 rollout.nnodes=1 rollout.n_gpus_per_node=${n_gpus_rollout} rollout.total_rollout_steps=${total_rollout_steps} trainer.total_epochs=2 trainer.test_freq=${test_freq} # Fully async specific configurations async_training.staleness_threshold=${staleness_threshold} async_training.partial_rollout="${partial_rollout}" async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" async_training.use_trainer_do_validate=${use_trainer_do_validate} actor_rollout_ref.rollout.checkpoint_engine.backend='nccl' actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=1024 ) # Detect device device_name=$(python3 - <<'EOF' from verl.utils.device import get_device_name print(get_device_name()) EOF ) if [ "${ACTOR_STRATEGY}" == "fsdp2" ]; then echo "Running fully async training with FSDP2 strategy..." # FSDP2 specific parameters gen_tp=1 sp_size=1 fsdp_size=1 ref_offload=True actor_offload=False if [ -n "$device_name" ] && [ "$device_name" == "npu" ]; then common_params+=( # Todo The checkpoint_engine.backend should be unified to nccl # actor_rollout_ref.rollout.checkpoint_engine.backend='hccl' actor_rollout_ref.rollout.gpu_memory_utilization=0.70 ) actor_offload=True fi python3 -m verl.experimental.fully_async_policy.fully_async_main \ "${common_params[@]}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} $@ elif [ "${ACTOR_STRATEGY}" == "megatron" ]; then echo "Running fully async training with Megatron strategy..." # Megatron specific parameters gen_tp=2 train_tp=1 train_pp=2 ref_offload=True actor_offload=False if [ -n "$device_name" ] && [ "$device_name" == "npu" ]; then train_tp=2 actor_offload=True common_params+=( # Todo The checkpoint_engine.backend should be unified to nccl # actor_rollout_ref.rollout.checkpoint_engine.backend='hccl' actor_rollout_ref.rollout.gpu_memory_utilization=0.60 ) fi python3 -m verl.experimental.fully_async_policy.fully_async_main \ --config-path=config \ --config-name='fully_async_ppo_megatron_trainer.yaml' \ "${common_params[@]}" \ actor_rollout_ref.actor.strategy=megatron \ critic.strategy=megatron \ actor_rollout_ref.actor.optim.lr_decay_steps=10000000 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.param_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.grad_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.param_offload=${ref_offload} $@ else echo "Error: Unknown strategy ${ACTOR_STRATEGY}. Please use 'fsdp2' or 'megatron'" exit 1 fi echo "Fully async policy E2E test completed successfully with ${ACTOR_STRATEGY} strategy" ================================================ FILE: tests/special_e2e/run_geo3k_fsdp_sgl_multiturn_w_tool.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x #hf download Qwen/Qwen2.5-VL-3B-Instruct --local-dir $HOME/models/Qwen/Qwen2.5-VL-3B-Instruct ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" FSDP_STRATEGY=${FSDP_STRATEGY:-fsdp} python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='geo3k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=64 \ data.max_prompt_length=2048 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=$HOME/models/Qwen/Qwen2.5-VL-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=64 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.strategy=$FSDP_STRATEGY \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \ actor_rollout_ref.ref.strategy=$FSDP_STRATEGY \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='geo3k_async_rl' \ trainer.experiment_name=qwen2.5-vl-3b_function_rm-geo3k-sgl-multi-w-tool-$FSDP_STRATEGY-rebased-0619-verify-n8 \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ data.train_files=$HOME/data/geo3k_verl_sgl_multi_turn_preprocessed/train.parquet \ data.val_files=$HOME/data/geo3k_verl_sgl_multi_turn_preprocessed/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/geo3k_tool_config.yaml" \ trainer.val_before_train=False \ trainer.total_training_steps=1 $@ ================================================ FILE: tests/special_e2e/run_grpo_lora_with_merge.sh ================================================ #!/usr/bin/env bash # # An e2e test script for testing the GRPO LoRA training process # and processing the generated checkpoint using the merge_model.py script. set -xeuo pipefail MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} if [ ! -d "$MODEL_PATH" ]; then echo "Downloading model to ${MODEL_PATH}..." # hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" else echo "Model directory ${MODEL_PATH} already exists, skip downloading." fi BATCH_SIZE=16 EXP_NAME="qwen2.5_0.5b_grpo_lora" # step 1. train model with grpo-lora for 1 step python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=${BATCH_SIZE} \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.shuffle=False \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.model.use_shm=True \ actor_rollout_ref.model.lora_rank=64 \ actor_rollout_ref.model.lora_alpha=32 \ actor_rollout_ref.actor.optim.lr=3e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${BATCH_SIZE} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name=${EXP_NAME} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.total_training_steps=1 \ trainer.save_freq=1 \ trainer.test_freq=5 \ trainer.total_epochs=1 $@ # step 2. merge model python3 -m verl.model_merger merge \ --backend fsdp \ --local_dir checkpoints/verl_grpo_example_gsm8k/${EXP_NAME}/global_step_1/actor/ \ --target_dir checkpoints/verl_grpo_example_gsm8k/${EXP_NAME}/global_step_1/actor/hf # step 3. assert # make sure adapter_model.safetensors exists and its size is larger than 1MB file_path="checkpoints/verl_grpo_example_gsm8k/${EXP_NAME}/global_step_1/actor/hf/lora_adapter/adapter_model.safetensors" if [ ! -f "$file_path" ]; then echo "Error: File $file_path does not exist!" exit 1 fi file_size=$(stat -c %s "$file_path") min_size_mb=1 min_size=$((min_size_mb * 1024 * 1024)) # 1MB = 1048576 bytes if [ "$file_size" -lt "$min_size" ]; then echo "Error: File $file_path is too small! Current size: $((file_size/1024))KB, Required: ${min_size_mb}MB" exit 1 fi echo "Check passed: File exists and size is $(($file_size/1024/1024))MB" exit 0 ================================================ FILE: tests/special_e2e/run_gsm8k_fsdp_sgl_multiturn_sf_tool.sh ================================================ # run on 8xH20 # make sure your current working directory is the root of the project set -x export PYTHONUNBUFFERED=1 export RAY_DEDUP_LOGS=0 export RUST_BACKTRACE=1 export HYDRA_FULL_ERROR=1 ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_sf_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=128 \ data.max_prompt_length=2048 \ data.max_response_length=16384 \ data.filter_overlong_prompts=False \ data.truncation='error' \ data.return_raw_chat=True \ data.train_files=$HOME/data/retool_dapo/train.parquet \ data.val_files=$HOME/data/retool_aime2024/train.parquet \ actor_rollout_ref.model.path=Qwen/Qwen3-4B \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.use_liger=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ +actor_rollout_ref.model.enable_activation_offload=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=32768 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.0 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/sandbox_fusion_tool_config.yaml" \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='retool_async_rl' \ trainer.experiment_name='qwen3-4b_function_rm-retool-async-sgl-no-sft-n8-v2505271300' \ trainer.val_before_train=False \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=100 \ trainer.test_freq=20 \ trainer.total_training_steps=1000 \ trainer.total_epochs=1 $@ ================================================ FILE: tests/special_e2e/run_gsm8k_fsdp_sgl_multiturn_w_tool.sh ================================================ # run on 8xH100 # make sure your current working directory is the root of the project set -x #hf download Qwen/Qwen2.5-3B-Instruct --local-dir $HOME/models/Qwen/Qwen2.5-3B-Instruct ulimit -n 65535 PROJECT_DIR="$(pwd)" CONFIG_PATH="$PROJECT_DIR/examples/sglang_multiturn/config" FSDP_STRATEGY=${FSDP_STRATEGY:-fsdp} python3 -m verl.trainer.main_ppo \ --config-path="$CONFIG_PATH" \ --config-name='gsm8k_multiturn_grpo' \ algorithm.adv_estimator=grpo \ data.train_batch_size=256 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.return_raw_chat=True \ actor_rollout_ref.model.path=$HOME/models/Qwen/Qwen2.5-3B-Instruct \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=256 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=16 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.strategy=$FSDP_STRATEGY \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.strategy=$FSDP_STRATEGY \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='gsm8k_async_rl' \ trainer.experiment_name=qwen2.5-3b_function_rm-gsm8k-sgl-multi-w-tool-$FSDP_STRATEGY-rebased-0427-verify-n16 \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ data.train_files=$HOME/data/gsm8k_verl_sgl_multi_turn_preprocessed/train.parquet \ data.val_files=$HOME/data/gsm8k_verl_sgl_multi_turn_preprocessed/test.parquet \ actor_rollout_ref.rollout.multi_turn.tool_config_path="$PROJECT_DIR/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml" \ trainer.val_before_train=False \ trainer.total_training_steps=1 $@ ================================================ FILE: tests/special_e2e/run_one_step_off_policy.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail # Test script for one_step_off_policy E2E regression testing # This script runs one_step_off_policy with both FSDP2 and Megatron backends # to ensure the asynchronous training mechanism works correctly NUM_GPUS=${NUM_GPUS:-8} ACTOR_STRATEGY=${ACTOR_STRATEGY:-"fsdp2"} # fsdp2 or megatron # Download model if not exists MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=1024 max_response_length=2048 enable_overlong_buffer=True overlong_buffer_len=128 overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" train_prompt_bsz=8 n_resp_per_prompt=3 train_prompt_mini_bsz=4 # Temperature parameters temperature=1.0 top_p=1.0 top_k=-1 val_top_p=0.7 # One-step-off-policy specific parameters # Allocate 2 GPUs for rollout, remaining for training n_gpus_rollout=2 n_gpus_training=$((NUM_GPUS - n_gpus_rollout)) exp_name="$(basename "${MODEL_ID,,}")-one-step-off-policy-${ACTOR_STRATEGY}-minimal" echo "Running one_step_off_policy with ${ACTOR_STRATEGY} strategy" echo "Total GPUs: ${NUM_GPUS}, Rollout GPUs: ${n_gpus_rollout}, Training GPUs: ${n_gpus_training}" # Common parameters for both FSDP2 and Megatron common_params=( data.train_files="${HOME}/data/gsm8k/train.parquet" data.val_files="${HOME}/data/gsm8k/test.parquet" data.prompt_key=prompt data.truncation='left' data.max_prompt_length=${max_prompt_length} data.max_response_length=${max_response_length} data.train_batch_size=${train_prompt_bsz} actor_rollout_ref.rollout.n=${n_resp_per_prompt} algorithm.adv_estimator=${adv_estimator} algorithm.use_kl_in_reward=${use_kl_in_reward} algorithm.kl_ctrl.kl_coef=${kl_coef} actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} actor_rollout_ref.actor.clip_ratio_c=10.0 actor_rollout_ref.model.path="${MODEL_PATH}" actor_rollout_ref.actor.optim.lr=1e-6 actor_rollout_ref.actor.optim.lr_warmup_steps=-1 actor_rollout_ref.actor.optim.weight_decay=0.1 actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} actor_rollout_ref.actor.entropy_coeff=0 actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} actor_rollout_ref.rollout.gpu_memory_utilization=0.80 actor_rollout_ref.rollout.temperature=${temperature} actor_rollout_ref.rollout.top_p=${top_p} actor_rollout_ref.rollout.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} actor_rollout_ref.rollout.val_kwargs.do_sample=True actor_rollout_ref.rollout.val_kwargs.n=1 actor_rollout_ref.rollout.enable_chunked_prefill=True actor_rollout_ref.rollout.name=vllm actor_rollout_ref.rollout.checkpoint_engine.backend='nccl' actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=1024 reward.reward_manager.name=dapo +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} +reward.reward_kwargs.overlong_buffer_cfg.log=False +reward.reward_kwargs.max_resp_len=${max_response_length} trainer.logger=['console'] trainer.project_name='verl-test' trainer.experiment_name="${exp_name}" trainer.val_before_train=True trainer.test_freq=-1 trainer.save_freq=-1 trainer.total_epochs=2 trainer.total_training_steps=2 trainer.resume_mode=disable trainer.nnodes=1 trainer.n_gpus_per_node=${n_gpus_training} rollout.nnodes=1 rollout.n_gpus_per_node=${n_gpus_rollout} ) # Detect device device_name=$(python3 - <<'EOF' from verl.utils.device import get_device_name print(get_device_name()) EOF ) if [ "${ACTOR_STRATEGY}" == "fsdp2" ]; then echo "Running with FSDP2 strategy..." # FSDP2 specific parameters gen_tp=2 sp_size=2 fsdp_size=2 ref_offload=True actor_offload=False if [ "$device_name" ] && [ "$device_name" == "npu" ]; then common_params+=( # Todo The checkpoint_engine.backend should be unified to nccl # actor_rollout_ref.rollout.checkpoint_engine.backend='hccl' actor_rollout_ref.rollout.gpu_memory_utilization=0.60 ) actor_offload=True fi python3 -m verl.experimental.one_step_off_policy.main_ppo \ "${common_params[@]}" \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} $@ elif [ "${ACTOR_STRATEGY}" == "megatron" ]; then echo "Running with Megatron strategy..." # Megatron specific parameters gen_tp=2 train_tp=1 train_pp=2 ref_offload=True actor_offload=False if [ "$device_name" ] && [ "$device_name" == "npu" ]; then common_params+=( # Todo The checkpoint_engine.backend should be unified to nccl # actor_rollout_ref.rollout.checkpoint_engine.backend='hccl' actor_rollout_ref.rollout.gpu_memory_utilization=0.70 trainer.n_gpus_per_node=4 rollout.n_gpus_per_node=4 actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ ) train_tp=2 actor_offload=True fi python3 -m verl.experimental.one_step_off_policy.main_ppo \ --config-path=config \ --config-name='one_step_off_ppo_megatron_trainer.yaml' \ "${common_params[@]}" \ actor_rollout_ref.actor.strategy=megatron \ critic.strategy=megatron \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.actor.megatron.param_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.grad_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.param_offload=${ref_offload} $@ else echo "Error: Unknown strategy ${ACTOR_STRATEGY}. Please use 'fsdp2' or 'megatron'" exit 1 fi echo "One-step-off-policy E2E test completed successfully with ${ACTOR_STRATEGY} strategy" ================================================ FILE: tests/special_e2e/run_ppo_trainer_megatron.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping export VERL_LOGGING_LEVEL=INFO export VERL_PPO_LOGGING_LEVEL=INFO NUM_GPUS=${NUM_GPUS:-8} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} RM_MODEL_PATH=${RM_MODEL_PATH:-${HOME}/models/Skywork/Skywork-Reward-V2-Llama-3.2-1B} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" USE_DUMMY_MODEL=${USE_DUMMY_MODEL:-False} DUMMY_MODEL_PATH=${DUMMY_MODEL_PATH:-${HOME}/dummy_models/${MODEL_ID}} if [ "$USE_DUMMY_MODEL" = "True" ]; then if [ -z "${DUMMY_MODEL_CONFIG_PATH}" ]; then echo "[ERROR] DUMMY_MODEL_CONFIG_PATH not set" exit 1 fi python scripts/init_random_model.py \ --hf_model_path "${MODEL_PATH}" \ --new_config_path "${DUMMY_MODEL_CONFIG_PATH}" \ --output_path "${DUMMY_MODEL_PATH}" MODEL_PATH="${DUMMY_MODEL_PATH}" fi TRAIN_FILES=${TRAIN_FILES:-${HOME}/data/gsm8k/train.parquet} VAL_FILES=${VAL_FILES:-${HOME}/data/gsm8k/test.parquet} ADV_ESTIMATOR=${ADV_ESTIMATOR:-gae} # Validation VAL_BEFORE_TRAIN=${VAL_BEFORE_TRAIN:-False} TEST_FREQ=${TEST_FREQ:--1} # Save & Resume RESUME_MODE=${RESUME_MODE:-disable} SAVE_FREQ=${SAVE_FREQ:--1} TOTAL_TRAIN_STEPS=${TOTAL_TRAIN_STEPS:-1} USE_DYNAMIC_BSZ=${USE_DYNAMIC_BSZ:-True} ppo_max_token_len_per_gpu=${PPO_MAX_TOKEN_LEN:-2400} forward_max_token_len_per_gpu=${FWD_MAX_TOKEN_LEN:-4800} train_traj_micro_bsz_per_gpu=${MICRO_BSZ:-2} # b n_resp_per_prompt=4 # g train_traj_micro_bsz=$((train_traj_micro_bsz_per_gpu * NUM_GPUS)) # b * n train_traj_mini_bsz=$((train_traj_micro_bsz * 2)) # 2 * b * n train_prompt_mini_bsz=$((train_traj_mini_bsz * n_resp_per_prompt)) # 2 * b * n / g train_prompt_bsz=$((train_prompt_mini_bsz * 2)) # 4 * b * n / g LORA_RANK=${LORA_RANK:-0} CRITIC_LORA_RANK=${CRITIC_LORA_RANK:-$LORA_RANK} LORA_ALPHA=${LORA_ALPHA:-${LORA_RANK}} LORA_TARGET_MODULES=${LORA_TARGET_MODULES:-"['linear_qkv','linear_proj','linear_fc1','linear_fc2']"} LORA_MERGE=${LORA_MERGE:-False} MAX_PROMPT_LENGTH=${MAX_PROMPT_LENGTH:-512} MAX_RESPONSE_LENGTH=${MAX_RESPONSE_LENGTH:-512} MAX_RM_LENGTH=$((MAX_PROMPT_LENGTH + MAX_RESPONSE_LENGTH)) COMMON_PP=${COMMON_PP:-2} COMMON_VPP=${COMMON_VPP:-2} COMMON_CP=${COMMON_CP:-2} COMMON_TP=${COMMON_TP:-2} COMMON_EP=${COMMON_EP:-1} COMMON_ETP=${COMMON_ETP:-1} TRAIN_TP=${TRAIN_TP:-$COMMON_TP} INFER_TP=${INFER_TP:-$COMMON_TP} ACTOR_PP=${ACTOR_PP:-$COMMON_PP} ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP} ACTOR_CP=${ACTOR_CP:-$COMMON_CP} ACTOR_TP=${ACTOR_TP:-$TRAIN_TP} ACTOR_EP=${ACTOR_EP:-$COMMON_EP} ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP} ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP} REF_PP=${REF_PP:-$COMMON_PP} REF_VPP=${REF_VPP:-$COMMON_VPP} REF_CP=${REF_CP:-$COMMON_CP} REF_TP=${REF_TP:-$TRAIN_TP} REF_EP=${REF_EP:-$COMMON_EP} REF_ETP=${REF_ETP:-$COMMON_ETP} CRITIC_PP=${CRITIC_PP:-$COMMON_PP} CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP} CRITIC_CP=${CRITIC_CP:-$COMMON_CP} CRITIC_TP=${CRITIC_TP:-$TRAIN_TP} CRITIC_EP=${CRITIC_EP:-$COMMON_EP} CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP} ALL_OFFLOAD=${ALL_OFFLOAD:-False} COMMON_PARAM_OFFLOAD=${COMMON_PARAM_OFFLOAD:-$ALL_OFFLOAD} COMMON_GRAD_OFFLOAD=${COMMON_GRAD_OFFLOAD:-$ALL_OFFLOAD} COMMON_OPTIMIZER_OFFLOAD=${COMMON_OPTIMIZER_OFFLOAD:-$ALL_OFFLOAD} ACTOR_PARAM_OFFLOAD=${ACTOR_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} ACTOR_GRAD_OFFLOAD=${ACTOR_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} ACTOR_OPTIMIZER_OFFLOAD=${ACTOR_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} REF_PARAM_OFFLOAD=${REF_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} CRITIC_PARAM_OFFLOAD=${CRITIC_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} CRITIC_GRAD_OFFLOAD=${CRITIC_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD} CRITIC_OPTIMIZER_OFFLOAD=${CRITIC_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD} RM_PARAM_OFFLOAD=${RM_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD} USE_MBRIDGE=${USE_MBRIDGE:-False} VANILLA_MBRIDGE=${VANILLA_MBRIDGE:-True} VALUE_VANILLA_MBRIDGE=${VALUE_VANILLA_MBRIDGE:-$VANILLA_MBRIDGE} USE_FUSED_KERNELS=${USE_FUSED_KERNELS:-False} LR_WARMUP_STEPS=${LR_WARMUP_STEPS:-null} CHECKPOINT_CONTENTS=['model','hf_model','optimizer','extra'] SKIP_SAVE_HF_MODEL=${SKIP_SAVE_HF_MODEL:-0} if [ $SKIP_SAVE_HF_MODEL -eq 1 ]; then CHECKPOINT_CONTENTS=['model','optimizer','extra'] fi USE_DIST_CKPT=${USE_DIST_CKPT:-False} DIST_CKPT_PATH=${DIST_CKPT_PATH:-${HOME}/dist_ckpt/${MODEL_ID}} if [ "$USE_DIST_CKPT" = "True" ]; then if [ "$USE_DUMMY_MODEL" = "True" ]; then DIST_CKPT_PATH=${HOME}/dist_ckpt_dummy/${MODEL_ID} fi python scripts/converter_hf_to_mcore.py \ --hf_model_path "${MODEL_PATH}" \ --output_path "${DIST_CKPT_PATH}" fi ENGINE=${ENGINE:-"vllm"} if [ "$ENGINE" = "vllm" ]; then export VLLM_USE_V1=1 fi exp_name="$(basename "${MODEL_ID,,}")-megatron-gsm8k-minimal" ROLLOUT_MODE="async" ROLLOUT_QUANTIZATION=${ROLLOUT_QUANTIZATION:-null} RETURN_RAW_CHAT="True" SKIP_TOKENIZER_INIT="True" OPTIM_MEMORY_EFFICIENT=${OPTIM_MEMORY_EFFICIENT:-False} PROFILE_ENABLE=${PROFILE_ENABLE:-False} PROFILE_STEPS=${PROFILE_STEPS:-[1]} PROFILE_RANKS_ALL=${PROFILE_RANKS_ALL:-True} PROFILE_RANKS=${PROFILE_RANKS:-[0,1,2,3]} DISCRETE=${DISCRETE:-True} # or True USE_LEGACY_WORKER_IMPL=${USE_LEGACY_WORKER_IMPL:-"enable"} USE_REMOVE_PADDING=${USE_REMOVE_PADDING:-False} ROUTING_REPLAY_MODE=${ROUTING_REPLAY_MODE:-"disabled"} if [ "$ROUTING_REPLAY_MODE" = "R3" ]; then ENABLE_ROLLOUT_ROUTING_REPLAY=True else ENABLE_ROLLOUT_ROUTING_REPLAY=False fi python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml'\ algorithm.adv_estimator="${ADV_ESTIMATOR}" \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.train_batch_size=${train_prompt_bsz} \ data.max_prompt_length=${MAX_PROMPT_LENGTH} \ data.max_response_length=${MAX_RESPONSE_LENGTH} \ data.return_raw_chat=${RETURN_RAW_CHAT} \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.use_fused_kernels=${USE_FUSED_KERNELS} \ actor_rollout_ref.model.use_remove_padding=${USE_REMOVE_PADDING} \ actor_rollout_ref.model.lora.rank=${LORA_RANK} \ actor_rollout_ref.model.lora.alpha=${LORA_ALPHA} \ actor_rollout_ref.model.lora.target_modules=${LORA_TARGET_MODULES} \ actor_rollout_ref.model.lora.merge=${LORA_MERGE} \ +actor_rollout_ref.model.lora.fully_sharded_loras=True \ actor_rollout_ref.actor.optim.lr_warmup_steps=$LR_WARMUP_STEPS \ actor_rollout_ref.actor.megatron.router_replay.mode=${ROUTING_REPLAY_MODE} \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=$OPTIM_MEMORY_EFFICIENT \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=$OPTIM_MEMORY_EFFICIENT \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=$OPTIM_MEMORY_EFFICIENT \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.actor.use_dynamic_bsz=${USE_DYNAMIC_BSZ} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${ppo_max_token_len_per_gpu} \ actor_rollout_ref.actor.megatron.use_mbridge=${USE_MBRIDGE} \ actor_rollout_ref.actor.megatron.vanilla_mbridge=${VANILLA_MBRIDGE} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$ACTOR_PP \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=$ACTOR_VPP \ actor_rollout_ref.actor.megatron.context_parallel_size=$ACTOR_CP \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$ACTOR_TP \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=$ACTOR_EP \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=$ACTOR_ETP \ actor_rollout_ref.actor.megatron.param_offload=${ACTOR_PARAM_OFFLOAD} \ actor_rollout_ref.actor.megatron.optimizer_offload=${ACTOR_OPTIMIZER_OFFLOAD} \ actor_rollout_ref.actor.megatron.grad_offload=${ACTOR_GRAD_OFFLOAD} \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.checkpoint.save_contents=$CHECKPOINT_CONTENTS \ actor_rollout_ref.actor.profiler.enable=$PROFILE_ENABLE \ actor_rollout_ref.actor.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.rollout.name="${ENGINE}" \ actor_rollout_ref.rollout.mode="${ROLLOUT_MODE}" \ actor_rollout_ref.rollout.tensor_model_parallel_size=$ROLLOUT_TP \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ ++actor_rollout_ref.rollout.quantization=${ROLLOUT_QUANTIZATION} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.rollout.enable_rollout_routing_replay=${ENABLE_ROLLOUT_ROUTING_REPLAY} \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ actor_rollout_ref.ref.megatron.use_mbridge=${USE_MBRIDGE} \ actor_rollout_ref.ref.megatron.vanilla_mbridge=${VANILLA_MBRIDGE} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$REF_PP \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=$REF_VPP \ actor_rollout_ref.ref.megatron.context_parallel_size=$REF_CP \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=$REF_TP \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=$REF_EP \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=$REF_ETP \ actor_rollout_ref.ref.megatron.param_offload=${REF_PARAM_OFFLOAD} \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ critic.optim.lr=2e-5 \ critic.optim.lr_warmup_steps=$LR_WARMUP_STEPS \ +critic.optim.override_optimizer_config.optimizer_cpu_offload=$OPTIM_MEMORY_EFFICIENT \ +critic.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=$OPTIM_MEMORY_EFFICIENT \ +critic.optim.override_optimizer_config.use_precision_aware_optimizer=$OPTIM_MEMORY_EFFICIENT \ critic.model.path="${MODEL_PATH}" \ critic.model.lora.rank=${CRITIC_LORA_RANK} \ critic.model.lora.alpha=${LORA_ALPHA} \ critic.model.lora.target_modules=${LORA_TARGET_MODULES} \ critic.ppo_micro_batch_size_per_gpu=${train_traj_micro_bsz_per_gpu} \ critic.ppo_max_token_len_per_gpu=${forward_max_token_len_per_gpu} \ critic.megatron.use_mbridge=${USE_MBRIDGE} \ critic.megatron.vanilla_mbridge=${VALUE_VANILLA_MBRIDGE} \ critic.megatron.pipeline_model_parallel_size=$CRITIC_PP \ critic.megatron.virtual_pipeline_model_parallel_size=$CRITIC_VPP \ critic.megatron.context_parallel_size=$CRITIC_CP \ critic.megatron.tensor_model_parallel_size=$CRITIC_TP \ critic.megatron.expert_model_parallel_size=$CRITIC_EP \ critic.megatron.expert_tensor_parallel_size=$CRITIC_ETP \ critic.megatron.param_offload=${CRITIC_PARAM_OFFLOAD} \ critic.megatron.optimizer_offload=${CRITIC_OPTIMIZER_OFFLOAD} \ critic.megatron.grad_offload=${CRITIC_GRAD_OFFLOAD} \ critic.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ critic.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ critic.checkpoint.save_contents=$CHECKPOINT_CONTENTS \ critic.profiler.enable=$PROFILE_ENABLE \ critic.profiler.ranks=$PROFILE_RANKS \ critic.profiler.all_ranks=$PROFILE_RANKS_ALL \ reward.num_workers=8 \ reward.reward_model.enable=True \ reward.reward_model.model_path="${RM_MODEL_PATH}" \ reward.reward_model.rollout.name=${ENGINE} \ reward.reward_model.rollout.gpu_memory_utilization=0.6 \ reward.reward_model.rollout.tensor_model_parallel_size=${INFER_TP} \ reward.reward_model.rollout.prompt_length=${MAX_RM_LENGTH} \ reward.reward_model.rollout.response_length=${MAX_RESPONSE_LENGTH} \ algorithm.use_kl_in_reward=False \ algorithm.kl_penalty=kl \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl-test' \ trainer.experiment_name="${exp_name}" \ trainer.nnodes=1 \ trainer.n_gpus_per_node=${NUM_GPUS} \ trainer.val_before_train="${VAL_BEFORE_TRAIN}" \ trainer.test_freq="${TEST_FREQ}" \ trainer.save_freq="${SAVE_FREQ}" \ trainer.resume_mode="${RESUME_MODE}" \ trainer.total_epochs=2 \ trainer.total_training_steps="${TOTAL_TRAIN_STEPS}" \ trainer.use_legacy_worker_impl=${USE_LEGACY_WORKER_IMPL} \ global_profiler.profile_continuous_steps=True \ global_profiler.tool=nsys \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.global_tool_config.nsys.discrete=$DISCRETE $@ ================================================ FILE: tests/special_e2e/run_ppo_trainer_torchtitan.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail # Download model if not exists MODEL_ID=${MODEL_ID:-Qwen/Qwen3-0.6B} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #huggingface-cli download "${MODEL_ID}" --local-dir "${MODEL_PATH}" VAL_BEFORE_TRAIN=${VAL_BEFORE_TRAIN:-False} NUM_GPUS=${NUM_GPUS:-1} FSDP_SIZE=${FSDP_SIZE:-1} TP_SIZE=${TP_SIZE:-1} CP_SIZE=${CP_SIZE:-1} EP_SIZE=${EP_SIZE:-1} VERL_EXP_NAME=${VERL_EXP_NAME:-Titan_Qwen3_30B_A3B_DP8_EP8} MAX_PROMPT_LENGTH=${MAX_PROMPT_LENGTH:-512} MAX_RESPONSE_LENGTH=${MAX_RESPONSE_LENGTH:-2048} MAX_SEQ_LEN=${MAX_SEQ_LEN:-$((MAX_PROMPT_LENGTH + MAX_RESPONSE_LENGTH))} python3 -m verl.trainer.main_ppo \ model_engine=torchtitan \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=8 \ data.max_prompt_length="${MAX_PROMPT_LENGTH}" \ data.max_response_length="${MAX_RESPONSE_LENGTH}" \ data.seed=42 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.min_lr_factor=1.0 \ actor_rollout_ref.actor.ppo_mini_batch_size=4 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.torchtitan.data_parallel_shard_size="${FSDP_SIZE}" \ actor_rollout_ref.actor.torchtitan.tensor_parallel_size="${TP_SIZE}" \ actor_rollout_ref.actor.torchtitan.context_parallel_size="${CP_SIZE}" \ actor_rollout_ref.actor.torchtitan.expert_parallel_size="${EP_SIZE}" \ actor_rollout_ref.actor.torchtitan.attn_type=flex \ actor_rollout_ref.actor.torchtitan.use_torch_compile=False \ actor_rollout_ref.actor.torchtitan.param_offload=True \ actor_rollout_ref.actor.torchtitan.optimizer_offload=True \ actor_rollout_ref.actor.torchtitan.max_seq_len="${MAX_SEQ_LEN}" \ actor_rollout_ref.ref.torchtitan.max_seq_len="${MAX_SEQ_LEN}" \ actor_rollout_ref.ref.torchtitan.use_torch_compile=False \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=8 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.35 \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.max_model_len="${MAX_SEQ_LEN}" \ critic.optim.lr=1e-5 \ critic.model.path="${MODEL_PATH}" \ critic.ppo_micro_batch_size_per_gpu=2 \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.use_legacy_worker_impl=disable \ trainer.logger=['console','file','wandb'] \ trainer.project_name='verl_grpo_example_gsm8k_0302' \ trainer.experiment_name="${VERL_EXP_NAME}" \ trainer.val_before_train="${VAL_BEFORE_TRAIN}" \ trainer.log_val_generations=1 \ trainer.test_freq=1 \ trainer.n_gpus_per_node="${NUM_GPUS}" \ trainer.nnodes=1 \ trainer.total_training_steps=100 $@ ================================================ FILE: tests/special_e2e/run_ppo_trainer_veomni.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail SAVE_PATH=tests/utils/ci/profiler_data rm -rf "$SAVE_PATH" CONTENTS=['cuda'] PROFILE_STEPS=[1] PROFILE_RANKS_ALL=False PROFILE_RANKS=[0] DISCRETE=True # Download model if not exists MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #huggingface-cli download "${MODEL_ID}" --local-dir "${MODEL_PATH}" TRAIN_FILES=${TRAIN_FILES:-${HOME}/data/gsm8k/train.parquet} VAL_FILES=${VAL_FILES:-${HOME}/data/gsm8k/test.parquet} VAL_BEFORE_TRAIN=${VAL_BEFORE_TRAIN:-True} NUM_GPUS=${NUM_GPUS:-8} FSDP_SIZE=${FSDP_SIZE:-4} SP_SIZE=${SP_SIZE:-2} EP_SIZE=${EP_SIZE:-1} MODEL_NAME_ONLY=${MODEL_ID##*/} VERL_EXP_NAME=${VERL_EXP_NAME:-${MODEL_NAME_ONLY}-function-reward-minimal-fsdp-size${FSDP_SIZE}} python3 -m verl.trainer.main_ppo \ model_engine=veomni \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.train_batch_size=16 \ data.max_prompt_length=512 \ data.max_response_length=128 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=5e-7 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.veomni.param_offload=True \ actor_rollout_ref.actor.veomni.optimizer_offload=True \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.actor.veomni.fsdp_size="${FSDP_SIZE}" \ actor_rollout_ref.actor.veomni.ulysses_parallel_size="${SP_SIZE}" \ actor_rollout_ref.actor.veomni.expert_parallel_size="${EP_SIZE}" \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.veomni.param_offload=True \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=2 \ actor_rollout_ref.ref.veomni.optimizer_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_veomni_test' \ trainer.experiment_name="${VERL_EXP_NAME}" \ trainer.n_gpus_per_node="${NUM_GPUS}" \ trainer.val_before_train="${VAL_BEFORE_TRAIN}" \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 \ trainer.total_training_steps=1 \ actor_rollout_ref.actor.profiler.enable=True \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.actor.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.actor.profiler.tool_config.torch.discrete=$DISCRETE \ actor_rollout_ref.actor.profiler.tool_config.torch.contents=$CONTENTS \ actor_rollout_ref.ref.profiler.enable=True \ actor_rollout_ref.ref.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.ref.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.ref.profiler.tool_config.torch.discrete=$DISCRETE \ actor_rollout_ref.ref.profiler.tool_config.torch.contents=$CONTENTS \ global_profiler.tool=torch \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.save_path="$SAVE_PATH" $@ python3 "tests/utils/test_check_profiler_output.py" --profiler_dir="$SAVE_PATH" --device="gpu" rm -rf "$SAVE_PATH" ================================================ FILE: tests/special_e2e/run_test.sh ================================================ #!/bin/bash set -xeuo pipefail # Get the configuration name and engine name from arguments CONFIG_NAME="$1" ENGINE="${2:-vllm}" # Download model if needed #hf download Qwen/Qwen2.5-0.5B --local-dir "$HOME/models/Qwen/Qwen2.5-0.5B" # Run the training with the specified configuration python3 -m verl.trainer.main_ppo \ --config-name "$CONFIG_NAME" "$@" ================================================ FILE: tests/special_e2e/sft/compare_sft_engine_results.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import torch def get_result(file): file = os.path.expanduser(file) result = [] with open(file) as f: lines = f.readlines() for line in lines: result.append(json.loads(line)) return result def compare_results(golden_results, other_result): golden_loss = golden_results[0]["data"]["train/loss"] golden_grad_norm = golden_results[0]["data"]["train/grad_norm"] loss = other_result[0]["data"]["train/loss"] grad_norm = other_result[0]["data"]["train/grad_norm"] torch.testing.assert_close(golden_loss, loss, atol=1e-2, rtol=1e-2) torch.testing.assert_close(golden_grad_norm, grad_norm, atol=1e-4, rtol=3e-2) if __name__ == "__main__": golden_results = get_result("~/verl/test/log/golden.jsonl") # get all other results other_results = {} # walk through all files in ~/verl/test/log for file in os.listdir(os.path.expanduser("~/verl/test/log/verl_sft_test")): if file.endswith(".jsonl"): other_results[file] = get_result(os.path.join(os.path.expanduser("~/verl/test/log/verl_sft_test"), file)) # # compare results for file, other_result in other_results.items(): print(f"compare results {file}") compare_results(golden_results, other_result) print(f"compare results {file} done") print("All results are close to golden results") ================================================ FILE: tests/special_e2e/sft/run_sft.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"} NUM_GPUS=${NUM_GPUS:-8} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" TRAIN_FILES=${TRAIN_FILES:-$HOME/data/gsm8k_sft/train.parquet} VAL_FILES=${VAL_FILES:-$HOME/data/gsm8k_sft/test.parquet} SP_SIZE=${SP_SIZE:-1} LIGER=${LIGER:-False} MULTITURN=${MULTITURN:-False} LORA_RANK=${LORA_RANK:-0} RM_PAD=${RM_PAD:-True} TOTAL_TRAIN_STEP=${TOTAL_TRAIN_STEP:-1} RESUME_MODE=${RESUME_MODE:-disable} SAVE_FREQ=${SAVE_FREQ:-1} micro_bsz=2 NUM_GPUS=8 project_name="verl-test" exp_name="$(basename "${MODEL_ID,,}")-sft-minimal" ckpts_home=${ckpts_home:-$HOME/${project_name}/${exp_name}} mkdir -p "${ckpts_home}" torchrun --standalone --nnodes=1 --nproc_per_node=${NUM_GPUS} ${ENTRYPOINT} \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.messages_key=messages \ data.micro_batch_size_per_gpu=${micro_bsz} \ optim.lr=1e-4 \ engine=fsdp \ engine.ulysses_sequence_parallel_size="${SP_SIZE}" \ model.path="${MODEL_PATH}" \ model.lora_rank="${LORA_RANK}" \ model.lora_alpha=16 \ model.target_modules=all-linear \ model.use_liger="${LIGER}" \ model.use_remove_padding="${RM_PAD}" \ trainer.default_local_dir="${ckpts_home}" \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.total_training_steps=${TOTAL_TRAIN_STEP} \ trainer.save_freq=${SAVE_FREQ} \ checkpoint.save_contents=[model,optimizer,extra,hf_model] \ trainer.max_ckpt_to_keep=1 \ trainer.resume_mode=${RESUME_MODE} \ trainer.logger=['console'] $@ rm -rf "${ckpts_home:?}/*" ================================================ FILE: tests/special_e2e/sft/run_sft_engine.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail NUM_GPUS=${NUM_GPUS:-1} mode=${mode:-spmd} if [ "$mode" = "spmd" ]; then ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"} COMMAND="torchrun --standalone --nnodes=${NNODES:-1} --nproc-per-node=${NUM_GPUS:-1} ${ENTRYPOINT}" else ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer_ray"} COMMAND="python ${ENTRYPOINT} trainer.nnodes=${NNODES:-1} trainer.n_gpus_per_node=${NUM_GPUS:-1}" fi DATASET_DIR=${DATASET_DIR:-~/data/gsm8k_sft} TRAIN_FILES=${DATASET_DIR}/train.parquet VAL_FILES=${DATASET_DIR}/test.parquet backend=${BACKEND:-fsdp} project_name=verl_sft_test RESUME_MODE=disable ckpts_home=${ckpts_home:-~/verl/test/gsm8k-sft-${backend}} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B} MODEL_PATH=${MODEL_PATH:-${HOME}/models/${MODEL_ID}} #hf download "${MODEL_ID}" --local-dir "${MODEL_PATH}" SP_SIZE=${SP_SIZE:-1} FSDP_SIZE=${FSDP_SIZE:-1} FSDP_STRATEGY=${FSDP_STRATEGY:-"fsdp"} TP_SIZE=${TP_SIZE:-1} PP_SIZE=${PP_SIZE:-1} VPP_SIZE=${VPP_SIZE:-null} CP_SIZE=${CP_SIZE:-1} PAD_MODE=${PAD_MODE:-no_padding} USE_REMOVE_PADDING=${USE_REMOVE_PADDING:-True} FSDP_ENGINE_CONFIG="\ engine=${backend} \ model=hf_model \ model.path=$MODEL_PATH \ optim=${backend} \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.min_lr_ratio=0.1 \ optim.lr_scheduler_type=cosine \ engine.ulysses_sequence_parallel_size=${SP_SIZE} \ engine.strategy=${FSDP_STRATEGY} \ engine.fsdp_size=${FSDP_SIZE}" VEOMNI_ENGINE_CONFIG="\ engine=${backend} \ model=hf_model \ model.path=$MODEL_PATH \ optim=${backend} \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.lr_min=1e-6 \ optim.lr_scheduler_type=cosine \ engine.ulysses_parallel_size=${SP_SIZE} \ engine.fsdp_size=${FSDP_SIZE}" MEGATRON_ENGINE_CONFIG="\ engine=${backend} \ model=hf_model \ model.path=$MODEL_PATH \ optim=${backend} \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.lr_warmup_init=0 \ optim.lr_decay_style=cosine \ optim.min_lr=1e-6 \ engine.tensor_model_parallel_size=${TP_SIZE} \ engine.pipeline_model_parallel_size=${PP_SIZE} \ engine.virtual_pipeline_model_parallel_size=${VPP_SIZE} \ engine.context_parallel_size=${CP_SIZE} \ +engine.override_transformer_config.context_parallel_size=${CP_SIZE} \ engine.use_mbridge=True" TORCHTITAN_ENGINE_CONFIG="\ engine=${backend} \ model=hf_model \ model.path=${MODEL_PATH} \ optim=${backend} \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.min_lr_factor=0.1 \ optim.decay_type=cosine \ optim.total_training_steps=1000 \ engine.tensor_parallel_size=${TP_SIZE} \ engine.pipeline_parallel_size=${PP_SIZE} \ engine.context_parallel_size=${CP_SIZE} \ engine.data_parallel_shard_size=${FSDP_SIZE} \ engine.use_torch_compile=False" AUTOMODEL_ENGINE_CONFIG="\ engine=${backend} \ model=hf_model \ model.path=${MODEL_PATH} \ optim=${backend} \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.min_lr_ratio=0.1 \ optim.lr_scheduler_type=cosine \ engine.tp_size=${TP_SIZE} \ engine.cp_size=${CP_SIZE} \ engine.use_torch_compile=False" if [ "$backend" = "fsdp" ]; then ENGINE_CONFIG="$FSDP_ENGINE_CONFIG" echo "Using fsdp engine" exp_name=gsm8k-${backend}-${FSDP_STRATEGY}-sp${SP_SIZE}-fsdp${FSDP_SIZE}-pad-${PAD_MODE}-use_remove_padding-${USE_REMOVE_PADDING}-mode-${mode} elif [ "$backend" = "veomni" ]; then ENGINE_CONFIG="$VEOMNI_ENGINE_CONFIG" echo "Using veomni engine" exp_name=gsm8k-${backend}-sp${SP_SIZE}-fsdp${FSDP_SIZE}-pad-${PAD_MODE}-use_remove_padding-${USE_REMOVE_PADDING}-mode-${mode} elif [ "$backend" = "torchtitan" ]; then ENGINE_CONFIG="$TORCHTITAN_ENGINE_CONFIG" echo "Using torchtitan engine" exp_name=gsm8k-${backend}-tp${TP_SIZE}-pp${PP_SIZE}-cp${CP_SIZE}-dp${FSDP_SIZE}-pad-${PAD_MODE}-use_remove_padding-${USE_REMOVE_PADDING}-mode-${mode} elif [ "$backend" = "automodel" ]; then ENGINE_CONFIG="$AUTOMODEL_ENGINE_CONFIG" echo "Using automodel engine" exp_name=gsm8k-${backend}-tp${TP_SIZE}-pp${PP_SIZE}-cp${CP_SIZE}-pad-${PAD_MODE}-use_remove_padding-${USE_REMOVE_PADDING}-mode-${mode} else ENGINE_CONFIG="$MEGATRON_ENGINE_CONFIG" echo "Using megatron engine" exp_name=gsm8k-${backend}-tp${TP_SIZE}-pp${PP_SIZE}-vpp${VPP_SIZE}-cp${CP_SIZE}-pad-${PAD_MODE}-use_remove_padding-${USE_REMOVE_PADDING}-mode-${mode} fi mkdir -p "${ckpts_home}" $COMMAND \ data.train_files="${TRAIN_FILES}" \ data.val_files="${VAL_FILES}" \ data.train_batch_size=128 \ data.pad_mode=${PAD_MODE} \ data.truncation=error \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=2048 \ data.messages_key=messages \ model.use_remove_padding=${USE_REMOVE_PADDING} \ data.ignore_input_ids_mismatch=True \ ${ENGINE_CONFIG} \ trainer.test_freq=after_each_epoch \ trainer.save_freq=-1 \ trainer.logger=['console','file'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.total_epochs=2 \ trainer.total_training_steps=2 \ trainer.default_local_dir="${ckpts_home}" \ trainer.resume_mode=${RESUME_MODE} \ # trainer.total_training_steps=${TOTAL_TRAIN_STEP} \ # trainer.checkpoint.save_contents=[model,optimizer,extra,hf_model] \ # trainer.max_ckpt_to_keep=1 \ rm -rf "${ckpts_home:?}/*" ================================================ FILE: tests/special_e2e/sft/test_sft_engine_all.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail rm -rf ~/verl/test/log mkdir -p ~/verl/test/log export VERL_FILE_LOGGER_ROOT=~/verl/test/log VPP_SIZE=${VPP_SIZE:-2} # test with single gpu as golden echo "run with single gpu as golden" BACKEND=fsdp SP_SIZE=1 FSDP_SIZE=1 NUM_GPUS=1 FSDP_STRATEGY=fsdp VERL_FILE_LOGGER_PATH=~/verl/test/log/golden.jsonl bash tests/special_e2e/sft/run_sft_engine.sh # test with fsdp 1 echo "run with sp2 fsdp_size2 num_gpus8 fsdp_strategy fsdp pad_mode no_padding" BACKEND=fsdp SP_SIZE=2 FSDP_SIZE=2 NUM_GPUS=8 FSDP_STRATEGY=fsdp PAD_MODE=no_padding bash tests/special_e2e/sft/run_sft_engine.sh # test with fsdp 1 use_remove_padding and pad_mode no_padding echo "run with sp4 fsdp_size4 num_gpus8 fsdp_strategy fsdp pad_mode no_padding use_remove_padding False" BACKEND=fsdp SP_SIZE=1 FSDP_SIZE=-1 NUM_GPUS=8 FSDP_STRATEGY=fsdp PAD_MODE=no_padding USE_REMOVE_PADDING=False bash tests/special_e2e/sft/run_sft_engine.sh # test with fsdp 2 echo "run with sp2 fsdp_size2 num_gpus8 fsdp_strategy fsdp2" BACKEND=fsdp SP_SIZE=2 FSDP_SIZE=2 NUM_GPUS=8 FSDP_STRATEGY=fsdp2 bash tests/special_e2e/sft/run_sft_engine.sh # test with veomni echo "run with sp2 fsdp_size4 num_gpus8 fsdp_strategy fsdp2" BACKEND=veomni SP_SIZE=2 FSDP_SIZE=4 NUM_GPUS=8 FSDP_STRATEGY=fsdp2 bash tests/special_e2e/sft/run_sft_engine.sh # test with megatron echo "run with tp2 pp2 vpp2 cp2 num_gpus8" BACKEND=megatron TP_SIZE=2 PP_SIZE=2 VPP_SIZE=${VPP_SIZE} CP_SIZE=2 NUM_GPUS=8 bash tests/special_e2e/sft/run_sft_engine.sh # test with cp in ray echo "run with tp2 pp2 vpp2 cp2 num_gpus8 mode=ray" BACKEND=megatron TP_SIZE=2 PP_SIZE=2 VPP_SIZE=${VPP_SIZE} CP_SIZE=2 NUM_GPUS=8 mode=ray bash tests/special_e2e/sft/run_sft_engine.sh # TODO: Will add back torchtitan CI once everything is ready # # test with torchtitan fsdp=2 # echo "run with tp1 pp1 cp1 fsdp2 num_gpus2" # BACKEND=torchtitan TP_SIZE=1 PP_SIZE=1 CP_SIZE=1 FSDP_SIZE=2 NUM_GPUS=2 bash tests/special_e2e/sft/run_sft_engine.sh # # test with torchtitan tp2 fsdp=2 # echo "run with tp2 pp1 cp1 fsdp2 num_gpus4" # BACKEND=torchtitan TP_SIZE=2 PP_SIZE=1 CP_SIZE=1 FSDP_SIZE=2 NUM_GPUS=4 bash tests/special_e2e/sft/run_sft_engine.sh # # test with automodel dp=2 # echo "run with automodel tp1 pp1 cp1 dp2 num_gpus2" # BACKEND=automodel TP_SIZE=1 PP_SIZE=1 CP_SIZE=1 FSDP_SIZE=2 NUM_GPUS=2 bash tests/special_e2e/sft/run_sft_engine.sh # # test with automodel tp2 dp=2 # echo "run with automodel tp2 pp1 cp1 dp2 num_gpus4" # BACKEND=automodel TP_SIZE=2 PP_SIZE=1 CP_SIZE=1 FSDP_SIZE=2 NUM_GPUS=4 bash tests/special_e2e/sft/run_sft_engine.sh python3 tests/special_e2e/sft/compare_sft_engine_results.py rm -rf ~/verl/test/log ================================================ FILE: tests/special_npu/nightly_ci_ascend/run_grpo_qwen25-7b-instruct_fsdp_npu.sh ================================================ set -x # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-7B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=32 \ data.max_prompt_length=1024 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=5e-8 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_5_7b_instruct_fsdp' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_training_steps=15 2>&1 | tee /root/.cache/grpo_qwen25-7b-instruct_fsdp_npu.log ================================================ FILE: tests/special_npu/nightly_ci_ascend/run_grpo_qwen25-vl-3b-instruct_fsdp_npu.sh ================================================ set -x ENGINE=${1:-vllm} # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. export USE_OPTIMIZED_MODEL=0 MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-VL-3B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=16 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=16 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.use_legacy_worker_impl=disable \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_3b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_training_steps=15 2>&1 | tee /root/.cache/grpo_qwen25-vl-3b-instruct_fsdp_npu.log ================================================ FILE: tests/special_npu/nightly_ci_ascend/run_ppo_qwen3-8b_fsdp_npu.sh ================================================ set -x # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. MODEL_ID=${MODEL_ID:-Qwen/Qwen3-8B} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=32 \ data.max_prompt_length=2000 \ data.max_response_length=2000 \ data.shuffle=False \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.max_num_batched_tokens=4000 \ actor_rollout_ref.rollout.max_num_seqs=64 \ actor_rollout_ref.rollout.checkpoint_engine.update_weights_bucket_megabytes=4096 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enforce_eager=False \ actor_rollout_ref.rollout.calculate_log_probs=True \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path="${MODEL_PATH}" \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=1 \ critic.ulysses_sequence_parallel_size=2 \ critic.model.fsdp_config.param_offload=True \ critic.model.fsdp_config.optimizer_offload=True \ critic.use_dynamic_bsz=True \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_example_ppo_gsm8k' \ trainer.experiment_name='qwen3_8b_fsdp' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.val_before_train=False \ trainer.max_actor_ckpt_to_keep=1 \ trainer.max_critic_ckpt_to_keep=1 \ trainer.total_training_steps=15 2>&1 | tee /root/.cache/ppo_qwen3-8b_fsdp_npu.log ================================================ FILE: tests/special_npu/run_qwen2_5_05b_grpo.sh ================================================ set -x MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} SAVE_PATH=tests/utils/ci/profiler_data rm -rf "$SAVE_PATH" LEVEL="level0" CONTENTS=['npu','cpu'] ANALYSIS=False PROFILE_STEPS=[1] PROFILE_RANKS_ALL=False PROFILE_RANKS=[0] DISCRETE=True python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=16 \ data.max_prompt_length=512 \ data.max_response_length=128 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=5e-7 \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_AND_PIECEWISE" \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 \ trainer.total_training_steps=1 \ actor_rollout_ref.actor.profiler.enable=True \ actor_rollout_ref.actor.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.actor.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.actor.profiler.tool_config.npu.discrete=$DISCRETE \ actor_rollout_ref.actor.profiler.tool_config.npu.contents=$CONTENTS \ actor_rollout_ref.actor.profiler.tool_config.npu.level=$LEVEL \ actor_rollout_ref.actor.profiler.tool_config.npu.analysis=$ANALYSIS \ actor_rollout_ref.ref.profiler.enable=True \ actor_rollout_ref.ref.profiler.all_ranks=$PROFILE_RANKS_ALL \ actor_rollout_ref.ref.profiler.ranks=$PROFILE_RANKS \ actor_rollout_ref.ref.profiler.tool_config.npu.discrete=$DISCRETE \ actor_rollout_ref.ref.profiler.tool_config.npu.contents=$CONTENTS \ actor_rollout_ref.ref.profiler.tool_config.npu.level=$LEVEL \ actor_rollout_ref.ref.profiler.tool_config.npu.analysis=$ANALYSIS \ global_profiler.tool=npu \ global_profiler.steps=$PROFILE_STEPS \ global_profiler.save_path="$SAVE_PATH" $@ python3 "tests/utils/test_check_profiler_output.py" --profiler_dir="$SAVE_PATH" --device="npu" rm -rf "$SAVE_PATH" ================================================ FILE: tests/special_npu/run_qwen2_5_05b_grpo_mindspeed.sh ================================================ set -x MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} USE_DIST_CKPT=${USE_DIST_CKPT:-False} DIST_CKPT_PATH=${DIST_CKPT_PATH:-${HOME}/dist_ckpt/qwen2_5_05b_grpo_mindspeed} if [ "$USE_DIST_CKPT" = "True" ]; then if [ "$USE_DUMMY_MODEL" = "True" ]; then DIST_CKPT_PATH=${HOME}/dist_ckpt_dummy/${MODEL_ID} fi python scripts/converter_hf_to_mcore.py \ --hf_model_path "${MODEL_PATH}" \ --output_path "${DIST_CKPT_PATH}" fi python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=16 \ data.max_prompt_length=512 \ data.max_response_length=128 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=${MODEL_PATH} \ actor_rollout_ref.actor.optim.lr=5e-7 \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.strategy=megatron \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=1 \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_AND_PIECEWISE" \ actor_rollout_ref.rollout.n=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.strategy=megatron \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=1 \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ actor_rollout_ref.ref.use_torch_compile=False \ algorithm.kl_ctrl.kl_coef=0.001 \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen2_7b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 \ trainer.total_training_steps=1 \ +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True $@ ================================================ FILE: tests/special_npu/run_qwen2_5_05b_sft_peft_sp2.sh ================================================ set -x NUM_GPUS=${NUM_GPUS:-4} mode=${mode:-spmd} if [ "$mode" = "spmd" ]; then ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"} COMMAND="torchrun --standalone --nnodes=${NNODES:-1} --nproc-per-node=${NUM_GPUS:-1} ${ENTRYPOINT}" else ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer_ray"} COMMAND="python ${ENTRYPOINT} trainer.nnodes=${NNODES:-1} trainer.n_gpus_per_node=${NUM_GPUS:-1}" fi RESUME_MODE=disable ckpts_home=${ckpts_home:-~/verl/test/gsm8k-sft-fsdp} MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} DATASET_DIR=${DATASET_DIR:-$HOME/data/gsm8k_sft} TRAIN_FILES=${DATASET_DIR}/train.parquet VAL_FILES=${DATASET_DIR}/test.parquet exp_name=gsm8k-sft-qwen-2.5-0.5b-instruct-mode-${mode} mkdir -p "${ckpts_home}" $COMMAND \ data.train_files=$TRAIN_FILES \ data.val_files=$VAL_FILES \ data.pad_mode=no_padding \ data.truncation=error \ data.use_dynamic_bsz=True \ data.max_token_len_per_gpu=2048 \ data.messages_key=messages \ model.path=$MODEL_PATH \ model.use_remove_padding=True \ model.lora_rank=32 \ model.lora_alpha=16 \ model.target_modules=all-linear \ engine=fsdp \ optim=fsdp \ optim.lr=1e-5 \ optim.lr_warmup_steps_ratio=0.2 \ optim.weight_decay=0.1 \ optim.betas="[0.9,0.95]" \ optim.clip_grad=1.0 \ optim.min_lr_ratio=0.1 \ optim.lr_scheduler_type=cosine \ engine.ulysses_sequence_parallel_size=2 \ engine.strategy=fsdp2 \ engine.fsdp_size=2 \ trainer.test_freq=after_each_epoch \ trainer.save_freq=-1 \ trainer.logger=['console','file'] \ trainer.project_name=gsm8k-sft \ trainer.experiment_name=gsm8k-sft-qwen-2.5-0.5b-instruct \ trainer.total_epochs=2 \ trainer.total_training_steps=2 \ trainer.default_local_dir="${ckpts_home}" \ trainer.resume_mode=${RESUME_MODE} \ rm -rf "${ckpts_home:?}/*" ================================================ FILE: tests/special_npu/run_qwen2_5_vl_3b_npu.sh ================================================ set -x ENGINE=${1:-vllm} # Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, # the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model. export USE_OPTIMIZED_MODEL=0 MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-VL-3B-Instruct} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/geo3k/train.parquet \ data.val_files=$HOME/data/geo3k/test.parquet \ data.train_batch_size=16 \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.image_key=images \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.enforce_eager=True \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_AND_PIECEWISE" \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.rollout.n=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=console \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_3b_function_rm' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 \ trainer.total_training_steps=1 $@ ================================================ FILE: tests/special_npu/run_qwen3_06b_ppo.sh ================================================ set -x MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct} # TODO: change to Qwen3-0.6B when CI server is ready MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=gae \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=16 \ data.max_prompt_length=512 \ data.max_response_length=128 \ data.shuffle=False \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=2 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.enforce_eager=False \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_AND_PIECEWISE" \ critic.optim.lr=1e-5 \ critic.model.use_remove_padding=True \ critic.model.path="${MODEL_PATH}" \ critic.model.enable_gradient_checkpointing=True \ critic.ppo_micro_batch_size_per_gpu=1 \ critic.ulysses_sequence_parallel_size=2 \ critic.model.fsdp_config.param_offload=True \ critic.model.fsdp_config.optimizer_offload=True \ critic.use_dynamic_bsz=True \ trainer.critic_warmup=0 \ trainer.logger='["console"]' \ trainer.project_name='verl_ppo_example_gsm8k_qwen3' \ trainer.experiment_name='qwen3_06b_fsdp' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 \ trainer.total_training_steps=1 $@ ================================================ FILE: tests/special_npu/run_qwen3_30b_grpo_mindspeed.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail MODEL_ID=${MODEL_ID:-Qwen/Qwen3-30B-A3B-Instruct-2507} MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} USE_DIST_CKPT=${USE_DIST_CKPT:-False} DIST_CKPT_PATH=${DIST_CKPT_PATH:-${HOME}/dist_ckpt/qwen3_30b_grpo_mindspeed} # use dummy model if [[ "$USE_DUMMY_MODEL" == "True" ]]; then DUMMY_MODEL_PATH=${DUMMY_MODEL_PATH:-${HOME}/models_dummy/${MODEL_ID}} if [ -z "${DUMMY_MODEL_CONFIG_PATH}" ]; then echo "[ERROR] DUMMY_MODEL_CONFIG_PATH not set" exit 1 fi # make sure the path is empty if [[ -d $DUMMY_MODEL_PATH && $DUMMY_MODEL_PATH != "/" ]]; then rm -rf $DUMMY_MODEL_PATH fi # init model python scripts/init_random_model.py \ --hf_model_path "${MODEL_PATH}" \ --new_config_path "${DUMMY_MODEL_CONFIG_PATH}" \ --output_path "${DUMMY_MODEL_PATH}" # replace model path MODEL_PATH=$DUMMY_MODEL_PATH fi # convert to megatron if [[ "$USE_DIST_CKPT" == "True" ]]; then if [[ "$USE_DUMMY_MODEL" == "True" ]]; then DIST_CKPT_PATH=${HOME}/dist_ckpt/qwen3_30b_grpo_mindspeed_dummy if [[ -d $DIST_CKPT_PATH && $DIST_CKPT_PATH != "/" ]];then rm -rf $DIST_CKPT_PATH fi fi torchrun --nproc_per_node 2 --nnodes 1 scripts/converter_hf_to_mcore.py \ --hf_model_path "${MODEL_PATH}" \ --output_path "${DIST_CKPT_PATH}" fi exp_name='Qwen3-30B-A3B-GRPO-MindSpeed' max_prompt_length=512 max_response_length=1024 train_prompt_bsz=16 actor_ppo_max_token_len=$(((max_prompt_length + max_response_length))) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length))) python3 -m verl.trainer.main_ppo --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ data.train_files=${HOME}/data/gsm8k/train.parquet \ data.val_files=${HOME}/data/gsm8k/test.parquet \ data.train_batch_size=${train_prompt_bsz} \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.filter_overlong_prompts=True \ data.shuffle=False \ data.truncation='left' \ algorithm.adv_estimator=grpo \ algorithm.use_kl_in_reward=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.enable_chunked_prefill=False \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=2 \ actor_rollout_ref.rollout.temperature=1.0 \ actor_rollout_ref.rollout.top_p=1.0 \ actor_rollout_ref.rollout.top_k=-1 \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.actor.strategy=megatron \ actor_rollout_ref.actor.kl_loss_coef=0.0 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.ppo_epochs=1 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.ppo_mini_batch_size=8 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ +actor_rollout_ref.rollout.engine_kwargs.vllm.compilation_config.cudagraph_mode="FULL_AND_PIECEWISE" \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=2 \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.actor.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.loss_agg_mode="token-mean" \ actor_rollout_ref.ref.strategy=megatron \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=2 \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.ref.megatron.dist_checkpointing_path=${DIST_CKPT_PATH} \ reward.reward_manager.name=naive \ algorithm.kl_ctrl.kl_coef=0.0 \ trainer.logger=['console'] \ trainer.project_name='verl_gsm8k_example' \ trainer.experiment_name='qwen3_30b_a3b_cut_gsm8k_mindspeed' \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=-1 \ trainer.total_epochs=1 \ trainer.total_training_steps=1 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True $@ # clean up if [[ "$USE_DUMMY_MODEL" == "True" ]]; then rm -rf $DUMMY_MODEL_PATH if [[ "$USE_DIST_CKPT" == "True" ]]; then rm -rf $DIST_CKPT_PATH fi fi ================================================ FILE: tests/special_sanity/check_api_docs.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fail CI if any function or class that is publicly exported via ``__all__`` lacks a docstring. Usage ----- # Check specific modules or packages python check_docstrings.py mypkg.core mypkg.utils # Check an entire source tree (all top-level packages under cwd) python check_docstrings.py """ from __future__ import annotations import argparse import importlib import inspect import pkgutil import sys from pathlib import Path from types import ModuleType from typing import Iterable _ALLOW_LIST = [ "verl.third_party.vllm.LLM", "verl.third_party.vllm.parallel_state", "verl.utils.profiler.WorkerProfiler", "verl.utils.profiler.WorkerProfilerExtension", "verl.utils.profiler.log_gpu_memory_usage", "verl.utils.profiler.log_print", "verl.utils.profiler.mark_annotate", "verl.utils.profiler.mark_end_range", "verl.utils.profiler.mark_start_range", "verl.models.mcore.qwen2_5_vl.get_vision_model_config", "verl.models.mcore.qwen2_5_vl.get_vision_projection_config", "verl.models.mcore.mbridge.freeze_moe_router", "verl.models.mcore.mbridge.make_value_model", "verl.utils.transformers_compat.flash_attn_supports_top_left_mask", ] def iter_submodules(root: ModuleType) -> Iterable[ModuleType]: """Yield *root* and every sub-module inside it.""" yield root def print_pkg_error(pkg_name): print(f"[warn] Skipping {pkg_name!r}", file=sys.stderr) if getattr(root, "__path__", None): # only packages have __path__ for mod_info in pkgutil.walk_packages(root.__path__, prefix=f"{root.__name__}.", onerror=print_pkg_error): try: yield importlib.import_module(mod_info.name) except Exception as exc: print(f"[warn] Skipping {mod_info.name!r}: {exc}", file=sys.stderr) def names_missing_doc(mod: ModuleType) -> list[str]: """Return fully-qualified names that need docstrings.""" missing: list[str] = [] public = getattr(mod, "__all__", []) for name in public: obj = getattr(mod, name, None) if f"{mod.__name__}.{name}" in _ALLOW_LIST: continue if obj is None: # Exported but not found in the module: flag it anyway. missing.append(f"{mod.__name__}.{name} (not found)") continue if inspect.isfunction(obj) or inspect.isclass(obj): doc = inspect.getdoc(obj) if not doc or not doc.strip(): missing.append(f"{mod.__name__}.{name}") return missing def check_module(qualname: str) -> list[str]: """Import *qualname* and check it (and sub-modules).""" try: module = importlib.import_module(qualname) except ModuleNotFoundError as exc: print(f"[error] Cannot import '{qualname}': {exc}", file=sys.stderr) return [qualname] missing: list[str] = [] for submod in iter_submodules(module): missing.extend(names_missing_doc(submod)) return missing def autodiscover_packages() -> list[str]: """Detect top-level packages under CWD when no argument is given.""" pkgs: list[str] = [] for p in Path.cwd().iterdir(): if p.is_dir() and (p / "__init__.py").exists(): pkgs.append(p.name) return pkgs def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "modules", nargs="*", help="Fully-qualified module or package names (defaults to every top-level package found in CWD).", ) args = parser.parse_args() targets = args.modules or autodiscover_packages() if not targets: raise ValueError("[error] No modules specified and none detected automatically.") all_missing: list[str] = [] for modname in targets: all_missing.extend(check_module(modname)) if all_missing: print("\nMissing docstrings:") for name in sorted(all_missing): print(f" - {name}") raise ValueError("Missing docstrings detected. Please enhance them with docs accordingly.") print("✅ All exported functions/classes have docstrings.") if __name__ == "__main__": main() ================================================ FILE: tests/special_sanity/check_dataproto_usage.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This CI test is used for checking whether DataProto is used in the code of some directory """ import os from argparse import ArgumentParser from pathlib import Path SEARCH_WHITELIST = [] SEARCH_KEYWORDS = ["DataProto"] if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--directory", "-d", required=True, type=str) args = parser.parse_args() directory_in_str = args.directory pathlist = Path(directory_in_str).glob("**/*.py") for path in pathlist: path_in_str = str(path.absolute()) # judge whether current path is in pre-defined search whitelist or not. path_in_whitelist = False for sw in SEARCH_WHITELIST: # for easy debugging in non-linux system sw = sw.replace("/", os.sep) if sw in path_in_str: print(f"[SKIP] File {path_in_str} is in device api usage check whitelist, checking is skipped.") path_in_whitelist = True break if path_in_whitelist: continue with open(path_in_str, encoding="utf-8") as f: file_content = f.read() find_invalid_device_management = False for sk in SEARCH_KEYWORDS: if sk in file_content: find_invalid_device_management = True break print( f"[CHECK] File {path_in_str} is detected for DataProto usage check, check result: " f"{'success' if not find_invalid_device_management else f'failed, because detect {sk}'}." ) assert not find_invalid_device_management, ( f"file {path_in_str} contains DataProto usage, please use TensorDict directly!" ) ================================================ FILE: tests/special_sanity/check_device_api_usage.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This CI test is used for checking whether device api usage is irregular, suggest using api in `verl/utils/device.py`. Search targets include .py files in verl/recipe and verl/verl. Some files that must contain ".cuda", "cuda" or "nccl" keyword is pre-defined in whitelist below. """ import os from argparse import ArgumentParser from pathlib import Path # directory or file path must contain keyword ".cuda" or "cuda" CUDA_KEYWORD_CHECK_WHITELIST = [ "verl/utils/device.py", "verl/utils/torch_functional.py", # import flash_attn only on cuda "verl/utils/profiler/nvtx_profile.py", # appear in NsightSystemsProfiler "verl/utils/profiler/torch_profile.py", # appear in TorchProfiler "verl/utils/profiler/config.py", # appear in TorchProfilerToolConfig "verl/utils/kernel/linear_cross_entropy.py", # appear in nvidia nvtx "verl/utils/rendezvous/ray_backend.py", # appear in cupy importance "verl/single_controller/ray/base.py", # appear in default device_name "verl/trainer/ppo/ray_trainer.py", # appear in default device_name "verl/experimental/transfer_queue/ray_trainer.py", # appear in docstring as default device_name "verl/experimental/one_step_off_policy/ray_trainer.py", # appear in docstring as default device_name "verl/utils/reward_score/sandbox_fusion/utils.py", # appear in sandbox language type "verl/third_party/torch/distributed/_state_dict_utils.py", # torch monkey patch fixes "verl/third_party/torch/distributed/checkpoint/state_dict.py", # torch monkey patch fixes "verl/workers/engine/base.py", # appear in default device_name "verl/workers/engine/utils.py", # appear in enable_full_determinism "verl/workers/engine/fsdp/transformer_impl.py", # appear in default device_name "verl/workers/engine/veomni/transformer_impl.py", # appear in default device_name "verl/workers/engine/torchtitan/transformer_impl.py", # appear in default device_name "verl/workers/engine/torchtitan/utils.py", # appear in torch.cuda.empty_cache() "verl/workers/engine/automodel/transformer_impl.py", # appear in default device_name "verl/workers/rollout/vllm_rollout/vllm_async_server.py", # appear in config.cudagraph_capture_sizes "verl/workers/rollout/sglang_rollout/async_sglang_server.py", # manually set CUDA_VISIBLE_DEVICES "verl/workers/rollout/trtllm_rollout/trtllm_async_server.py", # appear in config.cudagraph_capture_sizes "verl/workers/rollout/replica.py", # appear in default device_name "verl/checkpoint_engine", # checkpoint engine backend are device specific ] # directory or file path must contain keyword "nccl" NCCL_KEYWORD_CHECK_WHITELIST = [ "verl/utils/device.py", "verl/third_party/sglang/parallel_state.py", # appear in default backend ] SEARCH_WHITELIST = CUDA_KEYWORD_CHECK_WHITELIST + NCCL_KEYWORD_CHECK_WHITELIST SEARCH_KEYWORDS = [".cuda", '"cuda"', '"nccl"'] if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--directory", "-d", required=True, type=str) args = parser.parse_args() directory_in_str = args.directory pathlist = Path(directory_in_str).glob("**/*.py") for path in pathlist: path_in_str = str(path.absolute()) # judge whether current path is in pre-defined search whitelist or not. path_in_whitelist = False for sw in SEARCH_WHITELIST: # for easy debugging in non-linux system sw = sw.replace("/", os.sep) if sw in path_in_str: print(f"[SKIP] File {path_in_str} is in device api usage check whitelist, checking is skipped.") path_in_whitelist = True break if path_in_whitelist: continue with open(path_in_str, encoding="utf-8") as f: file_content = f.read() find_invalid_device_management = False for sk in SEARCH_KEYWORDS: if sk in file_content: find_invalid_device_management = True break print( f"[CHECK] File {path_in_str} is detected for device api usage check, check result: " f"{'success' if not find_invalid_device_management else f'failed, because detect {sk}'}." ) assert not find_invalid_device_management, ( f'file {path_in_str} contains .cuda/"cuda"/"nccl" usage, please use api in ' f"verl/utils/device.py directly." ) ================================================ FILE: tests/special_sanity/check_docs_time_info.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Check that every .md and .rst file under docs/ contains the substring "Last updated", with an allow-list for exceptions. """ import sys from pathlib import Path # === CONFIGURATION === # Relative paths (to docs/) or glob patterns to skip checking ALLOW_LIST = { "docs/README.md", # you can list individual files "docs/legacy/*.rst", # or glob patterns "docs/index.rst", "docs/start/install.rst", "docs/start/quickstart.rst", "docs/README_vllm0.7.md", } # The folder to scan DOCS_DIR = Path("docs") # === SCRIPT === def is_allowed(path: Path) -> bool: """ Return True if `path` matches any entry in ALLOW_LIST. """ rel = str(path) for pattern in ALLOW_LIST: if Path(rel).match(pattern): return True return False def main(): if not DOCS_DIR.exists(): print(f"Error: Documentation directory '{DOCS_DIR}' does not exist.", file=sys.stderr) sys.exit(1) missing = [] # Gather all .md and .rst files under docs/ for ext in ("*.md", "*.rst"): for path in DOCS_DIR.rglob(ext): if is_allowed(path): continue text = path.read_text(encoding="utf-8", errors="ignore") if "Last updated" not in text: missing.append(path) # Report if missing: print("\nThe following files are missing the 'Last updated' string:\n") for p in missing: print(f" - {p}") print(f"\nTotal missing: {len(missing)}\n", file=sys.stderr) raise AssertionError( "Some documentation files lack a 'Last updated' line. Please include info such as " "'Last updated: mm/dd/yyyy' to indicate the last update time of the document." ) else: print("✅ All checked files contain 'Last updated'.") if __name__ == "__main__": main() ================================================ FILE: tests/special_sanity/check_docstrings.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Python script to check docstrings for functions and classes in specified files. Checks that every public function and class has proper docstring documentation. """ import ast import os import sys class DocstringChecker(ast.NodeVisitor): """AST visitor to check for missing docstrings in functions and classes.""" def __init__(self, filename: str): self.filename = filename self.missing_docstrings: list[tuple[str, str, int]] = [] self.current_class = None self.function_nesting_level = 0 def visit_FunctionDef(self, node: ast.FunctionDef): """Visit function definitions and check for docstrings.""" if not node.name.startswith("_") and self.function_nesting_level == 0: if not self._has_docstring(node): func_name = f"{self.current_class}.{node.name}" if self.current_class else node.name self.missing_docstrings.append((func_name, self.filename, node.lineno)) self.function_nesting_level += 1 self.generic_visit(node) self.function_nesting_level -= 1 def visit_AsyncFunctionDef(self, node: ast.AsyncFunctionDef): """Visit async function definitions and check for docstrings.""" if not node.name.startswith("_") and self.function_nesting_level == 0: if not self._has_docstring(node): func_name = f"{self.current_class}.{node.name}" if self.current_class else node.name self.missing_docstrings.append((func_name, self.filename, node.lineno)) self.function_nesting_level += 1 self.generic_visit(node) self.function_nesting_level -= 1 def visit_ClassDef(self, node: ast.ClassDef): """Visit class definitions and check for docstrings.""" if not node.name.startswith("_"): if not self._has_docstring(node): self.missing_docstrings.append((node.name, self.filename, node.lineno)) old_class = self.current_class self.current_class = node.name self.generic_visit(node) self.current_class = old_class def _has_docstring(self, node) -> bool: """Check if a node has a docstring.""" return ast.get_docstring(node) is not None def check_file_docstrings(filepath: str) -> list[tuple[str, str, int]]: """Check docstrings in a single file.""" try: with open(filepath, encoding="utf-8") as f: content = f.read() tree = ast.parse(content, filename=filepath) checker = DocstringChecker(filepath) checker.visit(tree) return checker.missing_docstrings except Exception as e: print(f"Error processing {filepath}: {e}") return [] def main(): """Main function to check docstrings in specified files.""" files_to_check = [ "verl/trainer/ppo/ray_trainer.py", "verl/trainer/main_ppo.py", "verl/trainer/ppo/reward.py", "verl/utils/reward_score/__init__.py", "verl/trainer/ppo/core_algos.py", "verl/experimental/agent_loop/agent_loop.py", "verl/workers/sharding_manager/fsdp_vllm.py", "verl/workers/sharding_manager/fsdp_ulysses.py", ] script_dir = os.path.dirname(os.path.abspath(__file__)) repo_path = os.path.dirname(os.path.dirname(script_dir)) if not os.path.exists(repo_path): print(f"Repository path {repo_path} does not exist!") sys.exit(1) os.chdir(repo_path) all_missing_docstrings = [] print("Checking docstrings in specified files...") print("=" * 60) for file_path in files_to_check: if not os.path.exists(file_path): print(f"Warning: File {file_path} does not exist!") continue print(f"Checking {file_path}...") missing = check_file_docstrings(file_path) all_missing_docstrings.extend(missing) if missing: print(f" Found {len(missing)} missing docstrings") else: print(" All functions and classes have docstrings [OK]") print("=" * 60) if all_missing_docstrings: print(f"\nSUMMARY: Found {len(all_missing_docstrings)} functions/classes missing docstrings:") print("-" * 60) by_file = {} for name, filepath, lineno in all_missing_docstrings: if filepath not in by_file: by_file[filepath] = [] by_file[filepath].append((name, lineno)) for filepath in sorted(by_file.keys()): print(f"\n{filepath}:") for name, lineno in sorted(by_file[filepath], key=lambda x: x[1]): print(f" - {name} (line {lineno})") print(f"\nTotal missing docstrings: {len(all_missing_docstrings)}") raise Exception(f"Found {len(all_missing_docstrings)} functions/classes without proper docstrings!") else: print("\n[OK] All functions and classes have proper docstrings!") if __name__ == "__main__": main() ================================================ FILE: tests/special_sanity/check_license.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser from pathlib import Path from typing import Iterable license_head_bytedance = "Copyright 2024 Bytedance Ltd. and/or its affiliates" license_head_bytedance_25 = "Copyright 2025 Bytedance Ltd. and/or its affiliates" license_head_bytedance_26 = "Copyright 2026 Bytedance Ltd. and/or its affiliates" # Add custom license headers below license_head_prime = "Copyright 2024 PRIME team and/or its affiliates" license_head_individual = "Copyright 2025 Individual Contributor:" license_head_sglang = "Copyright 2023-2024 SGLang Team" license_head_modelbest = "Copyright 2025 ModelBest Inc. and/or its affiliates" license_head_amazon = "Copyright 2025 Amazon.com Inc and/or its affiliates" license_head_amazon_26 = "Copyright 2026 Amazon.com Inc and/or its affiliates" license_head_facebook = "Copyright (c) 2016- Facebook, Inc" license_head_meituan = "Copyright 2025 Meituan Ltd. and/or its affiliates" license_head_huawei = "Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved." license_head_nvidia = "Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved." license_headers = [ license_head_bytedance, license_head_bytedance_25, license_head_bytedance_26, license_head_prime, license_head_individual, license_head_sglang, license_head_modelbest, license_head_amazon, license_head_amazon_26, license_head_facebook, license_head_meituan, license_head_huawei, license_head_nvidia, ] def get_py_files(path_arg: Path) -> Iterable[Path]: """get py files under a dir. if already py file return it Args: path_arg (Path): path to scan for py files Returns: Iterable[Path]: list of py files """ if path_arg.is_dir(): return path_arg.glob("**/*.py") elif path_arg.is_file() and path_arg.suffix == ".py": return [path_arg] return [] if __name__ == "__main__": parser = ArgumentParser() parser.add_argument( "--directories", "-d", required=True, type=Path, nargs="+", help="List of directories to check for license headers", ) args = parser.parse_args() # Collect all Python files from specified directories pathlist = set(path for path_arg in args.directories for path in get_py_files(path_arg)) for path in pathlist: # because path is object not string path_in_str = str(path.absolute()) print(path_in_str) with open(path_in_str, encoding="utf-8") as f: file_content = f.read() has_license = False for lh in license_headers: if lh in file_content: has_license = True break assert has_license, f"file {path_in_str} does not contain license" ================================================ FILE: tests/special_sanity/check_pr_description.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #!/usr/bin/env python3 import json import os # Number of lines to check NUM_LINES = 5 # Custom exception types for clear error handling class TemplateFileError(Exception): pass class PRBodyLoadError(Exception): pass class PRDescriptionError(Exception): pass # Path to the PR template file template_file = os.path.join(os.getenv("GITHUB_WORKSPACE", "."), ".github", "PULL_REQUEST_TEMPLATE.md") def load_template(path): """ Load only the first NUM_LINES of the PR template file as a list of lines, without stripping any characters. """ lines = [] try: with open(path, encoding="utf-8") as f: for _ in range(NUM_LINES): line = f.readline() if not line: break lines.append(line.strip()) return lines except Exception as e: raise TemplateFileError(f"Failed to read PR template (first {NUM_LINES} lines) at {path}: {e}") from e def load_pr_body(event_path): try: with open(event_path, encoding="utf-8") as f: payload = json.load(f) return payload.get("pull_request", {}).get("body", "") or "" except Exception as e: raise PRBodyLoadError(f"Failed to read PR body from {event_path}: {e}") from e def check_pr_description(body, template_lines): """ Compare the first NUM_LINES lines of the PR body to the template lines. If they match exactly, the placeholder was not modified. """ pr_lines = body.splitlines(keepends=True) pr_first = [x.strip() for x in pr_lines[:NUM_LINES]] if pr_first == template_lines: raise PRDescriptionError( "It looks like you haven't updated the '### What does this PR do?' section. Please replace " "the placeholder text with a concise description of what your PR does." ) else: print(pr_first) print(template_lines) def main(): event_path = os.getenv("GITHUB_EVENT_PATH") if not event_path: raise OSError("GITHUB_EVENT_PATH is not set.") template_lines = load_template(template_file) pr_body = load_pr_body(event_path) check_pr_description(pr_body, template_lines) print("✅ '### What does this PR do?' section has been filled out.") if __name__ == "__main__": main() ================================================ FILE: tests/special_sanity/check_pr_title.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import re # Get PR title from environment pr_title = os.environ.get("PR_TITLE", "").strip() # Define rules allowed_modules = ["fsdp", "megatron", "veomni", "sglang", "vllm", "trtllm", "rollout", "trainer"] allowed_modules += ["tests", "training_utils", "recipe", "hardware", "deployment"] allowed_modules += ["ray", "worker", "single_controller", "misc", "docker", "ci"] allowed_modules += ["perf", "model", "algo", "env", "tool", "ckpt", "doc", "data", "cfg", "reward"] allowed_modules += ["fully_async", "one_step_off"] allowed_types = ["feat", "fix", "refactor", "chore", "test"] # Check for [1/N] prefix and extract the rest of the title progress_match = re.match(r"^\[\d/[\dNn]\]\s*(.+)$", pr_title, re.IGNORECASE) if progress_match: pr_title = progress_match.group(1).strip() # Check for [BREAKING] prefix and extract the rest of the title breaking_match = re.match(r"^\[BREAKING\]\s*(.+)$", pr_title, re.IGNORECASE) if breaking_match: core_pr_title = breaking_match.group(1).strip() is_breaking = True else: core_pr_title = pr_title is_breaking = False # Build dynamic regex pattern for modules (now working on core_pr_title) re_modules_pattern = re.compile(r"^\[([a-z_,\s]+)\]", re.IGNORECASE) re_modules = re_modules_pattern.match(core_pr_title) if not re_modules: print(f"❌ Invalid PR title: '{pr_title}'") print("Expected format: [BREAKING][module] type: description") print(f"Allowed modules: {', '.join(allowed_modules)}") raise Exception("Invalid PR title") else: modules = re.findall(r"[a-z_]+", re_modules.group(1).lower()) if not all(module in allowed_modules for module in modules): invalid_modules = [module for module in modules if module not in allowed_modules] print(f"❌ Invalid modules: {', '.join(invalid_modules)}") print(f"Allowed modules: {', '.join(allowed_modules)}") raise Exception("Invalid PR title") types_pattern = "|".join(re.escape(t) for t in allowed_types) re_types_pattern = re.compile(rf"^\[[a-z_,\s]+\]\s+({types_pattern}):\s+.+$", re.IGNORECASE) match = re_types_pattern.match(core_pr_title) if not match: print(f"❌ Invalid PR title: '{pr_title}'") print("Expected format: [BREAKING][module] type: description") print(f"Allowed types: {', '.join(allowed_types)}") raise Exception("Invalid PR title") change_type = match.group(1).lower() # Build the success message breaking_info = " (BREAKING CHANGE)" if is_breaking else "" print(f"✅ PR title is valid: {pr_title}, modules: {modules}, type: {change_type}{breaking_info}") ================================================ FILE: tests/special_sanity/test_config_docs.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re from pathlib import Path def validate_yaml_format(yaml_lines): errors = [] i = 0 while i < len(yaml_lines): line = yaml_lines[i] stripped = line.strip() # Skip empty lines if stripped == "": i += 1 continue # Match YAML keys like "field:" or "field: value" key_match = re.match(r"^(\s*)([a-zA-Z0-9_]+):", line) if key_match: # Check if there's a comment above if i == 0 or not yaml_lines[i - 1].strip().startswith("#"): errors.append(f"Missing comment above line {i + 1}: {line.strip()}") # Check for inline comment if "#" in line and not stripped.startswith("#"): comment_index = line.index("#") colon_index = line.index(":") if comment_index > colon_index: errors.append(f"Inline comment found on line {i + 1}: {line.strip()}") # Check for blank line after this key line (unless next is a deeper indent) if i + 1 < len(yaml_lines): next_line = yaml_lines[i + 1] next_stripped = next_line.strip() # If next is not empty and not a deeper nested line, enforce blank line if next_stripped != "": errors.append(f"Missing blank line after line {i + 1}: {line.strip()}") i += 1 return errors def test_trainer_config_doc(): yamls_to_inspect = [ "verl/trainer/config/ppo_trainer.yaml", "verl/trainer/config/actor/actor.yaml", "verl/trainer/config/actor/dp_actor.yaml", "verl/trainer/config/critic/critic.yaml", "verl/trainer/config/critic/dp_critic.yaml", "verl/trainer/config/ref/ref.yaml", "verl/trainer/config/ref/dp_ref.yaml", "verl/trainer/config/rollout/rollout.yaml", ] success = True for yaml_to_inspect in yamls_to_inspect: yaml_path = Path(yaml_to_inspect) # path to your YAML file with open(yaml_path) as f: lines = f.readlines() validation_errors = validate_yaml_format(lines) if validation_errors: success = False print("YAML documentation format check failed:") print(f"Please read the top block of {yaml_to_inspect} to see format rules:\n") for err in validation_errors: print(" -", err) if not success: raise Exception("Please fix documentation format.") else: print("YAML format check passed ✅") ================================================ FILE: tests/special_sanity/test_import.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. def test_import(): import verl print(verl.__version__) def test_single_controller_import(): import verl.single_controller print(verl.single_controller.__version__) ================================================ FILE: tests/special_sanity/type_coverage_check.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Custom type annotation check tool. To inspect the type annotation for functions in the entire codebase, please run: find verl -type f -name "*.py" | xargs -n 1 python3 tests/special_sanity/type_coverage_check.py --all-lines --debug --target-file """ import argparse import ast import linecache import subprocess from pathlib import Path def get_changed_files() -> list[Path]: result = subprocess.run( ["git", "diff", "--name-only", "--diff-filter=AM", "origin/main...HEAD"], stdout=subprocess.PIPE, text=True ) return [Path(f) for f in result.stdout.splitlines() if f.endswith(".py")] def get_changed_lines(file_path: Path) -> set[int]: result = subprocess.run( ["git", "diff", "-U0", "origin/main...HEAD", "--", str(file_path)], stdout=subprocess.PIPE, text=True, ) lines: set[int] = set() for line in result.stdout.splitlines(): if line.startswith("@@"): for part in line.split(): try: if part.startswith("+") and "," in part: start, count = map(int, part[1:].split(",")) lines.update(range(start, start + count)) elif part.startswith("+") and "," not in part: lines.add(int(part[1:])) except Exception: # (vermouth1992) There are many edge cases here because + can be in the changed program pass return lines CHECK_SUCCESS = 0 CHECK_WARNING = 1 CHECK_FAILURE = -1 def should_check_type(arg_name: str) -> bool: if arg_name in ("self", "cls"): return False if arg_name.startswith("*"): return False return True def has_type_annotations(node: ast.AST, debug: bool = False) -> int: if isinstance(node, ast.FunctionDef): is_private = node.name.startswith("_") if node.args.vararg is not None or node.args.kwarg is not None: return CHECK_SUCCESS has_ann = ( all(arg.annotation is not None for arg in node.args.args if should_check_type(arg.arg)) and node.returns is not None ) if has_ann or is_private: return CHECK_SUCCESS else: if debug: print(node, [(arg.annotation, arg.arg) for arg in node.args.args if should_check_type(arg.arg)]) return CHECK_FAILURE return CHECK_SUCCESS def check_file( file_path: Path, changed_lines: set[int], debug: bool = False ) -> tuple[int, int, list[tuple[Path, int, str]], list[tuple[Path, int, str]]]: with open(file_path) as f: source: str = f.read() tree = ast.parse(source, filename=str(file_path)) annotated = 0 total = 0 warning_lines: list[tuple[Path, int, str]] = [] failure_lines: list[tuple[Path, int, str]] = [] for node in ast.walk(tree): if hasattr(node, "lineno") and node.lineno in changed_lines: if isinstance(node, ast.FunctionDef | ast.Assign | ast.AnnAssign): total += 1 result = has_type_annotations(node, debug) if result == CHECK_SUCCESS or result == CHECK_WARNING: annotated += 1 if result == CHECK_WARNING: warning_lines.append( (file_path, node.lineno, linecache.getline(str(file_path), node.lineno).strip()) ) else: source_line = linecache.getline(str(file_path), node.lineno).strip() failure_lines.append((file_path, node.lineno, source_line)) return annotated, total, warning_lines, failure_lines def main() -> None: parser = argparse.ArgumentParser() parser.add_argument( "--threshold", type=float, default=0.3, help="Minimum ratio of annotated lines required (0.0 - 1.0)" ) parser.add_argument("--target-file", type=str, default=None, help="Path to the Python source file to analyse") parser.add_argument( "--all-lines", action="store_true", help="Check all lines in the file instead of only changed lines based on git", ) parser.add_argument("--debug", action="store_true", help="Add debugging logs") args = parser.parse_args() total_changed = 0 total_annotated = 0 all_warnings: list[tuple[Path, int, str]] = [] all_failures: list[tuple[Path, int, str]] = [] target_files = [args.target_file] if args.target_file is not None else get_changed_files() for fpath in target_files: if "tests/" in str(fpath): continue if args.all_lines: changed_lines = [i + 1 for i in range(len(open(fpath).readlines()))] else: changed_lines = get_changed_lines(fpath) annotated, total, warning_lines, failure_lines = check_file(fpath, changed_lines, args.debug) total_annotated += annotated total_changed += total all_warnings.extend(warning_lines) all_failures.extend(failure_lines) ratio = (total_annotated / total_changed) if total_changed else 1.0 print( f"🔍 Type coverage on {'all' if args.all_lines else 'changed'} lines: " f"{total_annotated}/{total_changed} = {ratio:.2%}. Files inspected: {target_files}" ) if all_warnings: print("\n⚠️ Suggest Improve: Lines missing type annotations for inputs and outputs:\n") for fname, lineno, line in all_warnings: print(f"{fname}:{lineno}: {line}") if all_failures: print("⚠️ [ERROR] Lines missing type annotations for inputs and outputs:\n") for fname, lineno, line in all_failures: print(f"{fname}:{lineno}: {line}") if ratio < args.threshold: print( f"Please add type annotations for inputs and outputs to meet threshold {args.threshold}. " f"Cases exempt from checking:" ) print("1. Private methods.") print("2. Args with name in ('self', 'cls'), or *args / **kwargs") print("3. Files under tests/") raise Exception(f"\n❌ Type coverage below threshold ({args.threshold:.0%}).") else: if all_warnings or all_failures: print("") print("✅ Type annotation coverage acceptable.\n") if __name__ == "__main__": main() ================================================ FILE: tests/special_sanity/validate_imported_docs.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ verify_imported_docs.py Assert that every function or class *explicitly imported* (via `from import `) in a given Python file has a docstring. """ from __future__ import annotations import argparse import ast import importlib import inspect import pathlib import sys def _parse_args() -> argparse.Namespace: p = argparse.ArgumentParser(description="Verify that imported functions/classes have docstrings.") p.add_argument( "--target-file", default="verl/trainer/ppo/ray_trainer.py", help="Path to the Python source file to analyse (e.g. verl/trainer/ppo/ray_trainer.py)", ) p.add_argument( "--allow-list", default=["omegaconf.open_dict"], help="a list of third_party dependencies that do not have proper docs :(", ) p.add_argument( "--project-root", default=".", help="Directory to prepend to PYTHONPATH so local packages resolve (default: .)", ) p.add_argument( "--quiet", action="store_true", help="Suppress success message (still prints errors).", ) return p.parse_args() def _import_attr(module_name: str, attr_name: str): """Import `module_name` then return `getattr(module, attr_name)`.""" module = importlib.import_module(module_name) return getattr(module, attr_name) def _check_file(py_file: pathlib.Path, project_root: pathlib.Path, allow_list: list[str]) -> list[str]: """Return a list of error strings (empty == success).""" # Ensure local packages resolve sys.path.insert(0, str(project_root.resolve())) tree = ast.parse(py_file.read_text(), filename=str(py_file)) problems: list[str] = [] for node in ast.walk(tree): if not isinstance(node, ast.ImportFrom): continue # Relative imports (level > 0) get the leading dots stripped module_name = "." * node.level + (node.module or "") for alias in node.names: if alias.name == "*": problems.append( f"{py_file}:{node.lineno} - wildcard import `from {module_name} import *` cannot be verified." ) continue imported_name = alias.name try: obj = _import_attr(module_name, imported_name) except Exception: # pragma: no cover – wide net for import quirks pass # For some reason the module cannot be imported, skip for now # problems.append( # f"{py_file}:{node.lineno} - could not resolve " # f"`{imported_name}` from `{module_name}` ({exc})" # ) continue if f"{module_name}.{imported_name}" in allow_list: continue if inspect.isfunction(obj) or inspect.isclass(obj): doc = inspect.getdoc(obj) if not (doc and doc.strip()): kind = "class" if inspect.isclass(obj) else "function" problems.append( f"{py_file}:{node.lineno} - {kind} `{module_name}.{imported_name}` is missing a docstring." ) return problems def main() -> None: args = _parse_args() target_path = pathlib.Path(args.target_file).resolve() project_root = pathlib.Path(args.project_root).resolve() if not target_path.is_file(): raise Exception(f"❌ Target file not found: {target_path}") errors = _check_file(target_path, project_root, args.allow_list) if errors: print("Docstring verification failed:\n") print("\n".join(f" • {e}" for e in errors)) raise Exception("❌ Docstring verification failed.") if not args.quiet: print(f"✅ All explicitly imported functions/classes in {target_path} have docstrings.") if __name__ == "__main__": main() ================================================ FILE: tests/special_sanity/validate_structure.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #!/usr/bin/env python3 """ Validate that test file subfolders mirror the top-level package layout. Usage examples -------------- # Typical run (defaults: impl_root=my_project, tests_root=tests) python check_tests_structure.py # Custom layout and extra allowed folders python check_tests_structure.py \ --impl-root verl \ --tests-root tests \ --allow-dirs special_e2e special_sanity special_standalone special_distributed """ from __future__ import annotations import argparse import sys from pathlib import Path def discover_allowed_modules(impl_root: Path, extra: list[str]) -> set[str]: """Return the set of first-level directories that tests may live under.""" allowed = {p.name for p in impl_root.iterdir() if p.is_dir()} allowed.update(extra) return allowed def find_violations(tests_root: Path, allowed: set[str], allowed_files: list[str]) -> list[str]: """Return a list of error strings for test files in the wrong place.""" errors: list[str] = [] for test_file in tests_root.rglob("test*.py"): if str(test_file) in allowed_files: continue rel_parts = test_file.relative_to(tests_root).parts if len(rel_parts) < 2: errors.append(f"{test_file}: must be inside one of {sorted(allowed)} (not at tests root)") continue first_folder = rel_parts[0] if first_folder not in allowed: errors.append( f"{test_file}: subfolder '{first_folder}' under tests/ is not an allowed module. " f"The valid ones are: {sorted(allowed)}" ) return errors def main() -> None: parser = argparse.ArgumentParser(description="Check that test files follow tests//… layout.") parser.add_argument( "--impl-root", type=Path, default="verl", help="Implementation root (default: my_project)", ) parser.add_argument( "--tests-root", type=Path, default="tests", help="Root of test tree (default: tests)", ) parser.add_argument( "--allow-dirs", nargs="*", default=["special_e2e", "special_sanity", "special_standalone", "special_distributed"], help="Extra top-level test folders that are exempt from the rule", ) parser.add_argument( "--allow-files", nargs="*", default=[ "tests/test_protocol_on_cpu.py", "tests/test_base_config_on_cpu.py", "tests/test_protocol_v2_on_cpu.py", ], help="Extra top-level test folders that are exempt from the rule", ) args = parser.parse_args() if not args.impl_root.is_dir(): raise Exception(f"Implementation root '{args.impl_root}' does not exist.") if not args.tests_root.is_dir(): raise Exception(f"Tests root '{args.tests_root}' does not exist.") allowed = discover_allowed_modules(args.impl_root, args.allow_dirs) violations = find_violations(args.tests_root, allowed, args.allow_files) if violations: print("❌ Test layout violations found:\n", file=sys.stderr) for err in violations: print(" -", err, file=sys.stderr) print( f"\nGuideline:\n Place each test file under tests//…\n where is " f"one of the top-level packages inside '{args.impl_root}', or is explicitly listed via --allow-dirs.\n", file=sys.stderr, ) raise Exception("❌ Test layout violations found.") print("✅ Tests folder structure looks good.") if __name__ == "__main__": main() ================================================ FILE: tests/special_standalone/README.md ================================================ The standalone test folder is reserved for tests that require dedicated environment (e.g. memory stress tests) ================================================ FILE: tests/special_standalone/test_memory_buffers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test memory buffers - We start with two models with the same weights - We use Memory buffer to make one of the models and then compare the parameters """ import gc import torch from transformers import LlamaConfig, LlamaModel def test_memory_buffers(): llama_config = LlamaConfig( vocab_size=256, hidden_size=4096, intermediate_size=11008, num_hidden_layers=2, num_attention_heads=16, num_key_value_heads=16, ) model = LlamaModel(config=llama_config).cuda() model_copy = LlamaModel(config=llama_config).cuda() model_copy.load_state_dict(model.state_dict()) norm_factor = 1024**3 t_before = torch.cuda.get_device_properties(0).total_memory / norm_factor r_before = torch.cuda.memory_reserved(0) / norm_factor a_before = torch.cuda.memory_allocated(0) / norm_factor print(f"Before Total memory: {t_before} GB, reserved: {r_before} GB, allocated: {a_before} GB") t = torch.cuda.get_device_properties(0).total_memory / norm_factor r = torch.cuda.memory_reserved(0) / norm_factor a = torch.cuda.memory_allocated(0) / norm_factor gc.collect() torch.cuda.empty_cache() print(f"After Total memory: {t} GB, reserved: {r} GB, allocated: {a} GB") change_ratio = (a - a_before) / a_before assert change_ratio < 0.01, f"make sure the allocated change is less than 1%, Got {change_ratio}" for (name1, param1), (name2, param2) in zip(model.named_parameters(), model_copy.named_parameters(), strict=True): assert name1 == name2 assert torch.eq(param1.data, param2.data).all(), f"{param1.data}, {param2.data}, {name1}" if __name__ == "__main__": test_memory_buffers() ================================================ FILE: tests/test_base_config_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from verl.base_config import BaseConfig @pytest.fixture def base_config_mock(): """Fixture to create a mock BaseConfig instance with test attributes.""" mock_config = BaseConfig() mock_config.test_attr = "test_value" return mock_config def test_getitem_success(base_config_mock): """Test __getitem__ with existing attribute (happy path).""" assert base_config_mock["test_attr"] == "test_value" def test_getitem_nonexistent_attribute(base_config_mock): """Test __getitem__ with non-existent attribute (exception path 1).""" with pytest.raises(AttributeError): _ = base_config_mock["nonexistent_attr"] def test_getitem_invalid_key_type(base_config_mock): """Test __getitem__ with invalid key type (exception path 2).""" with pytest.raises(TypeError): _ = base_config_mock[123] # type: ignore ================================================ FILE: tests/test_protocol_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import numpy as np import pytest import tensordict import torch from packaging.version import parse as parse_version from tensordict import TensorDict from verl import DataProto from verl.protocol import ( deserialize_single_tensor, deserialize_tensordict, serialize_single_tensor, serialize_tensordict, union_numpy_dict, union_tensor_dict, ) from verl.utils import tensordict_utils as tu def test_union_tensor_dict(): obs = torch.randn(100, 10) data1 = TensorDict({"obs": obs, "act": torch.randn(100, 3)}, batch_size=[100]) data2 = TensorDict({"obs": obs, "next_obs": torch.randn(100, 10), "rew": torch.randn(100)}, batch_size=[100]) data_with_copied_obs = TensorDict( {"obs": obs.clone(), "next_obs": torch.randn(100, 10), "rew": torch.randn(100)}, batch_size=[100] ) union_tensor_dict(data1, data2) with pytest.raises(AssertionError): union_tensor_dict(data1, data_with_copied_obs) def test_union_numpy_dict(): """ A comprehensive test suite for union_numpy_dict, covering standard use cases, N-dimensional arrays, object-dtype arrays, and NaN value handling. """ arr_3d = np.arange(8).reshape((2, 2, 2)) union_numpy_dict({"a": arr_3d}, {"a": arr_3d}) arr1 = np.array([1, "hello", np.array([2, 3])], dtype=object) arr2 = np.array([1, "hello", np.array([2, 3])], dtype=object) union_numpy_dict({"a": arr1}, {"a": arr2}) # --- Test Case 1: The original test with mixed object/float types --- # This test case from the original test file is preserved. data = np.random.random(100) # This array intentionally mixes float('nan') and the string 'nan' nan_data = [float("nan") for _ in range(99)] nan_data.append("nan") nan_data_arr = np.array(nan_data, dtype=object) dict1 = {"a": data, "b": nan_data_arr} dict2_same = {"a": data.copy(), "b": nan_data_arr.copy()} dict3_different = {"a": np.random.random(100)} union_numpy_dict(dict1, dict2_same) # Should pass with pytest.raises(AssertionError): union_numpy_dict(dict1, dict3_different) # --- Test Case 2: Standard 3D arrays (fixes the core bug) --- arr_3d = np.arange(24, dtype=np.int32).reshape((2, 3, 4)) dict_3d_1 = {"nd_array": arr_3d} dict_3d_2_same = {"nd_array": arr_3d.copy()} dict_3d_3_different = {"nd_array": arr_3d + 1} union_numpy_dict(dict_3d_1, dict_3d_2_same) # Should pass with pytest.raises(AssertionError, match="`nd_array` in tensor_dict1 and tensor_dict2 are not the same object."): union_numpy_dict(dict_3d_1, dict_3d_3_different) # --- Test Case 3: Nested 2D and 4D object-dtype arrays --- sub_arr1 = np.array([1, 2]) sub_arr2 = np.array([3.0, 4.0]) # 2D object array arr_2d_obj = np.array([[sub_arr1, "text"], [sub_arr2, None]], dtype=object) arr_2d_obj_diff = np.array([[sub_arr1, "text"], [sub_arr2, "other"]], dtype=object) union_numpy_dict({"data": arr_2d_obj}, {"data": arr_2d_obj.copy()}) # Should pass with pytest.raises(AssertionError): union_numpy_dict({"data": arr_2d_obj}, {"data": arr_2d_obj_diff}) # 4D object array to ensure deep recursion is robust arr_4d_obj = np.array([[[[sub_arr1]]], [[[sub_arr2]]]], dtype=object) arr_4d_obj_diff = np.array([[[[sub_arr1]]], [[[np.array([9, 9])]]]], dtype=object) union_numpy_dict({"data": arr_4d_obj}, {"data": arr_4d_obj.copy()}) # Should pass with pytest.raises(AssertionError): union_numpy_dict({"data": arr_4d_obj}, {"data": arr_4d_obj_diff}) # --- Test Case 4: Explicit NaN value comparison --- # This verifies that our new _deep_equal logic correctly handles NaNs. nan_arr = np.array([1.0, np.nan, 3.0]) dict_nan_1 = {"data": nan_arr} dict_nan_2_same = {"data": np.array([1.0, np.nan, 3.0])} # A new array with same values dict_nan_3_different_val = {"data": np.array([1.0, 2.0, 3.0])} dict_nan_4_different_pos = {"data": np.array([np.nan, 1.0, 3.0])} # NaNs in the same position should be considered equal for merging. union_numpy_dict(dict_nan_1, dict_nan_2_same) # Should pass with pytest.raises(AssertionError): union_numpy_dict(dict_nan_1, dict_nan_3_different_val) with pytest.raises(AssertionError): union_numpy_dict(dict_nan_1, dict_nan_4_different_pos) # --- Test Case 5: Circular reference handling --- # Create two separate, but structurally identical, circular references. # This should pass without a RecursionError. circ_arr_1 = np.array([None], dtype=object) circ_arr_1[0] = circ_arr_1 circ_arr_2 = np.array([None], dtype=object) circ_arr_2[0] = circ_arr_2 union_numpy_dict({"data": circ_arr_1}, {"data": circ_arr_2}) # Should pass # Create a circular reference and a non-circular one. # This should fail with an AssertionError because they are different. non_circ_arr = np.array([None], dtype=object) with pytest.raises(AssertionError): union_numpy_dict({"data": circ_arr_1}, {"data": non_circ_arr}) def test_tensor_dict_constructor(): obs = torch.randn(100, 10) act = torch.randn(100, 10, 3) data = DataProto.from_dict(tensors={"obs": obs, "act": act}) assert data.batch.batch_size == torch.Size([100]) with pytest.raises(AssertionError): data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=2) with pytest.raises(AssertionError): data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=3) def test_tensor_dict_make_iterator(): obs = torch.randn(100, 10) labels = [random.choice(["abc", "cde"]) for _ in range(100)] dataset = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}) data_iter_1 = dataset.make_iterator(mini_batch_size=10, epochs=2, seed=1) data_list_1 = [] for data in data_iter_1: data_list_1.append(data) data_iter_2 = dataset.make_iterator(mini_batch_size=10, epochs=2, seed=1) data_list_2 = [] for data in data_iter_2: data_list_2.append(data) for data1, data2 in zip(data_list_1, data_list_2, strict=True): assert isinstance(data1, DataProto) assert isinstance(data2, DataProto) result = torch.all(torch.eq(data1.batch["obs"], data2.batch["obs"])) if not result.item(): print(data1.batch["obs"]) print(data2.batch["obs"]) raise AssertionError() non_tensor_result = np.all(np.equal(data1.non_tensor_batch["labels"], data2.non_tensor_batch["labels"])) if not non_tensor_result.item(): print(data1.non_tensor_batch["labels"]) print(data2.non_tensor_batch["labels"]) def test_reorder(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) labels = ["a", "b", "c", "d", "e", "f"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"}) data.reorder(torch.tensor([3, 4, 2, 0, 1, 5])) assert torch.all(torch.eq(data.batch["obs"], torch.tensor([4, 5, 3, 1, 2, 6]))) assert np.all(data.non_tensor_batch["labels"] == np.array(["d", "e", "c", "a", "b", "f"])) assert data.meta_info == {"name": "abdce"} def test_chunk_concat(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) labels = ["a", "b", "c", "d", "e", "f"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"}) with pytest.raises(AssertionError): data.chunk(5) data_split = data.chunk(2) assert len(data_split) == 2 assert torch.all(torch.eq(data_split[0].batch["obs"], torch.tensor([1, 2, 3]))) assert np.all(data_split[0].non_tensor_batch["labels"] == np.array(["a", "b", "c"])) assert data_split[0].meta_info == {"name": "abdce"} assert torch.all(torch.eq(data_split[1].batch["obs"], torch.tensor([4, 5, 6]))) assert np.all(data_split[1].non_tensor_batch["labels"] == np.array(["d", "e", "f"])) assert data_split[1].meta_info == {"name": "abdce"} concat_data = DataProto.concat(data_split) assert torch.all(torch.eq(concat_data.batch["obs"], data.batch["obs"])) assert np.all(concat_data.non_tensor_batch["labels"] == data.non_tensor_batch["labels"]) assert concat_data.meta_info == data.meta_info def test_concat_metrics_from_multiple_workers(): """Test that concat() properly merges metrics from all workers in distributed training.""" # Simulate 3 workers each with their own metrics obs1 = torch.tensor([1, 2]) obs2 = torch.tensor([3, 4]) obs3 = torch.tensor([5, 6]) # Each worker has different metrics (as list of dict format) worker1_metrics = [{"loss": 0.5, "accuracy": 0.9}] worker2_metrics = [{"loss": 0.6, "accuracy": 0.85}] worker3_metrics = [{"loss": 0.55, "accuracy": 0.88}] data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"metrics": worker1_metrics, "config_flag": True}) data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"metrics": worker2_metrics, "config_flag": True}) data3 = DataProto.from_dict(tensors={"obs": obs3}, meta_info={"metrics": worker3_metrics, "config_flag": True}) # Concat all workers' data concat_data = DataProto.concat([data1, data2, data3]) # Verify tensors are concatenated assert torch.all(torch.eq(concat_data.batch["obs"], torch.tensor([1, 2, 3, 4, 5, 6]))) # Verify ALL workers' metrics are flattened to dict of lists expected_metrics = {"loss": [0.5, 0.6, 0.55], "accuracy": [0.9, 0.85, 0.88]} assert concat_data.meta_info["metrics"] == expected_metrics # Verify config flags are preserved from first worker assert concat_data.meta_info["config_flag"] is True def test_concat_with_empty_and_non_list_meta_info(): """Test concat() handles edge cases: empty meta_info, non-list values, and None.""" obs1 = torch.tensor([1, 2]) obs2 = torch.tensor([3, 4]) # Worker 1 has metrics, worker 2 doesn't data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"metrics": [{"loss": 0.5}], "flag": True}) data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"flag": True}) concat_data = DataProto.concat([data1, data2]) # Should flatten worker1's metrics to dict of lists assert concat_data.meta_info["metrics"] == {"loss": [0.5]} assert concat_data.meta_info["flag"] is True # Test with non-list meta_info value data3 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"single_value": 42}) data4 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"single_value": 42}) concat_data2 = DataProto.concat([data3, data4]) assert concat_data2.meta_info["single_value"] == 42 def test_concat_first_worker_missing_metrics(): """Test that metrics from other workers are preserved even when first worker has no metrics. This is a critical edge case - the old buggy implementation only checked data[0].meta_info and would lose all metrics if the first worker didn't have any. """ obs1 = torch.tensor([1, 2]) obs2 = torch.tensor([3, 4]) obs3 = torch.tensor([5, 6]) # First worker has NO metrics, but workers 2 and 3 do data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"config_flag": True}) data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"metrics": {"loss": 0.6}, "config_flag": True}) data3 = DataProto.from_dict(tensors={"obs": obs3}, meta_info={"metrics": {"loss": 0.55}, "config_flag": True}) concat_data = DataProto.concat([data1, data2, data3]) # Should flatten metrics from workers 2 and 3 into dict of lists expected_metrics = {"loss": [0.6, 0.55]} assert concat_data.meta_info["metrics"] == expected_metrics assert concat_data.meta_info["config_flag"] is True def test_concat_non_list_metrics(): """Test that concat() handles non-list metrics (single dict) correctly. In some cases, metrics might be a single dict instead of a list. The implementation should flatten them into a dict of lists. """ obs1 = torch.tensor([1, 2]) obs2 = torch.tensor([3, 4]) # Metrics as single dict (not wrapped in list) data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"metrics": {"loss": 0.5, "accuracy": 0.9}}) data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"metrics": {"loss": 0.6, "accuracy": 0.85}}) concat_data = DataProto.concat([data1, data2]) # Should flatten to dict of lists expected_metrics = {"loss": [0.5, 0.6], "accuracy": [0.9, 0.85]} assert concat_data.meta_info["metrics"] == expected_metrics def test_concat_merge_different_non_metric_keys(): """Test that concat() merges non-metric meta_info keys from all workers. When different workers have different non-metric keys, all keys should be preserved. This prevents silent data loss and aligns with the docstring stating meta_info is "merged". """ obs1 = torch.tensor([1, 2]) obs2 = torch.tensor([3, 4]) obs3 = torch.tensor([5, 6]) # Each worker has some unique non-metric keys data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"config": "A", "shared_key": "X"}) data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"extra_key": "B", "shared_key": "X"}) data3 = DataProto.from_dict(tensors={"obs": obs3}, meta_info={"another_key": "C", "shared_key": "X"}) concat_data = DataProto.concat([data1, data2, data3]) # All unique keys should be preserved assert concat_data.meta_info["config"] == "A" assert concat_data.meta_info["extra_key"] == "B" assert concat_data.meta_info["another_key"] == "C" assert concat_data.meta_info["shared_key"] == "X" def test_concat_conflicting_non_metric_keys(): """Test that concat() raises an assertion error when non-metric keys have conflicting values. This ensures data integrity by catching cases where workers have different values for what should be the same configuration parameter. """ obs1 = torch.tensor([1, 2]) obs2 = torch.tensor([3, 4]) # Same key "config" but different values data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"config": "A"}) data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"config": "B"}) # Should raise an assertion error due to conflicting values with pytest.raises(AssertionError, match="Conflicting values for meta_info key 'config'"): DataProto.concat([data1, data2]) def test_pop(): obs = torch.randn(100, 10) act = torch.randn(100, 3) dataset = DataProto.from_dict({"obs": obs, "act": act}, meta_info={"2": 2, "1": 1}) poped_dataset = dataset.pop(batch_keys=["obs"], meta_info_keys=["2"]) assert poped_dataset.batch.keys() == {"obs"} assert poped_dataset.meta_info.keys() == {"2"} assert dataset.batch.keys() == {"act"} assert dataset.meta_info.keys() == {"1"} def test_repeat(): # Create a DataProto object with some batch and non-tensor data obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) # Test interleave=True repeated_data_interleave = data.repeat(repeat_times=2, interleave=True) expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [3, 4], [3, 4], [5, 6], [5, 6]]) expected_labels_interleave = ["a", "a", "b", "b", "c", "c"] assert torch.all(torch.eq(repeated_data_interleave.batch["obs"], expected_obs_interleave)) assert (repeated_data_interleave.non_tensor_batch["labels"] == expected_labels_interleave).all() assert repeated_data_interleave.meta_info == {"info": "test_info"} # Test interleave=False repeated_data_no_interleave = data.repeat(repeat_times=2, interleave=False) expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6]]) expected_labels_no_interleave = ["a", "b", "c", "a", "b", "c"] assert torch.all(torch.eq(repeated_data_no_interleave.batch["obs"], expected_obs_no_interleave)) assert (repeated_data_no_interleave.non_tensor_batch["labels"] == expected_labels_no_interleave).all() assert repeated_data_no_interleave.meta_info == {"info": "test_info"} def test_dataproto_pad_unpad(): obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) from verl.protocol import pad_dataproto_to_divisor, unpad_dataproto padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=2) assert pad_size == 1 expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2]]) expected_labels = ["a", "b", "c", "a"] assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs)) assert (padded_data.non_tensor_batch["labels"] == expected_labels).all() assert padded_data.meta_info == {"info": "test_info"} unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size) assert torch.all(torch.eq(unpadd_data.batch["obs"], obs)) assert (unpadd_data.non_tensor_batch["labels"] == labels).all() assert unpadd_data.meta_info == {"info": "test_info"} padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=3) assert pad_size == 0 expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) expected_labels = ["a", "b", "c"] assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs)) assert (padded_data.non_tensor_batch["labels"] == expected_labels).all() assert padded_data.meta_info == {"info": "test_info"} unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size) assert torch.all(torch.eq(unpadd_data.batch["obs"], obs)) assert (unpadd_data.non_tensor_batch["labels"] == labels).all() assert unpadd_data.meta_info == {"info": "test_info"} padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=7) assert pad_size == 4 expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6], [1, 2]]) expected_labels = ["a", "b", "c", "a", "b", "c", "a"] assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs)) assert (padded_data.non_tensor_batch["labels"] == expected_labels).all() assert padded_data.meta_info == {"info": "test_info"} unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size) assert torch.all(torch.eq(unpadd_data.batch["obs"], obs)) assert (unpadd_data.non_tensor_batch["labels"] == labels).all() assert unpadd_data.meta_info == {"info": "test_info"} def test_dataproto_fold_unfold(): from verl.protocol import DataProto, fold_batch_dim, unfold_batch_dim obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) data1 = data.repeat(repeat_times=2, interleave=True) data2 = fold_batch_dim(data1, new_batch_size=3) torch.testing.assert_close(data2.batch["obs"], torch.tensor([[[1, 2], [1, 2]], [[3, 4], [3, 4]], [[5, 6], [5, 6]]])) assert (data2.non_tensor_batch["labels"] == [["a", "a"], ["b", "b"], ["c", "c"]]).all() data2.reorder(indices=torch.tensor([1, 2, 0])) data3 = unfold_batch_dim(data2, batch_dims=2) torch.testing.assert_close(data3.batch["obs"], torch.tensor([[3, 4], [3, 4], [5, 6], [5, 6], [1, 2], [1, 2]])) assert (data3.non_tensor_batch["labels"] == ["b", "b", "c", "c", "a", "a"]).all() assert data3.meta_info == {"info": "test_info"} def test_torch_save_data_proto(): obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) data.save_to_disk("test_data.pt") loaded_data = DataProto.load_from_disk("test_data.pt") assert torch.all(torch.eq(loaded_data.batch["obs"], data.batch["obs"])) assert (loaded_data.non_tensor_batch["labels"] == data.non_tensor_batch["labels"]).all() assert loaded_data.meta_info == data.meta_info import os os.remove("test_data.pt") def test_len(): obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = np.array(["a", "b", "c"], dtype=object) data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) assert len(data) == 3 data = DataProto(batch=None, non_tensor_batch={"labels": labels}, meta_info={"info": "test_info"}) assert len(data) == 3 data = DataProto(batch=None, non_tensor_batch={}, meta_info={"info": "test_info"}) assert len(data) == 0 data = DataProto(batch=None, non_tensor_batch=None, meta_info={"info": "test_info"}) assert len(data) == 0 def test_dataproto_index(): data_len = 100 idx_num = 10 obs = torch.randn(data_len, 10) labels = [random.choice(["abc", "cde"]) for _ in range(data_len)] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}) labels_np = np.array(labels) idx_np_int = np.random.randint(0, data_len, size=(idx_num,)) result_np_int = data[idx_np_int] assert result_np_int.batch.keys() == data.batch.keys() assert result_np_int.non_tensor_batch.keys() == data.non_tensor_batch.keys() assert result_np_int.batch["obs"].shape[0] == idx_num assert result_np_int.non_tensor_batch["labels"].shape[0] == idx_num assert np.array_equal(result_np_int.batch["obs"].cpu().numpy(), obs[idx_np_int].numpy()) assert np.array_equal(result_np_int.non_tensor_batch["labels"], labels_np[idx_np_int]) idx_torch_int = torch.randint(0, data_len, size=(idx_num,)) result_torch_int = data[idx_torch_int] assert result_torch_int.batch.keys() == data.batch.keys() assert result_torch_int.non_tensor_batch.keys() == data.non_tensor_batch.keys() assert result_torch_int.batch["obs"].shape[0] == idx_num assert result_torch_int.non_tensor_batch["labels"].shape[0] == idx_num assert np.array_equal(result_torch_int.batch["obs"].cpu().numpy(), obs[idx_torch_int].cpu().numpy()) assert np.array_equal(result_torch_int.non_tensor_batch["labels"], labels_np[idx_torch_int.cpu().numpy()]) idx_list_int = [np.random.randint(0, data_len) for _ in range(idx_num)] result_list_int = data[idx_list_int] assert result_list_int.batch.keys() == data.batch.keys() assert result_list_int.non_tensor_batch.keys() == data.non_tensor_batch.keys() assert result_list_int.batch["obs"].shape[0] == idx_num assert result_list_int.non_tensor_batch["labels"].shape[0] == idx_num assert np.array_equal(result_list_int.batch["obs"].cpu().numpy(), obs[idx_list_int].cpu().numpy()) assert np.array_equal(result_list_int.non_tensor_batch["labels"], labels_np[idx_list_int]) idx_np_bool = np.random.randint(0, 2, size=(data_len,), dtype=bool) result_np_bool = data[idx_np_bool] assert result_np_bool.batch.keys() == data.batch.keys() assert result_np_bool.non_tensor_batch.keys() == data.non_tensor_batch.keys() assert result_np_bool.batch["obs"].shape[0] == idx_np_bool.sum() assert result_np_bool.non_tensor_batch["labels"].shape[0] == idx_np_bool.sum() assert np.array_equal(result_np_bool.batch["obs"].cpu().numpy(), obs[idx_np_bool].cpu().numpy()) assert np.array_equal(result_np_bool.non_tensor_batch["labels"], labels_np[idx_np_bool]) idx_torch_bool = torch.randint(0, 2, size=(data_len,), dtype=torch.bool) result_torch_bool = data[idx_torch_bool] assert result_torch_bool.batch.keys() == data.batch.keys() assert result_torch_bool.non_tensor_batch.keys() == data.non_tensor_batch.keys() assert result_torch_bool.batch["obs"].shape[0] == idx_torch_bool.sum().item() assert result_torch_bool.non_tensor_batch["labels"].shape[0] == idx_torch_bool.sum().item() assert np.array_equal(result_torch_bool.batch["obs"].cpu().numpy(), obs[idx_torch_bool].cpu().numpy()) assert np.array_equal(result_torch_bool.non_tensor_batch["labels"], labels_np[idx_torch_bool]) idx_list_bool = [np.random.randint(0, 2, dtype=bool) for _ in range(data_len)] result_list_bool = data[idx_list_bool] assert result_list_bool.batch.keys() == data.batch.keys() assert result_list_bool.non_tensor_batch.keys() == data.non_tensor_batch.keys() assert result_list_bool.batch["obs"].shape[0] == sum(idx_list_bool) assert result_list_bool.non_tensor_batch["labels"].shape[0] == sum(idx_list_bool) assert np.array_equal(result_list_bool.batch["obs"].cpu().numpy(), obs[idx_list_bool].cpu().numpy()) assert np.array_equal(result_list_bool.non_tensor_batch["labels"], labels_np[idx_list_bool]) def test_old_vs_new_from_single_dict(): class CustomProto(DataProto): """Uses the new, fixed from_single_dict.""" pass class OriginProto(DataProto): """Mimics the *old* from_single_dict (always returns a DataProto).""" @classmethod def from_single_dict(cls, data, meta_info=None, auto_padding=False): tensors, non_tensors = {}, {} for k, v in data.items(): if torch.is_tensor(v): tensors[k] = v else: non_tensors[k] = v # always calls DataProto.from_dict, ignoring `cls` return DataProto.from_dict( tensors=tensors, non_tensors=non_tensors, meta_info=meta_info, auto_padding=auto_padding, ) sample = {"x": torch.tensor([0])} orig = OriginProto.from_single_dict(sample) # old behavior: always DataProto, not a CustomOriginProto assert type(orig) is DataProto assert type(orig) is not OriginProto cust = CustomProto.from_single_dict(sample) # new behavior: respects subclass assert type(cust) is CustomProto def test_dataproto_no_batch(): labels = ["a", "b", "c"] data = DataProto.from_dict(non_tensors={"labels": labels}, meta_info={"info": "test_info"}) selected = data.select(non_tensor_batch_keys=["labels"]) assert (selected.non_tensor_batch["labels"] == labels).all() pop_data = data.pop(non_tensor_batch_keys=["labels"]) assert (pop_data.non_tensor_batch["labels"] == labels).all() assert data.non_tensor_batch == {} def test_sample_level_repeat(): # Create a DataProto object with some batch and non-tensor data obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"}) # list repeated_data_interleave = data.sample_level_repeat(repeat_times=[3, 1, 2]) expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [1, 2], [3, 4], [5, 6], [5, 6]]) expected_labels_interleave = ["a", "a", "a", "b", "c", "c"] assert torch.all(torch.eq(repeated_data_interleave.batch["obs"], expected_obs_interleave)) assert (repeated_data_interleave.non_tensor_batch["labels"] == expected_labels_interleave).all() assert repeated_data_interleave.meta_info == {"info": "test_info"} # torch.tensor repeated_data_no_interleave = data.sample_level_repeat(repeat_times=torch.tensor([1, 2, 3])) expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [3, 4], [5, 6], [5, 6], [5, 6]]) expected_labels_no_interleave = ["a", "b", "b", "c", "c", "c"] assert torch.all(torch.eq(repeated_data_no_interleave.batch["obs"], expected_obs_no_interleave)) assert (repeated_data_no_interleave.non_tensor_batch["labels"] == expected_labels_no_interleave).all() assert repeated_data_no_interleave.meta_info == {"info": "test_info"} def test_dataproto_unfold_column_chunks(): obs1 = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) obs2 = torch.tensor([[1, 2], [5, 6], [9, 10]]) labels = ["a", "b", "c"] data = DataProto.from_dict( tensors={"obs1": obs1, "obs2": obs2}, non_tensors={"labels": labels}, meta_info={"name": "abc"} ) ret = data.unfold_column_chunks(2, split_keys=["obs1"]) expect_obs1 = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]]) expect_obs2 = torch.tensor([[1, 2], [1, 2], [5, 6], [5, 6], [9, 10], [9, 10]]) expect_labels = ["a", "a", "b", "b", "c", "c"] assert torch.all(torch.eq(ret.batch["obs1"], expect_obs1)) assert torch.all(torch.eq(ret.batch["obs2"], expect_obs2)) assert (ret.non_tensor_batch["labels"] == expect_labels).all() assert ret.meta_info == {"name": "abc"} obs1 = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) obs2 = torch.tensor([[1, 2], [5, 6], [9, 10]]) labels = [["a1", "a2"], ["b1", "b2"], ["c1", "c2"]] data = DataProto.from_dict( tensors={"obs1": obs1, "obs2": obs2}, non_tensors={"labels": labels}, meta_info={"name": "abc"} ) ret = data.unfold_column_chunks(2, split_keys=["obs1", "labels"]) expect_obs1 = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]]) expect_obs2 = torch.tensor([[1, 2], [1, 2], [5, 6], [5, 6], [9, 10], [9, 10]]) expect_labels = [["a1"], ["a2"], ["b1"], ["b2"], ["c1"], ["c2"]] assert torch.all(torch.eq(ret.batch["obs1"], expect_obs1)) assert torch.all(torch.eq(ret.batch["obs2"], expect_obs2)) assert (ret.non_tensor_batch["labels"] == expect_labels).all() assert ret.meta_info == {"name": "abc"} obs1 = torch.tensor( [[[1, 1], [2, 2], [3, 3], [4, 4]], [[5, 5], [6, 6], [7, 7], [8, 8]], [[9, 9], [10, 10], [11, 11], [12, 12]]] ) obs2 = torch.tensor([[[1, 1], [2, 2]], [[5, 5], [6, 6]], [[9, 9], [10, 10]]]) labels = ["a", "b", "c"] data = DataProto.from_dict( tensors={"obs1": obs1, "obs2": obs2}, non_tensors={"labels": labels}, meta_info={"name": "abc"} ) ret = data.unfold_column_chunks(2, split_keys=["obs1"]) expect_obs1 = torch.tensor( [ [[1, 1], [2, 2]], [[3, 3], [4, 4]], [[5, 5], [6, 6]], [[7, 7], [8, 8]], [[9, 9], [10, 10]], [[11, 11], [12, 12]], ] ) expect_obs2 = torch.tensor( [[[1, 1], [2, 2]], [[1, 1], [2, 2]], [[5, 5], [6, 6]], [[5, 5], [6, 6]], [[9, 9], [10, 10]], [[9, 9], [10, 10]]] ) expect_labels = ["a", "a", "b", "b", "c", "c"] assert torch.all(torch.eq(ret.batch["obs1"], expect_obs1)) assert torch.all(torch.eq(ret.batch["obs2"], expect_obs2)) assert (ret.non_tensor_batch["labels"] == expect_labels).all() assert ret.meta_info == {"name": "abc"} def test_dataproto_chunk_after_index(): data_len = 4 obs = torch.randn(data_len, 4) labels = [f"label_{i}" for i in range(data_len)] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abc"}) # Test with boolean numpy array bool_mask = np.array([True, False, True, False]) selected = data[bool_mask] assert isinstance(selected.batch.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch.batch_size) # int or List[int] # Test with integer numpy array int_mask = np.array([0, 2]) selected = data[int_mask] assert isinstance(selected.batch.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch.batch_size) # Test with boolean list list_mask = [True, False, True, False] selected = data[list_mask] assert isinstance(selected.batch.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch.batch_size) # Test with list list_mask = [0, 2] selected = data[list_mask] assert isinstance(selected.batch.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch.batch_size) # Test with torch tensor (bool) torch_bool_mask = torch.tensor([True, False, True, False]) selected = data[torch_bool_mask] assert isinstance(selected.batch.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch.batch_size) # Test with torch tensor (int) torch_int_mask = torch.tensor([0, 2]) selected = data[torch_int_mask] assert isinstance(selected.batch.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch.batch_size) @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_to_tensordict(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) labels = ["a", "b", "c", "d", "e", "f"] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"}) output = data.to_tensordict() assert torch.all(torch.eq(output["obs"], obs)).item() assert output["labels"] == labels assert output["name"] == "abdce" @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_from_tensordict(): tensor_dict = { "obs": torch.tensor([1, 2, 3, 4, 5, 6]), "labels": ["a", "b", "c", "d", "e", "f"], } non_tensor_dict = {"name": "abdce"} tensordict = tu.get_tensordict(tensor_dict, non_tensor_dict) data = DataProto.from_tensordict(tensordict) assert data.non_tensor_batch["labels"].tolist() == tensor_dict["labels"] assert torch.all(torch.eq(data.batch["obs"], tensor_dict["obs"])).item() assert data.meta_info["name"] == "abdce" @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_to_tensordict_with_nested_lists(): """Test converting DataProto with nested lists to TensorDict (lists of lists).""" obs = torch.tensor([1, 2, 3]) # Simulate turn_scores or tool_rewards: array of lists with varying lengths turn_scores = [[], [0.5, 0.8], [0.9]] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"turn_scores": turn_scores}) # This should not raise an error tensordict_output = data.to_tensordict() # Verify the data is preserved assert torch.all(torch.eq(tensordict_output["obs"], obs)).item() # Verify nested structure is accessible (TensorDict wraps NonTensorStack as LinkedList) retrieved_scores = tensordict_output["turn_scores"] assert len(retrieved_scores) == len(turn_scores) # Verify content matches assert list(retrieved_scores[0]) == [] assert list(retrieved_scores[1]) == [0.5, 0.8] assert list(retrieved_scores[2]) == [0.9] @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_to_tensordict_with_nested_dicts(): """Test converting DataProto with lists of dicts to TensorDict.""" obs = torch.tensor([1, 2, 3]) # Simulate reward_extra_info: array of dicts reward_extra_info = [{"acc": 1.0}, {"acc": 0.0}, {"acc": 1.0}] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"reward_extra_info": reward_extra_info}) # This should not raise an error - this was the original bug tensordict_output = data.to_tensordict() # Verify the data is preserved assert torch.all(torch.eq(tensordict_output["obs"], obs)).item() # Verify nested dicts are accessible retrieved_info = tensordict_output["reward_extra_info"] assert len(retrieved_info) == len(reward_extra_info) # Verify content matches for i, expected_dict in enumerate(reward_extra_info): assert dict(retrieved_info[i]) == expected_dict @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_to_tensordict_with_complex_nested_structures(): """Test converting DataProto with complex nested structures (lists of lists of dicts).""" obs = torch.tensor([1, 2, 3]) # Simulate raw_prompt: array of lists containing dicts raw_prompt = [ [{"content": "Question 1", "role": "user"}], [{"content": "Question 2", "role": "user"}, {"content": "Answer 2", "role": "assistant"}], [{"content": "Question 3", "role": "user"}], ] data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"raw_prompt": raw_prompt}) # This should not raise an error tensordict_output = data.to_tensordict() # Verify the data is preserved assert torch.all(torch.eq(tensordict_output["obs"], obs)).item() # Verify complex nested structure is accessible retrieved_prompt = tensordict_output["raw_prompt"] assert len(retrieved_prompt) == len(raw_prompt) # Spot check: verify first prompt has correct structure assert len(retrieved_prompt[0]) == 1 assert dict(retrieved_prompt[0][0]) == {"content": "Question 1", "role": "user"} @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_to_tensordict_and_back_with_nested_data(): """Test round-trip conversion: DataProto → TensorDict → DataProto with nested structures.""" obs = torch.tensor([1, 2, 3, 4]) labels = ["a", "b", "c", "d"] # Multiple types of nested structures turn_scores = [[], [0.5], [0.8, 0.9], [0.7]] reward_extra_info = [ {"acc": 1.0, "loss": 0.1}, {"acc": 0.5, "loss": 0.3}, {"acc": 1.0, "loss": 0.05}, {"acc": 0.0, "loss": 0.9}, ] raw_prompt = [ [{"content": "Q1", "role": "user"}], [{"content": "Q2", "role": "user"}], [{"content": "Q3", "role": "user"}, {"content": "A3", "role": "assistant"}], [{"content": "Q4", "role": "user"}], ] # Create original DataProto original_data = DataProto.from_dict( tensors={"obs": obs}, non_tensors={ "labels": labels, "turn_scores": turn_scores, "reward_extra_info": reward_extra_info, "raw_prompt": raw_prompt, }, meta_info={"experiment": "test_nested"}, ) # Convert to TensorDict tensordict_output = original_data.to_tensordict() # Convert back to DataProto reconstructed_data = DataProto.from_tensordict(tensordict_output) # Verify tensors are preserved assert torch.all(torch.eq(reconstructed_data.batch["obs"], obs)).item() # Verify non-tensor data is preserved assert reconstructed_data.non_tensor_batch["labels"].tolist() == labels # Verify nested structures are preserved assert len(reconstructed_data.non_tensor_batch["turn_scores"]) == len(turn_scores) for orig, recon in zip(turn_scores, reconstructed_data.non_tensor_batch["turn_scores"], strict=True): assert list(orig) == list(recon) assert len(reconstructed_data.non_tensor_batch["reward_extra_info"]) == len(reward_extra_info) for orig, recon in zip(reward_extra_info, reconstructed_data.non_tensor_batch["reward_extra_info"], strict=True): assert orig == recon assert len(reconstructed_data.non_tensor_batch["raw_prompt"]) == len(raw_prompt) for orig, recon in zip(raw_prompt, reconstructed_data.non_tensor_batch["raw_prompt"], strict=True): assert orig == list(recon) # Verify meta_info is preserved assert reconstructed_data.meta_info["experiment"] == "test_nested" @pytest.mark.skipif( parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10" ) def test_to_tensordict_agent_loop_scenario(): """Test the exact scenario from agent loop: DataProto with tool rewards, acc, etc. This test reproduces the exact error from the agent loop where nested structures (lists of lists, lists of dicts) failed to convert to TensorDict. """ # Simulate real agent loop data structure prompts = torch.tensor([[1, 2, 3], [4, 5, 6]]) responses = torch.tensor([[7, 8], [9, 10]]) # Non-tensor data with nested structures from agent loop data_source = ["lighteval/MATH", "lighteval/MATH"] uid = ["uuid-1", "uuid-2"] num_turns = np.array([2, 4], dtype=np.int32) acc = np.array([1.0, 0.0]) turn_scores = [[], [0.5, 0.8]] # Lists of varying lengths reward_extra_info = [{"acc": 1.0}, {"acc": 0.0}] # List of dicts raw_prompt = [ [{"content": "Compute 4 @ 2", "role": "user"}], [{"content": "Compute 8 @ 7", "role": "user"}], ] tool_rewards = [[0.0], []] # List of lists data = DataProto.from_dict( tensors={"prompts": prompts, "responses": responses}, non_tensors={ "data_source": data_source, "uid": uid, "num_turns": num_turns, "acc": acc, "turn_scores": turn_scores, "reward_extra_info": reward_extra_info, "raw_prompt": raw_prompt, "tool_rewards": tool_rewards, }, meta_info={"global_steps": 42}, ) # THE KEY TEST: This should not raise ValueError about TensorDict conversion tensordict_output = data.to_tensordict() # Verify tensors are accessible assert torch.all(torch.eq(tensordict_output["prompts"], prompts)).item() assert torch.all(torch.eq(tensordict_output["responses"], responses)).item() # Verify all nested structures are accessible (content check, not type check) assert len(tensordict_output["turn_scores"]) == 2 assert list(tensordict_output["turn_scores"][0]) == [] assert list(tensordict_output["turn_scores"][1]) == [0.5, 0.8] assert len(tensordict_output["reward_extra_info"]) == 2 assert dict(tensordict_output["reward_extra_info"][0]) == {"acc": 1.0} assert len(tensordict_output["raw_prompt"]) == 2 assert dict(tensordict_output["raw_prompt"][0][0]) == {"content": "Compute 4 @ 2", "role": "user"} assert len(tensordict_output["tool_rewards"]) == 2 assert list(tensordict_output["tool_rewards"][0]) == [0.0] assert list(tensordict_output["tool_rewards"][1]) == [] # Verify round-trip conversion works perfectly reconstructed = DataProto.from_tensordict(tensordict_output) assert len(reconstructed) == 2 assert reconstructed.meta_info["global_steps"] == 42 assert torch.all(torch.eq(reconstructed.batch["prompts"], prompts)).item() def test_serialize_deserialize_single_tensor(): """Test serialization and deserialization of a single tensor""" # Create test tensor original_tensor = torch.randn(3, 4, 5) # Serialize dtype, shape, data = serialize_single_tensor(original_tensor) # Deserialize reconstructed_tensor = deserialize_single_tensor((dtype, shape, data)) # Verify results assert torch.allclose(original_tensor, reconstructed_tensor) assert original_tensor.shape == reconstructed_tensor.shape assert original_tensor.dtype == reconstructed_tensor.dtype def test_serialize_deserialize_tensordict_regular_tensors(): """Test serialization and deserialization of TensorDict with regular tensors""" # Create test data batch_size = (5, 3) tensor1 = torch.randn(*batch_size, 4) tensor2 = torch.randint(0, 10, (*batch_size, 2)) # Create TensorDict original_tensordict = TensorDict({"tensor1": tensor1, "tensor2": tensor2}, batch_size=batch_size) # Serialize batch_size_serialized, device, encoded_items = serialize_tensordict(original_tensordict) # Deserialize reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device, encoded_items)) # Verify results assert original_tensordict.batch_size == reconstructed_tensordict.batch_size assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys()) for key in original_tensordict.keys(): original_tensor = original_tensordict[key] reconstructed_tensor = reconstructed_tensordict[key] assert torch.allclose(original_tensor, reconstructed_tensor) assert original_tensor.shape == reconstructed_tensor.shape assert original_tensor.dtype == reconstructed_tensor.dtype def test_serialize_deserialize_tensordict_nested_tensors(): """Test serialization and deserialization of TensorDict with nested tensors""" # Create nested tensor tensor_list = [torch.randn(2, 3), torch.randn(3, 4), torch.randn(1, 5)] nested_tensor = torch.nested.as_nested_tensor(tensor_list) # Create regular tensor for comparison regular_tensor = torch.randn(3, 4, 5) # Create TensorDict original_tensordict = TensorDict({"nested": nested_tensor, "regular": regular_tensor}, batch_size=(3,)) # Serialize batch_size_serialized, device, encoded_items = serialize_tensordict(original_tensordict) # Deserialize reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device, encoded_items)) # Verify results assert original_tensordict.batch_size == reconstructed_tensordict.batch_size assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys()) # Verify regular tensor original_regular = original_tensordict["regular"] reconstructed_regular = reconstructed_tensordict["regular"] assert torch.allclose(original_regular, reconstructed_regular) assert original_regular.shape == reconstructed_regular.shape assert original_regular.dtype == reconstructed_regular.dtype # Verify nested tensor original_nested = original_tensordict["nested"] reconstructed_nested = reconstructed_tensordict["nested"] # Check if it's a nested tensor assert original_nested.is_nested assert reconstructed_nested.is_nested # Check layout assert original_nested.layout == reconstructed_nested.layout # Check each tensor after unbinding original_unbind = original_nested.unbind() reconstructed_unbind = reconstructed_nested.unbind() assert len(original_unbind) == len(reconstructed_unbind) for orig, recon in zip(original_unbind, reconstructed_unbind, strict=False): assert torch.allclose(orig, recon) assert orig.shape == recon.shape assert orig.dtype == recon.dtype def test_serialize_deserialize_tensordict_mixed_types(): """Test serialization and deserialization of TensorDict with mixed tensor types""" # Create tensors with different data types float_tensor = torch.randn(2, 3).float() double_tensor = torch.randn(2, 3).double() int_tensor = torch.randint(0, 10, (2, 3)).int() long_tensor = torch.randint(0, 10, (2, 3)).long() bool_tensor = torch.tensor([[True, False], [False, True]]) bfloat16_tensor = torch.randn(2, 3).bfloat16() # Add fp8 tensor (if available) # Note: FP8 is not natively supported in all PyTorch versions # We'll check if it's available and conditionally include it has_fp8 = hasattr(torch, "float8_e5m2") or hasattr(torch, "float8_e4m3fn") if has_fp8: try: # Try to create an FP8 tensor (implementation may vary) # This is a placeholder - actual FP8 support might require specific hardware fp8_tensor = torch.randn(2, 3) if hasattr(torch, "float8_e5m2"): fp8_tensor = fp8_tensor.to(torch.float8_e5m2) elif hasattr(torch, "float8_e4m3fn"): fp8_tensor = fp8_tensor.to(torch.float8_e4m3fn) except Exception: has_fp8 = False # Create nested tensor tensor_list = [ torch.randn(2, 3), torch.randn(3, 4), ] nested_tensor = torch.nested.as_nested_tensor(tensor_list) # Create TensorDict with all available types tensordict_data = { "float": float_tensor, "double": double_tensor, "int": int_tensor, "long": long_tensor, "bool": bool_tensor, "bfloat16": bfloat16_tensor, "nested": nested_tensor, } # Conditionally add fp8 tensor if available if has_fp8: tensordict_data["fp8"] = fp8_tensor original_tensordict = TensorDict( tensordict_data, batch_size=(2,), ) # Serialize batch_size_serialized, device, encoded_items = serialize_tensordict(original_tensordict) # Deserialize reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device, encoded_items)) # Verify results assert original_tensordict.batch_size == reconstructed_tensordict.batch_size assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys()) for key in original_tensordict.keys(): original_tensor = original_tensordict[key] reconstructed_tensor = reconstructed_tensordict[key] if original_tensor.is_nested: # For nested tensors, check each tensor after unbinding original_unbind = original_tensor.unbind() reconstructed_unbind = reconstructed_tensor.unbind() assert len(original_unbind) == len(reconstructed_unbind) for orig, recon in zip(original_unbind, reconstructed_unbind, strict=False): assert torch.allclose(orig, recon, equal_nan=True) assert orig.shape == recon.shape assert orig.dtype == recon.dtype else: # For regular tensors, compare directly assert torch.all(original_tensor == reconstructed_tensor) assert original_tensor.shape == reconstructed_tensor.shape assert original_tensor.dtype == reconstructed_tensor.dtype def test_serialize_deserialize_tensordict_with_device(): """Test serialization and deserialization of TensorDict with device information""" # Create test data batch_size = (2, 3) tensor1 = torch.randn(*batch_size, 4) tensor2 = torch.randint(0, 10, (*batch_size, 2)) # Create TensorDict with device information device = "cpu" original_tensordict = TensorDict({"tensor1": tensor1, "tensor2": tensor2}, batch_size=batch_size, device=device) # Serialize batch_size_serialized, device_serialized, encoded_items = serialize_tensordict(original_tensordict) # Deserialize reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device_serialized, encoded_items)) # Verify results assert original_tensordict.batch_size == reconstructed_tensordict.batch_size assert str(original_tensordict.device) == str(reconstructed_tensordict.device) assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys()) for key in original_tensordict.keys(): original_tensor = original_tensordict[key] reconstructed_tensor = reconstructed_tensordict[key] assert torch.allclose(original_tensor.cpu(), reconstructed_tensor.cpu()) assert original_tensor.shape == reconstructed_tensor.shape assert original_tensor.dtype == reconstructed_tensor.dtype def test_serialize_dataproto_with_empty_tensordict(): """Tests that serializing a DataProto with an empty TensorDict does not crash. This test verifies the fix for the torch.cat error that occurs when calling consolidate() on an empty TensorDict during serialization. """ import pickle # This test requires tensordict >= 0.5.0 to trigger the code path if parse_version(tensordict.__version__) < parse_version("0.5.0"): pytest.skip("Test requires tensordict>=0.5.0") # Create a DataProto with an empty TensorDict but with a batch size empty_td = TensorDict({}, batch_size=[10]) data = DataProto(batch=empty_td) # This would crash before the fix with: # RuntimeError: torch.cat(): expected a non-empty list of Tensors try: serialized_data = pickle.dumps(data) except Exception as e: pytest.fail(f"Serializing DataProto with empty TensorDict failed with: {e}") # Verify deserialization works as expected deserialized_data = pickle.loads(serialized_data) assert len(deserialized_data.batch.keys()) == 0 assert deserialized_data.batch.batch_size == torch.Size([10]) ================================================ FILE: tests/test_protocol_v2_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Replace DataProto with raw TensorDict """ import copy import random import numpy as np import pytest import torch from tensordict.tensorclass import NonTensorData, NonTensorStack from verl.utils import tensordict_utils as tu def test_union_tensor_dict(): obs = torch.randn(100, 10) meta_info1 = {"top_p": 0.8} meta_info2 = {"top_p": 0.9} data1 = {"obs": obs, "act": torch.randn(100, 3), "data_sources": ["gsm8k"] * 100} data2 = {"obs": obs, "next_obs": torch.randn(100, 10), "rew": torch.randn(100), "data_sources": ["gsm8k"] * 100} data_with_copied_obs = {"obs": obs.clone(), "next_obs": torch.randn(100, 10), "rew": torch.randn(100)} data1 = tu.get_tensordict(tensor_dict=data1) data2 = tu.get_tensordict(tensor_dict=data2) data_with_copied_obs = tu.get_tensordict(data_with_copied_obs) tu.union_tensor_dict(data1, data2) with pytest.raises(AssertionError): # conflict in tensor values tu.union_tensor_dict(data1, data_with_copied_obs) data1 = tu.assign_non_tensor(data1, **meta_info1) tu.union_tensor_dict(data1, data2) # works ok data2 = tu.assign_non_tensor(data2, **meta_info2) with pytest.raises(AssertionError): # conflict in NonTensorData tu.union_tensor_dict(data1, data2) data1.pop("top_p") data2.pop("top_p") data2["data_sources"][0] = "math" with pytest.raises(AssertionError): # conflict in NonTensorData tu.union_tensor_dict(data1, data2) def test_tensor_dict_constructor(): obs = torch.ones(100, 10) act = torch.zeros(100, 10, 3) data_source = ["gsm8k"] * 100 non_tensor_dict = {"name": "abdce"} data = tu.get_tensordict( tensor_dict={"obs": obs, "act": act, "data_source": data_source}, non_tensor_dict=non_tensor_dict ) assert data.batch_size == torch.Size([100]) # test slicing assert torch.all(torch.eq(data[0]["obs"], torch.ones(10))).item() assert torch.all(torch.eq(data[0]["act"], torch.zeros(10, 3))).item() assert data[0]["data_source"] == "gsm8k" assert torch.all(torch.eq(data[0:2]["obs"], torch.ones(2, 10))).item() assert torch.all(torch.eq(data[0:2]["act"], torch.zeros(2, 10, 3))).item() assert data[0:2]["data_source"] == ["gsm8k"] * 2 # test non tensor data assert data["name"] == "abdce" def test_index_select_tensor_dict(): vocab_size = 128 a = torch.randint(low=0, high=vocab_size, size=(11,)) b = torch.randint(low=0, high=vocab_size, size=(13,)) c = torch.randint(low=0, high=vocab_size, size=(12,)) d = torch.randint(low=0, high=vocab_size, size=(15,)) input_ids = [a, b, c, d] input_ids = torch.nested.as_nested_tensor(input_ids, layout=torch.jagged) padded_tensor = torch.randn(4, 10) non_tensor_dict = {"global_batch_size": "4"} data = tu.get_tensordict( tensor_dict={ "input_ids": input_ids, "padded_tensor": padded_tensor, }, non_tensor_dict=non_tensor_dict, ) assert data.batch_size == torch.Size([4]) # test index select indices = torch.tensor([1, 3]) selected_data = tu.index_select_tensor_dict(data, indices) assert selected_data.batch_size == torch.Size([2]) target_input_ids = torch.nested.as_nested_tensor([input_ids[idx] for idx in indices], layout=torch.jagged) target_select_data = tu.get_tensordict( tensor_dict={ "input_ids": target_input_ids, "padded_tensor": padded_tensor[indices], }, non_tensor_dict=non_tensor_dict, ) tu.assert_tensordict_eq(selected_data, target_select_data) def test_tensordict_with_images(): # each sample contains a sequence with multiple images of different sizes vocab_size = 128 a = torch.randint(low=0, high=vocab_size, size=(11,)) b = torch.randint(low=0, high=vocab_size, size=(13,)) input_ids = [a, b] input_ids = torch.nested.as_nested_tensor(input_ids, layout=torch.jagged) # must be numpy # TODO(vermouth1992). We may use nested tensor too. But this requires nested over nested a_images = [ torch.randint(low=0, high=255, size=(3, 256, 256), dtype=torch.uint8).numpy(), torch.randint(low=0, high=255, size=(3, 128, 128), dtype=torch.uint8).numpy(), ] b_images = [ torch.randint(low=0, high=255, size=(3, 256, 256), dtype=torch.uint8).numpy(), torch.randint(low=0, high=255, size=(3, 128, 128), dtype=torch.uint8).numpy(), torch.randint(low=0, high=255, size=(3, 64, 64), dtype=torch.uint8).numpy(), ] images = [a_images, b_images] data = tu.get_tensordict({"input_ids": input_ids, "images": images}) assert np.all(np.equal(data[0]["images"][0], a_images[0])) assert torch.all(torch.eq(data[0]["input_ids"], a)) def test_tensordict_with_packing(): vocab_size = 128 a = torch.randint(low=0, high=vocab_size, size=(11,)) b = torch.randint(low=0, high=vocab_size, size=(13,)) input_ids = [a, b] input_ids = torch.nested.as_nested_tensor(input_ids, layout=torch.jagged) data = tu.get_tensordict({"input_ids": input_ids}) # test cu_seqlens cu_seqlens = torch.tensor([0, 11, 24]) assert torch.all(torch.eq(cu_seqlens, data["input_ids"].offsets())) # test index assert torch.all(torch.eq(data["input_ids"][0], a)) assert torch.all(torch.eq(data["input_ids"][1], b)) assert torch.all(torch.eq(data[0]["input_ids"], a)) assert torch.all(torch.eq(data[1]["input_ids"], b)) data_lst = data.chunk(2) assert torch.all(torch.eq(data_lst[0]["input_ids"][0], a)) assert torch.all(torch.eq(data_lst[1]["input_ids"][0], b)) def test_tensordict_eq(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) data_sources = ["abc", "def", "abc", "def", "pol", "klj"] non_tensor_dict = {"train_sample_kwargs": {"top_p": 1.0}, "val_sample_kwargs": {"top_p": 0.7}} data = tu.get_tensordict({"obs": obs, "data_sources": data_sources}, non_tensor_dict=non_tensor_dict) obs = torch.tensor([1, 2, 3, 4, 5, 6]) data_sources = ["abc", "def", "abc", "def", "pol", "klj"] non_tensor_dict = {"train_sample_kwargs": {"top_p": 1.0}, "val_sample_kwargs": {"top_p": 0.7}} data1 = tu.get_tensordict({"obs": obs, "data_sources": data_sources}, non_tensor_dict=non_tensor_dict) tu.assert_tensordict_eq(data, data1) data2 = copy.deepcopy(data1) data2["obs"][0] += 1 with pytest.raises(AssertionError): tu.assert_tensordict_eq(data, data2) data2 = copy.deepcopy(data1) data2["data_sources"][0] = "math" with pytest.raises(AssertionError): tu.assert_tensordict_eq(data, data2) data2 = copy.deepcopy(data1) data2["train_sample_kwargs"]["top_p"] = 0.9 with pytest.raises(AssertionError): tu.assert_tensordict_eq(data, data2) tensor_list = [ torch.tensor([1, 2, 3, 3, 2]), torch.tensor([4, 5]), torch.tensor([7, 8, 10, 14]), torch.tensor([10, 11, 12]), torch.tensor([13, 14, 15, 18]), torch.tensor([16, 17]), ] obs = torch.nested.as_nested_tensor(tensor_list, layout=torch.jagged) data_sources = ["abc", "def", "abc", "def", "pol", "klj"] non_tensor_dict = {"train_sample_kwargs": {"top_p": 1.0}, "val_sample_kwargs": {"top_p": 0.7}} data3 = tu.get_tensordict({"obs": obs, "data_sources": data_sources}, non_tensor_dict=non_tensor_dict) tensor_list[0] = torch.tensor([1, 2, 3, 3, 2]) obs = torch.nested.as_nested_tensor(tensor_list, layout=torch.jagged) data4 = tu.get_tensordict({"obs": obs, "data_sources": data_sources}, non_tensor_dict=non_tensor_dict) tu.assert_tensordict_eq(data3, data4) tensor_list[0] = torch.tensor([1, 2, 4]) obs = torch.nested.as_nested_tensor(tensor_list, layout=torch.jagged) data5 = tu.get_tensordict({"obs": obs, "data_sources": data_sources}, non_tensor_dict=non_tensor_dict) with pytest.raises(AssertionError): tu.assert_tensordict_eq(data3, data5) tensor_list[0] = torch.tensor([4, 5]) tensor_list[1] = torch.tensor([1, 2, 3, 3, 2]) obs = torch.nested.as_nested_tensor(tensor_list, layout=torch.jagged) data6 = tu.get_tensordict({"obs": obs, "data_sources": data_sources}, non_tensor_dict=non_tensor_dict) with pytest.raises(AssertionError): tu.assert_tensordict_eq(data3, data6) def test_tensor_dict_make_iterator(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) input_ids = torch.nested.as_nested_tensor( [ torch.tensor([0, 1]), torch.tensor([2]), torch.tensor([3, 4]), torch.tensor([5]), torch.tensor([6, 7, 8]), torch.tensor([9]), ], layout=torch.jagged, ) data_sources = ["abc", "def", "abc", "def", "pol", "klj"] non_tensor_dict = {"train_sample_kwargs": {"top_p": 1.0}, "val_sample_kwargs": {"top_p": 0.7}} dataset = tu.get_tensordict( {"obs": obs, "data_sources": data_sources, "input_ids": input_ids}, non_tensor_dict=non_tensor_dict ) dataloader = tu.make_iterator( dataset, mini_batch_size=2, epochs=2, seed=0, dataloader_kwargs={"shuffle": False, "drop_last": False} ) expected_tensor_dict = [ tu.index_select_tensor_dict(dataset, indices=list(range(0, 2))), tu.index_select_tensor_dict(dataset, indices=list(range(2, 4))), tu.index_select_tensor_dict(dataset, indices=list(range(4, 6))), tu.index_select_tensor_dict(dataset, indices=list(range(0, 2))), tu.index_select_tensor_dict(dataset, indices=list(range(2, 4))), tu.index_select_tensor_dict(dataset, indices=list(range(4, 6))), ] i = 0 for d in dataloader: tu.assert_tensordict_eq(d, expected_tensor_dict[i]) i += 1 data_iter_1 = tu.make_iterator(dataset, mini_batch_size=3, epochs=1, seed=1, dataloader_kwargs={"shuffle": True}) data_list_1 = [] for data in data_iter_1: data_list_1.append(data) data_iter_2 = tu.make_iterator(dataset, mini_batch_size=3, epochs=1, seed=1, dataloader_kwargs={"shuffle": True}) data_list_2 = [] for data in data_iter_2: data_list_2.append(data) for data1, data2 in zip(data_list_1, data_list_2, strict=True): tu.assert_tensordict_eq(data1, data2) def test_reorder(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) labels = ["a", "b", "c", "d", "e", "f"] non_tensor_dict = {"name": "abdce"} data = tu.get_tensordict(tensor_dict={"obs": obs, "labels": labels}, non_tensor_dict=non_tensor_dict) data = data[torch.tensor([3, 4, 2, 0, 1, 5])] assert torch.all(torch.eq(data["obs"], torch.tensor([4, 5, 3, 1, 2, 6]))) assert np.all(data["labels"] == np.array(["d", "e", "c", "a", "b", "f"])) assert data["name"] == "abdce" def test_chunk_concat(): obs = torch.tensor([1, 2, 3, 4, 5, 6]) labels = ["a", "b", "c", "d", "e", "f"] data = tu.get_tensordict({"obs": obs, "labels": labels}, non_tensor_dict={"name": "abcde"}) data_split = data.tensor_split(indices_or_sections=5, dim=0) expected_idx_lst = [[0, 1], [2], [3], [4], [5]] for d, expected_idx in zip(data_split, expected_idx_lst, strict=False): tu.assert_tensordict_eq(d, data[expected_idx]) data_split = data.chunk(2) assert len(data_split) == 2 assert torch.all(torch.eq(data_split[0]["obs"], torch.tensor([1, 2, 3]))) assert np.all(data_split[0]["labels"] == np.array(["a", "b", "c"])) assert data_split[0]["name"] == "abcde" assert torch.all(torch.eq(data_split[1]["obs"], torch.tensor([4, 5, 6]))) assert np.all(data_split[1]["labels"] == np.array(["d", "e", "f"])) assert data_split[1]["name"] == "abcde" concat_data = torch.cat(data_split, dim=0) assert torch.all(torch.eq(concat_data["obs"], data["obs"])) assert np.all(concat_data["labels"] == data["labels"]) assert concat_data["name"] == data["name"] data1 = tu.get_tensordict(tensor_dict={"obs": obs, "labels": labels}, non_tensor_dict={"name": "abcde"}) data2 = tu.get_tensordict(tensor_dict={"obs": obs, "labels": labels}, non_tensor_dict={"name": "def"}) data3 = tu.get_tensordict(tensor_dict={"obs": obs, "labels": labels}, non_tensor_dict={"name": "cfg"}) output = torch.cat([data1, data2, data3], dim=0) # concat NonTensorData will keep the first one. assert output["name"] == "abcde" def test_pop(): obs = torch.randn(3, 10) act = torch.randn(3, 3) labels = ["a", ["b"], []] dataset = tu.get_tensordict({"obs": obs, "act": act, "labels": labels}, non_tensor_dict={"2": 2, "1": 1}) dataset1 = copy.deepcopy(dataset) # test pop keys popped_dataset = tu.pop_keys(dataset, keys=["obs", "2"]) assert popped_dataset.batch_size[0] == 3 assert popped_dataset.keys() == {"obs", "2"} assert torch.all(torch.eq(popped_dataset["obs"], obs)).item() assert popped_dataset["2"] == 2 assert dataset.keys() == {"act", "1", "labels"} # test pop non-exist key with pytest.raises(KeyError): tu.pop_keys(dataset, keys=["obs", "2"]) # test single pop # NonTensorData assert tu.pop(dataset1, key="2") == 2 # NonTensorStack assert tu.pop(dataset1, key="labels") == ["a", ["b"], []] # Tensor assert torch.all(torch.eq(tu.pop(dataset1, key="obs"), obs)).item() def test_get(): obs = torch.randn(3, 10) act = torch.randn(3, 3) labels = ["a", ["b"], []] dataset = tu.get_tensordict({"obs": obs, "act": act, "labels": labels}, non_tensor_dict={"2": 2, "1": 1}) # test pop keys popped_dataset = tu.get_keys(dataset, keys=["obs", "2"]) assert popped_dataset.batch_size[0] == 3 assert torch.all(torch.eq(popped_dataset["obs"], dataset["obs"])).item() assert popped_dataset["2"] == dataset["2"] # test pop non-exist key with pytest.raises(KeyError): tu.get_keys(dataset, keys=["obs", "3"]) # test single pop # NonTensorData assert tu.get(dataset, key="2") == 2 # NonTensorStack assert tu.get(dataset, key="labels") == ["a", ["b"], []] # Tensor assert torch.all(torch.eq(tu.get(dataset, key="obs"), obs)).item() # Non-exist key assert tu.get(dataset, key="3", default=3) == 3 def test_repeat(): # Create a DataProto object with some batch and non-tensor data obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = tu.get_tensordict({"obs": obs, "labels": labels}, non_tensor_dict={"info": "test_info"}) # Test interleave=True repeated_data_interleave = data.repeat_interleave(repeats=2) expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [3, 4], [3, 4], [5, 6], [5, 6]]) expected_labels_interleave = ["a", "a", "b", "b", "c", "c"] assert torch.all(torch.eq(repeated_data_interleave["obs"], expected_obs_interleave)) assert repeated_data_interleave["labels"] == expected_labels_interleave assert repeated_data_interleave["info"] == "test_info" # Test interleave=False repeated_data_no_interleave = data.repeat(2) expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6]]) expected_labels_no_interleave = ["a", "b", "c", "a", "b", "c"] assert torch.all(torch.eq(repeated_data_no_interleave["obs"], expected_obs_no_interleave)) assert repeated_data_no_interleave["labels"] == expected_labels_no_interleave assert repeated_data_no_interleave["info"] == "test_info" def test_dataproto_pad_unpad(): obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = tu.get_tensordict(tensor_dict={"obs": obs, "labels": labels}, non_tensor_dict={"info": "test_info"}) padded_data, pad_size = tu.pad_to_divisor(data, size_divisor=2) assert pad_size == 1 expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2]]) expected_labels = ["a", "b", "c", "a"] assert torch.all(torch.eq(padded_data["obs"], expected_obs)) assert padded_data["labels"] == expected_labels assert padded_data["info"] == "test_info" unpadd_data = tu.unpad(padded_data, pad_size=pad_size) assert torch.all(torch.eq(unpadd_data["obs"], obs)) assert unpadd_data["labels"] == labels assert unpadd_data["info"] == "test_info" padded_data, pad_size = tu.pad_to_divisor(data, size_divisor=3) assert pad_size == 0 expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) expected_labels = ["a", "b", "c"] assert torch.all(torch.eq(padded_data["obs"], expected_obs)) assert padded_data["labels"] == expected_labels assert padded_data["info"] == "test_info" unpadd_data = tu.unpad(padded_data, pad_size=pad_size) assert torch.all(torch.eq(unpadd_data["obs"], obs)) assert unpadd_data["labels"] == labels assert unpadd_data["info"] == "test_info" padded_data, pad_size = tu.pad_to_divisor(data, size_divisor=7) assert pad_size == 4 expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6], [1, 2]]) expected_labels = ["a", "b", "c", "a", "b", "c", "a"] assert torch.all(torch.eq(padded_data["obs"], expected_obs)) assert padded_data["labels"] == expected_labels assert padded_data["info"] == "test_info" unpadd_data = tu.unpad(padded_data, pad_size=pad_size) assert torch.all(torch.eq(unpadd_data["obs"], obs)) assert unpadd_data["labels"] == labels assert unpadd_data["info"] == "test_info" def test_torch_save_data_proto(): obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = tu.get_tensordict({"obs": obs, "labels": labels}, non_tensor_dict={"info": "test_info"}) filename = "test_data.pt" torch.save(data, filename) loaded_data = torch.load(filename, weights_only=False) assert torch.all(torch.eq(loaded_data["obs"], data["obs"])) assert loaded_data["labels"] == data["labels"] assert loaded_data["info"] == data["info"] import os os.remove(filename) def test_len(): obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = np.array(["a", "b", "c"], dtype=object) data = tu.get_tensordict({"obs": obs, "labels": labels.tolist()}, non_tensor_dict={"info": "test_info"}) assert len(data) == 3 data = tu.get_tensordict({"labels": labels.tolist()}, non_tensor_dict={"info": "test_info"}) assert len(data) == 3 data_item = data[0] assert len(data_item) == 0 data = tu.get_tensordict({}, non_tensor_dict={"info": "test_info"}) assert len(data) == 0 def test_dataproto_index(): data_len = 100 idx_num = 10 obs = torch.randn(data_len, 10) labels = [random.choice(["abc", "cde"]) for _ in range(data_len)] data = tu.get_tensordict({"obs": obs, "labels": labels}) labels_np = np.array(labels) idx_np_int = np.random.randint(0, data_len, size=(idx_num,)) result_np_int = data[idx_np_int] assert result_np_int.keys() == data.keys() assert result_np_int["obs"].shape[0] == idx_num assert len(result_np_int["labels"]) == idx_num assert np.array_equal(result_np_int["obs"].cpu().numpy(), obs[idx_np_int].numpy()) assert np.array_equal(result_np_int["labels"], labels_np[idx_np_int]) idx_torch_int = torch.randint(0, data_len, size=(idx_num,)) result_torch_int = data[idx_torch_int] assert result_torch_int.keys() == data.keys() assert result_torch_int["obs"].shape[0] == idx_num assert len(result_torch_int["labels"]) == idx_num assert np.array_equal(result_torch_int["obs"].cpu().numpy(), obs[idx_torch_int].cpu().numpy()) assert np.array_equal(result_torch_int["labels"], labels_np[idx_torch_int.cpu().numpy()]) idx_list_int = [np.random.randint(0, data_len) for _ in range(idx_num)] result_list_int = data[idx_list_int] assert result_list_int.keys() == data.keys() assert result_list_int["obs"].shape[0] == idx_num assert len(result_list_int["labels"]) == idx_num assert np.array_equal(result_list_int["obs"].cpu().numpy(), obs[idx_list_int].cpu().numpy()) assert np.array_equal(result_list_int["labels"], labels_np[idx_list_int]) # idx_np_bool = np.random.randint(0, 2, size=(data_len,), dtype=bool) # result_np_bool = data[idx_np_bool] # assert result_np_bool.keys() == data.keys() # assert result_np_bool["obs"].shape[0] == idx_np_bool.sum() # assert len(result_np_bool["labels"]) == idx_np_bool.sum() # assert np.array_equal(result_np_bool["obs"].cpu().numpy(), obs[idx_np_bool].cpu().numpy()) # assert np.array_equal(result_np_bool["labels"], labels_np[idx_np_bool]) idx_torch_bool = torch.randint(0, 2, size=(data_len,), dtype=torch.bool) result_torch_bool = data[idx_torch_bool] assert result_torch_bool.keys() == data.keys() assert result_torch_bool["obs"].shape[0] == idx_torch_bool.sum().item() assert len(result_torch_bool["labels"]) == idx_torch_bool.sum().item() assert np.array_equal(result_torch_bool["obs"].cpu().numpy(), obs[idx_torch_bool].cpu().numpy()) assert np.array_equal(result_torch_bool["labels"], labels_np[idx_torch_bool]) # idx_list_bool = [np.random.randint(0, 2, dtype=bool) for _ in range(data_len)] # result_list_bool = data[idx_list_bool] # assert result_list_bool.keys() == data.keys() # assert result_list_bool["obs"].shape[0] == sum(idx_list_bool) # assert len(result_list_bool["labels"]) == sum(idx_list_bool) # assert np.array_equal(result_list_bool["obs"].cpu().numpy(), obs[idx_list_bool].cpu().numpy()) # assert np.array_equal(result_list_bool["labels"], labels_np[idx_list_bool]) def test_select(): obs = torch.randn(100, 10) act = torch.randn(100, 3) dataset = tu.get_tensordict({"obs": obs, "act": act}, non_tensor_dict={"2": 2, "1": 1}) subset = dataset.select("obs", "2") assert torch.all(torch.eq(subset["obs"], dataset["obs"])) assert subset["2"] == dataset["2"] assert "act" not in subset.keys() assert "1" not in subset.keys() def test_dataproto_no_batch(): labels = ["a", "b", "c"] data = tu.get_tensordict(tensor_dict={"labels": labels}, non_tensor_dict={"info": "test_info"}) selected = data.select("labels") assert selected["labels"] == labels pop_data = tu.pop_keys(data, keys=["labels"]) assert pop_data["labels"] == labels assert "labels" not in data def test_sample_level_repeat(): # Create a DataProto object with some batch and non-tensor data obs = torch.tensor([[1, 2], [3, 4], [5, 6]]) labels = ["a", "b", "c"] data = tu.get_tensordict({"obs": obs, "labels": labels}, non_tensor_dict={"info": "test_info"}) # list repeated_data_interleave = data.repeat_interleave(repeats=torch.tensor([3, 1, 2])) expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [1, 2], [3, 4], [5, 6], [5, 6]]) expected_labels_interleave = ["a", "a", "a", "b", "c", "c"] assert torch.all(torch.eq(repeated_data_interleave["obs"], expected_obs_interleave)) assert repeated_data_interleave["labels"] == expected_labels_interleave assert repeated_data_interleave["info"] == "test_info" # torch.tensor repeated_data_no_interleave = data.repeat_interleave(repeats=torch.tensor([1, 2, 3])) expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [3, 4], [5, 6], [5, 6], [5, 6]]) expected_labels_no_interleave = ["a", "b", "b", "c", "c", "c"] assert torch.all(torch.eq(repeated_data_no_interleave["obs"], expected_obs_no_interleave)) assert repeated_data_no_interleave["labels"] == expected_labels_no_interleave assert repeated_data_no_interleave["info"] == "test_info" def test_dataproto_chunk_after_index(): data_len = 4 obs = torch.randn(data_len, 4) labels = [f"label_{i}" for i in range(data_len)] data = tu.get_tensordict(tensor_dict={"obs": obs, "labels": labels}, non_tensor_dict={"name": "abc"}) # Test with boolean numpy array bool_mask = torch.tensor([True, False, True, False]) selected = data[bool_mask] assert isinstance(selected.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch_size) # int or List[int] # Test with integer numpy array int_mask = torch.tensor([0, 2]) selected = data[int_mask] assert isinstance(selected.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch_size) # Test with boolean list list_mask = [True, False, True, False] selected = data[list_mask] assert isinstance(selected.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch_size) # Test with list list_mask = [0, 2] selected = data[list_mask] assert isinstance(selected.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch_size) # Test with torch tensor (bool) torch_bool_mask = torch.tensor([True, False, True, False]) selected = data[torch_bool_mask] assert isinstance(selected.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch_size) # Test with torch tensor (int) torch_int_mask = torch.tensor([0, 2]) selected = data[torch_int_mask] assert isinstance(selected.batch_size, torch.Size) assert all(isinstance(d, int) for d in selected.batch_size) def test_concat_nested_tensor(): # Test 2D nested tensors vocab_size = 128 a = torch.randint(low=0, high=vocab_size, size=(11,)) b = torch.randint(low=0, high=vocab_size, size=(13,)) c = torch.randint(low=0, high=vocab_size, size=(12,)) d = torch.randint(low=0, high=vocab_size, size=(15,)) nested_a_b = torch.nested.as_nested_tensor([a, b], layout=torch.jagged) nested_c_d = torch.nested.as_nested_tensor([c, d], layout=torch.jagged) output = tu.concat_nested_tensors([nested_a_b, nested_c_d]) output_values = output.values() expected = torch.cat([a, b, c, d], dim=0) assert torch.all(torch.eq(output_values, expected)).item() # Test 3D nested tensors a_3d = torch.randint(low=0, high=vocab_size, size=(4, 4)) b_3d = torch.randint(low=0, high=vocab_size, size=(4, 5)) c_3d = torch.randint(low=0, high=vocab_size, size=(4, 6)) d_3d = torch.randint(low=0, high=vocab_size, size=(4, 7)) nested_a_b_3d = torch.nested.as_nested_tensor([a_3d, b_3d], layout=torch.jagged) nested_c_d_3d = torch.nested.as_nested_tensor([c_3d, d_3d], layout=torch.jagged) output_3d = tu.concat_nested_tensors([nested_a_b_3d, nested_c_d_3d]) assert output_3d.shape[0] == 4 output_3d_unbind = output_3d.unbind(0) assert torch.all(torch.eq(output_3d_unbind[0], a_3d)).item() assert torch.all(torch.eq(output_3d_unbind[1], b_3d)).item() assert torch.all(torch.eq(output_3d_unbind[2], c_3d)).item() assert torch.all(torch.eq(output_3d_unbind[3], d_3d)).item() # Test 4D nested tensors a_4d = torch.randint(low=0, high=vocab_size, size=(2, 3, 4)) b_4d = torch.randint(low=0, high=vocab_size, size=(2, 3, 5)) c_4d = torch.randint(low=0, high=vocab_size, size=(2, 3, 3)) d_4d = torch.randint(low=0, high=vocab_size, size=(2, 3, 6)) nested_a_b_4d = torch.nested.as_nested_tensor([a_4d, b_4d], layout=torch.jagged) nested_c_d_4d = torch.nested.as_nested_tensor([c_4d, d_4d], layout=torch.jagged) output_4d = tu.concat_nested_tensors([nested_a_b_4d, nested_c_d_4d]) assert output_4d.shape[0] == 4 output_4d_unbind = output_4d.unbind(0) assert torch.all(torch.eq(output_4d_unbind[0], a_4d)).item() assert torch.all(torch.eq(output_4d_unbind[1], b_4d)).item() assert torch.all(torch.eq(output_4d_unbind[2], c_4d)).item() assert torch.all(torch.eq(output_4d_unbind[3], d_4d)).item() def test_concat_tensordict(): vocab_size = 128 a = torch.randint(low=0, high=vocab_size, size=(11,)) b = torch.randint(low=0, high=vocab_size, size=(13,)) c = torch.randint(low=0, high=vocab_size, size=(12,)) d = torch.randint(low=0, high=vocab_size, size=(15,)) nested_a_b = torch.nested.as_nested_tensor([a, b], layout=torch.jagged) nested_c_d = torch.nested.as_nested_tensor([c, d], layout=torch.jagged) tensordict1 = tu.get_tensordict( tensor_dict={"input_ids": nested_a_b, "labels": ["a", "b"]}, non_tensor_dict={"temp": 1.0} ) tensordict2 = tu.get_tensordict( tensor_dict={"input_ids": nested_c_d, "labels": ["c", "d"]}, non_tensor_dict={"temp": 2.0} ) tensordict1_copy = copy.deepcopy(tensordict1) tensordict2_copy = copy.deepcopy(tensordict2) output = tu.concat_tensordict([tensordict1, tensordict2]) assert torch.all(torch.eq(output["input_ids"].values(), torch.cat([a, b, c, d]))).item() assert output["labels"] == ["a", "b", "c", "d"] assert output["temp"] == 1.0 # make sure tensordict1 and tensordict2 is untouched tu.assert_tensordict_eq(tensordict1, tensordict1_copy) tu.assert_tensordict_eq(tensordict2, tensordict2_copy) # test concat tensordict with only NonTensorStack and NonTensorData tensordict1 = tu.get_tensordict(tensor_dict={"labels": ["a", "b"]}, non_tensor_dict={"temp": 1.0}) tensordict2 = tu.get_tensordict(tensor_dict={"labels": ["c", "d"]}, non_tensor_dict={"temp": 2.0}) output = tu.concat_tensordict([tensordict1, tensordict2]) assert output["labels"] == ["a", "b", "c", "d"] assert output["temp"] == 1.0 assert output.batch_size[0] == 4 # test concat tensordict with only NonTensorData tensordict1 = tu.get_tensordict(tensor_dict={}, non_tensor_dict={"temp": 1.0}) tensordict2 = tu.get_tensordict(tensor_dict={}, non_tensor_dict={"temp": 2.0}) output = tu.concat_tensordict([tensordict1, tensordict2]) assert len(output.batch_size) == 0 assert output["temp"] == 1.0 def test_chunk_tensordict(): # Qwen-VL 3d position_ids position_ids = torch.nested.as_nested_tensor( [ torch.arange(4).expand(4, 4), torch.arange(5).expand(4, 5), torch.arange(6).expand(4, 6), torch.arange(7).expand(4, 7), ], layout=torch.jagged, ) input_ids = torch.nested.as_nested_tensor( [torch.arange(4), torch.arange(5), torch.arange(6), torch.arange(7)], layout=torch.jagged ) attention_mask = torch.nested.as_nested_tensor( [ torch.randint(low=0, high=2, size=[3, 4]), torch.randint(low=0, high=2, size=[3, 5]), torch.randint(low=0, high=2, size=[3, 6]), torch.randint(low=0, high=2, size=[3, 7]), ], layout=torch.jagged, ) multi_modal_inputs = torch.stack( [ NonTensorData({"pixel_values": torch.randn(3, 224, 224)}), NonTensorData(None), NonTensorData({"pixel_values": torch.randn(3, 128, 128)}), NonTensorData({"pixel_values": torch.randn(3, 128, 128)}), ] ) td = tu.get_tensordict( { "input_ids": input_ids, "position_ids": position_ids, "attention_mask": attention_mask, "multi_modal_inputs": multi_modal_inputs, }, ) assert len(td) == 4 chunks = tu.chunk_tensordict(td, chunks=2) for i, chunk in enumerate(chunks): assert len(chunk) == 2 for key, val in chunk.items(): if isinstance(val, torch.Tensor) and val.is_nested: tensors = td[key].unbind(dim=0) expected = torch.nested.as_nested_tensor(tensors[i * 2 : (i + 1) * 2], layout=torch.jagged) assert torch.all(torch.eq(val.values(), expected.values())).item() else: expected = td[key][i * 2 : (i + 1) * 2] for tensor, expect in zip(val, expected, strict=False): if tensor.data is None: assert expect is None else: assert torch.all(torch.eq(tensor.data["pixel_values"], expect["pixel_values"])).item() def test_assign_non_tensor_stack_with_nested_lists(): """Test assign_non_tensor_stack with lists of lists.""" td = tu.get_tensordict({"obs": torch.randn(3, 4)}, non_tensor_dict={}) # Lists of varying lengths (like turn_scores or tool_rewards) turn_scores = [[], [0.5, 0.8], [0.9]] tu.assign_non_tensor_stack(td, "turn_scores", turn_scores) # Verify data is accessible assert len(td["turn_scores"]) == 3 assert list(td["turn_scores"][0]) == [] assert list(td["turn_scores"][1]) == [0.5, 0.8] assert list(td["turn_scores"][2]) == [0.9] def test_assign_non_tensor_stack_with_nested_dicts(): """Test assign_non_tensor_stack with lists of dicts.""" td = tu.get_tensordict({"obs": torch.randn(3, 4)}, non_tensor_dict={}) # Lists of dicts (like reward_extra_info) reward_extra_info = [{"acc": 1.0, "loss": 0.1}, {"acc": 0.0, "loss": 0.9}, {"acc": 1.0, "loss": 0.05}] tu.assign_non_tensor_stack(td, "reward_extra_info", reward_extra_info) # Verify data is accessible assert len(td["reward_extra_info"]) == 3 assert dict(td["reward_extra_info"][0]) == {"acc": 1.0, "loss": 0.1} assert dict(td["reward_extra_info"][1]) == {"acc": 0.0, "loss": 0.9} assert dict(td["reward_extra_info"][2]) == {"acc": 1.0, "loss": 0.05} def test_assign_non_tensor_stack_with_complex_nested(): """Test assign_non_tensor_stack with lists of lists of dicts.""" td = tu.get_tensordict({"obs": torch.randn(2, 4)}, non_tensor_dict={}) # Lists of lists of dicts (like raw_prompt) raw_prompt = [ [{"content": "Question 1", "role": "user"}], [{"content": "Question 2", "role": "user"}, {"content": "Answer 2", "role": "assistant"}], ] tu.assign_non_tensor_stack(td, "raw_prompt", raw_prompt) # Verify data is accessible assert len(td["raw_prompt"]) == 2 assert len(td["raw_prompt"][0]) == 1 assert dict(td["raw_prompt"][0][0]) == {"content": "Question 1", "role": "user"} assert len(td["raw_prompt"][1]) == 2 assert dict(td["raw_prompt"][1][0]) == {"content": "Question 2", "role": "user"} def test_assign_non_tensor_handles_wrappers(): td = tu.get_tensordict({"obs": torch.randn(3, 4)}, non_tensor_dict={}) meta = {"top_p": 0.8} tu.assign_non_tensor(td, **meta) assert td["top_p"] == 0.8 wrapped = NonTensorData(0.3) stack = NonTensorStack.from_list([NonTensorData(1.0), NonTensorData(2.0), NonTensorData(3.0)]) tu.assign_non_tensor(td, wrapped=wrapped, stack=stack) assert td["wrapped"] == 0.3 assert td["stack"] == [1.0, 2.0, 3.0] def test_assign_non_tensor_stack_batch_size_check(): td = tu.get_tensordict({"obs": torch.randn(3, 4)}, non_tensor_dict={}) stack = NonTensorStack.from_list([NonTensorData(1.0), NonTensorData(2.0)]) with pytest.raises(RuntimeError): tu.assign_non_tensor(td, stack=stack) def test_assign_non_tensor_with_auto_detection(): """Test assign_non_tensor automatically detects and handles nested structures.""" td = tu.get_tensordict({"obs": torch.randn(3, 4)}, non_tensor_dict={}) # Mix of simple and nested data tu.assign_non_tensor( td, metadata="experiment_1", # Simple value turn_scores=[[], [0.5, 0.8], [0.9]], # Nested list reward_extra_info=[{"acc": 1.0}, {"acc": 0.0}, {"acc": 1.0}], # List of dicts simple_list=["a", "b", "c"], # Simple list (also uses NonTensorStack for consistency) ) # Verify all data is accessible assert td["metadata"] == "experiment_1" assert len(td["turn_scores"]) == 3 assert list(td["turn_scores"][1]) == [0.5, 0.8] assert len(td["reward_extra_info"]) == 3 assert dict(td["reward_extra_info"][0]) == {"acc": 1.0} assert len(td["simple_list"]) == 3 assert td["simple_list"][0] == "a" def test_get_tensordict_with_nested_lists(): """Test get_tensordict automatically handles nested lists.""" obs = torch.randn(3, 4) turn_scores = [[], [0.5, 0.8], [0.9]] # This should automatically convert turn_scores to NonTensorStack td = tu.get_tensordict({"obs": obs, "turn_scores": turn_scores}) # Verify tensors and nested data are both accessible assert torch.all(torch.eq(td["obs"], obs)) assert len(td["turn_scores"]) == 3 assert list(td["turn_scores"][0]) == [] assert list(td["turn_scores"][1]) == [0.5, 0.8] def test_get_tensordict_with_nested_dicts(): """Test get_tensordict automatically handles lists of dicts.""" obs = torch.randn(3, 4) reward_extra_info = [{"acc": 1.0}, {"acc": 0.0}, {"acc": 1.0}] td = tu.get_tensordict({"obs": obs, "reward_extra_info": reward_extra_info}) assert torch.all(torch.eq(td["obs"], obs)) assert len(td["reward_extra_info"]) == 3 assert dict(td["reward_extra_info"][0]) == {"acc": 1.0} def test_get_tensordict_with_complex_nested_structures(): """Test get_tensordict with lists of lists of dicts.""" obs = torch.randn(2, 4) raw_prompt = [ [{"content": "Q1", "role": "user"}], [{"content": "Q2", "role": "user"}, {"content": "A2", "role": "assistant"}], ] td = tu.get_tensordict({"obs": obs, "raw_prompt": raw_prompt}) assert torch.all(torch.eq(td["obs"], obs)) assert len(td["raw_prompt"]) == 2 assert dict(td["raw_prompt"][0][0]) == {"content": "Q1", "role": "user"} def test_get_tensordict_agent_loop_scenario(): """Test the complete agent loop scenario with all nested types. This simulates the exact use case from agent loops with: - turn_scores: lists of lists - reward_extra_info: lists of dicts - raw_prompt: lists of lists of dicts - tool_rewards: lists of lists """ prompts = torch.randn(2, 10) responses = torch.randn(2, 5) # Nested structures from agent loop data_source = ["lighteval/MATH", "lighteval/MATH"] uid = ["uuid-1", "uuid-2"] turn_scores = [[], [0.5, 0.8]] # Lists of varying lengths reward_extra_info = [{"acc": 1.0, "loss": 0.1}, {"acc": 0.0, "loss": 0.9}] raw_prompt = [ [{"content": "Compute 4 @ 2", "role": "user"}], [{"content": "Compute 8 @ 7", "role": "user"}], ] tool_rewards = [[0.0], []] # List of lists # This should handle all nested structures automatically td = tu.get_tensordict( tensor_dict={ "prompts": prompts, "responses": responses, "data_source": data_source, "uid": uid, "turn_scores": turn_scores, "reward_extra_info": reward_extra_info, "raw_prompt": raw_prompt, "tool_rewards": tool_rewards, }, non_tensor_dict={"global_steps": 42}, ) # Verify all data types are accessible assert torch.all(torch.eq(td["prompts"], prompts)) assert torch.all(torch.eq(td["responses"], responses)) assert td["data_source"] == data_source assert td["uid"] == uid # Verify nested structures assert len(td["turn_scores"]) == 2 assert list(td["turn_scores"][0]) == [] assert list(td["turn_scores"][1]) == [0.5, 0.8] assert len(td["reward_extra_info"]) == 2 assert dict(td["reward_extra_info"][0]) == {"acc": 1.0, "loss": 0.1} assert len(td["raw_prompt"]) == 2 assert dict(td["raw_prompt"][0][0]) == {"content": "Compute 4 @ 2", "role": "user"} assert len(td["tool_rewards"]) == 2 assert list(td["tool_rewards"][0]) == [0.0] assert list(td["tool_rewards"][1]) == [] # Verify metadata assert td["global_steps"] == 42 def test_contiguous(): # create a tensordict that contains normal tensor, nested tensor, # nontensorstack with numpy, nontensorstack with tensor, NonTensorData with numpy and NonTensorData with tensor a = torch.randn(3, 4) # contiguous tensor b = torch.randn(3, 4)[:, :-1] # non contiguous tensor c = torch.nested.as_nested_tensor([torch.randn(3), torch.randn(4), torch.randn(5)], layout=torch.jagged) d = torch.randn(10, 12) e = torch.randn(11, 12) f = torch.randn(13, 12) data = tu.get_tensordict( tensor_dict={"a": a, "b": b, "c": c, "nt": [{"pixel": d}, {"pixel": e}, {"pixel": f}]}, non_tensor_dict={"ntd": a.clone()}, ) with pytest.raises(RuntimeError): # b is not contiguous data.consolidate() data1 = copy.deepcopy(data) data_cont = tu.contiguous(data1) tu.assert_tensordict_eq(data_cont, data) data_cont.consolidate() tu.assert_tensordict_eq(data_cont, data) ================================================ FILE: tests/trainer/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tests for the trainer module. """ ================================================ FILE: tests/trainer/config/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/trainer/config/legacy_ppo_megatron_trainer.yaml ================================================ data: tokenizer: null train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 # set to -1 to use full dataset val_max_samples: -1 # set to -1 to use full dataset prompt_key: prompt reward_fn_key: data_source max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null # DEPRECATED: Validation datasets are sent to inference engines as a whole batch, which will schedule the memory themselves return_raw_input_ids: False # This should be set to true when the tokenizer between policy and rm differs return_raw_chat: True return_full_prompt: False shuffle: True seed: null # An integer seed to use when shuffling the data. If not set or set to `null`, the data shuffling will not be seeded, resulting in a different data order on each run. filter_overlong_prompts: False # for large-scale dataset, filtering overlong prompts could be timeconsuming. You cat set the filter_overlong_prompts_workers to use multiprocessing to speed up. filter_overlong_prompts_workers: 1 truncation: error trust_remote_code: False # main_ppo will check this config to determine whether to use remote code for tokenizer custom_cls: path: null name: null sampler: class_path: null class_name: null dataloader_num_workers: 8 return_multi_modal_inputs: True actor_rollout_ref: hybrid_engine: True nccl_timeout: 600 # seconds, default is 10 minutes for torch, you can set it to a larger value if you have long-running operations like 32B or 72B model using megatron model: path: ~/models/deepseek-llm-7b-chat custom_chat_template: null external_lib: null override_config: model_config: {} moe_config: freeze_moe_router: False enable_gradient_checkpointing: True gradient_checkpointing_kwargs: ## Activation Checkpointing activations_checkpoint_method: null # 'uniform', 'block'; not used with 'selective' # 'uniform' divides the total number of transformer layers and checkpoints the input activation of each chunk # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity activations_checkpoint_granularity: null # 'selective' or 'full' # 'full' will checkpoint the entire transformer layer and 'selective' only checkpoints memory intensive part of attention activations_checkpoint_num_layers: null # not used with 'selective' trust_remote_code: False actor: strategy: megatron # This is for backward-compatibility ppo_mini_batch_size: 256 ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: False ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length} use_torch_compile: True # False to disable torch compile # pg_losses2 = -advantages * torch.clamp(ratio, 1 - cliprange_low, 1 + cliprange_high) clip_ratio: 0.2 # default value if clip_ratio_low and clip_ratio_high are not specified clip_ratio_low: 0.2 clip_ratio_high: 0.2 clip_ratio_c: 3.0 # lower bound of the value for Dual-clip PPO from https://arxiv.org/pdf/1912.09729 loss_agg_mode: "token-mean" # / "seq-mean-token-sum" / "seq-mean-token-mean" / "seq-mean-token-sum-norm" # NOTE: "token-mean" is the default behavior loss_scale_factor: null # Scale factor for "seq-mean-token-sum-norm" mode. If null, uses response_length. entropy_coeff: 0 use_kl_loss: False # True for GRPO kl_loss_coef: 0.001 # for grpo kl_loss_type: low_var_kl # for grpo ppo_epochs: 1 data_loader_seed: 42 shuffle: False policy_loss: # policy loss config loss_mode: "vanilla" # Loss function mode: vanilla / clip-cov / kl-cov / gpg from https://arxiv.org/abs/2505.22617, clip_cov_ratio: 0.0002 # Ratio of tokens to be clipped for clip-cov loss clip_cov_lb: 1.0 # Lower bound for clip-cov loss clip_cov_ub: 5.0 # Upper bound for clip-cov loss kl_cov_ratio: 0.0002 # Ratio of tokens to be applied kl penalty for kl-cov loss ppo_kl_coef: 0.1 # KL divergence penalty coefficient optim: optimizer: adam lr: 1e-6 clip_grad: 1.0 total_training_steps: -1 # must be override by program lr_warmup_init: 0.0 # initial learning rate for warmup, default to 0.0 lr_warmup_steps: null # Prioritized. None, 0 or Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime lr_decay_steps: null lr_decay_style: constant # select from constant/linear/cosine/inverse_square_root min_lr: 0.0 # minimum learning rate, default to 0.0 weight_decay: 0.01 weight_decay_incr_style: constant # select from constant/linear/cosine lr_wsd_decay_style: exponential # select from constant/exponential/cosine lr_wsd_decay_steps: null use_checkpoint_opt_param_scheduler: False # use checkpoint optimizer parameter scheduler megatron: param_offload: False grad_offload: False optimizer_offload: False tensor_model_parallel_size: 1 expert_model_parallel_size: 1 expert_tensor_parallel_size: null pipeline_model_parallel_size: 1 virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests context_parallel_size: 1 sequence_parallel: True use_distributed_optimizer: True use_dist_checkpointing: False dist_checkpointing_path: null seed: 42 override_transformer_config: {} # additional transformer config like: num_layers_in_first(/last)_pipeline_stage use_mbridge: True vanilla_mbridge: True profile: # profile the actor model in `update_policy` use_profile: False # open it when you want to profile the actor model profile_ranks: null # list, you can specify the ranks to profile step_start: -1 # start step in update_policy step_end: -1 # end step save_path: null # the path to save the profile result load_weight: True checkpoint: async_save: False # save checkpoint asynchronously # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] # For more flexibility, you can specify the contents to load from the checkpoint. load_contents: ${actor_rollout_ref.actor.checkpoint.save_contents} ref: strategy: ${actor_rollout_ref.actor.strategy} use_torch_compile: ${actor_rollout_ref.actor.use_torch_compile} megatron: param_offload: False tensor_model_parallel_size: 1 expert_model_parallel_size: 1 expert_tensor_parallel_size: null pipeline_model_parallel_size: 1 virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests context_parallel_size: 1 sequence_parallel: True use_distributed_optimizer: True use_dist_checkpointing: False dist_checkpointing_path: null seed: ${actor_rollout_ref.actor.megatron.seed} override_transformer_config: ${actor_rollout_ref.actor.megatron.override_transformer_config} use_mbridge: ${actor_rollout_ref.actor.megatron.use_mbridge} vanilla_mbridge: ${actor_rollout_ref.actor.megatron.vanilla_mbridge} profile: use_profile: False profile_ranks: null step_start: -1 step_end: -1 save_path: null load_weight: True log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} rollout: name: vllm mode: async # sync: LLM, async: AsyncLLM temperature: 1.0 top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1 prompt_length: ${data.max_prompt_length} # for xperf_gpt response_length: ${data.max_response_length} # for vllm rollout dtype: bfloat16 # should align with FSDP gpu_memory_utilization: 0.5 ignore_eos: False enforce_eager: False free_cache_engine: True load_format: dummy tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} disable_log_stats: True enable_chunked_prefill: True # could get higher throughput # for hf rollout do_sample: True layer_name_map: qkv_layer_name: qkv gate_proj_layer_name: gate_up # number of responses (i.e. num sample times) n: 1 engine_kwargs: # inference engine parameters, please refer vllm/sglang official doc for detail vllm: {} sglang: {} val_kwargs: # sampling parameters for validation top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1.0 temperature: 0 n: 1 do_sample: False # default eager for validation # Multi-turn interaction config for tools or chat. multi_turn: # set to True for multi-turn tool interaction tasks; should set rollout.name to sglang as well enable: False # null for no limit (default max_length // 3) max_assistant_turns: null # null for no tool tool_config_path: null # null for no limit (default max_length // 3) max_user_turns: null # max parallel call for tools in single turn max_parallel_calls: 1 # max length of tool response max_tool_response_length: 256 # truncate side of tool response: left, middle, right tool_response_truncate_side: middle # null for no interaction interaction_config_path: null # - When set to True, the model's default chat template is used for multi-turn rollout, which typically matches production behavior. # - When set to False, the token ids recorded for training are used instead; unlike the default chat template, these always include the model's full output, # which may contain additional content such as reasoning content. This maintains the consistency between training and rollout, but it will lead to longer prompts. use_inference_chat_template: False # Tokenization is performed turn by turn and the resulting token ids are concatenated to form the full conversation. # To ensure this matches the result of tokenizing the entire conversation at once, a sanity check is run at the end of each multi-turn rollout to compare the two sets of token ids. # Some models are known to produce different tokenization results when tokenizing turn by turn vs. all at once. aThis behavior has already been validated for them. # To reduce excessive warnings, you can turn off the sanity check for these models if you are using their default chat template: # Qwen/QwQ-32B, Qwen/Qwen3-xxB # - disable: disable tokenization sanity check # - strict: enable strict tokenization sanity check (default) # - ignore_strippable: ignore strippable tokens when checking tokenization sanity tokenization_sanity_check_mode: strict # Format of the multi-turn interaction. Options: hermes, llama3_json, ... format: hermes # [Experimental] agent loop based rollout configs agent: # Number of agent loop workers num_workers: 8 custom_async_server: path: null name: null # support logging rollout prob for debugging purpose calculate_log_probs: False # Nsight system profiler configs profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig discrete: False all_ranks: False ranks: [] critic: rollout_n: ${actor_rollout_ref.rollout.n} strategy: ${actor_rollout_ref.actor.strategy} nccl_timeout: 600 # seconds, default is 10 minutes for torch, you can set it to a larger value if you have long-running operations like 32B or 72B model using megatron optim: optimizer: adam lr: 1e-6 clip_grad: 1.0 total_training_steps: -1 # must be override by program lr_warmup_init: 0.0 # initial learning rate for warmup, default to 0.0 lr_warmup_steps: null # Prioritized. None, 0 or Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime lr_decay_steps: null lr_decay_style: constant # select from constant/linear/cosine/inverse_square_root min_lr: 0.0 # minimum learning rate, default to 0.0 weight_decay: 0.01 weight_decay_incr_style: constant # select from constant/linear/cosine lr_wsd_decay_style: exponential # select from constant/exponential/cosine lr_wsd_decay_steps: null use_checkpoint_opt_param_scheduler: False # use checkpoint optimizer parameter scheduler model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${actor_rollout_ref.model.path} override_config: model_config: {} moe_config: freeze_moe_router: False external_lib: ${actor_rollout_ref.model.external_lib} trust_remote_code: False enable_gradient_checkpointing: True gradient_checkpointing_kwargs: ## Activation Checkpointing activations_checkpoint_method: null activations_checkpoint_granularity: null activations_checkpoint_num_layers: null megatron: param_offload: False grad_offload: False optimizer_offload: False tensor_model_parallel_size: 1 expert_model_parallel_size: 1 expert_tensor_parallel_size: null pipeline_model_parallel_size: 1 virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests context_parallel_size: 1 sequence_parallel: True use_distributed_optimizer: True use_dist_checkpointing: False dist_checkpointing_path: null seed: ${actor_rollout_ref.actor.megatron.seed} override_transformer_config: ${actor_rollout_ref.actor.megatron.override_transformer_config} use_mbridge: ${actor_rollout_ref.actor.megatron.use_mbridge} vanilla_mbridge: ${actor_rollout_ref.actor.megatron.vanilla_mbridge} load_weight: True ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size} ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} ppo_max_token_len_per_gpu: 32768 # (${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}) * 2 forward_max_token_len_per_gpu: ${critic.ppo_max_token_len_per_gpu} ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs} data_loader_seed: ${actor_rollout_ref.actor.data_loader_seed} shuffle: ${actor_rollout_ref.actor.shuffle} cliprange_value: 0.5 loss_agg_mode: ${actor_rollout_ref.actor.loss_agg_mode} checkpoint: async_save: False # save checkpoint asynchronously # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] load_contents: ${critic.checkpoint.save_contents} # Nsight system profiler configs profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig discrete: False all_ranks: False ranks: [] reward_model: enable: False strategy: ${actor_rollout_ref.actor.strategy} nccl_timeout: 600 # seconds, default is 10 minutes for torch, you can set it to a larger value if you have long-running operations like 32B or 72B model using megatron megatron: param_offload: False tensor_model_parallel_size: 1 expert_model_parallel_size: 1 expert_tensor_parallel_size: null pipeline_model_parallel_size: 1 virtual_pipeline_model_parallel_size: null # change VPP interface for parallelism tests context_parallel_size: 1 sequence_parallel: True use_distributed_optimizer: False use_dist_checkpointing: False dist_checkpointing_path: null seed: ${actor_rollout_ref.actor.megatron.seed} override_transformer_config: {} use_mbridge: ${actor_rollout_ref.actor.megatron.use_mbridge} vanilla_mbridge: ${actor_rollout_ref.actor.megatron.vanilla_mbridge} model: input_tokenizer: ${actor_rollout_ref.model.path} # set this to null if the chat template is identical path: ~/models/FsfairX-LLaMA3-RM-v0.1 trust_remote_code: False external_lib: ${actor_rollout_ref.model.external_lib} load_weight: True micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu micro_batch_size_per_gpu: null use_dynamic_bsz: ${critic.use_dynamic_bsz} forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu} max_length: null reward_manager: naive launch_reward_fn_async: False # custom reward function executed async on CPU, during log_prob sandbox_fusion: url: null # faas url to run code in cloud sandbox max_concurrent: 64 # max concurrent requests to sandbox memory_limit_mb: 1024 # Max memory limit for each sandbox process in MB # Nsight system profiler configs profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig discrete: False all_ranks: False ranks: [] custom_reward_function: path: null name: compute_score algorithm: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.AlgoConfig gamma: 1.0 lam: 1.0 adv_estimator: gae norm_adv_by_std_in_grpo: True use_kl_in_reward: False kl_penalty: kl # how to estimate kl divergence kl_ctrl: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.KLControlConfig type: fixed kl_coef: 0.001 horizon: 10000 target_kl: 0.1 use_pf_ppo: False pf_ppo: reweight_method: pow # ["pow", "max_min", "max_random"] weight_pow: 2.0 trainer: balance_batch: True total_epochs: 30 total_training_steps: null profile_steps: null # [1,2,5] or [] or null project_name: verl_examples experiment_name: gsm8k logger: ['console', 'wandb'] log_val_generations: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 esi_redundant_time: 0 # auto: find the last ckpt to resume. If can't find, start from scratch resume_mode: auto # or disable or resume_path if resume_from_path is set resume_from_path: null del_local_ckpt_after_load: False val_before_train: True test_freq: -1 critic_warmup: 0 default_hdfs_dir: null default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} max_actor_ckpt_to_keep: null max_critic_ckpt_to_keep: null # The timeout for ray worker group to wait for the register center to be ready ray_wait_register_center_timeout: 300 device: cuda # see ppo_trainer.yaml for more details controller_nsight_options: trace: "cuda,nvtx,cublas,ucx" cuda-memory-usage: "true" cuda-graph-trace: "graph" worker_nsight_options: trace: "cuda,nvtx,cublas,ucx" cuda-memory-usage: "true" cuda-graph-trace: "graph" capture-range: "cudaProfilerApi" capture-range-end: null kill: none npu_profile: options: save_path: ./profiler_data roles: ["all"] level: level0 with_memory: False record_shapes: False with_npu: True with_cpu: True with_module: False with_stack: False analysis: True ray_kwargs: ray_init: num_cpus: null # `None` means using all CPUs, which might cause hang if limited in systems like SLURM. Please set to a number allowed then. timeline_json_file: null ================================================ FILE: tests/trainer/config/legacy_ppo_trainer.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. # dataset config data: # Tokenizer class or path. If null, it will be inferred from the model. tokenizer: null # Whether to use shared memory for data loading. use_shm: False # Training set parquet. Can be a list or a single file. # The program will read all files into memory, so it can't be too large (< 100GB). # The path can be either a local path or an HDFS path. # For HDFS path, we provide utils to download it to DRAM and convert it to a local path. train_files: ~/data/rlhf/gsm8k/train.parquet # Validation parquet. Can be a list or a single file. val_files: ~/data/rlhf/gsm8k/test.parquet # Maximum sample length to be used. # Set to -1 to use full dataset, otherwise, randomly # select the specified number of samples from train dataset train_max_samples: -1 # Maximum sample length to be used. # Set to -1 to use full dataset, otherwise, randomly # select the specified number of samples from val dataset val_max_samples: -1 # The field in the dataset where the prompt is located. Default is 'prompt'. prompt_key: prompt # The field used to select the reward function (if using different ones per example). reward_fn_key: data_source # Maximum prompt length. All prompts will be left-padded to this length. # An error will be reported if the length is too long. max_prompt_length: 512 # Maximum response length. Rollout in RL algorithms (e.g. PPO) generates up to this length. max_response_length: 512 # Batch size sampled for one training iteration of different RL algorithms. train_batch_size: 1024 # Batch size used during validation. Can be null. val_batch_size: null # Whether to return the original input_ids without adding chat template. # This is used when the reward model's chat template differs from the policy. # If using a model-based RM with different templates, this should be True. return_raw_input_ids: False # Whether to return the original chat (prompt) without applying chat template. return_raw_chat: True # Whether to return the full prompt with chat template. return_full_prompt: False # Whether to shuffle the data in the dataloader. shuffle: True # An integer seed to use when shuffling the data. If not set or set to # `null`, the data shuffling will not be seeded, resulting in a different data order on each run. seed: null # num dataloader workers dataloader_num_workers: 8 # Whether to shuffle the validation set. validation_shuffle: False # Whether to filter overlong prompts. filter_overlong_prompts: False # Number of workers for filtering overlong prompts. # For large-scale datasets, filtering can be time-consuming. # Use multiprocessing to speed up. Default is 1. filter_overlong_prompts_workers: 1 # Truncate the input_ids or prompt if they exceed max_prompt_length. # Options: 'error', 'left', or 'right'. Default is 'error'. truncation: error # The field in the multi-modal dataset where the image is located. Default is 'images'. image_key: images # The field in the multi-modal dataset where the video is located. video_key: videos # If the remote tokenizer has a Python file, this flag determines whether to allow using it. trust_remote_code: False # Optional: specify a custom dataset class path and name if overriding default loading behavior. custom_cls: # The path to the file containing your customized dataset class. If not specified, pre-implemented dataset will be used. path: null # The name of the dataset class within the specified file. name: null # Whether to return multi-modal inputs in the dataset. Set to False if rollout generates new multi-modal inputs. return_multi_modal_inputs: True # Data generation configuration for augmenting the dataset. datagen: # The path to the file containing your customized data generation class. # E.g. 'pkg://verl.experimental.dynamic_dataset.dynamicgen_dataset' path: null # The class name of the data generation class within the specified file. # E.g. 'MockDataGenerator' name: null # settings related to data sampler sampler: # the path to the module containing a curriculum class which implements the # AbstractSampler interface class_path: null # the name of the curriculum class like `MySampler` class_name: null # Additional kwargs when calling tokenizer.apply_chat_template apply_chat_template_kwargs: {} # config for actor, rollout and reference model actor_rollout_ref: # Whether it's a hybrid engine, currently only supports hybrid engine hybrid_engine: true # common configs for the model model: _target_: verl.workers.config.HFModelConfig # Huggingface model path. This can be either local path or HDFS path. path: ~/models/deepseek-llm-7b-chat # Custom chat template for the model. custom_chat_template: null # Whether to use shared memory (SHM) for accelerating the loading of model weights use_shm: false # Additional Python packages to register huggingface models/tokenizers. external_lib: null # Used to override model's original configurations, mainly dropout override_config: {} # Enable gradient checkpointing for actor enable_gradient_checkpointing: true # Enable activation offloading for actor enable_activation_offload: false # Whether to remove padding tokens in inputs during training use_remove_padding: true # Set to positive value to enable LoRA (e.g., 32) lora_rank: 0 # LoRA scaling factor lora_alpha: 16 # Target modules to apply LoRA. Options: "all-linear" (not recommended for VLMs) or # [q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj] target_modules: all-linear # Exclude modules from applying Lora. Similar usage to target_modules and Peft. # Example: '.*visual.*' for excluding the ViT in Qwen2.5-VL, as currently vllm does not support ViT Lora. exclude_modules: null # Whether to use Liger for linear layer fusion use_liger: false # Whether to use custom fused kernels (e.g., FlashAttention, fused MLP) use_fused_kernels: false # Options for fused kernels. If use_fused_kernels is true, this will be used. fused_kernel_options: # Implementation backend for fused kernels. Options: "triton" or "torch". impl_backend: torch # Whether to enable loading a remote code model trust_remote_code: false # actor configs actor: # fsdp, fsdp2 or megatron. fsdp backend used here. strategy: fsdp # Split each sample into sub-batches of this size for PPO ppo_mini_batch_size: 256 # [Deprecated] Global micro batch size ppo_micro_batch_size: null # Local per-GPU micro batch size ppo_micro_batch_size_per_gpu: null # Whether to automatically adjust batch size at runtime use_dynamic_bsz: false # Max tokens per GPU in one PPO batch; affects gradient accumulation # Typically it should be: n * ${data.max_prompt_length} + ${data.max_response_length} ppo_max_token_len_per_gpu: 16384 # Gradient clipping for actor updates grad_clip: 1.0 # PPO clip ratio clip_ratio: 0.2 # Lower bound for asymmetric clipping (used in dual-clip PPO) clip_ratio_low: 0.2 # Upper bound for asymmetric clipping (used in dual-clip PPO) clip_ratio_high: 0.2 # policy loss config policy_loss: # Loss function mode: vanilla / clip-cov / kl-cov /gpg from https://arxiv.org/abs/2505.22617 loss_mode: "vanilla" # Ratio of tokens to be clipped for clip-cov loss clip_cov_ratio: 0.0002 # Lower bound for clip-cov loss clip_cov_lb: 1.0 # Upper bound for clip-cov loss clip_cov_ub: 5.0 # Ratio of tokens to be applied kl penalty for kl-cov loss kl_cov_ratio: 0.0002 # KL divergence penalty coefficient ppo_kl_coef: 0.1 # Constant C in Dual-clip PPO; clips when advantage < 0 and ratio > C clip_ratio_c: 3.0 # Loss aggregation mode: "token-mean", "seq-mean-token-sum", "seq-mean-token-mean", or "seq-mean-token-sum-norm" loss_agg_mode: token-mean # Scale factor for "seq-mean-token-sum-norm" loss aggregation mode. # If null, uses response_length. Set to a constant to ensure consistent normalization. loss_scale_factor: null # Entropy regularization coefficient in PPO loss entropy_coeff: 0 # Whether to use KL loss instead of KL reward penalty. True for GRPO use_kl_loss: false # Whether to use torch.compile() use_torch_compile: true # KL loss coefficient when use_kl_loss is enabled. For GRPO kl_loss_coef: 0.001 # Type of KL divergence loss. Options: "kl"(k1), "abs", "mse"(k2), "low_var_kl"(k3), "full" kl_loss_type: low_var_kl # Number of PPO epochs per batch ppo_epochs: 1 # Shuffle training data across PPO epochs shuffle: false # Sequence parallelism size for Ulysses-style model parallelism ulysses_sequence_parallel_size: 1 # calculate entropy with chunking to reduce memory peak entropy_from_logits_with_chunking: False # recompute entropy entropy_checkpointing: False # checkpoint configs checkpoint: # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] # For more flexibility, you can specify the contents to load from the checkpoint. load_contents: ${actor_rollout_ref.actor.checkpoint.save_contents} # optimizer configs optim: # Learning rate lr: 1e-6 # Warmup steps; negative value delegates to lr_warmup_steps_ratio lr_warmup_steps: -1 # Warmup steps ratio (used if lr_warmup_steps is negative) lr_warmup_steps_ratio: 0.0 # Minimum LR ratio for cosine schedule min_lr_ratio: 0.0 # Number of cosine cycles in LR schedule num_cycles: 0.5 # LR scheduler type: "constant" or "cosine" lr_scheduler_type: constant # Total training steps (must be overridden at runtime) total_training_steps: -1 # Weight decay weight_decay: 0.01 # configs for FSDP fsdp_config: # policy for wrapping the model wrap_policy: # Minimum number of parameters to trigger wrapping a layer with FSDP min_num_params: 0 # Whether to offload model parameters to CPU (trades speed for memory) param_offload: false # Whether to offload optimizer state to CPU optimizer_offload: false # Only for FSDP2: offload param/grad/optimizer during train offload_policy: false # Only for FSDP2: Reshard after forward pass to reduce memory footprint reshard_after_forward: true # Number of GPUs in each FSDP shard group; -1 means auto fsdp_size: -1 # Only for FSDP1: FSDP1 configuration, prefetch the next forward-pass all-gather # before the current forward computation. forward_prefetch: False # Reference model config. # Reference model will be enabled when actor.use_kl_loss or/and algorithm.use_kl_in_reward is/are True. ref: # actor_rollout_ref.ref: FSDP config same as actor. For models larger than 7B, it’s recommended to turn on offload for ref by default strategy: ${actor_rollout_ref.actor.strategy} # config for FSDP strategy fsdp_config: # whether to offload parameters in FSDP param_offload: False # whether to perform reshard after model forward to save memory. # only for fsdp2, [True, False, int between 1 and fsdp_size] reshard_after_forward: True # Only for FSDP1: FSDP1 configuration, prefetch the next forward-pass all-gather # before the current forward computation. forward_prefetch: False # the wrap policy for FSDP model wrap_policy: # minimum number of params in a wrapped module min_num_params: 0 # whether to enable torch.compile use_torch_compile: ${actor_rollout_ref.actor.use_torch_compile} # [Will be deprecated, use log_prob_micro_batch_size_per_gpu] # The batch size for one forward pass in the computation of log_prob. Global batch size. log_prob_micro_batch_size: null # The batch size for one forward pass in the computation of log_prob. Local batch size per GPU. log_prob_micro_batch_size_per_gpu: null # enable dynamic batch size (sequence packing) for log_prob computation log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} # the max token length per GPU log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} # sequence parallel size ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # calculate entropy with chunking to reduce memory peak entropy_from_logits_with_chunking: False # recompute entropy entropy_checkpointing: False # Rollout model config. rollout: # actor_rollout_ref.rollout.name: hf/vllm/sglang. name: vllm # sync: LLM, async: AsyncLLM mode: async # Sampling temperature for rollout. temperature: 1.0 # Top-k sampling parameter. -1 for vLLM rollout, 0 for HF rollout. top_k: -1 # Top-p sampling parameter. Default 1.0. top_p: 1 # typically the same as data max prompt length prompt_length: ${data.max_prompt_length} # typically the same as data max response length response_length: ${data.max_response_length} # for vllm rollout # Rollout model parameters type. Align with actor model's FSDP/Megatron type. dtype: bfloat16 # Fraction of GPU memory used by vLLM/SGLang for KV cache. gpu_memory_utilization: 0.5 # Whether to ignore EOS and continue generating after EOS is hit. ignore_eos: False # Whether to disable CUDA graph. Default True to allow cache freeing. enforce_eager: False # Whether to free engine KVCache after generation. Set enforce_eager=True when enabled. free_cache_engine: True # Which loader to use for rollout model weights: dummy_dtensor, hf, megatron, etc. # safetensors (for huge model, and set use_shm=True); dummy_dtensor: randomly init model weight load_format: dummy # for huge model, layered summon can save memory (prevent OOM) but make it slower layered_summon: False # TP size for rollout. Only effective for vLLM. tensor_model_parallel_size: 2 # max number of tokens in a batch max_num_batched_tokens: 8192 # max length for rollout max_model_len: null # max length of sequences max_num_seqs: 1024 # [Will be deprecated, use log_prob_micro_batch_size_per_gpu] The batch size for one forward pass in the computation of log_prob. Global batch size. log_prob_micro_batch_size: null # The batch size for one forward pass in the computation of log_prob. Local batch size per GPU. log_prob_micro_batch_size_per_gpu: null # enable dynamic batch size (sequence packing) for log_prob computation log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} # max token length for log_prob computation log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} # disable logging statistics disable_log_stats: True # may get higher throughput when set to True. When activated, Please increase max_num_batched_tokens or decrease max_model_len. enable_chunked_prefill: True # for hf rollout # Whether to sample during training rollout. False uses greedy sampling. do_sample: True # number of responses (i.e. num sample times). > 1 for grpo n: 1 # Whether to wake up inference engine in multi-stage to reduce peak memory during training-rollout transition. multi_stage_wake_up: false # Extra inference engine arguments, please refer vllm/sglang official doc for detail engine_kwargs: # vllm engine config vllm: {} # sglang engine config sglang: {} # Sampling parameters used during validation. val_kwargs: # sampling parameters for validation # Top-k sampling parameter. -1 for vLLM rollout, 0 for HF rollout. top_k: -1 # Top-p sampling parameter. Default 1.0. top_p: 1.0 # Sampling temperature for rollout. temperature: 0 # whether to repeat n times for validation n: 1 # Whether to sample during training rollout. False uses greedy sampling. do_sample: False # Multi-turn interaction config for tools or chat. multi_turn: # set to True for multi-turn tool interaction tasks; should set rollout.name to sglang as well enable: False # null for no limit (default max_length // 3) max_assistant_turns: null # null for no tool tool_config_path: null # null for no limit (default max_length // 3) max_user_turns: null # max parallel call for tools in single turn max_parallel_calls: 1 # max length of tool response max_tool_response_length: 256 # truncate side of tool response: left, middle, right tool_response_truncate_side: middle # null for no interaction interaction_config_path: null # - When set to True, the model's default chat template is used for multi-turn rollout, which typically matches production behavior. # - When set to False, the token ids recorded for training are used instead; unlike the default chat template, these always include the model's full output, # which may contain additional content such as reasoning content. This maintains the consistency between training and rollout, but it will lead to longer prompts. use_inference_chat_template: False # Tokenization is performed turn by turn and the resulting token ids are concatenated to form the full conversation. # To ensure this matches the result of tokenizing the entire conversation at once, a sanity check is run at the end of each multi-turn rollout to compare the two sets of token ids. # Some models are known to produce different tokenization results when tokenizing turn by turn vs. all at once. aThis behavior has already been validated for them. # To reduce excessive warnings, you can turn off the sanity check for these models if you are using their default chat template: # Qwen/QwQ-32B, Qwen/Qwen3-xxB # - disable: disable tokenization sanity check # - strict: enable strict tokenization sanity check (default) # - ignore_strippable: ignore strippable tokens when checking tokenization sanity tokenization_sanity_check_mode: strict # Format of the multi-turn interaction. Options: hermes, llama3_json, ... format: hermes # support logging rollout prob for debugging purpose calculate_log_probs: False # [Experimental] agent loop based rollout configs agent: # Number of agent loop workers num_workers: 8 # custom async server configs custom_async_server: # Path to the custom async server implementation path: null # Class name of the custom async server class (e.g. AsyncvLLMServer) name: null # profiler configs profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # configs for the critic critic: # Number of rollouts per update (mirrors actor rollout_n) rollout_n: ${actor_rollout_ref.rollout.n} # fsdp or fsdp2 strategy used for critic model training strategy: ${actor_rollout_ref.actor.strategy} # optimizer configs optim: # Learning rate lr: 1e-5 # Warmup steps ratio; total steps will be injected at runtime lr_warmup_steps_ratio: 0. # Minimum LR ratio for cosine schedule min_lr_ratio: 0.0 # LR scheduler type: "constant" or "cosine" lr_scheduler_type: constant # Total training steps (must be overridden at runtime) total_training_steps: -1 # Weight decay weight_decay: 0.01 # model config for the critic model: # Path to pretrained model weights path: ~/models/deepseek-llm-7b-chat # Whether to use shared memory for loading the model use_shm: False # Tokenizer path (defaults to actor's model path) tokenizer_path: ${actor_rollout_ref.model.path} # Hugging Face config override override_config: { } # External model implementation (optional) external_lib: ${actor_rollout_ref.model.external_lib} # Enable gradient checkpointing to save memory enable_gradient_checkpointing: True # Offload activations to CPU to reduce GPU memory usage enable_activation_offload: False # Use remove padding optimization (saves compute) use_remove_padding: False # Whether to trust remote code from Hugging Face models trust_remote_code: ${actor_rollout_ref.model.trust_remote_code} # FSDP-specific config fsdp_config: # Whether to offload model parameters to CPU param_offload: False # Whether to offload optimizer state to CPU optimizer_offload: False # Only for FSDP2: offload param/grad/optimizer during train offload_policy: False # Only for FSDP2: Reshard after forward pass to reduce memory footprint reshard_after_forward: True # Policy for wrapping layers with FSDP wrap_policy: # Minimum number of parameters to trigger wrapping min_num_params: 0 # Number of GPUs in each FSDP shard group; -1 means auto fsdp_size: -1 # Only for FSDP1: FSDP1 configuration, prefetch the next forward-pass all-gather # before the current forward computation. forward_prefetch: False # Set to positive value to enable LoRA (e.g., 32) lora_rank: 0 # LoRA scaling factor lora_alpha: 16 # LoRA target modules: "all-linear" or list of linear projection layers target_modules: all-linear # PPO mini-batch size per update ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size} # [Deprecated] Global micro batch size ppo_micro_batch_size: null # Local per-GPU micro batch size ppo_micro_batch_size_per_gpu: null # Forward-only batch size (global) forward_micro_batch_size: ${critic.ppo_micro_batch_size} # Forward-only batch size (per GPU) forward_micro_batch_size_per_gpu: ${critic.ppo_micro_batch_size_per_gpu} # Whether to automatically adjust batch size at runtime use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} # Max tokens per GPU in one PPO batch (doubled for critic) ppo_max_token_len_per_gpu: 32768 # Max token length per GPU in forward pass forward_max_token_len_per_gpu: ${critic.ppo_max_token_len_per_gpu} # Sequence parallelism size for Ulysses-style model parallelism ulysses_sequence_parallel_size: 1 # Number of PPO epochs per batch ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs} # Shuffle training data across PPO epochs shuffle: ${actor_rollout_ref.actor.shuffle} # Gradient clipping for critic updates grad_clip: 1.0 # PPO value function clipping range cliprange_value: 0.5 # Loss aggregation mode: "token-mean", "seq-mean-token-sum", or "seq-mean-token-mean" loss_agg_mode: ${actor_rollout_ref.actor.loss_agg_mode} # checkpoint configs checkpoint: # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] # What to include when loading checkpoints load_contents: ${critic.checkpoint.save_contents} # profiler configs # the corresponding dataclass is verl.utils.profiler.ProfilerConfig. profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # configs for the reward model reward_model: # Whether to enable reward model. If False, we compute the reward only with the user-defined reward functions. # In GSM8K and Math examples, we disable reward model. # For RLHF alignment example using full_hh_rlhf, we utilize reward model to assess the responses. # If False, the following parameters are not effective enable: False # FSDP strategy: "fsdp" or "fsdp2" strategy: ${actor_rollout_ref.actor.strategy} # model config for reward scoring model: # Input tokenizer. If the reward model’s chat template is inconsistent with the policy, # we need to first decode to plaintext, then apply the rm’s chat_template. # Then score with RM. If chat_templates are consistent, it can be set to null. input_tokenizer: ${actor_rollout_ref.model.path} # RM’s HDFS path or local path. Note that RM only supports AutoModelForSequenceClassification. # Other model types need to define their own RewardModelWorker and pass it from the code. path: ~/models/FsfairX-LLaMA3-RM-v0.1 # Whether to use shared memory for loading the model use_shm: False # External model implementation (optional) external_lib: ${actor_rollout_ref.model.external_lib} # Use remove padding optimization (saves compute) use_remove_padding: False # Whether to use fused reward kernels for speedup use_fused_kernels: ${actor_rollout_ref.model.use_fused_kernels} # Whether to enable loading a remote code model, default to False trust_remote_code: False # FSDP-specific config fsdp_config: # Policy for wrapping layers with FSDP wrap_policy: # Minimum number of parameters to trigger wrapping min_num_params: 0 # Whether to offload model parameters to CPU param_offload: False # Only for FSDP2: Reshard after forward pass to reduce memory footprint reshard_after_forward: True # Number of GPUs in each FSDP shard group; -1 means auto fsdp_size: -1 # Only for FSDP1: FSDP1 configuration, prefetch the next forward-pass all-gather # before the current forward computation. forward_prefetch: False # [Deprecated] Global micro batch size micro_batch_size: null # Local per-GPU micro batch size micro_batch_size_per_gpu: null # Maximum sequence length to process for scoring max_length: null # Sequence parallelism size for Ulysses-style model parallelism ulysses_sequence_parallel_size: 1 # Whether to dynamically adjust batch size at runtime use_dynamic_bsz: ${critic.use_dynamic_bsz} # Maximum number of tokens per GPU in one forward pass forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu} # Reward Manager. This defines the mechanism of computing rule-based reward and handling different reward sources. # Default is naive. If all verification functions are multiprocessing-safe, # the reward manager can be set to prime for parallel verification. reward_manager: naive # Whether to launch custom reward function asynchronously during log_prob launch_reward_fn_async: False # Cloud/local sandbox fusion configuration for custom reward logic sandbox_fusion: # Cloud/local function URL for sandbox execution url: null # Max concurrent requests allowed to sandbox max_concurrent: 64 # Max memory limit for each sandbox process in MB memory_limit_mb: 1024 # profiler configs profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # custom reward function definition custom_reward_function: # The path to the file containing your customized reward function. # If not specified, pre-implemented reward functions will be used. path: null # The name of the reward function within the specified file. Default is 'compute_score'. name: compute_score # config for the algorithm algorithm: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.AlgoConfig # Discount factor for future rewards gamma: 1.0 # Trade-off between bias and variance in the GAE estimator lam: 1.0 # Advantage estimator type: "gae", "grpo", "reinforce_plus_plus", etc. adv_estimator: gae # Whether to normalize advantages by std (specific to GRPO) norm_adv_by_std_in_grpo: True # Whether to enable in-reward KL penalty use_kl_in_reward: False # How to estimate KL divergence: "kl", "abs", "mse", "low_var_kl", or "full" kl_penalty: kl # KL control configuration kl_ctrl: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.KLControlConfig # KL control type: "fixed" or "adaptive" type: fixed # Initial coefficient for KL penalty kl_coef: 0.001 # Horizon value for adaptive controller (if enabled) horizon: 10000 # Target KL divergence (used for adaptive controller) target_kl: 0.1 # Whether to enable preference feedback PPO use_pf_ppo: False # Preference feedback PPO settings pf_ppo: # Method for reweighting samples: "pow", "max_min", or "max_random" reweight_method: pow # Power used for weight scaling in "pow" method weight_pow: 2.0 # config for the trainer trainer: # Whether to balance batch sizes across distributed workers balance_batch: True # Number of epochs in training total_epochs: 30 # Total training steps (can be set explicitly or derived from epochs) total_training_steps: null # The steps that will be profiled. null means no profiling. null or [1,2,5,...] profile_steps: null # controller Nvidia Nsight Systems Options. Must set when profile_steps is not None. ## reference https://docs.nvidia.com/nsight-systems/UserGuide/index.html ## reference https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html controller_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # worker Nvidia Nsight Systems Options. Must set when profile_steps is not None. worker_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # Profiling only in a range of torch.cuda.profiler.start and stop. Do not change this config. capture-range: "cudaProfilerApi" # Specify the desired behavior when a capture range ends. # In verl we need the orch.cuda.profiler.start/stop pair to repeats n times. # valid values are "repeat-shutdown:n" or null. # For normal whole step profiling, n = len(profile_steps); # but for discrete profiling, n = len(profile_steps) * Number(subtasks). # Or you can just leave it null and the program will use n = len(profile_steps) * 6; capture-range-end: null # Send signal to the target application's process group. We let the program to exit by itself. kill: none # Config for npu profiler. Must set when profile_steps is not None and torch_npu is available. npu_profile: # Options for the npu profiler options: # Storage path of collected data. save_path: ./profiler_data # The roles that will be profiled. Only takes effect in discrete mode. # optional values: all, rollout_generate, actor_compute_log_prob, actor_update and ref_compute_log_prob. # "all" means all roles will be profiled. roles: ["all"] # Collection level, optional values: level_none, level0, level1, level2. level: level0 # Whether to enable memory analysis. with_memory: False # Whether to record tensor shape. record_shapes: False # Whether to record Device-side performance data. with_npu: True # Whether to record Host-side performance data. with_cpu: True # Whether to record Python call stack information. with_module: False # Whether to record operator call stack information. with_stack: False # Whether to automatically parse the data. analysis: True # Project name for experiment tracking (e.g., wandb) project_name: verl_examples # Experiment name for run identification in tracking tools experiment_name: gsm8k # Logging backends to use: "console", "wandb", etc. logger: [ 'console', 'wandb' ] # Number of generations to log during validation log_val_generations: 0 # Directory for logging rollout data; no dump if null rollout_data_dir: null # Directory for logging validation data; no dump if null validation_data_dir: null # Number of nodes used in the training nnodes: 1 # Number of GPUs per node n_gpus_per_node: 8 # Save frequency (by iteration) for model checkpoints save_freq: -1 # ESI refers to the elastic server instance used during training, similar to the training plan. For example, # if you purchase 10 hours of computing power, the ESI will automatically shut down after 10 hours of training. # To ensure a checkpoint is saved before ESI shuts down, the system will start saving a checkpoint in advance. # The advance time is calculated as: Advance Time = Longest historical step duration + Checkpoint save duration + esi_redundant_time. # Here, esi_redundant_time is a user-defined value that further extends the advance time for added safety. esi_redundant_time: 0 # Resume mode: "auto", "disable", or "resume_path" # "auto": resume from last checkpoint if available # "disable": start from scratch # "resume_path": resume from a user-defined path resume_mode: auto # Path to resume training from (only used when resume_mode is "resume_path") resume_from_path: null # Whether to run validation before training begins val_before_train: True # Whether to run validation only val_only: False # Validation frequency (in training iterations) test_freq: -1 # Number of iterations to warm up the critic before updating policy critic_warmup: 0 # Default path to distributed filesystem for saving checkpoints default_hdfs_dir: null # Whether to delete local checkpoints after loading del_local_ckpt_after_load: False # Default local directory for saving checkpoints default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} # Maximum number of actor checkpoints to keep max_actor_ckpt_to_keep: null # Maximum number of critic checkpoints to keep max_critic_ckpt_to_keep: null # Timeout (in seconds) for Ray worker to wait for registration ray_wait_register_center_timeout: 300 # Device to run training on (e.g., "cuda", "cpu") device: cuda # configs related to ray ray_kwargs: # configs related to ray initialization ray_init: # Number of CPUs for Ray. Use a fixed number instead of null when using SLURM. num_cpus: null # Path to save Ray timeline JSON for performance profiling timeline_json_file: null ================================================ FILE: tests/trainer/config/test_algo_config_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np import torch from omegaconf import OmegaConf from verl.trainer.config import AlgoConfig, KLControlConfig from verl.trainer.ppo.core_algos import ( compute_gae_advantage_return, compute_grpo_outcome_advantage, get_adv_estimator_fn, ) from verl.utils.config import omega_conf_to_dataclass class TestAlgoConfig(unittest.TestCase): """Test the AlgoConfig dataclass and its integration with core algorithms.""" def setUp(self): """Set up test fixtures.""" # Create a sample algorithm config as DictConfig (similar to what comes from YAML) self.config_dict = { "_target_": "verl.trainer.config.AlgoConfig", "gamma": 0.99, "lam": 0.95, "adv_estimator": "gae", "norm_adv_by_std_in_grpo": True, "use_kl_in_reward": True, "kl_penalty": "kl", "kl_ctrl": { "_target_": "verl.trainer.config.KLControlConfig", "type": "adaptive", "kl_coef": 0.002, "horizon": 5000, "target_kl": 0.05, }, "use_pf_ppo": True, "pf_ppo": {"reweight_method": "max_min", "weight_pow": 3.0}, } self.omega_config = OmegaConf.create(self.config_dict) def test_dataclass_creation_from_dict(self): """Test creating AlgoConfig from dictionary.""" config = omega_conf_to_dataclass(self.config_dict) self.assertIsInstance(config, AlgoConfig) self.assertEqual(config.gamma, 0.99) self.assertEqual(config.lam, 0.95) self.assertEqual(config.adv_estimator, "gae") self.assertTrue(config.norm_adv_by_std_in_grpo) self.assertTrue(config.use_kl_in_reward) self.assertEqual(config.kl_penalty, "kl") self.assertTrue(config.use_pf_ppo) def test_dataclass_creation_from_omega_config(self): """Test creating AlgoConfig from OmegaConf DictConfig.""" config = omega_conf_to_dataclass(self.omega_config) self.assertIsInstance(config, AlgoConfig) self.assertEqual(config.gamma, 0.99) self.assertEqual(config.lam, 0.95) def test_nested_configs(self): """Test that nested configurations are properly converted.""" config = omega_conf_to_dataclass(self.omega_config) # Test KL control config self.assertIsInstance(config.kl_ctrl, KLControlConfig) self.assertEqual(config.kl_ctrl.type, "adaptive") self.assertEqual(config.kl_ctrl.kl_coef, 0.002) self.assertEqual(config.kl_ctrl.horizon, 5000) self.assertEqual(config.kl_ctrl.target_kl, 0.05) # Test PF PPO config self.assertEqual(config.pf_ppo.get("reweight_method"), "max_min") self.assertEqual(config.pf_ppo.get("weight_pow"), 3.0) def test_default_values(self): """Test that default values are properly set.""" minimal_config = {"gamma": 0.8} config = omega_conf_to_dataclass(minimal_config, AlgoConfig) self.assertEqual(config.gamma, 0.8) self.assertEqual(config.lam, 1.0) # default value self.assertEqual(config.adv_estimator, "gae") # default value self.assertTrue(config.norm_adv_by_std_in_grpo) # default value self.assertFalse(config.use_kl_in_reward) # default value self.assertEqual(config.kl_penalty, "kl") # default value self.assertFalse(config.use_pf_ppo) # default value def test_get_method_backward_compatibility(self): """Test the get method for backward compatibility.""" config = omega_conf_to_dataclass(self.omega_config) # Test existing attribute self.assertEqual(config.get("gamma"), 0.99) self.assertEqual(config.get("gamma", 1.0), 0.99) # Test non-existing attribute self.assertIsNone(config.get("non_existing")) self.assertEqual(config.get("non_existing", "default"), "default") def test_post_init_nested_configs(self): """Test that __post_init__ properly initializes nested configs when None.""" # Create config without nested configs minimal_config = AlgoConfig(gamma=0.9) # Check that nested configs are initialized self.assertIsNotNone(minimal_config.kl_ctrl) self.assertIsInstance(minimal_config.kl_ctrl, KLControlConfig) assert not minimal_config.pf_ppo def test_config_init_from_yaml(self): import os from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): cfg = compose(config_name="ppo_trainer") algo_config = omega_conf_to_dataclass(cfg.algorithm) from verl.trainer.config import AlgoConfig assert isinstance(algo_config, AlgoConfig) class TestAlgoCompute(unittest.TestCase): """Test the AlgoConfig dataclass and its integration with core algorithms.""" def setUp(self): """Set up test fixtures.""" self.algo_config = AlgoConfig( gamma=0.99, lam=0.95, adv_estimator="gae", norm_adv_by_std_in_grpo=True, use_kl_in_reward=True, kl_penalty="kl", kl_ctrl=KLControlConfig(type="adaptive", kl_coef=0.002, horizon=5000, target_kl=0.05), use_pf_ppo=True, pf_ppo={"reweight_method": "max_min", "weight_pow": 3.0}, ) def test_advantage_estimator_with_cfg(self): """Test integration with advantage estimators from core_algos.""" config = self.algo_config # Test GAE advantage estimator adv_fn = get_adv_estimator_fn(config.adv_estimator) self.assertIsNotNone(adv_fn) # Test with actual GAE computation batch_size, seq_len = 2, 5 token_level_rewards = torch.randn(batch_size, seq_len) values = torch.randn(batch_size, seq_len) response_mask = torch.ones(batch_size, seq_len) advantages, returns = compute_gae_advantage_return( token_level_rewards=token_level_rewards, values=values, response_mask=response_mask, gamma=config.gamma, lam=config.lam, ) self.assertEqual(advantages.shape, (batch_size, seq_len)) self.assertEqual(returns.shape, (batch_size, seq_len)) def test_grpo_advantage_estimator_with_cfg(self): """Test integration with GRPO advantage estimator.""" grpo_config = AlgoConfig(adv_estimator="grpo", norm_adv_by_std_in_grpo=True) # Test GRPO advantage computation batch_size, seq_len = 4, 3 token_level_rewards = torch.tensor([[1.0, 0.5, 0.0], [2.0, 1.0, 0.0], [0.5, 0.2, 0.0], [1.5, 0.8, 0.0]]) response_mask = torch.ones(batch_size, seq_len) index = np.array([0, 0, 1, 1]) # Two groups advantages, returns = compute_grpo_outcome_advantage( token_level_rewards=token_level_rewards, response_mask=response_mask, index=index, norm_adv_by_std_in_grpo=grpo_config.norm_adv_by_std_in_grpo, ) self.assertEqual(advantages.shape, (batch_size, seq_len)) self.assertEqual(returns.shape, (batch_size, seq_len)) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/trainer/config/test_legacy_config_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import warnings from hydra import compose, initialize_config_dir from hydra.core.global_hydra import GlobalHydra from omegaconf import OmegaConf _BREAKING_CHANGES = [ "critic.optim.lr", # mcore critic lr init value 1e-6 -> 1e-5 "actor_rollout_ref.actor.optim.lr_warmup_steps", # None -> -1 "critic.optim.lr_warmup_steps", # None -> -1 "actor_rollout_ref.rollout.name", # vllm -> ??? "actor_rollout_ref.actor.megatron.expert_tensor_parallel_size", "actor_rollout_ref.ref.megatron.expert_tensor_parallel_size", "critic.megatron.expert_tensor_parallel_size", "reward_model.megatron.expert_tensor_parallel_size", ] class TestConfigComparison(unittest.TestCase): """Test that current configs match their legacy counterparts exactly.""" ignored_keys = [ "enable_gradient_checkpointing", "gradient_checkpointing_kwargs", "activations_checkpoint_method", "activations_checkpoint_granularity", "activations_checkpoint_num_layers", "discrete", "profiler", "profile", "use_profile", "npu_profile", "profile_steps", "worker_nsight_options", "controller_nsight_options", ] ignored_paths = ["reward_model", "custom_reward_function"] def _compare_configs_recursively( self, current_config, legacy_config, path="", legacy_allow_missing=True, current_allow_missing=False ): """Recursively compare two OmegaConf configs and assert they are identical. Args: legacy_allow_missing (bool): sometimes the legacy megatron config contains fewer keys and we allow that to happen """ if path in self.ignored_paths: return if isinstance(current_config, dict) and isinstance(legacy_config, dict): current_keys = set(current_config.keys()) legacy_keys = set(legacy_config.keys()) missing_in_current = legacy_keys - current_keys missing_in_legacy = current_keys - legacy_keys # Ignore specific keys that are allowed to be missing for key in self.ignored_keys: if key in missing_in_current: missing_in_current.remove(key) if key in missing_in_legacy: missing_in_legacy.remove(key) if missing_in_current: msg = f"Keys missing in current config at {path}: {missing_in_current}" if current_allow_missing: warnings.warn(msg, stacklevel=1) else: self.fail(f"Keys missing in current config at {path}: {missing_in_current}") if missing_in_legacy: # if the legacy msg = f"Keys missing in legacy config at {path}: {missing_in_legacy}" if legacy_allow_missing: warnings.warn(msg, stacklevel=1) else: self.fail(msg) for key in current_keys: current_path = f"{path}.{key}" if path else key if key in legacy_config: self._compare_configs_recursively(current_config[key], legacy_config[key], current_path) elif isinstance(current_config, list) and isinstance(legacy_config, list): self.assertEqual( len(current_config), len(legacy_config), f"List lengths differ at {path}: current={len(current_config)}, legacy={len(legacy_config)}", ) for i, (current_item, legacy_item) in enumerate(zip(current_config, legacy_config, strict=True)): self._compare_configs_recursively(current_item, legacy_item, f"{path}[{i}]") elif path not in _BREAKING_CHANGES: self.assertEqual( current_config, legacy_config, f"Values differ at {path}: current={current_config}, legacy={legacy_config}", ) def test_ppo_trainer_config_matches_legacy(self): """Test that ppo_trainer.yaml matches legacy_ppo_trainer.yaml exactly.""" import os from hydra import compose, initialize_config_dir from hydra.core.global_hydra import GlobalHydra GlobalHydra.instance().clear() try: with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): current_config = compose(config_name="ppo_trainer") legacy_config = OmegaConf.load("tests/trainer/config/legacy_ppo_trainer.yaml") current_dict = OmegaConf.to_container(current_config, resolve=True) legacy_dict = OmegaConf.to_container(legacy_config, resolve=True) if "defaults" in current_dict: del current_dict["defaults"] self._compare_configs_recursively(current_dict, legacy_dict) finally: GlobalHydra.instance().clear() def test_ppo_megatron_trainer_config_matches_legacy(self): """Test that ppo_megatron_trainer.yaml matches legacy_ppo_megatron_trainer.yaml exactly.""" GlobalHydra.instance().clear() try: with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): current_config = compose(config_name="ppo_megatron_trainer") legacy_config = OmegaConf.load("tests/trainer/config/legacy_ppo_megatron_trainer.yaml") current_dict = OmegaConf.to_container(current_config, resolve=True) legacy_dict = OmegaConf.to_container(legacy_config, resolve=True) if "defaults" in current_dict: del current_dict["defaults"] self._compare_configs_recursively( current_dict, legacy_dict, legacy_allow_missing=True, current_allow_missing=False ) finally: GlobalHydra.instance().clear() def test_load_component(self): """Test that ppo_megatron_trainer.yaml matches legacy_ppo_megatron_trainer.yaml exactly.""" GlobalHydra.instance().clear() configs_to_load = [ ("verl/trainer/config/actor", "dp_actor"), ("verl/trainer/config/actor", "megatron_actor"), ("verl/trainer/config/ref", "dp_ref"), ("verl/trainer/config/ref", "megatron_ref"), ("verl/trainer/config/rollout", "rollout"), ] for config_dir, config_file in configs_to_load: try: with initialize_config_dir(config_dir=os.path.abspath(config_dir)): compose(config_name=config_file) finally: GlobalHydra.instance().clear() if __name__ == "__main__": unittest.main() ================================================ FILE: tests/trainer/ppo/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tests for the PPO trainer module. """ ================================================ FILE: tests/trainer/ppo/test_core_algos_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import unittest import numpy as np import pytest import torch import verl.trainer.ppo.core_algos from verl.trainer.ppo.core_algos import ( compute_gae_advantage_return, compute_grpo_outcome_advantage, compute_grpo_vectorized_outcome_advantage, compute_rloo_outcome_advantage, compute_rloo_vectorized_outcome_advantage, get_adv_estimator_fn, register_adv_est, ) def mock_test_fn(): pass class TestRegisterAdvEst(unittest.TestCase): def setUp(self): """Clear the registry before each test""" verl.trainer.ppo.core_algos.ADV_ESTIMATOR_REGISTRY.clear() verl.trainer.ppo.core_algos.ADV_ESTIMATOR_REGISTRY = { "gae": lambda x: x * 2, "vtrace": lambda x: x + 1, } self.ADV_ESTIMATOR_REGISTRY = verl.trainer.ppo.core_algos.ADV_ESTIMATOR_REGISTRY def tearDown(self) -> None: verl.trainer.ppo.core_algos.ADV_ESTIMATOR_REGISTRY.clear() return super().tearDown() def test_register_new_function(self): """Test registering a new function with a string name""" @register_adv_est("test_estimator") def test_fn(): pass self.assertIn("test_estimator", self.ADV_ESTIMATOR_REGISTRY) self.assertEqual(self.ADV_ESTIMATOR_REGISTRY["test_estimator"], test_fn) def test_register_with_enum(self): """Test registering with an enum value (assuming AdvantageEstimator exists)""" from enum import Enum class AdvantageEstimator(Enum): TEST = "test_enum_estimator" @register_adv_est(AdvantageEstimator.TEST) def test_fn(): pass self.assertIn("test_enum_estimator", self.ADV_ESTIMATOR_REGISTRY) self.assertEqual(self.ADV_ESTIMATOR_REGISTRY["test_enum_estimator"], test_fn) def test_duplicate_registration_same_function(self): """Test that registering the same function twice doesn't raise an error""" register_adv_est("duplicate_test")(mock_test_fn) register_adv_est("duplicate_test")(mock_test_fn) self.assertEqual(self.ADV_ESTIMATOR_REGISTRY["duplicate_test"], mock_test_fn) def test_duplicate_registration_different_function(self): """Test that registering different functions with same name raises ValueError""" @register_adv_est("conflict_test") def test_fn1(): pass with self.assertRaises(ValueError): @register_adv_est("conflict_test") def test_fn2(): pass def test_decorator_preserves_function(self): """Test that the decorator returns the original function""" def test_fn(): return "original" decorated = register_adv_est("preserve_test")(test_fn) self.assertEqual(decorated(), "original") def test_multiple_registrations(self): """Test registering multiple different functions""" init_adv_count = len(self.ADV_ESTIMATOR_REGISTRY) @register_adv_est("estimator1") def fn1(): pass @register_adv_est("estimator2") def fn2(): pass self.assertEqual(len(self.ADV_ESTIMATOR_REGISTRY), 2 + init_adv_count) self.assertEqual(self.ADV_ESTIMATOR_REGISTRY["estimator1"], fn1) self.assertEqual(self.ADV_ESTIMATOR_REGISTRY["estimator2"], fn2) def test_get_adv_estimator_fn_valid_names(self): """Test that valid names return the correct function from registry.""" # Test GAE gae_fn = get_adv_estimator_fn("gae") assert gae_fn(5) == 10 # 5 * 2 = 10 # Test Vtrace vtrace_fn = get_adv_estimator_fn("vtrace") assert vtrace_fn(5) == 6 # 5 + 1 = 6 def test_get_adv_estimator_fn_invalid_name(self): """Test that invalid names raise ValueError.""" with pytest.raises(ValueError) as excinfo: get_adv_estimator_fn("invalid_name") assert "Unknown advantage estimator simply: invalid_name" in str(excinfo.value) def test_get_adv_estimator_fn_case_sensitive(self): """Test that name lookup is case-sensitive.""" with pytest.raises(ValueError): get_adv_estimator_fn("GAE") # Different case def test_multi_turn_compute_gae_advantage_return(): """Test multi-turn GAE skip observation tokens.""" gamma = random.uniform(0.0, 1.0) lam = random.uniform(0.0, 1.0) rewards = torch.tensor([[0.0, 0.0, 0.1, 0.1, 0.1, 0.0, 0.0, 0.1, 1.0, 0.0, 0.0]], dtype=torch.float) values1 = torch.tensor( [ [ random.uniform(-100.0, 100.0), random.random(), 4.0, 5.0, 6.0, random.uniform(-100.0, 0), random.random(), 7.0, 9.0, 0.0, 0.0, ] ], dtype=torch.float, ) values2 = torch.tensor( [ [ random.random(), random.uniform(-100.0, 100.0), 4.0, 5.0, 6.0, random.random(), random.uniform(0.0, 100.0), 7.0, 9.0, 0.0, 0.0, ] ], dtype=torch.float, ) response_mask = torch.tensor([[0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0]], dtype=torch.float) adv1, ret1 = compute_gae_advantage_return(rewards, values1, response_mask, gamma, lam) adv2, ret2 = compute_gae_advantage_return(rewards, values2, response_mask, gamma, lam) ret1 *= response_mask ret2 *= response_mask assert torch.equal(adv1, adv2), f"{adv1=}, {adv2=}" assert torch.equal(ret1, ret2), f"{ret1=}, {ret2=}" print(f" [CORRECT] \n\n{adv1=}, \n\n{ret1=}") def _make_group_index(batch_size: int, num_groups: int) -> np.ndarray: """Create a numpy index array ensuring each group has at least 2 samples.""" assert num_groups * 2 <= batch_size, "batch_size must allow >=2 samples per group" counts: list[int] = [2] * num_groups remaining = batch_size - 2 * num_groups for _ in range(remaining): counts[random.randrange(num_groups)] += 1 index = [] for gid, c in enumerate(counts): index.extend([gid] * c) random.shuffle(index) return np.asarray(index, dtype=np.int64) def _rand_mask(batch_size: int, seq_len: int) -> torch.Tensor: mask = torch.randint(0, 2, (batch_size, seq_len), dtype=torch.int64).float() rows_without_one = (mask.sum(dim=-1) == 0).nonzero(as_tuple=True)[0] if len(rows_without_one) > 0: mask[rows_without_one, -1] = 1.0 return mask @pytest.mark.parametrize( "batch_size,seq_len,num_groups,seed", [ (64, 128, 5, 0), (128, 256, 8, 1), (512, 512, 10, 2), ], ) def test_rloo_and_vectorized_equivalence(batch_size: int, seq_len: int, num_groups: int, seed: int): torch.manual_seed(seed) random.seed(seed) np.random.seed(seed) index = _make_group_index(batch_size, num_groups) response_mask = _rand_mask(batch_size, seq_len) base_rewards = torch.randn(batch_size, seq_len, dtype=torch.float32) token_level_rewards = base_rewards * response_mask adv1, ret1 = compute_rloo_outcome_advantage( token_level_rewards=token_level_rewards, response_mask=response_mask, index=index, ) adv2, ret2 = compute_rloo_vectorized_outcome_advantage( token_level_rewards=token_level_rewards, response_mask=response_mask, index=index, ) # Print concise diagnostics for visibility during test runs adv_max_diff = (adv1 - adv2).abs().max().item() ret_max_diff = (ret1 - ret2).abs().max().item() total_mask_tokens = int(response_mask.sum().item()) print( f"[RLOO] seed={seed} groups={num_groups} shape={adv1.shape} " f"mask_tokens={total_mask_tokens} adv_max_diff={adv_max_diff:.3e} ret_max_diff={ret_max_diff:.3e}" ) assert adv1.shape == adv2.shape == (batch_size, seq_len) assert ret1.shape == ret2.shape == (batch_size, seq_len) assert torch.allclose(adv1, adv2, rtol=1e-5, atol=1e-6) assert torch.allclose(ret1, ret2, rtol=1e-5, atol=1e-6) @pytest.mark.parametrize( "batch_size,seq_len,num_groups,seed", [ (64, 128, 5, 0), (128, 256, 8, 1), (512, 512, 10, 2), ], ) def test_grpo_and_vectorized_equivalence(batch_size: int, seq_len: int, num_groups: int, seed: int): # Set seeds for reproducibility torch.manual_seed(seed) random.seed(seed) np.random.seed(seed) # Generate group indices (numpy array of shape [batch_size]) index = _make_group_index(batch_size, num_groups) # Generate binary response mask (at least one valid token per row) response_mask = _rand_mask(batch_size, seq_len) # Generate token-level rewards and apply mask base_rewards = torch.randn(batch_size, seq_len, dtype=torch.float32) token_level_rewards = base_rewards * response_mask # Compute GRPO outcome advantage (original implementation) adv1, ret1 = compute_grpo_outcome_advantage( token_level_rewards=token_level_rewards, response_mask=response_mask, index=index, ) # Compute GRPO outcome advantage (vectorized implementation) adv2, ret2 = compute_grpo_vectorized_outcome_advantage( token_level_rewards=token_level_rewards, response_mask=response_mask, index=index, ) # Diagnostic info for visibility (same style as RLOO test) adv_max_diff = (adv1 - adv2).abs().max().item() ret_max_diff = (ret1 - ret2).abs().max().item() total_mask_tokens = int(response_mask.sum().item()) print( f"[GRPO] seed={seed} groups={num_groups} shape={adv1.shape} " f"mask_tokens={total_mask_tokens} adv_max_diff={adv_max_diff:.3e} ret_max_diff={ret_max_diff:.3e}" ) # Assert shape and numerical equivalence assert adv1.shape == adv2.shape == (batch_size, seq_len) assert ret1.shape == ret2.shape == (batch_size, seq_len) assert torch.allclose(adv1, adv2, rtol=1e-5, atol=1e-6) assert torch.allclose(ret1, ret2, rtol=1e-5, atol=1e-6) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/trainer/ppo/test_metric_utils_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tests for the metric utilities in verl.trainer.ppo.metric_utils. """ import unittest from unittest.mock import MagicMock, patch import numpy as np import torch from verl.trainer.ppo.metric_utils import ( bootstrap_metric, calc_maj_val, compute_data_metrics, compute_throughout_metrics, compute_timing_metrics, process_validation_metrics, ) from verl.utils.metric import ( reduce_metrics, ) from verl.utils.metric.utils import ( AggregationType, Metric, ) class TestReduceMetrics(unittest.TestCase): """Tests for the reduce_metrics function.""" def test_reduce_metrics_basic(self): """Test that reduce_metrics correctly computes means.""" metrics = { "loss": [1.0, 2.0, 3.0], "accuracy": [0.0, 0.5, 1.0], } result = reduce_metrics(metrics) self.assertEqual(result["loss"], 2.0) self.assertEqual(result["accuracy"], 0.5) def test_reduce_metrics_empty(self): """Test that reduce_metrics handles empty lists.""" metrics = { "empty": [], } result = reduce_metrics(metrics) self.assertTrue(np.isnan(result["empty"])) def test_reduce_metrics_single_value(self): """Test that reduce_metrics works with single values.""" metrics = { "single": [5.0], } result = reduce_metrics(metrics) self.assertEqual(result["single"], 5.0) class TestMetric(unittest.TestCase): """Tests for the Metric class.""" def test_init_with_string_aggregation(self): """Test Metric initialization with string aggregation type.""" metric = Metric(aggregation="mean") self.assertEqual(metric.aggregation, AggregationType.MEAN) self.assertEqual(metric.values, []) def test_init_with_enum_aggregation(self): """Test Metric initialization with AggregationType enum.""" metric = Metric(aggregation=AggregationType.SUM) self.assertEqual(metric.aggregation, AggregationType.SUM) self.assertEqual(metric.values, []) def test_init_with_value(self): """Test Metric initialization with an initial value.""" metric = Metric(aggregation="mean", value=5.0) self.assertEqual(metric.values, [5.0]) def test_init_with_invalid_aggregation(self): """Test Metric initialization with invalid aggregation type.""" with self.assertRaises(ValueError): Metric(aggregation="invalid") def test_append_float(self): """Test appending float values.""" metric = Metric(aggregation="mean") metric.append(1.0) metric.append(2.0) self.assertEqual(metric.values, [1.0, 2.0]) def test_append_int(self): """Test appending int values.""" metric = Metric(aggregation="mean") metric.append(1) metric.append(2) self.assertEqual(metric.values, [1, 2]) def test_append_tensor(self): """Test appending scalar tensor values.""" metric = Metric(aggregation="mean") metric.append(torch.tensor(3.0)) metric.append(torch.tensor(4.0)) self.assertEqual(metric.values, [3.0, 4.0]) def test_append_non_scalar_tensor_raises(self): """Test that appending non-scalar tensor raises ValueError.""" metric = Metric(aggregation="mean") with self.assertRaises(ValueError): metric.append(torch.tensor([1.0, 2.0])) def test_append_metric(self): """Test appending another Metric extends values.""" metric1 = Metric(aggregation="mean", value=1.0) metric1.append(2.0) metric2 = Metric(aggregation="mean", value=3.0) metric2.append(metric1) self.assertEqual(metric2.values, [3.0, 1.0, 2.0]) def test_extend_with_list(self): """Test extending with a list of values.""" metric = Metric(aggregation="mean") metric.extend([1.0, 2.0, 3.0]) self.assertEqual(metric.values, [1.0, 2.0, 3.0]) def test_extend_with_metric(self): """Test extending with another Metric.""" metric1 = Metric(aggregation="mean") metric1.extend([1.0, 2.0]) metric2 = Metric(aggregation="mean") metric2.extend([3.0, 4.0]) metric2.extend(metric1) self.assertEqual(metric2.values, [3.0, 4.0, 1.0, 2.0]) def test_extend_aggregation_mismatch_raises(self): """Test that extending with mismatched aggregation raises ValueError.""" metric1 = Metric(aggregation="mean") metric2 = Metric(aggregation="sum") with self.assertRaises(ValueError): metric1.extend(metric2) def test_aggregate_mean(self): """Test aggregation with mean.""" metric = Metric(aggregation="mean") metric.extend([1.0, 2.0, 3.0, 4.0]) self.assertEqual(metric.aggregate(), 2.5) def test_aggregate_sum(self): """Test aggregation with sum.""" metric = Metric(aggregation="sum") metric.extend([1.0, 2.0, 3.0, 4.0]) self.assertEqual(metric.aggregate(), 10.0) def test_aggregate_min(self): """Test aggregation with min.""" metric = Metric(aggregation="min") metric.extend([3.0, 1.0, 4.0, 2.0]) self.assertEqual(metric.aggregate(), 1.0) def test_aggregate_max(self): """Test aggregation with max.""" metric = Metric(aggregation="max") metric.extend([3.0, 1.0, 4.0, 2.0]) self.assertEqual(metric.aggregate(), 4.0) def test_aggregate_dp_sum_mean(self): """Test aggregate_dp with SUM and MEAN aggregations.""" # Test with SUM: mean over DP ranks, then sum metric1 = Metric(aggregation="sum") metric1.extend([1.0, 2.0]) metric2 = Metric(aggregation="sum") metric2.extend([3.0, 4.0]) result = Metric.aggregate_dp([metric1, metric2]) # value_arrays = [[1.0, 2.0], [3.0, 4.0]] # mean over axis 0 = [2.0, 3.0] # sum = 5.0 self.assertEqual(result, 5.0) # Test with MEAN: mean over DP ranks, then mean metric4 = Metric(aggregation="mean") metric4.extend([1.0, 2.0]) metric5 = Metric(aggregation="mean") metric5.extend([3.0, 4.0]) result = Metric.aggregate_dp([metric4, metric5]) # value_arrays = [[1.0, 2.0], [3.0, 4.0]] # mean over axis 0 = [2.0, 3.0] # mean = 2.5 self.assertEqual(result, 2.5) def test_aggregate_dp_min_max(self): """Test aggregate_dp with MIN and MAX aggregations.""" # Test with MAX: flatten, then max metric1 = Metric(aggregation="max") metric1.extend([1.0, 2.0]) metric2 = Metric(aggregation="max") metric2.extend([3.0, 4.0]) result = Metric.aggregate_dp([metric1, metric2]) # value_arrays = [[1.0, 2.0], [3.0, 4.0]] # flatten = [1.0, 2.0, 3.0, 4.0] # max = 4.0 self.assertEqual(result, 4.0) # Test with MIN: flatten, then min metric4 = Metric(aggregation="min") metric4.extend([1.0, 2.0]) metric5 = Metric(aggregation="min") metric5.extend([3.0, 4.0]) result = Metric.aggregate_dp([metric4, metric5]) # value_arrays = [[1.0, 2.0], [3.0, 4.0]] # flatten = [1.0, 2.0, 3.0, 4.0] # min = 1.0 self.assertEqual(result, 1.0) def test_aggregate_dp_mismatched_lengths(self): """Test aggregate_dp raises error with mismatched value lengths.""" metric1 = Metric(aggregation="sum") metric1.extend([1.0, 2.0]) metric2 = Metric(aggregation="sum") metric2.extend([3.0, 4.0, 5.0]) # Different length with self.assertRaises(ValueError): Metric.aggregate_dp([metric1, metric2]) def test_from_dict(self): """Test from_dict creates Metrics from dictionary.""" data = {"loss": 1.0, "accuracy": 0.9} metrics = Metric.from_dict(data, aggregation="mean") self.assertIn("loss", metrics) self.assertIn("accuracy", metrics) self.assertEqual(metrics["loss"].values, [1.0]) self.assertEqual(metrics["accuracy"].values, [0.9]) self.assertEqual(metrics["loss"].aggregation, AggregationType.MEAN) def test_init_list(self): """Test init_list creates new empty Metric with same aggregation.""" metric = Metric(aggregation="max") metric.extend([1.0, 2.0]) new_metric = metric.init_list() self.assertEqual(new_metric.aggregation, AggregationType.MAX) self.assertEqual(new_metric.values, []) def test_reduce_metrics_with_metric(self): """Test reduce_metrics correctly handles Metric objects.""" metric = Metric(aggregation="mean") metric.extend([1.0, 2.0, 3.0]) metrics = { "custom_metric": metric, "list_metric": [4.0, 5.0, 6.0], } result = reduce_metrics(metrics) self.assertEqual(result["custom_metric"], 2.0) self.assertEqual(result["list_metric"], 5.0) class TestComputeDataMetrics(unittest.TestCase): """Tests for the compute_data_metrics function.""" def setUp(self): """Set up common test data.""" # Create a mock DataProto object self.batch = MagicMock() self.batch.batch = { "token_level_scores": torch.tensor([[1.0, 2.0], [3.0, 4.0]]), "token_level_rewards": torch.tensor([[0.5, 1.0], [1.5, 2.0]]), "advantages": torch.tensor([[0.1, 0.2], [0.3, 0.4]]), "returns": torch.tensor([[1.1, 1.2], [1.3, 1.4]]), "responses": torch.zeros((2, 2)), # 2 samples, 2 tokens each "attention_mask": torch.tensor( [ [1, 1, 1, 1], # 2 prompt tokens, 2 response tokens [1, 1, 1, 1], ] ), "response_mask": torch.tensor( [ [1, 1], # 2 response tokens [1, 1], ] ), "values": torch.tensor([[0.9, 1.0], [1.1, 1.2]]), } def test_compute_data_metrics_with_critic(self): """Test compute_data_metrics with critic enabled.""" metrics = compute_data_metrics(self.batch, use_critic=True) # Check that all expected metrics are present self.assertIn("critic/score/mean", metrics) self.assertIn("critic/rewards/mean", metrics) self.assertIn("critic/advantages/mean", metrics) self.assertIn("critic/returns/mean", metrics) self.assertIn("critic/values/mean", metrics) self.assertIn("critic/vf_explained_var", metrics) self.assertIn("response_length/mean", metrics) self.assertIn("prompt_length/mean", metrics) # Check some specific values self.assertAlmostEqual(metrics["critic/score/mean"], 5.0) # Sum of token_level_scores self.assertAlmostEqual(metrics["critic/rewards/mean"], 2.5) # Sum of token_level_rewards def test_compute_data_metrics_without_critic(self): """Test compute_data_metrics with critic disabled.""" metrics = compute_data_metrics(self.batch, use_critic=False) # Check that critic-specific metrics are not present self.assertNotIn("critic/values/mean", metrics) self.assertNotIn("critic/vf_explained_var", metrics) # Check that other metrics are still present self.assertIn("critic/score/mean", metrics) self.assertIn("critic/rewards/mean", metrics) self.assertIn("response_length/mean", metrics) class TestComputeTimingMetrics(unittest.TestCase): """Tests for the compute_timing_metrics function.""" def setUp(self): """Set up common test data.""" # Create a mock DataProto object self.batch = MagicMock() self.batch.batch = { "responses": torch.zeros((2, 3)), # 2 samples, 3 response tokens each "attention_mask": torch.tensor( [ [1, 1, 1, 1, 1, 1], # 3 prompt tokens, 3 response tokens [1, 1, 1, 1, 1, 1], ] ), } # Mock the _compute_response_info function to return known values self.response_info = { "prompt_length": torch.tensor([3.0, 3.0]), "response_length": torch.tensor([3.0, 3.0]), "response_mask": torch.ones((2, 3)), } @patch("verl.trainer.ppo.metric_utils._compute_response_info") def test_compute_timing_metrics(self, mock_compute_response_info): """Test compute_timing_metrics with various timing data.""" mock_compute_response_info.return_value = self.response_info timing_raw = { "gen": 0.5, # 500ms "ref": 0.3, # 300ms "values": 0.2, # 200ms } metrics = compute_timing_metrics(self.batch, timing_raw) # Check raw timing metrics self.assertEqual(metrics["timing_s/gen"], 0.5) self.assertEqual(metrics["timing_s/ref"], 0.3) self.assertEqual(metrics["timing_s/values"], 0.2) # Check per-token timing metrics # gen uses only response tokens (6 tokens) self.assertAlmostEqual(metrics["timing_per_token_ms/gen"], 0.5 * 1000 / 6, places=5) # ref and values use all tokens (12 tokens) self.assertAlmostEqual(metrics["timing_per_token_ms/ref"], 0.3 * 1000 / 12, places=5) self.assertAlmostEqual(metrics["timing_per_token_ms/values"], 0.2 * 1000 / 12, places=5) class TestComputeThroughputMetrics(unittest.TestCase): """Tests for the compute_throughout_metrics function.""" def setUp(self): """Set up common test data.""" # Create a mock DataProto object self.batch = MagicMock() self.batch.meta_info = { "global_token_num": [100, 200, 300], # 600 tokens total } def test_compute_throughout_metrics(self): """Test compute_throughout_metrics with various timing data.""" timing_raw = { "step": 2.0, # 2 seconds per step } # Test with 1 GPU metrics = compute_throughout_metrics(self.batch, timing_raw, n_gpus=1) self.assertEqual(metrics["perf/total_num_tokens"], 600) self.assertEqual(metrics["perf/time_per_step"], 2.0) self.assertEqual(metrics["perf/throughput"], 600 / 2.0) # 300 tokens/sec # Test with 2 GPUs metrics = compute_throughout_metrics(self.batch, timing_raw, n_gpus=2) self.assertEqual(metrics["perf/total_num_tokens"], 600) self.assertEqual(metrics["perf/time_per_step"], 2.0) self.assertEqual(metrics["perf/throughput"], 600 / (2.0 * 2)) # 150 tokens/sec/GPU class TestBootstrapMetric(unittest.TestCase): """Tests for the bootstrap_metric function.""" def test_bootstrap_metric_basic(self): """Test bootstrap_metric with simple data and functions.""" data = [1, 2, 3, 4, 5] reduce_fns = [np.mean, np.max] # Use a fixed seed for reproducibility result = bootstrap_metric(data, subset_size=3, reduce_fns=reduce_fns, n_bootstrap=100, seed=42) # Check that we get two results (one for each reduce_fn) self.assertEqual(len(result), 2) # Each result should be a tuple of (mean, std) mean_result, max_result = result self.assertEqual(len(mean_result), 2) self.assertEqual(len(max_result), 2) # The mean of means should be close to the true mean (3.0) self.assertAlmostEqual(mean_result[0], 3.0, delta=0.3) # The mean of maxes should be close to the expected value for samples of size 3 # For samples of size 3 from [1,2,3,4,5], the expected max is around 4.0-4.5 self.assertGreater(max_result[0], 3.5) self.assertLess(max_result[0], 5.0) def test_bootstrap_metric_empty(self): """Test bootstrap_metric with empty data.""" with self.assertRaises(ValueError): bootstrap_metric([], subset_size=1, reduce_fns=[np.mean]) class TestCalcMajVal(unittest.TestCase): """Tests for the calc_maj_val function.""" def test_calc_maj_val_basic(self): """Test calc_maj_val with simple data.""" data = [ {"pred": "A", "val": 0.9}, {"pred": "B", "val": 0.8}, {"pred": "A", "val": 0.7}, ] result = calc_maj_val(data, vote_key="pred", val_key="val") # "A" is the majority vote, so we should get the first "val" for "A" self.assertEqual(result, 0.9) def test_calc_maj_val_tie(self): """Test calc_maj_val with tied votes.""" data = [ {"pred": "A", "val": 0.9}, {"pred": "B", "val": 0.8}, {"pred": "B", "val": 0.7}, {"pred": "A", "val": 0.6}, ] # In case of a tie, the first key in sorted order wins # This depends on Python's dict implementation, but for this test # we just verify that one of the valid values is returned result = calc_maj_val(data, vote_key="pred", val_key="val") self.assertTrue(result in [0.9, 0.8]) class TestProcessValidationMetrics(unittest.TestCase): """Tests for the process_validation_metrics function.""" def test_process_validation_metrics_basic(self): """Test process_validation_metrics with simple data.""" data_sources = ["source1", "source1", "source2"] sample_inputs = ["prompt1", "prompt1", "prompt2"] infos_dict = { "score": [0.8, 0.9, 0.7], } result = process_validation_metrics(data_sources, sample_inputs, infos_dict, seed=42) # Check the structure of the result self.assertIn("source1", result) self.assertIn("source2", result) # Check that source1 has metrics for score self.assertIn("score", result["source1"]) # Check that mean@2 is present for source1/score self.assertIn("mean@2", result["source1"]["score"]) # Check the value of mean@2 for source1/score self.assertAlmostEqual(result["source1"]["score"]["mean@2"], 0.85) def test_process_validation_metrics_with_pred(self): """Test process_validation_metrics with prediction data.""" data_sources = ["source1", "source1", "source1"] sample_inputs = ["prompt1", "prompt1", "prompt1"] infos_dict = { "score": [0.8, 0.9, 0.7], "pred": ["A", "B", "A"], } result = process_validation_metrics(data_sources, sample_inputs, infos_dict, seed=42) # Check that majority voting metrics are present self.assertIn("maj@2/mean", result["source1"]["score"]) # For bootstrap with n=2, the majority vote could be either A or B # depending on the random sampling, so we don't check the exact value if __name__ == "__main__": unittest.main() ================================================ FILE: tests/trainer/ppo/test_rollout_corr.py ================================================ #!/usr/bin/env python3 # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Quick Sanity Test for Rollout Correction This is a standalone test script that can be run without pytest to quickly verify the rollout correction implementation is working correctly. For comprehensive integration tests, see: tests/trainer/ppo/test_rollout_corr_integration.py Usage: python test_rollout_corr.py This tests: - Basic rollout correction functionality (IS weights + rejection sampling) - Metrics completeness (IS metrics + rejection metrics + off-policy metrics) - Edge cases """ import pytest import torch from verl.trainer.ppo.rollout_corr_helper import ( SUPPORTED_ROLLOUT_RS_OPTIONS, compute_offpolicy_metrics, compute_rollout_correction_and_rejection_mask, ) def test_basic_rollout_correction(): """Test basic rollout correction functionality.""" print("Testing basic rollout correction functionality...") # Create test data batch_size, seq_length = 4, 10 device = "cuda" if torch.cuda.is_available() else "cpu" # Create slightly different log probs (simulating BF16 vs FP32 mismatch) old_log_prob = torch.randn(batch_size, seq_length, device=device) rollout_log_prob = old_log_prob + torch.randn(batch_size, seq_length, device=device) * 0.1 eos_mask = torch.ones(batch_size, seq_length, device=device) # Test token-level truncate mode print("\n1. Testing token-level truncate mode...") weights_proto, modified_response_mask, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=eos_mask, rollout_is="token", # Compute IS weights at token level rollout_is_threshold=2.0, rollout_rs=None, # No rejection sampling (truncate mode) ) weights = weights_proto.batch["rollout_is_weights"] print(f" Weights shape: {weights.shape}") print(f" Mean weight: {metrics['rollout_corr/rollout_is_mean']:.4f}") print(f" Max weight: {metrics['rollout_corr/rollout_is_max']:.4f}") print(f" Min weight: {metrics['rollout_corr/rollout_is_min']:.4f}") assert weights.shape == old_log_prob.shape assert weights.max() <= 2.0, "Weights should be capped at threshold" print(" ✓ Token-level truncate mode passed") # Test sequence-level mode print("\n2. Testing sequence-level mode...") weights_seq_proto, _, metrics_seq = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=eos_mask, rollout_is="sequence", # Compute IS weights at sequence level rollout_is_threshold=5.0, rollout_rs=None, # No rejection sampling (truncate mode) ) weights_seq = weights_seq_proto.batch["rollout_is_weights"] print(f" Mean weight: {metrics_seq['rollout_corr/rollout_is_mean']:.4f}") print(f" Effective sample size: {metrics_seq['rollout_corr/rollout_is_eff_sample_size']:.4f}") # Check that all tokens in a sequence have the same weight for i in range(batch_size): seq_weights = weights_seq[i, eos_mask[i].bool()] assert torch.allclose(seq_weights, seq_weights[0]), "All tokens in sequence should have same weight" print(" ✓ Sequence-level mode passed") # Test K1 sequence mean rejection sampling (mask mode) print("\n3. Testing K1 (sequence mean) rejection sampling...") weights_geo_proto, modified_mask_geo, metrics_geo = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=eos_mask, rollout_is=None, # No IS weights (pure mask mode) rollout_rs="seq_mean_k1", # Rejection sampling with sequence-mean log ratio bounds rollout_rs_threshold="0.5_1.5", ) print(f" Masked fraction: {metrics_geo['rollout_corr/rollout_rs_masked_fraction']:.4f}") print(" ✓ K1 sequence mean rejection sampling passed") # Test disabled IS (rollout_is=None, rollout_rs=None) print("\n4. Testing disabled IS...") weights_disabled, modified_response_mask_disabled, metrics_disabled = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=eos_mask, rollout_is=None, rollout_rs=None, ) assert weights_disabled is None, "Should return None when IS is disabled" assert torch.equal(modified_response_mask_disabled, eos_mask), "Should return original mask unchanged" # Note: off-policy metrics are still computed even when IS/RS are disabled assert "rollout_corr/kl" in metrics_disabled, "Should still compute off-policy metrics" print(" ✓ Disabled IS passed") print("\n✓ All tests passed!") @pytest.mark.parametrize( ("option", "threshold"), [ ("token_k1", "0.5_1.5"), ("token_k2", 2.0), ("token_k3", 2.0), ("seq_sum_k1", "0.6_1.4"), ("seq_sum_k2", 2.5), ("seq_sum_k3", 2.5), ("seq_mean_k1", "0.5_1.5"), ("seq_mean_k2", 2.0), ("seq_mean_k3", 2.0), ("seq_max_k2", 2.0), ("seq_max_k3", 2.0), ], ) def test_each_supported_rollout_rs_option(option: str, threshold): """Ensure every supported RS option produces metrics without error.""" assert option in SUPPORTED_ROLLOUT_RS_OPTIONS batch_size, seq_length = 3, 7 device = "cuda" if torch.cuda.is_available() else "cpu" old_log_prob = torch.randn(batch_size, seq_length, device=device) rollout_log_prob = old_log_prob + torch.randn(batch_size, seq_length, device=device) * 0.15 response_mask = torch.ones(batch_size, seq_length, device=device) _, modified_mask, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, rollout_is=None, rollout_rs=option, rollout_rs_threshold=threshold, ) expected_key = f"rollout_corr/rollout_rs_{option}_mean" assert expected_key in metrics, f"Missing metric for {option}" assert modified_mask.shape == response_mask.shape def test_rollout_rs_multiple_options(): """Verify multiple RS options with mixed threshold formats.""" batch_size, seq_length = 2, 6 device = "cuda" if torch.cuda.is_available() else "cpu" old_log_prob = torch.randn(batch_size, seq_length, device=device) rollout_log_prob = old_log_prob + torch.randn(batch_size, seq_length, device=device) * 0.2 response_mask = torch.ones(batch_size, seq_length, device=device) rollout_rs = "token_k1,seq_max_k3" rollout_rs_threshold = "0.4_1.8,3.0" _, _, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, rollout_is=None, rollout_rs=rollout_rs, rollout_rs_threshold=rollout_rs_threshold, ) for option in rollout_rs.split(","): key = f"rollout_corr/rollout_rs_{option}_mean" assert key in metrics, f"Metrics missing for chained option {option}" def test_metrics_completeness(): """Test that all expected metrics are returned.""" print("\nTesting metrics completeness...") batch_size, seq_length = 3, 8 device = "cuda" if torch.cuda.is_available() else "cpu" old_log_prob = torch.randn(batch_size, seq_length, device=device) rollout_log_prob = old_log_prob + torch.randn(batch_size, seq_length, device=device) * 0.2 eos_mask = torch.ones(batch_size, seq_length, device=device) _, _, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=eos_mask, rollout_is="token", rollout_is_threshold=2.5, rollout_rs=None, ) # Expected IS metrics expected_is_metrics = [ "rollout_corr/rollout_is_mean", "rollout_corr/rollout_is_max", "rollout_corr/rollout_is_min", "rollout_corr/rollout_is_std", "rollout_corr/rollout_is_eff_sample_size", "rollout_corr/rollout_is_ratio_fraction_high", "rollout_corr/rollout_is_ratio_fraction_low", ] # Expected off-policy diagnostic metrics (also included now) expected_offpolicy_metrics = [ "rollout_corr/training_ppl", "rollout_corr/training_log_ppl", "rollout_corr/kl", "rollout_corr/k3_kl", "rollout_corr/rollout_ppl", "rollout_corr/rollout_log_ppl", "rollout_corr/log_ppl_diff", "rollout_corr/log_ppl_abs_diff", "rollout_corr/log_ppl_diff_max", "rollout_corr/log_ppl_diff_min", "rollout_corr/ppl_ratio", "rollout_corr/chi2_token", "rollout_corr/chi2_seq", ] expected_metrics = expected_is_metrics + expected_offpolicy_metrics missing_metrics = [m for m in expected_metrics if m not in metrics] if missing_metrics: print(f" ✗ Missing metrics: {missing_metrics}") return False print(f" ✓ All {len(expected_metrics)} expected metrics present") print(f" Total metrics returned: {len(metrics)}") return True def test_offpolicy_metrics(): """Test off-policy metrics computation.""" print("\nTesting off-policy metrics computation...") batch_size, seq_length = 4, 12 device = "cuda" if torch.cuda.is_available() else "cpu" # Create test data with some mismatch old_log_prob = torch.randn(batch_size, seq_length, device=device) - 2.0 # training policy rollout_log_prob = torch.randn(batch_size, seq_length, device=device) - 1.5 # rollout policy (more confident) response_mask = torch.ones(batch_size, seq_length, device=device) # Test with rollout log probs metrics = compute_offpolicy_metrics( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, ) expected_metrics = [ "training_ppl", "training_log_ppl", "kl", "k3_kl", "rollout_ppl", "rollout_log_ppl", "log_ppl_diff", "log_ppl_abs_diff", "log_ppl_diff_max", "log_ppl_diff_min", "ppl_ratio", "chi2_token", "chi2_seq", ] for metric in expected_metrics: assert metric in metrics, f"Missing metric: {metric}" print(f" Training PPL: {metrics['training_ppl']:.4f}") print(f" Rollout PPL: {metrics['rollout_ppl']:.4f}") print(f" KL divergence: {metrics['kl']:.6f}") print(f" K3 KL: {metrics['k3_kl']:.6f}") print(f" PPL ratio: {metrics['ppl_ratio']:.4f}") print(f" ✓ All {len(expected_metrics)} off-policy metrics present") # Test without rollout log probs metrics_no_rollout = compute_offpolicy_metrics( old_log_prob=old_log_prob, rollout_log_prob=None, response_mask=response_mask, ) assert "training_ppl" in metrics_no_rollout assert "rollout_ppl" not in metrics_no_rollout print(" ✓ Off-policy metrics work without rollout log probs") def test_mask_mode(): """Test mask mode applies rejection via response_mask, keeps true IS weights.""" print("\nTesting mask mode behavior...") batch_size = 2 seq_length = 5 device = "cuda" if torch.cuda.is_available() else "cpu" # Sequence 0: ratio ≈ 0.37 (below 0.5, should be rejected) # Sequence 1: ratio ≈ 1.65 (in [0.5, 2.0], should be accepted) old_log_prob = torch.tensor([[-2.0] * seq_length, [-2.0] * seq_length], device=device) rollout_log_prob = torch.tensor( [ [-1.0] * seq_length, # exp(-2.0 - (-1.0)) = exp(-1.0) ≈ 0.37 [-2.5] * seq_length, # exp(-2.0 - (-2.5)) = exp(0.5) ≈ 1.65 ], device=device, ) response_mask = torch.ones(batch_size, seq_length, device=device) weights_proto, modified_response_mask, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, rollout_is="token", # Compute IS weights rollout_is_threshold=2.0, rollout_rs="token_k1", # Also apply rejection sampling (mask mode) rollout_rs_threshold="0.5_2.0", ) weights = weights_proto.batch["rollout_is_weights"] # KEY FIX: Weights should be safety-bounded ratios (NOT zeroed) assert torch.all(weights[0, :] > 0), "Weights should remain as safety-bounded ratios (not zeroed)" assert torch.allclose(weights[0, 0], torch.tensor(0.368, device=device), atol=0.01), ( "First seq ratio should be ≈0.37" ) assert torch.allclose(weights[1, 0], torch.tensor(1.649, device=device), atol=0.01), ( "Second seq ratio should be ≈1.65" ) # Rejection should be applied via response_mask assert torch.all(modified_response_mask[0, :] == 0), "First sequence should be rejected via mask" assert torch.all(modified_response_mask[1, :] == 1), "Second sequence should be accepted" # Verify rejection sampling metrics exist assert "rollout_corr/rollout_rs_masked_fraction" in metrics, "Should have rollout_rs_masked_fraction metric" assert abs(metrics["rollout_corr/rollout_rs_masked_fraction"] - 0.5) < 0.01, "Should reject 50% of tokens" print(f" First seq IS weight: {weights[0, 0]:.4f} (expected ≈0.37)") print(f" Second seq IS weight: {weights[1, 0]:.4f} (expected ≈1.65)") print(f" First seq mask: {modified_response_mask[0, 0]:.0f} (expected 0 - rejected)") print(f" Second seq mask: {modified_response_mask[1, 0]:.0f} (expected 1 - accepted)") print(f" Masked fraction: {metrics['rollout_corr/rollout_rs_masked_fraction']:.2f}") print(" ✓ Mask mode correctly separates IS weights from rejection") if __name__ == "__main__": print("=" * 60) print("Rollout Correction Test Suite") print("=" * 60) try: test_basic_rollout_correction() test_metrics_completeness() test_offpolicy_metrics() test_mask_mode() print("\n" + "=" * 60) print("ALL TESTS PASSED ✓") print("=" * 60) except Exception as e: print(f"\n✗ Test failed with error: {e}") import traceback traceback.print_exc() exit(1) ================================================ FILE: tests/trainer/ppo/test_rollout_corr_integration.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Integration tests for Rollout Correction.""" import pytest import torch from verl.trainer.config.algorithm import RolloutCorrectionConfig from verl.trainer.ppo.core_algos import compute_policy_loss_vanilla from verl.trainer.ppo.rollout_corr_helper import ( compute_offpolicy_metrics, compute_rollout_correction_and_rejection_mask, ) from verl.workers.config.actor import ActorConfig class TestRolloutISIntegration: """Integration tests for Rollout Correction with PPO.""" @pytest.fixture def sample_data(self): """Create sample training data.""" batch_size, seq_length = 4, 16 device = "cuda" if torch.cuda.is_available() else "cpu" return { "old_log_prob": torch.randn(batch_size, seq_length, device=device), "log_prob": torch.randn(batch_size, seq_length, device=device), "rollout_log_prob": torch.randn(batch_size, seq_length, device=device), "advantages": torch.randn(batch_size, seq_length, device=device), "response_mask": torch.ones(batch_size, seq_length, device=device), } @pytest.fixture def config_with_rollout_is(self): """Create config for policy loss computation. Note: rollout_is config has been moved to algorithm config. This config only needs fields used by policy loss (clip_ratio, etc). """ config = ActorConfig( strategy="fsdp", rollout_n=1, ppo_micro_batch_size=2, clip_ratio=0.2, ) return config def test_policy_loss_with_rollout_is(self, sample_data, config_with_rollout_is): """Test that policy loss computation works with rollout correction weights. Note: In production, IS weights are computed centrally in the trainer (before advantage computation) and passed to policy loss. This test simulates that workflow. """ # First compute IS weights (as trainer would do centrally) rollout_is_weights_proto, _, _ = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is="token", rollout_is_threshold=2.0, rollout_rs=None, ) rollout_is_weights = rollout_is_weights_proto.batch["rollout_is_weights"] # Policy loss function receives pre-computed IS weights pg_loss, _ = compute_policy_loss_vanilla( old_log_prob=sample_data["old_log_prob"], log_prob=sample_data["log_prob"], advantages=sample_data["advantages"], response_mask=sample_data["response_mask"], loss_agg_mode="token-mean", config=config_with_rollout_is, rollout_is_weights=rollout_is_weights, ) # Check loss is valid assert isinstance(pg_loss, torch.Tensor) assert pg_loss.ndim == 0 # Scalar assert not torch.isnan(pg_loss) assert not torch.isinf(pg_loss) def test_rollout_is_weights_computation(self, sample_data): """Test rollout correction weights and metrics computation.""" weights_proto, _, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is="token", rollout_is_threshold=2.0, rollout_rs=None, ) # Check weights from verl.protocol import DataProto assert isinstance(weights_proto, DataProto) weights = weights_proto.batch["rollout_is_weights"] assert isinstance(weights, torch.Tensor) assert weights.shape == sample_data["old_log_prob"].shape # Check metrics are returned assert isinstance(metrics, dict) assert len(metrics) > 0 assert "rollout_corr/rollout_is_mean" in metrics def test_all_aggregation_levels(self, sample_data): """Test all aggregation levels (token, sequence for IS; K1 for RS).""" # Test IS weight levels is_levels = ["token", "sequence"] for level in is_levels: _, _, metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is=level, rollout_is_threshold=2.0, rollout_rs=None, ) assert "rollout_corr/rollout_is_mean" in metrics # Test rejection sampling with K1 sequence mean level _, _, metrics_geo = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is=None, rollout_rs="seq_mean_k1", rollout_rs_threshold="0.999_1.001", ) assert "rollout_corr/rollout_rs_seq_mean_k1_mean" in metrics_geo def test_both_bounding_modes(self, sample_data): """Test both truncate and mask modes.""" # Test truncate mode (IS weights only) _, _, metrics_truncate = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is="token", rollout_is_threshold=2.0, rollout_rs=None, ) assert "rollout_corr/rollout_is_mean" in metrics_truncate # Test mask mode (rejection sampling) _, _, metrics_mask = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is="token", # Can also compute IS weights in mask mode rollout_is_threshold=2.0, rollout_rs="token_k1", # Enable rejection sampling rollout_rs_threshold=1.3, # Float upper bound (lower inferred automatically) ) assert "rollout_corr/rollout_is_mean" in metrics_mask assert "rollout_corr/rollout_rs_token_k1_mean" in metrics_mask def test_offpolicy_metrics(self, sample_data): """Test off-policy diagnostic metrics computation.""" metrics = compute_offpolicy_metrics( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], ) # Check key metrics are present assert "training_ppl" in metrics assert "rollout_ppl" in metrics assert "kl" in metrics assert isinstance(metrics["kl"], float) def test_metrics_only_mode(self, sample_data, config_with_rollout_is): """Test metrics-only mode: compute IS weights/metrics but don't apply to loss. This tests the use case where rollout_is_threshold is set (enables computation) but rollout_is=False (disables weight application to policy loss). """ # Compute IS weights (as trainer would do) rollout_is_weights_proto, _, is_metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=sample_data["old_log_prob"], rollout_log_prob=sample_data["rollout_log_prob"], response_mask=sample_data["response_mask"], rollout_is="token", rollout_is_threshold=2.0, rollout_rs=None, ) # Metrics should be computed assert len(is_metrics) > 0 assert "rollout_corr/rollout_is_mean" in is_metrics # In metrics-only mode, we compute loss WITHOUT applying weights # (simulating rollout_is=False) pg_loss_no_weights, _ = compute_policy_loss_vanilla( old_log_prob=sample_data["old_log_prob"], log_prob=sample_data["log_prob"], advantages=sample_data["advantages"], response_mask=sample_data["response_mask"], loss_agg_mode="token-mean", config=config_with_rollout_is, rollout_is_weights=None, # Don't apply weights ) # Compare to loss WITH weights (rollout_is=True) rollout_is_weights = rollout_is_weights_proto.batch["rollout_is_weights"] pg_loss_with_weights, _ = compute_policy_loss_vanilla( old_log_prob=sample_data["old_log_prob"], log_prob=sample_data["log_prob"], advantages=sample_data["advantages"], response_mask=sample_data["response_mask"], loss_agg_mode="token-mean", config=config_with_rollout_is, rollout_is_weights=rollout_is_weights, ) # Losses should be different (weights have an effect) assert not torch.allclose(pg_loss_no_weights, pg_loss_with_weights) class TestRolloutCorrectionConfigNormalization: """Unit tests for RolloutCorrectionConfig canonicalization logic.""" def test_alias_normalization_and_threshold_parsing(self): config = RolloutCorrectionConfig( rollout_is="token", rollout_is_threshold=2.5, rollout_rs="seq_mean_k1,seq_max_k3", rollout_rs_threshold="0.8_1.2,3.0", ) assert config.rollout_is == "token" assert config.rollout_is_threshold == pytest.approx(2.5) assert config.rollout_rs == "seq_mean_k1,seq_max_k3" assert config.rollout_rs_threshold == "0.8_1.2,3.0" def test_missing_threshold_raises(self): config = RolloutCorrectionConfig(rollout_rs="token_k1") assert config.rollout_rs == "token_k1" assert config.rollout_rs_threshold is None def test_float_threshold_conversion_in_factory(self): config = RolloutCorrectionConfig.decoupled_geo_rs_seq_tis(rs_threshold=1.001) assert config.rollout_rs == "seq_mean_k1" assert config.rollout_rs_threshold == 1.001 if __name__ == "__main__": pytest.main([__file__, "-v", "-s"]) ================================================ FILE: tests/utils/_test_module.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Test module for import_utils.load_extern_object testing class TestClass: """A test class to be imported by load_extern_object""" def __init__(self, value=None): self.value = value or "default" def get_value(self): return self.value TEST_CONSTANT = "test_constant_value" def test_function(): return "test_function_result" ================================================ FILE: tests/utils/ckpt/test_checkpoint_cleanup_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil import tempfile import pytest class TestCheckpointCleanupLogic: """Tests for checkpoint cleanup methods in BaseCheckpointManager.""" @pytest.fixture(autouse=True) def setup(self): """Set up test fixtures.""" self.test_dir = tempfile.mkdtemp() yield shutil.rmtree(self.test_dir, ignore_errors=True) @pytest.fixture def manager(self, monkeypatch): """Create a minimal BaseCheckpointManager for testing.""" import torch.distributed monkeypatch.setattr(torch.distributed, "get_rank", lambda: 0) monkeypatch.setattr(torch.distributed, "get_world_size", lambda: 1) from verl.utils.checkpoint.checkpoint_manager import BaseCheckpointManager class MockModel: pass class MockOptimizer: pass return BaseCheckpointManager( model=MockModel(), optimizer=MockOptimizer(), lr_scheduler=None, processing_class=None, checkpoint_config=None, ) def _create_checkpoint_dir(self, step: int) -> str: """Create a mock checkpoint directory.""" path = os.path.join(self.test_dir, f"global_step_{step}") os.makedirs(path, exist_ok=True) with open(os.path.join(path, "checkpoint.txt"), "w") as f: f.write(f"step={step}") return path def test_max_ckpt_1_preserves_existing_before_save(self, manager): """ Regression test: max_ckpt_to_keep=1 must NOT delete existing checkpoint before save. """ ckpt_100 = self._create_checkpoint_dir(100) manager.previous_saved_paths = [ckpt_100] manager.ensure_checkpoint_capacity(max_ckpt_to_keep=1) assert os.path.exists(ckpt_100), "Bug: checkpoint deleted before save!" assert manager.previous_saved_paths == [ckpt_100] def test_max_ckpt_1_deletes_old_after_save(self, manager): """After save succeeds, old checkpoint should be deleted.""" ckpt_100 = self._create_checkpoint_dir(100) manager.previous_saved_paths = [ckpt_100] ckpt_200 = self._create_checkpoint_dir(200) manager.register_checkpoint(ckpt_200, max_ckpt_to_keep=1) assert not os.path.exists(ckpt_100) assert os.path.exists(ckpt_200) assert manager.previous_saved_paths == [ckpt_200] def test_max_ckpt_2_keeps_one_before_save(self, manager): """With max_ckpt_to_keep=2, pre-save cleanup keeps 1 checkpoint.""" ckpt_100 = self._create_checkpoint_dir(100) ckpt_200 = self._create_checkpoint_dir(200) manager.previous_saved_paths = [ckpt_100, ckpt_200] manager.ensure_checkpoint_capacity(max_ckpt_to_keep=2) assert not os.path.exists(ckpt_100) assert os.path.exists(ckpt_200) assert len(manager.previous_saved_paths) == 1 def test_max_ckpt_0_keeps_all(self, manager): """max_ckpt_to_keep=0 means unlimited - no deletions.""" ckpt_100 = self._create_checkpoint_dir(100) ckpt_200 = self._create_checkpoint_dir(200) manager.previous_saved_paths = [ckpt_100, ckpt_200] manager.ensure_checkpoint_capacity(max_ckpt_to_keep=0) ckpt_300 = self._create_checkpoint_dir(300) manager.register_checkpoint(ckpt_300, max_ckpt_to_keep=0) assert os.path.exists(ckpt_100) assert os.path.exists(ckpt_200) assert os.path.exists(ckpt_300) assert len(manager.previous_saved_paths) == 3 def test_full_save_cycle_max_ckpt_1(self, manager): """Simulate multiple save cycles with max_ckpt_to_keep=1.""" # First save manager.ensure_checkpoint_capacity(1) ckpt_100 = self._create_checkpoint_dir(100) manager.register_checkpoint(ckpt_100, 1) assert manager.previous_saved_paths == [ckpt_100] # Second save - existing checkpoint must survive pre-save manager.ensure_checkpoint_capacity(1) assert os.path.exists(ckpt_100), "Bug: checkpoint deleted before save!" ckpt_200 = self._create_checkpoint_dir(200) manager.register_checkpoint(ckpt_200, 1) assert not os.path.exists(ckpt_100) assert manager.previous_saved_paths == [ckpt_200] # Third save manager.ensure_checkpoint_capacity(1) assert os.path.exists(ckpt_200), "Bug: checkpoint deleted before save!" ckpt_300 = self._create_checkpoint_dir(300) manager.register_checkpoint(ckpt_300, 1) assert not os.path.exists(ckpt_200) assert manager.previous_saved_paths == [ckpt_300] ================================================ FILE: tests/utils/ckpt/test_esi_save_ckpt_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import time from datetime import datetime, timedelta from unittest import TestCase from verl.utils.checkpoint.checkpoint_manager import should_save_ckpt_esi class TestShouldSaveCkptEsi(TestCase): def test_no_expiration_timestamp(self): """Test case when no expiration timestamp is set""" os.environ.pop("MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP", None) os.environ.pop("SAGEMAKER_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP", None) self.assertFalse(should_save_ckpt_esi(100)) def test_mlp_expiration_valid(self): """Test valid MLP expiration timestamp requiring save""" current_time = time.time() os.environ["MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = str(current_time + 90) self.assertTrue(should_save_ckpt_esi(30)) # max_steps_duration=30 seconds def test_mlp_expiration_passed(self): """Test expired MLP timestamp""" current_time = time.time() os.environ["MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = str(current_time - 10) self.assertFalse(should_save_ckpt_esi(30)) def test_mlp_invalid_timestamp(self): """Test invalid MLP timestamp format""" os.environ["MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = "invalid" self.assertFalse(should_save_ckpt_esi(30)) def test_mlp_expiration_not_reached(self): """Test MLP expiration timestamp with insufficient remaining time""" current_time = time.time() os.environ["MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = str(current_time + 200) self.assertFalse(should_save_ckpt_esi(30)) # max_steps_duration=30 def test_aws_expiration_not_reached(self): """Test AWS expiration timestamp with sufficient remaining time""" now = datetime.now() expiration = now + timedelta(minutes=100) # Exceeds 90-minute threshold os.environ["SAGEMAKER_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = str(int(expiration.timestamp())) self.assertFalse(should_save_ckpt_esi(30 * 60)) def test_redundant_time(self): """Test redundant_time parameter effect""" current_time = time.time() # Total required: 60+30+30=120 seconds os.environ["MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = str(current_time + 120) self.assertTrue(should_save_ckpt_esi(30, redundant_time=30)) def test_zero_max_steps_duration(self): """Test zero max_steps_duration""" current_time = time.time() os.environ["MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP"] = str(current_time + 60) self.assertFalse(should_save_ckpt_esi(0)) ================================================ FILE: tests/utils/dataset/test_create_rl_sampler_on_cpu.py ================================================ # Copyright 2025 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ test create_rl_sampler """ from collections.abc import Sized import pytest import torch from omegaconf import DictConfig, OmegaConf from torch.utils.data import Dataset, RandomSampler from verl.experimental.dataset.sampler import AbstractCurriculumSampler from verl.trainer.main_ppo import create_rl_sampler class RandomCurriculumSampler(AbstractCurriculumSampler): def __init__( self, data_source: Sized, data_config: DictConfig, ): train_dataloader_generator = torch.Generator() train_dataloader_generator.manual_seed(1) sampler = RandomSampler(data_source=data_source) self.sampler = sampler def __iter__(self): return self.sampler.__iter__() def __len__(self) -> int: return len(self.sampler) def update(self, batch) -> None: return class MockIncorrectSampler: """A fake sampler class that does not adhere to the AbstractCurriculumSampler interface.""" def __init__(self, data_source, data_config): pass class MockChatDataset(Dataset): def __init__(self): self.data = [ {"prompt": "What's your name?", "response": "My name is Assistant."}, {"prompt": "How are you?", "response": "I'm doing well, thank you."}, {"prompt": "What is the capital of France?", "response": "Paris."}, { "prompt": "Tell me a joke.", "response": "Why did the chicken cross the road? To get to the other side!", }, {"prompt": "What is 2+2?", "response": "4"}, ] def __getitem__(self, index): return self.data[index] def __len__(self): return len(self.data) def test_create_custom_curriculum_samper(): data_config = OmegaConf.create( { "dataloader_num_workers": 0, "sampler": { "class_path": "pkg://tests.utils.dataset.test_create_rl_sampler_on_cpu", "class_name": "RandomCurriculumSampler", }, } ) dataset = MockChatDataset() # doesn't raise create_rl_sampler(data_config, dataset) def test_create_custom_curriculum_samper_wrong_class(): data_config = OmegaConf.create( { "sampler": { "class_path": "pkg://tests.utils.dataset.test_create_rl_sampler_on_cpu", "class_name": "MockIncorrectSampler", } } ) dataset = MockChatDataset() # MockIncorrectSampler is not an instance of AbstractCurriculumSampler, so raises with pytest.raises(AssertionError): create_rl_sampler(data_config, dataset) ================================================ FILE: tests/utils/dataset/test_multiturn_sft_dataset_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test the MultiTurnSFTDataset implementation """ import os from io import BytesIO from pathlib import Path import pandas as pd import pytest import torch from PIL import Image from tensordict import TensorDict from torch.utils.data import DistributedSampler from torchdata.stateful_dataloader import StatefulDataLoader from transformers.utils import get_json_schema from verl.utils import hf_processor, hf_tokenizer from verl.utils.dataset.dataset_utils import DatasetPadMode, SFTTensorCollator from verl.utils.dataset.multiturn_sft_dataset import MultiTurnSFTDataset from verl.utils.model import extract_multi_modal_inputs custom_model_prefix = Path("~/models").expanduser().resolve() @pytest.mark.parametrize( "model_path, ignore_input_ids_mismatch", [ (f"{custom_model_prefix}/Qwen/Qwen2.5-0.5B", False), (f"{custom_model_prefix}/Qwen/Qwen3-0.6B", True), (f"{custom_model_prefix}/Qwen/Qwen3.5-0.8B", False), ], ) def test_multiturn_sft_dataset(model_path: str, ignore_input_ids_mismatch: bool): print(f"Starting test... model_path={model_path}, ignore_input_ids_mismatch={ignore_input_ids_mismatch}") # Create a temporary parquet file with test data test_data = { "messages": [ [ {"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "2+2 equals 4."}, {"role": "tool", "content": "And what is 4+4?"}, {"role": "assistant", "content": "4+4 equals 8."}, ], [ # {"role": "system", "content": "You are a powerful assistant."}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why did the chicken cross the road?"}, {"role": "tool", "content": "Why?"}, {"role": "assistant", "content": "To get to the other side!"}, ], ] } # Create test directory if it doesn't exist os.makedirs("test_data", exist_ok=True) test_file = "test_data/test.parquet" # Save test data to parquet df = pd.DataFrame(test_data) df.to_parquet(test_file) # Initialize tokenizer and dataset tokenizer = hf_tokenizer(model_path) # processor = hf_processor(model_path) processor = None config = { "max_length": 512, "truncation": "error", "multiturn": {"messages_key": "messages"}, "ignore_input_ids_mismatch": ignore_input_ids_mismatch, } dataset = MultiTurnSFTDataset(parquet_files=test_file, tokenizer=tokenizer, processor=processor, config=config) # Test 1: Dataset Length assert len(dataset) == 2, f"Expected dataset length 2, got {len(dataset)}" # Get items for testing item0 = dataset[0] # Math conversation item1 = dataset[1] # Joke conversation # Test 2: Required Keys and Types required_keys = ["input_ids", "attention_mask", "position_ids", "loss_mask"] for key in required_keys: assert key in item0, f"Missing key {key} in dataset item" assert isinstance(item0[key], torch.Tensor), f"Expected torch.Tensor for {key}" assert item0[key].dtype == torch.long, f"Expected torch.long for {key}, got {item0[key].dtype}" # Test 3: Shape Consistency assert item0["loss_mask"].shape == item0["input_ids"].shape, "Loss mask shape doesn't match input_ids shape" assert item0["attention_mask"].shape == item0["input_ids"].shape, ( "Attention mask shape doesn't match input_ids shape" ) assert item0["position_ids"].shape == item0["input_ids"].shape, "Position IDs shape doesn't match input_ids shape" # Test 4: Loss Mask Pattern - Math Conversation loss_mask0 = item0["loss_mask"] input_ids0 = item0["input_ids"] # Find assistant response positions assistant_positions0 = torch.where(loss_mask0 == 1)[0] assert len(assistant_positions0) > 0, "No assistant positions found in loss mask" # Decode and verify assistant responses assistant_text0 = tokenizer.decode(input_ids0[loss_mask0 == 1]) print(f"Math conversation assistant text: {assistant_text0}") assert "2+2 equals 4" in assistant_text0, "First assistant response not found" assert "4+4 equals 8" in assistant_text0, "Second assistant response not found" # Test 5: Loss Mask Pattern - Joke Conversation loss_mask1 = item1["loss_mask"] input_ids1 = item1["input_ids"] # Find assistant response positions assistant_positions1 = torch.where(loss_mask1 == 1)[0] assert len(assistant_positions1) > 0, "No assistant positions found in loss mask" # Decode and verify assistant responses assistant_text1 = tokenizer.decode(input_ids1[loss_mask1 == 1]) print(f"Joke conversation assistant text: {assistant_text1}") assert "chicken cross the road" in assistant_text1, "First assistant response not found" assert "other side" in assistant_text1, "Second assistant response not found" # Test 6: Attention Mask Pattern attention_mask0 = item0["attention_mask"] sequence_length = torch.sum(attention_mask0) assert sequence_length > 0, "No tokens marked as attended in attention mask" assert torch.all(attention_mask0[:sequence_length] == 1), "Incorrect attention mask pattern" if sequence_length < len(attention_mask0): assert torch.all(attention_mask0[sequence_length:] == 0), "Padding not properly masked" # Test 7: Position IDs Pattern position_ids0 = item0["position_ids"] assert torch.equal(position_ids0[:sequence_length], torch.arange(sequence_length)), ( "Position IDs not sequential for non-padded tokens" ) if sequence_length < len(position_ids0): assert torch.all(position_ids0[sequence_length:] == 0), "Padding position IDs not zero" # Test 8: Verify loss mask for assistant responses # Get the full conversation text full_text = tokenizer.decode(input_ids0) print(f"\nFull conversation text:\n{full_text}") # Get the assistant responses assistant_text = tokenizer.decode(input_ids0[loss_mask0 == 1]) print(f"\nAssistant responses (from loss mask):\n{assistant_text}") # Verify that loss mask is set for all assistant responses for msg in test_data["messages"][0]: # First conversation if msg["role"] == "assistant": # The content should appear in the masked text assert msg["content"] in assistant_text, f"Assistant message '{msg['content']}' not found in masked text" # The content should NOT appear in the non-masked text non_assistant_text = tokenizer.decode(input_ids0[loss_mask0 == 0]) assert msg["content"] not in non_assistant_text, ( f"Assistant message '{msg['content']}' found in non-assistant text" ) # Test 9: Verify non-assistant parts have loss_mask=0 # Get non-assistant text non_assistant_text = tokenizer.decode(input_ids0[loss_mask0 == 0]) print(f"\nNon-assistant text (from loss mask):\n{non_assistant_text}") # Verify that system and user messages are in the non-assistant text for msg in test_data["messages"][0]: # First conversation if msg["role"] in ["system", "user"]: assert msg["content"] in non_assistant_text, ( f"{msg['role'].title()} message '{msg['content']}' not found in non-assistant text" ) # And verify they're NOT in the assistant text assert msg["content"] not in assistant_text, ( f"{msg['role'].title()} message '{msg['content']}' found in assistant text" ) # Test 10: Verify padding behavior padding_config = { "max_length": 1024, "truncation": "error", "multiturn": {"messages_key": "messages"}, "ignore_input_ids_mismatch": ignore_input_ids_mismatch, } small_dataset = MultiTurnSFTDataset( parquet_files=test_file, tokenizer=tokenizer, processor=processor, config=padding_config ) padded_item = small_dataset[0] # Get actual sequence length (before padding) actual_length = torch.sum(padded_item["attention_mask"]) # Verify padding tokens assert torch.all(padded_item["input_ids"][actual_length:] == tokenizer.pad_token_id), ( "Padding tokens not set correctly" ) assert torch.all(padded_item["attention_mask"][actual_length:] == 0), "Attention mask not set correctly for padding" assert torch.all(padded_item["loss_mask"][actual_length:] == 0), "Loss mask not set correctly for padding" # test no-padding config = { "max_length": 512, "truncation": "error", "multiturn": {"messages_key": "messages"}, "pad_mode": "no_padding", "ignore_input_ids_mismatch": ignore_input_ids_mismatch, } dataset = MultiTurnSFTDataset(parquet_files=test_file, tokenizer=tokenizer, processor=processor, config=config) item0 = dataset[0] # Verify that the output contains expected keys for no-padding mode required_keys = ["input_ids", "position_ids", "loss_mask"] for key in required_keys: assert key in item0, f"Missing key {key} in no-padding mode dataset item" assert isinstance(item0[key], torch.Tensor), f"Expected torch.Tensor for {key} in no-padding mode" # make sure assistant_text matches with expected assistant_text = tokenizer.decode(item0["input_ids"][item0["loss_mask"] == 1]) assert assistant_text == "2+2 equals 4.<|im_end|>\n4+4 equals 8.<|im_end|>\n" print("All tests passed!") print("Starting test...") def generate_image(description: str, size: str = "256x256"): """Generate a simple image based on description. Args: description: The description of the image to generate. size: The size of the image. Defaults to "256x256". (choices: ["256x256", "512x512"]) Returns: A generated image """ ... @pytest.fixture def vlm_data_file(): test_data = [ # sample 0: single turn with image input { "messages": [ { "role": "user", "content": "Describe this image.", }, { "role": "assistant", "content": "The image is a red square.", }, ], "images": [Image.new("RGB", (300, 300), color="red")], "tools": [], }, # sample 1: single turn with multiple images input { "messages": [ { "role": "user", "content": "Compare these images.", }, { "role": "assistant", "content": "The first image is a red square and the second image is a green square.", }, ], "images": [Image.new("RGB", (100, 100), color="red"), Image.new("RGB", (100, 300), color="green")], "tools": [], }, # sample 2: multi turn with image input and tool generated image { "messages": [ { "role": "user", "content": "Describe this image.", }, { "role": "assistant", "content": "Let's generate a zoom-in image.", "tool_calls": [ { "function": {"arguments": {"bbox_2d": "[0, 1, 2, 4]"}, "name": "image_zoom_in_tool"}, "type": "function", } ], }, { "role": "tool", "content": "Generated image.", }, {"role": "assistant", "content": "The zoom-in image is a red square."}, ], "images": [Image.new("RGB", (300, 500), color="red"), Image.new("RGB", (100, 100), color="red")], "tools": [get_json_schema(generate_image)], }, # sample 3: single turn without image input { "messages": [ {"role": "user", "content": "How is the weather today?"}, {"role": "assistant", "content": "The weather is sunny."}, ], "images": [], "tools": [], }, ] # Create test directory if it doesn't exist os.makedirs("test_data", exist_ok=True) test_file = "test_data/test_vlm.parquet" # Save test data to parquet df = pd.DataFrame(test_data) def serialize_image(img): if isinstance(img, Image.Image): img_byte_arr = BytesIO() img.save(img_byte_arr, format="PNG") return {"bytes": img_byte_arr.getvalue()} return img df["images"] = df["images"].apply(lambda x: [serialize_image(img) for img in x]) df.to_parquet(test_file) return test_file @pytest.mark.parametrize( "model_path", [ f"{custom_model_prefix}/Qwen/Qwen3-VL-2B-Instruct", f"{custom_model_prefix}/Qwen/Qwen3.5-0.8B", ], ) def test_multiturn_sft_vlm_dataset_on_cpu(model_path, vlm_data_file): df = pd.read_parquet(vlm_data_file) tokenizer = hf_tokenizer(model_path) processor = hf_processor(model_path) config = {"max_length": 1024, "pad_mode": "no_padding", "truncation": "error", "messages_key": "messages"} dataset = MultiTurnSFTDataset(parquet_files=vlm_data_file, tokenizer=tokenizer, processor=processor, config=config) assert dataset.pad_mode == DatasetPadMode.NO_PADDING for i in range(len(dataset)): item = dataset[i] input_ids = item["input_ids"] loss_mask = item["loss_mask"] position_ids = item["position_ids"] pixel_values = item.get("multi_modal_inputs", {}).get("pixel_values") image_grid_thw = item.get("multi_modal_inputs", {}).get("image_grid_thw") assert input_ids.shape == loss_mask.shape, "Shapes of input_ids and loss_mask must be equal" assert position_ids.dim() == 2, "position_ids must be 2-dimensional" assert position_ids.shape[0] == 4, f"position_ids[0] should be 4: {position_ids[0]}" assert position_ids.shape[1] == input_ids.shape[0] # 1. verify input_ids without assistant text text = tokenizer.decode(input_ids[loss_mask == 0], skip_special_tokens=True) print(f"Text without assistant: {repr(text)}") for message in df["messages"][i]: if message["role"] != "assistant": content = message["content"].replace("", "") assert content in text, f"user/tool text should be in the input_ids: {text}" # 2. verify input_ids with assistant text text = tokenizer.decode(input_ids[loss_mask == 1], skip_special_tokens=True) print(f"Text with assistant: {repr(text)}") for message in df["messages"][i]: if message["role"] == "assistant": assert message["content"] in text, f"Assistant text should be in the input_ids: {text}" assert "assistant" not in text, f"Assistant token should not be in the input_ids: {text}" # 3. verify image token match with image_grid_thw if len(df["images"][i]) > 0: patch_size = processor.image_processor.patch_size temporal_patch_size = processor.image_processor.temporal_patch_size merge_size = processor.image_processor.merge_size num_patches = image_grid_thw.prod(dim=1).sum() assert image_grid_thw.shape == (len(df["images"][i]), 3), ( f"image_grid_thw: {image_grid_thw.shape} should have shape ({len(df['images'][i])}, 3)" ) assert pixel_values.shape == (num_patches, 3 * temporal_patch_size * patch_size * patch_size), ( f"pixel_values: {pixel_values.shape} should have shape ({num_patches}, {3 * patch_size * patch_size})" ) assert (input_ids == processor.image_token_id).sum() == num_patches // (merge_size**2) else: assert pixel_values is None, "pixel_values should be None when no image is provided" assert image_grid_thw is None, "image_grid_thw should be None when no image is provided" @pytest.mark.parametrize( "model_path", [ f"{custom_model_prefix}/Qwen/Qwen3-VL-2B-Instruct", f"{custom_model_prefix}/Qwen/Qwen3.5-0.8B", ], ) def test_multiturn_sft_vlm_dataloader_on_cpu(model_path, vlm_data_file): df = pd.read_parquet(vlm_data_file) tokenizer = hf_tokenizer(model_path) processor = hf_processor(model_path) config = {"max_length": 1024, "pad_mode": "no_padding", "truncation": "error", "messages_key": "messages"} dataset = MultiTurnSFTDataset(parquet_files=vlm_data_file, tokenizer=tokenizer, processor=processor, config=config) assert dataset.pad_mode == DatasetPadMode.NO_PADDING collate_fn = SFTTensorCollator(DatasetPadMode.NO_PADDING) sampler = DistributedSampler(dataset, shuffle=False, num_replicas=1, rank=0, drop_last=True) batch_size = 2 dataloader = StatefulDataLoader( dataset=dataset, batch_size=batch_size, sampler=sampler, collate_fn=collate_fn, num_workers=0, pin_memory=False, drop_last=True, ) for i, batch in enumerate(dataloader): # 1. verify input_ids, loss_mask input_ids = batch["input_ids"] loss_mask = batch["loss_mask"] assert input_ids.is_nested, "input_ids should be a nested tensor" assert loss_mask.is_nested, "loss_mask should be a nested tensor" assert input_ids.shape[0] == loss_mask.shape[0] == batch_size, "Shapes of input_ids, loss_mask must be equal" # 2. verify position_ids: (bs, 4, seq_len) position_ids = batch["position_ids"] assert position_ids.is_nested, "position_ids should be a nested tensor" assert position_ids.dim() == 3, "position_ids must be 3-dimensional" assert position_ids.shape[0] == batch_size assert position_ids.shape[1] == 4 values = position_ids.values() assert values.shape == (4, len(input_ids.values())) # 3. verify multi-modal data td = TensorDict(**batch, batch_size=batch_size) multi_modal_inputs = extract_multi_modal_inputs(td["multi_modal_inputs"]) pixel_values = multi_modal_inputs["pixel_values"] image_grid_thw = multi_modal_inputs["image_grid_thw"] num_images = sum([len(images) for images in df["images"][i * batch_size : (i + 1) * batch_size]]) assert image_grid_thw.shape == (num_images, 3), ( f"image_grid_thw: {image_grid_thw.shape} should have shape ({num_images}, 3)" ) patch_size = processor.image_processor.patch_size temporal_patch_size = processor.image_processor.temporal_patch_size num_patches = image_grid_thw.prod(dim=1).sum() assert pixel_values.shape[0] == num_patches, ( f"pixel_values: {pixel_values.shape} should have shape " f"({num_patches}, 3 * {temporal_patch_size} * {patch_size} * {patch_size})" ) ================================================ FILE: tests/utils/dataset/test_rl_collate_fn_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch def test_rl_collate_fn(): from verl.utils.dataset.rl_dataset import collate_fn max_prompt_length = 5 test_data = [ { # test tensor "input_ids": torch.randint(0, 10, (max_prompt_length,)), # test fixed length (1) list within a batch "messages": [{"role": "user", "content": "Hi."}], # test variable length list within a batch "raw_prompt_ids": [1, 2, 3, 4], # test string "ability": "math", # test dict "reward_model": {"ground_truth": 5, "style": "rule"}, # test empty dict "tools_kwargs": {}, }, { "input_ids": torch.randint(0, 10, (max_prompt_length,)), "messages": [{"role": "user", "content": "Hello."}], "raw_prompt_ids": [1, 2, 3], "ability": "toolcall", "reward_model": { "ground_truth": '[{"name": "rgb_to_cmyk", "arguments": {"r": 0, "g": 0, "b": 255}}]', "style": "rule", }, "tools_kwargs": {}, }, ] batch_size = len(test_data) batch = collate_fn(test_data) # Tensor part assert batch["input_ids"].shape == (batch_size, max_prompt_length) assert isinstance(batch["input_ids"], torch.Tensor) # Non-tensor parts expected_types = { "messages": list, "raw_prompt_ids": list, "ability": str, "reward_model": dict, "tools_kwargs": dict, } for key, dtype in expected_types.items(): assert batch[key].shape == (batch_size,), ( f"Expected shape {(batch_size,)} for '{key}', but got {batch[key].shape}" ) assert isinstance(batch[key][0], dtype), ( f"'{key}' should contain elements of type {dtype}, but got {type(batch[key][0])}" ) ================================================ FILE: tests/utils/dataset/test_rl_dataset_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import pytest import torch from omegaconf import OmegaConf from PIL import Image from torch.utils.data import DataLoader from verl import DataProto from verl.utils import hf_processor, hf_tokenizer from verl.utils.dataset.rl_dataset import RLHFDataset, collate_fn def get_gsm8k_data(): # prepare test dataset local_folder = os.path.expanduser("~/data/gsm8k/") local_path = os.path.join(local_folder, "train.parquet") os.makedirs(local_folder, exist_ok=True) return local_path def test_rl_dataset(): tokenizer = hf_tokenizer(os.path.expanduser("~/models/deepseek-ai/deepseek-coder-1.3b-instruct")) local_path = get_gsm8k_data() config = OmegaConf.create( { "prompt_key": "prompt", "max_prompt_length": 256, "filter_overlong_prompts": True, "filter_overlong_prompts_workers": 2, } ) dataset = RLHFDataset(data_files=local_path, tokenizer=tokenizer, config=config) dataloader = DataLoader(dataset=dataset, batch_size=16, shuffle=True, drop_last=True, collate_fn=collate_fn) a = next(iter(dataloader)) tensors = {} non_tensors = {} for key, val in a.items(): if isinstance(val, torch.Tensor): tensors[key] = val else: non_tensors[key] = val data_proto = DataProto.from_dict(tensors=tensors, non_tensors=non_tensors) assert len(data_proto) == 16 assert "raw_prompt" in data_proto.non_tensor_batch def test_rl_dataset_with_max_samples(): tokenizer = hf_tokenizer(os.path.expanduser("~/models/deepseek-ai/deepseek-coder-1.3b-instruct")) local_path = get_gsm8k_data() config = OmegaConf.create( { "prompt_key": "prompt", "max_prompt_length": 256, "filter_overlong_prompts": True, "filter_overlong_prompts_workers": 2, "max_samples": 5, } ) dataset = RLHFDataset(data_files=local_path, tokenizer=tokenizer, config=config, max_samples=5) assert len(dataset) == 5 def test_image_rl_data(): tokenizer = hf_tokenizer(os.path.expanduser("~/models/Qwen/Qwen2-VL-2B-Instruct")) processor = hf_processor(os.path.expanduser("~/models/Qwen/Qwen2-VL-2B-Instruct")) config = OmegaConf.create( { "prompt_key": "prompt", "max_prompt_length": 1024, "filter_overlong_prompts": True, "filter_overlong_prompts_workers": None, # num_workers=1 hang in ci } ) dataset = RLHFDataset( data_files=os.path.expanduser("~/data/geo3k/train.parquet"), tokenizer=tokenizer, config=config, processor=processor, ) dataloader = DataLoader(dataset=dataset, batch_size=16, shuffle=True, drop_last=True, collate_fn=collate_fn) a = next(iter(dataloader)) tensors = {} non_tensors = {} for key, val in a.items(): if isinstance(val, torch.Tensor): tensors[key] = val else: non_tensors[key] = val data_proto = DataProto.from_dict(tensors=tensors, non_tensors=non_tensors) assert len(data_proto) == 16 assert "images" not in data_proto.non_tensor_batch for prompt in data_proto.non_tensor_batch["raw_prompt"]: assert len(prompt) == 1 prompt = prompt[0] role, content = prompt["role"], prompt["content"] assert role == "user" assert len(content) == 2 assert content[0]["type"] == "image" and isinstance(content[0]["image"], Image.Image) assert content[1]["type"] == "text" and isinstance(content[1]["text"], str) print("raw_prompt", data_proto.non_tensor_batch["raw_prompt"][0]) @pytest.fixture def video_data_file(): data = [ { "problem_id": 17, "problem": "How does the crowd's excitement change as the match progresses?", "data_type": "video", "prompt": [ { "role": "user", "content": [ {"type": "video", "video": "LLaVA-Video-178K/academic_source/activitynet/v_2g9GrshWQrU.mp4"}, { "type": "text", "text": "How does the crowd's excitement change as the match progresses? " "A. It fluctuates; B. It decreases; C. It builds up; D. It remains the same. " "Put your answer in ", }, ], } ], "problem_type": "multiple choice", "solution": "C", "data_source": "LLaVA-Video-178K/2_3_m_academic_v0_1", } ] * 30 # Create test directory if it doesn't exist os.makedirs("test_data", exist_ok=True) test_file = "test_data/test_video.json" with open(test_file, "w") as f: json.dump(data, f, indent=2) return test_file def test_video_rl_data(video_data_file): tokenizer = hf_tokenizer(os.path.expanduser("~/models/Qwen/Qwen2-VL-2B-Instruct")) processor = hf_processor(os.path.expanduser("~/models/Qwen/Qwen2-VL-2B-Instruct")) config = OmegaConf.create( { "prompt_key": "prompt", "max_prompt_length": 1024, "filter_overlong_prompts": False, } ) dataset = RLHFDataset( data_files=video_data_file, tokenizer=tokenizer, config=config, processor=processor, ) dataloader = DataLoader(dataset=dataset, batch_size=16, shuffle=True, drop_last=True, collate_fn=collate_fn) batch = next(iter(dataloader)) tensors = {} non_tensors = {} for key, val in batch.items(): if isinstance(val, torch.Tensor): tensors[key] = val else: non_tensors[key] = val data_proto = DataProto.from_dict(tensors=tensors, non_tensors=non_tensors) assert len(data_proto) == 16 assert "images" not in data_proto.non_tensor_batch print("raw_prompt", data_proto.non_tensor_batch["raw_prompt"][0]) ================================================ FILE: tests/utils/debug/test_metrics.py ================================================ # Copyright 2025 Individual Contributor: TomQunChaoA # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import torch from verl.protocol import DataProto from verl.utils.debug.metrics import calculate_debug_metrics class TestMetrics(unittest.TestCase): def test_calculate_debug_metrics(self): data = DataProto.from_dict( { "rollout_log_probs": torch.tensor( [ [-1.5085, -0.1200, -0.6650, -0.4823, -0.1426, -1.5557, -2.8532, -0.3919, -0.4294, -0.4700], [-0.0585, -0.0573, -0.4681, -0.5187, -0.7451, -1.2737, -0.0682, -0.4284, -0.5754, -0.0611], ] ), "old_log_probs": torch.tensor( [ [-1.8636, -0.7863, -0.2136, -0.4376, -2.0257, -0.2579, -1.1547, -0.5203, -0.3802, -0.9872], [-0.3507, -0.5426, -0.2725, -0.4637, -0.3577, -0.3733, -1.7560, -1.9542, -0.4229, -1.3098], ] ), "loss_mask": torch.tensor([[1, 0, 0, 0, 1, 1, 0, 1, 1, 0], [1, 0, 1, 0, 1, 1, 1, 0, 1, 1]]), "responses": torch.zeros((2, 10)), } ) metrics = calculate_debug_metrics(data) print(metrics) assert metrics["training/rollout_probs_diff_valid"] == 1 if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/megatron/test_pipeline_parallel.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from verl.model_merger.megatron_model_merger import get_dynamic_pipeline_shards from verl.utils.megatron.pipeline_parallel import make_batch_generator def test_make_batch_generator_no_vpp(): batches = [1, 2, 3] vpp_size = 1 generator = make_batch_generator(batches, vpp_size) assert list(generator) == batches def test_make_batch_generator_with_vpp(): batches = [{"data": 1}, {"data": 2}] vpp_size = 2 generators = make_batch_generator(batches, vpp_size) assert isinstance(generators, list) assert len(generators) == vpp_size # Check each generator yields the original batches for gen in generators: assert list(gen) == batches def test_make_batch_generator_empty(): batches = [] vpp_size = 1 generator = make_batch_generator(batches, vpp_size) assert list(generator) == [] vpp_size = 3 generators = make_batch_generator(batches, vpp_size) assert len(generators) == vpp_size for gen in generators: assert list(gen) == [] @pytest.mark.parametrize( "layer_num,pp_size,gt", [ (61, 8, [6, 8, 8, 8, 8, 8, 8, 7]), (61, 7, [8, 9, 9, 9, 9, 9, 8]), (61, 1, [61]), (61, 0, ValueError), (10, 16, ValueError), ], ) def test_get_dynamic_pipeline_shards(layer_num, pp_size, gt): if isinstance(gt, list): shards = get_dynamic_pipeline_shards(layer_num, pp_size) assert len(shards) == len(gt) == pp_size, f"Expected {pp_size} shards, got {len(shards)}" assert all([shard == gt[i] for i, shard in enumerate(shards)]), f"Expected shards {gt}, got {shards}" elif issubclass(gt, Exception): with pytest.raises(gt): shards = get_dynamic_pipeline_shards(layer_num, pp_size) ================================================ FILE: tests/utils/reward_score/reward_score/test_sandbox_fusion_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import multiprocessing import os import time from concurrent.futures import ProcessPoolExecutor from unittest.mock import patch import pytest # Import the function to be tested from verl.utils.reward_score.sandbox_fusion.utils import check_correctness # Get SANDBOX_URL from environment variable SANDBOX_URL = os.environ.get("SANDBOX_FUSION_URL") # Define skip condition and reason skip_reason = "SANDBOX_FUSION_URL environment variable not set" skip_condition = not SANDBOX_URL # --- Test code (for real API calls) --- CODE_SUCCESS = """ import sys data = sys.stdin.read() if data == 'input1': print('output1\\n', end='') elif data == 'input2': print('output2\\n', end='') else: print('unexpected input', end='') """ CODE_WRONG_OUTPUT = """ print('wrong_output\\n', end='') """ CODE_COMPILE_ERROR = """ a=b """ CODE_RUNTIME_ERROR = """ import sys print("About to raise error", file=sys.stderr) raise ValueError("This is a runtime error") """ CODE_TIMEOUT = """ import time import sys print("Sleeping...", file=sys.stderr) time.sleep(10) # Sleep time should be longer than the timeout set in the test print("Finished sleeping", file=sys.stderr) """ # --- Test input/output data --- INPUT_OUTPUT_VALID = {"inputs": ["input1", "input2"], "outputs": ["output1\n", "output2\n"]} INPUT_OUTPUT_SINGLE = {"inputs": ["input1"], "outputs": ["output1\n"]} INPUT_OUTPUT_MISMATCH = {"inputs": ["input1"], "outputs": ["output1\n", "output2\n"]} INPUT_OUTPUT_INVALID_MISSING_KEY = {"inputs": ["input1"]} # --- Integration test cases (calling real API) --- @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_success_correct(): """Integration test: Code is correct, output is correct""" results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_VALID, CODE_SUCCESS) assert results == [True, True] assert metadata_list[0]["status"] == "success" assert metadata_list[0]["stdout"] == "output1\n" assert metadata_list[1]["status"] == "success" assert metadata_list[1]["stdout"] == "output2\n" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_success_wrong_output(): """Integration test: Code runs successfully, but output is wrong""" results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_VALID, CODE_WRONG_OUTPUT) assert results == [False, False] assert metadata_list[0]["status"] == "wrong_answer" assert metadata_list[0]["stdout"] == "wrong_output\n" assert metadata_list[1]["status"] == "wrong_answer" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_compile_error(): """Integration test: Code causes compile error""" results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_VALID, CODE_COMPILE_ERROR, language="cpp") assert results == [-4, -4] assert metadata_list[0]["status"] == "compile_error" assert metadata_list[1]["status"] == "compile_error" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_runtime_error(): """Integration test: Code causes runtime error""" results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_SINGLE, CODE_RUNTIME_ERROR) assert results == [-2] assert metadata_list[0]["status"] == "runtime_error" # More assertions can be added based on the actual API response, e.g., exit_code, stderr @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_runtime_timeout(): """Integration test: Code causes runtime timeout""" test_timeout = 5 # Set a timeout shorter than the sleep time in CODE_TIMEOUT results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_SINGLE, CODE_TIMEOUT, timeout=test_timeout) assert results == [-3] assert metadata_list[0]["status"] == "timeout" # More assertions can be added based on the actual API response, e.g., run_status @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_concurrency_high_load(): """Integration test: High concurrency (100 cases) against real API with mixed results (success, wrong answer, timeout)""" concurrency_level = 100 # Indices for different expected outcomes wrong_answer_indices = {10, 25, 50} timeout_indices = {5, 30, 60, 90} # Indices where we expect a timeout # Generate 100 input/output pairs and code high_load_inputs = [] high_load_outputs = [] expected_results_map = {} # Store expected result for each index for i in range(concurrency_level): if i in timeout_indices: # Use a special input to trigger timeout in the code high_load_inputs.append(f"input_timeout_{i}") # Output doesn't matter for timeout, but keep it consistent high_load_outputs.append(f"output_{i}\n") expected_results_map[i] = -3 # Expect timeout elif i in wrong_answer_indices: high_load_inputs.append(f"input_{i}") # Intentionally set wrong expected output high_load_outputs.append(f"wrong_output_{i}\n") expected_results_map[i] = False # Expect wrong answer else: high_load_inputs.append(f"input_{i}") # Correct expected output high_load_outputs.append(f"output_{i}\n") expected_results_map[i] = True # Expect success high_load_in_outs = {"inputs": high_load_inputs, "outputs": high_load_outputs} # Code that handles normal inputs, and sleeps on specific "timeout" inputs code_mixed_concurrent = """ import sys import time data = sys.stdin.read() if data.startswith('input_timeout_'): time.sleep(20) # Sleep longer than the test timeout print(f"output_{data.split('_')[-1]}\\n", end='') # Still print something in case it finishes early elif data.startswith('input_'): print(f"output_{data.split('_')[-1]}\\n", end='') else: print("unknown_input\\n", end='') """ # Set a reasonable timeout per case (must be less than the sleep time in the code) test_timeout = 15 # Allow slightly more time due to potential API load, but less than 20s sleep start_time = time.time() results, metadata_list = check_correctness( SANDBOX_URL, high_load_in_outs, code_mixed_concurrent, # Use the new code timeout=test_timeout, ) end_time = time.time() duration = end_time - start_time print( f"\nHigh concurrency test ({concurrency_level} cases with {len(wrong_answer_indices)} wrong answers, " f"{len(timeout_indices)} timeouts) duration: {duration:.2f} seconds" ) # Verify results against the expected map assert len(results) == concurrency_level, f"Expected {concurrency_level} results, got {len(results)}" correct_count = 0 wrong_count = 0 timeout_count = 0 unexpected_results = [] for i, r in enumerate(results): expected = expected_results_map[i] if r == expected: if expected is True: correct_count += 1 elif expected is False: wrong_count += 1 elif expected == -3: timeout_count += 1 else: unexpected_results.append((i, r, f"Expected {expected}")) print( f"Correct results (True): {correct_count}/" f"{concurrency_level - len(wrong_answer_indices) - len(timeout_indices)}" ) print(f"Expected wrong answers (False, correctly identified): {wrong_count}/{len(wrong_answer_indices)}") print(f"Expected timeouts (-3, correctly identified): {timeout_count}/{len(timeout_indices)}") if unexpected_results: print("Unexpected results found:") for idx, res, expected_str in unexpected_results[:10]: # Print first 10 unexpected print(f" Index {idx}: Got {res}, {expected_str}. Metadata: {metadata_list[idx]}") raise AssertionError(f"Found {len(unexpected_results)} unexpected results.") assert correct_count == concurrency_level - len(wrong_answer_indices) - len(timeout_indices), ( "Incorrect number of successful results" ) assert wrong_count == len(wrong_answer_indices), "Incorrect number of identified wrong answers" assert timeout_count == len(timeout_indices), "Incorrect number of identified timeouts" # Verify metadata count and basic status of one of each type assert len(metadata_list) == concurrency_level # Find the first correct index first_correct_index = next( i for i in range(concurrency_level) if i not in wrong_answer_indices and i not in timeout_indices ) assert metadata_list[first_correct_index]["status"] == "success" assert metadata_list[first_correct_index]["stdout"] == f"output_{first_correct_index}\n" # Check the status of the first intentionally wrong case first_wrong_index = min(wrong_answer_indices) assert metadata_list[first_wrong_index]["status"] == "wrong_answer" assert metadata_list[first_wrong_index]["stdout"] == f"output_{first_wrong_index}\n" assert metadata_list[first_wrong_index]["expected_output"] == f"wrong_output_{first_wrong_index}\n" # Check the status of the first intentionally timeout case first_timeout_index = min(timeout_indices) assert metadata_list[first_timeout_index]["status"] == "timeout" # For timeout, stdout might be None or empty depending on when the timeout occurred # assert metadata_list[first_timeout_index]["stdout"] is None or metadata_list[first_timeout_index]["stdout"] == "" # --- Unit test cases (using mock) --- @patch("verl.utils.reward_score.sandbox_fusion.utils.call_sandbox_api") def test_unit_concurrency_order(mock_call_sandbox_api): sandbox_url = "mock_url" generation = "print(input())" language = "python" timeout = 5 in_outs = {"inputs": ["input1", "input2", "input3"], "outputs": ["output1", "output2", "output3"]} def side_effect(*args, **kwargs): stdin = kwargs.get("stdin") if stdin == "input1": return ( {"status": "Success", "run_result": {"status": "Finished", "stdout": "output1", "return_code": 0}}, None, ) elif stdin == "input2": time.sleep(0.1) return ( {"status": "Success", "run_result": {"status": "Finished", "stdout": "output2", "return_code": 0}}, None, ) elif stdin == "input3": return ( {"status": "Success", "run_result": {"status": "Finished", "stdout": "output3", "return_code": 0}}, None, ) else: return (None, "Unknown input in mock") mock_call_sandbox_api.side_effect = side_effect results, metadata_list = check_correctness(sandbox_url, in_outs, generation, timeout, language) assert results == [True, True, True] assert len(metadata_list) == 3 assert metadata_list[0]["case_index"] == 0 assert metadata_list[0]["status"] == "success" assert metadata_list[1]["case_index"] == 1 assert metadata_list[1]["status"] == "success" assert metadata_list[2]["case_index"] == 2 assert metadata_list[2]["status"] == "success" assert mock_call_sandbox_api.call_count == 3 @patch("verl.utils.reward_score.sandbox_fusion.utils.call_sandbox_api") def test_unit_api_timeout_error_concurrent(mock_call_sandbox_api): sandbox_url = "mock_url" generation = "print(input())" language = "python" timeout = 5 in_outs = {"inputs": ["input1", "input2_timeout", "input3"], "outputs": ["output1", "output2", "output3"]} api_error_message = "API Call Failed: Gateway Timeout (504) on attempt 3/3" def side_effect(*args, **kwargs): stdin = kwargs.get("stdin") if stdin == "input1": return ( {"status": "Success", "run_result": {"status": "Finished", "stdout": "output1", "return_code": 0}}, None, ) elif stdin == "input2_timeout": return (None, api_error_message) elif stdin == "input3": return ( {"status": "Success", "run_result": {"status": "Finished", "stdout": "output3", "return_code": 0}}, None, ) else: return (None, "Unknown input in mock") mock_call_sandbox_api.side_effect = side_effect results, metadata_list = check_correctness(sandbox_url, in_outs, generation, timeout, language) assert results == [True, -1, True] assert len(metadata_list) == 3 assert metadata_list[0]["status"] == "success" assert metadata_list[1]["status"] == "api_error" assert metadata_list[1]["api_request_error"] == api_error_message assert metadata_list[2]["status"] == "success" assert mock_call_sandbox_api.call_count == 3 # --- Constants for the new concurrency test --- # Define a low global concurrency limit to test the semaphore's effect MAX_GLOBAL_CONCURRENCY_LIMIT_TEST = 5 # Define the number of processes used in the test NUM_PROCESSES_TEST = 4 # Define the number of tasks processed by check_correctness in each process (i.e., internal # ThreadPoolExecutor's concurrency potential) NUM_TASKS_PER_PROCESS_TEST = 3 # Simulate API call duration to ensure calls can overlap SIMULATED_API_CALL_DURATION_TEST = 0.2 # seconds # --- Mock API call function for concurrency tracking --- # This function will replace the real call_sandbox_api and use shared variables to track concurrency def _mock_api_call_for_concurrency_tracking( active_calls_counter, # multiprocessing.Value max_calls_tracker, # multiprocessing.Value call_lock, # multiprocessing.Lock # Standard call_sandbox_api parameters sandbox_fusion_url, code, stdin, compile_timeout, run_timeout, memory_limit_mb, language, ): # entry_time = time.time() # For detailed logging with call_lock: active_calls_counter.value += 1 if active_calls_counter.value > max_calls_tracker.value: max_calls_tracker.value = active_calls_counter.value # Optional debug log: # print(f"[PID:{os.getpid()}-TID:{threading.get_ident()}] API Call Start. Active: " # f"{active_calls_counter.value}, Max Observed: {max_calls_tracker.value}, Input: {stdin}") time.sleep(SIMULATED_API_CALL_DURATION_TEST) # Simulate actual work duration # exit_time = time.time() # For detailed logging with call_lock: active_calls_counter.value -= 1 # Optional debug log: # print(f"[PID:{os.getpid()}-TID:{threading.get_ident()}] API Call End. Active: " # f"{active_calls_counter.value}, Input: {stdin}, Duration: {exit_time - entry_time:.2f}s") # Return a simulated successful API response return { "status": "Success", "run_result": {"status": "Finished", "stdout": f"mock_output_for_{stdin}", "return_code": 0}, }, None # --- Worker function for ProcessPoolExecutor --- # This function runs in each child process of ProcessPoolExecutor def _process_pool_worker_for_concurrency_test( sandbox_url, in_outs, generation, memory_limit_mb, language, timeout, mp_semaphore_for_check_correctness, active_calls_counter, max_calls_tracker, call_lock, ): # Corrected lambda to accept keyword arguments matching call_sandbox_api's usage curried_mock_api_call = ( lambda sandbox_fusion_url, code, stdin, compile_timeout, run_timeout, memory_limit_mb, language: ( _mock_api_call_for_concurrency_tracking( active_calls_counter, max_calls_tracker, call_lock, sandbox_fusion_url, code, stdin, compile_timeout, run_timeout, memory_limit_mb, language, ) ) ) # ---- START DEBUG PRINTS ---- import os import verl.utils.reward_score.sandbox_fusion.utils print( f"[Worker PID:{os.getpid()}] Original call_sandbox_api: " f"{verl.utils.reward_score.sandbox_fusion.utils.call_sandbox_api}", flush=True, ) # ---- END DEBUG PRINTS ---- with patch( "verl.utils.reward_score.sandbox_fusion.utils.call_sandbox_api", side_effect=curried_mock_api_call ) as mock_obj: # ---- START DEBUG PRINTS ---- print( f"[Worker PID:{os.getpid()}] Patched call_sandbox_api: " f"{verl.utils.reward_score.sandbox_fusion.utils.call_sandbox_api}", flush=True, ) print(f"[Worker PID:{os.getpid()}] Mock object: {mock_obj}", flush=True) # ---- END DEBUG PRINTS ---- results, metadata_list = check_correctness( sandbox_fusion_url=sandbox_url, in_outs=in_outs, generation=generation, timeout=timeout, memory_limit_mb=memory_limit_mb, language=language, concurrent_semaphore=mp_semaphore_for_check_correctness, # Pass multiprocessing.Semaphore ) # print(f"Process {os.getpid()} finished check_correctness. Processed {len(results)} tasks.") return len(results) # Return the number of processed tasks for basic validation # --- The actual test case for multiprocess concurrency control --- def test_multiprocess_global_concurrency_limit_with_semaphore(): """ Tests that the global concurrent_semaphore (multiprocessing.Semaphore) correctly limits the number of concurrent calls to call_sandbox_api across multiple processes, each potentially running multiple threads via check_correctness's internal ThreadPoolExecutor. """ manager = multiprocessing.Manager() active_calls_counter = manager.Value("i", 0) # Current active mock API calls max_calls_tracker = manager.Value("i", 0) # Observed maximum concurrent mock API calls call_lock = manager.Lock() # Lock to protect counters # Create a multiprocessing.Semaphore instance, this is the global semaphore we are testing. # It will be passed to check_correctness and used by _process_single_case to limit calls to call_sandbox_api. global_mp_semaphore = manager.Semaphore(MAX_GLOBAL_CONCURRENCY_LIMIT_TEST) mock_sandbox_url = "mock_url_for_concurrency_test" mock_generation = "pass" # Specific code content is not important as API call is mocked mock_memory_limit_mb = 1024 mock_language = "python" mock_timeout = 5 # Timeout setting, not critical for mock calls # Input/output data for each process # NUM_TASKS_PER_PROCESS_TEST tasks will be handled by check_correctness's internal ThreadPoolExecutor process_in_outs = { "inputs": [f"task_input_{i}" for i in range(NUM_TASKS_PER_PROCESS_TEST)], "outputs": [f"task_output_{i}" for i in range(NUM_TASKS_PER_PROCESS_TEST)], } futures = [] total_tasks_expected_to_run = NUM_PROCESSES_TEST * NUM_TASKS_PER_PROCESS_TEST test_start_time = time.time() with ProcessPoolExecutor(max_workers=NUM_PROCESSES_TEST) as executor: for i in range(NUM_PROCESSES_TEST): future = executor.submit( _process_pool_worker_for_concurrency_test, # Worker function mock_sandbox_url, process_in_outs, mock_generation, mock_memory_limit_mb, mock_language, mock_timeout, global_mp_semaphore, # Global semaphore to test active_calls_counter, # Shared variables for tracking max_calls_tracker, call_lock, ) futures.append(future) # Wait for all processes to complete and collect results num_tasks_processed_per_worker = [f.result() for f in futures] test_end_time = time.time() total_execution_time = test_end_time - test_start_time # Print some test statistics for debugging and validation print("\n--- Global Concurrency Test Stats ---") print(f"Semaphore Limit (MAX_GLOBAL_CONCURRENCY_LIMIT_TEST): {MAX_GLOBAL_CONCURRENCY_LIMIT_TEST}") print(f"Number of Processes (NUM_PROCESSES_TEST): {NUM_PROCESSES_TEST}") print(f"Tasks per Process (NUM_TASKS_PER_PROCESS_TEST): {NUM_TASKS_PER_PROCESS_TEST}") print(f"Total Tasks Submitted: {total_tasks_expected_to_run}") print(f"Simulated API Call Duration: {SIMULATED_API_CALL_DURATION_TEST}s") print(f"Total Test Execution Time: {total_execution_time:.2f}s") print(f"Max Concurrent Mock API Calls Observed: {max_calls_tracker.value}") # print(f"Tasks processed per worker: {num_tasks_processed_per_worker}") # Verify that all submitted tasks have been processed assert sum(num_tasks_processed_per_worker) == total_tasks_expected_to_run, ( "Mismatch in the number of tasks processed." ) # Verify that the mock API was called at least once assert max_calls_tracker.value > 0, "The mocked API call_sandbox_api was not called." # Core assertion: Observed maximum concurrent calls should not exceed the semaphore's limit assert max_calls_tracker.value <= MAX_GLOBAL_CONCURRENCY_LIMIT_TEST, ( f"Observed concurrency ({max_calls_tracker.value}) exceeded semaphore limit " f"({MAX_GLOBAL_CONCURRENCY_LIMIT_TEST})." ) # Optional: Rough check on execution time to verify semaphore is working to limit concurrency # Theoretical minimum execution time = (Total tasks / Concurrency limit) * Single task duration # Actual time will be longer due to various overheads min_expected_duration = ( total_tasks_expected_to_run * SIMULATED_API_CALL_DURATION_TEST ) / MAX_GLOBAL_CONCURRENCY_LIMIT_TEST # print(f"Minimum Expected Execution Time (approx): {min_expected_duration:.2f}s") # Allow some margin, e.g., 80% of theoretical minimum time assert total_execution_time >= min_expected_duration * 0.8, ( f"Total execution time ({total_execution_time:.2f}s) was unexpectedly short, suggesting the " f"semaphore might not be effectively limiting concurrency as expected " f"(min expected: {min_expected_duration * 0.8:.2f}s)." ) # Ensure there is no more code after this point if these were the last functions. # If there was other code, it would follow here. def test_unit_invalid_input_format(): """Unit test: Invalid in_outs format passed""" results, metadata_list = check_correctness(SANDBOX_URL, None, CODE_SUCCESS) assert results == [-1] assert metadata_list[0]["error"] == "Invalid input/output data" results, metadata_list = check_correctness(SANDBOX_URL, {}, CODE_SUCCESS) assert results == [-1] assert metadata_list[0]["error"] == "Invalid input/output data" results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_INVALID_MISSING_KEY, CODE_SUCCESS) assert results == [-1] assert metadata_list[0]["error"] == "Invalid input/output data" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_unit_input_output_mismatch(): """Unit test: Mismatch between the number of inputs and outputs""" results, metadata_list = check_correctness(SANDBOX_URL, INPUT_OUTPUT_MISMATCH, CODE_SUCCESS) assert results == [-1] assert len(metadata_list) == 1 assert metadata_list[0]["error"] == "Input/output count mismatch" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_integration_concurrency_all_timeout(): """Integration test: High concurrency (100 cases) against real API, all causing timeout""" concurrency_level = 100 code_infinite_loop = """ def knight_moves(X, Y): MOD = 10**9 + 7 dp = [[0] * (Y + 1) for _ in range(X + 1)] dp[0][0] = 1 for i in range(1, X + 1): for j in range(1, Y + 1): dp[i][j] = (dp[i - 1][j] + dp[i][j - 1]) % MOD return dp[X][Y] def solve(): X, Y = map(int, input().split()) print(knight_moves(X, Y)) if __name__ == "__main__": solve() """ # Generate 100 simple input/output pairs (content doesn't matter) timeout_inputs = ["324 384429" for i in range(concurrency_level)] timeout_outputs = [f"output_{i}\n" for i in range(concurrency_level)] timeout_in_outs = {"inputs": timeout_inputs, "outputs": timeout_outputs} # Set a timeout for the test cases test_timeout = 10 # Set a timeout value start_time = time.time() results, metadata_list = check_correctness(SANDBOX_URL, timeout_in_outs, code_infinite_loop, timeout=test_timeout) end_time = time.time() duration = end_time - start_time print(f"\nHigh concurrency all timeout test ({concurrency_level} cases) duration: {duration:.2f} seconds") # Verify all results are -3 (timeout) assert len(results) == concurrency_level, f"Expected {concurrency_level} results, got {len(results)}" all_timed_out = all(r == -3 for r in results) if not all_timed_out: non_timeout_indices = [i for i, r in enumerate(results) if r != -3] print(f"Indices that did not time out: {non_timeout_indices}") # Print metadata for the first few non-timeout cases for debugging for i in non_timeout_indices[:5]: print(f"Metadata for non-timeout case {i}: {metadata_list[i]}") assert all_timed_out, f"Not all {concurrency_level} concurrent tests resulted in timeout (-3). Results: {results}" # Verify metadata count and status of the first case assert len(metadata_list) == concurrency_level assert metadata_list[0]["status"] == "timeout" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_fn_name_success_single_case(): """Tests successful execution for a single test case with fn_name. from livecodebench/code_generation_lite test 510 """ generation_code = """ class Solution: def occurrencesOfElement(self, nums: List[int], queries: List[int], x: int) -> List[int]: positions = defaultdict(list) for idx, num in enumerate(nums): positions[num].append(idx) x_positions = positions[x] answer = [] for k in queries: if k > len(x_positions): answer.append(-1) else: answer.append(x_positions[k-1]) return answer """ in_outs = { "fn_name": "occurrencesOfElement", "inputs": ["[1, 3, 1, 7]\n[1, 3, 2, 4]\n1", "[1, 2, 3]\n[10]\n5"], "outputs": ["[0, -1, 2, -1]", "[-1]"], } # Use a short timeout for fast tests results, metadata_list = check_correctness(SANDBOX_URL, in_outs, generation_code, timeout=5) # from verl.utils.reward_score.prime_code import apps_check_correctness # results, metadata_list = apps_check_correctness(in_outs=in_outs, generation=generation_code, # timeout=50000, debug=True) assert results == [True, True] assert "error" not in metadata_list[0] assert metadata_list[0].get("status") != "compile_error" assert metadata_list[0].get("status") != "runtime_error" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_none_and_empty_stdin_passed_correctly(): """ Tests that when stdin data is set to an empty string or None, it is still is passed correctly to Sandbox Fusion as an empty string. """ echo_code = """ import sys print(f"You said '{sys.stdin.readline().strip()}'") """ in_outs = { "inputs": [None, "", "hello"], "outputs": ["You said ''", "You said ''", "You said 'hello'"], } # Use a short timeout for fast tests results, metadata_list = check_correctness(SANDBOX_URL, in_outs, echo_code, timeout=5) assert results == [True, True, True] assert "error" not in metadata_list[0] assert metadata_list[0].get("status") != "compile_error" assert metadata_list[0].get("status") != "runtime_error" @pytest.mark.skipif(skip_condition, reason=skip_reason) def test_assert_case_success(): """Tests successful execution for assert case. from KodCode """ generation_code = """ from typing import List, Tuple def merge_intervals(intervals: List[Tuple[int, int]]) -> List[Tuple[int, int]]: if not intervals: return [] # Sort intervals by the start time intervals.sort(key=lambda x: x[0]) merged = [intervals[0]] for current in intervals[1:]: last = merged[-1] # If intervals overlap, merge them if current[0] <= last[1]: merged[-1] = (last[0], max(last[1], current[1])) else: merged.append(current) return merged """ test_cases = { "fn_name": "merge_intervals", "assert_case": [ "assert merge_intervals([(0, 1), (3, 5), (4, 7), (6, 8), (10, 12)," " (12, 14)]) == [(0, 1), (3, 8), (10, 14)]", "assert merge_intervals([(1, 2), (2, 3), (3, 4)]) == [(1, 4)]", "assert merge_intervals([(1, 2), (3, 4), (5, 6)]) == [(1, 2), (3, 4), (5, 5)]", ], } assert_cases = test_cases.get("assert_case") test_cases.setdefault("inputs", ["" for _ in assert_cases]) test_cases.setdefault("outputs", [None for _ in assert_cases]) # Use a short timeout for fast tests results, metadata_list = check_correctness(SANDBOX_URL, test_cases, generation_code, timeout=5) assert results == [True, True, -2] for i in range(2): assert "error" not in metadata_list[i] assert metadata_list[i].get("status") == "success" assert metadata_list[i].get("expected_output") is None assert metadata_list[i].get("status") != "runtime_error" assert "error" not in metadata_list[2] assert metadata_list[2].get("status") != "success" assert metadata_list[2].get("expected_output") is None assert metadata_list[2].get("status") == "runtime_error" ================================================ FILE: tests/utils/reward_score/test_sandbox_on_cpu.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import json import os import pytest from verl.utils.reward_score import default_compute_score, sandbox_fusion from verl.workers.reward_manager.prime import parallel_compute_score_async prime_math_answers = [ """\\begin{bmatrix}\n -7 & 6 & -8 \\\\\n 11 & -9 & 12 \\\\\n 15 & -16 & 19 \n \\end{bmatrix}""", """\\frac{\\sqrt{505}}{7}""", """x^2 + y^2 + 4x - 6y + 13""", ] prime_math_gts = [ """\\begin{pmatrix}\n -7 & 6 & -8 \\\\\n 11 & -9 & 12 \\\\\n 15 & -16 & 19\n \\end{pmatrix}""", # mat test """\\frac{\\sqrt{505}}{7}""", # frac test """(x + 2)^2 + (y - 3)^2 """, # symbolic test ] prime_code_answers = [ """import sys from collections import deque def main(): data = sys.stdin.read().split() it = iter(data) # Read start and target positions x0, y0, x1, y1 = int(next(it)), int(next(it)), int(next(it)), int(next(it)) n = int(next(it)) allowed = set() # The total number of allowed cells is at most 10^5. for _ in range(n): r = int(next(it)) a = int(next(it)) b = int(next(it)) for c in range(a, b + 1): allowed.add((r, c)) # Directions for the king (8 neighboring cells) directions = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)] start = (x0, y0) target = (x1, y1) # BFS initialization queue = deque() queue.append((x0, y0, 0)) # Mark the starting cell as visited by removing it from allowed set. allowed.discard(start) while queue: x, y, moves = queue.popleft() if (x, y) == target: print(moves) return for dx, dy in directions: nx, ny = x + dx, y + dy if (nx, ny) in allowed: allowed.remove((nx, ny)) queue.append((nx, ny, moves + 1)) print(-1) if __name__ == '__main__': main() """ ] * 2 prime_code_gts = [ """{\n \"inputs\": [\n \"5 7 6 11\\n3\\n5 3 8\\n6 7 11\\n5 2 5\\n\",\n \"3 4 3 10\\n3\\n3 1 4\\n4 5 9\\n3 10 10\\n\",\n \"1 1 2 10\\n2\\n1 1 3\\n2 6 10\\n\",\n \"9 8 7 8\\n9\\n10 6 6\\n10 6 6\\n7 7 8\\n9 5 6\\n8 9 9\\n9 5 5\\n9 8 8\\n8 5 6\\n9 10 10\\n\",\n \"6 15 7 15\\n9\\n6 15 15\\n7 14 14\\n6 15 15\\n9 14 14\\n7 14 16\\n6 15 15\\n6 15 15\\n7 14 14\\n8 15 15\\n\",\n \"13 16 20 10\\n18\\n13 16 16\\n20 10 10\\n19 10 10\\n12 15 15\\n20 10 10\\n18 11 11\\n19 10 10\\n19 10 10\\n20 10 10\\n19 10 10\\n20 10 10\\n20 10 10\\n19 10 10\\n18 11 11\\n13 16 16\\n12 15 15\\n19 10 10\\n19 10 10\\n\",\n \"89 29 88 30\\n16\\n87 31 31\\n14 95 95\\n98 88 89\\n96 88 88\\n14 97 97\\n13 97 98\\n100 88 88\\n88 32 32\\n99 88 89\\n90 29 29\\n87 31 31\\n15 94 96\\n89 29 29\\n88 32 32\\n97 89 89\\n88 29 30\\n\",\n \"30 14 39 19\\n31\\n35 7 11\\n37 11 12\\n32 13 13\\n37 5 6\\n46 13 13\\n37 14 14\\n31 13 13\\n43 13 19\\n45 15 19\\n46 13 13\\n32 17 17\\n41 14 19\\n30 14 14\\n43 13 17\\n34 16 18\\n44 11 19\\n38 13 13\\n40 12 20\\n37 16 18\\n46 16 18\\n34 10 14\\n36 9 10\\n36 15 19\\n38 15 19\\n42 13 19\\n33 14 15\\n35 15 19\\n33 17 18\\n39 12 20\\n36 5 7\\n45 12 12\\n\",\n \"2 1 1 1\\n2\\n1 1 2\\n2 1 2\\n\",\n \"1 1 1 2\\n5\\n1000000000 1 10000\\n19920401 1188 5566\\n1000000000 1 10000\\n1 1 10000\\n5 100 200\\n\",\n \"1 1 1000000000 2\\n5\\n1000000000 1 10000\\n19920401 1188 5566\\n1000000000 1 10000\\n1 1 10000\\n5 100 200\\n\"\n ],\n \"outputs\": [\n \"4\\n\",\n \"6\\n\",\n \"-1\\n\",\n \"2\\n\",\n \"1\\n\",\n \"-1\\n\",\n \"1\\n\",\n \"9\\n\",\n \"1\\n\",\n \"1\\n\",\n \"-1\\n\"\n ]\n}""", # A correct sample # noqa: E501 """{\n \"inputs\": [\n \"5 7 6 11\\n3\\n5 3 8\\n6 7 11\\n5 2 5\\n\",\n \"3 4 3 10\\n3\\n3 1 4\\n4 5 9\\n3 10 10\\n\",\n \"1 1 2 10\\n2\\n1 1 3\\n2 6 10\\n\",\n \"9 8 7 8\\n9\\n10 6 6\\n10 6 6\\n7 7 8\\n9 5 6\\n8 9 9\\n9 5 5\\n9 8 8\\n8 5 6\\n9 10 10\\n\",\n \"6 15 7 15\\n9\\n6 15 15\\n7 14 14\\n6 15 15\\n9 14 14\\n7 14 16\\n6 15 15\\n6 15 15\\n7 14 14\\n8 15 15\\n\",\n \"13 16 20 10\\n18\\n13 16 16\\n20 10 10\\n19 10 10\\n12 15 15\\n20 10 10\\n18 11 11\\n19 10 10\\n19 10 10\\n20 10 10\\n19 10 10\\n20 10 10\\n20 10 10\\n19 10 10\\n18 11 11\\n13 16 16\\n12 15 15\\n19 10 10\\n19 10 10\\n\",\n \"89 29 88 30\\n16\\n87 31 31\\n14 95 95\\n98 88 89\\n96 88 88\\n14 97 97\\n13 97 98\\n100 88 88\\n88 32 32\\n99 88 89\\n90 29 29\\n87 31 31\\n15 94 96\\n89 29 29\\n88 32 32\\n97 89 89\\n88 29 30\\n\",\n \"30 14 39 19\\n31\\n35 7 11\\n37 11 12\\n32 13 13\\n37 5 6\\n46 13 13\\n37 14 14\\n31 13 13\\n43 13 19\\n45 15 19\\n46 13 13\\n32 17 17\\n41 14 19\\n30 14 14\\n43 13 17\\n34 16 18\\n44 11 19\\n38 13 13\\n40 12 20\\n37 16 18\\n46 16 18\\n34 10 14\\n36 9 10\\n36 15 19\\n38 15 19\\n42 13 19\\n33 14 15\\n35 15 19\\n33 17 18\\n39 12 20\\n36 5 7\\n45 12 12\\n\",\n \"2 1 1 1\\n2\\n1 1 2\\n2 1 2\\n\",\n \"1 1 1 2\\n5\\n1000000000 1 10000\\n19920401 1188 5566\\n1000000000 1 10000\\n1 1 10000\\n5 100 200\\n\",\n \"1 1 1000000000 2\\n5\\n1000000000 1 10000\\n19920401 1188 5566\\n1000000000 1 10000\\n1 1 10000\\n5 100 200\\n\"\n ],\n \"outputs\": [\n \"4\\n\",\n \"6\\n\",\n \"-1\\n\",\n \"-1\\n\",\n \"1\\n\",\n \"-1\\n\",\n \"1\\n\",\n \"9\\n\",\n \"1\\n\",\n \"1\\n\",\n \"-1\\n\"\n ]\n}""", # noqa: E501 ] # A failed sample with first several in-out passed prime_code_scores = [1.0, 0.9] def test_parallelism(): """ Test if process pool works properly """ sequences_str = [] ground_truth = [] data_sources = [] while len(sequences_str) < 32: sequences_str.extend(prime_code_answers) ground_truth.extend(prime_code_gts) data_sources.extend(["codecontests"] * len(prime_code_answers)) sequences_str.extend(prime_math_answers) ground_truth.extend(prime_math_gts) data_sources.extend(["numina_aops_forum"] * len(prime_math_answers)) scores = asyncio.run( parallel_compute_score_async(default_compute_score, sequences_str, ground_truth, data_sources, num_processes=16) ) print(scores) @pytest.mark.skip("pyext not compatible with python 3.12") def test_prime_code(): """ Test PRIME code sandbox. """ data_source = "codecontests" for completion, ground_truth, score_ in zip(prime_code_answers, prime_code_gts, prime_code_scores, strict=True): score = default_compute_score(data_source, completion, ground_truth) assert float(score) == score_ # Use the pytest.mark.skipif decorator to skip the test @pytest.mark.skipif(not os.environ.get("SANDBOX_FUSION_URL"), reason="SANDBOX_FUSION_URL environment variable not set") def test_prime_code_sandbox_fusion(): """ Test PRIME code on sandbox fusion. Skips if SANDBOX_FUSION_URL is not set. """ data_source = "codecontests" # Get the URL from the environment variable, as skipif ensures it is set at this point sandbox_fusion_url = os.environ.get("SANDBOX_FUSION_URL") # Removed the previous 'if not sandbox_url' check block for completion, ground_truth, score_ in zip(prime_code_answers, prime_code_gts, prime_code_scores, strict=True): score = default_compute_score( data_source, completion, ground_truth, extra_info={"sandbox_fusion_url": sandbox_fusion_url} ) # <-- Use the URL obtained from the environment variable assert float(score) == score_ @pytest.mark.skipif(not os.environ.get("SANDBOX_FUSION_URL"), reason="SANDBOX_FUSION_URL environment variable not set") def test_continuous_score_consistency(): """ Verify that continuous score calculation is consistent between prime_code and sandbox_fusion. Uses a test case where the first 9 out of 11 sub-cases pass (expected score 0.9). """ from verl.utils.reward_score import prime_code completion = prime_code_answers[1] # Use the second sample ground_truth = prime_code_gts[1] # Use the second sample (9/11 pass, first 9 pass) expected_continuous_score = 0.9 # 1. Calculate score using prime_code (default) with continuous=True prime_score, _ = sandbox_fusion.compute_score( os.environ.get("SANDBOX_FUSION_URL"), None, completion, ground_truth, continuous=True ) # 2. Calculate score using sandbox_fusion with continuous=True # Ensure the extra_info key triggers the sandbox_fusion path in default_compute_score fusion_score, _ = prime_code.compute_score(completion, ground_truth, continuous=True) # 3. Assert scores are equal (using pytest.approx for float comparison) assert float(prime_score) == pytest.approx(expected_continuous_score) assert float(fusion_score) == pytest.approx(expected_continuous_score) assert float(prime_score) == pytest.approx(float(fusion_score)) print(f"Continuous Score (Prime Code): {prime_score}") print(f"Continuous Score (Sandbox Fusion): {fusion_score}") @pytest.mark.skip("pyext not compatible with python 3.12") def test_check_correctness(): from verl.utils.reward_score.prime_code import apps_check_correctness completion = prime_code_answers[0] ground_truth = json.loads(prime_code_gts[0]) ground_truth_single = {"inputs": ground_truth["inputs"][:1], "outputs": ground_truth["outputs"][:1]} res, meta = apps_check_correctness(in_outs=ground_truth_single, generation=completion, timeout=5, debug=False) print(res, meta) def test_prime_math(): data_source = "numina_aops_forum" for completion, ground_truth in zip(prime_math_answers, prime_math_gts, strict=True): score = default_compute_score(data_source, completion, ground_truth) assert float(score) == 1.0 ================================================ FILE: tests/utils/test_activation_offload.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil import tempfile import pytest import torch import torch.distributed import torch.multiprocessing as mp from torch.distributed import init_device_mesh from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import MixedPrecision, ShardingStrategy from transformers import AutoModelForCausalLM, AutoTokenizer, Qwen2Config from verl.utils.activation_offload import enable_activation_offloading from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device from verl.utils.fsdp_utils import MixedPrecisionPolicy, apply_fsdp2, get_fsdp_wrap_policy def create_random_input_ids(batch_size, seq_len, vocab_size): if get_device_name() == "cuda": from flash_attn.bert_padding import unpad_input elif get_device_name() == "npu": from verl.utils.attention_utils import unpad_input from verl.utils.model import compute_position_id_with_mask, create_random_mask input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=get_device_name()) attention_mask = create_random_mask( input_ids, max_ratio_of_left_padding=0.1, min_ratio_of_valid_token=0.5, max_ratio_of_valid_token=0.7 ) position_ids = compute_position_id_with_mask(attention_mask) input_ids = unpad_input(input_ids.unsqueeze(-1), attention_mask)[0].transpose(0, 1) position_ids = unpad_input(position_ids.unsqueeze(-1), attention_mask)[0].transpose(0, 1) return input_ids, position_ids def _fsdp_activation_offloading_test(rank, world_size, rendezvous_file, strategy="fsdp"): get_torch_device().set_device(rank) torch.distributed.init_process_group( backend=get_nccl_backend(), init_method=f"file://{rendezvous_file}", rank=rank, world_size=world_size, ) device_mesh = init_device_mesh(get_device_name(), mesh_shape=(world_size,), mesh_dim_names=("dp",)) model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") config = Qwen2Config(num_hidden_layers=4) with torch.device(get_device_name()): model = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model = model.to(device=get_device_name()) # Wrap model with FSDP mixed_precision = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32) if strategy == "fsdp": model = FSDP( model, use_orig_params=False, device_id=get_torch_device().current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, device_mesh=device_mesh, auto_wrap_policy=get_fsdp_wrap_policy(module=model), ) else: mp_policy = MixedPrecisionPolicy( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, cast_forward_inputs=True ) fsdp_kwargs = { "mesh": device_mesh, "mp_policy": mp_policy, } apply_fsdp2(model, fsdp_kwargs, {}) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9) # Create checkpoint manager tokenizer = AutoTokenizer.from_pretrained(model_name) checkpoint_manager = FSDPCheckpointManager( model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, tokenizer=tokenizer ) # Generate sample input batch_size = 2 seq_len = 32 vocab_size = 32000 # First input for initial update input_ids1, position_ids1 = create_random_input_ids(batch_size, seq_len, vocab_size) # Second input for verification input_ids2, position_ids2 = create_random_input_ids(batch_size, seq_len, vocab_size) # Step 1: Initial update and save checkpoint outputs1 = model(input_ids=input_ids1, position_ids=position_ids1) loss1 = outputs1.logits.mean() loss1.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Save checkpoint after first update temp_dir = tempfile.mkdtemp() checkpoint_path = os.path.join(temp_dir, "checkpoint") checkpoint_manager.save_checkpoint(local_path=checkpoint_path, hdfs_path=None, global_step=0) # Step 2: Second update and forward pass outputs2 = model(input_ids=input_ids2, position_ids=position_ids2) loss2 = outputs2.logits.mean() loss2.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Record logits after second update with torch.no_grad(): logits_without_offloading = model(input_ids=input_ids2, position_ids=position_ids2).logits # Step 3: wrap module with activation offloading and load checkpoint enable_activation_offloading(model, strategy=strategy) checkpoint_manager.load_checkpoint(checkpoint_path) # Step 4: Repeat the second update with same input outputs3 = model(input_ids=input_ids2, position_ids=position_ids2) loss3 = outputs3.logits.mean() loss3.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Record logits after loaded checkpoint and update with torch.no_grad(): logits_with_offloading = model(input_ids=input_ids2, position_ids=position_ids2).logits # Step 4: Verify outputs match torch.testing.assert_close(logits_without_offloading, logits_with_offloading, atol=0.0, rtol=0.0) print(f"Activaiton offloading for {strategy} test passed on {world_size} GPUs!") # Cleanup shutil.rmtree(temp_dir) torch.distributed.barrier() torch.distributed.destroy_process_group() @pytest.mark.parametrize("world_size", (2, 4)) @pytest.mark.parametrize("strategy", ("fsdp", "fsdp2")) def test_activation_offloading(world_size, strategy, tmp_path): rendezvous_file = str(tmp_path / "rdzv_file") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) mp.spawn( fn=_fsdp_activation_offloading_test, args=(world_size, rendezvous_file, strategy), nprocs=world_size, join=True, ) ================================================ FILE: tests/utils/test_bucketed_weight_transfer.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for BucketedWeightSender and BucketedWeightReceiver. Sender and receiver run in separate processes to match real-world usage and because CUDA IPC requires distinct processes. """ import asyncio import multiprocessing as mp import uuid import pytest import torch from verl.utils.device import get_device_name, get_torch_device, is_support_ipc PROCESS_TIMEOUT = 60 # Use string checks to avoid initializing CUDA in the main pytest process, # which would make subsequent fork-based multiprocessing in other tests unsafe. HAS_ACCELERATOR = get_device_name() != "cpu" HAS_CUDA = "cuda" in get_device_name() def _unique_zmq_handle(): return f"ipc:///tmp/test-bwt-{uuid.uuid4().hex}.sock" def _generate_weights(weight_specs, seed): """Deterministically generate weights on the best available device from specs. Args: weight_specs: list of (name, shape, dtype) tuples seed: random seed for reproducibility Returns: list of (name, tensor_on_device) tuples """ device_name = get_device_name() device = torch.device(f"{device_name}:0") get_torch_device().manual_seed(seed) weights = [] for name, shape, dtype in weight_specs: # Generate in float32 then cast, since torch.randn doesn't support all dtypes t = torch.randn(shape, dtype=torch.float32, device=device).to(dtype) weights.append((name, t)) return weights # --------------------------------------------------------------------------- # Process entry points (must be module-level for pickling with spawn) # --------------------------------------------------------------------------- def _sender_fn(zmq_handle, weight_specs, seed, bucket_size_mb, use_shm): """Sender process: generate weights, move to device, send.""" from verl.workers.rollout.vllm_rollout.bucketed_weight_transfer import BucketedWeightSender weights = _generate_weights(weight_specs, seed) sender = BucketedWeightSender( zmq_handle=zmq_handle, bucket_size_mb=bucket_size_mb, use_shm=use_shm, ) asyncio.run(sender.async_send_weights(iter(weights))) def _receiver_fn(zmq_handle, use_shm, result_queue): """Receiver process: receive weights, send back (name, dtype, shape, checksum).""" from verl.utils.device import get_device_name from verl.workers.rollout.vllm_rollout.bucketed_weight_transfer import BucketedWeightReceiver device = torch.device(f"{get_device_name()}:0") receiver = BucketedWeightReceiver( zmq_handle=zmq_handle, device=device, use_shm=use_shm, ) received = [] receiver.receive_weights(on_bucket_received=lambda w: received.extend(w)) # Only send lightweight metadata + checksum back through the queue summaries = [(name, t.dtype, tuple(t.shape), t.float().sum().item()) for name, t in received] result_queue.put(summaries) # --------------------------------------------------------------------------- # Test helper # --------------------------------------------------------------------------- def _transfer_and_validate(weight_specs, bucket_size_mb, use_shm): """Spawn sender + receiver processes, then validate received tensors.""" zmq_handle = _unique_zmq_handle() seed = 42 ctx = mp.get_context("spawn") result_queue = ctx.Queue() sender_p = ctx.Process( target=_sender_fn, args=(zmq_handle, weight_specs, seed, bucket_size_mb, use_shm), ) receiver_p = ctx.Process( target=_receiver_fn, args=(zmq_handle, use_shm, result_queue), ) # Start sender first (it binds), then receiver (it connects) sender_p.start() receiver_p.start() sender_p.join(timeout=PROCESS_TIMEOUT) receiver_p.join(timeout=PROCESS_TIMEOUT) assert sender_p.exitcode == 0, f"Sender process failed with exit code {sender_p.exitcode}" assert receiver_p.exitcode == 0, f"Receiver process failed with exit code {receiver_p.exitcode}" summaries = result_queue.get(timeout=5) # Regenerate expected weights on device with the same seed expected = _generate_weights(weight_specs, seed) assert len(summaries) == len(expected), f"Expected {len(expected)} weights, got {len(summaries)}" for (exp_name, exp_tensor), (recv_name, recv_dtype, recv_shape, recv_cksum) in zip( expected, summaries, strict=False ): assert exp_name == recv_name, f"Name mismatch: expected {exp_name}, got {recv_name}" assert tuple(exp_tensor.shape) == recv_shape, ( f"Shape mismatch for {exp_name}: expected {tuple(exp_tensor.shape)}, got {recv_shape}" ) assert exp_tensor.dtype == recv_dtype, ( f"Dtype mismatch for {exp_name}: expected {exp_tensor.dtype}, got {recv_dtype}" ) exp_sum = exp_tensor.float().sum().item() assert exp_sum == recv_cksum, f"Data mismatch for {exp_name}" # --------------------------------------------------------------------------- # Shared memory tests # --------------------------------------------------------------------------- @pytest.mark.skipif(not (HAS_ACCELERATOR and not HAS_CUDA), reason="Requires (shm only tested)") class TestBucketedWeightTransferSHM: """Test BucketedWeightSender/Receiver via shared memory path.""" def test_single_small_weight(self): specs = [("layer.weight", (32, 16), torch.float32)] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=True) def test_multiple_weights_single_bucket(self): specs = [ ("layer0.weight", (16, 16), torch.float32), ("layer0.bias", (16,), torch.float32), ("layer1.weight", (16, 8), torch.bfloat16), ] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=True) def test_multiple_buckets(self): # ~64 KB each x 20 = ~1.25 MB, bucket = 1 MB => spans 2 buckets specs = [(f"layer{i}.weight", (128, 128), torch.float32) for i in range(20)] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=True) def test_mixed_dtypes(self): specs = [ ("fp32_param", (64, 64), torch.float32), ("bf16_param", (64, 64), torch.bfloat16), ("fp16_param", (32, 32), torch.float16), ] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=True) def test_empty_weights(self): _transfer_and_validate([], bucket_size_mb=1, use_shm=True) # --------------------------------------------------------------------------- # CUDA IPC tests (CUDA only — IPC is not supported on NPU) # --------------------------------------------------------------------------- @pytest.mark.skipif(not is_support_ipc(), reason="Requires IPC support") class TestBucketedWeightTransferIPC: """Test BucketedWeightSender/Receiver via CUDA IPC path.""" def test_single_small_weight(self): specs = [("layer.weight", (32, 16), torch.float32)] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=False) def test_multiple_weights_single_bucket(self): specs = [ ("layer0.weight", (16, 16), torch.float32), ("layer0.bias", (16,), torch.float32), ("layer1.weight", (16, 8), torch.bfloat16), ] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=False) def test_multiple_buckets(self): specs = [(f"layer{i}.weight", (128, 128), torch.float32) for i in range(20)] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=False) def test_mixed_dtypes(self): specs = [ ("fp32_param", (64, 64), torch.float32), ("bf16_param", (64, 64), torch.bfloat16), ("fp16_param", (32, 32), torch.float16), ] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=False) def test_empty_weights(self): _transfer_and_validate([], bucket_size_mb=1, use_shm=False) def test_exact_bucket_boundary(self): # 1 MB bucket = 1048576 bytes; float32 = 4 bytes => 262144 elements numel = (1 << 20) // 4 specs = [("exact_fit", (numel,), torch.float32)] _transfer_and_validate(specs, bucket_size_mb=1, use_shm=False) ================================================ FILE: tests/utils/test_check_ipc_version_support_on_npu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import unittest from unittest.mock import Mock, mock_open, patch from verl.utils.device import check_ipc_version_support, get_npu_versions class TestCheckIPCVersionSupport(unittest.TestCase): """Test cases for the check_ipc_version_support function.""" def setUp(self): """Set up test logging to suppress INFO messages.""" # Suppress INFO log messages during testing logging.disable(logging.INFO) def tearDown(self): """Restore logging.""" logging.disable(logging.NOTSET) def test_standard_version_with_support(self): """Test standard version that meets minimum requirements.""" # Software 25.5.0 >= 25.3.rc1, CANN 8.3.0 >= 8.3.rc1 result = check_ipc_version_support("25.5.0", "8.3.0") self.assertTrue(result) def test_standard_version_newer(self): """Test newer standard versions.""" # Software 26.0.0 >= 25.3.rc1, CANN 9.0.0 >= 8.3.rc1 result = check_ipc_version_support("26.0.0", "9.0.0") self.assertTrue(result) def test_rc_version_format(self): """Test RC version format with additional parts.""" # Software 25.3.rc1.2 -> 25.3.rc1 >= 25.3.rc1 # CANN 8.3.rc1.2 -> 8.3.rc1 >= 8.3.rc1 result = check_ipc_version_support("25.3.rc1.2", "8.3.rc1.2") self.assertTrue(result) def test_exact_rc_version(self): """Test exact RC version.""" # Software 25.3.rc1 >= 25.3.rc1 # CANN 8.3.rc1 >= 8.3.rc1 result = check_ipc_version_support("25.3.rc1", "8.3.rc1") self.assertTrue(result) def test_t_suffix_version(self): """Test version with lowercase t suffix.""" # Software 25.5.t3.b001 -> 25.5 >= 25.3.rc1 # CANN 8.3.rc1 >= 8.3.rc1 result = check_ipc_version_support("25.5.t3.b001", "8.3.rc1") self.assertTrue(result) def test_t_suffix_version_older(self): """Test version with lowercase t suffix that's too old.""" # Software 25.5.t3.b001 -> 25.5 >= 25.3.rc1 (should pass) # CANN 8.2.rc1 < 8.3.rc1 (should fail) result = check_ipc_version_support("25.5.t3.b001", "8.2.rc1") self.assertFalse(result) def test_software_version_below_minimum(self): """Test software version below minimum requirement.""" # Software 25.2.0 < 25.3.rc1 result = check_ipc_version_support("25.2.0", "8.3.0") self.assertFalse(result) def test_cann_version_below_minimum(self): """Test CANN version below minimum requirement.""" # Software 25.5.0 >= 25.3.rc1 # CANN 8.2.0 < 8.3.rc1 result = check_ipc_version_support("25.5.0", "8.2.0") self.assertFalse(result) def test_both_versions_below_minimum(self): """Test both versions below minimum requirement.""" # Software 25.2.0 < 25.3.rc1 # CANN 8.2.0 < 8.3.rc1 result = check_ipc_version_support("25.2.0", "8.2.0") self.assertFalse(result) def test_invalid_software_version(self): """Test invalid software version format.""" with self.assertRaises(RuntimeError) as context: check_ipc_version_support("invalid.version", "8.3.0") self.assertIn("Invalid software version format", str(context.exception)) def test_invalid_cann_version(self): """Test invalid CANN version format.""" with self.assertRaises(RuntimeError) as context: check_ipc_version_support("25.5.0", "invalid.version") self.assertIn("Invalid CANN version format", str(context.exception)) def test_rc_with_more_parts(self): """Test RC version with more than 3 parts.""" # Should extract only first 3 parts: 25.3.rc1 result = check_ipc_version_support("25.3.rc1.2.3.4", "8.3.rc1.2.3.4") self.assertTrue(result) def test_standard_with_more_parts(self): """Test standard version with more than 3 parts.""" # Should extract only first 3 parts: 25.5.0 result = check_ipc_version_support("25.5.0.1.2.3", "8.3.0.1.2.3") self.assertTrue(result) def test_rc_edge_case_versions(self): """Test edge case RC versions.""" # RC1 is the minimum result = check_ipc_version_support("25.3.rc1", "8.3.rc1") self.assertTrue(result) # RC0 should fail result = check_ipc_version_support("25.3.rc0", "8.3.rc1") self.assertFalse(result) def test_major_version_differences(self): """Test major version number differences.""" # Much newer major versions result = check_ipc_version_support("30.0.0", "10.0.0") self.assertTrue(result) # Older major versions result = check_ipc_version_support("24.0.0", "7.0.0") self.assertFalse(result) class TestGetNPUVersions(unittest.TestCase): """Test cases for the get_npu_versions function.""" @patch("subprocess.run") @patch("platform.machine") @patch("os.path.exists") @patch("builtins.open", new_callable=mock_open, read_data="version=8.3.rc1\n") def test_get_npu_versions_success(self, mock_file, mock_exists, mock_machine, mock_run): """Test successful retrieval of versions.""" # Mock npu-smi output mock_run.return_value = Mock(stdout="Software Version : 25.5.0\nOther Info\n", check=True) # Mock architecture mock_machine.return_value = "x86_64" # Mock path exists mock_exists.return_value = True software_version, cann_version = get_npu_versions() self.assertEqual(software_version, "25.5.0") self.assertEqual(cann_version, "8.3.rc1") @patch("subprocess.run") def test_get_npu_versions_missing_software_version(self, mock_run): """Test error when Software Version is missing.""" mock_run.return_value = Mock(stdout="Other Info Without Software Version\n", check=True) with self.assertRaises(RuntimeError) as context: get_npu_versions() self.assertIn("Could not find Software Version", str(context.exception)) @patch("subprocess.run") @patch("platform.machine") @patch("os.path.exists") @patch("builtins.open", new_callable=mock_open, read_data="version=8.3.rc1\n") def test_get_npu_versions_unsupported_architecture(self, mock_file, mock_exists, mock_machine, mock_run): """Test error with unsupported architecture.""" mock_run.return_value = Mock(stdout="Software Version : 25.5.0\n", check=True) mock_machine.return_value = "armv7l" # Unsupported architecture mock_exists.return_value = True with self.assertRaises(RuntimeError) as context: get_npu_versions() self.assertIn("Unsupported architecture", str(context.exception)) @patch("subprocess.run") @patch("platform.machine") @patch("os.path.exists") @patch("builtins.open", new_callable=mock_open, read_data="version=8.3.rc1\n") def test_get_npu_versions_cann_path_not_exists(self, mock_file, mock_exists, mock_machine, mock_run): """Test error when CANN path doesn't exist.""" mock_run.return_value = Mock(stdout="Software Version : 25.5.0\n", check=True) mock_machine.return_value = "x86_64" mock_exists.return_value = False # Path doesn't exist with self.assertRaises(RuntimeError) as context: get_npu_versions() self.assertIn("CANN toolkit path does not exist", str(context.exception)) @patch("subprocess.run") @patch("platform.machine") @patch("os.path.exists") @patch("builtins.open") def test_get_npu_versions_info_file_not_exists(self, mock_file, mock_exists, mock_machine, mock_run): """Test error when CANN info file doesn't exist.""" mock_run.return_value = Mock(stdout="Software Version : 25.5.0\n", check=True) mock_machine.return_value = "x86_64" # First call is for CANN path exists, second call is for info file exists mock_exists.side_effect = [True, False] with self.assertRaises(RuntimeError) as context: get_npu_versions() self.assertIn("CANN toolkit info file does not exist", str(context.exception)) @patch("subprocess.run") @patch("platform.machine") @patch("os.path.exists") @patch("builtins.open", new_callable=mock_open, read_data="other_info=no_version\n") def test_get_npu_versions_missing_cann_version(self, mock_file, mock_exists, mock_machine, mock_run): """Test error when CANN version is missing from info file.""" mock_run.return_value = Mock(stdout="Software Version : 25.5.0\n", check=True) mock_machine.return_value = "x86_64" mock_exists.return_value = True with self.assertRaises(RuntimeError) as context: get_npu_versions() self.assertIn("Could not find version in CANN toolkit info file", str(context.exception)) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_check_profiler_output.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import glob import logging import os import sys from dataclasses import dataclass from typing import Callable # Initialize logger logger = logging.getLogger(__file__) logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") @dataclass class DeviceCheckConfig: """Device check configuration: encapsulates device-specific validation rules""" # Search path pattern search_pattern: str # Directory count validation function: takes stage and dir list, returns bool dir_count_validator: Callable[[str, list[str]], bool] # PROF file/dir validation function: takes directory path, returns bool prof_validator: Callable[[str], bool] class ProfilerChecker: """Unified Profiler checker supporting GPU/NPU devices""" TARGET_STAGES = ["actor_update", "*_rollout_*", "ref_*"] def __init__(self, device_type: str, profiler_dir: str): self.device_type = device_type.lower() self.profiler_dir = profiler_dir # Validate device type if self.device_type not in ["gpu", "npu"]: raise ValueError(f"Unsupported device type: {device_type}, only gpu/npu are supported") # Initialize device-specific configuration self._init_device_config() def _init_device_config(self): """Initialize validation rules for different devices (core: device differences as config)""" if self.device_type == "gpu": self.config = DeviceCheckConfig( # GPU search pattern: match stage directory directly search_pattern=os.path.join(self.profiler_dir, "{stage}"), # GPU: all stages must have exactly 1 directory dir_count_validator=lambda stage, dirs: len(dirs) == 1, # GPU: any file/subdirectory exists under the directory prof_validator=lambda d: len(glob.glob(os.path.join(d, "*"))) > 0, ) else: # NPU self.config = DeviceCheckConfig( # NPU search pattern: match ascend subdirectory under stage search_pattern=os.path.join(self.profiler_dir, "{stage}", "*_ascend_*"), # NPU: rollout requires >1 dir, others require exactly 1 dir dir_count_validator=lambda stage, dirs: (len(dirs) > 1 if stage == "*_rollout_*" else len(dirs) == 1), # NPU: PROF_* subdirectory must exist and be a valid directory prof_validator=lambda d: ( len(glob.glob(os.path.join(d, "PROF_*"))) > 0 and os.path.isdir(glob.glob(os.path.join(d, "PROF_*"))[0]) ), ) def _validate_stage_dirs(self, stage: str) -> bool: """Generic stage directory validation: extracted common logic for GPU/NPU""" # 1. Generate search path and match directories search_pattern = self.config.search_pattern.format(stage=stage) dirs = glob.glob(search_pattern, recursive=True) # 2. Log found directories for d in dirs: logger.info(f"[{stage}] Found: {d}") # 3. Validate directory count if not self.config.dir_count_validator(stage, dirs): expected = ">1" if stage == "*_rollout_*" and self.device_type == "npu" else 1 logger.error(f"[{stage}] Expected {expected} directories, found {len(dirs)}") return False # 4. Validate PROF files/directories for target_dir in dirs: if not self.config.prof_validator(target_dir): logger.error(f"[{stage}] PROF not found in {target_dir}") return False return True def check(self) -> bool: """Unified check entry point""" logger.info(f"Starting profiler deliverables check for {self.device_type.upper()}...") # Validate root directory exists if not os.path.exists(self.profiler_dir): logger.error(f"Profiler data directory not found: {self.profiler_dir}") return False # Run validation for all target stages for stage in self.TARGET_STAGES: if not self._validate_stage_dirs(stage): return False logger.info(f"All {self.device_type.upper()} validation stages passed") return True def parse_args(): """Parse command line arguments""" parser = argparse.ArgumentParser(description="Check Profiler deliverables (support GPU/NPU)") parser.add_argument( "--device", type=str, required=True, choices=["gpu", "npu"], help="Device type, available values: gpu/npu (required)", ) parser.add_argument( "--profiler_dir", type=str, default="./profiler_data", help="Path to profiler data directory (default: ./profiler_data)", ) return parser.parse_args() def main(): args = parse_args() try: checker = ProfilerChecker(device_type=args.device, profiler_dir=args.profiler_dir) if checker.check(): logger.info(f"All {args.device.upper()} profiler deliverables check passed!") sys.exit(0) else: logger.error(f"{args.device.upper()} profiler check failed!") sys.exit(1) except Exception as e: logger.exception(f"Check failed with error: {str(e)}") sys.exit(1) if __name__ == "__main__": main() ================================================ FILE: tests/utils/test_config_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from dataclasses import dataclass, field from omegaconf import OmegaConf from verl.base_config import BaseConfig from verl.utils import omega_conf_to_dataclass @dataclass class TestDataclass(BaseConfig): hidden_size: int = 0 activation: str = "relu" @dataclass class TestTrainConfig(BaseConfig): batch_size: int = 0 model: TestDataclass = field(default_factory=TestDataclass) override_config: dict = field(default_factory=dict) _cfg_str = """train_config: _target_: tests.utils.test_config_on_cpu.TestTrainConfig batch_size: 32 model: hidden_size: 768 activation: relu override_config: {}""" class TestConfigOnCPU(unittest.TestCase): """Test cases for configuration utilities on CPU. Test Plan: 1. Test basic OmegaConf to dataclass conversion for simple nested structures 2. Test nested OmegaConf to dataclass conversion for complex hierarchical configurations 3. Verify all configuration values are correctly converted and accessible """ def setUp(self): self.config = OmegaConf.create(_cfg_str) def test_omega_conf_to_dataclass(self): sub_cfg = self.config.train_config.model cfg = omega_conf_to_dataclass(sub_cfg, TestDataclass) self.assertEqual(cfg.hidden_size, 768) self.assertEqual(cfg.activation, "relu") assert isinstance(cfg, TestDataclass) def test_nested_omega_conf_to_dataclass(self): cfg = omega_conf_to_dataclass(self.config.train_config, TestTrainConfig) self.assertEqual(cfg.batch_size, 32) self.assertEqual(cfg.model.hidden_size, 768) self.assertEqual(cfg.model.activation, "relu") assert isinstance(cfg, TestTrainConfig) assert isinstance(cfg.model, TestDataclass) class TestPrintCfgCommand(unittest.TestCase): """Test suite for the print_cfg.py command-line tool.""" def test_command_with_override(self): """Test that the command runs without error when overriding config values.""" import subprocess # Run the command result = subprocess.run( ["python3", "scripts/print_cfg.py"], capture_output=True, text=True, ) # Verify the command exited successfully self.assertEqual(result.returncode, 0, f"Command failed with stderr: {result.stderr}") # Verify the output contains expected config information self.assertIn("critic", result.stdout) self.assertIn("profiler", result.stdout) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_flops_counter.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import pytest from verl.utils.flops_counter import FlopsCounter VALID_CONFIG_TYPE = {"llama", "qwen2", "qwen3", "qwen3_moe", "deepseek_v3", "mistral", "gemma3_text", "apertus"} class Config: def __init__(self, config_dict): for key, value in config_dict.items(): if isinstance(value, dict): value = Config(value) setattr(self, key, value) CONFIG = { "llama": { "config": { # llama2-7B "model_type": "llama", "vocab_size": 32000, "hidden_size": 4096, "intermediate_size": 11008, "num_hidden_layers": 32, "num_attention_heads": 32, "num_key_value_heads": 32, }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # 6*(vocab*hidden*2+layer*(hidden*(q+k+v+head*head_dim)+ hidden*inter*3))*token_sum + # 6*sum(seqlen^2)*layer*head*head_dim # 6*(32000*4096*2+32*(4096*4096*4+4096*11008*3))*(512+1024+2048) + # 6*(512*512+1024*1024+2048*2048)*32*4096 # 6*(32000*4096*2+32*(4096*4096*4+4096*11008*3))*(4096+4096+4096) + # 6*(4096*4096+4096*4096+4096*4096)*32*4096 "expected_flops_tuple": (149226491215872 / 1e12, 536372695793664 / 1e12), }, "qwen2": { "config": { # Qwen/Qwen2.5-7B-Instruct "model_type": "qwen2", "vocab_size": 152064, "hidden_size": 3584, "intermediate_size": 18944, "num_hidden_layers": 28, "num_attention_heads": 28, "num_key_value_heads": 4, }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # 6*(vocab*hidden*2+layer*(hidden*(q+k+v+head*head_dim)+ hidden*inter*3))*token_sum + # 6*sum(seqlen^2)*layer*head*head_dim # 6*(152064*3584*2+28*(3584*(3584+512+512+3584)+3584*18944*3))*(512+1024+2048) + # 6*(512*512+1024*1024+2048*2048)*28*3584 # 6*(152064*3584*2+28*(3584*(3584+512+512+3584)+3584*18944*3))*(4096+4096+4096) + # 6*(4096*4096+4096*4096+4096*4096)*28*3584 "expected_flops_tuple": (167073690943488 / 1e12, 591764889010176 / 1e12), }, "qwen3": { "config": { # Qwen/Qwen3-8B "model_type": "qwen3", "vocab_size": 151936, "hidden_size": 4096, "intermediate_size": 12288, "num_hidden_layers": 36, "num_attention_heads": 32, "num_key_value_heads": 8, "head_dim": 128, }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # 6*(vocab*hidden*2+layer*(hidden*(q+k+v+head*head_dim)+ hidden*inter*3))*token_sum + # 6*sum(seqlen^2)*layer*head*head_dim # 6*(151936*4096*2+36*(4096*(128*32+128*8*2+128*32)+4096*12288*3))*(512+1024+2048) + # 6*(512*512+1024*1024+2048*2048)*36*128*32 # 6*(151936*4096*2+36*(4096*(128*32+128*8*2+128*32)+4096*12288*3))*(4096+4096+4096) + # 6*(4096*4096+4096*4096+4096*4096)*36*128*32 "expected_flops_tuple": (180997438046208 / 1e12, 648394032807936 / 1e12), }, "qwen3_moe": { "config": { # Qwen/Qwen3-30B-A3B-Base "model_type": "qwen3_moe", "hidden_size": 2048, "vocab_size": 151936, "num_hidden_layers": 48, "num_key_value_heads": 4, "num_attention_heads": 32, "head_dim": 128, "moe_intermediate_size": 768, "num_experts_per_tok": 8, "num_experts": 128, }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # 6*(vocab*hidden*2+layer*(hidden*(q+k+v+head*head_dim)+hidden*inter*top_k_exp*3 + # hidden*num_experts))*token_sum + 6*sum(seqlen^2)*layer*head*head_dim # 6*(151936*2048*2+48*(2048*(128*32+128*4*2+128*32)+2048*768*8*3+2048*128))*(512+1024+2048) + # 6*(512*512+1024*1024+2048*2048)*48*128*32 # 6*(151936*2048*2+48*(2048*(128*32+128*4*2+128*32)+2048*768*8*3+2048*128))*(4096+4096+4096) + # 6*(4096*4096+4096*4096+4096*4096)*48*128*32 "expected_flops_tuple": (78593069678592 / 1e12, 306570470621184 / 1e12), }, "deepseek_v3": { "config": { # deepseek-ai/DeepSeek-Prover-V2-671B "model_type": "deepseek_v3", "hidden_size": 7168, "vocab_size": 129280, "moe_intermediate_size": 2048, "num_hidden_layers": 61, "first_k_dense_replace": 3, "num_attention_heads": 128, "n_routed_experts": 256, "num_experts_per_tok": 8, "n_shared_experts": 1, "kv_lora_rank": 512, "qk_rope_head_dim": 64, "v_head_dim": 128, "intermediate_size": 18432, "qk_nope_head_dim": 128, "q_lora_rank": 1536, }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # (1536*7168+128*192*1536+7168*(512+64)+128*(128+128)*512+128*128*7168) = 187105280 # 6*(129280*7168*2+ 3*(7168*18432*3+187105280)+ 58*(187105280+7168*256+7168*2048*9*3))*(512+1024+2048) + # 3*(512*512+1024*1024+2048*2048)*61*(192+128)*128 # 6*(129280*7168*2+ 3*(7168*18432*3+187105280)+ 58*(187105280+7168*256+7168*2048*9*3))*(4096+4096+4096) + # 3*(4096*4096+4096*4096+4096*4096)*61*(192+128)*128 "expected_flops_tuple": (848766538088448 / 1e12, 3145850406567936 / 1e12), }, "mistral": { "config": { # mistralai/Mistral-Small-24B-Instruct-2501 "model_type": "mistral", "vocab_size": 131072, "hidden_size": 5120, "intermediate_size": 32768, "num_hidden_layers": 40, "num_attention_heads": 32, "num_key_value_heads": 8, "head_dim": 128, }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # Mistral uses same architecture as Llama, with GQA # 6*(vocab*hidden*2+layer*(hidden*(q+k+v+head*head_dim)+ hidden*inter*3))*token_sum + # 12*sum(seqlen^2)*layer*head*head_dim # vocab part: 131072*5120*2 = 1342177280 # attn part per layer: 5120*(128*32+128*8+128*8+128*32) = 5120*10240 = 52428800 # mlp part per layer: 5120*32768*3 = 503316480 # total per layer: 52428800 + 503316480 = 555745280 # all layers: 1342177280 + 40*555745280 = 23571988480 # For batch [512, 1024, 2048], tokens_sum = 3584: # dense flops: 6 * 23571988480 * 3584 = 506892040273920 # attn flops: 6 * 5505024 * 40 * 128 * 32 = 10823317585920 # total: 517715357859840 / 1e12 = 517.71535785984 # For batch [4096, 4096, 4096], tokens_sum = 12288: # dense flops: 6 * 23571988480 * 12288 = 1737915566653440 # attn flops: 6 * 50331648 * 40 * 128 * 32 = 98956046499840 # total: 1836871613153280 / 1e12 = 1836.87161315328 "expected_flops_tuple": (512303699066880 / 1e12, 1787393589903360 / 1e12), }, "gemma3_text": { "config": { # Gemma3-12B-IT-TextOnly "model_type": "gemma3_text", "vocab_size": 262208, "hidden_size": 3840, "intermediate_size": 15360, "num_hidden_layers": 48, "num_attention_heads": 16, "num_key_value_heads": 8, "head_dim": 256, "sliding_window": 1024, "layer_types": None, # Will be auto-generated based on sliding_window_pattern "sliding_window_pattern": 6, # Every 6th layer is full attention }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # Gemma3 has alternating sliding window attention # With sliding_window_pattern=6: layers 5,11,17,23,29,35,41,47 use full attention (8 layers) # Other 40 layers use sliding window attention with window_size=1024 # # Non-attention FLOPs: # vocab part: 262208*3840*2 = 2013757440 # attn part per layer: 3840*(256*16+256*8+256*8+256*16) = 3840*12288 = 47185920 # mlp part per layer: 3840*15360*3 = 176947200 # total per layer: 47185920 + 176947200 = 224133120 # all layers: 2013757440 + 48*224133120 = 12772147200 # # For batch [512, 1024, 2048], tokens_sum = 3584: # dense flops: 6 * 12772147200 * 3584 = 274652253388800 # seqlen_square_sum: 180355072 (calculated with sliding window logic) # attn flops: 6 * 180355072 * 256 * 16 = 8864812498944 # total: 283517065887744 / 1e12 = 283.517065887744 # # For batch [4096, 4096, 4096], tokens_sum = 12288: # dense flops: 6 * 12772147200 * 12288 = 941664868761600 # seqlen_square_sum: 905969664 (calculated with sliding window logic) # attn flops: 6 * 905969664 * 256 * 16 = 44530220924928 # total: 986195089686528 / 1e12 = 986.195089686528 "expected_flops_tuple": (279084659638272 / 1e12, 963929979224064 / 1e12), }, "gpt_oss": { "config": { "model_type": "gpt_oss", "vocab_size": 201088, "hidden_size": 2880, "num_hidden_layers": 24, "num_attention_heads": 64, "num_key_value_heads": 8, "head_dim": 64, "intermediate_size": 2880, "num_local_experts": 32, "num_experts_per_tok": 4, "sliding_window": 128, "layer_types": [ "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", "sliding_attention", "full_attention", ], }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # GPT-OSS has alternating sliding / full attention # Even layers (12 layers) use sliding window attention with window_size = 128 # Odd layers (12 layers) use full attention # # Non-attention FLOPs: # vocab part: 201088 * 2880 * 2 = 1158266880 # attn linear part per layer: # Q: 2880 * (64 * 64) = 11796480 # K: 2880 * (8 * 64) = 1474560 # V: 2880 * (8 * 64) = 1474560 # O: (64 * 64) * 2880 = 11796480 # attn linear total = 26542080 # mlp (MoE, SwiGLU) part per layer: # gate: 2880 * 32 = 92160 # active experts: 3 * 2880 * 2880 * 4 = 99532800 # mlp total = 99624960 # total per layer: 26542080 + 99624960 = 126167040 # all layers: # 126167040 * 24 = 3028008960 # total dense params: # 3028008960 + 1158266880 = 4186275840 # # For batch [512, 1024, 2048], tokens_sum = 3584: # dense flops: 6 * 4186275840 * 3584 = 90021675663360 # seqlen_square_sum: 71565312 (calculated with sliding window logic) # attn flops: 6 * 71565312 * 64 * 64 = 3517578215424 # total: 93539253878784 / 1e12 = 93.539253878784 # # For batch [4096, 4096, 4096], tokens_sum = 12288: # dense flops: 6 * 4186275840 * 12288 = 308646629068800 # seqlen_square_sum: 622854144 (calculated with sliding window logic) # attn flops: 6 * 622854144 * 64 * 64 = 30613642948608 # total: 339260272017408 / 1e12 = 339.260272017408 "expected_flops_tuple": (91780464771072 / 1e12, 323953008574464 / 1e12), }, "apertus": { "config": { # swiss-ai/Apertus-8B "model_type": "apertus", "vocab_size": 131072, "hidden_size": 4096, "intermediate_size": 21504, "num_hidden_layers": 32, "num_attention_heads": 32, "num_key_value_heads": 32, "hidden_act": "xielu", # head_dim will be derived as 4096 / 32 = 128 }, "batch_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # Calculation for Apertus (hidden_act="xielu" -> MLP uses [k_mlp=2]*H*I params; qk_norm=True -> [k_qkn=2]*H): # V=131072, H=4096, I=21504, L=32, k_mlp=2 (XIELU), k_qkn=2 (QK norm), S=6 # S*(2*V*H + L*(4*H**2 + k_mlp*H*I + k_qkn*H)) * (SUM[seqlen]) + 6*SUM[seqlen**2]*L*H "expected_flops_tuple": (194825353691136 / 1e12, 692711652851712 / 1e12), }, "qwen3_vl": { "config": { # Qwen/Qwen3-VL-8B "model_type": "qwen3_vl", # -------- Text config -------- "text_config": { "vocab_size": 151936, "hidden_size": 4096, "intermediate_size": 12288, "num_hidden_layers": 36, "num_attention_heads": 32, "num_key_value_heads": 8, "head_dim": 128, }, # -------- Vision config (ViT) -------- "vision_config": { "deepstack_visual_indexes": [8, 16, 24], "num_heads": 16, "depth": 27, "hidden_size": 1152, "intermediate_size": 4304, "out_hidden_size": 4096, "spatial_merge_size": 2, "temporal_patch_size": 2, "in_channels": 3, "patch_size": 16, }, }, "batch_seqlens_tuple": ( [512, 1024, 2048], [4096, 4096, 4096], ), "images_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # -----Text----- # 6*(vocab*hidden*2 # + layer*(hidden*(q+k+v+o) + hidden*inter*3) # )*token_sum # + 6*sum(seqlen^2)*layer*hidden # # -----ViT----- # patch_embed_N =hidden*temporal_patch_size*in_channels* patch_size^2 # attn_linear_N =hidden*(4*hidden) # mlp_N =hidden*inter*2 # merger_N =((o+hidden*spatial_merge_size^2) * (hidden*spatial_merge_size^2)) # deepstack_merger_N =merger_N * 3 # dense_N =patch_embed_N + (attn_linear_N + mlp_N) * 27 + deepstack_merger_N + merger_N # # 6*(151936*4096*2 # + 36*(4096*(4096+1024+1024+4096) + 4096*12288*3) # )*(512+1024+2048) # + 12*(512*512+1024*1024+2048*2048)*36*4096 # + 6 * dense_N * (512 + 1024 + 2048) # + 12 * (512**2 + 1024**2 + 2048**2) * 27 * 16 * 72 # # 6*(151936*4096*2 # + 36*(4096*(4096+1024+1024+4096) + 4096*12288*3) # )*(4096+4096+4096) # + 12*(4096*4096+4096*4096+4096*4096)*36*4096 # + 6 * dense_N * (4096 + 4096 + 2048) # + 12 * (4096**2 + 4096**2 + 4096**2) * 27 * 16 * 72 "expected_flops_tuple": ( 195379819708416 / 1e12, 709446422495232 / 1e12, ), }, "qwen3_vl_moe": { "config": { # Qwen/Qwen3-VL-30B-A3B "model_type": "qwen3_vl_moe", # -------- Text config -------- "text_config": { "vocab_size": 151936, "hidden_size": 2048, "num_hidden_layers": 48, "num_attention_heads": 32, "num_key_value_heads": 4, "head_dim": 128, "moe_intermediate_size": 768, "num_experts": 128, "num_experts_per_tok": 8, }, # -------- Vision config (ViT) -------- "vision_config": { "deepstack_visual_indexes": [8, 16, 24], "num_heads": 16, "depth": 27, "hidden_size": 1152, "intermediate_size": 4304, "out_hidden_size": 4096, "spatial_merge_size": 2, "temporal_patch_size": 2, "in_channels": 3, "patch_size": 16, }, }, "batch_seqlens_tuple": ( [512, 1024, 2048], [4096, 4096, 4096], ), "images_seqlens_tuple": ([512, 1024, 2048], [4096, 4096, 4096]), # -----Text----- # 6*(vocab*hidden*2 # + layer*(hidden*(q+k+v+head*head_dim)+hidden*inter*top_k_exp*3+hidden*num_experts) # )*token_sum # + 6*sum(seqlen^2)*layer*hidden # # -----ViT----- # patch_embed_N =hidden*temporal_patch_size*in_channels* patch_size^2 # attn_linear_N =hidden*(4*hidden) # mlp_N =hidden*inter*2 # merger_N =((o+hidden*spatial_merge_size^2) * (hidden*spatial_merge_size^2)) # deepstack_merger_N =merger_N * 3 # dense_N =patch_embed_N + (attn_linear_N + mlp_N) * 27 + deepstack_merger_N + merger_N # # 6*(151936*2048*2 # + 48*(2048*(128*32+128*4*2+128*32)+2048*768*8*3+2048*128) # )*(512+1024+2048) # + 12*(512*512+1024*1024+2048*2048)*48*4096 # + 6 * dense_N * (512 + 1024 + 2048) # + 12 * (512**2 + 1024**2 + 2048**2) * 27 * 16 * 72 # # 6*(151936*2048*2 # 48*(2048*(128*32+128*4*2+128*32)+2048*768*8*3+2048*128) # )*(4096+4096+4096) # + 12*(4096*4096+4096*4096+4096*4096)*48*4096 # + 6 * dense_N * (4096 + 4096 + 2048) # + 12 * (4096**2 + 4096**2 + 4096**2) * 27 * 16 * 72 "expected_flops_tuple": ( 92975451340800 / 1e12, 367622860308480 / 1e12, ), }, } @pytest.mark.parametrize( "config_type", [ "llama", "qwen2", "qwen3", "qwen3_moe", "deepseek_v3", "mistral", "gemma3_text", "apertus", "gpt_oss", "qwen3_vl", "qwen3_vl_moe", ], ) def test_flops_counter(config_type: str): test_config = CONFIG[config_type] config = Config(test_config["config"]) flops_counter = FlopsCounter(config) if "images_seqlens_tuple" in test_config: for batch_seqlens, images_seqlens, expected_flops in zip( test_config["batch_seqlens_tuple"], test_config["images_seqlens_tuple"], test_config["expected_flops_tuple"], strict=True, ): # set delta time to 1 to get the flops counted_flops, _ = flops_counter.estimate_flops(batch_seqlens, 1, images_seqlens=images_seqlens) print(f"Expect flops for {test_config['config']} is {expected_flops}, but get {counted_flops}") assert math.isclose(counted_flops, expected_flops), ( f"Expect flops for {test_config['config']} is {expected_flops}, but get {counted_flops}" ) else: for batch_seqlens, expected_flops in zip( test_config["batch_seqlens_tuple"], test_config["expected_flops_tuple"], strict=True ): # set delta time to 1 to get the flops counted_flops, _ = flops_counter.estimate_flops(batch_seqlens, 1) print(f"Expect flops for {test_config['config']} is {expected_flops}, but get {counted_flops}") assert math.isclose(counted_flops, expected_flops), ( f"Expect flops for {test_config['config']} is {expected_flops}, but get {counted_flops}" ) ================================================ FILE: tests/utils/test_fs_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from pathlib import Path import verl.utils.fs as fs def test_record_and_check_directory_structure(tmp_path): # Create test directory structure test_dir = tmp_path / "test_dir" test_dir.mkdir() (test_dir / "file1.txt").write_text("test") (test_dir / "subdir").mkdir() (test_dir / "subdir" / "file2.txt").write_text("test") # Create structure record record_file = fs._record_directory_structure(test_dir) # Verify record file exists assert os.path.exists(record_file) # Initial check should pass assert fs._check_directory_structure(test_dir, record_file) is True # Modify structure and verify check fails (test_dir / "new_file.txt").write_text("test") assert fs._check_directory_structure(test_dir, record_file) is False def test_copy_from_hdfs_with_mocks(tmp_path, monkeypatch): # Mock HDFS dependencies monkeypatch.setattr(fs, "is_non_local", lambda path: True) # side_effect will simulate the copy by creating parent dirs + empty file def fake_copy(src: str, dst: str, *args, **kwargs): dst_path = Path(dst) dst_path.parent.mkdir(parents=True, exist_ok=True) dst_path.write_bytes(b"") # touch an empty file monkeypatch.setattr(fs, "copy", fake_copy) # Mock actual HDFS copy # Test parameters test_cache = tmp_path / "cache" hdfs_path = "hdfs://test/path/file.txt" # Test initial copy local_path = fs.copy_to_local(hdfs_path, cache_dir=test_cache) expected_path = os.path.join(test_cache, fs.md5_encode(hdfs_path), os.path.basename(hdfs_path)) assert local_path == expected_path assert os.path.exists(local_path) def test_always_recopy_flag(tmp_path, monkeypatch): # Mock HDFS dependencies monkeypatch.setattr(fs, "is_non_local", lambda path: True) copy_call_count = 0 def fake_copy(src: str, dst: str, *args, **kwargs): nonlocal copy_call_count copy_call_count += 1 dst_path = Path(dst) dst_path.parent.mkdir(parents=True, exist_ok=True) dst_path.write_bytes(b"") monkeypatch.setattr(fs, "copy", fake_copy) # Mock actual HDFS copy test_cache = tmp_path / "cache" hdfs_path = "hdfs://test/path/file.txt" # Initial copy (always_recopy=False) fs.copy_to_local(hdfs_path, cache_dir=test_cache) assert copy_call_count == 1 # Force recopy (always_recopy=True) fs.copy_to_local(hdfs_path, cache_dir=test_cache, always_recopy=True) assert copy_call_count == 2 # Subsequent normal call (always_recopy=False) fs.copy_to_local(hdfs_path, cache_dir=test_cache) assert copy_call_count == 2 # Should not increment ================================================ FILE: tests/utils/test_fsdp2_peft_wrapping.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Test that apply_fsdp2's module selection handles peft-wrapped models. peft wraps embed_tokens in a ModulesToSaveWrapper, so isinstance(module, nn.Embedding) fails. Without name-based matching, embed_tokens + lm_head land in the root FSDP unit, causing OOM from oversized allgather. These tests verify the module selection logic works for: (1) vanilla models, (2) peft-wrapped models, (3) tied embeddings. """ import unittest from types import SimpleNamespace import torch.nn as nn from verl.utils.fsdp_utils import _select_fsdp2_wrap_targets class MockDecoderLayer(nn.Module): """Simulates a transformer decoder layer (e.g. Qwen3DecoderLayer).""" def __init__(self, hidden_size=64): super().__init__() self.self_attn = nn.Linear(hidden_size, hidden_size) self.mlp = nn.Linear(hidden_size, hidden_size) class MockModulesToSaveWrapper(nn.Module): """Simulates peft's ModulesToSaveWrapper around nn.Embedding. peft wraps modules listed in modules_to_save (like embed_tokens) in this wrapper, which breaks isinstance(module, nn.Embedding) checks. """ def __init__(self, original_module): super().__init__() self.original_module = original_module self.weight = original_module.weight # peft exposes weight class MockCausalLM(nn.Module): """Simulates a causal LM with embed_tokens, decoder layers, and lm_head.""" _no_split_modules = ["MockDecoderLayer"] def __init__(self, vocab_size=1000, hidden_size=64, num_layers=2, tie_word_embeddings=False): super().__init__() self.config = SimpleNamespace(tie_word_embeddings=tie_word_embeddings) self.model = nn.Module() self.model.embed_tokens = nn.Embedding(vocab_size, hidden_size) self.model.layers = nn.ModuleList([MockDecoderLayer(hidden_size) for _ in range(num_layers)]) self.lm_head = nn.Linear(hidden_size, vocab_size, bias=False) if tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight class TestFSDP2PeftWrapping(unittest.TestCase): """Test module selection in apply_fsdp2 for vanilla and peft-wrapped models.""" def _get_wrapped_names(self, model, cls_names): """Return names of modules selected for wrapping.""" selected = _select_fsdp2_wrap_targets(model, cls_names) # _select_fsdp2_wrap_targets returns module objects; map back to names module_to_name = {id(m): n for n, m in model.named_modules()} return [module_to_name[id(m)] for m in selected] def test_vanilla_model_wraps_layers_and_embedding(self): """Vanilla model (no peft): embed_tokens matched by isinstance, layers by class name.""" model = MockCausalLM(tie_word_embeddings=False) names = self._get_wrapped_names(model, ["MockDecoderLayer"]) self.assertIn("model.embed_tokens", names) self.assertIn("lm_head", names) self.assertTrue(any("layers.0" in n for n in names)) self.assertTrue(any("layers.1" in n for n in names)) def test_peft_wrapped_model_wraps_embed_tokens_by_name(self): """peft-wrapped model: embed_tokens fails isinstance but is matched by name.""" model = MockCausalLM(tie_word_embeddings=False) original_embed = model.model.embed_tokens model.model.embed_tokens = MockModulesToSaveWrapper(original_embed) names = self._get_wrapped_names(model, ["MockDecoderLayer"]) self.assertIn("model.embed_tokens", names) self.assertIn("lm_head", names) self.assertTrue(any("layers.0" in n for n in names)) def test_tied_embeddings_skips_name_based_wrapping(self): """With tie_word_embeddings=True, embed_tokens/lm_head are NOT wrapped separately.""" model = MockCausalLM(tie_word_embeddings=True) names = self._get_wrapped_names(model, ["MockDecoderLayer"]) self.assertNotIn("model.embed_tokens", names) self.assertNotIn("lm_head", names) self.assertTrue(any("layers.0" in n for n in names)) def test_peft_wrapped_tied_embeddings_skips_wrapping(self): """peft + tied embeddings: name-based matching is disabled, no wrapping.""" model = MockCausalLM(tie_word_embeddings=True) original_embed = model.model.embed_tokens model.model.embed_tokens = MockModulesToSaveWrapper(original_embed) names = self._get_wrapped_names(model, ["MockDecoderLayer"]) self.assertNotIn("model.embed_tokens", names) self.assertNotIn("lm_head", names) def test_no_duplicate_wrapping_for_vanilla_embedding(self): """Vanilla nn.Embedding should not be wrapped twice (by isinstance AND by name).""" model = MockCausalLM(tie_word_embeddings=False) names = self._get_wrapped_names(model, ["MockDecoderLayer"]) embed_count = sum(1 for n in names if n == "model.embed_tokens") self.assertEqual(embed_count, 1, f"embed_tokens wrapped {embed_count} times, expected 1") if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_fsdp_lora_merge.py ================================================ # Copyright 2026 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest import torch import torch.distributed import torch.multiprocessing as mp from peft import LoraConfig, get_peft_model from torch.distributed import init_device_mesh from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import MixedPrecision, ShardingStrategy from transformers import AutoModelForCausalLM, GptOssConfig, Qwen2Config from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device from verl.utils.fsdp_utils import ( MixedPrecisionPolicy, apply_fsdp2, get_fsdp_wrap_policy, merged_lora_context, ) def _test_merged_lora_context_worker( rank, world_size, rendezvous_file, strategy, model_config, lora_config_dict, backup_adapters ): """Worker function for testing merged_lora_context with FSDP. Args: rank: Process rank world_size: Total number of processes rendezvous_file: Path to rendezvous file for distributed init strategy: FSDP strategy ("fsdp" or "fsdp2") model_config: Model configuration object (Qwen2Config, GptOssConfig, etc.) lora_config_dict: Dictionary of LoRA configuration parameters backup_adapters: Whether to backup adapter weights before merging """ get_torch_device().set_device(rank) torch.distributed.init_process_group( backend=get_nccl_backend(), init_method=f"file://{rendezvous_file}", rank=rank, world_size=world_size, ) device_mesh = init_device_mesh(get_device_name(), mesh_shape=(world_size,), mesh_dim_names=("dp",)) # Create model from provided config with torch.device(get_device_name()): model = AutoModelForCausalLM.from_config( config=model_config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model = model.to(device=get_device_name()) # Add LoRA with provided config lora_config = LoraConfig(**lora_config_dict) model = get_peft_model(model, lora_config) # Initialize LoRA adapter weights to non-zero values for testing from peft.tuners.lora import LoraLayer with torch.no_grad(): for name, module in model.named_modules(): if isinstance(module, LoraLayer): for adapter_name in module.lora_A.keys(): if adapter_name in module.lora_A: # Initialize lora_A with values around 1.0 module.lora_A[adapter_name].weight.data.uniform_(0.5, 1.5) if adapter_name in module.lora_B: # Initialize lora_B with values around 2.0 module.lora_B[adapter_name].weight.data.uniform_(1.5, 2.5) # Wrap model with FSDP if strategy == "fsdp": mixed_precision = MixedPrecision( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32 ) model = FSDP( model, use_orig_params=True, device_id=get_torch_device().current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, device_mesh=device_mesh, auto_wrap_policy=get_fsdp_wrap_policy(module=model, is_lora=True), ) else: mp_policy = MixedPrecisionPolicy( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, cast_forward_inputs=True ) fsdp_kwargs = { "mesh": device_mesh, "mp_policy": mp_policy, } apply_fsdp2(model, fsdp_kwargs, {}) # Test: backup adapter weights, merge, restore from peft.tuners.lora import LoraLayer lora_layers = [m for m in model.modules() if isinstance(m, LoraLayer)] # Verify LoRA layers exist assert len(lora_layers) > 0, "Model should have LoRA layers" # Initially not merged for layer in lora_layers: assert not getattr(layer, "merged", False), "LoRA should not be merged initially" # Backup adapter weights before merge from peft.utils.save_and_load import get_peft_model_state_dict original_adapter_weights = get_peft_model_state_dict(model) # Use merged_lora_context with the specified backup_adapters flag for _ in range(3): with merged_lora_context(model, backup_adapters=backup_adapters): # Inside context, LoRA should be merged for layer in lora_layers: assert getattr(layer, "merged", False), "LoRA should be merged inside context" # After context, check the state based on backup_adapters flag for layer in lora_layers: assert not getattr(layer, "merged", False), "LoRA should be unmerged after context" restored_adapter_weights = get_peft_model_state_dict(model) # Verify adapter weights are restored exactly for key in original_adapter_weights.keys(): assert key in restored_adapter_weights, f"Key {key} should be in restored weights" torch.testing.assert_close( original_adapter_weights[key].cpu(), restored_adapter_weights[key].cpu(), rtol=1e-5, atol=1e-6, msg=f"Adapter weight {key} should be restored to original value", ) if rank == 0: model_name = model_config.__class__.__name__ backup_mode = "with backup" if backup_adapters else "without backup" print(f"merged_lora_context test with {model_name} {strategy} {backup_mode} passed on {world_size} GPUs!") torch.distributed.barrier() torch.distributed.destroy_process_group() @pytest.mark.parametrize("world_size", (2,)) @pytest.mark.parametrize("strategy", ("fsdp", "fsdp2")) @pytest.mark.parametrize("backup_adapters", (True, False)) def test_merged_lora_context_qwen2(world_size, strategy, backup_adapters, tmp_path): """Test merged_lora_context with FSDP on Qwen2 model.""" rendezvous_file = str(tmp_path / f"rdzv_file_qwen2_{backup_adapters}") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) # Create Qwen2 model config model_config = Qwen2Config(num_hidden_layers=2, num_attention_heads=2, hidden_size=128) # Create LoRA config for Qwen2 lora_config_dict = { "r": 8, "lora_alpha": 16, "target_modules": ["q_proj", "v_proj"], "lora_dropout": 0.0, "bias": "none", "task_type": "CAUSAL_LM", } mp.spawn( fn=_test_merged_lora_context_worker, args=(world_size, rendezvous_file, strategy, model_config, lora_config_dict, backup_adapters), nprocs=world_size, join=True, ) @pytest.mark.parametrize("world_size", (2,)) @pytest.mark.parametrize("strategy", ("fsdp", "fsdp2")) @pytest.mark.parametrize("backup_adapters", (True, False)) def test_merged_lora_context_gptoss(world_size, strategy, backup_adapters, tmp_path): """Test merged_lora_context with FSDP on GPT-OSS model.""" rendezvous_file = str(tmp_path / f"rdzv_file_gptoss_{backup_adapters}") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) # Create GPT-OSS model config model_config = GptOssConfig( num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=2, hidden_size=128, intermediate_size=256, ) # Create LoRA config for GPT-OSS lora_config_dict = { "r": 8, "lora_alpha": 16, "target_modules": "all-linear", "target_parameters": ["mlp.experts.gate_up_proj", "mlp.experts.down_proj"], "exclude_modules": ["mlp.router"], "lora_dropout": 0.0, "bias": "none", "task_type": "CAUSAL_LM", } mp.spawn( fn=_test_merged_lora_context_worker, args=(world_size, rendezvous_file, strategy, model_config, lora_config_dict, backup_adapters), nprocs=world_size, join=True, ) ================================================ FILE: tests/utils/test_groupwise.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os os.environ.setdefault("VERL_FORCE_DEVICE", "cpu") # ensure CPU for tests import numpy as np import pytest import torch from verl.utils import as_torch_index, group_mean_std def test_as_torch_index_basic_integers(): g = as_torch_index([2, 2, 5, 7, 5, 2]) assert g.dtype == torch.long assert g.device.type == "cpu" # Values should be contiguous 0..G-1, keeping equal labels equal assert g.tolist()[0] == g.tolist()[1] assert len(torch.unique(g)) == 3 # {2,5,7} -> 3 groups def test_as_torch_index_near_integer_floats(): arr = np.array([1.0000001, 2.0, 1.0, 3.0000000001], dtype=np.float64) g = as_torch_index(arr) # should round to integers then factorize assert g.dtype == torch.long assert len(torch.unique(g)) == 3 # {1,2,3} def test_as_torch_index_factorization_mixed(): labels = ["a", "b", "a", "c", "0042", 42] g = as_torch_index(labels) # "0042" and 42 should NOT be the same group (strings are not coerced here) assert g.tolist()[4] != g.tolist()[5] assert len(torch.unique(g)) == 5 def test_group_mean_std_simple(): # groups: 0 -> [1, 3], 1 -> [2] scores = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32) gidx = as_torch_index([0, 1, 0]) mean_g, std_g, cnt_g = group_mean_std(scores, gidx) # group 0: mean = (1+3)/2 = 2 # sample std (unbiased) = sqrt( (sum(x^2) - (sum(x)^2)/n) / (n-1) ) # = sqrt( (1^2+3^2) - (1+3)^2/2 ) / (2-1) = sqrt(10 - 16/2) = sqrt(2) assert torch.allclose(mean_g, torch.tensor([2.0, 0.0])) assert torch.allclose(cnt_g, torch.tensor([2.0, 1.0])) # singleton group -> std = 1.0 assert mean_g[1].item() == 0.0 assert std_g[1].item() == 1.0 assert pytest.approx(std_g[0].item(), rel=1e-6) == (2.0**0.5) def test_group_mean_std_empty(): scores = torch.tensor([], dtype=torch.float32) gidx = torch.tensor([], dtype=torch.long) mean_g, std_g, cnt_g = group_mean_std(scores, gidx) assert mean_g.numel() == 0 and std_g.numel() == 0 and cnt_g.numel() == 0 def test_group_mean_std_default_device_no_force_env(monkeypatch): """ Regression test: - group_mean_std(device=None) must not pass a device *module* (e.g., torch.cuda) into Tensor.to(device=...), which crashes with: TypeError: to() received an invalid combination of arguments - got (..., device=module, ...) """ # Simulate a non-pytest environment (training code path) while keeping the test CPU-only. monkeypatch.delenv("VERL_FORCE_DEVICE", raising=False) monkeypatch.delenv("PYTEST_CURRENT_TEST", raising=False) # Force device selection to CPU even if CUDA is available on the test machine. import verl.utils.device as device_mod monkeypatch.setattr(device_mod, "is_cuda_available", False) monkeypatch.setattr(device_mod, "is_npu_available", False) scores = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32) gidx = torch.tensor([0, 1, 0], dtype=torch.long) mean_g, std_g, cnt_g = group_mean_std(scores, gidx) assert mean_g.device.type == "cpu" assert std_g.device.type == "cpu" assert cnt_g.device.type == "cpu" ================================================ FILE: tests/utils/test_import_utils_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from verl.utils.import_utils import load_extern_object # Path to the test module TEST_MODULE_PATH = os.path.join(os.path.dirname(__file__), "_test_module.py") def test_load_extern_object_class(): """Test loading a class from an external file""" TestClass = load_extern_object(TEST_MODULE_PATH, "TestClass") # Verify the class was loaded correctly assert TestClass is not None assert TestClass.__name__ == "TestClass" # Test instantiation and functionality instance = TestClass() assert instance.value == "default" # Test with a custom value custom_instance = TestClass("custom") assert custom_instance.get_value() == "custom" def test_load_extern_object_function(): """Test loading a function from an external file""" test_function = load_extern_object(TEST_MODULE_PATH, "test_function") # Verify the function was loaded correctly assert test_function is not None assert callable(test_function) # Test function execution result = test_function() assert result == "test_function_result" def test_load_extern_object_constant(): """Test loading a constant from an external file""" constant = load_extern_object(TEST_MODULE_PATH, "TEST_CONSTANT") # Verify the constant was loaded correctly assert constant is not None assert constant == "test_constant_value" def test_load_extern_object_nonexistent_file(): """Test behavior when file doesn't exist""" with pytest.raises(FileNotFoundError): load_extern_object("/nonexistent/path.py", "SomeType") def test_load_extern_object_nonexistent_type(): """Test behavior when type doesn't exist in the file""" with pytest.raises(AttributeError): load_extern_object(TEST_MODULE_PATH, "NonExistentType") def test_load_extern_object_none_path(): """Test behavior when file path is None""" with pytest.raises(AttributeError): load_extern_object(None, "SomeType") def test_load_extern_object_invalid_module(): """Test behavior when module has syntax errors""" # Create a temporary file with syntax errors import tempfile with tempfile.NamedTemporaryFile(suffix=".py", mode="w+", delete=False) as temp_file: temp_file.write("This is not valid Python syntax :") temp_path = temp_file.name try: with pytest.raises(RuntimeError): load_extern_object(temp_path, "SomeType") finally: # Clean up the temporary file if os.path.exists(temp_path): os.remove(temp_path) ================================================ FILE: tests/utils/test_linear_cross_entropy.py ================================================ # # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import torch import verl.utils.torch_functional as verl_F from verl.utils.device import is_torch_npu_available from verl.utils.experimental.torch_functional import FusedLinearForPPO from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy from verl.utils.torch_functional import logprobs_from_logits compute_entropy_from_logits = torch.compile(verl_F.entropy_from_logits, dynamic=True) fused_linear_for_ppo = FusedLinearForPPO() fused_linear_for_ppo.compile(dynamic=True) MAX_TEST_CASES = os.environ.get("MAX_TEST_CASES", 5) def run_torch_entropy( hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, temperature: float, reduction="none" ) -> list[torch.Tensor]: hidden = hidden.squeeze(0).to(torch.float32) weight = weight.transpose(0, 1).to(torch.float32) logits = torch.matmul(hidden, weight) # [num_tokens, vocab_size] logits /= temperature pd = torch.nn.functional.softmax(logits, dim=-1) # [num_tokens, vocab_size] entropy_a = torch.logsumexp(logits, dim=-1) # [num_tokens] entropy_b = torch.sum(pd * logits, dim=-1) # [num_tokens] entropy = entropy_a - entropy_b logprobs = torch.nn.functional.cross_entropy(logits, labels.squeeze(0), reduction=reduction) # [num_tokens] logprobs = torch.neg(logprobs) return logprobs, entropy def run_verl_original_entropy( hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, temperature: float, ) -> list[torch.Tensor]: hidden = hidden.squeeze(0).to(torch.float32) weight = weight.transpose(0, 1).to(torch.float32) logits = torch.matmul(hidden, weight) # [num_tokens, vocab_size] logits /= temperature # compute entropy entropy = compute_entropy_from_logits(logits) # ((total_nnz / sp) + pad) # if use_sp: ((total_nnz / sp) + pad) ; if not use_sp: (batch, seqlen) logprobs = logprobs_from_logits(logits=logits, labels=labels, inplace_backward=False) return logprobs, entropy # To be tested def run_verl_torch_fused_entropy( hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, temperature: float, ): hidden = hidden.to(torch.float32) weight = weight.to(torch.float32) logprobs, entropy = fused_linear_for_ppo( hidden, weight, labels, temperature=temperature, ) return logprobs.squeeze(0), entropy.squeeze(0) class TestLinearCrossEntropy: def __init__(self, test_case_idx: int, temperature: float = 1.5) -> None: self.test_case_idx = test_case_idx self.temperature = temperature def cleanup(self): torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() import gc gc.collect() torch.cuda.synchronize() def generate_hyper(self): global MAX_TEST_CASES self.dtype = torch.bfloat16 if self.test_case_idx == 0: self.batch_size = 1 self.num_tokens = 1937 self.hidden_size = 3584 self.vocab_size = 152064 elif self.test_case_idx == 1: self.batch_size = 1 self.num_tokens = 2169 self.hidden_size = 896 self.vocab_size = 151936 elif self.test_case_idx == 2: self.batch_size = 1 self.num_tokens = 1530 self.hidden_size = 2048 self.vocab_size = 32256 elif self.test_case_idx == 3: self.batch_size = 1 self.num_tokens = 1388 self.hidden_size = 4096 self.vocab_size = 102400 elif self.test_case_idx == 4: self.batch_size = 1 self.num_tokens = 8192 self.hidden_size = 4096 self.vocab_size = 102400 else: raise ValueError(f"Invalid test case index: {self.test_case_idx}") assert MAX_TEST_CASES <= 5, "MAX_TEST_CASES should be less than or equal to 5." def generate_forward_inputs(self): hidden = ( torch.empty((self.batch_size, self.num_tokens, self.hidden_size), dtype=self.dtype, device="cuda") .uniform_(-0.5, 0.5) .requires_grad_() ) weight = ( torch.empty((self.vocab_size, self.hidden_size), dtype=self.dtype, device="cuda") .uniform_(-0.5, 0.5) .requires_grad_() ) labels = torch.randint(0, self.vocab_size, (self.batch_size, self.num_tokens), device="cuda") return hidden, weight, labels def generate_backward_inputs(self): g_entropy = torch.empty((self.num_tokens,), dtype=self.dtype, device="cuda").uniform_(-0.5, 0.5) g_logprobs = torch.empty((self.num_tokens,), dtype=self.dtype, device="cuda").uniform_(-1, 1) return g_entropy, g_logprobs def verify_correctness(self, iterations=5): self.cleanup() self.generate_hyper() torch_forward_latency = list() torch_backward_latency = list() verl_forward_latency = list() verl_backward_latency = list() verl_fused_forward_latency = list() verl_fused_backward_latency = list() kernel_forward_latency = list() kernel_backward_latency = list() start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) for i in range(iterations): print(f"[INFO]: Iteration {i + 1} / {iterations}...", end="\r") hidden, weight, labels = self.generate_forward_inputs() start_event.record() (torch_logprobs, torch_entropy) = run_torch_entropy(hidden, weight, labels, self.temperature) end_event.record() torch.cuda.synchronize() torch_forward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (verl_logprobs, verl_entropy) = run_verl_original_entropy(hidden, weight, labels, self.temperature) end_event.record() torch.cuda.synchronize() verl_forward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (verl_fused_logprobs, verl_fused_entropy) = run_verl_torch_fused_entropy( hidden, weight, labels, self.temperature ) end_event.record() torch.cuda.synchronize() verl_fused_forward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (kernel_logprobs, kernel_entropy) = linear_cross_entropy(hidden, weight, labels, self.temperature) end_event.record() torch.cuda.synchronize() kernel_forward_latency.append(start_event.elapsed_time(end_event)) torch.testing.assert_close(torch_logprobs, verl_logprobs, atol=1e-4, rtol=1e-4) torch.testing.assert_close(torch_entropy, verl_entropy, atol=1e-4, rtol=1e-4) torch.testing.assert_close(torch_logprobs, verl_fused_logprobs, atol=1e-4, rtol=1e-4) torch.testing.assert_close(torch_entropy, verl_fused_entropy, atol=1e-4, rtol=1e-4) torch.testing.assert_close(verl_logprobs, verl_fused_logprobs, atol=1e-4, rtol=1e-4) torch.testing.assert_close(verl_entropy, verl_fused_entropy, atol=1e-4, rtol=1e-4) torch.testing.assert_close(torch_logprobs, kernel_logprobs, atol=1e-3, rtol=2e-4) torch.testing.assert_close(torch_entropy, kernel_entropy, atol=5e-3, rtol=5e-4) torch.testing.assert_close(verl_logprobs, kernel_logprobs, atol=1e-3, rtol=2e-4) torch.testing.assert_close(verl_entropy, kernel_entropy, atol=5e-3, rtol=5e-4) torch.testing.assert_close(verl_fused_logprobs, kernel_logprobs, atol=1e-3, rtol=2e-4) torch.testing.assert_close(verl_fused_entropy, kernel_entropy, atol=5e-3, rtol=5e-4) # backward g_entropy, g_logprobs = self.generate_backward_inputs() start_event.record() (d_torch_hidden, d_torch_weight) = torch.autograd.grad( (torch_entropy, torch_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) end_event.record() torch.cuda.synchronize() torch_backward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (d_verl_hidden, d_verl_weight) = torch.autograd.grad( (verl_entropy, verl_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) end_event.record() torch.cuda.synchronize() verl_backward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (d_verl_fused_hidden, d_verl_fused_weight) = torch.autograd.grad( (verl_fused_entropy, verl_fused_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) end_event.record() torch.cuda.synchronize() verl_fused_backward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (d_kernel_hidden, d_kernel_weight) = torch.autograd.grad( (kernel_entropy, kernel_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) end_event.record() torch.cuda.synchronize() kernel_backward_latency.append(start_event.elapsed_time(end_event)) torch.testing.assert_close(d_torch_hidden, d_verl_hidden, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_torch_weight, d_verl_weight, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_torch_hidden, d_verl_fused_hidden, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_torch_weight, d_verl_fused_weight, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_verl_hidden, d_verl_fused_hidden, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_verl_weight, d_verl_fused_weight, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_torch_hidden, d_verl_hidden, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_torch_weight, d_verl_weight, atol=1e-2, rtol=1e-4) torch.testing.assert_close(d_torch_hidden, d_kernel_hidden, atol=2e-2, rtol=4e-2) torch.testing.assert_close(d_torch_weight, d_kernel_weight, atol=2e-2, rtol=4e-2) torch.testing.assert_close(d_verl_hidden, d_kernel_hidden, atol=2e-2, rtol=4e-2) torch.testing.assert_close(d_verl_weight, d_kernel_weight, atol=2e-2, rtol=4e-2) torch.testing.assert_close(d_verl_fused_hidden, d_kernel_hidden, atol=2e-2, rtol=4e-2) torch.testing.assert_close(d_verl_fused_weight, d_kernel_weight, atol=2e-2, rtol=4e-2) # remove first latency torch_forward_latency = torch_forward_latency[1:] torch_backward_latency = torch_backward_latency[1:] verl_forward_latency = verl_forward_latency[1:] verl_backward_latency = verl_backward_latency[1:] verl_fused_forward_latency = verl_fused_forward_latency[1:] verl_fused_backward_latency = verl_fused_backward_latency[1:] kernel_forward_latency = kernel_forward_latency[1:] kernel_backward_latency = kernel_backward_latency[1:] print("\n[INFO]: Verified forward & backward correctness.") print( f"[INFO]: Forward pass: Torch implementation average time: " f"{sum(torch_forward_latency) / len(torch_forward_latency):.2f} ms" ) print( f"[INFO]: Backward pass: torch implementation average time: " f"{sum(torch_backward_latency) / len(torch_backward_latency):.2f} ms" ) print( f"[INFO]: Forward pass: VeRL implementation average time: " f"{sum(verl_forward_latency) / len(verl_forward_latency):.2f} ms" ) print( f"[INFO]: Backward pass: VeRL implementation average time: " f"{sum(verl_backward_latency) / len(verl_backward_latency):.2f} ms" ) print( f"[INFO]: Forward pass: VeRL Fused Entropy implementation average time: " f"{sum(verl_fused_forward_latency) / len(verl_fused_forward_latency):.2f} ms" ) print( f"[INFO]: Backward pass: VeRL Fused Entropy implementation average time: " f"{sum(verl_fused_backward_latency) / len(verl_fused_backward_latency):.2f} ms" ) print( f"[INFO]: Forward pass: Kernel implementation average time: " f"{sum(kernel_forward_latency) / len(kernel_forward_latency):.2f} ms" ) print( f"[INFO]: Backward pass: kernel implementation average time: " f"{sum(kernel_backward_latency) / len(kernel_backward_latency):.2f} ms" ) def check_storage(self, method_name, run_forward): self.cleanup() self.generate_hyper() hidden, weight, labels = self.generate_forward_inputs() torch.cuda.reset_peak_memory_stats() (logprobs, entropy) = run_forward(hidden, weight, labels, self.temperature) torch.cuda.synchronize() torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 print(f"[INFO]: {method_name} Forward pass peak memory: {torch_max_memory:.2f} MB") g_entropy, g_logprobs = self.generate_backward_inputs() torch.cuda.reset_peak_memory_stats() (d_torch_hidden, d_torch_weight) = torch.autograd.grad( (entropy, logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) torch.cuda.synchronize() torch_backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 print(f"[INFO]: {method_name} Backward pass peak memory: {torch_backward_max_memory:.2f} MB") def check_storage_all(self): self.check_storage("Torch", run_torch_entropy) self.check_storage("VeRL", run_verl_original_entropy) self.check_storage("VeRL Torch Fused", run_verl_torch_fused_entropy) self.check_storage("Kernel", linear_cross_entropy) def test_lce_non_divisible_vocab_padding(): """Regression test for the logsumexp padding bug. When vocab_size % BLOCK_SIZE_N != 0 the last tile has fewer than BLOCK_SIZE_N valid entries. Without the fix, out-of-bounds positions are loaded as weight=0 → logit=0 → exp(0)=1, adding phantom probability mass to the logsumexp denominator. For peaked softmax distributions (small denominator) this causes large log-prob errors. Reproducing construction: one token-logit at +3, all others at -15 → denominator ≈ 20, phantom adds ≈ 25 → error ≈ 0.82 per token. """ if not torch.cuda.is_available() or is_torch_npu_available(check_device=False): return torch.manual_seed(0) V = 152064 # vocab_size % 1024 == 512 (triggers bug) V_div = 149 * 1024 # vocab_size % 1024 == 0 (control) D = 3584 N = 512 T = 1.5 def reference(hidden, weight, labels): h = hidden.squeeze(0).float() logits = torch.matmul(h, weight.float().T) / T lp = -torch.nn.functional.cross_entropy(logits, labels.squeeze(0), reduction="none") pd = torch.nn.functional.softmax(logits, dim=-1) ent = torch.logsumexp(logits, dim=-1) - (pd * logits).sum(-1) return lp, ent for vocab_size, desc in [(V, "non-divisible vocab (mod1024=512)"), (V_div, "divisible vocab (mod1024=0)")]: w = torch.zeros(vocab_size, D, dtype=torch.bfloat16, device="cuda") w[:, 0] = -15.0 * T w[0, 0] = 3.0 * T h = torch.zeros(1, N, D, dtype=torch.bfloat16, device="cuda") h[:, :, 0] = 1.0 labels = torch.zeros(1, N, dtype=torch.long, device="cuda") ref_lp, ref_ent = reference(h, w, labels) ker_lp, ker_ent = linear_cross_entropy(h, w, labels, T) torch.testing.assert_close(ref_lp, ker_lp, atol=1e-3, rtol=1e-3, msg=f"logprob mismatch: {desc}") torch.testing.assert_close(ref_ent, ker_ent, atol=1e-3, rtol=1e-3, msg=f"entropy mismatch: {desc}") if __name__ == "__main__": # torch.cuda.memory._record_memory_history() for test_case_idx in range(MAX_TEST_CASES): print(f"[INFO] Running test case {test_case_idx}") test = TestLinearCrossEntropy(test_case_idx) test.verify_correctness() test.check_storage_all() test_lce_non_divisible_vocab_padding() # torch.cuda.memory._dump_snapshot("test_linear_cross_entropy.pkl") ================================================ FILE: tests/utils/test_mlflow_key_sanitization.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from unittest.mock import patch from verl.utils.tracking import _MlflowLoggingAdapter class TestMlflowLoggingAdapter(unittest.TestCase): def test_sanitize_key_and_warning(self): """Test key sanitization for invalid characters and consecutive slashes with warnings.""" adapter = _MlflowLoggingAdapter() data = { "valid_key": 1.0, "invalid@key!": 2.0, "another/valid-key": 3.0, "bad key#": 4.0, "val-aux//reward/mean_at_1": 5.0, "val-core///acc/best_at_5": 6.0, "metric////with/many////slashes": 7.0, } # Patch mlflow.log_metrics to capture the metrics actually sent with ( patch("mlflow.log_metrics") as mock_log_metrics, patch.object(adapter, "logger") as mock_logger, ): adapter.log(data, step=5) # Check that invalid characters are sanitized sent_metrics = mock_log_metrics.call_args[1]["metrics"] self.assertIn("invalid_at_key_", sent_metrics) # @ becomes _at_, ! becomes _ self.assertIn("bad key_", sent_metrics) # # becomes _, space remains self.assertNotIn("invalid@key!", sent_metrics) self.assertNotIn("bad key#", sent_metrics) # Check that consecutive slashes are collapsed to single slashes self.assertIn("val-aux/reward/mean_at_1", sent_metrics) self.assertIn("val-core/acc/best_at_5", sent_metrics) self.assertIn("metric/with/many/slashes", sent_metrics) self.assertNotIn("val-aux//reward/mean_at_1", sent_metrics) self.assertNotIn("val-core///acc/best_at_5", sent_metrics) # Check that warnings were logged for all sanitized keys warning_msgs = [str(call) for call in mock_logger.warning.call_args_list] # Warnings for invalid characters self.assertTrue(any("invalid@key!" in msg and "invalid_at_key_" in msg for msg in warning_msgs)) self.assertTrue(any("bad key#" in msg and "bad key_" in msg for msg in warning_msgs)) # Warnings for consecutive slashes self.assertTrue(any("val-aux//reward/mean_at_1" in msg for msg in warning_msgs)) self.assertTrue(any("val-core///acc/best_at_5" in msg for msg in warning_msgs)) self.assertTrue(any("metric////with/many////slashes" in msg for msg in warning_msgs)) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_model_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from types import SimpleNamespace # Or use a mock object library import pytest from verl.utils.model import update_model_config # Parametrize with different override scenarios @pytest.mark.parametrize( "override_kwargs", [ {"param_a": 5, "new_param": "plain_added"}, {"param_a": 2, "nested_params": {"sub_param_x": "updated_x", "sub_param_z": True}}, ], ) def test_update_model_config(override_kwargs): """ Tests that update_model_config correctly updates attributes, handling both plain and nested overrides via parametrization. """ # Create a fresh mock config object for each test case mock_config = SimpleNamespace( param_a=1, nested_params=SimpleNamespace(sub_param_x="original_x", sub_param_y=100), other_param="keep_me" ) # Apply the updates using the parametrized override_kwargs update_model_config(mock_config, override_kwargs) # Assertions to check if the config was updated correctly if "nested_params" in override_kwargs: # Case 2: Nested override override_nested = override_kwargs["nested_params"] assert mock_config.nested_params.sub_param_x == override_nested["sub_param_x"], "Nested sub_param_x mismatch" assert mock_config.nested_params.sub_param_y == 100, "Nested sub_param_y should be unchanged" assert hasattr(mock_config.nested_params, "sub_param_z"), "Expected nested sub_param_z to be added" assert mock_config.nested_params.sub_param_z == override_nested["sub_param_z"], "Value of sub_param_z mismatch" else: # Case 1: Plain override (nested params untouched) assert mock_config.nested_params.sub_param_x == "original_x", "Nested sub_param_x should be unchanged" assert mock_config.nested_params.sub_param_y == 100, "Nested sub_param_y should be unchanged" assert not hasattr(mock_config.nested_params, "sub_param_z"), "Nested sub_param_z should not exist" ================================================ FILE: tests/utils/test_normalize_peft_param_name.py ================================================ # Copyright 2026 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest import torch import torch.distributed import torch.multiprocessing as mp from peft import LoraConfig, get_peft_model from torch.distributed import init_device_mesh from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import MixedPrecision, ShardingStrategy, StateDictType from transformers import AutoModelForCausalLM, Qwen3Config from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device from verl.utils.fsdp_utils import ( MixedPrecisionPolicy, apply_fsdp2, get_fsdp_wrap_policy, normalize_peft_param_name, ) from verl.utils.model import convert_weight_keys def _test_normalize_peft_with_fsdp_worker(rank, world_size, rendezvous_file, strategy): """Worker function for testing normalize_peft_param_name with FSDP-wrapped models. Args: rank: Process rank world_size: Total number of processes rendezvous_file: Path to rendezvous file for distributed init strategy: FSDP strategy ("fsdp" or "fsdp2") """ get_torch_device().set_device(rank) torch.distributed.init_process_group( backend=get_nccl_backend(), init_method=f"file://{rendezvous_file}", rank=rank, world_size=world_size, ) device_mesh = init_device_mesh(get_device_name(), mesh_shape=(world_size,), mesh_dim_names=("dp",)) # Create model config config = Qwen3Config( num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=2, hidden_size=128, intermediate_size=256, ) # Create base model with torch.device(get_device_name()): base_model = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) base_model = base_model.to(device=get_device_name()) # Create PEFT model with LoRA lora_config = LoraConfig( r=8, lora_alpha=16, target_modules="all-linear", lora_dropout=0.0, bias="none", task_type="CAUSAL_LM" ) peft_model = get_peft_model(base_model, lora_config) # Wrap base model with FSDP (create a fresh copy for base model) with torch.device(get_device_name()): base_model_for_fsdp = AutoModelForCausalLM.from_config( config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) base_model_for_fsdp = base_model_for_fsdp.to(device=get_device_name()) if strategy == "fsdp": mixed_precision = MixedPrecision( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32 ) # Wrap base model with FSDP fsdp_base_model = FSDP( base_model_for_fsdp, use_orig_params=True, device_id=get_torch_device().current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, device_mesh=device_mesh, auto_wrap_policy=get_fsdp_wrap_policy(module=base_model_for_fsdp, is_lora=False), ) # Wrap PEFT model with FSDP fsdp_peft_model = FSDP( peft_model, use_orig_params=True, device_id=get_torch_device().current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, device_mesh=device_mesh, auto_wrap_policy=get_fsdp_wrap_policy(module=peft_model, is_lora=True), ) else: # FSDP2 mp_policy = MixedPrecisionPolicy( param_dtype=torch.bfloat16, reduce_dtype=torch.float32, cast_forward_inputs=True ) fsdp_kwargs = { "mesh": device_mesh, "mp_policy": mp_policy, } # Wrap base model with FSDP2 apply_fsdp2(base_model_for_fsdp, fsdp_kwargs, {}) fsdp_base_model = base_model_for_fsdp # Wrap PEFT model with FSDP2 apply_fsdp2(peft_model, fsdp_kwargs, {}) fsdp_peft_model = peft_model # Get state dicts from FSDP models if strategy == "fsdp": # FSDP v1: Use full_state_dict context with FSDP.state_dict_type(fsdp_base_model, StateDictType.FULL_STATE_DICT): base_state_dict = fsdp_base_model.state_dict() with FSDP.state_dict_type(fsdp_peft_model, StateDictType.FULL_STATE_DICT): peft_state_dict = fsdp_peft_model.state_dict() else: # FSDP2: Direct state_dict call base_state_dict = fsdp_base_model.state_dict() peft_state_dict = fsdp_peft_model.state_dict() # Normalize PEFT model state dict normalized_peft_state_dict = normalize_peft_param_name(peft_state_dict) base_state_dict = convert_weight_keys( base_state_dict, getattr(fsdp_base_model, "_fsdp_wrapped_module", fsdp_base_model) ) normalized_peft_state_dict = convert_weight_keys( normalized_peft_state_dict, getattr(fsdp_peft_model, "_fsdp_wrapped_module", fsdp_peft_model) ) # Get key sets base_keys = set(base_state_dict.keys()) normalized_peft_keys = set(normalized_peft_state_dict.keys()) # if rank == 0: print(f"\n=== FSDP {strategy} Test Results ===") print(f"Base model keys: {base_keys=}") print(f"Normalized PEFT keys: {normalized_peft_keys=}") # Check for missing keys missing_keys = base_keys - normalized_peft_keys if missing_keys: print(f"Missing keys from base model: {missing_keys}") # Check for extra keys extra_keys = normalized_peft_keys - base_keys if extra_keys: print(f"Extra keys not in base model: {extra_keys}") # Verify that all base model keys are in the normalized PEFT keys missing_keys = base_keys - normalized_peft_keys assert len(missing_keys) == 0, f"Missing keys from base model: {missing_keys}" # Verify that all normalized PEFT keys are in the base model extra_keys = normalized_peft_keys - base_keys assert len(extra_keys) == 0, f"Extra keys not in base model: {extra_keys}" # Verify exact match assert base_keys == normalized_peft_keys, "Normalized PEFT keys should exactly match FSDP base model keys" # Verify tensor shapes match for key in base_keys: base_shape = base_state_dict[key].shape peft_shape = normalized_peft_state_dict[key].shape assert base_shape == peft_shape, f"Shape mismatch for {key}: base={base_shape}, peft={peft_shape}" # Verify no LoRA keys remain in normalized state dict lora_keys = [k for k in normalized_peft_keys if "lora_" in k or "adapter_" in k] assert len(lora_keys) == 0, f"Normalized state dict should not contain LoRA keys, but found: {lora_keys}" if rank == 0: print(f"✓ All tests passed for FSDP {strategy}") torch.distributed.barrier() torch.distributed.destroy_process_group() @pytest.mark.parametrize("world_size", (2,)) @pytest.mark.parametrize("strategy", ("fsdp", "fsdp2")) def test_normalize_peft_param_name_with_fsdp(world_size, strategy, tmp_path): """Test normalize_peft_param_name with FSDP-wrapped models. This test verifies that after applying FSDP to both base and PEFT models, the normalized PEFT model keys match the FSDP base model keys. """ rendezvous_file = str(tmp_path / f"rdzv_file_normalize_{strategy}") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) mp.spawn( fn=_test_normalize_peft_with_fsdp_worker, args=(world_size, rendezvous_file, strategy), nprocs=world_size, join=True, ) if __name__ == "__main__": pytest.main([__file__, "-v"]) ================================================ FILE: tests/utils/test_normalize_peft_param_name_on_cpu.py ================================================ # Copyright 2026 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import torch from peft import LoraConfig, get_peft_model from transformers import AutoModelForCausalLM, Qwen3Config from verl.utils.fsdp_utils import normalize_peft_param_name def create_base_model(): """Create a simple base model for testing.""" config = Qwen3Config( num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=2, hidden_size=128, intermediate_size=256, ) model = AutoModelForCausalLM.from_config(config) return model def create_peft_model(): lora_config = LoraConfig( r=8, lora_alpha=16, target_modules="all-linear", lora_dropout=0.0, bias="none", task_type="CAUSAL_LM" ) model = create_base_model() model = get_peft_model(model, lora_config) return model @pytest.fixture def base_model(): """Create a simple base model for testing.""" return create_base_model() @pytest.fixture def peft_model(): """Create a PEFT model with LoRA adapters.""" return create_peft_model() def test_normalize_peft_param_name_keys_match_base_model(): """Test that normalized PEFT model keys match base model keys.""" # Get state dicts base_model = create_base_model() peft_model = create_peft_model() base_state_dict = base_model.state_dict() peft_state_dict = peft_model.state_dict() # Normalize PEFT model keys normalized_peft_state_dict = normalize_peft_param_name(peft_state_dict) # Get key sets base_keys = set(base_state_dict.keys()) normalized_peft_keys = set(normalized_peft_state_dict.keys()) print(f"{base_keys=}") print(f"{normalized_peft_keys=}") # Verify that all base model keys are in the normalized PEFT keys missing_keys = base_keys - normalized_peft_keys assert len(missing_keys) == 0, f"Missing keys from base model: {missing_keys}" # Verify that all normalized PEFT keys are in the base model extra_keys = normalized_peft_keys - base_keys assert len(extra_keys) == 0, f"Extra keys not in base model: {extra_keys}" # Verify exact match assert base_keys == normalized_peft_keys, "Normalized PEFT keys should exactly match base model keys" def test_normalize_peft_param_name_removes_lora_keys(peft_model): """Test that LoRA-specific parameters are removed after normalization.""" peft_state_dict = peft_model.state_dict() # Before normalization, should have lora_A and lora_B keys lora_keys_before = [k for k in peft_state_dict.keys() if "lora_" in k] assert len(lora_keys_before) > 0, "PEFT model should have LoRA parameters" # After normalization, should not have any lora keys normalized_state_dict = normalize_peft_param_name(peft_state_dict) lora_keys_after = [k for k in normalized_state_dict.keys() if "lora_" in k] assert len(lora_keys_after) == 0, ( f"Normalized state dict should not contain LoRA keys, but found: {lora_keys_after}" ) def test_normalize_peft_param_name_removes_base_model_prefix(peft_model): """Test that base_model prefix is removed from parameter names.""" peft_state_dict = peft_model.state_dict() # Before normalization, should have base_model prefix base_model_keys = [k for k in peft_state_dict.keys() if "base_model" in k] assert len(base_model_keys) > 0, "PEFT model should have base_model prefix" # After normalization, should not have base_model prefix normalized_state_dict = normalize_peft_param_name(peft_state_dict) base_model_keys_after = [k for k in normalized_state_dict.keys() if "base_model" in k] assert len(base_model_keys_after) == 0, ( f"Normalized keys should not contain base_model prefix, but found: {base_model_keys_after}" ) def test_normalize_peft_param_name_removes_base_layer_suffix(peft_model): """Test that .base_layer suffix is removed from parameter names.""" peft_state_dict = peft_model.state_dict() # Before normalization, should have .base_layer suffix base_layer_keys = [k for k in peft_state_dict.keys() if ".base_layer" in k] assert len(base_layer_keys) > 0, "PEFT model should have .base_layer suffix" # After normalization, should not have .base_layer suffix normalized_state_dict = normalize_peft_param_name(peft_state_dict) base_layer_keys_after = [k for k in normalized_state_dict.keys() if ".base_layer" in k] assert len(base_layer_keys_after) == 0, ( f"Normalized keys should not contain .base_layer suffix, but found: {base_layer_keys_after}" ) def test_normalize_peft_param_name_tensor_shapes_match(base_model, peft_model): """Test that tensor shapes match between base model and normalized PEFT model.""" base_state_dict = base_model.state_dict() peft_state_dict = peft_model.state_dict() # Normalize PEFT model keys normalized_peft_state_dict = normalize_peft_param_name(peft_state_dict) # Check that shapes match for all common keys for key in base_state_dict.keys(): assert key in normalized_peft_state_dict, f"Key {key} not found in normalized PEFT state dict" base_shape = base_state_dict[key].shape peft_shape = normalized_peft_state_dict[key].shape assert base_shape == peft_shape, f"Shape mismatch for {key}: base={base_shape}, peft={peft_shape}" def test_normalize_peft_param_name_empty_dict(): """Test that normalize_peft_param_name handles empty dict.""" result = normalize_peft_param_name({}) assert result == {}, "Empty dict should return empty dict" @pytest.mark.parametrize( "lora_key_pattern", [ "model.layers.0.self_attn.q_proj.lora_A.default.weight", "model.layers.0.self_attn.q_proj.lora_B.default.weight", "model.layers.0.adapter_layer.weight", "base_model.model.layers.0.lora_embedding_A", ], ) def test_normalize_peft_param_name_filters_lora_patterns(lora_key_pattern): """Test that various LoRA key patterns are filtered out.""" test_dict = { lora_key_pattern: torch.randn(10, 10), "model.layers.0.weight": torch.randn(10, 10), } normalized = normalize_peft_param_name(test_dict) # LoRA key should be filtered out assert lora_key_pattern not in normalized, f"LoRA key {lora_key_pattern} should be filtered out" # Regular key should remain assert len(normalized) == 1, "Should have exactly one key remaining" assert "model.layers.0.weight" in normalized, "Regular weight should remain" if __name__ == "__main__": pytest.main([__file__, "-v"]) ================================================ FILE: tests/utils/test_nvtx_profile.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from unittest.mock import MagicMock, patch from verl.utils import omega_conf_to_dataclass from verl.utils.profiler.config import NsightToolConfig, ProfilerConfig from verl.utils.profiler.profile import DistProfiler class TestProfilerConfig(unittest.TestCase): def test_config_init(self): import os from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): cfg = compose(config_name="ppo_trainer") for config in [ cfg.actor_rollout_ref.actor.profiler, cfg.actor_rollout_ref.rollout.profiler, cfg.actor_rollout_ref.ref.profiler, cfg.critic.profiler, ]: profiler_config = omega_conf_to_dataclass(config) self.assertEqual(profiler_config.tool, config.tool) self.assertEqual(profiler_config.enable, config.enable) self.assertEqual(profiler_config.all_ranks, config.all_ranks) self.assertEqual(profiler_config.ranks, config.ranks) self.assertEqual(profiler_config.save_path, config.save_path) self.assertEqual(profiler_config.ranks, config.ranks) assert isinstance(profiler_config, ProfilerConfig) with self.assertRaises(AttributeError): _ = profiler_config.non_existing_key assert config.get("non_existing_key") == profiler_config.get("non_existing_key") assert config.get("non_existing_key", 1) == profiler_config.get("non_existing_key", 1) def test_frozen_config(self): """Test that modifying frozen keys in ProfilerConfig raises exceptions.""" from dataclasses import FrozenInstanceError from verl.utils.profiler.config import ProfilerConfig # Create a new ProfilerConfig instance config = ProfilerConfig(all_ranks=False, ranks=[0]) with self.assertRaises(FrozenInstanceError): config.all_ranks = True with self.assertRaises(FrozenInstanceError): config.ranks = [1, 2, 3] with self.assertRaises(TypeError): config["all_ranks"] = True with self.assertRaises(TypeError): config["ranks"] = [1, 2, 3] class TestNsightSystemsProfiler(unittest.TestCase): """Test suite for NsightSystemsProfiler functionality. Test Plan: 1. Initialization: Verify profiler state after creation 2. Basic Profiling: Test start/stop functionality 3. Discrete Mode: TODO: Test discrete profiling behavior 4. Annotation: Test the annotate decorator in both normal and discrete modes 5. Config Validation: Verify proper config initialization from OmegaConf """ def setUp(self): self.config = ProfilerConfig(tool="nsys", enable=True, all_ranks=True) self.rank = 0 self.profiler = DistProfiler(self.rank, self.config, tool_config=NsightToolConfig(discrete=False)) def test_initialization(self): self.assertEqual(self.profiler.check_this_rank(), True) self.assertEqual(self.profiler.check_this_step(), False) def test_start_stop_profiling(self): with patch("torch.cuda.profiler.start") as mock_start, patch("torch.cuda.profiler.stop") as mock_stop: # Test start self.profiler.start() self.assertTrue(self.profiler.check_this_step()) mock_start.assert_called_once() # Test stop self.profiler.stop() self.assertFalse(self.profiler.check_this_step()) mock_stop.assert_called_once() # def test_discrete_profiling(self): # discrete_config = ProfilerConfig(discrete=True, all_ranks=True) # profiler = NsightSystemsProfiler(self.rank, discrete_config) # with patch("torch.cuda.profiler.start") as mock_start, patch("torch.cuda.profiler.stop") as mock_stop: # profiler.start() # self.assertTrue(profiler.this_step) # mock_start.assert_not_called() # Shouldn't start immediately in discrete mode # profiler.stop() # self.assertFalse(profiler.this_step) # mock_stop.assert_not_called() # Shouldn't stop immediately in discrete mode def test_annotate_decorator(self): mock_self = MagicMock() mock_self.profiler = self.profiler mock_self.profiler.start() decorator = mock_self.profiler.annotate(message="test") @decorator def test_func(self, *args, **kwargs): return "result" with ( patch("torch.cuda.profiler.start") as mock_start, patch("torch.cuda.profiler.stop") as mock_stop, patch("verl.utils.profiler.nvtx_profile.mark_start_range") as mock_start_range, patch("verl.utils.profiler.nvtx_profile.mark_end_range") as mock_end_range, ): result = test_func(mock_self) self.assertEqual(result, "result") mock_start_range.assert_called_once() mock_end_range.assert_called_once() mock_start.assert_not_called() # Not discrete mode mock_stop.assert_not_called() # Not discrete mode # def test_annotate_discrete_mode(self): # discrete_config = ProfilerConfig(discrete=True, all_ranks=True) # profiler = NsightSystemsProfiler(self.rank, discrete_config) # mock_self = MagicMock() # mock_self.profiler = profiler # mock_self.profiler.this_step = True # @NsightSystemsProfiler.annotate(message="test") # def test_func(self, *args, **kwargs): # return "result" # with ( # patch("torch.cuda.profiler.start") as mock_start, # patch("torch.cuda.profiler.stop") as mock_stop, # patch("verl.utils.profiler.nvtx_profile.mark_start_range") as mock_start_range, # patch("verl.utils.profiler.nvtx_profile.mark_end_range") as mock_end_range, # ): # result = test_func(mock_self) # self.assertEqual(result, "result") # mock_start_range.assert_called_once() # mock_end_range.assert_called_once() # mock_start.assert_called_once() # Should start in discrete mode # mock_stop.assert_called_once() # Should stop in discrete mode if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_padding_on_cpu.py ================================================ # Copyright 2026 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from tensordict import TensorDict from verl.workers.utils.padding import left_right_2_no_padding, no_padding_2_padding def test_padding_conversion_with_log_probs(): """Test that log probability tensors remain in padded format after conversion This test verifies the fix for the bug where ratio values were ~451,728 instead of ~1.0. The key insight is that old_log_probs should STAY in padded format and be sliced in the loss computation to match log_prob from model output, rather than being converted to nested format. """ batch_size = 4 max_seq_len = 128 max_response_len = 64 # Create test data with varying sequence lengths input_ids = torch.randint(0, 1000, (batch_size, max_seq_len)) # Create attention masks with different valid lengths per sample attention_mask = torch.zeros(batch_size, max_seq_len) valid_lens = [100, 120, 90, 128] # Different lengths for each batch item for i, vlen in enumerate(valid_lens): attention_mask[i, :vlen] = 1 # Create response masks aligned with the end of each sequence response_mask = torch.zeros(batch_size, max_response_len) response_lens = [50, 60, 45, 64] # Different response lengths for i, rlen in enumerate(response_lens): response_mask[i, :rlen] = 1 # Create position IDs position_ids = torch.arange(max_seq_len).unsqueeze(0).expand(batch_size, -1) # Add log probability tensors in padded format old_log_probs = torch.randn(batch_size, max_seq_len) ref_log_prob = torch.randn(batch_size, max_seq_len) advantages = torch.randn(batch_size, max_response_len) rollout_log_probs = torch.randn(batch_size, max_seq_len) data = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "response_mask": response_mask, "position_ids": position_ids, "old_log_probs": old_log_probs, "ref_log_prob": ref_log_prob, "advantages": advantages, "rollout_log_probs": rollout_log_probs, } ) # Convert to no-padding format data_converted = left_right_2_no_padding(data) # Verify input_ids and position_ids are nested tensors assert isinstance(data_converted["input_ids"], torch.Tensor) assert data_converted["input_ids"].is_nested assert data_converted["position_ids"].is_nested # Verify log probs REMAIN in padded format (NOT converted to nested) # They will be sliced in the loss computation to match log_prob format assert isinstance(data_converted["old_log_probs"], torch.Tensor) assert not data_converted["old_log_probs"].is_nested, "old_log_probs should remain in padded format" assert not data_converted["ref_log_prob"].is_nested, "ref_log_prob should remain in padded format" assert not data_converted["advantages"].is_nested, "advantages should remain in padded format" assert not data_converted["rollout_log_probs"].is_nested, "rollout_log_probs should remain in padded format" # Verify they maintain their original shapes assert data_converted["old_log_probs"].shape == (batch_size, max_seq_len) assert data_converted["ref_log_prob"].shape == (batch_size, max_seq_len) assert data_converted["advantages"].shape == (batch_size, max_response_len) assert data_converted["rollout_log_probs"].shape == (batch_size, max_seq_len) # Verify that nested tensors (input_ids, position_ids) have correct number of elements per batch item for i, vlen in enumerate(valid_lens): assert data_converted["input_ids"][i].numel() == vlen, ( f"Batch {i}: input_ids should have {vlen} elements, got {data_converted['input_ids'][i].numel()}" ) def test_padding_conversion_without_log_probs(): """Test that padding conversion works correctly when log prob tensors are not present""" batch_size = 4 max_seq_len = 128 max_response_len = 64 # Create minimal test data input_ids = torch.randint(0, 1000, (batch_size, max_seq_len)) attention_mask = torch.ones(batch_size, max_seq_len) response_mask = torch.ones(batch_size, max_response_len) position_ids = torch.arange(max_seq_len).unsqueeze(0).expand(batch_size, -1) data = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "response_mask": response_mask, "position_ids": position_ids, } ) # Convert to no-padding format data_converted = left_right_2_no_padding(data) # Verify basic conversion works assert data_converted["input_ids"].is_nested assert data_converted["position_ids"].is_nested assert "old_log_probs" not in data_converted assert "ref_log_prob" not in data_converted def test_padding_roundtrip(): """Test that converting from padding to nested and back preserves values in the response region""" batch_size = 2 max_seq_len = 64 max_response_len = 32 prompt_len = max_seq_len - max_response_len # 32 # Create simple test data with known values input_ids = torch.arange(1, max_seq_len + 1).unsqueeze(0).expand(batch_size, -1).clone() attention_mask = torch.ones(batch_size, max_seq_len) response_mask = torch.ones(batch_size, max_response_len) position_ids = torch.arange(max_seq_len).unsqueeze(0).expand(batch_size, -1) # Create nested prompts and responses (required by no_padding_2_padding) prompt_list = [input_ids[i, :prompt_len] for i in range(batch_size)] response_list = [input_ids[i, prompt_len:] for i in range(batch_size)] prompts_nested = torch.nested.as_nested_tensor(prompt_list, layout=torch.jagged) responses_nested = torch.nested.as_nested_tensor(response_list, layout=torch.jagged) data = TensorDict( { "input_ids": input_ids, "prompts": prompts_nested, "responses": responses_nested, "attention_mask": attention_mask, "response_mask": response_mask, "position_ids": position_ids, } ) # Convert to nested format data_nested = left_right_2_no_padding(data) # Verify input_ids is nested assert data_nested["input_ids"].is_nested # Convert back to padding format recovered = no_padding_2_padding(data_nested["input_ids"], data_nested) # Verify the shape is correct (response region only) assert recovered.shape == (batch_size, max_response_len) # Verify values are correct (left-shifted by 1 for log_probs alignment) # Response tokens are 33,34,...,64 -> left-shifted: 32,33,...,63 expected = torch.arange(prompt_len, max_seq_len, dtype=torch.long).unsqueeze(0).expand(batch_size, -1) torch.testing.assert_close(recovered, expected) def test_no_padding_2_padding_varying_lengths(): """Test no_padding_2_padding with varied prompt/response lengths.""" batch_size = 4 max_seq_len = 100 max_response_len = 50 prompt_lens = [10, 30, 5, 40] response_lens = [40, 20, 45, 10] input_ids = torch.zeros(batch_size, max_seq_len, dtype=torch.long) for i in range(batch_size): total_len = prompt_lens[i] + response_lens[i] input_ids[i, :total_len] = torch.arange(1, total_len + 1) attention_mask = torch.zeros(batch_size, max_seq_len) for i in range(batch_size): attention_mask[i, : prompt_lens[i] + response_lens[i]] = 1 response_mask = torch.zeros(batch_size, max_response_len) for i in range(batch_size): response_mask[i, : response_lens[i]] = 1 position_ids = torch.arange(max_seq_len).unsqueeze(0).expand(batch_size, -1).clone() prompt_list = [input_ids[i, : prompt_lens[i]] for i in range(batch_size)] response_list = [input_ids[i, prompt_lens[i] : prompt_lens[i] + response_lens[i]] for i in range(batch_size)] prompts_nested = torch.nested.as_nested_tensor(prompt_list, layout=torch.jagged) responses_nested = torch.nested.as_nested_tensor(response_list, layout=torch.jagged) data = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "response_mask": response_mask, "position_ids": position_ids, "prompts": prompts_nested, "responses": responses_nested, } ) data_nested = left_right_2_no_padding(data) input_ids_nested = data_nested["input_ids"] log_probs_values = input_ids_nested.values().float() log_probs_nested = torch.nested.nested_tensor_from_jagged(log_probs_values, offsets=input_ids_nested.offsets()) result_slice_response = no_padding_2_padding(log_probs_nested, data_nested) # Verify no_padding_2_padding produces correct values (left-shifted by 1) for i in range(batch_size): resp_len = response_lens[i] expected_start = prompt_lens[i] expected_values = torch.arange(expected_start, expected_start + resp_len, dtype=torch.float) torch.testing.assert_close( result_slice_response[i, :resp_len], expected_values, rtol=1e-5, atol=1e-6, msg=f"Batch {i} (prompt_len={prompt_lens[i]}, resp_len={resp_len}): values incorrect", ) print("All varied length tests passed") if __name__ == "__main__": test_padding_conversion_with_log_probs() test_padding_conversion_without_log_probs() test_padding_roundtrip() test_no_padding_2_padding_varying_lengths() print("All padding conversion tests passed!") ================================================ FILE: tests/utils/test_prepare_micro_batches_with_group_size.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tests for prepare_micro_batches with force_group_size > 1 and use_dynamic_bsz=True. Focuses on verifying that: 1. Samples within the same group (consecutive force_group_size samples) always end up in the same micro-batch. 2. All original samples are covered exactly once across all micro-batches. 3. The returned batch_idx_list correctly maps micro-batch positions back to original batch positions. 4. Token budget (max_token_len) is respected per micro-batch. """ import torch from tensordict import TensorDict from verl.utils import tensordict_utils as tu from verl.workers.engine.utils import prepare_micro_batches def _make_batch(seq_lens: list[int], force_group_size: int, max_token_len_per_gpu: int) -> TensorDict: """Build a minimal TensorDict accepted by prepare_micro_batches. Args: seq_lens: Effective sequence length for each sample. force_group_size: Group size constraint to embed in the batch. max_token_len_per_gpu: Token budget per GPU to embed in the batch. Returns: A TensorDict with ``input_ids``, ``attention_mask``, and the required non-tensor metadata fields. """ batch_size = len(seq_lens) max_len = max(seq_lens) # Build padded attention_mask: each row has seq_lens[i] ones followed by zeros. attention_mask = torch.zeros(batch_size, max_len, dtype=torch.long) for i, sl in enumerate(seq_lens): attention_mask[i, :sl] = 1 input_ids = torch.randint(1, 100, (batch_size, max_len)) batch = TensorDict( {"input_ids": input_ids, "attention_mask": attention_mask}, batch_size=[batch_size], ) # Embed metadata that prepare_micro_batches reads via get_non_tensor_data. tu.assign_non_tensor_data(batch, "use_dynamic_bsz", True) tu.assign_non_tensor_data(batch, "sp_size", 1) tu.assign_non_tensor_data(batch, "force_group_size", force_group_size) tu.assign_non_tensor_data(batch, "max_token_len_per_gpu", max_token_len_per_gpu) return batch def _verify_group_integrity(batch_idx_list: list[list[int]], force_group_size: int, batch_size: int): """Assert that every group of force_group_size consecutive samples stays together. Args: batch_idx_list: Index lists returned by prepare_micro_batches. force_group_size: Expected group size. batch_size: Total number of samples in the original batch. """ # Build a mapping: original_sample_idx -> micro_batch_id sample_to_mb = {} for mb_id, indices in enumerate(batch_idx_list): for idx in indices: assert idx not in sample_to_mb, f"Sample {idx} appears in multiple micro-batches" sample_to_mb[idx] = mb_id # Every sample must be assigned. assert set(sample_to_mb.keys()) == set(range(batch_size)), ( f"Not all samples covered. Missing: {set(range(batch_size)) - set(sample_to_mb.keys())}" ) # Samples within the same group must share the same micro-batch. num_groups = batch_size // force_group_size for g in range(num_groups): start = g * force_group_size group_indices = list(range(start, start + force_group_size)) mb_ids = {sample_to_mb[i] for i in group_indices} assert len(mb_ids) == 1, f"Group {g} (samples {group_indices}) was split across micro-batches {mb_ids}" def test_force_group_size_2_basic(): """Basic test: batch_size=8, force_group_size=2, dynamic bsz enabled.""" # 4 groups of 2; alternating short/long sequences within each group. seq_lens = [50, 60, 80, 90, 40, 45, 100, 110] force_group_size = 2 batch_size = len(seq_lens) max_token_len_per_gpu = 200 batch = _make_batch(seq_lens, force_group_size, max_token_len_per_gpu) micro_batches, batch_idx_list = prepare_micro_batches(batch) assert batch_idx_list is not None, "batch_idx_list must not be None when use_dynamic_bsz=True" assert len(micro_batches) > 0 _verify_group_integrity(batch_idx_list, force_group_size, batch_size) def test_force_group_size_4_basic(): """Test with force_group_size=4 (e.g., 4 responses per prompt in RM training).""" # 4 groups of 4 samples each. seq_lens = [ 100, 110, 90, 95, # group 0 200, 210, 190, 205, # group 1 50, 55, 45, 60, # group 2 150, 160, 140, 155, # group 3 ] force_group_size = 4 batch_size = len(seq_lens) max_token_len_per_gpu = 500 batch = _make_batch(seq_lens, force_group_size, max_token_len_per_gpu) micro_batches, batch_idx_list = prepare_micro_batches(batch) assert batch_idx_list is not None assert len(micro_batches) > 0 _verify_group_integrity(batch_idx_list, force_group_size, batch_size) def test_force_group_size_reconstruction(): """Verify that micro-batches can be reconstructed back to the original batch order.""" seq_lens = [80, 85, 120, 130, 60, 65, 200, 210] force_group_size = 2 max_token_len_per_gpu = 300 batch = _make_batch(seq_lens, force_group_size, max_token_len_per_gpu) micro_batches, batch_idx_list = prepare_micro_batches(batch) assert batch_idx_list is not None # Flatten micro-batches and index lists. flat_input_ids = torch.cat([mb["input_ids"] for mb in micro_batches], dim=0) flat_indices = [idx for indices in batch_idx_list for idx in indices] # Build reverse mapping and reconstruct. reverse_idx = [0] * len(flat_indices) for new_pos, orig_pos in enumerate(flat_indices): reverse_idx[orig_pos] = new_pos reconstructed = flat_input_ids[torch.tensor(reverse_idx)] torch.testing.assert_close(reconstructed, batch["input_ids"]) def test_force_group_size_single_micro_batch(): """When all samples fit in one micro-batch, grouping constraint is trivially satisfied.""" seq_lens = [10, 12, 15, 11, 8, 9, 14, 13] force_group_size = 2 max_token_len_per_gpu = 10000 # very large budget batch = _make_batch(seq_lens, force_group_size, max_token_len_per_gpu) micro_batches, batch_idx_list = prepare_micro_batches(batch) assert batch_idx_list is not None # All samples should be in a single micro-batch. assert len(micro_batches) == 1 assert len(batch_idx_list[0]) == len(seq_lens) _verify_group_integrity(batch_idx_list, force_group_size, len(seq_lens)) def test_force_group_size_large_group(): """Test with a larger batch and force_group_size=3.""" # 6 groups of 3 samples each. seq_lens = [ 100, 105, 95, # group 0 200, 205, 195, # group 1 50, 55, 45, # group 2 150, 155, 145, # group 3 80, 85, 75, # group 4 120, 125, 115, # group 5 ] force_group_size = 3 batch_size = len(seq_lens) max_token_len_per_gpu = 400 batch = _make_batch(seq_lens, force_group_size, max_token_len_per_gpu) micro_batches, batch_idx_list = prepare_micro_batches(batch) assert batch_idx_list is not None assert len(micro_batches) > 0 _verify_group_integrity(batch_idx_list, force_group_size, batch_size) def test_force_group_size_1_unchanged(): """force_group_size=1 should behave identically to the default (no grouping constraint).""" seq_lens = [100, 200, 50, 150, 80, 120] force_group_size = 1 max_token_len_per_gpu = 300 batch = _make_batch(seq_lens, force_group_size, max_token_len_per_gpu) micro_batches, batch_idx_list = prepare_micro_batches(batch) assert batch_idx_list is not None assert len(micro_batches) > 0 # All samples covered exactly once. all_indices = [idx for indices in batch_idx_list for idx in indices] assert sorted(all_indices) == list(range(len(seq_lens))) ================================================ FILE: tests/utils/test_rollout_skip_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil import tempfile from pathlib import Path from unittest.mock import MagicMock import pytest import torch from verl.utils.rollout_skip import DataProto, RolloutSkip len_prompt = 50 len_response = 100 def temp_dir(): # Create a temporary directory temp_dir = Path(tempfile.mkdtemp()) yield temp_dir # Cleanup shutil.rmtree(temp_dir) def build_generate_fn(gen_bs, n): len_tokenizer = 1024 def iterate(): while True: prompt = torch.randint(len_tokenizer, size=(gen_bs, len_prompt)).repeat_interleave(n, dim=0) generate = torch.randint(len_tokenizer, size=(gen_bs * n, len_response)) data = DataProto.from_dict(tensors={"prompt": prompt, "response": generate}) yield data mock_infer_engine = iterate() def fn(batch, **kwargs): # Simulate the inference engine returning the next batch return next(mock_infer_engine) return fn @pytest.fixture(params=[(32, 4), (64, 4), (64, 8)]) def mock_rollout_wg(request): gen_bs, n = request.param rollout_wg = MagicMock() config = MagicMock() config.actor_rollout_ref.rollout = { "n": n, "skip_dump_dir": next(temp_dir()), } config.data = {"gen_batch_size": gen_bs} rollout_wg.generate_sequences = build_generate_fn(gen_bs, n) yield config, rollout_wg # Cleanup shutil.rmtree(next(temp_dir())) class TestRolloutSkip: def test_initialization(self, capsys): """Test that RolloutSkip initializes correctly""" config = MagicMock() config.actor_rollout_ref.rollout = { "n": 16, "skip_dump_dir": "tmp/rollout_dump", } config.data = {"gen_batch_size": 128} mock_rollout_wg = MagicMock() skip = RolloutSkip(config, mock_rollout_wg) assert skip.n == 16 assert skip.gbs == 128 assert str(skip.dumped_dir) == "tmp/rollout_dump" assert skip._rollout_wg == mock_rollout_wg skip.wrap_generate_sequences() captured = capsys.readouterr() assert "Successfully patched" in captured.out def test_generate_without_wrap(self, mock_rollout_wg): """Test that generate_sequences works without wrapping""" config, rollout_wg = mock_rollout_wg _ = RolloutSkip(config, rollout_wg) _result = rollout_wg.generate_sequences(MagicMock()) for _ in range(10): result = rollout_wg.generate_sequences(MagicMock()) assert isinstance(result, DataProto) # * make sure the data is different assert torch.abs(_result.batch["prompt"] - result.batch["prompt"]).sum() > 0 assert torch.abs(_result.batch["response"] - result.batch["response"]).sum() > 0 _result = result def test_dump(self, mock_rollout_wg, capsys): config, rollout_wg = mock_rollout_wg skip = RolloutSkip(config, rollout_wg) skip.wrap_generate_sequences() result = rollout_wg.generate_sequences(MagicMock()) # * check if dump is OK assert skip.curr_path_dump.exists() captured = capsys.readouterr() assert "Successfully dump data in" in captured.out # * get file size, estimate file size file_size = skip.curr_path_dump.stat().st_size est_file_size = (len_prompt + len_response) * skip.gbs * skip.n * result.batch["prompt"].dtype.itemsize assert file_size >= est_file_size, "Dumped file size is smaller than expected" def test_generate_with_wrap(self, mock_rollout_wg, capsys): """Test that generate_sequences works without wrapping""" config, rollout_wg = mock_rollout_wg skip = RolloutSkip(config, rollout_wg) skip.wrap_generate_sequences() _result = rollout_wg.generate_sequences(MagicMock()) for _ in range(10): result = rollout_wg.generate_sequences(MagicMock()) assert isinstance(result, DataProto) # * make sure the data is different assert torch.abs(_result.batch["prompt"] - result.batch["prompt"]).sum() == 0 assert torch.abs(_result.batch["response"] - result.batch["response"]).sum() == 0 captured = capsys.readouterr() assert "Successfully load pre-generated data from" in captured.out _result = result ================================================ FILE: tests/utils/test_rollout_trace_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys from unittest.mock import MagicMock, patch import pytest from verl.utils.rollout_trace import RolloutTraceConfig, rollout_trace_attr, rollout_trace_op @pytest.fixture(autouse=True) def reset_rollout_trace_config_singleton(): """Fixture to reset the RolloutTraceConfig singleton before each test.""" RolloutTraceConfig.reset() @pytest.fixture def mock_weave_client(): """Mocks the weave module and its client, yielding the mock client.""" mock_weave = MagicMock() mock_client = MagicMock() mock_call = MagicMock() mock_client.create_call.return_value = mock_call mock_weave.init.return_value = mock_client # Also mock the call_context if it's used internally by the decorator mock_weave.trace.context.call_context.return_value = MagicMock() with patch.dict(sys.modules, {"weave": mock_weave, "weave.trace.context": mock_weave.trace.context}): yield mock_client class TracedClass: @rollout_trace_op # @weave.op # @mlflow.trace async def my_method(self, a, b="default"): return f"result: {a}, {b}" @rollout_trace_op # @weave.op # @mlflow.trace async def middle_method(self, a, b="default"): await self.my_method("test_a1", b="test_b1") return f"result: {a}, {b}" @rollout_trace_op # @mlflow.trace async def my_method_with_exception(self): raise ValueError("Test Exception") async def upper_method(self): await self.my_method("test_a0", b="test_b0") await self.middle_method("test_a2", b="test_b2") return True class UntracedClass: @rollout_trace_op async def my_method(self, x): return x * 2 async def test_rollout_trace_on_untraced_class(): """Tests that the decorator works correctly when no backend is configured.""" instance = UntracedClass() assert await instance.my_method(10) == 20 async def test_rollout_trace_with_tracer(mock_weave_client): """Tests that the decorator calls the tracer's methods correctly.""" RolloutTraceConfig.init(project_name="my-project", experiment_name="my-experiment", backend="weave") instance = TracedClass() assert RolloutTraceConfig.get_client() is mock_weave_client result = await instance.my_method("test_a", b="test_b") assert result == "result: test_a, test_b" mock_weave_client.create_call.assert_called_once() call_kwargs = mock_weave_client.create_call.call_args.kwargs assert call_kwargs["op"] == "TracedClass.my_method" expected_inputs = {"a": "test_a", "b": "test_b"} assert call_kwargs["inputs"] == expected_inputs mock_call = mock_weave_client.create_call.return_value mock_weave_client.finish_call.assert_called_once_with(mock_call, output=result) async def test_rollout_trace_with_exception(mock_weave_client): """Tests that `finish` is called with the exception when one is raised.""" RolloutTraceConfig.init(project_name="my-project", experiment_name="my-experiment", backend="weave") instance = TracedClass() with pytest.raises(ValueError, match="Test Exception"): await instance.my_method_with_exception() mock_weave_client.create_call.assert_called_once() mock_call = mock_weave_client.create_call.return_value mock_weave_client.finish_call.assert_called_once() # Check that finish_call was called with the exception args, kwargs = mock_weave_client.finish_call.call_args assert args[0] == mock_call assert "exception" in kwargs assert isinstance(kwargs["exception"], ValueError) async def test_rollout_trace_with_dummy_backend(mock_weave_client): """Tests that the tracer is not called when the backend is 'dummy'.""" RolloutTraceConfig.init(project_name="my-project", experiment_name="my-experiment", backend="dummy") instance = TracedClass() await instance.my_method("test_a") mock_weave_client.create_call.assert_not_called() async def test_trace_disabled_with_trace_false(mock_weave_client): """Tests that tracing is disabled when trace=False.""" RolloutTraceConfig.init( project_name="my-project", experiment_name="my-experiment", backend="weave", ) instance = TracedClass() assert RolloutTraceConfig.get_backend() == "weave" with rollout_trace_attr(step=1, sample_index=0, rollout_n=0, trace=False): result = await instance.my_method("test_a", b="test_b") assert result == "result: test_a, test_b" # No tracing should have occurred mock_weave_client.create_call.assert_not_called() # Verify that tracing works again with trace=True (default) with rollout_trace_attr(step=1, sample_index=0, rollout_n=0): result = await instance.my_method("test_a", b="test_b") assert result == "result: test_a, test_b" assert mock_weave_client.create_call.call_count == 1 async def test_trace_false_disables_nested_trace_ops(mock_weave_client): """Tests that trace=False disables all nested @rollout_trace_op calls.""" RolloutTraceConfig.init( project_name="my-project", experiment_name="my-experiment", backend="weave", ) instance = TracedClass() with rollout_trace_attr(step=1, sample_index=0, rollout_n=0, trace=False): # Call upper_method which internally calls my_method and middle_method # All of these are decorated with @rollout_trace_op result = await instance.upper_method() assert result is True # No tracing should have occurred for any of the nested calls mock_weave_client.create_call.assert_not_called() with rollout_trace_attr(step=1, sample_index=0, rollout_n=0): result = await instance.my_method("test_a", b="test_b") assert result == "result: test_a, test_b" assert mock_weave_client.create_call.call_count == 1 async def test_trace_enabled_restored_after_exception(mock_weave_client): """Tests that trace state is restored even if an exception occurs when trace=False.""" RolloutTraceConfig.init( project_name="my-project", experiment_name="my-experiment", backend="weave", ) instance = TracedClass() assert RolloutTraceConfig.get_backend() == "weave" # Use trace=False and raise an exception try: with rollout_trace_attr(step=1, sample_index=0, rollout_n=0, trace=False): raise RuntimeError("Test exception with trace disabled") except RuntimeError: pass with rollout_trace_attr(step=1, sample_index=0, rollout_n=0): result = await instance.my_method("test_a", b="test_b") assert result == "result: test_a, test_b" assert mock_weave_client.create_call.call_count == 1 @pytest.mark.skipif( os.environ.get("RUN_WEAVE_INTEGRATION_TESTS", "false").lower() != "true", reason="Skipping weave integration test. Set RUN_WEAVE_INTEGRATION_TESTS=true to run.", ) async def test_rollout_trace_with_real_weave_backend(): """Integration test with a real weave backend.""" # This assumes that the weave environment (e.g., project) is configured RolloutTraceConfig.init(project_name="my-project", experiment_name="my-experiment", backend="weave") instance = TracedClass() with rollout_trace_attr(step=1, sample_index=2, rollout_n=3): await instance.upper_method() with pytest.raises(ValueError, match="Test Exception"): await instance.my_method_with_exception() print("\nWeave integration test ran successfully. Check your weave project for the trace.") @pytest.mark.skipif( os.environ.get("RUN_MLFLOW_INTEGRATION_TESTS", "false").lower() != "true", reason="Skipping mlflow integration test. Set RUN_MLFLOW_INTEGRATION_TESTS=true to run.", ) async def test_rollout_trace_with_real_mlflow_backend(): """Integration test with a real mlflow backend.""" # This assumes that the mlflow environment (e.g., project) is configured RolloutTraceConfig.init(project_name="my-project", experiment_name="my-experiment", backend="mlflow") instance = TracedClass() with rollout_trace_attr(step=1, sample_index=2, rollout_n=3, name="agent_run"): assert await instance.upper_method() # with pytest.raises(ValueError, match="Test Exception"): # await instance.my_method_with_exception() print("\nWeave integration test ran successfully. Check your weave project for the trace.") ================================================ FILE: tests/utils/test_seqlen_balancing.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.distributed as dist import torch.multiprocessing as mp from verl import DataProto from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device from verl.utils.model import create_random_mask from verl.utils.seqlen_balancing import ( ceildiv, get_reverse_idx, prepare_dynamic_batch, rearrange_micro_batches, restore_dynamic_batch, ) def test_seqlen_balancing(): input_ids = torch.randint(low=0, high=10, size=(20, 100)) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0.1, max_ratio_of_valid_token=0.9, min_ratio_of_valid_token=0.5 ) data = {"input_ids": input_ids, "attention_mask": attention_mask} dataproto = DataProto.from_single_dict(data) micro_batches, micro_bsz_idx_lst = rearrange_micro_batches(dataproto.batch, max_token_len=300) batch = torch.cat(micro_batches) micro_bsz_idx = [] for idx in micro_bsz_idx_lst: micro_bsz_idx.extend(idx) reverse_idx_map = get_reverse_idx(micro_bsz_idx) reverse_idx_map = torch.tensor(reverse_idx_map) new_batch = batch[reverse_idx_map] torch.testing.assert_close(new_batch, dataproto.batch) def test_dynamic_batch(): input_ids = torch.randint(low=0, high=10, size=(20, 100)) attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0.1, max_ratio_of_valid_token=0.9, min_ratio_of_valid_token=0.5 ) data = {"input_ids": input_ids, "attention_mask": attention_mask} dataproto = DataProto.from_single_dict(data) micro_batches, micro_bsz_idx_lst = prepare_dynamic_batch(dataproto, max_token_len=300) input_ids = torch.cat([micro_batch.batch["input_ids"] for micro_batch in micro_batches], dim=0) input_ids = restore_dynamic_batch(input_ids, micro_bsz_idx_lst) torch.testing.assert_close(input_ids, dataproto.batch["input_ids"]) def _worker(rank, world_size, init_method, max_token_len, use_same_dp, min_mb): # 1) init process group & CUDA get_torch_device().set_device(rank) dist.init_process_group( backend=get_nccl_backend(), init_method=init_method, world_size=world_size, rank=rank, ) # 2) build a small random batch (each rank different length to force mismatch) torch.manual_seed(42 + rank) input_ids = torch.randint(0, 10, (20 + rank * 5, 100), device=f"{get_device_name()}:{rank}") attention_mask = create_random_mask( input_ids=input_ids, max_ratio_of_left_padding=0.1, max_ratio_of_valid_token=0.9, min_ratio_of_valid_token=0.5, ) dp = {"input_ids": input_ids, "attention_mask": attention_mask} proto = DataProto.from_single_dict(dp) batch = proto.batch # 3) call rearrange_micro_batches with one of the two params under test micros, idx_lst = rearrange_micro_batches( batch, max_token_len=max_token_len, dp_group=dist.group.WORLD, same_micro_num_in_dp=use_same_dp, min_num_micro_batch=min_mb, ) # 4) check the enforced counts seq_len_effective: torch.Tensor = batch["attention_mask"].sum(dim=1) total_seqlen = seq_len_effective.sum().item() local = min(len(seq_len_effective), ceildiv(total_seqlen, max_token_len)) if min_mb is not None: expected = max(local, min_mb) assert len(micros) == expected if use_same_dp: # gather all local_counts counts = [torch.zeros(1, device=f"{get_device_name()}:{rank}") for _ in range(world_size)] counts[rank].fill_(local) dist.all_gather(counts, counts[rank]) expected = max(int(c.item()) for c in counts) assert len(micros) == expected else: # if neither, we get the local natural count assert len(micros) == local # 5) reconstruction sanity: concat→reverse_idx→orig flat = torch.cat(micros, dim=0) idx = [] for sub in idx_lst: idx.extend(sub) inv = get_reverse_idx(idx) inv = torch.tensor(inv, device=flat.device) reconstructed = flat[inv] torch.testing.assert_close(reconstructed, batch) dist.destroy_process_group() def test_dataproto_split_uneven(): """Test DataProto.split with uneven splits""" # Create test data with 10 items input_ids = torch.randint(low=0, high=10, size=(10, 5)) attention_mask = torch.ones(10, 5) data = {"input_ids": input_ids, "attention_mask": attention_mask} dataproto = DataProto.from_single_dict(data) # Test split with size 3 (should create chunks of [3, 3, 3, 1]) splits = dataproto.split(3) assert len(splits) == 4 assert len(splits[0]) == 3 assert len(splits[1]) == 3 assert len(splits[2]) == 3 assert len(splits[3]) == 1 reconstructed = DataProto.concat(splits) torch.testing.assert_close(reconstructed.batch["input_ids"], dataproto.batch["input_ids"]) torch.testing.assert_close(reconstructed.batch["attention_mask"], dataproto.batch["attention_mask"]) # Test split with size equal to length (should create one chunk) splits = dataproto.split(10) assert len(splits) == 1 assert len(splits[0]) == 10 # Test split with size larger than length (should create one chunk with all data) splits = dataproto.split(15) assert len(splits) == 1 assert len(splits[0]) == 10 # Test with non-tensor batch data import numpy as np data_with_non_tensor = { "input_ids": input_ids, "attention_mask": attention_mask, "labels": np.array([f"label_{i}" for i in range(10)], dtype=object), } dataproto_with_non_tensor = DataProto.from_single_dict(data_with_non_tensor) splits = dataproto_with_non_tensor.split(3) assert len(splits) == 4 assert len(splits[0]) == 3 assert len(splits[1]) == 3 assert len(splits[2]) == 3 assert len(splits[3]) == 1 # Verify non-tensor data integrity reconstructed = DataProto.concat(splits) np.testing.assert_array_equal( reconstructed.non_tensor_batch["labels"], dataproto_with_non_tensor.non_tensor_batch["labels"] ) def test_seqlen_balancing_distributed_params(tmp_path): world_size = 2 init_file = tmp_path / "dist_init" init_file.write_text("") # empty file init_method = f"file://{init_file}" # test min_num_micro_batch only mp.spawn( _worker, args=(world_size, init_method, 300, False, 4), nprocs=world_size, join=True, ) # test same_micro_num_in_dp only mp.spawn( _worker, args=(world_size, init_method, 300, True, None), nprocs=world_size, join=True, ) def test_group_balanced_partitions(): """Test group-level balancing keeps same-uid samples together.""" from verl.utils.seqlen_balancing import get_group_balanced_partitions # Create test data: 4 groups with different sizes # Group 0 (uid=0): indices 0,1,2,3 with seqlens [100, 100, 100, 100] # Group 1 (uid=1): indices 4,5,6,7 with seqlens [200, 200, 200, 200] # Group 2 (uid=2): indices 8,9,10,11 with seqlens [150, 150, 150, 150] # Group 3 (uid=3): indices 12,13,14,15 with seqlens [50, 50, 50, 50] seqlen_list = [100] * 4 + [200] * 4 + [150] * 4 + [50] * 4 uid_list = [0] * 4 + [1] * 4 + [2] * 4 + [3] * 4 # Partition into 2 groups partitions = get_group_balanced_partitions(seqlen_list, uid_list, k_partitions=2) assert len(partitions) == 2 # Verify all indices are covered all_indices = set() for partition in partitions: all_indices.update(partition) assert all_indices == set(range(16)) # Verify same-uid samples stay together for partition in partitions: uids_in_partition = set(uid_list[i] for i in partition) for uid in uids_in_partition: # All samples with this uid should be in this partition uid_indices = [i for i, u in enumerate(uid_list) if u == uid] assert all(i in partition for i in uid_indices), f"uid {uid} samples split across partitions" def test_group_balanced_partitions_single_sample_groups(): """Test group balancing with single-sample groups (n=1).""" from verl.utils.seqlen_balancing import get_group_balanced_partitions # Each sample is its own group seqlen_list = [100, 200, 150, 50, 300, 250] uid_list = [0, 1, 2, 3, 4, 5] partitions = get_group_balanced_partitions(seqlen_list, uid_list, k_partitions=2) assert len(partitions) == 2 all_indices = set() for partition in partitions: all_indices.update(partition) assert all_indices == set(range(6)) def test_group_balanced_partitions_equal_size(): """Test group balancing with equal_size constraint simulation.""" from verl.utils.seqlen_balancing import get_group_balanced_partitions # 8 groups, partition into 4 (simulating world_size=4) # Each group has 2 samples seqlen_list = [100, 100, 200, 200, 150, 150, 50, 50, 300, 300, 250, 250, 180, 180, 120, 120] uid_list = [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7] partitions = get_group_balanced_partitions(seqlen_list, uid_list, k_partitions=4) assert len(partitions) == 4 # Verify all indices are covered all_indices = set() for partition in partitions: all_indices.update(partition) assert all_indices == set(range(16)) # Verify same-uid samples stay together for partition in partitions: uids_in_partition = set(uid_list[i] for i in partition) for uid in uids_in_partition: uid_indices = [i for i, u in enumerate(uid_list) if u == uid] assert all(i in partition for i in uid_indices) ================================================ FILE: tests/utils/test_server_profiler.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from unittest.mock import AsyncMock, MagicMock, patch from verl.utils.profiler.config import ( ProfilerConfig, TorchProfilerToolConfig, build_sglang_profiler_args, build_vllm_profiler_args, ) class TestServerProfilerArgs(unittest.TestCase): def test_build_vllm_profiler_args(self): # Case 1: All features enabled tool_config = TorchProfilerToolConfig(contents=["stack", "shapes", "memory"]) config = ProfilerConfig(save_path="/tmp/test", tool_config=tool_config) # Patch environ to avoid side effects and verify calls with patch.dict(os.environ, {}, clear=True): args = build_vllm_profiler_args(config, tool_config, rank=0) # Check Env vars (backward compatibility) self.assertEqual(os.environ.get("VLLM_TORCH_PROFILER_DIR"), "/tmp/test/agent_loop_rollout_replica_0") self.assertEqual(os.environ.get("VLLM_TORCH_PROFILER_WITH_STACK"), "1") self.assertEqual(os.environ.get("VLLM_TORCH_PROFILER_RECORD_SHAPES"), "1") self.assertEqual(os.environ.get("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY"), "1") # Check Args (new API) self.assertIn("profiler_config", args) profiler_config_dict = json.loads(args["profiler_config"]) self.assertEqual(profiler_config_dict["torch_profiler_dir"], "/tmp/test/agent_loop_rollout_replica_0") self.assertTrue(profiler_config_dict["torch_profiler_with_stack"]) self.assertTrue(profiler_config_dict["torch_profiler_record_shapes"]) self.assertTrue(profiler_config_dict["torch_profiler_with_memory"]) def test_build_sglang_profiler_args(self): # Case 1: Basic features tool_config = TorchProfilerToolConfig(contents=["stack", "shapes", "memory"]) config = ProfilerConfig(save_path="/tmp/test", tool_config=tool_config) with self.assertWarns(UserWarning): args = build_sglang_profiler_args(config, tool_config, rank=0) self.assertEqual(args["output_dir"], "/tmp/test/agent_loop_rollout_replica_0") self.assertTrue(args["with_stack"]) self.assertTrue(args["record_shapes"]) class TestServerProfilerFunctionality(unittest.IsolatedAsyncioTestCase): async def test_vllm_start_stop_profile(self): try: # Import strictly inside test to avoid import errors if dependencies missing from verl.workers.rollout.vllm_rollout.vllm_async_server import vLLMHttpServer except ImportError: self.skipTest("vllm or dependencies not installed") return # Mock dependencies mock_profiler = MagicMock() mock_profiler.check_enable.return_value = True mock_profiler.check_this_rank.return_value = True mock_profiler.is_discrete_mode.return_value = True mock_engine = AsyncMock() # Mock self object mock_self = MagicMock() mock_self.profiler_controller = mock_profiler mock_self.engine = mock_engine # Test start_profile using the unbound method await vLLMHttpServer.start_profile(mock_self) mock_engine.start_profile.assert_called_once() # Test stop_profile await vLLMHttpServer.stop_profile(mock_self) mock_engine.stop_profile.assert_called_once() async def test_sglang_start_stop_profile(self): try: # Import strictly inside test to avoid import errors if dependencies missing from verl.workers.rollout.sglang_rollout.async_sglang_server import SGLangHttpServer except ImportError: self.skipTest("sglang or dependencies not installed") return # Mock dependencies mock_profiler = MagicMock() mock_profiler.check_enable.return_value = True mock_profiler.check_this_rank.return_value = True mock_profiler.is_discrete_mode.return_value = True mock_profiler.config = MagicMock() mock_profiler.tool_config = MagicMock() mock_tokenizer_manager = AsyncMock() mock_self = MagicMock() mock_self.profiler_controller = mock_profiler mock_self.tokenizer_manager = mock_tokenizer_manager mock_self.replica_rank = 0 # Mock build_sglang_profiler_args to return known dict with patch("verl.workers.rollout.sglang_rollout.async_sglang_server.build_sglang_profiler_args") as mock_build: mock_args = {"arg1": "val1"} mock_build.return_value = mock_args # Test start_profile await SGLangHttpServer.start_profile(mock_self) mock_build.assert_called_once_with(mock_profiler.config, mock_profiler.tool_config, mock_self.replica_rank) mock_tokenizer_manager.start_profile.assert_called_once_with(**mock_args) # Test stop_profile await SGLangHttpServer.stop_profile(mock_self) mock_tokenizer_manager.stop_profile.assert_called_once() if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_shared_memory.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import multiprocessing import unittest from multiprocessing import shared_memory import torch from verl.workers.rollout.vllm_rollout.bucketed_weight_transfer import create_shared_memory, rebuild_shared_memory class TestSharedMemory(unittest.TestCase): """Test cases for shared memory utility functions.""" def setUp(self): """Set up test fixtures before each test method.""" # Use short unique names to avoid POSIX shared memory name length limits import uuid short_id = uuid.uuid4().hex[:8] self.test_name = f"shm_{short_id}" def tearDown(self): """Clean up shared memory after each test method.""" # Note: We're relying on the OS to clean up shared memory # as we properly delete all references in the tests pass def test_create_shared_memory_new(self): """Test creating new shared memory with unique name.""" size = 1024 shm = create_shared_memory(size, self.test_name) # Verify shared memory object is created correctly self.assertIsNotNone(shm) # Note: shared memory may have system-dependent size rounding self.assertGreaterEqual(shm.size, size) self.assertEqual(shm.name, self.test_name) # Clean up - delete tensor references first del shm def test_create_shared_memory_attach_existing(self): """Test that create_shared_memory attaches to existing shared memory when FileExistsError occurs.""" size = 2048 # First, create shared memory shm1 = create_shared_memory(size, self.test_name) self.assertGreaterEqual(shm1.size, size) # Second call should attach to existing memory shm2 = create_shared_memory(size, self.test_name) # Verify we attached to the same shared memory self.assertIsNotNone(shm2) self.assertGreaterEqual(shm2.size, size) self.assertEqual(shm2.name, self.test_name) # Both should reference the same shared memory self.assertEqual(shm1.name, shm2.name) # Clean up del shm1, shm2 def test_rebuild_shared_memory_default_dtype(self): """Test rebuilding tensor from shared memory with default dtype (uint8).""" size = 1024 # Create and write to shared memory shm = create_shared_memory(size, self.test_name) test_data = torch.arange(size, dtype=torch.uint8) shm.buf[:size] = test_data.numpy().tobytes() # Rebuild tensor from shared memory tensor, _ = rebuild_shared_memory(self.test_name, size) # Verify tensor properties self.assertEqual(tensor.dtype, torch.uint8) self.assertEqual(len(tensor), size) # Verify data integrity reconstructed = torch.frombuffer(shm.buf[:size], dtype=torch.uint8) self.assertTrue(torch.equal(tensor, reconstructed)) # Clean up - delete references before closing del tensor, reconstructed def test_rebuild_shared_memory_custom_dtype(self): """Test rebuilding tensor from shared memory with custom dtype.""" size = 256 # 256 bytes = 64 float32 values # Create and write to shared memory shm = create_shared_memory(size, self.test_name) test_data = torch.arange(64, dtype=torch.float32) shm.buf[:size] = test_data.numpy().tobytes() # Rebuild tensor with custom dtype tensor, _ = rebuild_shared_memory(self.test_name, size, dtype=torch.float32) # Verify tensor properties self.assertEqual(tensor.dtype, torch.float32) self.assertEqual(len(tensor), 64) # Verify data integrity reconstructed = torch.frombuffer(shm.buf[:size], dtype=torch.float32) self.assertTrue(torch.equal(tensor, reconstructed)) # Clean up - delete references before closing del tensor, reconstructed def test_shared_memory_data_integrity(self): """Test that data remains intact between create and rebuild operations.""" size = 512 # Create test data with various patterns test_data = torch.randint(0, 256, (size,), dtype=torch.uint8) # Create shared memory and write data shm = create_shared_memory(size, self.test_name) shm.buf[:size] = test_data.numpy().tobytes() # Rebuild tensor tensor, _ = rebuild_shared_memory(self.test_name, size) # Verify data integrity reconstructed = torch.frombuffer(shm.buf[:size], dtype=torch.uint8) self.assertTrue(torch.equal(test_data, reconstructed)) # Clean up - delete references before closing del tensor, reconstructed def test_shared_memory_different_dtypes(self): """Test shared memory operations with different tensor dtypes.""" test_cases = [ (torch.float32, 256, 64), # 256 bytes / 4 bytes = 64 values (torch.float64, 256, 32), # 256 bytes / 8 bytes = 32 values (torch.int32, 256, 64), # 256 bytes / 4 bytes = 64 values (torch.int64, 256, 32), # 256 bytes / 8 bytes = 32 values (torch.uint8, 256, 256), # 256 bytes / 1 byte = 256 values ] for dtype, size, expected_len in test_cases: # Create test data test_data = torch.arange(expected_len, dtype=dtype) # Create shared memory and write data shm = create_shared_memory(size, self.test_name) shm.buf[:size] = test_data.numpy().tobytes() # Rebuild tensor tensor, _ = rebuild_shared_memory(self.test_name, size, dtype=dtype) # Verify properties and data self.assertEqual(tensor.dtype, dtype) self.assertEqual(len(tensor), expected_len) reconstructed = torch.frombuffer(shm.buf[:size], dtype=dtype) self.assertTrue(torch.equal(test_data, reconstructed)) # Clean up - delete references before closing del tensor, reconstructed def test_shared_memory_multiple_operations(self): """Test multiple create/rebuild operations with the same name.""" size = 512 # First iteration test_data1 = torch.arange(size, dtype=torch.uint8) shm1 = create_shared_memory(size, self.test_name) shm1.buf[:size] = test_data1.numpy().tobytes() tensor1, _ = rebuild_shared_memory(self.test_name, size) reconstructed1 = torch.frombuffer(shm1.buf[:size], dtype=torch.uint8) self.assertTrue(torch.equal(test_data1, reconstructed1)) del tensor1, reconstructed1, shm1 # Second iteration with different data test_data2 = torch.arange(size, dtype=torch.uint8) * 2 shm2 = create_shared_memory(size, self.test_name) shm2.buf[:size] = test_data2.numpy().tobytes() tensor2, _ = rebuild_shared_memory(self.test_name, size) reconstructed2 = torch.frombuffer(shm2.buf[:size], dtype=torch.uint8) self.assertTrue(torch.equal(test_data2, reconstructed2)) del tensor2, reconstructed2, shm2 # Module-level function for cross-process testing def child_process_function(name, size, test_data_bytes): """Child process function to rebuild and verify tensor.""" shm = None tensor = None test_data = None try: # Convert bytes back to tensor test_data = torch.frombuffer(test_data_bytes, dtype=torch.uint8) # Attach to shared memory shm = shared_memory.SharedMemory(name=name) # Rebuild tensor from shared memory tensor = torch.frombuffer(shm.buf[:size], dtype=torch.uint8) # Verify data integrity assert torch.equal(test_data, tensor), "Data mismatch in child process" return True except Exception as e: print(f"Error in child process: {e}") return False finally: # Clean up shared memory in child process # Delete all references first del tensor, test_data if shm is not None: shm.close() # Note: Don't unlink in child process, parent will clean up class TestSharedMemoryIntegration(unittest.TestCase): """Integration tests for shared memory operations across process boundaries.""" def test_cross_process_shared_memory(self): """Test shared memory can be created in one process and accessed in another.""" size = 1024 test_data = torch.arange(size, dtype=torch.uint8) # Create shared memory in parent process shm = create_shared_memory(size, "test_cross_proc") shm.buf[:size] = test_data.numpy().tobytes() # Convert tensor to bytes for passing to child process test_data_bytes = test_data.numpy().tobytes() # Start child process process = multiprocessing.Process( target=child_process_function, args=("test_cross_proc", size, test_data_bytes) ) process.start() process.join(timeout=5) # Verify child process completed successfully self.assertEqual(process.exitcode, 0, "Child process failed") # Clean up del shm if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_special_linear_cross_entropy_tp.py ================================================ # # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import torch import torch.distributed as dist try: from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy except ImportError: # FIXME: remove these manually included paths import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../"))) finally: from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy import verl.utils.torch_functional as verl_F compute_entropy_from_logits = torch.compile(verl_F.entropy_from_logits, dynamic=True) MAX_TEST_CASES = os.environ.get("MAX_TEST_CASES", 5) VERIFY_TORCH_SELF = os.environ.get("VERIFY_TORCH_SELF", False) LOW_MEMORY = os.environ.get("LOW_MEMORY", False) LOW_MEMORY_DIV_FACTOR = os.environ.get("LOW_MEMORY_DIV_FACTOR", 16) def run_torch_entropy( hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, temperature: float, reduction="none" ) -> list[torch.Tensor]: # [num_tokens, vocab_size] if len(hidden.shape) > 2: hidden = hidden.view(-1, hidden.shape[-1]) # [num_tokens, hidden_size] if len(labels.shape) > 1: labels = labels.view(-1) logits = torch.matmul( hidden.to(torch.float32), weight.to(torch.float32) if weight.size(0) == hidden.size(1) else weight.T.to(torch.float32), ) logits /= temperature pd = torch.nn.functional.softmax(logits, dim=-1) # [num_tokens, vocab_size] entropy_a = torch.logsumexp(logits, dim=-1) # [num_tokens] entropy_b = torch.sum(pd * logits, dim=-1) # [num_tokens] entropy = entropy_a - entropy_b logprobs = torch.nn.functional.cross_entropy(logits, labels, reduction=reduction) # [num_tokens] logprobs = torch.neg(logprobs) return logprobs, entropy class TorchEntropyTP(torch.autograd.Function): """ it is used for testing the correctness of the kernel it is not efficient and is not recommended to use in practice """ @staticmethod def forward( ctx, hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, temperature: float, dist_process_group: torch.distributed.ProcessGroup, ): # weight has shape [vocab_size, hidden_size], hidden has shape [num_tokens, hidden_size] ctx.original_hidden_shape = hidden.shape if len(hidden.shape) > 2: hidden = hidden.view(-1, hidden.shape[-1]) # [num_tokens, hidden_size] if len(labels.shape) > 1: labels = labels.view(-1) logits = torch.matmul(hidden.to(torch.float32), weight.to(torch.float32).T) # [num_tokens, vocab_size] logits /= temperature whole_logits = torch.empty( (logits.shape[0], logits.shape[1] * dist.get_world_size(dist_process_group)), dtype=logits.dtype, device=logits.device, ) whole_logits_ref = [ whole_logits[:, i * logits.shape[1] : (i + 1) * logits.shape[1]] for i in range(dist.get_world_size(dist_process_group)) ] dist.all_gather(whole_logits_ref, logits, group=dist_process_group) pd = torch.nn.functional.softmax(whole_logits, dim=-1) entropy_a = torch.logsumexp(whole_logits, dim=-1) # [num_tokens] entropy_b = torch.sum(pd * whole_logits, dim=-1) # [num_tokens] entropy = entropy_a - entropy_b logprobs = torch.nn.functional.cross_entropy(whole_logits, labels, reduction="none") logprobs = torch.neg(logprobs) ctx.save_for_backward(hidden, weight, labels, whole_logits, entropy_b) ctx.dist_process_group = dist_process_group ctx.temperature = temperature return logprobs, entropy @staticmethod def backward(ctx, g_logprobs: torch.Tensor, g_entropy: torch.Tensor): hidden, weight, labels, whole_logits, entropy_b = ctx.saved_tensors dist_process_group = ctx.dist_process_group temperature = ctx.temperature batch_size, hidden_size = hidden.shape vocab_size, hidden_size = weight.shape rank = dist.get_rank(dist_process_group) # Compute softmax probabilities maximum, _ = torch.max(whole_logits, dim=-1, keepdim=True) exp_logits = torch.exp(whole_logits - maximum) accumulate = exp_logits.sum(dim=-1, keepdim=True) pd = exp_logits / accumulate # Gradient for entropy # entropy = entropy_a - entropy_b # entropy_a = log(sum(exp(logits))) # entropy_b = sum(pd * logits) # d_entropy_a/d_logits = pd # d_entropy_b/d_logits = pd * (logits - b.unsqueeze(1) + 1) # d_entropy/d_logits = d_entropy_a - d_entropy_b # d_entropy/d_logits = pd - pd * (logits - b.unsqueeze(1) + 1) # d_entropy/d_logits = -pd * (logits - b.unsqueeze(1)) d_logits_entropy = g_entropy.unsqueeze(1) * (-pd * (whole_logits - entropy_b.unsqueeze(1))) # Gradient for logprobs # logprobs = -cross_entropy = -log(pd[labels]) # d_logprobs/d_logits = (pd - one_hot(labels)) one_hot = torch.zeros_like(whole_logits) one_hot.scatter_(1, labels.unsqueeze(1), 1) g_logprobs = torch.neg(g_logprobs) d_logits_logprobs = g_logprobs.unsqueeze(1) * (pd - one_hot) # NOTE: This will lead to wrong result # d_logits_logprobs = g_logprobs.unsqueeze(1) * (pd - 1) * one_hot # Combine gradients d_logits = d_logits_entropy + d_logits_logprobs d_logits /= temperature # Get local slice of gradients local_d_logits = d_logits[:, rank * vocab_size : (rank + 1) * vocab_size] # Compute gradients for hidden and weight d_hidden = torch.matmul(local_d_logits, weight.to(torch.float32)) d_weight = torch.matmul(local_d_logits.T, hidden.to(torch.float32)) d_hidden = d_hidden.view(ctx.original_hidden_shape) return d_hidden, d_weight, None, None, None run_torch_entropy_tp = TorchEntropyTP.apply class TestLinearCrossEntropy_TensorParallel: def __init__(self): dist.init_process_group(backend="nccl") self.group = dist.group.WORLD self.local_rank = dist.get_rank(self.group) self.world_size = dist.get_world_size(self.group) device = torch.device(f"cuda:{self.local_rank}") torch.cuda.set_device(device) print(f"[INFO]: Local rank: {self.local_rank}, World size: {self.world_size}") def initialize(self, test_case_idx: int, temperature: float = 1.5): self.test_case_idx = test_case_idx self.temperature = temperature def shutdown(self): dist.destroy_process_group() def cleanup(self): torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() import gc gc.collect() torch.cuda.synchronize() def generate_hyper(self): global LOW_MEMORY, LOW_MEMORY_DIV_FACTOR, MAX_TEST_CASES self.dtype = torch.bfloat16 if self.test_case_idx == 0: self.batch_size = 1 self.num_tokens = 1937 self.hidden_size = 3584 self.vocab_size = 152064 elif self.test_case_idx == 1: self.batch_size = 1 self.num_tokens = 2169 self.hidden_size = 896 self.vocab_size = 151936 elif self.test_case_idx == 2: self.batch_size = 1 self.num_tokens = 1530 self.hidden_size = 2048 self.vocab_size = 32256 elif self.test_case_idx == 3: self.batch_size = 1 self.num_tokens = 1388 self.hidden_size = 4096 self.vocab_size = 102400 elif self.test_case_idx == 4: self.batch_size = 1 self.num_tokens = 8192 self.hidden_size = 4096 self.vocab_size = 102400 else: raise ValueError(f"Invalid test case index: {self.test_case_idx}") if LOW_MEMORY: self.vocab_size = int(self.vocab_size / LOW_MEMORY_DIV_FACTOR) assert MAX_TEST_CASES <= 5, "MAX_TEST_CASES should be less than or equal to 5." def generate_forward_inputs(self): hidden = ( torch.empty((self.batch_size, self.num_tokens, self.hidden_size), dtype=self.dtype, device="cuda") .uniform_(-0.5, 0.5) .requires_grad_() ) weight = ( torch.empty((self.vocab_size, self.hidden_size), dtype=self.dtype, device="cuda") .uniform_(-0.5, 0.5) .requires_grad_() ) labels = torch.randint(0, self.vocab_size, (self.batch_size, self.num_tokens), device="cuda") return hidden, weight, labels def generate_backward_inputs(self): g_entropy = torch.empty((self.num_tokens,), dtype=self.dtype, device="cuda").uniform_(-0.5, 0.5) g_logprobs = torch.empty((self.num_tokens,), dtype=self.dtype, device="cuda").uniform_(-1, 1) return g_entropy, g_logprobs def verify_torch_itself(self, iterations: int = 5): self.cleanup() self.generate_hyper() for i in range(iterations): hidden, weight, labels = self.generate_forward_inputs() # NOTE: we need to manually synchronize hidden and labels among Process Group dist.broadcast(hidden, src=0, group=self.group) dist.broadcast(labels, src=0, group=self.group) # forward pass # Create a tensor to hold the gathered weights from all ranks # weight has shape [vocab_size, hidden_size] # We want to gather along the first dimension to get [vocab_size * world_size, hidden_size] # Create a single contiguous tensor to hold all gathered weights whole_weight = torch.empty( (self.vocab_size * self.world_size, self.hidden_size), dtype=weight.dtype, device=weight.device ) # Create views into the tensor for each rank's portion whole_weight_views = [ whole_weight[i * self.vocab_size : (i + 1) * self.vocab_size] for i in range(self.world_size) ] # Perform all_gather operation using the views dist.all_gather(whole_weight_views, weight, group=self.group) # Set requires_grad for autograd whole_weight.requires_grad_() (single_logprobs, single_entropy) = run_torch_entropy(hidden, whole_weight, labels, self.temperature) (tp_logprobs, tp_entropy) = run_torch_entropy_tp(hidden, weight, labels, self.temperature, self.group) torch.testing.assert_close(single_logprobs, tp_logprobs, atol=1e-4, rtol=1e-4) torch.testing.assert_close(single_entropy, tp_entropy, atol=1e-4, rtol=1e-4) # backward pass g_entropy, g_logprobs = self.generate_backward_inputs() # NOTE: we need to manually synchronize g_entropy and g_logprobs among Process Group dist.broadcast(g_entropy, src=0, group=self.group) dist.broadcast(g_logprobs, src=0, group=self.group) (single_d_hidden, single_d_weight) = torch.autograd.grad( (single_entropy, single_logprobs), (hidden, whole_weight), (g_entropy, g_logprobs), retain_graph=False ) (tp_d_hidden, tp_d_weight) = torch.autograd.grad( (tp_entropy, tp_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) # NOTE: all-reduce on hidden is conducted outside the kernel dist.all_reduce(tp_d_hidden, op=dist.ReduceOp.SUM, group=self.group) torch.testing.assert_close(tp_d_hidden, single_d_hidden, atol=1e-2, rtol=1e-4) # Extract the corresponding slice from single_d_weight for comparison # tp_d_weight has shape [vocab_size, hidden_size] # single_d_weight has shape [vocab_size * world_size, hidden_size] torch.testing.assert_close( tp_d_weight, single_d_weight[self.local_rank * self.vocab_size : (self.local_rank + 1) * self.vocab_size], atol=1e-2, rtol=1e-4, ) # atol=1e-3, rtol=1e-4) if self.local_rank == 0: print("[PASS] torch TP correctness is verified") def check_torch_storage(self): self.cleanup() self.generate_hyper() hidden, weight, labels = self.generate_forward_inputs() # NOTE: we need to manually synchronize hidden and labels among Process Group dist.broadcast(hidden, src=0, group=self.group) dist.broadcast(labels, src=0, group=self.group) torch.cuda.reset_peak_memory_stats() (tp_logprobs, tp_entropy) = run_torch_entropy_tp(hidden, weight, labels, self.temperature, self.group) torch.cuda.synchronize() forward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 g_entropy, g_logprobs = self.generate_backward_inputs() # NOTE: we need to manually synchronize g_entropy and g_logprobs among Process Group dist.broadcast(g_entropy, src=0, group=self.group) dist.broadcast(g_logprobs, src=0, group=self.group) torch.cuda.reset_peak_memory_stats() (d_tp_hidden, d_tp_weight) = torch.autograd.grad( (tp_entropy, tp_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) torch.cuda.synchronize() backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 # NOTE: all-reduce on hidden is conducted outside the kernel dist.all_reduce(d_tp_hidden, op=dist.ReduceOp.SUM, group=self.group) if self.local_rank == 0: print(f"[INFO]: Torch Forward pass peak memory: {forward_max_memory:.2f} MB") print(f"[INFO]: Torch Backward pass peak memory: {backward_max_memory:.2f} MB") def verify_kernel_correctness(self, iterations: int = 5): self.cleanup() self.generate_hyper() torch_forward_latency = list() torch_backward_latency = list() kernel_forward_latency = list() kernel_backward_latency = list() start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) for i in range(iterations): hidden, weight, labels = self.generate_forward_inputs() # NOTE: we need to manually synchronize hidden and labels among Process Group dist.broadcast(hidden, src=0, group=self.group) dist.broadcast(labels, src=0, group=self.group) start_event.record() (torch_logprobs, torch_entropy) = run_torch_entropy_tp(hidden, weight, labels, self.temperature, self.group) end_event.record() torch.cuda.synchronize() torch_forward_latency.append(start_event.elapsed_time(end_event)) start_event.record() (kernel_logprobs, kernel_entropy) = linear_cross_entropy( hidden, weight, labels, self.temperature, "none", self.group ) end_event.record() torch.cuda.synchronize() kernel_forward_latency.append(start_event.elapsed_time(end_event)) torch.testing.assert_close(torch_logprobs, kernel_logprobs, atol=1e-1, rtol=1e-2) torch.testing.assert_close(torch_entropy, kernel_entropy, atol=1e-1, rtol=1e-2) # backward pass g_entropy, g_logprobs = self.generate_backward_inputs() # NOTE: we need to manually synchronize g_entropy and g_logprobs among Process Group dist.broadcast(g_entropy, src=0, group=self.group) dist.broadcast(g_logprobs, src=0, group=self.group) start_event.record() (torch_d_hidden, torch_d_weight) = torch.autograd.grad( (torch_entropy, torch_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) end_event.record() torch.cuda.synchronize() torch_backward_latency.append(start_event.elapsed_time(end_event)) # NOTE: all-reduce on hidden is conducted outside the kernel dist.all_reduce(torch_d_hidden, op=dist.ReduceOp.SUM, group=self.group) start_event.record() (kernel_d_hidden, kernel_d_weight) = torch.autograd.grad( (kernel_entropy, kernel_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) end_event.record() torch.cuda.synchronize() kernel_backward_latency.append(start_event.elapsed_time(end_event)) # NOTE: all-reduce on hidden is conducted outside the kernel dist.all_reduce(kernel_d_hidden, op=dist.ReduceOp.SUM, group=self.group) torch.testing.assert_close(torch_d_hidden, kernel_d_hidden, atol=2e-2, rtol=4e-2) torch.testing.assert_close(torch_d_weight, kernel_d_weight, atol=2e-2, rtol=4e-2) # remove first latency torch_forward_latency = torch_forward_latency[1:] torch_backward_latency = torch_backward_latency[1:] kernel_forward_latency = kernel_forward_latency[1:] kernel_backward_latency = kernel_backward_latency[1:] if self.local_rank == 0: print("\n[PASS]: Verified kernel forward & backward correctness.") print( f"[INFO]: Forward pass: Torch implementation average time: " f"{sum(torch_forward_latency) / len(torch_forward_latency):.2f} ms" ) print( f"[INFO]: Backward pass: torch implementation average time: " f"{sum(torch_backward_latency) / len(torch_backward_latency):.2f} ms" ) print( f"[INFO]: Forward pass: Kernel implementation average time: " f"{sum(kernel_forward_latency) / len(kernel_forward_latency):.2f} ms" ) print( f"[INFO]: Backward pass: kernel implementation average time: " f"{sum(kernel_backward_latency) / len(kernel_backward_latency):.2f} ms" ) def check_kernel_storage(self): self.cleanup() self.generate_hyper() hidden, weight, labels = self.generate_forward_inputs() # NOTE: we need to manually synchronize hidden and labels among Process Group dist.broadcast(hidden, src=0, group=self.group) dist.broadcast(labels, src=0, group=self.group) torch.cuda.reset_peak_memory_stats() (kernel_logprobs, kernel_entropy) = linear_cross_entropy( hidden, weight, labels, self.temperature, "none", self.group ) torch.cuda.synchronize() kernel_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 g_entropy, g_logprobs = self.generate_backward_inputs() # NOTE: we need to manually synchronize g_entropy and g_logprobs among Process Group dist.broadcast(g_entropy, src=0, group=self.group) dist.broadcast(g_logprobs, src=0, group=self.group) torch.cuda.reset_peak_memory_stats() (d_kernel_hidden, d_kernel_weight) = torch.autograd.grad( (kernel_entropy, kernel_logprobs), (hidden, weight), (g_entropy, g_logprobs), retain_graph=False ) torch.cuda.synchronize() kernel_backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 # NOTE: all-reduce on hidden is conducted outside the kernel dist.all_reduce(d_kernel_hidden, op=dist.ReduceOp.SUM, group=self.group) if self.local_rank == 0: print(f"[INFO]: Kernel Forward pass peak memory: {kernel_max_memory:.2f} MB") print(f"[INFO]: Kernel Backward pass peak memory: {kernel_backward_max_memory:.2f} MB") if __name__ == "__main__": # TP command: torchrun --standalone --nnodes=1 --nproc-per-node=2 tests/kernels/test_linear_cross_entropy_tp.py # Check if running with torchrun (distributed mode) assert int(os.environ["WORLD_SIZE"]) > 1, ( "[ERROR]: This test is designed to run in distributed mode with torchrun. Please use torchrun to " "execute this script." ) torch.manual_seed(233376 + int(os.environ.get("RANK", 0))) # set_backward_method(BackwardEnum._Total_Fuse_MN) # set_backward_method(BackwardEnum._Split_Dlogits_N) test = TestLinearCrossEntropy_TensorParallel() for test_case_idx in range(MAX_TEST_CASES): print(f"[INFO] Running test case {test_case_idx}") test.initialize(test_case_idx) if VERIFY_TORCH_SELF: test.verify_torch_itself() test.check_torch_storage() test.verify_kernel_correctness() test.check_kernel_storage() test.shutdown() ================================================ FILE: tests/utils/test_special_mstx_profile.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from unittest.mock import MagicMock, patch from verl.utils.profiler.config import NPUToolConfig, ProfilerConfig from verl.utils.profiler.mstx_profile import NPUProfiler from verl.utils.profiler.profile import DistProfiler class TestNPUProfilerInitialization(unittest.TestCase): def setUp(self): NPUProfiler._define_count = 0 def test_init_with_default_config(self): tool_config = NPUToolConfig() config = ProfilerConfig(tool="npu") profiler = DistProfiler(rank=0, config=config, tool_config=tool_config) self.assertFalse(profiler.check_enable()) def test_init_with_disabled_config(self): config = ProfilerConfig(enable=False, tool="npu") tool_config = NPUToolConfig() profiler = DistProfiler(rank=0, config=config, tool_config=tool_config) self.assertFalse(profiler.check_enable()) def test_init_with_all_ranks_true(self): config = ProfilerConfig(enable=True, all_ranks=True, tool="npu") tool_config = NPUToolConfig() profiler = DistProfiler(rank=0, config=config, tool_config=tool_config) self.assertTrue(profiler.check_this_rank()) def test_init_with_ranks_list(self): config = ProfilerConfig(enable=True, ranks=[1, 2], tool="npu") tool_config = NPUToolConfig() profiler = DistProfiler(rank=1, config=config, tool_config=tool_config) self.assertTrue(profiler.check_this_rank()) def test_init_with_rank_not_in_ranks(self): config = ProfilerConfig(enable=True, ranks=[1, 2], tool="npu") tool_config = NPUToolConfig() profiler = DistProfiler(rank=3, config=config, tool_config=tool_config) self.assertFalse(profiler.check_this_rank()) class TestNPUProfilerStart(unittest.TestCase): def setUp(self): NPUProfiler._define_count = 0 self.config = ProfilerConfig(enable=True, ranks=[0], tool="npu") self.tool_config = NPUToolConfig(discrete=False) @patch("verl.utils.profiler.mstx_profile.get_npu_profiler") def test_start_when_enabled_and_this_rank(self, mock_get_profiler): profiler = DistProfiler(rank=0, config=self.config, tool_config=self.tool_config) profiler.start(role="worker", profile_step="1") self.assertTrue(profiler.check_this_step()) self.assertEqual(NPUProfiler._define_count, 1) mock_get_profiler.assert_called_once() @patch("verl.utils.profiler.mstx_profile.get_npu_profiler") def test_start_when_not_this_rank(self, mock_get_profiler): profiler = DistProfiler(rank=1, config=self.config, tool_config=self.tool_config) profiler.start() self.assertFalse(profiler.check_this_step()) self.assertEqual(NPUProfiler._define_count, 0) mock_get_profiler.assert_not_called() @patch("verl.utils.profiler.mstx_profile.get_npu_profiler") def test_start_discrete_mode_does_not_increase_count(self, mock_get_profiler): tool_config = NPUToolConfig(discrete=True) profiler = DistProfiler(rank=0, config=self.config, tool_config=tool_config) profiler.start() self.assertEqual(NPUProfiler._define_count, 0) mock_get_profiler.assert_not_called() @patch("verl.utils.profiler.mstx_profile.get_npu_profiler") def test_multiple_start_calls_do_not_increase_count(self, mock_get_profiler): profiler = DistProfiler(rank=0, config=self.config, tool_config=self.tool_config) profiler.start() profiler.start() self.assertEqual(NPUProfiler._define_count, 1) mock_get_profiler.assert_called_once() class TestNPUProfilerStartStopInteraction(unittest.TestCase): def setUp(self): NPUProfiler._define_count = 0 self.config = ProfilerConfig(enable=True, ranks=[0], tool="npu") self.tool_config = NPUToolConfig(discrete=False) @patch("verl.utils.profiler.mstx_profile.get_npu_profiler") def test_start_stop_cycle(self, mock_get_profiler): mock_profile_npu = MagicMock() mock_get_profiler.return_value = mock_profile_npu profiler = DistProfiler(rank=0, config=self.config, tool_config=self.tool_config) profiler.start() self.assertEqual(NPUProfiler._define_count, 1) self.assertEqual(mock_profile_npu.start.call_count, 1) profiler.stop() self.assertEqual(NPUProfiler._define_count, 0) self.assertEqual(mock_profile_npu.step.call_count, 1) self.assertEqual(mock_profile_npu.stop.call_count, 1) @patch("verl.utils.profiler.mstx_profile.get_npu_profiler") def test_multiple_instances_share_define_count(self, mock_get_profiler): mock_profile_npu = MagicMock() mock_get_profiler.return_value = mock_profile_npu profiler1 = DistProfiler(rank=0, config=self.config, tool_config=self.tool_config) profiler2 = DistProfiler(rank=0, config=self.config, tool_config=self.tool_config) profiler1.start() profiler2.start() self.assertEqual(NPUProfiler._define_count, 1) self.assertEqual(mock_profile_npu.start.call_count, 1) profiler1.stop() self.assertEqual(NPUProfiler._define_count, 0) class TestNPUProfilerAnnotate(unittest.TestCase): def setUp(self): self.config = ProfilerConfig(enable=True, all_ranks=True, tool="npu") self.tool_config = NPUToolConfig(discrete=False) self.rank = 0 def test_annotate_decorator_applied_correctly(self): mock_worker = MagicMock() mock_worker.profiler = DistProfiler(rank=self.rank, config=self.config, tool_config=self.tool_config) # Manually set private attribute for testing annotation in active step mock_worker.profiler._this_step = True mock_mark_range = "mocked_range_handle" with ( patch("verl.utils.profiler.mstx_profile.mark_start_range") as mock_start_patch, patch("verl.utils.profiler.mstx_profile.mark_end_range") as mock_end_patch, ): mock_start_patch.return_value = mock_mark_range with patch("verl.utils.profiler.mstx_profile.get_npu_profiler") as mock_get_profiler: decorator = mock_worker.profiler.annotate(message="test") @decorator def test_func(self, *args, **kwargs): return "result" result = test_func(mock_worker) self.assertEqual(result, "result") mock_start_patch.assert_called_once_with(message="test") mock_end_patch.assert_called_once_with(mock_mark_range) mock_get_profiler.assert_not_called() def test_annotate_when_profiler_disabled(self): disabled_config = ProfilerConfig(enable=False, tool="npu") mock_worker = MagicMock() mock_worker.profiler = DistProfiler(rank=self.rank, config=disabled_config, tool_config=self.tool_config) with ( patch("verl.utils.profiler.mstx_profile.mark_start_range") as mock_start_patch, patch("verl.utils.profiler.mstx_profile.mark_end_range") as mock_end_patch, patch("verl.utils.profiler.mstx_profile.get_npu_profiler") as mock_get_profiler, ): decorator = mock_worker.profiler.annotate(message="test") @decorator def test_func(self, *args, **kwargs): return "result" result = test_func(mock_worker) self.assertEqual(result, "result") mock_start_patch.assert_not_called() mock_end_patch.assert_not_called() mock_get_profiler.assert_not_called() def test_annotate_when_this_step_disabled(self): mock_worker = MagicMock() mock_worker.profiler = DistProfiler(rank=self.rank, config=self.config, tool_config=self.tool_config) mock_worker.profiler._this_step = False with ( patch("verl.utils.profiler.mstx_profile.mark_start_range") as mock_start_patch, patch("verl.utils.profiler.mstx_profile.mark_end_range") as mock_end_patch, patch("verl.utils.profiler.mstx_profile.get_npu_profiler") as mock_get_profiler, ): decorator = mock_worker.profiler.annotate(message="test") @decorator def test_func(self, *args, **kwargs): return "result" result = test_func(mock_worker) self.assertEqual(result, "result") mock_start_patch.assert_not_called() mock_end_patch.assert_not_called() mock_get_profiler.assert_not_called() def test_annotate_discrete_mode_enabled(self): discrete_tool_config = NPUToolConfig(discrete=True) mock_worker = MagicMock() mock_worker.profiler = DistProfiler(rank=self.rank, config=self.config, tool_config=discrete_tool_config) mock_worker.profiler._this_step = True mock_mark_range = "mocked_range_handle" mock_profile_npu = MagicMock() with ( patch("verl.utils.profiler.mstx_profile.mark_start_range") as mock_start_patch, patch("verl.utils.profiler.mstx_profile.mark_end_range") as mock_end_patch, patch("verl.utils.profiler.mstx_profile.get_npu_profiler") as mock_get_profiler, ): mock_start_patch.return_value = mock_mark_range mock_get_profiler.return_value = mock_profile_npu decorator = mock_worker.profiler.annotate(message="test", role="test_role") @decorator def test_func(self, *args, **kwargs): return "result" result = test_func(mock_worker) self.assertEqual(result, "result") mock_start_patch.assert_called_once_with(message="test") mock_end_patch.assert_called_once_with(mock_mark_range) mock_get_profiler.assert_called_once_with( contents=mock_worker.profiler._impl.profile_contents, profile_level=mock_worker.profiler._impl.profile_level, profile_save_path=mock_worker.profiler._impl.profile_save_path, analysis=mock_worker.profiler._impl.analysis, role="test_role", ) mock_profile_npu.start.assert_called_once() mock_profile_npu.step.assert_called_once() mock_profile_npu.stop.assert_called_once() def test_annotate_with_default_message(self): mock_worker = MagicMock() mock_worker.profiler = DistProfiler(rank=self.rank, config=self.config, tool_config=self.tool_config) mock_worker.profiler._this_step = True mock_mark_range = "mocked_range_handle" with ( patch("verl.utils.profiler.mstx_profile.mark_start_range") as mock_start_patch, patch("verl.utils.profiler.mstx_profile.mark_end_range") as mock_end_patch, ): mock_start_patch.return_value = mock_mark_range decorator = mock_worker.profiler.annotate() @decorator def test_func(self, *args, **kwargs): return "result" test_func(mock_worker) mock_start_patch.assert_called_once_with(message="test_func") mock_end_patch.assert_called_once_with(mock_mark_range) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/utils/test_temp_env_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from verl.utils.py_functional import temp_env_var @pytest.fixture(autouse=True) def clean_env(): """Fixture to clean up environment variables before and after each test.""" # Store original environment state original_env = dict(os.environ) # Clean up any test variables that might exist test_vars = ["TEST_VAR", "TEST_VAR_2", "EXISTING_VAR"] for var in test_vars: if var in os.environ: del os.environ[var] # Yield control to the test function yield # Restore original environment state after test os.environ.clear() os.environ.update(original_env) def test_set_new_env_var(): """Test setting a new environment variable that didn't exist before.""" # Ensure variable doesn't exist assert "TEST_VAR" not in os.environ with temp_env_var("TEST_VAR", "test_value"): # Variable should be set inside context assert os.environ["TEST_VAR"] == "test_value" assert "TEST_VAR" in os.environ # Variable should be removed after context assert "TEST_VAR" not in os.environ def test_restore_existing_env_var(): """Test restoring an environment variable that already existed.""" # Set up existing variable os.environ["EXISTING_VAR"] = "original_value" with temp_env_var("EXISTING_VAR", "temporary_value"): # Variable should be temporarily changed assert os.environ["EXISTING_VAR"] == "temporary_value" # Variable should be restored to original value assert os.environ["EXISTING_VAR"] == "original_value" def test_env_var_restored_on_exception(): """Test that environment variables are restored even when exceptions occur.""" # Set up existing variable os.environ["EXISTING_VAR"] = "original_value" with pytest.raises(ValueError): with temp_env_var("EXISTING_VAR", "temporary_value"): # Verify variable is set assert os.environ["EXISTING_VAR"] == "temporary_value" # Raise exception raise ValueError("Test exception") # Variable should still be restored despite exception assert os.environ["EXISTING_VAR"] == "original_value" def test_nested_context_managers(): """Test nested temp_env_var context managers.""" # Set up original variable os.environ["TEST_VAR"] = "original" with temp_env_var("TEST_VAR", "level1"): assert os.environ["TEST_VAR"] == "level1" with temp_env_var("TEST_VAR", "level2"): assert os.environ["TEST_VAR"] == "level2" # Should restore to level1 assert os.environ["TEST_VAR"] == "level1" # Should restore to original assert os.environ["TEST_VAR"] == "original" def test_multiple_different_vars(): """Test setting multiple different environment variables.""" # Set up one existing variable os.environ["EXISTING_VAR"] = "existing_value" with temp_env_var("EXISTING_VAR", "modified"): with temp_env_var("TEST_VAR", "new_value"): assert os.environ["EXISTING_VAR"] == "modified" assert os.environ["TEST_VAR"] == "new_value" # Check restoration assert os.environ["EXISTING_VAR"] == "existing_value" assert "TEST_VAR" not in os.environ def test_empty_string_value(): """Test setting environment variable to empty string.""" with temp_env_var("TEST_VAR", ""): assert os.environ["TEST_VAR"] == "" assert "TEST_VAR" in os.environ # Should be removed after context assert "TEST_VAR" not in os.environ def test_overwrite_with_empty_string(): """Test overwriting existing variable with empty string.""" os.environ["EXISTING_VAR"] = "original" with temp_env_var("EXISTING_VAR", ""): assert os.environ["EXISTING_VAR"] == "" # Should restore original value assert os.environ["EXISTING_VAR"] == "original" def test_context_manager_returns_none(): """Test that context manager yields None.""" with temp_env_var("TEST_VAR", "value") as result: assert result is None assert os.environ["TEST_VAR"] == "value" ================================================ FILE: tests/utils/test_timeout_decorator_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import multiprocessing import sys import threading import time import pytest # Import pytest from verl.utils.py_functional import timeout_limit as timeout # --- Test Task Functions --- TEST_TIMEOUT_SECONDS = 1.5 # Timeout duration for tests LONG_TASK_DURATION = TEST_TIMEOUT_SECONDS + 0.5 # Duration slightly longer than timeout @timeout(seconds=TEST_TIMEOUT_SECONDS) # Keep global decorator for mp tests def quick_task(x): """A task that completes quickly.""" time.sleep(0.1) return "quick_ok" @timeout(seconds=TEST_TIMEOUT_SECONDS) # Keep global decorator for mp tests def slow_task(x): """A task that takes longer than the timeout.""" time.sleep(LONG_TASK_DURATION) return "slow_finished" # This return value indicates it didn't time out # REMOVE global decorator here def task_raises_value_error(): # Now truly not globally decorated """A task that intentionally raises a ValueError.""" raise ValueError("Specific value error from task") # --- Top-level function for signal test in subprocess --- # Keep this decorated globally for the specific subprocess test case @timeout(seconds=TEST_TIMEOUT_SECONDS, use_signals=True) def top_level_decorated_quick_task_signal(): """A pickleable top-level function decorated with signal timeout.""" # Assuming this calls the logic of quick_task directly for the test purpose time.sleep(0.1) return "quick_ok_signal_subprocess" # Different return for clarity if needed # --- Top-level function for signal test in subprocess --- # Keep this decorated globally for the specific subprocess test case @timeout(seconds=TEST_TIMEOUT_SECONDS, use_signals=True) def top_level_decorated_slow_task_signal(): """A pickleable top-level function decorated with signal timeout.""" time.sleep(LONG_TASK_DURATION) return "slow_finished" # --- NEW: Top-level helper function to run target in process --- def run_target_and_put_in_queue(target_func, q): """ Top-level helper function to run a target function and put its result or exception into a queue. This function is pickleable and can be used as the target for multiprocessing.Process. """ try: result = target_func() q.put(("success", result)) except Exception as e: q.put(("error", e)) # Use a module-level fixture to set the start method on macOS @pytest.fixture(scope="module", autouse=True) # Changed scope to module def set_macos_start_method(): if sys.platform == "darwin": # Force fork method on macOS to avoid pickling issues with globally decorated functions # when running tests via pytest discovery. current_method = multiprocessing.get_start_method(allow_none=True) # Only set if not already set or if set to something else (less likely in test run) if current_method is None or current_method != "fork": try: multiprocessing.set_start_method("fork", force=True) except RuntimeError: # Might fail if context is already started, ignore in that case. pass def test_quick_task(): # Renamed from test_multiprocessing_quick_task """Tests timeout handles a quick task correctly.""" # Call the globally decorated function directly result = quick_task(1) assert result == "quick_ok" # Use pytest assert def test_slow_task_timeout(): # Renamed from test_multiprocessing_slow_task_timeout """Tests timeout correctly raises TimeoutError for a slow task.""" # Call the globally decorated function directly within pytest.raises with pytest.raises(TimeoutError) as excinfo: # Use pytest.raises slow_task(1) # Check the error message from the multiprocessing implementation assert f"timed out after {TEST_TIMEOUT_SECONDS} seconds" in str(excinfo.value) # Use pytest assert def test_internal_exception(): # Renamed from test_multiprocessing_internal_exception """Tests timeout correctly propagates internal exceptions.""" # Apply the default timeout decorator dynamically to the undecorated function decorated_task = timeout(seconds=TEST_TIMEOUT_SECONDS)(task_raises_value_error) # Apply decorator dynamically with pytest.raises(ValueError) as excinfo: # Use pytest.raises decorated_task() # Call the dynamically decorated function assert str(excinfo.value) == "Specific value error from task" # Use pytest assert # --- Test the signal implementation (use_signals=True) --- # Note: As per py_functional.py, use_signals=True currently falls back to # multiprocessing on POSIX. These tests verify that behavior. def test_signal_quick_task_main_process(): # Removed self """Tests signal timeout handles a quick task correctly in the main process.""" # Apply the signal decorator dynamically def plain_quick_task_logic(): time.sleep(0.1) return "quick_ok_signal" decorated_task = timeout(seconds=TEST_TIMEOUT_SECONDS, use_signals=True)(plain_quick_task_logic) assert decorated_task() == "quick_ok_signal" # Use pytest assert def test_signal_slow_task_main_process_timeout(): # Removed self """Tests signal timeout correctly raises TimeoutError for a slow task in the main process.""" # Apply the signal decorator dynamically def plain_slow_task_logic(): time.sleep(LONG_TASK_DURATION) return "slow_finished_signal" decorated_task = timeout(seconds=TEST_TIMEOUT_SECONDS, use_signals=True)(plain_slow_task_logic) with pytest.raises(TimeoutError) as excinfo: # Use pytest.raises decorated_task() # Check the error message (falls back to multiprocessing message on POSIX) assert f"timed out after {TEST_TIMEOUT_SECONDS} seconds" in str(excinfo.value) # Use pytest assert @pytest.mark.skip(reason="this test won't pass. Just to show why use_signals should not be used") def test_signal_in_thread_does_not_timeout(): """ Tests that signal-based timeout does NOT work reliably in a child thread. The TimeoutError from the signal handler is not expected to be raised. """ result_container = [] # Use a list to store result from thread exception_container = [] # Use a list to store exception from thread @timeout(seconds=TEST_TIMEOUT_SECONDS, use_signals=True) def slow_task_in_thread(): try: print("Thread: Starting slow task...") time.sleep(LONG_TASK_DURATION) print("Thread: Slow task finished.") return "slow_finished_in_thread" except Exception as e: # Catch any exception within the thread's target function print(f"Thread: Caught exception: {e}") exception_container.append(e) return None # Indicate failure def thread_target(): try: # Run the decorated function inside the thread res = slow_task_in_thread() if res is not None: result_container.append(res) except Exception as e: # This might catch exceptions happening *outside* the decorated function # but still within the thread target, though less likely here. print(f"Thread Target: Caught exception: {e}") exception_container.append(e) thread = threading.Thread(target=thread_target) print("Main: Starting thread...") thread.start() # Wait longer than the timeout + task duration to ensure the thread finishes # regardless of whether timeout worked or not. thread.join(timeout=LONG_TASK_DURATION + 1) assert len(exception_container) == 1 assert isinstance(exception_container[0], TimeoutError) assert not result_container def test_in_thread_timeout(): result_container = [] # Use a list to store result from thread exception_container = [] # Use a list to store exception from thread @timeout(seconds=TEST_TIMEOUT_SECONDS, use_signals=False) def slow_task_in_thread(): try: print("Thread: Starting slow task...") time.sleep(LONG_TASK_DURATION) print("Thread: Slow task finished.") return "slow_finished_in_thread" except Exception as e: # Catch any exception within the thread's target function print(f"Thread: Caught exception: {e}") exception_container.append(e) return None # Indicate failure def thread_target(): try: # Run the decorated function inside the thread res = slow_task_in_thread() if res is not None: result_container.append(res) except Exception as e: # This might catch exceptions happening *outside* the decorated function # but still within the thread target, though less likely here. print(f"Thread Target: Caught exception: {e}") exception_container.append(e) thread = threading.Thread(target=thread_target) print("Main: Starting thread...") thread.start() # Wait longer than the timeout + task duration to ensure the thread finishes # regardless of whether timeout worked or not. thread.join(timeout=LONG_TASK_DURATION + 1) assert len(exception_container) == 1 assert isinstance(exception_container[0], TimeoutError) assert not result_container ================================================ FILE: tests/utils/test_tokenizer_normalize_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import pytest from verl.utils.tokenizer import normalize_token_ids class DummyBatchEncoding: def __init__(self, input_ids): self.input_ids = input_ids class DummyToList: def __init__(self, data): self._data = data def tolist(self): return self._data @pytest.mark.parametrize( ("tokenized_output", "expected"), [ # transformers v4-style direct token ids ([1, 2, 3], [1, 2, 3]), ((1, 2, 3), [1, 2, 3]), # common list-like outputs with tolist()/ndarray paths (DummyToList([1, 2, 3]), [1, 2, 3]), (np.array([1, 2, 3], dtype=np.int64), [1, 2, 3]), # transformers v5-like mapping / BatchEncoding-style outputs ({"input_ids": [1, 2, 3]}, [1, 2, 3]), ({"input_ids": DummyToList([1, 2, 3])}, [1, 2, 3]), ({"input_ids": [[1, 2, 3]]}, [1, 2, 3]), (DummyBatchEncoding([1, 2, 3]), [1, 2, 3]), (DummyBatchEncoding(DummyToList([[1, 2, 3]])), [1, 2, 3]), # scalar item() support ([np.int64(1), np.int32(2), np.int16(3)], [1, 2, 3]), ], ) def test_normalize_token_ids_valid_outputs(tokenized_output, expected): assert normalize_token_ids(tokenized_output) == expected @pytest.mark.parametrize( "tokenized_output", [ "not-token-ids", {"attention_mask": [1, 1, 1]}, [[1, 2], [3, 4]], # ambiguous batched ids should fail fast [1, object(), 3], ], ) def test_normalize_token_ids_invalid_outputs(tokenized_output): with pytest.raises(TypeError): normalize_token_ids(tokenized_output) ================================================ FILE: tests/utils/test_torch_functional.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest import torch import torch.distributed as dist import torch.multiprocessing as mp from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device from verl.utils.torch_functional import ( distributed_masked_mean, distributed_mean_max_min_std, expand_as_nested, masked_mean, ) def _worker_mean(rank: int, world_size: int, rendezvous_file: str): # 1) set GPU and init NCCL get_torch_device().set_device(rank) dist.init_process_group( backend=get_nccl_backend(), init_method=f"file://{rendezvous_file}", rank=rank, world_size=world_size, ) # each rank holds tensor [rank+1] local = torch.tensor([float(rank + 1)], device=f"{get_device_name()}:{rank}") mean, gmax, gmin, gstd = distributed_mean_max_min_std(local, True, True, True) values = [float(i + 1) for i in range(world_size)] exp_mean = sum(values) / len(values) exp_max = max(values) exp_min = min(values) var = sum((x - exp_mean) ** 2 for x in values) / (len(values) - 1) exp_std = var**0.5 # all ranks should see the same result assert torch.allclose(mean.cpu(), torch.tensor(exp_mean)), f"mean@{rank}" assert torch.allclose(gmax.cpu(), torch.tensor(exp_max)), f"max@{rank}" assert torch.allclose(gmin.cpu(), torch.tensor(exp_min)), f"min@{rank}" assert torch.allclose(gstd.cpu(), torch.tensor(exp_std)), f"std@{rank}" dist.destroy_process_group() @pytest.mark.parametrize( "value,mask,gt", [ ([1.0, 2.0, 3.0, 4.0], [1, 0, 0, 1], 2.5), ([1.0, 2.0, float("nan"), 4.0], [1, 0, 0, 1], 2.5), ([1.0, 2.0, float("nan"), 4.0], [1, 0, 1, 0], float("nan")), ], ) def test_masked_mean(value, mask, gt): res = masked_mean(torch.tensor(value), torch.tensor(mask)) gt = torch.tensor(gt) assert torch.allclose(res, gt) or (torch.isnan(res) and torch.isnan(gt)) @pytest.mark.parametrize("world_size", [2, 4]) def test_distributed_mean_max_min_std(world_size, tmp_path): rendezvous_file = str(tmp_path / "rdzv_mean") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) mp.spawn( fn=_worker_mean, args=(world_size, rendezvous_file), nprocs=world_size, join=True, ) def _worker_mask(rank: int, world_size: int, rendezvous_file: str): get_torch_device().set_device(rank) dist.init_process_group( backend=get_nccl_backend(), init_method=f"file://{rendezvous_file}", rank=rank, world_size=world_size, ) # build per‐rank tensor and mask local_tensor = torch.tensor([rank * 2 + 1.0, rank * 2 + 2.0], device=f"{get_device_name()}:{rank}") if rank == 0: mask = torch.tensor([1, 0], device=f"{get_device_name()}:{rank}", dtype=torch.float32) else: mask = torch.tensor([0, 1], device=f"{get_device_name()}:{rank}", dtype=torch.float32) gmean = distributed_masked_mean(local_tensor, mask) valid_values = [1.0] + [2 * i + 2.0 for i in range(1, world_size)] expected_mean = sum(valid_values) / len(valid_values) assert torch.allclose(gmean.cpu(), torch.tensor(expected_mean)), f"masked_mean@{rank}" dist.destroy_process_group() @pytest.mark.parametrize("world_size", [2, 4]) def test_distributed_masked_mean(world_size, tmp_path): rendezvous_file = str(tmp_path / "rdzv_mask") os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True) mp.spawn( fn=_worker_mask, args=(world_size, rendezvous_file), nprocs=world_size, join=True, ) def test_expand_as_nested(): a = torch.randn(2) b = torch.randn(3) c = torch.randn(4) nested_tensor = torch.nested.as_nested_tensor([a, b, c], layout=torch.jagged) tensor = torch.tensor([1, 2, 3]) output = expand_as_nested(tensor, nested_tensor) assert output.values().tolist() == [1, 1, 2, 2, 2, 3, 3, 3, 3] assert torch.all(output.offsets() == nested_tensor.offsets()).item() # test exceptions with pytest.raises(AssertionError): expand_as_nested(tensor, tensor) other_tensor = torch.tensor([1, 2, 3, 4]) with pytest.raises(AssertionError): expand_as_nested(other_tensor, nested_tensor) other_tensor = torch.tensor([[1, 2, 3]]) with pytest.raises(AssertionError): expand_as_nested(other_tensor, nested_tensor) with pytest.raises(AssertionError): expand_as_nested(tensor, nested_tensor.unsqueeze(-1)) ================================================ FILE: tests/utils/test_torch_profile.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from unittest.mock import MagicMock, patch import torch from verl.utils.profiler.config import ProfilerConfig, TorchProfilerToolConfig from verl.utils.profiler.torch_profile import Profiler, get_torch_profiler class TestTorchProfile(unittest.TestCase): def setUp(self): # Reset Profiler class state Profiler._define_count = 0 @patch("torch.profiler.profile") def test_get_torch_profiler(self, mock_profile): # Test wrapper function get_torch_profiler(contents=["cpu", "cuda", "stack"], save_path="/tmp/test", rank=0) mock_profile.assert_called_once() _, kwargs = mock_profile.call_args # Verify activities activities = kwargs["activities"] self.assertIn(torch.profiler.ProfilerActivity.CPU, activities) self.assertIn(torch.profiler.ProfilerActivity.CUDA, activities) # Verify options self.assertTrue(kwargs["with_stack"]) self.assertFalse(kwargs["record_shapes"]) self.assertFalse(kwargs["profile_memory"]) @patch("verl.utils.profiler.torch_profile.get_torch_profiler") def test_profiler_lifecycle(self, mock_get_profiler): # Mock the underlying torch profiler object mock_prof_instance = MagicMock() mock_get_profiler.return_value = mock_prof_instance # Initialize tool_config = TorchProfilerToolConfig(contents=["cpu"], discrete=False) config = ProfilerConfig(save_path="/tmp/test", enable=True, tool_config=tool_config) profiler = Profiler(rank=0, config=config, tool_config=tool_config) # Test Start profiler.start() mock_get_profiler.assert_called_once() mock_prof_instance.start.assert_called_once() # Test Step profiler.step() mock_prof_instance.step.assert_called_once() # Test Stop profiler.stop() mock_prof_instance.stop.assert_called_once() @patch("verl.utils.profiler.torch_profile.get_torch_profiler") def test_discrete_mode(self, mock_get_profiler): # Mock for discrete mode mock_prof_instance = MagicMock() mock_get_profiler.return_value = mock_prof_instance tool_config = TorchProfilerToolConfig(contents=["cpu"], discrete=True) config = ProfilerConfig(save_path="/tmp/test", enable=True, tool_config=tool_config) profiler = Profiler(rank=0, config=config, tool_config=tool_config) # In discrete mode, start/stop shouldn't trigger global profiler immediately profiler.start() mock_get_profiler.assert_not_called() profiler.stop() mock_prof_instance.stop.assert_not_called() if __name__ == "__main__": unittest.main() ================================================ FILE: tests/workers/actor/test_special_dp_actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import torch import torch.nn as nn from tensordict import TensorDict from transformers import AutoModelForCausalLM, Qwen3Config from verl import DataProto from verl.utils.device import get_device_name from verl.workers.actor.dp_actor import DataParallelPPOActor from verl.workers.config import FSDPActorConfig, OptimizerConfig class MockTransformerModel(nn.Module): """Mock transformer model for testing DataParallelPPOActor""" def __init__(self, vocab_size=1000, hidden_size=64): super().__init__() self.vocab_size = vocab_size self.hidden_size = hidden_size self.embedding = nn.Embedding(vocab_size, hidden_size) self.transformer = nn.TransformerEncoder( nn.TransformerEncoderLayer(d_model=hidden_size, nhead=4, batch_first=True), num_layers=2 ) self.lm_head = nn.Linear(hidden_size, vocab_size) def forward(self, input_ids, attention_mask=None, position_ids=None, use_cache=False, **kwargs): batch_size, seq_len = input_ids.shape embeddings = self.embedding(input_ids) hidden_states = self.transformer(embeddings) logits = self.lm_head(hidden_states) class MockOutput: def __init__(self, logits): self.logits = logits return MockOutput(logits) class TestDataParallelPPOActor(unittest.TestCase): """Test DataParallelPPOActor compute_log_prob and update_policy methods""" @classmethod def setUpClass(cls): """Set up distributed environment""" if get_device_name() == "cuda": backend_name = "nccl" elif get_device_name() == "npu": backend_name = "hccl" else: backend_name = "gloo" if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend=backend_name, init_method="env://") cls.rank = torch.distributed.get_rank() cls.world_size = torch.distributed.get_world_size() if get_device_name() == "cuda": torch.cuda.set_device(cls.rank) cls.device = torch.device(f"cuda:{cls.rank}") elif get_device_name() == "npu": torch.npu.set_device(cls.rank) cls.device = torch.device(f"npu:{cls.rank}") else: cls.device = torch.device("cpu") def setUp(self): """Set up test fixtures""" self.config = FSDPActorConfig( strategy="fsdp2", ppo_mini_batch_size=4, ppo_micro_batch_size_per_gpu=2, ppo_epochs=1, clip_ratio=0.2, entropy_coeff=0.01, grad_clip=1.0, use_dynamic_bsz=False, use_torch_compile=False, # Disable torch.compile for testing ulysses_sequence_parallel_size=1, optim=OptimizerConfig(lr=1e-6), rollout_n=1, ) self.mock_model = MockTransformerModel(vocab_size=1000, hidden_size=64).to(self.device) self.mock_optimizer = torch.optim.Adam(self.mock_model.parameters(), lr=1e-4) self.actor = DataParallelPPOActor( config=self.config, actor_module=self.mock_model, actor_optimizer=self.mock_optimizer ) @classmethod def tearDownClass(cls): """Clean up distributed environment""" if torch.distributed.is_initialized(): torch.distributed.destroy_process_group() def _create_test_data_for_compute_log_prob(self): """Create test DataProto for compute_log_prob method""" batch_size = 2 prompt_length = 8 response_length = 4 total_length = prompt_length + response_length vocab_size = 1000 input_ids = torch.randint(0, vocab_size, (batch_size, total_length)).to(self.device) attention_mask = torch.ones(batch_size, total_length).to(self.device) position_ids = torch.arange(total_length).unsqueeze(0).expand(batch_size, -1).to(self.device) responses = input_ids[:, -response_length:] # Last part is the response tensor_dict = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "responses": responses, }, batch_size=[batch_size], ) meta_info = {"micro_batch_size": batch_size, "temperature": 1.0, "use_dynamic_bsz": False} return DataProto(batch=tensor_dict, meta_info=meta_info) def _create_test_data_for_update_policy(self): """Create test DataProto for update_policy method""" batch_size = 4 # Must match ppo_mini_batch_size prompt_length = 8 response_length = 4 total_length = prompt_length + response_length vocab_size = 1000 input_ids = torch.randint(0, vocab_size, (batch_size, total_length)).to(self.device) attention_mask = torch.ones(batch_size, total_length).to(self.device) position_ids = torch.arange(total_length).unsqueeze(0).expand(batch_size, -1).to(self.device) responses = input_ids[:, -response_length:] response_mask = torch.ones(batch_size, response_length).to(self.device) old_log_probs = torch.randn(batch_size, response_length).to(self.device) * 0.1 # Small values advantages = torch.randn(batch_size, response_length).to(self.device) * 0.5 tensor_dict = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "responses": responses, "response_mask": response_mask, "old_log_probs": old_log_probs, "advantages": advantages, }, batch_size=[batch_size], ) meta_info = {"temperature": 1.0} return DataProto(batch=tensor_dict, meta_info=meta_info) def test_compute_log_prob(self): """Test compute_log_prob method""" data = self._create_test_data_for_compute_log_prob() outputs = self.actor.compute_log_prob(data, calculate_entropy=True) log_probs = outputs["log_probs"] entropys = outputs["entropys"] batch_size = data.batch["responses"].shape[0] response_length = data.batch["responses"].shape[1] self.assertIsInstance(log_probs, torch.Tensor) self.assertEqual(log_probs.shape, (batch_size, response_length)) self.assertTrue(torch.all(torch.isfinite(log_probs))) self.assertIsInstance(entropys, torch.Tensor) self.assertEqual(entropys.shape, (batch_size, response_length)) self.assertTrue(torch.all(torch.isfinite(entropys))) self.assertTrue(torch.all(entropys >= 0)) # Entropy should be non-negative def test_compute_log_prob_without_entropy(self): """Test compute_log_prob method without entropy calculation""" data = self._create_test_data_for_compute_log_prob() outputs = self.actor.compute_log_prob(data, calculate_entropy=False) log_probs = outputs["log_probs"] entropys = outputs.get("entropys", None) batch_size = data.batch["responses"].shape[0] response_length = data.batch["responses"].shape[1] self.assertIsInstance(log_probs, torch.Tensor) self.assertEqual(log_probs.shape, (batch_size, response_length)) self.assertTrue(torch.all(torch.isfinite(log_probs))) self.assertIsNone(entropys) def test_update_policy(self): """Test update_policy method""" data = self._create_test_data_for_update_policy() metrics = self.actor.update_policy(data) self.assertIsInstance(metrics, dict) expected_metric_keys = [ "actor/pg_loss", "actor/pg_clipfrac", "actor/ppo_kl", "actor/pg_clipfrac_lower", "actor/grad_norm", ] for key in expected_metric_keys: self.assertIn(key, metrics) if isinstance(metrics[key], list): self.assertTrue(all(torch.isfinite(torch.tensor(v)) for v in metrics[key])) else: self.assertIsInstance(metrics[key], (float, int)) self.assertTrue(torch.isfinite(torch.tensor(metrics[key]))) def test_dataparallelppoactor_initialization(self): """Test DataParallelPPOActor initialization""" self.assertIsNotNone(self.actor.actor_module) self.assertIsNotNone(self.actor.actor_optimizer) self.assertEqual(self.actor.config, self.config) self.assertEqual(self.actor.config.strategy, "fsdp2") self.assertEqual(self.actor.config.ppo_mini_batch_size, 4) self.assertEqual(self.actor.config.clip_ratio, 0.2) def test_dataparallelppoactor_with_qwen3_model(self): """Test DataParallelPPOActor with real Qwen3ForCausalLM model""" qwen_config = Qwen3Config( vocab_size=1000, hidden_size=64, intermediate_size=128, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, max_position_embeddings=512, torch_dtype=torch.float32, use_cache=False, ) with torch.device(self.device): qwen_model = AutoModelForCausalLM.from_config(config=qwen_config, torch_dtype=torch.float32).to(self.device) qwen_optimizer = torch.optim.Adam(qwen_model.parameters(), lr=1e-4) qwen_actor = DataParallelPPOActor(config=self.config, actor_module=qwen_model, actor_optimizer=qwen_optimizer) data = self._create_test_data_for_compute_log_prob() outputs = qwen_actor.compute_log_prob(data, calculate_entropy=True) log_probs = outputs["log_probs"] entropys = outputs["entropys"] batch_size = data.batch["responses"].shape[0] response_length = data.batch["responses"].shape[1] self.assertIsInstance(log_probs, torch.Tensor) self.assertEqual(log_probs.shape, (batch_size, response_length)) self.assertTrue(torch.all(torch.isfinite(log_probs))) self.assertIsInstance(entropys, torch.Tensor) self.assertEqual(entropys.shape, (batch_size, response_length)) self.assertTrue(torch.all(torch.isfinite(entropys))) self.assertTrue(torch.all(entropys >= 0)) policy_data = self._create_test_data_for_update_policy() metrics = qwen_actor.update_policy(policy_data) self.assertIsInstance(metrics, dict) expected_metric_keys = [ "actor/pg_loss", "actor/pg_clipfrac", "actor/ppo_kl", "actor/pg_clipfrac_lower", "actor/grad_norm", ] for key in expected_metric_keys: self.assertIn(key, metrics) if isinstance(metrics[key], list): self.assertTrue(all(torch.isfinite(torch.tensor(v)) for v in metrics[key])) else: self.assertIsInstance(metrics[key], (float, int)) self.assertTrue(torch.isfinite(torch.tensor(metrics[key]))) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/workers/config/test_actor_config_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from verl.utils.config import omega_conf_to_dataclass from verl.workers.config import ( ActorConfig, FSDPActorConfig, McoreActorConfig, OptimizerConfig, ) class TestActorConfig(unittest.TestCase): """Test the ActorConfig dataclass and its variants.""" def test_config_inheritance(self): """Test that the inheritance hierarchy works correctly.""" megatron_dict = { "_target_": "verl.workers.config.McoreActorConfig", "strategy": "megatron", "ppo_mini_batch_size": 256, "ppo_micro_batch_size_per_gpu": 256, "clip_ratio": 0.2, "optim": { "_target_": "verl.workers.config.McoreOptimizerConfig", "lr": 0.1, }, "rollout_n": 1, } fsdp_dict = { "_target_": "verl.workers.config.FSDPActorConfig", "strategy": "fsdp", "ppo_mini_batch_size": 256, "ppo_micro_batch_size_per_gpu": 256, "clip_ratio": 0.2, "optim": { "_target_": "verl.workers.config.FSDPOptimizerConfig", "lr": 0.1, }, "rollout_n": 1, } megatron_config = omega_conf_to_dataclass(megatron_dict) fsdp_config = omega_conf_to_dataclass(fsdp_dict) self.assertIsInstance(megatron_config, ActorConfig) self.assertIsInstance(fsdp_config, ActorConfig) self.assertEqual(megatron_config.ppo_mini_batch_size, fsdp_config.ppo_mini_batch_size) self.assertEqual(megatron_config.clip_ratio, fsdp_config.clip_ratio) def test_actor_config_from_yaml(self): """Test creating ActorConfig from YAML file.""" from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config/actor")): cfg = compose(config_name="actor", overrides=["strategy=fsdp", "ppo_micro_batch_size_per_gpu=128"]) config = omega_conf_to_dataclass(cfg) self.assertIsInstance(config, ActorConfig) self.assertEqual(config.strategy, "fsdp") def test_fsdp_actor_config_from_yaml(self): """Test creating FSDPActorConfig from YAML file.""" from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config/actor")): cfg = compose(config_name="dp_actor", overrides=["strategy=fsdp2", "ppo_micro_batch_size_per_gpu=128"]) config = omega_conf_to_dataclass(cfg) self.assertIsInstance(config, FSDPActorConfig) self.assertEqual(config.strategy, "fsdp2") def test_megatron_actor_config_from_yaml(self): """Test creating McoreActorConfig from YAML file.""" from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config/actor")): cfg = compose(config_name="megatron_actor", overrides=["ppo_micro_batch_size_per_gpu=128"]) config = omega_conf_to_dataclass(cfg) self.assertIsInstance(config, McoreActorConfig) self.assertEqual(config.strategy, "megatron") def test_config_get_method(self): """Test the get method for backward compatibility.""" config_dict = { "_target_": "verl.workers.config.ActorConfig", "strategy": "fsdp", "ppo_mini_batch_size": 256, "ppo_micro_batch_size_per_gpu": 256, "optim": { "_target_": "verl.workers.config.OptimizerConfig", "lr": 0.1, }, "rollout_n": 1, } config = omega_conf_to_dataclass(config_dict) self.assertEqual(config.get("strategy"), "fsdp") self.assertEqual(config.get("ppo_mini_batch_size"), 256) self.assertIsNone(config.get("non_existing")) self.assertEqual(config.get("non_existing", "default"), "default") def test_config_dict_like_access(self): """Test dictionary-like access to config fields.""" config_dict = { "_target_": "verl.workers.config.ActorConfig", "strategy": "fsdp", "ppo_mini_batch_size": 256, "ppo_micro_batch_size_per_gpu": 256, "optim": { "_target_": "verl.workers.config.OptimizerConfig", "lr": 0.1, }, "rollout_n": 1, } config = omega_conf_to_dataclass(config_dict) self.assertEqual(config["strategy"], "fsdp") self.assertEqual(config["ppo_mini_batch_size"], 256) field_names = list(config) self.assertIn("strategy", field_names) self.assertIn("ppo_mini_batch_size", field_names) self.assertGreater(len(config), 0) def test_frozen_fields_modification_raises_exception(self): """Test that modifying frozen fields raises an exception.""" config_dict = { "_target_": "verl.workers.config.ActorConfig", "strategy": "fsdp", "ppo_mini_batch_size": 256, "ppo_micro_batch_size_per_gpu": 256, "optim": { "_target_": "verl.workers.config.OptimizerConfig", "lr": 0.1, }, "rollout_n": 1, } config = omega_conf_to_dataclass(config_dict) with self.assertRaises(AttributeError): config.strategy = "megatron" with self.assertRaises(AttributeError): config.clip_ratio = 0.5 config.ppo_mini_batch_size = 512 # This should work since it's not in frozen fields anymore self.assertEqual(config.ppo_mini_batch_size, 512) def test_actor_config_validation_exceptions(self): """Test that ActorConfig.__post_init__ raises appropriate validation exceptions.""" optim = OptimizerConfig(lr=0.1) with self.assertRaises((ValueError, AssertionError)) as cm: ActorConfig( strategy="fsdp", loss_agg_mode="invalid-mode", use_dynamic_bsz=True, optim=optim, ppo_micro_batch_size_per_gpu=4, rollout_n=1, ) self.assertIn("Invalid loss_agg_mode", str(cm.exception)) with self.assertRaises((ValueError, AssertionError)) as cm: ActorConfig( strategy="fsdp", use_dynamic_bsz=False, ppo_micro_batch_size=4, ppo_micro_batch_size_per_gpu=2, optim=optim, rollout_n=1, ) self.assertIn("You have set both", str(cm.exception)) with self.assertRaises((ValueError, AssertionError)) as cm: ActorConfig( strategy="fsdp", use_dynamic_bsz=False, ppo_micro_batch_size=None, ppo_micro_batch_size_per_gpu=None, optim=optim, rollout_n=1, ) self.assertIn("Please set at least one", str(cm.exception)) config = ActorConfig( strategy="fsdp", use_dynamic_bsz=True, ppo_micro_batch_size=None, ppo_micro_batch_size_per_gpu=None, optim=optim, rollout_n=1, ) self.assertIsNotNone(config) # Should not raise an exception def test_fsdp_actor_config_validation_exceptions(self): """Test that FSDPActorConfig.validate() raises appropriate validation exceptions.""" optim = OptimizerConfig(lr=0.1) config = FSDPActorConfig( strategy="fsdp", ulysses_sequence_parallel_size=2, use_dynamic_bsz=True, # Skip batch size validation to focus on FSDP validation optim=optim, rollout_n=1, ) model_config = {"use_remove_padding": False} with self.assertRaises(ValueError) as cm: config.validate(n_gpus=8, train_batch_size=256, model_config=model_config) self.assertIn("you must enable `use_remove_padding`", str(cm.exception)) def test_actor_config_validate_method_exceptions(self): """Test that ActorConfig.validate() raises appropriate validation exceptions.""" optim = OptimizerConfig(lr=0.1) config = ActorConfig( strategy="fsdp", use_dynamic_bsz=False, ppo_mini_batch_size=256, ppo_micro_batch_size=8, ppo_micro_batch_size_per_gpu=None, # Ensure only one batch size setting is used optim=optim, rollout_n=1, ) with self.assertRaises(ValueError) as cm: config.validate(n_gpus=8, train_batch_size=128) self.assertIn("train_batch_size", str(cm.exception)) with self.assertRaises(ValueError) as cm: config.validate(n_gpus=16, train_batch_size=512) self.assertIn("must be >= n_gpus", str(cm.exception)) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/workers/config/test_critic_config_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from pathlib import Path import pytest from hydra import compose, initialize_config_dir from verl.utils.config import omega_conf_to_dataclass from verl.utils.profiler import ProfilerConfig from verl.workers.config import ( CriticConfig, FSDPCriticConfig, FSDPOptimizerConfig, McoreCriticConfig, McoreOptimizerConfig, OptimizerConfig, ) @pytest.mark.skip(reason="This test is flaky when we actively load model config") class TestCriticConfig: """Test suite for critic configuration dataclasses.""" @pytest.fixture def config_dir(self): """Get the path to the config directory.""" return Path(__file__).parent.parent.parent.parent / "verl" / "trainer" / "config" / "critic" def test_megatron_critic_config_instantiation_from_yaml(self, config_dir): """Test that McoreCriticConfig can be instantiated from megatron_critic.yaml.""" yaml_path = config_dir / "megatron_critic.yaml" assert yaml_path.exists(), f"Config file not found: {yaml_path}" with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config/critic")): test_config = compose(config_name="megatron_critic", overrides=["ppo_micro_batch_size_per_gpu=1"]) megatron_config_obj = omega_conf_to_dataclass(test_config) assert isinstance(megatron_config_obj, McoreCriticConfig) assert isinstance(megatron_config_obj, CriticConfig) expected_attrs = [ "strategy", "rollout_n", "optim", "model", "ppo_mini_batch_size", "ppo_max_token_len_per_gpu", "cliprange_value", "get", "nccl_timeout", "megatron", "load_weight", ] for attr in expected_attrs: assert hasattr(megatron_config_obj, attr), f"Missing attribute: {attr}" assert callable(megatron_config_obj.get) assert megatron_config_obj.strategy == "megatron" def test_fsdp_critic_config_instantiation_from_yaml(self, config_dir): """Test that FSDPCriticConfig can be instantiated from dp_critic.yaml.""" yaml_path = config_dir / "dp_critic.yaml" assert yaml_path.exists(), f"Config file not found: {yaml_path}" with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config/critic")): test_config = compose(config_name="dp_critic", overrides=["ppo_micro_batch_size_per_gpu=1"]) fsdp_config_obj = omega_conf_to_dataclass(test_config) assert isinstance(fsdp_config_obj, FSDPCriticConfig) assert isinstance(fsdp_config_obj, CriticConfig) expected_attrs = [ "strategy", "rollout_n", "optim", "model", "ppo_mini_batch_size", "ppo_max_token_len_per_gpu", "cliprange_value", "get", "forward_micro_batch_size", "forward_micro_batch_size_per_gpu", "ulysses_sequence_parallel_size", "grad_clip", ] for attr in expected_attrs: assert hasattr(fsdp_config_obj, attr), f"Missing attribute: {attr}" assert callable(fsdp_config_obj.get) assert fsdp_config_obj.strategy == "fsdp" def test_config_inheritance_hierarchy(self): """Test that the inheritance hierarchy is correct.""" megatron_config = McoreCriticConfig(ppo_micro_batch_size_per_gpu=1, optim=McoreOptimizerConfig(lr=0.1)) assert isinstance(megatron_config, CriticConfig) assert isinstance(megatron_config, McoreCriticConfig) fsdp_config = FSDPCriticConfig(ppo_micro_batch_size_per_gpu=1, optim=FSDPOptimizerConfig(lr=0.1)) assert isinstance(fsdp_config, CriticConfig) assert isinstance(fsdp_config, FSDPCriticConfig) critic_config = CriticConfig(ppo_micro_batch_size_per_gpu=1, strategy="fsdp2", optim=OptimizerConfig(lr=0.1)) assert isinstance(critic_config, CriticConfig) assert not isinstance(critic_config, McoreCriticConfig) assert not isinstance(critic_config, FSDPCriticConfig) def test_config_dict_interface(self): """Test that configs provide dict-like interface from BaseConfig.""" optim = OptimizerConfig(lr=0.1) config = CriticConfig(ppo_micro_batch_size_per_gpu=1, strategy="fsdp2", optim=optim) assert "strategy" in config assert config["strategy"] == "fsdp2" assert config.get("strategy") == "fsdp2" assert config.get("nonexistent_key", "default") == "default" keys = list(config) assert "strategy" in keys assert "rollout_n" in keys assert len(config) > 0 def test_frozen_fields_immutability(self): """Test that frozen fields raise exceptions when modified after creation.""" critic_config = CriticConfig(ppo_micro_batch_size_per_gpu=1, strategy="fsdp2", optim=OptimizerConfig(lr=0.1)) frozen_fields = ["rollout_n", "strategy", "cliprange_value"] for field_name in frozen_fields: with pytest.raises((AttributeError, TypeError, ValueError)): setattr(critic_config, field_name, "modified_value") megatron_config = McoreCriticConfig(ppo_micro_batch_size_per_gpu=1, optim=McoreOptimizerConfig(lr=0.1)) megatron_frozen_fields = ["nccl_timeout", "load_weight", "data_loader_seed"] for field_name in megatron_frozen_fields: with pytest.raises((AttributeError, TypeError, ValueError)): setattr(megatron_config, field_name, "modified_value") fsdp_config = FSDPCriticConfig(ppo_micro_batch_size_per_gpu=1, optim=FSDPOptimizerConfig(lr=0.1)) fsdp_frozen_fields = ["ulysses_sequence_parallel_size", "grad_clip"] for field_name in fsdp_frozen_fields: with pytest.raises((AttributeError, TypeError, ValueError)): setattr(fsdp_config, field_name, "modified_value") def test_batch_size_fields_modifiable(self): """Test that batch size fields can be modified after creation.""" optim = OptimizerConfig(lr=0.1) critic_config = CriticConfig(ppo_micro_batch_size_per_gpu=1, strategy="fsdp2", optim=optim) critic_config.ppo_mini_batch_size = 8 critic_config.ppo_micro_batch_size = 4 critic_config.ppo_micro_batch_size_per_gpu = 2 assert critic_config.ppo_mini_batch_size == 8 assert critic_config.ppo_micro_batch_size == 4 assert critic_config.ppo_micro_batch_size_per_gpu == 2 fsdp_config = FSDPCriticConfig(ppo_micro_batch_size_per_gpu=1, optim=FSDPOptimizerConfig(lr=0.1)) fsdp_config.forward_micro_batch_size = 16 fsdp_config.forward_micro_batch_size_per_gpu = 8 assert fsdp_config.forward_micro_batch_size == 16 assert fsdp_config.forward_micro_batch_size_per_gpu == 8 def test_profiler_config_type_validation(self): """Test that profiler field has correct type and validation.""" optim = OptimizerConfig(lr=0.1) critic_config = CriticConfig(ppo_micro_batch_size_per_gpu=1, strategy="fsdp2", optim=optim) assert isinstance(critic_config.profiler, ProfilerConfig) assert critic_config.profiler.all_ranks is False assert critic_config.profiler.ranks == [] custom_profiler = ProfilerConfig(all_ranks=True, ranks=[0, 1]) critic_config_custom = CriticConfig( profiler=custom_profiler, ppo_micro_batch_size_per_gpu=1, strategy="fsdp2", optim=optim ) assert isinstance(critic_config_custom.profiler, ProfilerConfig) assert critic_config_custom.profiler.all_ranks is True assert critic_config_custom.profiler.ranks == [0, 1] profiler1 = ProfilerConfig(enable=True, ranks=[0, 1]) profiler2 = ProfilerConfig(all_ranks=True, ranks=[1, 2]) union_result = profiler1.union(profiler2) assert union_result.enable is True assert union_result.all_ranks is True assert set(union_result.ranks) == {0, 1, 2} intersect_result = profiler1.intersect(profiler2) assert intersect_result.all_ranks is False assert intersect_result.ranks == [1] def test_critic_config_validation_logic(self): """Test the __post_init__ validation logic for CriticConfig.""" optim = OptimizerConfig(lr=0.1) valid_config = CriticConfig( strategy="fsdp2", ppo_micro_batch_size_per_gpu=2, use_dynamic_bsz=False, optim=optim ) assert valid_config.ppo_micro_batch_size_per_gpu == 2 valid_config2 = CriticConfig( strategy="fsdp2", ppo_micro_batch_size_per_gpu=None, ppo_micro_batch_size=4, ppo_mini_batch_size=8, use_dynamic_bsz=False, optim=optim, ) assert valid_config2.ppo_micro_batch_size == 4 dynamic_config = CriticConfig( strategy="fsdp2", ppo_micro_batch_size_per_gpu=2, use_dynamic_bsz=True, optim=optim ) assert dynamic_config.use_dynamic_bsz is True with pytest.raises(ValueError, match="You have set both.*micro_batch_size.*AND.*micro_batch_size_per_gpu"): CriticConfig( strategy="fsdp2", ppo_micro_batch_size=4, ppo_micro_batch_size_per_gpu=2, use_dynamic_bsz=False, optim=optim, ) with pytest.raises( ValueError, match="Please set at least one of.*micro_batch_size.*or.*micro_batch_size_per_gpu" ): CriticConfig( strategy="fsdp2", ppo_micro_batch_size=None, ppo_micro_batch_size_per_gpu=None, use_dynamic_bsz=False, optim=optim, ) def test_micro_batch_size_divisibility_validation(self): """Test micro batch size divisibility validation in __post_init__.""" optim = OptimizerConfig(lr=0.1) valid_config = CriticConfig( strategy="fsdp2", ppo_micro_batch_size_per_gpu=2, ppo_mini_batch_size=8, use_dynamic_bsz=False, optim=optim ) assert valid_config.ppo_mini_batch_size == 8 assert valid_config.ppo_micro_batch_size_per_gpu == 2 valid_config_with_mbs = CriticConfig( strategy="fsdp2", ppo_mini_batch_size=8, ppo_micro_batch_size=4, use_dynamic_bsz=False, optim=optim ) assert valid_config_with_mbs.ppo_mini_batch_size == 8 assert valid_config_with_mbs.ppo_micro_batch_size == 4 with pytest.raises(ValueError, match="ppo_mini_batch_size.*must be divisible by.*ppo_micro_batch_size"): CriticConfig( strategy="fsdp2", ppo_mini_batch_size=7, ppo_micro_batch_size=4, use_dynamic_bsz=False, optim=optim ) dynamic_config = CriticConfig( strategy="fsdp2", ppo_mini_batch_size=7, ppo_micro_batch_size=4, use_dynamic_bsz=True, optim=optim ) assert dynamic_config.use_dynamic_bsz is True def test_fsdp_sequence_parallelism_validation(self): """Test FSDP sequence parallelism validation in FSDPCriticConfig.__post_init__.""" valid_config = FSDPCriticConfig( ppo_micro_batch_size_per_gpu=2, ulysses_sequence_parallel_size=2, model={"use_remove_padding": True}, optim=FSDPOptimizerConfig(lr=0.1), ) assert valid_config.ulysses_sequence_parallel_size == 2 with pytest.raises( ValueError, match="When using sequence parallelism for critic, you must enable.*use_remove_padding" ): FSDPCriticConfig( ppo_micro_batch_size_per_gpu=2, ulysses_sequence_parallel_size=2, model={"use_remove_padding": False}, optim=FSDPOptimizerConfig(lr=0.1), ) valid_config_no_sp = FSDPCriticConfig( ppo_micro_batch_size_per_gpu=2, ulysses_sequence_parallel_size=1, model={"use_remove_padding": False}, optim=FSDPOptimizerConfig(lr=0.1), ) assert valid_config_no_sp.ulysses_sequence_parallel_size == 1 ================================================ FILE: tests/workers/config/test_engine_config_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from verl.workers.config.engine import FSDPEngineConfig, McoreEngineConfig class TestMcoreEngineConfig: def test_default_values(self): config = McoreEngineConfig() assert config.tensor_model_parallel_size == 1 assert config.sequence_parallel is False # Should be auto-corrected assert config.seed == 42 def test_post_init_validation(self): # Test TP size 1 forces sequence_parallel=False config = McoreEngineConfig(tensor_model_parallel_size=1) assert config.sequence_parallel is False # Test TP >1 keeps sequence_parallel=True config = McoreEngineConfig(tensor_model_parallel_size=2) assert config.sequence_parallel is True def test_mutable_fields(self): config = McoreEngineConfig() config.sequence_parallel = True # Should be mutable with pytest.raises(AttributeError): config.tensor_model_parallel_size = 2 # Frozen field @pytest.mark.parametrize("offload_field", ["param_offload", "grad_offload", "optimizer_offload"]) def test_offload_flags(self, offload_field): config = McoreEngineConfig(**{offload_field: True}) assert getattr(config, offload_field) is True class TestFSDPEngineConfigCPU: def test_default_values(self): config = FSDPEngineConfig() assert config.param_offload is False assert config.optimizer_offload is False assert config.fsdp_size == -1 @pytest.mark.parametrize( "offload_params", [{"param_offload": True}, {"optimizer_offload": True}, {"param_offload": True, "optimizer_offload": True}], ) def test_offload_combinations(self, offload_params): config = FSDPEngineConfig(**offload_params) assert config.param_offload == offload_params.get("param_offload", False) assert config.optimizer_offload == offload_params.get("optimizer_offload", False) def test_wrap_policy_configuration(self): test_policy = {"layer_class": "TransformerBlock"} config = FSDPEngineConfig(wrap_policy=test_policy) assert config.wrap_policy == test_policy ================================================ FILE: tests/workers/config/test_model_config_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright Amazon.com and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from omegaconf import OmegaConf from verl.workers.config.model import HFModelConfig class TestHFModelConfigCPU: model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B") # Just a path string, not loaded def test_target_modules_accepts_list_via_omegaconf(self): """ Test that target_modules field accepts both string and list values when merging OmegaConf configs (simulates CLI override behavior). The purpose is to ensure we can pass actor_rollout_ref.model.target_modules='["k_proj","o_proj","down_proj","q_proj"]' """ # Create structured config from the dataclass defaults # This is what omega_conf_to_dataclass does internally cfg_from_dataclass = OmegaConf.structured(HFModelConfig) # Simulate CLI override with target_modules as a list cli_config = OmegaConf.create( { "path": self.model_path, "target_modules": ["k_proj", "o_proj", "q_proj", "v_proj"], } ) # This merge should NOT raise ValidationError # Before the fix (target_modules: str), this would fail with: # "Cannot convert 'ListConfig' to string" merged = OmegaConf.merge(cfg_from_dataclass, cli_config) # Verify the list was merged correctly assert list(merged.target_modules) == ["k_proj", "o_proj", "q_proj", "v_proj"] def test_target_modules_accepts_none_via_omegaconf(self): """Test that target_modules still accepts None values.""" cfg_from_dataclass = OmegaConf.structured(HFModelConfig) cli_config = OmegaConf.create( { "path": self.model_path, "target_modules": None, } ) merged = OmegaConf.merge(cfg_from_dataclass, cli_config) assert merged.target_modules is None def test_target_modules_accepts_string_via_omegaconf(self): """Test that target_modules still accepts string values.""" cfg_from_dataclass = OmegaConf.structured(HFModelConfig) cli_config = OmegaConf.create( { "path": self.model_path, "target_modules": "all-linear", } ) merged = OmegaConf.merge(cfg_from_dataclass, cli_config) assert merged.target_modules == "all-linear" def test_target_modules_raises_on_invalid_type(self): """Test that __post_init__ raises TypeError for invalid target_modules types.""" base_config = OmegaConf.structured(HFModelConfig) invalid_cli_config = OmegaConf.create( { "path": self.model_path, "target_modules": [1, 2, 3], # list of ints instead of strings } ) merged_config = OmegaConf.merge(base_config, invalid_cli_config) with pytest.raises(TypeError): OmegaConf.to_object(merged_config) ================================================ FILE: tests/workers/config/test_optim_config_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from verl.workers.config.optimizer import FSDPOptimizerConfig class TestFSDPOptimizerConfigCPU: def test_default_configuration(self): config = FSDPOptimizerConfig(lr=0.1) assert config.min_lr_ratio is None assert config.lr_scheduler_type == "constant" assert config.num_cycles == 0.5 @pytest.mark.parametrize("lr_scheduler_type", ["constant", "cosine"]) def test_valid_lr_scheduler_types(self, lr_scheduler_type): config = FSDPOptimizerConfig(lr_scheduler_type=lr_scheduler_type, lr=0.1) assert config.lr_scheduler_type == lr_scheduler_type @pytest.mark.parametrize("warmup_style", ["constant", "cosine"]) def test_valid_warmup_style_types(self, warmup_style): config = FSDPOptimizerConfig(warmup_style=warmup_style, lr=0.1) assert config.lr_scheduler_type == warmup_style def test_invalid_lr_scheduler_type(self): with pytest.raises((ValueError, AssertionError)): FSDPOptimizerConfig(lr_scheduler_type="invalid_style", lr=0.1) def test_invalid_warmup_style_type(self): with pytest.raises((ValueError, AssertionError)): FSDPOptimizerConfig(warmup_style="invalid_style", lr=0.1) @pytest.mark.parametrize("num_cycles", [0.1, 1.0, 2.5]) def test_num_cycles_configuration(self, num_cycles): config = FSDPOptimizerConfig(num_cycles=num_cycles, lr=0.1) assert config.num_cycles == num_cycles ================================================ FILE: tests/workers/critic/test_special_dp_critic.py ================================================ #!/usr/bin/env python3 # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import tempfile import unittest from unittest.mock import Mock, patch import torch import torch.distributed from omegaconf import OmegaConf from tensordict import TensorDict from transformers import AutoConfig from verl import DataProto from verl.workers.config import FSDPCriticConfig, FSDPOptimizerConfig from verl.workers.config.critic import FSDPCriticModelCfg from verl.workers.config.engine import FSDPEngineConfig from verl.workers.fsdp_workers import CriticWorker class TestCriticWorker(unittest.TestCase): @classmethod def setUpClass(cls): """Set up distributed environment""" if not torch.distributed.is_initialized(): torch.distributed.init_process_group( backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" ) cls.rank = torch.distributed.get_rank() cls.world_size = torch.distributed.get_world_size() if torch.cuda.is_available(): torch.cuda.set_device(cls.rank) cls.device = torch.device(f"cuda:{cls.rank}") else: cls.device = torch.device("cpu") @classmethod def tearDownClass(cls): """Clean up distributed environment""" if torch.distributed.is_initialized(): torch.distributed.destroy_process_group() def setUp(self): """Set up test fixtures""" self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.temp_dir = tempfile.mkdtemp() model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") config = AutoConfig.from_pretrained(model_path) config.save_pretrained(self.temp_dir) self.config = FSDPCriticConfig( strategy="fsdp2", ppo_mini_batch_size=4, ppo_micro_batch_size_per_gpu=2, forward_micro_batch_size_per_gpu=2, ppo_epochs=1, cliprange_value=0.5, grad_clip=1.0, use_dynamic_bsz=False, ulysses_sequence_parallel_size=1, rollout_n=1, optim=FSDPOptimizerConfig(lr=1e-6), model=FSDPCriticModelCfg( path=model_path, tokenizer_path=model_path, fsdp_config=FSDPEngineConfig(fsdp_size=-1), use_remove_padding=False, ), ) assert self.world_size <= 4 // 2 def tearDown(self): """Clean up test fixtures""" import shutil shutil.rmtree(self.temp_dir, ignore_errors=True) def _create_test_data_for_compute_values(self, batch_size=2, seq_len=10, response_len=5): """Create test data for compute_values method""" input_ids = torch.randint(0, 1000, (batch_size, seq_len), dtype=torch.long) attention_mask = torch.ones(batch_size, seq_len, dtype=torch.long) position_ids = torch.arange(seq_len).unsqueeze(0).expand(batch_size, -1) responses = torch.randint(0, 1000, (batch_size, response_len), dtype=torch.long) response_mask = torch.ones(batch_size, response_len, dtype=torch.float) batch = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "responses": responses, "response_mask": response_mask, }, batch_size=[batch_size], ) data = DataProto( batch=batch, meta_info={"micro_batch_size": 2, "max_token_len": seq_len, "use_dynamic_bsz": False} ) return data def _create_test_data_for_update_critic(self, batch_size=2, seq_len=10, response_len=5): """Create test data for update_critic method""" input_ids = torch.randint(0, 1000, (batch_size, seq_len), dtype=torch.long) attention_mask = torch.ones(batch_size, seq_len, dtype=torch.long) position_ids = torch.arange(seq_len).unsqueeze(0).expand(batch_size, -1) responses = torch.randint(0, 1000, (batch_size, response_len), dtype=torch.long) response_mask = torch.ones(batch_size, response_len, dtype=torch.float) values = torch.randn(batch_size, response_len, dtype=torch.float) returns = torch.randn(batch_size, response_len, dtype=torch.float) batch = TensorDict( { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "responses": responses, "response_mask": response_mask, "values": values, "returns": returns, }, batch_size=[batch_size], ) data = DataProto( batch=batch, meta_info={"global_token_num": [response_len] * batch_size, "batch_seqlens": [response_len] * batch_size}, ) return data def test_init_model(self): """Test CriticWorker.init_model() method""" worker = CriticWorker(self.config) worker.init_model() self.assertIsNotNone(worker.critic_module) self.assertIsNotNone(worker.critic_optimizer) self.assertIsNotNone(worker.critic) self.assertIsNotNone(worker.checkpoint_manager) def test_compute_values(self): """Test CriticWorker.compute_values() method""" worker = CriticWorker(self.config) worker.init_model() data = self._create_test_data_for_compute_values() result = worker.compute_values(data) self.assertIsInstance(result, DataProto) self.assertIn("values", result.batch) values = result.batch["values"] batch_size, response_len = 2, 5 self.assertEqual(values.shape, (batch_size, response_len)) self.assertTrue(torch.isfinite(values).all()) def test_update_critic(self): """Test CriticWorker.update_critic() method""" worker = CriticWorker(self.config) worker.init_model() data = self._create_test_data_for_update_critic() result = worker.update_critic(data) self.assertIsInstance(result, DataProto) self.assertIn("metrics", result.meta_info) metrics = result.meta_info["metrics"] expected_keys = ["critic/vf_loss", "critic/vf_clipfrac", "critic/vpred_mean", "critic/grad_norm"] for key in expected_keys: self.assertIn(key, metrics) for key, value in metrics.items(): if isinstance(value, list | tuple): for v in value: self.assertTrue(torch.isfinite(torch.tensor(v)).all()) else: self.assertTrue(torch.isfinite(torch.tensor(value)).all()) @patch("transformers.AutoConfig.from_pretrained") def test_critic_attn_implementation_override_functionality(self, mock_config_from_pretrained): """Test that CriticWorker correctly uses attn_implementation from override_config""" # Mock the AutoConfig return value mock_config = Mock() mock_config.tie_word_embeddings = False mock_config.architectures = ["LlamaForCausalLM"] mock_config.num_labels = 1 mock_config_from_pretrained.return_value = mock_config # Test different attn_implementation values test_cases = [ ("eager", "eager"), ("sdpa", "sdpa"), ("flash_attention_2", "flash_attention_2"), (None, "flash_attention_2"), # Default case ] for override_value, expected_value in test_cases: mock_config_from_pretrained.reset_mock() # Create config with override_config config_dict = { "model": { "path": "/test/model/path", "tokenizer_path": "/test/tokenizer/path", "fsdp_config": { "fsdp_size": 1, "param_offload": False, "optimizer_offload": False, }, }, "optim": {"lr": 1e-4, "type": "AdamW"}, "strategy": "fsdp", "ppo_mini_batch_size": 1, "ppo_epochs": 1, "rollout_n": 1, "checkpoint": {"save_contents": [], "load_contents": []}, } # Add override_config with attn_implementation if specified if override_value is not None: config_dict["model"]["override_config"] = {"attn_implementation": override_value} # Convert to OmegaConf test_config = OmegaConf.create(config_dict) # Test the extraction logic that should happen in CriticWorker._build_critic_model_optimizer override_config = OmegaConf.to_container(OmegaConf.create(test_config.model.get("override_config", {}))) extracted_attn_implementation = override_config.get("attn_implementation", "flash_attention_2") # Verify the extraction works correctly self.assertEqual( extracted_attn_implementation, expected_value, f"Expected {expected_value}, got {extracted_attn_implementation} for override_value {override_value}", ) def test_critic_model_config_structure(self): """Test that critic model config properly incorporates override settings""" # Test configuration scenarios test_scenarios = [ {"name": "default_flash_attention", "override_config": {}, "expected_attn": "flash_attention_2"}, {"name": "eager_override", "override_config": {"attn_implementation": "eager"}, "expected_attn": "eager"}, {"name": "sdpa_override", "override_config": {"attn_implementation": "sdpa"}, "expected_attn": "sdpa"}, { "name": "mixed_config", "override_config": {"attn_implementation": "eager", "dropout": 0.1, "num_labels": 1}, "expected_attn": "eager", }, ] for scenario in test_scenarios: with self.subTest(scenario=scenario["name"]): # Simulate the config processing logic from CriticWorker override_config = scenario["override_config"] # Test the extraction logic extracted_attn = override_config.get("attn_implementation", "flash_attention_2") # Verify correct extraction self.assertEqual(extracted_attn, scenario["expected_attn"], f"Failed for scenario {scenario['name']}") # Verify other configs are preserved if "dropout" in override_config: self.assertEqual(override_config["dropout"], 0.1) def test_critic_hydra_config_compatibility(self): """Test that Hydra +prefix configurations work correctly for CriticWorker""" # Simulate Hydra configuration with +prefix for critic # This would come from: +critic.model.override_config.attn_implementation=eager hydra_config_dict = { "critic": {"model": {"path": "/test/model/path", "override_config": {"attn_implementation": "eager"}}} } omegaconf = OmegaConf.create(hydra_config_dict) # Extract override config as would be done in CriticWorker override_model_config = OmegaConf.to_container( OmegaConf.create(omegaconf.critic.model.get("override_config", {})) ) # Test extraction attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") self.assertEqual(attn_implementation, "eager") def test_critic_backward_compatibility(self): """Test that CriticWorker maintains backward compatibility with existing configurations""" # Test cases for backward compatibility compatibility_tests = [ {"name": "no_override_config", "config": {}, "expected": "flash_attention_2"}, {"name": "empty_override_config", "config": {"override_config": {}}, "expected": "flash_attention_2"}, { "name": "other_overrides_only", "config": {"override_config": {"dropout": 0.1, "hidden_size": 768}}, "expected": "flash_attention_2", }, ] for test in compatibility_tests: with self.subTest(test=test["name"]): override_config = test["config"].get("override_config", {}) attn_implementation = override_config.get("attn_implementation", "flash_attention_2") self.assertEqual( attn_implementation, test["expected"], f"Backward compatibility failed for {test['name']}" ) def test_critic_and_actor_independent_configuration(self): """Test that critic and actor can have independent attention implementation configurations""" # Simulate a complete training configuration with both actor and critic complete_config = { "actor_rollout_ref": {"model": {"override_config": {"attn_implementation": "eager"}}}, "critic": {"model": {"override_config": {"attn_implementation": "sdpa"}}}, } omegaconf = OmegaConf.create(complete_config) # Extract actor config actor_override = OmegaConf.to_container( OmegaConf.create(omegaconf.actor_rollout_ref.model.get("override_config", {})) ) actor_attn = actor_override.get("attn_implementation", "flash_attention_2") # Extract critic config critic_override = OmegaConf.to_container(OmegaConf.create(omegaconf.critic.model.get("override_config", {}))) critic_attn = critic_override.get("attn_implementation", "flash_attention_2") # Verify independent configuration self.assertEqual(actor_attn, "eager") self.assertEqual(critic_attn, "sdpa") self.assertNotEqual(actor_attn, critic_attn) # Ensure they are indeed different if __name__ == "__main__": unittest.main() ================================================ FILE: tests/workers/reward_manager/test_registry_on_cpu.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest # Assuming REWARD_MANAGER_REGISTRY is defined somewhere in the module from verl.workers.reward_manager.registry import REWARD_MANAGER_REGISTRY, get_reward_manager_cls, register @pytest.fixture def setup(): """Setup test cases with a mock registry.""" REWARD_MANAGER_REGISTRY.clear() REWARD_MANAGER_REGISTRY.update({"manager1": "Manager1Class", "manager2": "Manager2Class"}) return REWARD_MANAGER_REGISTRY def test_get_existing_manager(setup): """Test getting an existing reward manager class.""" assert get_reward_manager_cls("manager1") == "Manager1Class" assert get_reward_manager_cls("manager2") == "Manager2Class" def test_get_nonexistent_manager(setup): """Test getting a non-existent reward manager raises ValueError.""" with pytest.raises(ValueError) as excinfo: get_reward_manager_cls("unknown_manager") assert "Unknown reward manager: unknown_manager" in str(excinfo.value) def test_case_sensitivity(setup): """Test that manager names are case-sensitive.""" with pytest.raises(ValueError): get_reward_manager_cls("MANAGER1") with pytest.raises(ValueError): get_reward_manager_cls("Manager1") def test_empty_registry(setup): """Test behavior when registry is empty.""" REWARD_MANAGER_REGISTRY.clear() with pytest.raises(ValueError) as excinfo: get_reward_manager_cls("any_manager") assert "Unknown reward manager: any_manager" in str(excinfo.value) def test_register_new_class(setup): """Test registering a new class with the decorator.""" @register("test_manager") class TestManager: pass assert "test_manager" in REWARD_MANAGER_REGISTRY assert REWARD_MANAGER_REGISTRY["test_manager"] == TestManager def test_register_different_classes_same_name(setup): """Test that registering different classes with same name raises ValueError.""" @register("conflict_manager") class Manager1: pass with pytest.raises(ValueError): @register("conflict_manager") class Manager2: pass assert REWARD_MANAGER_REGISTRY["conflict_manager"] == Manager1 def test_decorator_returns_original_class(setup): """Test that the decorator returns the original class unchanged.""" @register("return_test") class OriginalClass: def method(setup): return 42 assert OriginalClass().method() == 42 assert REWARD_MANAGER_REGISTRY["return_test"] == OriginalClass ================================================ FILE: tests/workers/rollout/perf/vllm_async_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Compare vLLM AsyncLLM backend: ExternalRayDistributedExecutor(remote call) vs RayDistributedExecutor(compiled graph) 1. Prepare openai/gsm8k dataset python3 examples/data_preprocess/gsm8k.py 2. Run perf test python3 tests/workers/rollout/perf/vllm_async_rollout.py >perf.log 2>&1 hardware: Nvidia 8*H20 packages: - torch==2.6.0 - vllm==0.8.5 [DEBUG] backend: sync, n_gpus_per_node: 8, batch_size: 2048, step: 0, step_time: 21.27 secs [DEBUG] backend: zeromq, n_gpus_per_node: 8, batch_size: 2048, step: 0, step_time: 23.40 secs [DEBUG] backend: ray, n_gpus_per_node: 8, batch_size: 2048, step: 0, step_time: 25.33 secs """ import os import time import ray from omegaconf import DictConfig from torch.utils.data import SequentialSampler from torchdata.stateful_dataloader import StatefulDataLoader from tests.experimental.agent_loop.agent_utils import AgentLoopManager, RayWorkerGroup, init_agent_loop_manager from verl.protocol import DataProto from verl.utils import hf_tokenizer from verl.utils.dataset import RLHFDataset from verl.utils.dataset.rl_dataset import collate_fn as default_collate_fn def init_config(n_gpus_per_node) -> DictConfig: import os from hydra import compose, initialize_config_dir with initialize_config_dir(config_dir=os.path.abspath("verl/trainer/config")): config = compose( config_name="ppo_trainer", overrides=[ "actor_rollout_ref.actor.use_dynamic_bsz=true", "actor_rollout_ref.actor.fsdp_config.param_offload=True", "actor_rollout_ref.actor.fsdp_config.optimizer_offload=True", ], ) config.trainer.n_gpus_per_node = n_gpus_per_node config.data.train_batch_size = 128 config.data.return_raw_chat = True config.actor_rollout_ref.model.path = "Qwen/Qwen2.5-7B-Instruct" config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = 2 config.actor_rollout_ref.rollout.gpu_memory_utilization = 0.9 config.actor_rollout_ref.rollout.multi_turn.format = "hermes" config.actor_rollout_ref.rollout.prompt_length = 4096 config.actor_rollout_ref.rollout.response_length = 4096 config.actor_rollout_ref.rollout.n = 16 return config def initialize(config, backend) -> tuple[AgentLoopManager | RayWorkerGroup, StatefulDataLoader]: env_vars = { "NCCL_DEBUG": "WARN", "VLLM_USE_V1": "1", "VERL_VLLM_DISTRIBUTED_BACKEND": backend, } ray.init(runtime_env={"env_vars": env_vars}) # STEP 1: init async llm server server = init_agent_loop_manager(config) # STEP 2: create dataloader tokenizer = hf_tokenizer(config.actor_rollout_ref.model.path) dataset = RLHFDataset( data_files=os.path.expanduser("~/data/gsm8k/train.parquet"), tokenizer=tokenizer, config=config.data, ) dataloader = StatefulDataLoader( dataset=dataset, batch_size=config.data.get("gen_batch_size", config.data.train_batch_size), num_workers=config.data.get("dataloader_num_workers", 8), drop_last=True, collate_fn=default_collate_fn, sampler=SequentialSampler(dataset), ) return server, dataloader def perf_rollout(mode, backend, n_gpus_per_node, num_steps): config = init_config(n_gpus_per_node) config.actor_rollout_ref.rollout.mode = mode agent_loop_manager, dataloader = initialize(config, backend) for step, batch in enumerate(dataloader): batch: DataProto = DataProto.from_single_dict(batch) batch = batch.pop( batch_keys=["input_ids", "attention_mask", "position_ids"], non_tensor_batch_keys=["raw_prompt_ids", "raw_prompt"], ) t_start = time.time() gen_batch = agent_loop_manager.generate_sequences(batch) t_end = time.time() print( f"[DEBUG] backend: {backend}, n_gpus_per_node: {n_gpus_per_node}, batch_size: {len(gen_batch)}, " f"step: {step}, step_time: {t_end - t_start:.2f} secs" ) if step + 1 >= num_steps: break ray.shutdown() if __name__ == "__main__": num_steps = 1 n_gpus_per_node = 8 # test_cases = [("sync", "sync"), ("async", "zeromq"), ("async", "ray")] test_cases = [("async", "zeromq"), ("async", "ray")] for mode, backend in test_cases: perf_rollout(mode=mode, backend=backend, n_gpus_per_node=n_gpus_per_node, num_steps=num_steps) ================================================ FILE: tests/workers/rollout/resource/tool_configs/mcp_server.json ================================================ { "mcpServers": { "Tavily Expert": { "url": "https://tavily.api.tadata.com/mcp/tavily/your_expert", "auth_token": "your_tavily_token" } } } ================================================ FILE: tests/workers/rollout/resource/tool_configs/mcp_tool_config ================================================ tools: - class_name: verl.tools.mcp_search_tool.MCPSearchTool config: rate_limit: 120 timeout: 120 type: mcp mcp: mcp_servers_config_path: ./resource/tool_configs/mcp_server.json # optional tool_selected_list: - tavily_search_tool ================================================ FILE: tests/workers/rollout/resource/tool_configs/sandbox_fusion_tool_config ================================================ tools: - class_name: "verl.tools.sandbox_fusion_tools.SandboxFusionTool" config: sandbox_fusion_url: "https://xxx.apigateway-cn-beijing.volceapi.com/run_code" type: native tool_schema: type: "function" function: name: "code_interpreter" description: "A tool for executing code." parameters: type: "object" properties: code: type: "string" description: "The code to execute." required: ["code"] ================================================ FILE: tests/workers/rollout/resource/tool_configs/search_tool_config ================================================ tools: - class_name: verl.tools.search_tool.SearchTool config: retrieval_service_url: http://127.0.0.1:8000/retrieve num_workers: 120 rate_limit: 120 timeout: 30 type: native tool_schema: type: function function: name: search description: Searches the web for relevant information based on the given query. parameters: type: object properties: query_list: type: array item: type: string description: A list of fully-formed semantic queries. The tool will return search results for each query. required: - query_list ================================================ FILE: tests/workers/rollout/rollout_sglang/test_http_server_engine.py ================================================ # Copyright 2025 z.ai # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # This file is adapted from multiple sources: # 1. THUDM/slime project # Original source: https://github.com/THUDM/slime/blob/main/slime/backends/sglang_utils/http_server_engine.py # Copyright 2025 z.ai # Licensed under the Apache License, Version 2.0 # 2. SGLang project # Original source: https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/entrypoints/http_server_engine.py # Copyright 2023-2024 SGLang Team # Licensed under the Apache License, Version 2.0 # # Modifications made by z.ai and ModelBest Inc. include but are not limited to: # - Enhanced error handling and retry logic # - Added async support with connection pooling # - Extended functionality for distributed weight updates # - Improved logging and monitoring capabilities # - Additional configuration options and optimizations """Complete unit tests for HTTP Server Engine Adapters. This module contains comprehensive unit tests for both HttpServerEngineAdapter and AsyncHttpServerEngineAdapter classes, covering all public methods, error handling scenarios, edge cases, and boundary conditions using pytest and mock frameworks. Tests use real SGLang modules for integration testing while mocking external dependencies. """ import asyncio from unittest.mock import AsyncMock, Mock, patch import aiohttp import pytest import requests from sglang.srt.managers.io_struct import ( UpdateWeightsFromTensorReqInput, ) from sglang.srt.utils import MultiprocessingSerializer # Import the module under test from verl.workers.rollout.sglang_rollout.http_server_engine import ( AsyncHttpServerAdapter, HttpServerAdapter, launch_server_process, ) @pytest.fixture(scope="session") def event_loop(): """Create an event loop for the entire test session.""" loop = asyncio.new_event_loop() yield loop loop.close() @pytest.fixture def basic_adapter_kwargs(): """Provide basic kwargs for creating HTTP server adapters.""" return { "host": "localhost", "port": 8000, "node_rank": 0, "model_path": "/tmp/test_model", } @pytest.fixture def router_adapter_kwargs(): """Provide kwargs for creating adapters with router configuration.""" return { "router_ip": "192.168.1.1", "router_port": 8080, "host": "localhost", "port": 8000, "node_rank": 0, "model_path": "/tmp/test_model", } @pytest.fixture def non_master_adapter_kwargs(): """Provide kwargs for creating non-master node adapters.""" return { "host": "localhost", "port": 8000, "node_rank": 1, # Non-master "model_path": "/tmp/test_model", } @pytest.fixture def mock_launch_server_process(): """Mock the launch_server_process function for testing without actual server startup.""" from unittest.mock import patch with patch("verl.workers.rollout.sglang_rollout.http_server_engine.launch_server_process") as mock_launch: mock_process = Mock() mock_process.is_alive.return_value = True mock_process.pid = 12345 mock_launch.return_value = mock_process yield mock_launch @pytest.fixture def mock_multiprocessing_process(): """Create mock multiprocessing.Process for testing without actual process creation.""" from unittest.mock import patch with patch("verl.workers.rollout.sglang_rollout.http_server_engine.multiprocessing.Process") as mock_process_class: mock_process = Mock() mock_process.is_alive.return_value = True mock_process.pid = 12345 mock_process_class.return_value = mock_process yield mock_process @pytest.fixture def mock_requests_session(): """Create mock requests.Session for testing HTTP interactions.""" from unittest.mock import patch with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.Session") as mock_session_class: mock_session = Mock() mock_response = Mock() mock_response.status_code = 200 mock_response.json.return_value = {"status": "success"} mock_session.get.return_value = mock_response mock_session.post.return_value = mock_response mock_session_class.return_value.__enter__.return_value = mock_session yield mock_session @pytest.fixture def mock_requests_post(): """Mock requests.post for testing HTTP POST requests.""" from unittest.mock import patch with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: mock_response = Mock() mock_response.status_code = 200 mock_response.json.return_value = {"status": "success"} mock_post.return_value = mock_response yield mock_post @pytest.fixture def mock_requests_get(): """Mock requests.get for testing HTTP GET requests.""" from unittest.mock import patch with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.get") as mock_get: mock_response = Mock() mock_response.status_code = 200 mock_response.json.return_value = {"status": "success"} mock_get.return_value = mock_response yield mock_get @pytest.fixture def mock_aiohttp_session(): """Create mock aiohttp.ClientSession for testing async HTTP interactions.""" mock_session = AsyncMock() mock_session.closed = False # Mock response mock_response = AsyncMock() mock_response.status = 200 mock_response.json = AsyncMock(return_value={"status": "success"}) mock_response.raise_for_status = Mock() # Mock context managers mock_session.get.return_value.__aenter__.return_value = mock_response mock_session.post.return_value.__aenter__.return_value = mock_response return mock_session @pytest.fixture def mock_kill_process_tree(): """Mock kill_process_tree function for testing cleanup without actual process termination.""" from unittest.mock import patch with patch("verl.workers.rollout.sglang_rollout.http_server_engine.kill_process_tree") as mock_kill: yield mock_kill # Test environment fixtures for real SGLang testing @pytest.fixture(scope="session") def sglang_test_model_path(): """Provide a test model path for SGLang tests. This can be overridden by environment variable SGLANG_TEST_MODEL_PATH for tests that need a real model. """ import os return os.getenv("SGLANG_TEST_MODEL_PATH", "/tmp/test_model") @pytest.fixture def real_adapter_kwargs(sglang_test_model_path): """Provide kwargs for creating adapters with real SGLang integration.""" return { "host": "localhost", "port": 8000, "node_rank": 0, "model_path": sglang_test_model_path, } @pytest.fixture(autouse=True) def mock_server_args_post_init(): """Mock ServerArgs.__post_init__ to skip model path validation.""" from unittest.mock import patch with patch( "verl.workers.rollout.sglang_rollout.http_server_engine.ServerArgs.__post_init__", return_value=None ) as mock_post_init: yield mock_post_init class TestLaunchServerProcess: """Test cases for launch_server_process function.""" def test_launch_server_process_success( self, mock_multiprocessing_process, mock_requests_session, real_adapter_kwargs ): """Test successful server process launch and health check.""" # Import real SGLang ServerArgs from sglang.srt.server_args import ServerArgs # Create server args using real ServerArgs server_args = ServerArgs(**real_adapter_kwargs) # Test with patch( "verl.workers.rollout.sglang_rollout.http_server_engine.multiprocessing.Process" ) as mock_process_class: mock_process_class.return_value = mock_multiprocessing_process with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.Session") as mock_session_class: mock_session_class.return_value.__enter__.return_value = mock_requests_session result = launch_server_process(server_args, first_rank_in_node=True) # Assertions assert result == mock_multiprocessing_process mock_multiprocessing_process.start.assert_called_once() assert mock_requests_session.get.call_count >= 2 # health_generate and flush_cache def test_launch_server_process_non_master(self, mock_multiprocessing_process, non_master_adapter_kwargs): """Test server launch for non-master nodes (should return immediately).""" from sglang.srt.server_args import ServerArgs server_args = ServerArgs(**non_master_adapter_kwargs) with patch( "verl.workers.rollout.sglang_rollout.http_server_engine.multiprocessing.Process" ) as mock_process_class: mock_process_class.return_value = mock_multiprocessing_process result = launch_server_process(server_args, first_rank_in_node=True) assert result == mock_multiprocessing_process mock_multiprocessing_process.start.assert_not_called() def test_launch_server_process_timeout(self, mock_multiprocessing_process, real_adapter_kwargs): """Test timeout during server health check.""" from sglang.srt.server_args import ServerArgs server_args = ServerArgs(**real_adapter_kwargs) with patch( "verl.workers.rollout.sglang_rollout.http_server_engine.multiprocessing.Process" ) as mock_process_class: mock_process_class.return_value = mock_multiprocessing_process with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.Session") as mock_session_class: mock_session = Mock() mock_session.get.side_effect = requests.RequestException("Connection failed") mock_session_class.return_value.__enter__.return_value = mock_session import itertools with patch( "verl.workers.rollout.sglang_rollout.http_server_engine.time.time", side_effect=itertools.chain([0], itertools.repeat(400)), # 第一次返回0，之后一直返回400 ): with pytest.raises(TimeoutError): launch_server_process(server_args, first_rank_in_node=True) mock_multiprocessing_process.terminate.assert_called_once() def test_launch_server_process_died(self, real_adapter_kwargs): """Test server process dies during startup.""" from sglang.srt.server_args import ServerArgs server_args = ServerArgs(**real_adapter_kwargs) with patch( "verl.workers.rollout.sglang_rollout.http_server_engine.multiprocessing.Process" ) as mock_process_class: mock_process = Mock() mock_process.is_alive.return_value = False mock_process_class.return_value = mock_process with pytest.raises(RuntimeError, match="Server process terminated unexpectedly"): launch_server_process(server_args, first_rank_in_node=True) class TestHttpServerEngineAdapter: """Test cases for HttpServerEngineAdapter class.""" def test_init_with_router_registration(self, mock_launch_server_process, mock_requests_post, router_adapter_kwargs): """Test initialization with router registration.""" adapter = HttpServerAdapter(**router_adapter_kwargs) assert adapter.router_ip == "192.168.1.1" assert adapter.router_port == 8080 assert adapter.process == mock_launch_server_process.return_value mock_requests_post.assert_called_once() def test_init_without_router(self, mock_launch_server_process, basic_adapter_kwargs): """Test initialization without router registration.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) assert adapter.router_ip is None assert adapter.router_port is None assert adapter.process == mock_launch_server_process.return_value def test_register_with_router_failure(self, mock_launch_server_process, router_adapter_kwargs): """Test router registration failure handling.""" with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: mock_post.side_effect = requests.RequestException("Connection failed") # Should not raise exception, just log error adapter = HttpServerAdapter(**router_adapter_kwargs) assert adapter.router_ip == "192.168.1.1" mock_post.assert_called_once() def test_make_request_success(self, mock_launch_server_process, basic_adapter_kwargs): """Test successful HTTP request.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: mock_response = Mock() mock_response.status_code = 200 mock_response.json.return_value = {"status": "success"} mock_post.return_value = mock_response result = adapter._make_request("test_endpoint", {"param": "value"}) assert result == {"status": "success"} mock_post.assert_called_with( "http://localhost:8000/test_endpoint", json={"param": "value"}, timeout=adapter.timeout, ) def test_make_request_get_method(self, mock_launch_server_process, basic_adapter_kwargs): """Test HTTP GET request.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.get") as mock_get: mock_response = Mock() mock_response.status_code = 200 mock_response.json.return_value = {"data": "test"} mock_get.return_value = mock_response result = adapter._make_request("test_endpoint", method="GET") assert result == {"data": "test"} mock_get.assert_called_with("http://localhost:8000/test_endpoint", timeout=adapter.timeout) def test_make_request_non_master(self, mock_launch_server_process): """Test request from non-master node returns empty dict.""" kwargs = {"host": "localhost", "port": 8000, "node_rank": 1, "model_path": "/tmp/test_model"} adapter = HttpServerAdapter(**kwargs) result = adapter._make_request("test_endpoint") assert result == {} def test_make_request_retry_logic(self, mock_launch_server_process, basic_adapter_kwargs): """Test retry logic for failed requests.""" adapter = HttpServerAdapter(max_attempts=3, **basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: with patch("time.sleep") as mock_sleep: # First two calls fail, third succeeds mock_post.side_effect = [ requests.exceptions.Timeout(), requests.exceptions.ConnectionError(), Mock(status_code=200, json=lambda: {"success": True}), ] result = adapter._make_request("test_endpoint") assert result == {"success": True} assert mock_post.call_count == 3 assert mock_sleep.call_count == 2 def test_make_request_http_error(self, mock_launch_server_process, basic_adapter_kwargs): """Test HTTP error handling.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: mock_response = Mock() mock_response.raise_for_status.side_effect = requests.exceptions.HTTPError("404 Not Found") mock_post.return_value = mock_response with pytest.raises(requests.exceptions.HTTPError): adapter._make_request("test_endpoint") def test_make_request_max_attempts_exceeded(self, mock_launch_server_process, basic_adapter_kwargs): """Test max retries exceeded.""" adapter = HttpServerAdapter(max_attempts=1, **basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: with patch("time.sleep"): mock_post.side_effect = requests.exceptions.Timeout() with pytest.raises(RuntimeError, match="Failed to complete request"): adapter._make_request("test_endpoint") assert mock_post.call_count == 1 # Initial retry def test_update_weights_from_tensor_strict(self, mock_launch_server_process, basic_adapter_kwargs): import base64 from sglang.srt.managers.io_struct import UpdateWeightsFromTensorReqInput from verl.workers.rollout.sglang_rollout.http_server_engine import HttpServerAdapter basic_adapter_kwargs.setdefault("node_rank", 0) adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "updated"} req = UpdateWeightsFromTensorReqInput( serialized_named_tensors=[b"tensor1", b"tensor2"], load_format="safetensors", flush_cache=True, ) result = adapter.update_weights_from_tensor(req) assert result == {"status": "updated"} expected_b64_1 = base64.b64encode(b"tensor1").decode("utf-8") expected_b64_2 = base64.b64encode(b"tensor2").decode("utf-8") mock_request.assert_called_once_with( "update_weights_from_tensor", { "serialized_named_tensors": [expected_b64_1, expected_b64_2], "load_format": "safetensors", "flush_cache": True, }, ) def test_update_weights_from_tensor_empty(self, mock_launch_server_process, basic_adapter_kwargs): from sglang.srt.managers.io_struct import UpdateWeightsFromTensorReqInput from verl.workers.rollout.sglang_rollout.http_server_engine import HttpServerAdapter basic_adapter_kwargs.setdefault("node_rank", 0) adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "updated"} req = UpdateWeightsFromTensorReqInput( serialized_named_tensors=[], load_format="safetensors", flush_cache=True, ) result = adapter.update_weights_from_tensor(req) assert result == {"status": "updated"} mock_request.assert_called_once_with( "update_weights_from_tensor", { "serialized_named_tensors": [], "load_format": "safetensors", "flush_cache": True, }, ) def test_update_weights_from_tensor_none(self, mock_launch_server_process, basic_adapter_kwargs): from sglang.srt.managers.io_struct import UpdateWeightsFromTensorReqInput from verl.workers.rollout.sglang_rollout.http_server_engine import HttpServerAdapter basic_adapter_kwargs.setdefault("node_rank", 0) adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "updated"} req = UpdateWeightsFromTensorReqInput( serialized_named_tensors=None, load_format="safetensors", flush_cache=True, ) result = adapter.update_weights_from_tensor(req) assert result == {"status": "updated"} mock_request.assert_called_once_with( "update_weights_from_tensor", { "serialized_named_tensors": [], "load_format": "safetensors", "flush_cache": True, }, ) def test_generate(self, mock_launch_server_process, basic_adapter_kwargs): """Test generate method.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"text": "Generated text"} result = adapter.generate( prompt="Hello world", sampling_params={"temperature": 0.7}, return_logprob=True, ) assert result == {"text": "Generated text"} mock_request.assert_called_once_with( "generate", { "text": "Hello world", "sampling_params": {"temperature": 0.7}, "return_logprob": True, }, only_master=False, ) def test_flush_cache(self, mock_launch_server_process, basic_adapter_kwargs): """Test flush_cache method.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.get") as mock_get: with patch("time.sleep") as mock_sleep: # First call fails, second succeeds mock_responses = [ Mock(status_code=503), # Service unavailable Mock(status_code=200, json=lambda: {"cache_flushed": True}), ] mock_get.side_effect = mock_responses result = adapter.flush_cache() assert result == {"cache_flushed": True} assert mock_get.call_count == 2 mock_sleep.assert_called_once() def test_flush_cache_non_master(self, mock_launch_server_process): """Test flush_cache for non-master node.""" kwargs = {"host": "localhost", "port": 8000, "node_rank": 1, "model_path": "/tmp/test_model"} adapter = HttpServerAdapter(**kwargs) result = adapter.flush_cache() assert result == {} def test_memory_management_methods(self, mock_launch_server_process, basic_adapter_kwargs): """Test memory release and resume methods.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "success"} # Test release_memory_occupation result = adapter.release_memory_occupation(["weights", "kv_cache"]) assert result == {"status": "success"} mock_request.assert_called_with("release_memory_occupation", {"tags": ["weights", "kv_cache"]}) # Test resume_memory_occupation result = adapter.resume_memory_occupation(["weights"]) assert result == {"status": "success"} mock_request.assert_called_with("resume_memory_occupation", {"tags": ["weights"]}) def test_generation_control_methods(self, mock_launch_server_process, basic_adapter_kwargs): """Test generation control methods.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "success"} def test_shutdown(self, mock_launch_server_process, mock_kill_process_tree, router_adapter_kwargs): """Test shutdown method.""" with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: mock_response = Mock() mock_response.status_code = 200 mock_post.return_value = mock_response adapter = HttpServerAdapter(**router_adapter_kwargs) adapter.shutdown() # Should unregister from router assert mock_post.call_count == 2 # Once for registration, once for unregistration # Should kill process mock_kill_process_tree.assert_called_once_with(mock_launch_server_process.return_value.pid) def test_shutdown_with_errors(self, mock_launch_server_process, mock_kill_process_tree, router_adapter_kwargs): """Test shutdown method with errors.""" with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: # Mock registration success but unregistration failure mock_post.side_effect = [ Mock(status_code=200), # Registration success requests.RequestException("Unregistration failed"), # Unregistration failure ] # Mock process kill failure mock_kill_process_tree.side_effect = Exception("Kill failed") adapter = HttpServerAdapter(**router_adapter_kwargs) # Should not raise exceptions adapter.shutdown() assert mock_post.call_count == 2 mock_kill_process_tree.assert_called_once_with(mock_launch_server_process.return_value.pid) # Edge cases for HttpServerEngineAdapter def test_empty_and_none_parameters(self, mock_launch_server_process, basic_adapter_kwargs): """Test handling of empty and None parameters.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "success"} req = UpdateWeightsFromTensorReqInput( serialized_named_tensors=None, load_format=None, flush_cache=None, ) # Test generate with all None parameters result = adapter.generate() assert result == {"status": "success"} # Test with empty lists result = adapter.update_weights_from_tensor(req) assert result == {"status": "success"} # Test with empty tags result = adapter.release_memory_occupation(req) assert result == {"status": "success"} def test_large_payload_handling(self, mock_launch_server_process, basic_adapter_kwargs): """Test handling of large payloads.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "success"} # Test with large tensor list large_tensor_list = [MultiprocessingSerializer.serialize(f"tensor_{i}") for i in range(1000)] req = UpdateWeightsFromTensorReqInput( serialized_named_tensors=large_tensor_list, load_format="safetensors", flush_cache=True, ) result = adapter.update_weights_from_tensor(req) assert result == {"status": "success"} # Test with large prompt large_prompt = "A" * 10000 result = adapter.generate(prompt=large_prompt) assert result == {"status": "success"} def test_timeout_edge_cases(self, mock_launch_server_process): """Test various timeout scenarios.""" # Test with very small timeout kwargs = {"host": "localhost", "port": 8000, "node_rank": 0, "model_path": "/tmp/test_model", "timeout": 0.001} adapter = HttpServerAdapter(**kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: mock_post.side_effect = requests.exceptions.Timeout() with pytest.raises(RuntimeError, match="Failed to complete request"): adapter._make_request("test_endpoint") def test_extreme_configuration_values(self, mock_launch_server_process): """Test extreme configuration values.""" # Test with extreme values kwargs = { "host": "localhost", "port": 8000, "node_rank": 0, "model_path": "/tmp/test_model", "timeout": 0.001, # Very small "max_attempts": 100, # Very large "retry_delay": 0.001, # Very small } adapter = HttpServerAdapter(**kwargs) assert adapter.timeout == 0.001 assert adapter.max_attempts == 100 assert adapter.retry_delay == 0.001 class TestAsyncHttpServerEngineAdapter: """Test cases for AsyncHttpServerEngineAdapter class.""" def test_init(self, mock_launch_server_process, basic_adapter_kwargs): """Test async adapter initialization.""" adapter = AsyncHttpServerAdapter(max_connections=50, **basic_adapter_kwargs) assert adapter.max_connections == 50 @pytest.mark.asyncio async def test_make_async_request_success(self, mock_launch_server_process, basic_adapter_kwargs): """Test successful async HTTP request.""" # Instantiate adapter adapter = AsyncHttpServerAdapter(**basic_adapter_kwargs) mock_response = AsyncMock() mock_response.status = 200 mock_response.json = AsyncMock(return_value={"status": "success"}) mock_response.raise_for_status = Mock() mock_post_context_manager = AsyncMock() mock_post_context_manager.__aenter__.return_value = mock_response mock_session = AsyncMock(spec=aiohttp.ClientSession) mock_session.closed = False mock_session.post.return_value = mock_post_context_manager mock_session_cm = AsyncMock() mock_session_cm.__aenter__.return_value = mock_session with patch.object(adapter, "_get_session", return_value=mock_session_cm): result = await adapter._make_async_request("test_endpoint", {"param": "value"}) # Assert result is correct assert result == {"status": "success"} # Verify post was called mock_session.post.assert_called_once_with( "http://localhost:8000/test_endpoint", json={"param": "value"}, timeout=adapter.timeout ) @pytest.mark.asyncio async def test_make_async_request_get_method(self, mock_launch_server_process, basic_adapter_kwargs): """Test async GET request using aiohttp and proper context mocking.""" # Instantiate the async adapter adapter = AsyncHttpServerAdapter(**basic_adapter_kwargs) mock_response = AsyncMock() mock_response.status = 200 mock_response.json = AsyncMock(return_value={"data": "test"}) mock_response.raise_for_status = Mock() mock_get_context_manager = AsyncMock() mock_get_context_manager.__aenter__.return_value = mock_response mock_session = AsyncMock(spec=aiohttp.ClientSession) mock_session.closed = False mock_session.get.return_value = mock_get_context_manager mock_session_cm = AsyncMock() mock_session_cm.__aenter__.return_value = mock_session with patch.object(adapter, "_get_session", return_value=mock_session_cm): result = await adapter._make_async_request("test_endpoint", method="GET") # Validate assert result == {"data": "test"} mock_session.get.assert_called_once_with("http://localhost:8000/test_endpoint", timeout=adapter.timeout) @pytest.mark.asyncio async def test_make_async_request_non_master(self, mock_launch_server_process): """Test async request from non-master node.""" kwargs = {"host": "localhost", "port": 8000, "node_rank": 1, "model_path": "/tmp/test_model"} adapter = AsyncHttpServerAdapter(**kwargs) result = await adapter._make_async_request("test_endpoint") assert result == {} @pytest.mark.asyncio async def test_async_generate(self, mock_launch_server_process, basic_adapter_kwargs): """Test async generate method.""" adapter = AsyncHttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_async_request", new_callable=AsyncMock) as mock_request: mock_request.return_value = {"text": "Generated text"} result = await adapter.generate( prompt="Hello world", sampling_params={"temperature": 0.7}, return_logprob=True, ) assert result == {"text": "Generated text"} mock_request.assert_called_once() @pytest.mark.asyncio async def test_async_memory_management(self, mock_launch_server_process, basic_adapter_kwargs): """Test async memory management methods.""" adapter = AsyncHttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_async_request", new_callable=AsyncMock) as mock_request: mock_request.return_value = {"status": "success"} # Test release_memory_occupation result = await adapter.release_memory_occupation(["weights"]) assert result == {"status": "success"} mock_request.assert_called_with("release_memory_occupation", {"tags": ["weights"]}) # Test resume_memory_occupation result = await adapter.resume_memory_occupation(["weights"]) assert result == {"status": "success"} mock_request.assert_called_with("resume_memory_occupation", {"tags": ["weights"]}) assert ( mock_request.call_count == 2 ) # resume memory occupation will also call release memory occupation once class TestErrorRecovery: """Test error recovery mechanisms.""" def test_flush_cache_recovery(self, mock_launch_server_process, basic_adapter_kwargs): """Test flush cache recovery from failures.""" adapter = HttpServerAdapter(max_attempts=2, **basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.get") as mock_get: # Simulate multiple failures then success mock_get.side_effect = [ requests.exceptions.ConnectionError(), requests.exceptions.Timeout(), Mock(status_code=503), # Service unavailable Mock(status_code=200, json=lambda: {"cache_flushed": True}), ] with patch("time.sleep"): result = adapter.flush_cache() assert result == {"cache_flushed": True} def test_flush_cache_max_attempts(self, mock_launch_server_process, basic_adapter_kwargs): """Test flush cache max retries exceeded.""" adapter = HttpServerAdapter(max_attempts=1, **basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.get") as mock_get: # All attempts fail mock_get.side_effect = requests.exceptions.ConnectionError() with patch("time.sleep"): result = adapter.flush_cache() assert result == {} # Should return empty dict on failure def test_network_partition_recovery(self, mock_launch_server_process, basic_adapter_kwargs): """Test recovery from network partition scenarios.""" adapter = HttpServerAdapter(max_attempts=3, **basic_adapter_kwargs) with patch("verl.workers.rollout.sglang_rollout.http_server_engine.requests.post") as mock_post: # Simulate network partition then recovery mock_post.side_effect = [ requests.exceptions.ConnectionError("Network unreachable"), requests.exceptions.ConnectionError("Network unreachable"), Mock(status_code=200, json=lambda: {"recovered": True}), ] with patch("time.sleep"): result = adapter._make_request("test_endpoint") assert result == {"recovered": True} class TestResourceManagement: """Test resource management and cleanup.""" def test_resource_cleanup_on_exception( self, mock_launch_server_process, mock_kill_process_tree, basic_adapter_kwargs ): """Test resource cleanup when exceptions occur.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) # Simulate exception during operation with patch.object(adapter, "_make_request", side_effect=Exception("Test error")): try: adapter.generate(prompt="test") except Exception: pass # Cleanup should still work adapter.shutdown() mock_kill_process_tree.assert_called_once_with(mock_launch_server_process.return_value.pid) def test_multiple_shutdown_calls(self, mock_launch_server_process, basic_adapter_kwargs): """Test multiple shutdown calls are safe.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) # Multiple shutdown calls should be safe adapter.shutdown() adapter.shutdown() adapter.shutdown() class TestDataTypeHandling: """Test handling of various data types.""" def test_complex_data_structures(self, mock_launch_server_process, basic_adapter_kwargs): """Test handling of complex data structures.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {"status": "success"} # Test with complex sampling params complex_sampling_params = { "temperature": 0.7, "top_p": 0.9, "top_k": 50, "repetition_penalty": 1.1, "stop_sequences": ["", "\n\n"], "max_tokens": 100, "logit_bias": {"token_123": 0.5, "token_456": -0.5}, "nested_config": { "beam_search": True, "num_beams": 4, "early_stopping": True, }, } result = adapter.generate( prompt="Test prompt", sampling_params=complex_sampling_params, ) assert result == {"status": "success"} # Verify the complex structure was passed through call_args = mock_request.call_args[0][1] assert call_args["sampling_params"] == complex_sampling_params class TestIntegration: """Integration tests for both adapters.""" def test_error_scenarios(self, mock_launch_server_process, basic_adapter_kwargs): """Test various error scenarios.""" adapter = HttpServerAdapter(**basic_adapter_kwargs) # Test with None payload with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {} result = adapter.generate() assert result == {} # Test with empty parameters with patch.object(adapter, "_make_request") as mock_request: mock_request.return_value = {} req = UpdateWeightsFromTensorReqInput( serialized_named_tensors=None, load_format=None, flush_cache=None, ) result = adapter.update_weights_from_tensor(req) assert result == {} ================================================ FILE: tests/workers/rollout/rollout_trtllm/__init__.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: tests/workers/rollout/rollout_trtllm/test_adapter.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import subprocess from unittest.mock import AsyncMock, Mock, patch import aiohttp import pytest import ray from verl.workers.rollout.trtllm_rollout.trtllm_async_server import TRTLLMReplica from verl.workers.rollout.trtllm_rollout.trtllm_rollout import AsyncTRTLLMHttpAdapter class TestAsyncTRTLLMHttpAdapter: def _build_async_session( self, *, adapter: AsyncTRTLLMHttpAdapter, method: str, response: AsyncMock | None = None, ) -> tuple[AsyncMock, AsyncMock]: mock_session = AsyncMock(spec=aiohttp.ClientSession) mock_session.closed = False if response is not None: mock_context_manager = AsyncMock() mock_context_manager.__aenter__.return_value = response getattr(mock_session, method).return_value = mock_context_manager mock_session_cm = AsyncMock() mock_session_cm.__aenter__.return_value = mock_session return mock_session_cm, mock_session @pytest.mark.asyncio async def test_make_async_request_get_method(self): """Test HTTP GET request.""" adapter = AsyncTRTLLMHttpAdapter(host="localhost", port=8000) get_response = AsyncMock() get_response.status = 200 get_response.headers = {"Content-Type": "application/json"} get_response.raise_for_status = Mock() get_response.json = AsyncMock(return_value={"data": "test"}) get_session_cm, get_session = self._build_async_session( adapter=adapter, method="get", response=get_response, ) with patch.object(adapter, "_get_session", return_value=get_session_cm): get_result = await adapter._make_async_request("test_endpoint", method="GET") assert get_result == {"data": "test"} get_session.get.assert_called_once_with("http://localhost:8000/test_endpoint", timeout=adapter.timeout) @pytest.mark.asyncio async def test_make_async_request_post_method(self): """Test HTTP POST request.""" adapter = AsyncTRTLLMHttpAdapter(host="localhost", port=8000) post_response = AsyncMock() post_response.status = 200 post_response.headers = {"Content-Type": "application/json"} post_response.raise_for_status = Mock() post_response.json = AsyncMock(return_value={"status": "ok"}) post_session_cm, post_session = self._build_async_session( adapter=adapter, method="post", response=post_response, ) with patch.object(adapter, "_get_session", return_value=post_session_cm): post_result = await adapter._make_async_request("test_endpoint", {"param": "value"}) assert post_result == {"status": "ok"} post_session.post.assert_called_once_with( "http://localhost:8000/test_endpoint", json={"param": "value"}, timeout=adapter.timeout ) @pytest.mark.asyncio async def test_make_async_request_http_error(self): """Test HTTP error handling.""" adapter = AsyncTRTLLMHttpAdapter(host="localhost", port=8000) mock_response = AsyncMock() mock_response.status = 500 mock_response.headers = {"Content-Type": "application/json"} mock_response.raise_for_status = Mock( side_effect=aiohttp.ClientResponseError( request_info=Mock(real_url="http://localhost:8000/test_endpoint"), history=(), status=500, message="server error", ) ) mock_session_cm, _mock_session = self._build_async_session( adapter=adapter, method="post", response=mock_response, ) with patch.object(adapter, "_get_session", return_value=mock_session_cm): with pytest.raises(aiohttp.ClientResponseError): await adapter._make_async_request("test_endpoint", {"param": "value"}) @pytest.mark.asyncio async def test_make_async_request_max_attempts_exceeded(self): """Test max retries exceeded.""" adapter = AsyncTRTLLMHttpAdapter(host="localhost", port=8000, max_attempts=1) mock_session_cm, mock_session = self._build_async_session( adapter=adapter, method="post", response=None, ) mock_session.post.side_effect = asyncio.TimeoutError() with patch.object(adapter, "_get_session", return_value=mock_session_cm): with pytest.raises(RuntimeError, match="Failed to complete async request"): await adapter._make_async_request("test_endpoint", {"param": "value"}) class TestTRTLLMServerAdapter: def test_init_without_device_mesh(self): """Test ServerAdapter init path without device mesh.""" from hydra import compose, initialize_config_dir prev_rank = os.environ.get("RANK") os.environ["RANK"] = "0" try: os.environ.setdefault("TLLM_RAY_FORCE_LOCAL_CLUSTER", "1") ray.init(address="local", ignore_reinit_error=True, include_dashboard=False) config_dir = os.path.abspath("verl/verl/trainer/config") if not os.path.exists(config_dir): config_dir = os.path.abspath("verl/trainer/config") with initialize_config_dir(config_dir=config_dir, version_base=None): config = compose(config_name="ppo_trainer") config.trainer.n_gpus_per_node = 2 config.trainer.nnodes = 1 model_root = os.path.expanduser(os.getenv("TRTLLM_TEST_MODEL_PATH_ROOT", "~/models")) config.actor_rollout_ref.model.path = os.path.join(model_root, "Qwen/Qwen2.5-1.5B-Instruct") config.actor_rollout_ref.rollout.name = "trtllm" config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = 2 rollout_config = config.actor_rollout_ref.rollout model_config = config.actor_rollout_ref.model replica = TRTLLMReplica( replica_rank=0, config=rollout_config, model_config=model_config, gpus_per_node=2, ) asyncio.run(replica.init_standalone()) assert len(replica.workers) == 2 worker0 = replica.workers[0] worker1 = replica.workers[1] replica_rank = ray.get(worker0.get_replica_rank.remote()) is_leader_rank_0 = ray.get(worker0.is_leader_rank.remote()) is_leader_rank_1 = ray.get(worker1.is_leader_rank.remote()) assert replica_rank == 0 assert is_leader_rank_0 is True assert is_leader_rank_1 is False finally: if prev_rank is None: os.environ.pop("RANK", None) else: os.environ["RANK"] = prev_rank ray.shutdown() subprocess.run(["ray", "stop"], capture_output=True) ================================================ FILE: tests/workers/rollout/rollout_trtllm/test_async_server.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import subprocess import time from unittest.mock import MagicMock, patch import ray import torch from ray.util import placement_group_table from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy from verl.single_controller.ray import RayResourcePool, SubRayResourcePool from verl.workers.rollout.replica import RolloutMode from verl.workers.rollout.trtllm_rollout.trtllm_async_server import TRTLLMHttpServer, TRTLLMReplica class TestTRTLLMReplica: def test_placement_group_with_sub_ray_resource_pool(self): """ Scenario: SubRayResourcePool, 1 node, 8 GPUs, TP=4, replica_rank=1 SubRayResourcePool pre-assigns start_bundle_index=4 for replica 1. Expected: Replica 1 gets bundles [4, 5, 6, 7] """ with patch("verl.workers.rollout.trtllm_rollout.trtllm_async_server.ray"): mock_config = MagicMock() mock_config.tensor_model_parallel_size = 4 mock_config.data_parallel_size = 1 mock_config.pipeline_model_parallel_size = 1 replica = TRTLLMReplica( replica_rank=1, config=mock_config, model_config=MagicMock(), gpus_per_node=8, ) mock_pg = MagicMock() mock_pg.bundle_count = 8 resource_pool = SubRayResourcePool( placement_groups=[mock_pg], start_bundle_index=4, subgroup_world_size=4, ) replica.resource_pool = resource_pool replica.world_size = 4 # TP=4 pgs, bundle_indices = replica.get_pgs_and_bundle_indices() assert len(pgs) == 1 assert pgs[0] == mock_pg assert len(bundle_indices) == 1 assert bundle_indices[0] == [4, 5, 6, 7] def test_placement_group_with_ray_resource_pool(self): """ Scenario: RayResourcePool, 1 node, 8 GPUs, TP=2, replica_rank=1 RayResourcePool calculates: local_bundle_index = world_size * replica_rank = 2 * 1 = 2 Expected: Replica 1 gets bundles [2, 3] """ with patch("verl.workers.rollout.trtllm_rollout.trtllm_async_server.ray"): mock_config = MagicMock() mock_config.tensor_model_parallel_size = 2 mock_config.data_parallel_size = 1 mock_config.pipeline_model_parallel_size = 1 replica = TRTLLMReplica( replica_rank=1, config=mock_config, model_config=MagicMock(), gpus_per_node=8, ) mock_pg = MagicMock() mock_pg.bundle_count = 8 resource_pool = RayResourcePool( process_on_nodes=[8], use_gpu=True, max_colocate_count=1, name_prefix="test_rollout", ) resource_pool.pgs = [mock_pg] replica.resource_pool = resource_pool replica.world_size = 2 # TP=2 pgs, bundle_indices = replica.get_pgs_and_bundle_indices() assert len(pgs) == 1 assert pgs[0] == mock_pg assert len(bundle_indices) == 1 assert bundle_indices[0] == [2, 3] class TestTRTLLMHttpServer: @staticmethod def _build_rollout_config(*, response_length: int | None = None, free_cache_engine: bool = False): from hydra import compose, initialize_config_dir config_dir = os.path.abspath("verl/verl/trainer/config") if not os.path.exists(config_dir): config_dir = os.path.abspath("verl/trainer/config") with initialize_config_dir(config_dir=config_dir, version_base=None): config = compose(config_name="ppo_trainer") config.trainer.n_gpus_per_node = 1 config.trainer.nnodes = 1 model_root = os.path.expanduser(os.getenv("TRTLLM_TEST_MODEL_PATH_ROOT", "~/models")) config.actor_rollout_ref.model.path = os.path.join(model_root, "Qwen/Qwen2.5-0.5B-Instruct") config.actor_rollout_ref.rollout.name = "trtllm" config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = 1 if response_length is not None: config.actor_rollout_ref.rollout.response_length = response_length if free_cache_engine: config.actor_rollout_ref.rollout.free_cache_engine = True return config.actor_rollout_ref.rollout, config.actor_rollout_ref.model @staticmethod def _create_server(rollout_config, model_config, *, name: str): resource_pool = RayResourcePool( process_on_nodes=[1], use_gpu=True, max_colocate_count=1, name_prefix="test_rollout", ) pgs = resource_pool.get_placement_groups() bundle_indices = [[0]] pg_data = placement_group_table(pgs[0]) node_id = pg_data["bundles_to_node_id"][bundle_indices[0][0]] return TRTLLMHttpServer.options( scheduling_strategy=NodeAffinitySchedulingStrategy( node_id=node_id, soft=False, ), runtime_env={"env_vars": {"RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES": "1"}}, name=name, ).remote( config=rollout_config, model_config=model_config, is_reward_model=False, rollout_mode=RolloutMode.COLOCATED, workers=[], replica_rank=0, max_colocate_count=1, pgs=pgs, bundle_indices=bundle_indices, ) def test_async_generate(self): """Test TRT-LLM generate method with real model.""" try: os.environ.setdefault("TLLM_RAY_FORCE_LOCAL_CLUSTER", "1") ray.init(address="local", ignore_reinit_error=True, include_dashboard=False) rollout_config, model_config = self._build_rollout_config(response_length=50) server = self._create_server( rollout_config, model_config, name="trtllm_server_test_generate", ) ray.get(server.launch_server.remote()) # Test generate with a simple prompt prompt_ids = [1, 2, 3, 4, 5] # Simple test prompt sampling_params = { "temperature": 1.0, "top_k": 0, "logprobs": 1, } request_id = "test_request_1" result = ray.get(server.generate.remote(prompt_ids, sampling_params, request_id)) print(f"Result: {result}") # Verify the result structure assert hasattr(result, "token_ids"), "Result should have token_ids attribute" assert hasattr(result, "log_probs"), "Result should have log_probs attribute" assert isinstance(result.token_ids, list), "token_ids should be a list" assert len(result.token_ids) > 0, "Generated tokens should not be empty" # Verify logprobs are returned when requested assert result.log_probs is not None, "log_probs should not be None when requested" assert len(result.log_probs) == len(result.token_ids), "log_probs length should match token_ids" print(f"Generated {len(result.token_ids)} tokens") print(f"Token IDs: {result.token_ids[:10]}...") # Print first 10 tokens print(f"Log probs: {result.log_probs[:10]}...") # Print first 10 log probs finally: ray.shutdown() subprocess.run(["ray", "stop"], capture_output=True) def test_async_memory_management(self): """Test TRT-LLM async memory management (sleep) reduces memory usage.""" try: os.environ.setdefault("TLLM_RAY_FORCE_LOCAL_CLUSTER", "1") ray.init(address="local", ignore_reinit_error=True, include_dashboard=False) rollout_config, model_config = self._build_rollout_config(free_cache_engine=True) server = self._create_server( rollout_config, model_config, name="trtllm_server_test_0", ) ray.get(server.launch_server.remote()) device_ids = ray.get(server.report_device_ids.remote()) print(f"TRTLLM device UUIDs: {device_ids}") def _uuid_to_device_index(device_uuid: str) -> int | None: for idx in range(torch.cuda.device_count()): props = torch.cuda.get_device_properties(idx) uuid = getattr(props, "uuid", None) if uuid is None: # fall back to rank 0 return 0 if isinstance(uuid, bytes): uuid_str = uuid.decode("utf-8", errors="ignore") else: uuid_str = str(uuid) if uuid_str == device_uuid or uuid_str in device_uuid: print(f"Mapped device UUID {device_uuid} to torch device index {idx}") return idx return 0 def get_gpu_memory_mb_for_device(device_uuid: str) -> float: device_index = _uuid_to_device_index(device_uuid) prev_device = torch.cuda.current_device() torch.cuda.set_device(device_index) mem_free, mem_total = torch.cuda.mem_get_info() torch.cuda.set_device(prev_device) return (mem_total - mem_free) / (1024**2) baseline_memory_mb = get_gpu_memory_mb_for_device(device_ids[0]) print(f" Baseline memory: {baseline_memory_mb:.2f} MB") ray.get(server.sleep.remote()) time.sleep(2) sleep_memory_mb = get_gpu_memory_mb_for_device(device_ids[0]) memory_freed_mb = baseline_memory_mb - sleep_memory_mb print(f" Memory after sleep: {sleep_memory_mb:.2f} MB") print(f" Memory freed: {memory_freed_mb:.2f} MB") assert memory_freed_mb >= baseline_memory_mb * 0.6, ( f"Expected sleep() to free >=60% of baseline memory. " f"Baseline: {baseline_memory_mb:.2f} MB, freed: {memory_freed_mb:.2f} MB." ) finally: ray.shutdown() subprocess.run(["ray", "stop"], capture_output=True) ================================================ FILE: tests/workers/rollout/rollout_trtllm/test_trtllm_rollout_utils.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import uuid import numpy as np import pytest import ray import torch from omegaconf import OmegaConf from PIL import Image from transformers import AutoTokenizer UNIMODAL_MODEL_PATH = "Qwen/Qwen2.5-Math-7B" MULTIMODAL_MODEL_PATH = "Qwen/Qwen2.5-VL-7B-Instruct" MAX_MODEL_LEN = 4096 RESPONSE_LENGTH = 256 MAX_NUM_SEQS = 16 GPU_MEMORY_UTILIZATION = 0.8 TENSOR_PARALLEL_SIZE = 1 def create_test_image(width: int = 224, height: int = 224) -> Image.Image: img_array = np.zeros((height, width, 3), dtype=np.uint8) for i in range(height): for j in range(width): img_array[i, j] = [ int(255 * i / height), int(255 * j / width), int(255 * (i + j) / (height + width)), ] return Image.fromarray(img_array) def create_rollout_config_dict(): config_dict = { "_target_": "verl.workers.config.RolloutConfig", "name": "trtllm", "mode": "async", "temperature": 0.7, "top_k": 50, "top_p": 0.9, "do_sample": True, "n": 1, "prompt_length": 512, "response_length": RESPONSE_LENGTH, "dtype": "bfloat16", "gpu_memory_utilization": GPU_MEMORY_UTILIZATION, "ignore_eos": False, "enforce_eager": True, "free_cache_engine": False, "data_parallel_size": 1, "tensor_model_parallel_size": TENSOR_PARALLEL_SIZE, "pipeline_model_parallel_size": 1, "max_num_batched_tokens": 8192, "max_model_len": MAX_MODEL_LEN, "max_num_seqs": MAX_NUM_SEQS, "load_format": "auto", "enable_chunked_prefill": True, "enable_prefix_caching": True, } return OmegaConf.create(config_dict) def create_model_config_dict(model_path: str): config_dict = { "_target_": "verl.workers.config.HFModelConfig", "path": model_path, "trust_remote_code": True, "load_tokenizer": True, } return OmegaConf.create(config_dict) def get_tokenizer(model_path: str): return AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def get_processor(model_path: str): from transformers import AutoProcessor return AutoProcessor.from_pretrained(model_path, trust_remote_code=True) @pytest.mark.skipif( not torch.cuda.is_available(), reason="CUDA not available", ) class TestUnimodalTRTLLMRollout: @pytest.fixture(scope="class") def ray_context(self): if ray.is_initialized(): ray.shutdown() ray.init(ignore_reinit_error=True) yield ray.shutdown() @pytest.fixture(scope="class") def trtllm_replica(self, ray_context): from verl.workers.rollout.trtllm_rollout.trtllm_async_server import TRTLLMReplica rollout_config = create_rollout_config_dict() model_config = create_model_config_dict(UNIMODAL_MODEL_PATH) replica = TRTLLMReplica( replica_rank=0, config=rollout_config, model_config=model_config, gpus_per_node=torch.cuda.device_count(), is_reward_model=False, ) loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(replica.init_standalone()) yield replica loop.close() @pytest.fixture(scope="class") def tokenizer(self): return get_tokenizer(UNIMODAL_MODEL_PATH) @pytest.mark.parametrize( "prompt", [ "What is 2 + 2?", "Solve for x: 3x + 5 = 20", "Calculate the derivative of x^2 + 3x + 1", ], ) def test_unimodal_generate(self, trtllm_replica, tokenizer, prompt): replica = trtllm_replica messages = [ {"role": "system", "content": "You are a helpful math assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) input_ids = tokenizer.encode(text, return_tensors="pt")[0].tolist() sampling_params = { "temperature": 0.7, "top_p": 0.9, "top_k": 50, "logprobs": True, } request_id = str(uuid.uuid4()) output = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=request_id, ) ) assert output is not None assert hasattr(output, "token_ids") assert len(output.token_ids) > 0 generated_text = tokenizer.decode(output.token_ids, skip_special_tokens=True) print("\n[Unimodal Test]") print(f"Prompt: {prompt}") print(f"Generated ({len(output.token_ids)} tokens): {generated_text[:300]}...") def test_unimodal_batch_generate(self, trtllm_replica, tokenizer): replica = trtllm_replica prompts = [ "What is 1 + 1?", "What is 2 * 3?", "What is 10 / 2?", ] sampling_params = { "temperature": 0.7, "top_p": 0.9, "top_k": 50, "logprobs": False, } results = [] for i, prompt in enumerate(prompts): messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) input_ids = tokenizer.encode(text, return_tensors="pt")[0].tolist() output = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=str(uuid.uuid4()), ) ) results.append(output) assert len(results) == len(prompts) for i, (prompt, result) in enumerate(zip(prompts, results, strict=False)): assert result is not None assert len(result.token_ids) > 0 generated = tokenizer.decode(result.token_ids, skip_special_tokens=True) print(f"\n[Batch {i}] Prompt: {prompt}") print(f"Generated: {generated[:100]}...") @pytest.mark.skipif( not torch.cuda.is_available(), reason="CUDA not available", ) class TestMultimodalTRTLLMRollout: @pytest.fixture(scope="class") def ray_context(self): if ray.is_initialized(): ray.shutdown() ray.init(ignore_reinit_error=True) yield ray.shutdown() @pytest.fixture(scope="class") def trtllm_vlm_replica(self, ray_context): from verl.workers.rollout.trtllm_rollout.trtllm_async_server import TRTLLMReplica rollout_config = create_rollout_config_dict() model_config = create_model_config_dict(MULTIMODAL_MODEL_PATH) replica = TRTLLMReplica( replica_rank=0, config=rollout_config, model_config=model_config, gpus_per_node=torch.cuda.device_count(), is_reward_model=False, ) loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(replica.init_standalone()) yield replica loop.close() @pytest.fixture(scope="class") def tokenizer(self): return get_tokenizer(MULTIMODAL_MODEL_PATH) @pytest.fixture(scope="class") def processor(self): return get_processor(MULTIMODAL_MODEL_PATH) @pytest.mark.parametrize( "prompt", [ "Describe this image in detail.", "What colors do you see in this image?", "What patterns are visible in this image?", ], ) def test_multimodal_generate_with_image(self, trtllm_vlm_replica, processor, tokenizer, prompt): replica = trtllm_vlm_replica test_image = create_test_image(224, 224) messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": prompt}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) print("text: ", text) input_ids = processor.tokenizer(text, return_tensors="pt", padding=True)["input_ids"][0].tolist() print( "input_ids decoded: ", processor.tokenizer.decode(input_ids, skip_special_tokens=False, add_special_tokens=False), ) sampling_params = { "temperature": 0.7, "top_p": 0.9, "top_k": 50, "logprobs": False, } output = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=str(uuid.uuid4()), image_data=[test_image], ) ) assert output is not None assert hasattr(output, "token_ids") assert len(output.token_ids) > 0 generated_text = tokenizer.decode(output.token_ids, skip_special_tokens=True) print("\n[Multimodal Test]") print(f"Prompt: {prompt}") print(f"Image size: {test_image.size}") print(f"Generated ({len(output.token_ids)} tokens): {generated_text[:300]}...") @pytest.mark.parametrize( "image_size", [(224, 224), (384, 384), (512, 512)], ) def test_multimodal_different_image_sizes(self, trtllm_vlm_replica, processor, tokenizer, image_size): replica = trtllm_vlm_replica width, height = image_size test_image = create_test_image(width, height) prompt = "What is shown in this image?" messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": prompt}, ], } ] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) input_ids = processor.tokenizer(text, return_tensors="pt", padding=True)["input_ids"][0].tolist() sampling_params = { "temperature": 0.7, "top_p": 0.9, "top_k": 50, "logprobs": False, } output = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=str(uuid.uuid4()), image_data=[test_image], ) ) assert output is not None assert len(output.token_ids) > 0 print(f"\n[Image Size {image_size}] Generated {len(output.token_ids)} tokens") def test_multimodal_text_only_fallback(self, trtllm_vlm_replica, tokenizer): replica = trtllm_vlm_replica prompt = "What is the capital of China?" messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) input_ids = tokenizer.encode(text, return_tensors="pt")[0].tolist() sampling_params = { "temperature": 0.7, "top_p": 0.9, "top_k": 50, "logprobs": False, } output = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=str(uuid.uuid4()), ) ) assert output is not None assert len(output.token_ids) > 0 generated_text = tokenizer.decode(output.token_ids, skip_special_tokens=True) print("\n[Text-only on VLM]") print(f"Prompt: {prompt}") print(f"Generated: {generated_text}") @pytest.mark.skipif( not torch.cuda.is_available(), reason="CUDA not available", ) class TestTRTLLMServerLifecycle: @pytest.fixture(scope="class") def ray_context(self): if ray.is_initialized(): ray.shutdown() ray.init(ignore_reinit_error=True) yield ray.shutdown() @pytest.fixture(scope="class") def trtllm_replica_lifecycle(self, ray_context): from verl.workers.rollout.trtllm_rollout.trtllm_async_server import TRTLLMReplica rollout_config = create_rollout_config_dict() model_config = create_model_config_dict(UNIMODAL_MODEL_PATH) replica = TRTLLMReplica( replica_rank=0, config=rollout_config, model_config=model_config, gpus_per_node=torch.cuda.device_count(), is_reward_model=False, ) loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(replica.init_standalone()) yield replica, loop loop.close() @pytest.fixture(scope="class") def tokenizer(self): return get_tokenizer(UNIMODAL_MODEL_PATH) def test_wake_sleep_cycle(self, trtllm_replica_lifecycle, tokenizer): replica, loop = trtllm_replica_lifecycle prompt = "Hello, world!" messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) input_ids = tokenizer.encode(text, return_tensors="pt")[0].tolist() sampling_params = {"temperature": 0.7, "top_p": 0.9, "top_k": 50, "logprobs": False} output1 = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=str(uuid.uuid4()), ) ) assert output1 is not None assert len(output1.token_ids) > 0 print(f"\n[Before Sleep] Generated {len(output1.token_ids)} tokens") loop.run_until_complete(replica.sleep()) print("[Sleep] Server put to sleep") loop.run_until_complete(replica.wake_up()) print("[Wake Up] Server woken up") output2 = ray.get( replica.server_handle.generate.remote( prompt_ids=input_ids, sampling_params=sampling_params, request_id=str(uuid.uuid4()), ) ) assert output2 is not None assert len(output2.token_ids) > 0 print(f"[After Wake Up] Generated {len(output2.token_ids)} tokens") ================================================ FILE: tests/workers/rollout/rollout_vllm/run_fsdp_vllm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import time import torch import torch.distributed as dist from torch.distributed.fsdp import CPUOffload, MixedPrecision from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.api import ShardedStateDictConfig, ShardingStrategy, StateDictType from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer from vllm import SamplingParams from verl.third_party.vllm import LLM from verl.utils.distributed import initialize_global_process_group def _pre_process_inputs(pad_token_id, prompt_token_ids: torch.Tensor) -> list[int]: """Remove left padding tokens before feeding prompts to vLLM.""" non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] return prompt_token_ids[non_pad_index:].tolist() def main(): assert torch.cuda.is_available(), "CUDA must be present to run FSDP vLLM example" local_rank, rank, world_size = initialize_global_process_group() local_cache_path = "~/.cache/verl/rlhf" local_cache_path = os.path.expanduser(local_cache_path) hdfs_path = "Qwen/Qwen2-7B-Instruct" from verl.utils.fs import copy_to_local local_model_path = copy_to_local(src=hdfs_path, cache_dir=local_cache_path) tokenizer = AutoTokenizer.from_pretrained(local_model_path, trust_remote_code=True) actor_model_config = AutoConfig.from_pretrained(local_model_path, trust_remote_code=True) with torch.device("cuda"): actor_model = AutoModelForCausalLM.from_pretrained(local_model_path, trust_remote_code=True) actor_model.to(torch.bfloat16) max_prompt_length = 16 response_length = 32 preencode_prompts = [ "The president of the United States is", "The capital of France is", "The future of AI is", ] tokenizer.pad_token = tokenizer.eos_token prompts = tokenizer(preencode_prompts, return_tensors="pt", padding=True) input_ids = prompts["input_ids"] attention_mask = prompts["attention_mask"] from verl.utils.torch_functional import pad_sequence_to_length input_ids = pad_sequence_to_length(input_ids, max_prompt_length, tokenizer.pad_token_id, left_pad=True).cuda() attention_mask = pad_sequence_to_length(attention_mask, max_prompt_length, 0, left_pad=True).cuda() from transformers import GenerationConfig generation_config = GenerationConfig(do_sample=False) actor_model.cuda() output = actor_model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=32, # max_length=max_length, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, generation_config=generation_config, # renormalize_logits=True, output_scores=False, # this is potentially very large return_dict_in_generate=True, use_cache=False, ) # may OOM when use_cache = True seq = output.sequences response = seq[:, max_prompt_length:] print(f"hf response: {tokenizer.batch_decode(response)}") tensor_model_parallel_size = 4 from torch.distributed.device_mesh import init_device_mesh device_mesh = init_device_mesh("cuda", mesh_shape=(world_size,), mesh_dim_names=["fsdp"]) mixed_precision = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32) fsdp_model = FSDP( actor_model, use_orig_params=True, auto_wrap_policy=None, device_id=torch.cuda.current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, cpu_offload=CPUOffload(offload_params=False), sync_module_states=False, device_mesh=device_mesh, ) FSDP.set_state_dict_type( fsdp_model, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig() ) state_dict = fsdp_model.state_dict() sampling_params = SamplingParams( temperature=0, top_p=1, n=1, max_tokens=response_length, logprobs=1, ignore_eos=True, detokenize=False ) print(actor_model_config) llm = LLM( model=None, tokenizer=tokenizer, model_hf_config=actor_model_config, tensor_parallel_size=tensor_model_parallel_size, enforce_eager=True, dtype="bfloat16", load_format="dummy_dtensor", gpu_memory_utilization=0.8, trust_remote_code=True, ) # Warmup iterations for _ in range(10): torch.cuda.synchronize() llm.sync_model_weights(actor_weights=state_dict, load_format="dtensor") torch.cuda.synchronize() dist.barrier() start_time = time.time() llm.sync_model_weights(actor_weights=state_dict, load_format="dtensor") torch.cuda.synchronize() dist.barrier() end_time = time.time() # Calculate elapsed time elapsed_time = end_time - start_time print(f"Time taken: {elapsed_time:.6f} seconds") input_ids = input_ids.cuda() attention_mask = attention_mask.cuda() idx_list = [] batch_size = input_ids.shape[0] pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id for i in range(batch_size): idx_list.append(_pre_process_inputs(pad_token_id, input_ids[i])) print("start generation") outputs = llm.generate(prompt_token_ids=idx_list, sampling_params=sampling_params, use_tqdm=False) vllm_output = outputs[0].cuda() if torch.distributed.get_rank() == 0: print(f"hf response: {tokenizer.batch_decode(response)}") print(f"vllm response: {tokenizer.batch_decode(vllm_output)}") if __name__ == "__main__": main() ================================================ FILE: tests/workers/rollout/rollout_vllm/test_vllm_abort.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test vLLM abort functionality. Usage: pytest tests/workers/rollout/rollout_vllm/test_vllm_abort.py -v -s or python tests/workers/rollout/rollout_vllm/test_vllm_abort.py """ import asyncio import os import time from uuid import uuid4 def test_vllm_abort(): # ==================== Configuration ==================== MODEL_PATH = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") # /root/models/Qwen/Qwen2.5-1.5B-Instruct GPUS_PER_NODE = 2 TP_SIZE = 1 ROLLOUT_NAME = "vllm" ABORT_DELAY = 0.5 # seconds to wait before aborting print("=" * 60) print("vLLM Abort Test") print("=" * 60) print(f"Model: {MODEL_PATH}") print(f"GPUs: {GPUS_PER_NODE}, TP Size: {TP_SIZE}") print(f"Abort Delay: {ABORT_DELAY}s") print("=" * 60) # ==================== Initialize Ray ==================== print("\n[1] Initializing Ray...") import ray ray.init( runtime_env={ "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "INFO", "VLLM_USE_V1": "1", } }, ignore_reinit_error=True, ) try: # ==================== Create Config ==================== print("\n[2] Creating config...") from hydra import compose, initialize_config_dir from verl.utils.tokenizer import normalize_token_ids config_dir = os.path.abspath("verl/verl/trainer/config") if not os.path.exists(config_dir): config_dir = os.path.abspath("verl/trainer/config") with initialize_config_dir(config_dir=config_dir, version_base=None): config = compose(config_name="ppo_trainer") config.trainer.n_gpus_per_node = GPUS_PER_NODE config.trainer.nnodes = 1 config.actor_rollout_ref.model.path = MODEL_PATH config.actor_rollout_ref.rollout.name = ROLLOUT_NAME config.actor_rollout_ref.rollout.mode = "async" config.actor_rollout_ref.rollout.tensor_model_parallel_size = TP_SIZE config.actor_rollout_ref.rollout.prompt_length = 512 config.actor_rollout_ref.rollout.response_length = 512 # Longer for abort test # ==================== Create Rollout Server ==================== print("\n[3] Creating rollout server (this may take a while)...") from verl.workers.rollout.replica import get_rollout_replica_class rollout_config = config.actor_rollout_ref.rollout model_config = config.actor_rollout_ref.model rollout_server_class = get_rollout_replica_class(ROLLOUT_NAME) server = rollout_server_class( replica_rank=0, config=rollout_config, model_config=model_config, gpus_per_node=GPUS_PER_NODE, ) asyncio.run(server.init_standalone()) server_handle = server._server_handle print(f"Server address: {server._server_address}") # ==================== Load Tokenizer ==================== print("\n[4] Loading tokenizer...") from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) # ==================== Prepare Prompts ==================== print("\n[5] Preparing prompts (to ensure generation takes time)...") NUM_PROMPTS = 8 prompts = [ "Write a very long story about a brave knight and dragon.", "Explain the history of the Roman Empire in great detail.", "Describe quantum computing and its applications thoroughly.", "Write an essay about climate change and its global effects.", "Who won the Champions League in 2019?", "Write a detailed analysis of Shakespeare's Hamlet.", "Describe the process of photosynthesis in plants.", "Write about the French Revolution and its consequences.", ] all_prompt_ids = [] for prompt in prompts[:NUM_PROMPTS]: messages = [{"role": "user", "content": prompt}] prompt_ids = normalize_token_ids( tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True) ) all_prompt_ids.append(prompt_ids) print(f"Prepared {NUM_PROMPTS} prompts") # ==================== Start Generations and Abort ==================== print("\n[6] Starting generations and then aborting...") sampling_params = { "temperature": 1.0, "top_p": 1.0, "logprobs": False, } # Start all generations concurrently print(f"\n Starting {NUM_PROMPTS} generations...") generate_refs = [] for i, prompt_ids in enumerate(all_prompt_ids): request_id = f"abort_test_{i}_{uuid4().hex[:8]}" ref = server_handle.generate.remote( request_id=request_id, prompt_ids=prompt_ids, sampling_params=sampling_params, image_data=None, ) generate_refs.append((i, request_id, ref)) print(f" Started request {i}: {request_id}") # Wait before aborting print(f"\n Waiting {ABORT_DELAY}s before abort...") time.sleep(ABORT_DELAY) # Call abort print(" Calling abort_all_requests...") abort_start = time.perf_counter() abort_result = ray.get(server_handle.abort_all_requests.remote()) abort_time = time.perf_counter() - abort_start print(f" Abort took: {abort_time * 1000:.2f}ms") print(f" Abort result: {abort_result}") # Wait for all generations to finish print("\n Waiting for all generations to complete...") outputs = [] for i, request_id, ref in generate_refs: try: output = ray.get(ref, timeout=10.0) outputs.append((i, request_id, output)) except ray.exceptions.GetTimeoutError: print(f" Request {i} timed out!") outputs.append((i, request_id, None)) # ==================== Print Results ==================== print("\n" + "=" * 60) print("RESULTS") print("=" * 60) aborted_count = 0 completed_count = 0 timeout_count = 0 for i, request_id, output in outputs: if output is None: timeout_count += 1 print(f"[{i}] {request_id}: TIMEOUT") elif output.stop_reason == "aborted": aborted_count += 1 print(f"[{i}] {request_id}: ABORTED ({len(output.token_ids)} tokens)") print(f"Partial Output: {tokenizer.decode(output.token_ids)}") else: completed_count += 1 print(f"[{i}] {request_id}: COMPLETED ({output.stop_reason}, {len(output.token_ids)} tokens)") print(f"Full Output: {tokenizer.decode(output.token_ids)}") print(f"\nSummary: {aborted_count} aborted, {completed_count} completed, {timeout_count} timeout") print("\n" + "=" * 60) print(f"Abort result: {abort_result}") print("=" * 60) print("Abort test completed!") # Assertions for pytest assert timeout_count == 0, "No requests should timeout" assert aborted_count + completed_count == NUM_PROMPTS, "All requests should finish" assert "aborted_count" in abort_result, "Abort result should contain aborted_count" assert abort_time < 1.0, "Abort should be fast (< 1 second)" finally: print("\nShutting down Ray...") ray.shutdown() if __name__ == "__main__": # Can still run as standalone script test_vllm_abort() ================================================ FILE: tests/workers/rollout/test_hf_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import torch from omegaconf import OmegaConf from torch.distributed.fsdp import CPUOffload, MixedPrecision from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.api import ShardedStateDictConfig, ShardingStrategy, StateDictType from transformers import AutoModelForCausalLM, AutoTokenizer from verl import DataProto from verl.utils.distributed import initialize_global_process_group from verl.utils.fs import copy_to_local from verl.utils.model import compute_position_id_with_mask from verl.workers.rollout.hf_rollout import HFRollout BASE_HF_ROLLOUT_CONFIG = { "temperature": 1.0, "top_k": -1, "top_p": 1, "prompt_length": 64, "response_length": 64, "do_sample": True, "n": 1, "val_kwargs": { "top_k": -1, "top_p": 1.0, "temperature": 0, "n": 1, "do_sample": False, }, } def prepare_input_dataproto(tokenizer, config, validate): preencode_prompts = [ [{"role": "user", "content": "Who won the Champions League in 2019?"}], [{"role": "user", "content": "The founder of Apple is"}], [{"role": "user", "content": "What's your name"}], ] formatted_prompts = [ tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) for conversation in preencode_prompts ] prompts = tokenizer(formatted_prompts, return_tensors="pt", padding="max_length", max_length=config.prompt_length) input_dataproto = DataProto.from_dict( { "input_ids": prompts["input_ids"], "attention_mask": prompts["attention_mask"], "position_ids": compute_position_id_with_mask(prompts["attention_mask"]), }, meta_info={ "bos_token_id": tokenizer.bos_token_id, "eos_token_id": tokenizer.eos_token_id, "pad_token_id": tokenizer.pad_token_id, "validate": validate, }, ) return input_dataproto def prepare_fsdp_model(model, world_size): from torch.distributed.device_mesh import init_device_mesh device_mesh = init_device_mesh("cuda", mesh_shape=(world_size,), mesh_dim_names=["fsdp"]) mixed_precision = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32) fsdp_model = FSDP( model, use_orig_params=True, auto_wrap_policy=None, device_id=torch.cuda.current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, cpu_offload=CPUOffload(offload_params=False), sync_module_states=False, device_mesh=device_mesh, ) FSDP.set_state_dict_type( fsdp_model, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig() ) return fsdp_model def test_hf_rollout(n: int = 1, do_sample: bool = True, validate: bool = False): config = OmegaConf.create(BASE_HF_ROLLOUT_CONFIG) config.update({"n": n, "do_sample": do_sample}) assert torch.cuda.device_count() >= 2, "At least 2 GPUs is required to run tp+dp tests." local_rank, rank, world_size = initialize_global_process_group() # Initialize model and tokenizer local_cache_path = "~/.cache/verl/rlhf" local_cache_path = os.path.expanduser(local_cache_path) hdfs_path = "Qwen/Qwen2-7B-Instruct" local_model_path = copy_to_local(src=hdfs_path, cache_dir=local_cache_path) tokenizer = AutoTokenizer.from_pretrained(local_model_path, padding_side="left", trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token # Initialize FSDP model actor_model = AutoModelForCausalLM.from_pretrained(local_model_path, trust_remote_code=True) actor_model.to(torch.bfloat16) fsdp_model = prepare_fsdp_model(actor_model, world_size) # Initialize HFRollout and start generate hf_rollout = HFRollout(fsdp_model, OmegaConf.create(config)) input = prepare_input_dataproto(tokenizer, config, validate).to(torch.cuda.current_device()) outputs = hf_rollout.generate_sequences(input) # check generated batch size is expected generated_batch_size = outputs.batch.batch_size[0] assert generated_batch_size == input.batch.batch_size[0] * config.n for i in range(generated_batch_size): prompt_tokens = outputs.batch["prompts"][i] prompt_mask = prompt_tokens != tokenizer.pad_token_id prompt_tokens = prompt_tokens[prompt_mask] decoded_prompt = tokenizer.decode(prompt_tokens, skip_special_tokens=False) response_tokens = outputs.batch["responses"][i] response_mask = response_tokens != tokenizer.pad_token_id response_tokens = response_tokens[response_mask] decoded_response = tokenizer.decode(response_tokens, skip_special_tokens=False) attention_mask = outputs.batch["attention_mask"][i] position_ids = outputs.batch["position_ids"][i] prompt_length = outputs.batch["prompts"].size(1) response_length = outputs.batch["responses"].size(1) assert attention_mask.size(0) == prompt_length + response_length assert position_ids.size(0) == prompt_length + response_length # check response attention mask is expected response_attention = attention_mask[prompt_length:] eos_positions = (outputs.batch["responses"][i] == tokenizer.pad_token_id).nonzero(as_tuple=True)[0] if len(eos_positions) > 0: first_eos_pos = eos_positions[0].item() assert response_attention[: first_eos_pos + 1].all(), "Response attention mask should be 1 until EOS" if first_eos_pos + 1 < response_length: assert not response_attention[first_eos_pos + 1 :].any(), ( "Response attention mask should be 0 after EOS" ) else: assert response_attention.all(), "Response attention mask should be all 1 if no EOS token" # check response position ids is expected prompt_positions = position_ids[:prompt_length] response_positions = position_ids[prompt_length:] valid_response_length = min(len(response_tokens), response_length) if valid_response_length > 0: assert response_positions[0] == prompt_positions[-1] + 1 for j in range(1, valid_response_length): assert response_positions[j] == response_positions[j - 1] + 1 # print generated text for inspection if torch.distributed.get_rank() == 0: print(f"prompt: {decoded_prompt}") print(f"response: {decoded_response}") print("=" * 30) if __name__ == "__main__": test_hf_rollout(n=2, do_sample=True, validate=False) # test_hf_rollout(n=1, do_sample=False, validate=True) # test_hf_rollout(n=1, do_sample=True, validate=False) ================================================ FILE: tests/workers/rollout/test_sglang_async_rollout_multimodal_delta.py ================================================ # Copyright 2025 Amazon.com, Inc. or its affiliates # Copyright 2023-2024 SGLang Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from verl.tools.schemas import ToolResponse from verl.utils.dataset.vision_utils import process_image from verl.utils.tokenizer import hf_processor from verl.workers.rollout.schemas import ( AsyncRolloutRequest, AsyncRolloutRequestStateEnum, TokenizationSanityCheckModeEnum, ) def _test_add_tool_response_messages_image_delta(processor, image_list, description_list, resize_image=False): assert len(image_list) == len(description_list) # Get the smallest dimensions across all images processed_images = [] for img_url in image_list: img = process_image(img_url) processed_images.append(img) min_width = min(img.size[0] for img in processed_images) min_height = min(img.size[1] for img in processed_images) min_size = (min_width, min_height) if resize_image: processed_images_resized = [] for img in processed_images: img = img.resize(min_size) processed_images_resized.append(img) processed_images = processed_images_resized # Initial message history system_prompt = ( "You will be provided with an image. Describe this image and then generate a new image for the next round" ) messages = [ { "role": "system", "content": system_prompt, }, { "role": "user", "content": [ {"type": "text", "text": "Here is the first image provided: "}, {"type": "image", "image": [processed_images[0]]}, ], }, ] # Initial multi_modal_data with one image multi_modal_data = {"image": [processed_images[0]], "video": []} # Minimal required fields for AsyncRolloutRequest req = AsyncRolloutRequest( batch_data_id=0, request_id="test-req-1", state=AsyncRolloutRequestStateEnum.PENDING, messages=messages, multi_modal_keys=["image", "video"], multi_modal_data=multi_modal_data.copy(), tool_schemas=[], tools_kwargs={}, interaction_kwargs={}, input_ids=None, prompt_ids=None, response_ids=None, attention_mask=None, prompt_attention_mask=None, response_attention_mask=None, position_ids=None, prompt_position_ids=None, response_position_ids=None, loss_mask=None, prompt_loss_mask=None, response_loss_mask=None, reward_scores={}, max_prompt_len=8192, max_response_len=8192, max_model_len=16384, metrics={}, use_inference_chat_template=True, tokenization_sanity_check_mode=TokenizationSanityCheckModeEnum.STRICT, generation_prompt_ids=None, base_conv_wo_gen_prompt_end_pos=0, base_conv_with_gen_prompt_end_pos=0, processing_class=processor, ) prev_generated_len = 0 # Add First Assistant Message and first tool response message(image) for idx, img in enumerate(processed_images): if idx == 0: continue _ = req.get_generation_prompt_ids(processor) req.add_assistant_message(processor, content=description_list[idx - 1]) before_tool_call_len = req.input_ids.shape[-1] req.add_tool_response_messages( processor, [ToolResponse(image=[img], text="Here is the new image you requested: ")] ) after_tool_call_len = req.input_ids.shape[-1] if prev_generated_len == 0: prev_generated_len = after_tool_call_len - before_tool_call_len else: if resize_image: assert after_tool_call_len - before_tool_call_len == prev_generated_len assert req.multi_modal_data["image"] == processed_images[: idx + 1] _ = req.get_generation_prompt_ids(processor) req.add_assistant_message(processor, content=description_list[-1]) messages = [msg.model_dump() for msg in req.messages] tools = [tool.model_dump() for tool in req.tool_schemas] if req.tool_schemas else None full_prompt_info = req._handle_apply_chat_template( processor, messages, multi_modal_data=req.multi_modal_data, tools=tools, add_generation_prompt=False, tokenize=True, return_dict=True, ) full_prompt_ids = full_prompt_info["input_ids"] assert full_prompt_ids.eq(req.input_ids).all() # We must use dict(full_prompt_info) to convert BatchFeature values to a new dict # because np.array() only keeps the keys for BatchFeature. full_prompt_multi_modal_inputs = full_prompt_info.copy() full_prompt_multi_modal_inputs.pop("input_ids", None) full_prompt_multi_modal_inputs.pop("attention_mask", None) for key in full_prompt_multi_modal_inputs: assert full_prompt_multi_modal_inputs[key].eq(req.multi_modal_inputs[key]).all() @pytest.mark.skipif( hf_processor(os.path.expanduser("~/models/Qwen/Qwen2.5-VL-3B-Instruct")) is None, reason="Processor not available for Qwen/Qwen2.5-VL-B-Instruct", ) def test_add_tool_response_messages_image_delta(): processor = hf_processor(os.path.expanduser("~/models/Qwen/Qwen2.5-VL-3B-Instruct")) # From Qwen2.5-VL-3B-Instruct HF example img_1_url = {"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"} img_1_description = "A woman sits on the beach at sunset, smiling as she shares a high five with her large dog." # GitHub Logo img_2_url = {"image": "https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png"} img_2_description = "A GitHub Logo image" # Octocat img_3_url = {"image": "https://octodex.github.com/images/orderedlistocat.png"} img_3_description = "An Octocat image" image_list = [img_1_url, img_2_url, img_3_url] description_list = [img_1_description, img_2_description, img_3_description] _test_add_tool_response_messages_image_delta(processor, image_list, description_list, resize_image=False) @pytest.mark.skipif( hf_processor(os.path.expanduser("~/models/Qwen/Qwen2.5-VL-3B-Instruct")) is None, reason="Processor not available for Qwen/Qwen2.5-VL-B-Instruct", ) def test_add_tool_response_messages_image_delta_resize_image(): processor = hf_processor(os.path.expanduser("~/models/Qwen/Qwen2.5-VL-3B-Instruct")) # From Qwen2.5-VL-3B-Instruct HF example img_1_url = {"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"} img_1_description = "A woman sits on the beach at sunset, smiling as she shares a high five with her large dog." # GitHub Logo img_2_url = {"image": "https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png"} img_2_description = "A GitHub Logo image" # Octocat img_3_url = {"image": "https://octodex.github.com/images/orderedlistocat.png"} img_3_description = "An Octocat image" image_list = [img_1_url, img_2_url, img_3_url] description_list = [img_1_description, img_2_description, img_3_description] _test_add_tool_response_messages_image_delta(processor, image_list, description_list, resize_image=True) ================================================ FILE: tests/workers/rollout/test_sglang_rollout_sharding_manager.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import torch from verl.workers.rollout.sglang_rollout.utils import get_named_tensor_buckets _TENSOR_1MB = torch.zeros(512, 512) _BYTES_1MB = 1 << 20 @pytest.mark.parametrize( "named_tensors, bucket_size_mb, gt_groups", [ ( [("a", _TENSOR_1MB), ("b", _TENSOR_1MB)], 0.5 * _BYTES_1MB, [["a"], ["b"]], ), ( [("a", _TENSOR_1MB), ("b", _TENSOR_1MB)], 1 * _BYTES_1MB, [["a"], ["b"]], ), ( [("a", _TENSOR_1MB), ("b", _TENSOR_1MB)], 1.5 * _BYTES_1MB, [["a"], ["b"]], ), ( [("a", _TENSOR_1MB), ("b", _TENSOR_1MB)], 2 * _BYTES_1MB, [["a", "b"]], ), ], ) def test_get_named_tensor_buckets(named_tensors, bucket_size_mb, gt_groups: list[list[str]]): named_tensors_iter = iter(named_tensors) groups = list(get_named_tensor_buckets(named_tensors_iter, bucket_size_mb)) assert len(groups) == len(gt_groups) for group, gt_group in zip(groups, gt_groups, strict=True): assert len(group) == len(gt_group) for (name, _), (gt_name) in zip(group, gt_group, strict=True): assert name == gt_name ================================================ FILE: tests/workers/rollout/test_vllm_cli_args_on_cpu.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import pytest from verl.workers.rollout.vllm_rollout.utils import build_cli_args_from_config class TestBuildCliArgsFromConfig: """Tests for CLI argument serialization from config dictionaries.""" def test_string_value(self): """String values become '--key value'.""" config = {"model": "gpt2"} result = build_cli_args_from_config(config) assert result == ["--model", "gpt2"] def test_integer_value(self): """Integer values are converted to strings.""" config = {"tensor-parallel-size": 4} result = build_cli_args_from_config(config) assert result == ["--tensor-parallel-size", "4"] def test_float_value(self): """Float values are converted to strings.""" config = {"temperature": 0.7} result = build_cli_args_from_config(config) assert result == ["--temperature", "0.7"] def test_bool_true(self): """Bool True adds flag without value.""" config = {"enable-prefix-caching": True} result = build_cli_args_from_config(config) assert result == ["--enable-prefix-caching"] def test_bool_false(self): """Bool False is skipped entirely.""" config = {"enable-prefix-caching": False} result = build_cli_args_from_config(config) assert result == [] def test_none_value(self): """None values are skipped.""" config = {"lora-path": None} result = build_cli_args_from_config(config) assert result == [] def test_list_values(self): """List values are expanded into multiple arguments.""" config = {"cudagraph-capture-sizes": [1, 2, 4, 8]} result = build_cli_args_from_config(config) assert result == ["--cudagraph-capture-sizes", "1", "2", "4", "8"] def test_empty_list(self): """Empty lists are skipped (vLLM nargs='+' requires at least one value).""" config = {"cudagraph-capture-sizes": []} result = build_cli_args_from_config(config) assert result == [] def test_list_with_strings(self): """List of strings is properly expanded.""" config = {"allowed-origins": ["http://localhost", "http://example.com"]} result = build_cli_args_from_config(config) assert result == ["--allowed-origins", "http://localhost", "http://example.com"] def test_dict_value(self): """Dict values are JSON serialized.""" config = {"extra-config": {"key": "value", "nested": True}} result = build_cli_args_from_config(config) assert result[0] == "--extra-config" # JSON output may have different key ordering, so parse and compare assert json.loads(result[1]) == {"key": "value", "nested": True} def test_mixed_config(self): """Test a realistic mixed configuration.""" config = { "tensor-parallel-size": 4, "enable-prefix-caching": True, "disable-log-requests": False, "lora-path": None, "cudagraph-capture-sizes": [1, 2, 4, 8], "max-model-len": 2048, } result = build_cli_args_from_config(config) # Check expected args are present assert "--tensor-parallel-size" in result assert "4" in result assert "--enable-prefix-caching" in result assert "--cudagraph-capture-sizes" in result assert "1" in result assert "8" in result assert "--max-model-len" in result assert "2048" in result # Check skipped values are not present assert "--disable-log-requests" not in result assert "--lora-path" not in result def test_preserves_order(self): """Arguments should preserve dictionary order (Python 3.7+).""" config = {"first": "a", "second": "b", "third": "c"} result = build_cli_args_from_config(config) assert result == ["--first", "a", "--second", "b", "--third", "c"] def test_empty_config(self): """Empty config returns empty list.""" config = {} result = build_cli_args_from_config(config) assert result == [] def test_single_element_list(self): """Single element list works correctly.""" config = {"sizes": [42]} result = build_cli_args_from_config(config) assert result == ["--sizes", "42"] if __name__ == "__main__": pytest.main([__file__, "-v"]) ================================================ FILE: tests/workers/test_fsdp_attn_implementation.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test for attn_implementation override configuration in FSDP workers. This test verifies that the fix for honoring attn_implementation override config works correctly in the ActorRolloutRefWorker._build_model_optimizer method. """ from unittest.mock import Mock, patch import pytest import torch from omegaconf import OmegaConf from transformers import AutoConfig, AutoModelForCausalLM # Only run these tests if we can import verl components try: from verl.workers.config import FSDPEngineConfig # noqa: F401 from verl.workers.fsdp_workers import ( ActorRolloutRefWorker, # noqa: F401 CriticWorker, # noqa: F401 ) VERL_AVAILABLE = True except ImportError: VERL_AVAILABLE = False @pytest.mark.skipif(not VERL_AVAILABLE, reason="VERL components not available") class TestFSDPAttnImplementation: """Test cases for attn_implementation override in FSDP workers.""" def test_attn_implementation_extraction_logic(self): """Test the core logic for extracting attn_implementation from override config.""" # Test case 1: Default behavior override_config = {} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "flash_attention_2" # Test case 2: Override to eager override_config = {"attn_implementation": "eager"} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "eager" # Test case 3: Override to sdpa override_config = {"attn_implementation": "sdpa"} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "sdpa" # Test case 4: Other configs don't affect attn_implementation override_config = {"other_setting": "value", "dropout": 0.1} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "flash_attention_2" @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModelForCausalLM.from_pretrained") def test_attn_implementation_passed_to_autoconfig(self, mock_model_from_pretrained, mock_config_from_pretrained): """Test that attn_implementation is correctly passed to AutoConfig.from_pretrained.""" # Mock the AutoConfig return value mock_config = Mock() mock_config.tie_word_embeddings = False mock_config.architectures = ["LlamaForCausalLM"] mock_config_from_pretrained.return_value = mock_config # Mock the model return value mock_model = Mock() mock_model_from_pretrained.return_value = mock_model # Test data test_cases = [ ({}, "flash_attention_2"), # Default ({"attn_implementation": "eager"}, "eager"), # Override to eager ({"attn_implementation": "sdpa"}, "sdpa"), # Override to sdpa ] for override_config, expected_attn_impl in test_cases: # Reset mocks mock_config_from_pretrained.reset_mock() mock_model_from_pretrained.reset_mock() # Simulate the logic from FSDP workers attn_implementation = override_config.get("attn_implementation", "flash_attention_2") # This simulates what happens in _build_model_optimizer AutoConfig.from_pretrained("test_path", trust_remote_code=False, attn_implementation=attn_implementation) # Verify AutoConfig.from_pretrained was called with correct attn_implementation mock_config_from_pretrained.assert_called_once_with( "test_path", trust_remote_code=False, attn_implementation=expected_attn_impl ) @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModelForCausalLM.from_pretrained") def test_attn_implementation_passed_to_model(self, mock_model_from_pretrained, mock_config_from_pretrained): """Test that attn_implementation is correctly passed to model.from_pretrained.""" # Mock the AutoConfig return value mock_config = Mock() mock_config.tie_word_embeddings = False mock_config.architectures = ["LlamaForCausalLM"] mock_config_from_pretrained.return_value = mock_config # Mock the model return value mock_model = Mock() mock_model_from_pretrained.return_value = mock_model # Test with override config override_config = {"attn_implementation": "eager"} attn_implementation = override_config.get("attn_implementation", "flash_attention_2") # This simulates what happens in _build_model_optimizer AutoModelForCausalLM.from_pretrained( pretrained_model_name_or_path="test_path", torch_dtype=torch.bfloat16, config=mock_config, trust_remote_code=False, attn_implementation=attn_implementation, ) # Verify AutoModelForCausalLM.from_pretrained was called with correct attn_implementation mock_model_from_pretrained.assert_called_once_with( pretrained_model_name_or_path="test_path", torch_dtype=torch.bfloat16, config=mock_config, trust_remote_code=False, attn_implementation="eager", ) def test_override_config_integration(self): """Test that override_config from Hydra configuration works correctly.""" # Simulate the OmegaConf configuration structure used in VERL config_dict = { "model": {"path": "/test/path", "override_config": {"attn_implementation": "eager", "dropout": 0.1}} } # Convert to OmegaConf structure omegaconf = OmegaConf.create(config_dict) # Simulate what happens in the FSDP worker override_model_config = OmegaConf.to_container(OmegaConf.create(omegaconf.model.get("override_config", {}))) # Test extraction attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "eager" # Test that other configs are preserved assert override_model_config.get("dropout") == 0.1 def test_hydra_plus_prefix_config(self): """Test that Hydra +prefix configurations work correctly.""" # This simulates the configuration when user specifies: # +actor_rollout_ref.model.override_config.attn_implementation=eager # The + prefix in Hydra adds new keys to the config config_dict = { "actor_rollout_ref": { "model": { "path": "/test/path", "override_config": { "attn_implementation": "eager" # This gets added via +prefix }, } } } omegaconf = OmegaConf.create(config_dict) # Extract override config as done in FSDP workers override_model_config = OmegaConf.to_container( OmegaConf.create(omegaconf.actor_rollout_ref.model.get("override_config", {})) ) # Verify extraction works attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "eager" def test_backward_compatibility(self): """Test that the fix maintains backward compatibility.""" # Test case 1: No override_config at all (old behavior) config_without_override = {} attn_implementation = config_without_override.get("attn_implementation", "flash_attention_2") assert attn_implementation == "flash_attention_2" # Test case 2: Empty override_config config_with_empty_override = {"override_config": {}} override_config = config_with_empty_override.get("override_config", {}) attn_implementation = override_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "flash_attention_2" # Test case 3: override_config with other settings but no attn_implementation config_with_other_overrides = {"override_config": {"dropout": 0.1, "hidden_size": 1024}} override_config = config_with_other_overrides.get("override_config", {}) attn_implementation = override_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "flash_attention_2" def test_critic_attn_implementation_extraction_logic(self): """Test the core logic for extracting attn_implementation from override config for CriticWorker.""" # Test case 1: Default behavior for critic override_config = {} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "flash_attention_2" # Test case 2: Override to eager for critic override_config = {"attn_implementation": "eager"} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "eager" # Test case 3: Override to sdpa for critic override_config = {"attn_implementation": "sdpa"} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "sdpa" # Test case 4: Other configs don't affect attn_implementation for critic override_config = {"other_setting": "value", "dropout": 0.1} attn_impl = override_config.get("attn_implementation", "flash_attention_2") assert attn_impl == "flash_attention_2" @patch("transformers.AutoConfig.from_pretrained") def test_critic_attn_implementation_passed_to_autoconfig(self, mock_config_from_pretrained): """Test that attn_implementation is correctly passed to AutoConfig.from_pretrained in CriticWorker.""" # Mock the AutoConfig return value mock_config = Mock() mock_config.tie_word_embeddings = False mock_config.architectures = ["LlamaForCausalLM"] mock_config.num_labels = 1 mock_config_from_pretrained.return_value = mock_config # Test data for critic model test_cases = [ ({}, "flash_attention_2"), # Default ({"attn_implementation": "eager"}, "eager"), # Override to eager ({"attn_implementation": "sdpa"}, "sdpa"), # Override to sdpa ] for override_config, expected_attn_impl in test_cases: # Reset mocks mock_config_from_pretrained.reset_mock() # Simulate the logic from CriticWorker _build_critic_model_optimizer attn_implementation = override_config.get("attn_implementation", "flash_attention_2") # This simulates what should happen in CriticWorker._build_critic_model_optimizer # (This is where the fix needs to be applied in the actual implementation) AutoConfig.from_pretrained( "test_path", attn_implementation=attn_implementation, trust_remote_code=False, ) # Verify AutoConfig.from_pretrained was called with correct attn_implementation mock_config_from_pretrained.assert_called_once_with( "test_path", attn_implementation=expected_attn_impl, trust_remote_code=False, ) def test_critic_override_config_integration(self): """Test that override_config from Hydra configuration works correctly for CriticWorker.""" # Simulate the OmegaConf configuration structure used in VERL for critic config_dict = { "critic": { "model": {"path": "/test/path", "override_config": {"attn_implementation": "eager", "dropout": 0.1}} } } # Convert to OmegaConf structure omegaconf = OmegaConf.create(config_dict) # Simulate what happens in the CriticWorker override_model_config = OmegaConf.to_container( OmegaConf.create(omegaconf.critic.model.get("override_config", {})) ) # Test extraction for critic attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "eager" # Test that other configs are preserved for critic assert override_model_config.get("dropout") == 0.1 def test_critic_hydra_plus_prefix_config(self): """Test that Hydra +prefix configurations work correctly for CriticWorker.""" # This simulates the configuration when user specifies: # +critic.model.override_config.attn_implementation=eager # The + prefix in Hydra adds new keys to the config config_dict = { "critic": { "model": { "path": "/test/path", "override_config": { "attn_implementation": "eager" # This gets added via +prefix for critic }, } } } omegaconf = OmegaConf.create(config_dict) # Extract override config as done in CriticWorker override_model_config = OmegaConf.to_container( OmegaConf.create(omegaconf.critic.model.get("override_config", {})) ) # Verify extraction works for critic attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "eager" def test_both_actor_and_critic_configuration(self): """Test that both actor and critic can have different attn_implementation overrides simultaneously.""" # This simulates a complete training configuration with both actor and critic overrides config_dict = { "actor_rollout_ref": {"model": {"override_config": {"attn_implementation": "eager"}}}, "critic": {"model": {"override_config": {"attn_implementation": "sdpa"}}}, } omegaconf = OmegaConf.create(config_dict) # Extract actor override config actor_override_config = OmegaConf.to_container( OmegaConf.create(omegaconf.actor_rollout_ref.model.get("override_config", {})) ) actor_attn_implementation = actor_override_config.get("attn_implementation", "flash_attention_2") # Extract critic override config critic_override_config = OmegaConf.to_container( OmegaConf.create(omegaconf.critic.model.get("override_config", {})) ) critic_attn_implementation = critic_override_config.get("attn_implementation", "flash_attention_2") # Verify both can be configured independently assert actor_attn_implementation == "eager" assert critic_attn_implementation == "sdpa" def test_critic_backward_compatibility(self): """Test that the CriticWorker fix maintains backward compatibility.""" # Test case 1: No override_config at all for critic (old behavior) config_without_override = {} attn_implementation = config_without_override.get("attn_implementation", "flash_attention_2") assert attn_implementation == "flash_attention_2" # Test case 2: Empty override_config for critic config_with_empty_override = {"override_config": {}} override_config = config_with_empty_override.get("override_config", {}) attn_implementation = override_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "flash_attention_2" # Test case 3: override_config with other settings but no attn_implementation for critic config_with_other_overrides = {"override_config": {"dropout": 0.1, "num_labels": 1}} override_config = config_with_other_overrides.get("override_config", {}) attn_implementation = override_config.get("attn_implementation", "flash_attention_2") assert attn_implementation == "flash_attention_2" def test_attn_implementation_fix_integration(): """Integration test to verify the entire fix works as expected.""" # This test simulates the complete flow from configuration to model creation # Step 1: Simulate Hydra configuration with +prefix # user_config = "+actor_rollout_ref.model.override_config.attn_implementation=eager" # This would result in a config structure like: config_dict = {"actor_rollout_ref": {"model": {"override_config": {"attn_implementation": "eager"}}}} # Step 2: Extract override_model_config as done in FSDP workers omegaconf = OmegaConf.create(config_dict) override_model_config = OmegaConf.to_container( OmegaConf.create(omegaconf.actor_rollout_ref.model.get("override_config", {})) ) # Step 3: Apply the fix logic attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") # Step 4: Verify the fix works assert attn_implementation == "eager" # Step 5: Verify this would be passed to both AutoConfig and Model creation # (This would normally be done with mocks, but we can test the parameter preparation) config_params = {"attn_implementation": attn_implementation} model_params = {"attn_implementation": attn_implementation} assert config_params["attn_implementation"] == "eager" assert model_params["attn_implementation"] == "eager" def test_critic_attn_implementation_fix_integration(): """Integration test to verify the entire fix works as expected for CriticWorker.""" # This test simulates the complete flow from configuration to model creation for critic # Step 1: Simulate Hydra configuration with +prefix for critic # user_config = "+critic.model.override_config.attn_implementation=sdpa" # This would result in a config structure like: config_dict = {"critic": {"model": {"override_config": {"attn_implementation": "sdpa"}}}} # Step 2: Extract override_model_config as should be done in CriticWorker omegaconf = OmegaConf.create(config_dict) override_model_config = OmegaConf.to_container(OmegaConf.create(omegaconf.critic.model.get("override_config", {}))) # Step 3: Apply the fix logic (what needs to be implemented in CriticWorker) attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") # Step 4: Verify the fix works for critic assert attn_implementation == "sdpa" # Step 5: Verify this would be passed to AutoConfig creation for critic config_params = {"attn_implementation": attn_implementation} assert config_params["attn_implementation"] == "sdpa" def test_complete_training_configuration(): """Integration test for a complete training configuration with both actor and critic overrides.""" # This test simulates a realistic training configuration where both # actor and critic have different attention implementations config_dict = { "actor_rollout_ref": { "model": { "path": "/shared/models/llama-7b", "override_config": {"attn_implementation": "eager", "torch_dtype": "bfloat16"}, } }, "critic": { "model": { "path": "/shared/models/llama-7b", "override_config": {"attn_implementation": "sdpa", "num_labels": 1}, } }, } omegaconf = OmegaConf.create(config_dict) # Extract configurations as would be done in the workers actor_override_config = OmegaConf.to_container( OmegaConf.create(omegaconf.actor_rollout_ref.model.get("override_config", {})) ) critic_override_config = OmegaConf.to_container(OmegaConf.create(omegaconf.critic.model.get("override_config", {}))) # Apply the fix logic for both actor_attn_implementation = actor_override_config.get("attn_implementation", "flash_attention_2") critic_attn_implementation = critic_override_config.get("attn_implementation", "flash_attention_2") # Verify both configurations work independently assert actor_attn_implementation == "eager" assert critic_attn_implementation == "sdpa" # Verify other configs are preserved assert actor_override_config.get("torch_dtype") == "bfloat16" assert critic_override_config.get("num_labels") == 1 if __name__ == "__main__": # Run basic tests test_attn_implementation_fix_integration() test_critic_attn_implementation_fix_integration() test_complete_training_configuration() if VERL_AVAILABLE: # Run class-based tests test_class = TestFSDPAttnImplementation() test_class.test_attn_implementation_extraction_logic() test_class.test_override_config_integration() test_class.test_hydra_plus_prefix_config() test_class.test_backward_compatibility() # Run new critic tests test_class.test_critic_attn_implementation_extraction_logic() test_class.test_critic_override_config_integration() test_class.test_critic_hydra_plus_prefix_config() test_class.test_both_actor_and_critic_configuration() test_class.test_critic_backward_compatibility() print("✓ All FSDP attn_implementation tests passed!") print("✓ All CriticWorker attn_implementation tests passed!") else: print("⚠ VERL components not available, skipping VERL-specific tests") print("✓ Integration tests passed!") print("✓ Critic integration tests passed!") ================================================ FILE: tests/workers/test_fsdp_workers.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from omegaconf import OmegaConf from verl.workers.fsdp_workers import ActorRolloutRefWorker def test_actor_rollout_ref_worker_actor_ref_model(): """Test specifying different reference/actor model""" os.environ["RANK"] = "0" os.environ["WORLD_SIZE"] = "1" os.environ["MASTER_ADDR"] = "127.0.0.1" os.environ["MASTER_PORT"] = "8888" actor_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct") ref_model_path = os.path.expanduser("~/models/Qwen/Qwen2.5-1.5B-Instruct") config_str = f""" model: path: {actor_model_path} actor: _target_: verl.workers.config.FSDPActorConfig strategy: fsdp fsdp_config: _target_: verl.workers.config.FSDPEngineConfig fsdp_size: -1 forward_prefetch: false profiler: tool: torch_memory save_path: ./mem_snapshots tool_config: torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: 100000 stack_depth: 32 ref: model: path: {ref_model_path} fsdp_config: _target_: verl.workers.config.FSDPEngineConfig fsdp_size: -1 profiler: tool: torch_memory save_path: ./mem_snapshots tool_config: torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: 100000 stack_depth: 32 log_prob_micro_batch_size: 1 ulysses_sequence_parallel_size: 1 entropy_from_logits_with_chunking: false """ dict_conf = OmegaConf.create(config_str) actor_rollout_ref_worker = ActorRolloutRefWorker(dict_conf, role="ref") actor_rollout_ref_worker.init_model() model_config = actor_rollout_ref_worker.ref_module_fsdp._fsdp_wrapped_module.config assert model_config.hidden_size == 1536 # set ref.model to null, fallback to default case where actor is the same as reference dict_conf["ref"]["model"] = None actor_rollout_ref_worker = ActorRolloutRefWorker(dict_conf, role="ref") actor_rollout_ref_worker.init_model() model_config = actor_rollout_ref_worker.ref_module_fsdp._fsdp_wrapped_module.config assert model_config.hidden_size == 896 ================================================ FILE: verl/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import logging import os from packaging.version import parse as parse_version from .protocol import DataProto from .utils.device import is_npu_available from .utils.import_utils import import_external_libs from .utils.logging_utils import set_basic_config version_folder = os.path.dirname(os.path.join(os.path.abspath(__file__))) with open(os.path.join(version_folder, "version/version")) as f: __version__ = f.read().strip() set_basic_config(level=logging.WARNING) __all__ = ["DataProto", "__version__"] modules = os.getenv("VERL_USE_EXTERNAL_MODULES", "") if modules: modules = modules.split(",") import_external_libs(modules) if os.getenv("VERL_USE_MODELSCOPE", "False").lower() == "true": if importlib.util.find_spec("modelscope") is None: raise ImportError("You are using the modelscope hub, please install modelscope by `pip install modelscope -U`") # Patch hub to download models from modelscope to speed up. from modelscope.utils.hf_util import patch_hub patch_hub() if is_npu_available: # Workaround for torch-npu's lack of support for creating nested tensors from NPU tensors. # # ``` # >>> a, b = torch.arange(3).npu(), torch.arange(5).npu() + 3 # >>> nt = torch.nested.nested_tensor([a, b], layout=torch.jagged) # ``` # throws "not supported in npu" on Ascend NPU. # See https://github.com/Ascend/pytorch/blob/294cdf5335439b359991cecc042957458a8d38ae/torch_npu/utils/npu_intercept.py#L109 # for details. import torch try: if hasattr(torch.nested.nested_tensor, "__wrapped__"): torch.nested.nested_tensor = torch.nested.nested_tensor.__wrapped__ if hasattr(torch.nested.as_nested_tensor, "__wrapped__"): torch.nested.as_nested_tensor = torch.nested.as_nested_tensor.__wrapped__ except AttributeError: pass # In verl, the driver process aggregates the computation results of workers via Ray. # Therefore, after a worker completes its computation job, it will package the output # using tensordict and transfer it to the CPU. Since the `to` operation of tensordict # is non-blocking, when transferring data from a device to the CPU, it is necessary to # ensure that a batch of data has been completely transferred before being used on the # host; otherwise, unexpected precision issues may arise. Tensordict has already noticed # this problem and fixed it. Ref: https://github.com/pytorch/tensordict/issues/725 # However, the relevant modifications only cover CUDA and MPS devices and do not take effect # for third-party devices such as NPUs. This patch fixes this issue, and the relevant # modifications can be removed once the fix is merged into tensordict. import tensordict if parse_version(tensordict.__version__) < parse_version("0.10.0"): from tensordict.base import TensorDictBase def _sync_all_patch(self): from torch._utils import _get_available_device_type, _get_device_module device_type = _get_available_device_type() if device_type is None: return device_module = _get_device_module(device_type) device_module.synchronize() TensorDictBase._sync_all = _sync_all_patch ================================================ FILE: verl/base_config.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections from dataclasses import FrozenInstanceError, dataclass, fields from typing import Any # BaseConfig class inherits from collections.abc.Mapping, which means it can act like a dictionary @dataclass class BaseConfig(collections.abc.Mapping): """The BaseConfig provides dict-like interface for a dataclass config. By default all fields in the config is not mutable, unless specified in "_mutable_fields". The BaseConfig class implements the Mapping Abstract Base Class. This allows instances of this class to be used like dictionaries. """ _mutable_fields = set() _target_: str = "" def __setattr__(self, name: str, value): """Set the value of an attribute. Check if the attr is mutable before setting the value.""" # If the field already exists, it's considered frozen unless it's in _mutable_fields if name in self.__dict__ and name not in getattr(self, "_mutable_fields", set()): raise FrozenInstanceError(f"Field '{name}' is frozen and cannot be modified") super().__setattr__(name, value) def get(self, key: str, default: Any = None) -> Any: """Get the value associated with the given key. If the key does not exist, return the default value. Args: key (str): The attribute name to retrieve. default (Any, optional): The value to return if the attribute does not exist. Defaults to None. Returns: Any: The value of the attribute or the default value. """ try: return getattr(self, key) except AttributeError: return default def __getitem__(self, key: str): """Implement the [] operator for the class. Allows accessing attributes like dictionary items. Args: key (str): The attribute name to retrieve. Returns: Any: The value of the attribute. Raises: AttributeError: If the attribute does not exist. TypeError: If the key type is not string """ return getattr(self, key) def __iter__(self): """Implement the iterator protocol. Allows iterating over the attribute names of the instance. Yields: str: The name of each field in the dataclass. """ for f in fields(self): yield f.name def __len__(self): """ Return the number of fields in the dataclass. Returns: int: The number of fields in the dataclass. """ return len(fields(self)) ================================================ FILE: verl/checkpoint_engine/README.md ================================================ Checkpoint Engine --- ### Overview Checkpoint Engine is an unified abstract layer to synchronize weights between various training backends and inference backends. It provides three unified APIs: - send_weights: get named tensors from generator and send them in streaming manner. - receive_weights: return a tensor generator that yield named tensors in streaming manner. - get_weights: return a tensor generator that yield named tensors in streaming manner, used for each inference instance update weight independently from local cache (e.g share memory, disk). ![checkpoint-engine](https://github.com/wuxibin89/verl/blob/wuxibin/doc_images/docs/_static/checkpoint_engine.png?raw=true) ### Supported Backends ||Comm Library|Topology|Hardware|Performance|Elastic|Use case| |----|----|----|----|----|----|----| |naive|torch.distributed|all_gather|NVIDIA/AMD/Ascend|Very High|NA|On-policy training
- Trainer/rollout colocated |nccl|NCCL|all_gather+broadcast|NVIDIA GPU & NCCL|Very High|Low: rebuild nccl group|Off-policy training
- Trainer/rollout disaggregated
- Fixed clusters |hccl|HCCL|all_gather+broadcast|Ascend NPU & HCCL| High|Low: rebuild hccl group|Off-policy training
- Trainer/rollout disaggregated
- Fixed clusters |nixl|NIXL|all_gather+ring p2p|Various transport backends (D2D, H2H, H2D, etc)
- UCX
- UCCL
- Mooncacke|Medium/High|High: dynamic adjust ring topology|Off-policy training
- Trainer/rollout disaggregated
- Elastic rollout
- Rollout fault tolerance
- Heterogeneous hardware rollout |kimi_ckpt_engine|MOONCAKE+NCCL/HCCL|p2p+broadcast|NVIDIA/Ascend|High|Low: rebuild communication group|Off-policy training
- Trainer/rollout disaggregated
- Save checkpoint each time |mooncake|Mooncake Transfer Engine|all_gather+ring p2p|NVIDIA/Ascend|High|High: dynamic adjust ring topology|Off-policy training
- Trainer/rollout disaggregated
- Fixed clusters ##### kimi_ckpt_engine detail: In the kimi_ckpt_engine workflow, the trainer first offloads the weights to the CPU, and the rollout creates a sub communication group that includes all the cards for the rollout. Then, using Mooncake transfer engine, these weights are transmitted via P2P to a specific worker in the rollout, followed by a broadcast to all other rollout workers. kimi-ckpt-engine This mode requires the P2P feature of checkpoint_engine. Please ensure you have installed it via pip install 'checkpoint-engine[p2p]' and that your version is 0.4.0 or higher. In addition, during the installation of checkpoint-engine[p2p], the transfer engine will be installed. However, This library has no prebuilt packages for Ascend devices and must be compiled from source. For detailed compilation instructions, see: [transfer-engine: ascend direct](https://github.com/kvcache-ai/Mooncake/blob/main/docs/source/design/transfer-engine/ascend_direct_transport.md) Note: Important Configuration for Ascend Devices If you are using CANN version >= 8.5.0 on Ascend devices, you must set the following environment variable to enable intra-node ROCE: ```bash export HCCL_INTRA_ROCE_ENABLE=1 ``` ### Benchmark 1. benchmark setup - model: Qwen/Qwen3-30B-A3B-Base - trainer: fsdp world_size=2 (since Ascend 910C has 64GB of HBM, we set world_size=4) - rollout: num_rollout=30 (only receive weight without cuda ipc to vllm/sglang) ```bash pytest tests/checkpoint_engine/test_correctness_on_gpu.py pytest tests/checkpoint_engine/test_correctness_on_npu.py pytest tests/checkpoint_engine/test_special_server_adapter.py ``` 2. benchmark result | hardware | backend | time cost (s) | Bandwidth(GB/s) | |----|----|----|----| |4*8 H100, ConnectX-7 400 Gbps (InfiniBand)| NCCL | ~7 | 8.25| |4*8 H100, ConnectX-7 400 Gbps (InfiniBand)| NIXL | ~7 | 8.25| |2*16 Ascend 910C, inner suppernode| HCCL | ~11 | 5.3| |2*16 Ascend 910C, inner suppernode| kimi_ckpt_engine | offload: 7 update: 3.5 | 16.5| |2*8 H100, ConnectX-7 400 Gbps (InfiniBand)| mooncake | 5.93 | 9.44| ================================================ FILE: verl/checkpoint_engine/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import ( CheckpointEngine, CheckpointEngineManager, CheckpointEngineRegistry, CheckpointEngineWorker, ColocatedCheckpointEngine, TensorMeta, ) __all__ = [ "CheckpointEngine", "CheckpointEngineRegistry", "TensorMeta", "ColocatedCheckpointEngine", "CheckpointEngineManager", "CheckpointEngineWorker", ] try: from .nccl_checkpoint_engine import NCCLCheckpointEngine __all__ += ["NCCLCheckpointEngine"] except ImportError: NCCLCheckpointEngine = None try: from .hccl_checkpoint_engine import HCCLCheckpointEngine __all__ += ["HCCLCheckpointEngine"] except ImportError: HCCLCheckpointEngine = None try: from .nixl_checkpoint_engine import NIXLCheckpointEngine __all__ += ["NIXLCheckpointEngine"] except ImportError: NIXLCheckpointEngine = None try: from .kimi_checkpoint_engine import KIMICheckpointEngine __all__ += ["KIMICheckpointEngine"] except ImportError: KIMICheckpointEngine = None try: from .mooncake_checkpoint_engine import MooncakeCheckpointEngine __all__ += ["MooncakeCheckpointEngine"] except ImportError: MooncakeCheckpointEngine = None ================================================ FILE: verl/checkpoint_engine/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio from abc import ABC, abstractmethod from typing import Any, Generator, TypedDict import ray import torch from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, register from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup from verl.utils.distributed import initialize_global_process_group_ray from verl.utils.ray_utils import auto_await from verl.workers.config import CheckpointEngineConfig, HFModelConfig, RolloutConfig from verl.workers.rollout import BaseRollout, RolloutReplica, get_rollout_class class TensorMeta(TypedDict): name: str shape: torch.Size dtype: torch.dtype offset: int class CheckpointEngineRegistry: """Checkpoint engine registry.""" _registry: dict[str, type["CheckpointEngine"]] = {} def register(backend: str): """Register a checkpoint engine. Args: backend: The backend of the checkpoint engine. """ def wrapper(cls: type["CheckpointEngine"]): CheckpointEngineRegistry._registry[backend] = cls return cls return wrapper @classmethod def get(cls, backend: str) -> type["CheckpointEngine"]: """Get the checkpoint engine class. Args: backend: The backend of the checkpoint engine. Returns: The checkpoint engine class. """ return cls._registry[backend] @classmethod def new(cls, backend: str, *args, **kwargs) -> "CheckpointEngine": """Create a new checkpoint engine instance. Args: backend: The backend of the checkpoint engine. *args: Variable length argument pass to the checkpoint engine constructor. **kwargs: Arbitrary keyword arguments pass to the checkpoint engine constructor. Returns: A new checkpoint engine instance. """ if backend not in cls._registry: raise ValueError(f"Checkpoint engine {backend} not registered") return cls._registry[backend](*args, **kwargs) class CheckpointEngine(ABC): """CheckpointEngine is an abstraction to transfer weights from trainer to rollout. In trainer process: >>> trainer = EngineRegistry.new(...) # FSDP, Megatron, VeOmini, TorchTitan, ... >>> engine = CheckpointEngine.new(...) # NCCLCheckpointEngine, NIXLCheckpointEngine, ... >>> await engine.send_weights(trainer.get_per_tensor_param()) In rollout process: >>> engine = CheckpointEngine.new(...) >>> server_adapter = ServerAdapter() >>> await server_adapter.update_weights(engine.get_weights()) # update weights via cuda ipc """ @abstractmethod def prepare(self) -> dict[str, Any]: """Prepare checkpoint engine before each step send_weights/receive_weights. 1. Allocate weight bucket. 2. [Optional] Register weight bucket for RDMA. 3. Return metadata to build communication topology: master ip:port, register RDMA description, etc. Args: worker_group: The worker group that the checkpoint engine will be used. Returns: A dictionary that contains the metadata of the worker group. """ raise NotImplementedError @classmethod @abstractmethod def build_topology( cls, trainer_world_size: int, rollout_world_size: int, metadata: list[dict] ) -> tuple[dict[str, list[Any]], dict[str, list[Any]]]: """Build communication topology between all workers. Args: trainer_world_size: The world size of the trainer worker group. rollout_world_size: The world size of the rollout replica. metadata: A list of metadata `prepare` from all workers. Returns: A tuple of two dictionaries that contains the communication topology for trainer and rollout worker group. Each dict value should be a list argument equal to the world size of the worker group to dispatch to `init_process_group`. ``` world_size = rollout.world_size + trainer.world_size kwargs = { "rank": list(range(world_size)), "world_size": [world_size] * world_size, "master_metadata": [metadata[0]] * world_size, } ``` """ raise NotImplementedError @abstractmethod def init_process_group(self, **kwargs): """Init process group for checkpoint engine. Args: **kwargs: Keyword arguments from `build_topology`. """ raise NotImplementedError @abstractmethod def finalize(self): """Finalize checkpoint engine after each step send_weights/receive_weights. 1. Free weight bucket. 1. [Optional] Deregister weight bucket for RDMA. 2. [Optional] Destroy process group. """ raise NotImplementedError @abstractmethod async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ raise NotImplementedError @abstractmethod async def receive_weights(self) -> Generator[tuple[str, torch.Tensor], None, None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ raise NotImplementedError class CheckpointEngineWithCache(CheckpointEngine): """Checkpoint engine with local cache: shm, disk, etc. This allow to synchronize weights without interrupting rollout ongoing requests (partial rollout). After requests exhausted, rollout can get weights from local cache. Laminar: https://arxiv.org/abs/2510.12633 """ @abstractmethod async def get_weights(self) -> Generator[tuple[str, torch.Tensor], None, None]: """Get the weights of the model from local cache. Yields: A tuple of the name of the weight tensor and the tensor itself. """ raise NotImplementedError @CheckpointEngineRegistry.register("naive") class ColocatedCheckpointEngine(CheckpointEngine): """Checkpoint engine for trainer and rollout colocated on same GPU. In trainer process: >>> engine = ColocatedCheckpointEngine() >>> trainer = Trainer() >>> server_adapter = ServerAdapter() >>> engine.send_weights(trainer.get_per_tensor_param()) >>> server_adapter.update_weights(engine.receive_weights()) """ def __init__(self, bucket_size: int, is_master: bool = False) -> None: self.bucket_size = bucket_size self.is_master = is_master def prepare(self): raise NotImplementedError def init_process_group(self, **kwargs): raise NotImplementedError def finalize(self): raise NotImplementedError @classmethod def build_topology(cls, *args, **kwargs): raise NotImplementedError def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ self.weights = weights def receive_weights(self) -> Generator[tuple[str, torch.Tensor], None, None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ yield from self.weights self.weights = None class CheckpointEngineWorker(Worker): """CheckpointEngineWorker colocated with inference engine's WorkerProc on same GPU. Args: rollout_config: The rollout configuration. model_config: The model configuration. server_adapter: The server adapter to update weights. """ def __init__( self, rollout_config: RolloutConfig, model_config: HFModelConfig, server_adapter: BaseRollout = None, *args, **kwargs, ) -> None: super().__init__() self.rollout_config = rollout_config self.model_config = model_config self.server_adapter: BaseRollout = server_adapter backend = self.rollout_config.checkpoint_engine.backend bucket_size = self.rollout_config.checkpoint_engine.update_weights_bucket_megabytes << 20 engine_kwargs = self.rollout_config.checkpoint_engine.engine_kwargs.get(backend, {}) self.checkpoint_engine: CheckpointEngine = CheckpointEngineRegistry.new( backend, bucket_size=bucket_size, **engine_kwargs ) self.extra_rollout_args = args self.extra_rollout_kwargs = kwargs if self.server_adapter is None: self.server_adapter = get_rollout_class(self.rollout_config.name, self.rollout_config.mode)( *self.extra_rollout_args, config=self.rollout_config, model_config=self.model_config, device_mesh=None, **self.extra_rollout_kwargs, ) # sglang and trt-llm need device_mesh for internal communication initialize_global_process_group_ray(timeout_second=None, backend="cpu:gloo") @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) async def update_weights(self, global_steps: int = None): weights = self.checkpoint_engine.receive_weights() await self.server_adapter.update_weights(weights, global_steps=global_steps) @register(dispatch_mode=Dispatch.DP_COMPUTE, blocking=False) def execute_checkpoint_engine(self, method: str, *args, **kwargs): return getattr(self.checkpoint_engine, method)(*args, **kwargs) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def get_replica_rank(self) -> int: """Get replica rank from the underlying rollout server adapter.""" return self.server_adapter.replica_rank @register(dispatch_mode=Dispatch.ONE_TO_ALL) def is_leader_rank(self) -> bool: """Get leader rank flag from the underlying rollout server adapter.""" return self.server_adapter.is_leader_rank _worker_cls = ray.remote(CheckpointEngineWorker) class CheckpointEngineManager: """Checkpoint engine manager to coordinate weight synchronization between trainer and rollout replicas. - ME: model engine, FSDP, MCore, VeOmni, export full tensor generator `get_per_tensor_param` - CE: checkpoint engine, NCCL, NIXL, etc In trainer, model engine and checkpoint engine are in same process. In rollout, checkpoint engine and rollout worker are in separate process, update weights via cuda ipc. ``` ┌────────┬────────┬─────┬────────┐ ┌───────────────────┬───────────────────┐ │ ┌────┐ │ ┌────┐ │ │ ┌────┐ │ │ Replica 0 │ Replica 1 │ │ │ ME0│ │ │ ME1│ │ │ │ MEn│ │ ├────┬────┬────┬────┼────┬────┬────┬────┤ │ └──┬─┘ │ └────┘ │ ... │ └────┘ │ │ 0 │ 1 │ 2 │ 3 │ 0 │ 1 │ 2 │ 3 │ │ v | | | | └──┬─┴──┬─┴──┬─┴──┬─┴──┬─┴──┬─┴──┬─┴──┬─┘ | ┌──┴─┐ │ ┌────┐ │ │ ┌────┐ │ ^ ^ ^ cuda ipc ^ ^ ^ │ │ CE │ │ │ CE │ │ │ │ CE │ │ ┌──┴─┬──┴─┬──┴─┬──┴─┬──┴─┬──┴─┬──┴─┬──┴─┐ │ └──┬─┘ │ └────┘ │ │ └────┘ │ │ CE │ CE │ CE │ CE │ CE │ CE │ CE │ CE | └────┼───┴────────┴─────┴────────┘ └──┬─┴──┬─┴──┬─┴──┬─┴──┬─┴──┬─┴──┬─┴──┬─┘ v | | | | | | | | └─────────────(nccl/nixl/..)─────────────┴────┴────┴────┴────┴────┴────┴────┘ ``` Args: config: The checkpoint engine config. trainer: The trainer worker group. replicas: The list of rollout replicas. """ def __init__( self, config: CheckpointEngineConfig, trainer: RayWorkerGroup, replicas: list[RolloutReplica], ) -> None: self.config = config self.backend = config.backend self.backend_cls = CheckpointEngineRegistry.get(config.backend) self.trainer = trainer self.replicas = replicas def build_process_group(self, rollout: RayWorkerGroup): """Build process group for trainer and rollout replicas.""" trainer = self.trainer # 1. prepare all workers metadata = ray.get( trainer.execute_checkpoint_engine(["prepare"] * trainer.world_size) + rollout.execute_checkpoint_engine(["prepare"] * rollout.world_size) ) # 2. build communication topology between all workers trainer_kwargs, rollout_kwargs = self.backend_cls.build_topology( trainer.world_size, rollout.world_size, metadata ) for k, v in trainer_kwargs.items(): assert len(v) == trainer.world_size, f"trainer_kwargs[{k}] must have length of {trainer.world_size}" for k, v in rollout_kwargs.items(): assert len(v) == rollout.world_size, f"rollout_kwargs[{k}] must have length of {rollout.world_size}" trainer_kwargs["method"] = ["init_process_group"] * trainer.world_size rollout_kwargs["method"] = ["init_process_group"] * rollout.world_size # 3. init process group between all workers ray.get( trainer.execute_checkpoint_engine(**trainer_kwargs) + rollout.execute_checkpoint_engine(**rollout_kwargs) ) def add_replicas(self, replicas: list[RolloutReplica]): """Add rollout replicas to the manager for elastic scale up, will rebuild process group. Args: replicas: The list of rollout replicas to add. """ self.replicas.extend(replicas) def remove_replicas(self, replicas: list[RolloutReplica]): """Remove rollout replicas from the manager for elastic scale down, will rebuild process group. Args: replicas: The list of rollout replicas to remove. """ replicas_set = set(replicas) self.replicas = [r for r in self.replicas if r not in replicas_set] @auto_await async def sleep_replicas(self): """Sleep all rollout replicas: free weight and kv_cache device memory.""" await asyncio.gather(*[r.sleep() for r in self.replicas]) @auto_await async def wake_up_replicas(self): """Resume all rollout replicas: recover kv_cache and weights device memory.""" await asyncio.gather(*[r.wake_up() for r in self.replicas]) @auto_await async def update_weights(self, global_steps: int = None): """Update weights from trainer to rollout replicas. Args: global_steps: The global steps of the trainer. """ # 0. update weights for sync training with colocated trainer and rollout if self.backend == "naive": ray.get(self.trainer.update_weights(global_steps=global_steps)) return # 1. abort and save all unfinished requests for partial rollout await asyncio.gather(*[r.abort_all_requests() for r in self.replicas]) # 2. create a temporay worker group for all replicas workers = [] for replica in self.replicas: workers.extend(replica.workers) rollout = RayWorkerGroup(worker_handles=workers, ray_cls_with_init=RayClassWithInitArgs(cls=_worker_cls)) trainer = self.trainer # 3. sleep replicas to free kv_cache before weight sync (if free_cache_engine is enabled) await self.sleep_replicas() # 4. build process group self.build_process_group(rollout) # 5. update weights of all workers ray.get(trainer.update_weights(global_steps=global_steps) + rollout.update_weights(global_steps=global_steps)) # 6. finalize all workers ray.get( trainer.execute_checkpoint_engine(["finalize"] * trainer.world_size) + rollout.execute_checkpoint_engine(["finalize"] * rollout.world_size) ) # 7. resume replicas to recover kv_cache (for free_cache_engine scenarios) await self.wake_up_replicas() # 8. resume all unfinished requests for partial rollout await asyncio.gather(*[r.resume_generation() for r in self.replicas]) ================================================ FILE: verl/checkpoint_engine/hccl_checkpoint_engine.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import time from dataclasses import dataclass from typing import AsyncGenerator, Generator import ray import torch import zmq from vllm.distributed.utils import StatelessProcessGroup from verl.checkpoint_engine.base import CheckpointEngine, CheckpointEngineRegistry, TensorMeta from verl.utils.device import is_torch_npu_available from verl.utils.distributed import stateless_init_process_group from verl.utils.net_utils import get_free_port, is_valid_ipv6_address if not is_torch_npu_available(check_device=False): raise ImportError("HCCLCheckpointEngine is unavailable because the torch.npu module is not available.") logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @dataclass class MasterMetadata: zmq_ip: str zmq_port: int dist_ip: str dist_port: int class BroadcastOperation: """Async broadcast operation with HCCL in separate thread. Args: rank (int): The rank of the current process. group_name (str): The name of the HCCL process group. bucket (torch.Tensor): The tensor to broadcast. metadata (dict[str, TensorMeta]): The metadata of the tensor. socket (zmq.Socket): The zeromq socket to communicate with master. topic (str): The topic to subscribe. """ def __init__( self, rank: int, process_group: StatelessProcessGroup | str, bucket: torch.Tensor, metadata: dict[str, TensorMeta], socket: zmq.Socket, topic: str, ) -> None: self.rank = rank self.pyhccl = process_group self.bucket = bucket self.metadata = metadata self.socket = socket self.topic = topic self._run() def _run(self): # broadcast tensor meta via zeromq PUB/SUB if self.rank == 0: self.socket.send_string(self.topic, flags=zmq.SNDMORE) self.socket.send_pyobj(self.metadata) else: self.socket.recv_string() self.metadata = self.socket.recv_pyobj() # broadcast tensor via HCCL self.pyhccl.broadcast(self.bucket, src=0) async def wait_for_complete(self) -> dict[str, TensorMeta]: """Wait for the broadcast operation to complete. Returns: dict[str, TensorMeta]: The bucket meta after broadcast. """ return self.metadata @CheckpointEngineRegistry.register("nccl") class HCCLCheckpointEngine(CheckpointEngine): """HCCL checkpoint engine with collective communication. Args: bucket_size (int): Bucket size in bytes to transfer multiple weights at one time. Note that we use two buffer to send and recv weights at same time, so the device memory overhead is 2 * bucket_size. group_name (str): The name of the HCCL process group. Defaults to "default". rebuild_group (bool): Whether to rebuild the HCCL process group in each update. Defaults to False. is_master (bool): Whether the current process is the master process. Defaults to False. rollout_dtype (torch.dtype): The dtype of the weights received from rollout workers. Defaults to torch.bfloat16. """ def __init__( self, bucket_size: int, group_name: str = "default", rebuild_group: bool = False, is_master: bool = False, rollout_dtype: torch.dtype = torch.bfloat16, ) -> None: self.bucket_size = bucket_size self.group_name = group_name self.rebuild_group = rebuild_group self.rollout_dtype = rollout_dtype self.pyhccl = None self.device = torch.npu.current_device() # start zeromq server for broadcasting bucket tensor metadata self.is_master = is_master self.topic = "bucket_metadata" if self.is_master: self._start_zmq_server() self.dist_port, _ = get_free_port(self.ip) def prepare(self) -> MasterMetadata: self.send_buf = torch.zeros(self.bucket_size, dtype=torch.uint8, device="npu") self.recv_buf = torch.zeros(self.bucket_size, dtype=torch.uint8, device="npu") return ( MasterMetadata(zmq_ip=self.ip, zmq_port=self.zmq_port, dist_ip=self.ip, dist_port=self.dist_port) if self.is_master else None ) def finalize(self): """Destroy the HCCL process group if rebuild_group is True.""" if self.rebuild_group: if self.rank >= 0: self.pyhccl.destroyComm(self.pyhccl.comm) self.pyhccl = None self.rank = None self.world_size = None self.send_buf = None self.recv_buf = None torch.npu.empty_cache() @classmethod def build_topology(cls, trainer_world_size: int, rollout_world_size: int, metadata: list[dict]): trainer_kwargs = { "rank": [0] + [-1] * (trainer_world_size - 1), "world_size": [rollout_world_size + 1] * trainer_world_size, "master_metadata": [metadata[0]] * trainer_world_size, } rollout_kwargs = { "rank": list(range(1, rollout_world_size + 1)), "world_size": [rollout_world_size + 1] * rollout_world_size, "master_metadata": [metadata[0]] * rollout_world_size, } return trainer_kwargs, rollout_kwargs def _start_zmq_server(self): self.ip = ray.util.get_node_ip_address().strip("[]") self.zmq_port, _ = get_free_port(self.ip) context = zmq.Context() self.socket = context.socket(zmq.PUB) if is_valid_ipv6_address(self.ip): address = f"tcp://[{self.ip}]:{self.zmq_port}" self.socket.setsockopt(zmq.IPV6, 1) else: address = f"tcp://{self.ip}:{self.zmq_port}" self.socket.bind(address) def _connect_zmq_client(self, metadata: MasterMetadata): assert not self.is_master, "Master process should not connect to other processes." context = zmq.Context() self.socket = context.socket(zmq.SUB) if is_valid_ipv6_address(metadata.zmq_ip): address = f"tcp://[{metadata.zmq_ip}]:{metadata.zmq_port}" self.socket.setsockopt(zmq.IPV6, 1) else: address = f"tcp://{metadata.zmq_ip}:{metadata.zmq_port}" self.socket.connect(address) self.socket.setsockopt_string(zmq.SUBSCRIBE, self.topic) def init_process_group(self, rank: int, world_size: int, master_metadata: MasterMetadata): """Initialize the HCCL process group. Args: rank (int): The rank of the current process. world_size (int): The total number of processes. """ # For trainer workers other than rank 0, their rank should be -1. if rank < 0: self.rank = rank self.world_size = world_size return if self.rebuild_group or self.pyhccl is None: self.pyhccl = stateless_init_process_group( master_metadata.dist_ip, master_metadata.dist_port, rank, world_size, self.device ) self.rank = rank self.world_size = world_size else: assert self.rank == rank, f"rank {rank} is not equal to self.rank {self.rank}" assert self.world_size == world_size, ( f"world_size {world_size} is not equal to self.world_size {self.world_size}" ) if self.rank > 0: self._connect_zmq_client(master_metadata) # barrier signal = torch.tensor([1], dtype=torch.int8, device=torch.npu.current_device()) self.pyhccl.all_reduce(signal) logger.info(f"init_process_group rank: {self.rank}, world_size: {self.world_size}") @torch.no_grad() async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ assert self.rank <= 0, "Trainer workers other than rank 0 should not send weights." # For trainer rank other than 0, consume weights without sending. if self.rank < 0: for name, weight in weights: pass return send_buf, recv_buf = self.send_buf, self.recv_buf broadcast_op = None start_time = time.time() bucket_meta: dict[str, TensorMeta] = {} offset = 0 for name, weight in weights: # fill the tensor bucket if offset + weight.nbytes > self.bucket_size: torch.npu.synchronize() # wait previous broadcast op finish if broadcast_op is not None: await broadcast_op.wait_for_complete() broadcast_op = BroadcastOperation( rank=self.rank, process_group=self.pyhccl, bucket=send_buf, metadata={"bucket_meta": bucket_meta, "is_last": False}, socket=self.socket, topic=self.topic, ) # swap send_buf and recv_buf send_buf, recv_buf = recv_buf, send_buf bucket_meta = {} offset = 0 assert offset + weight.nbytes <= self.bucket_size, ( f"Weight {name}({weight.shape}, {weight.dtype}) is too large to fit in the bucket." ) bucket_meta[name] = { "name": name, "shape": weight.shape, "dtype": weight.dtype, "offset": offset, } send_buf[offset : offset + weight.nbytes] = weight.view(-1).view(torch.uint8) offset += weight.nbytes # broadcast last bucket torch.npu.synchronize() if broadcast_op is not None: await broadcast_op.wait_for_complete() broadcast_op = BroadcastOperation( rank=self.rank, process_group=self.pyhccl, bucket=send_buf, metadata={"bucket_meta": bucket_meta, "is_last": True}, socket=self.socket, topic=self.topic, ) await broadcast_op.wait_for_complete() logger.info(f"Rank {self.rank} send weights done, time cost: {time.time() - start_time:.2f}s") @torch.no_grad() async def receive_weights(self) -> AsyncGenerator[tuple[str, torch.Tensor], None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ assert self.rank > 0, "Rank 0 should not receive weights." send_buf, recv_buf = self.send_buf, self.recv_buf total_bytes, total_params = 0, 0 # receive first bucket start_time = time.time() broadcast_op = BroadcastOperation( rank=self.rank, process_group=self.pyhccl, bucket=recv_buf, metadata=None, socket=self.socket, topic=self.topic, ) metadata = await broadcast_op.wait_for_complete() total_bytes += self.bucket_size total_params += len(metadata["bucket_meta"]) # swap send_buf and recv_buf send_buf, recv_buf = recv_buf, send_buf while not metadata["is_last"]: # 1. receive next bucket broadcast_op = BroadcastOperation( rank=self.rank, process_group=self.pyhccl, bucket=recv_buf, metadata=None, socket=self.socket, topic=self.topic, ) # 2. yield tensor from send_buf for name, meta in metadata["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = send_buf[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor # 3. wait for next bucket broadcast finish metadata = await broadcast_op.wait_for_complete() total_bytes += self.bucket_size total_params += len(metadata["bucket_meta"]) # 4. swap send_buf and recv_buf torch.npu.synchronize() # sync non-blocking copy send_buf, recv_buf = recv_buf, send_buf # yield tensor from send_buf for name, meta in metadata["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = send_buf[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor time_cost = time.time() - start_time bandwidth = total_bytes / time_cost / (1024 * 1024 * 1024) logger.info( f"Rank {self.rank} receive weights done, total_params: {total_params}, " f"time cost: {time_cost:.2f}s, bandwidth: {bandwidth:.2f} GB/s" ) ================================================ FILE: verl/checkpoint_engine/kimi_checkpoint_engine.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import concurrent.futures import logging import os import time import types from collections import defaultdict from dataclasses import dataclass from typing import AsyncGenerator, Generator import checkpoint_engine.distributed as dist import ray import torch from checkpoint_engine.ps import H2DBucket, ParameterMeta, ParameterServer, _gen_h2d_buckets, _to_named_tensor from verl.checkpoint_engine.base import CheckpointEngine, CheckpointEngineRegistry from verl.utils.device import get_nccl_backend, get_torch_device from verl.utils.net_utils import get_free_port logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def ckpt_get_named_tensor_buckets( iterable: Generator[tuple[str, torch.Tensor], None, None], bucket_bytes: int, world_size: int, rank_id: int, rollout_dtype: torch.dtype = torch.bfloat16, ) -> dict[str, torch.Tensor]: if bucket_bytes <= 0: raise ValueError(f"bucket_bytes must be greater than 0, got {bucket_bytes}") current_bucket = {} current_size = 0 for tensor_idx, (name, tensor) in enumerate(iterable): tensor = tensor.to(rollout_dtype) if tensor_idx % world_size == rank_id: tensor_size = tensor.element_size() * tensor.numel() if current_size + tensor_size > bucket_bytes: if current_bucket: yield current_bucket current_bucket = {} current_size = 0 current_bucket[name] = tensor current_size += tensor_size if current_bucket: yield current_bucket async def receive_tensor( self, checkpoint_name: str, ranks_group: int, ranks: list[int] | None = None, bucket_size: int = 2 << 30, disable_h2d_buffer: bool = False, ) -> AsyncGenerator[tuple[str, torch.Tensor], None]: assert len(self._current_global_parameter_metas) != 0, "parameter metas is empty" assert dist.is_initialized(), "process group is not initialized" assert self._p2p_store is not None, "p2p store is not initialized" assert ranks, "ranks should be set" # first execute a barrier to avoid subsequent device oom dist.barrier(group=ranks_group) buckets = _gen_h2d_buckets( self._current_global_parameter_metas, bucket_size, self._local_rdma_devices, self._remote_rdma_devices, ranks, ) h2d_buffer: torch.Tensor | None = ( None if disable_h2d_buffer else torch.empty(bucket_size, dtype=torch.uint8, device=self.device_manager.device_type) ) # p2p store need to register h2d_buffer to let other ranks read if ranks: h2d_buffer_name = "__h2d_buffer__" if h2d_buffer is not None and self._p2p_store is not None: self._p2p_store.register_named_tensors({h2d_buffer_name: h2d_buffer}) receiver_rank_buckets: list[tuple[int, H2DBucket]] = [] for receiver_rank, owner_rank, bucket in buckets: if receiver_rank != self._rank: continue receiver_rank_buckets.append((owner_rank, bucket)) buffer = torch.empty(bucket_size * 2, dtype=torch.uint8, device=self.device_manager.device_type) buckets_by_receiver_rank: dict[int, list[H2DBucket]] = defaultdict(list) max_len = 0 for receiver_rank, _, bucket in buckets: buckets_by_receiver_rank[receiver_rank].append(bucket) if len(buckets_by_receiver_rank[receiver_rank]) > max_len: max_len = len(buckets_by_receiver_rank[receiver_rank]) gidx = 0 metadata: list[ParameterMeta] try: for i in range(max_len): if i < len(receiver_rank_buckets) and not disable_h2d_buffer: self._copy_to_buffer( checkpoint_name, receiver_rank_buckets[i][1], h2d_buffer, receiver_rank_buckets[i][0] if ranks else None, ) for receiver_rank, _buckets in buckets_by_receiver_rank.items(): if i >= len(_buckets): continue bucket = _buckets[i] start = gidx % 2 * bucket_size buffer_b: torch.Tensor = buffer[start : start + bucket.size] if receiver_rank == self._rank: if disable_h2d_buffer: self._copy_to_buffer(checkpoint_name, bucket, buffer_b) else: buffer_b.data.copy_(h2d_buffer[: bucket.size]) broadcast_op = BroadcastOperation( rank=receiver_rank, ranks_group=ranks_group, bucket=buffer_b, metadata=bucket.items, ) if gidx == 0: metadata = await broadcast_op.wait_for_complete() gidx += 1 continue meta_list = _to_named_tensor(metadata, (gidx - 1) % 2 * bucket_size) for item in meta_list: shape = item["shape"] if isinstance(shape, list | tuple): shape = torch.Size(shape) assert isinstance(shape, torch.Size) dtype, offset = item["dtype"], item["offset"] size = dtype.itemsize * shape.numel() tensor = buffer[offset : offset + size].view(dtype=dtype).view(shape) yield item["name"], tensor metadata = await broadcast_op.wait_for_complete() self.device_manager.device_module.synchronize() gidx += 1 meta_list = _to_named_tensor(metadata, (gidx - 1) % 2 * bucket_size) for item in meta_list: shape = item["shape"] if isinstance(shape, list | tuple): shape = torch.Size(shape) assert isinstance(shape, torch.Size) dtype, offset = item["dtype"], item["offset"] size = dtype.itemsize * shape.numel() tensor = buffer[offset : offset + size].view(dtype=dtype).view(shape) yield item["name"], tensor finally: dist.barrier(group=ranks_group) if ranks and h2d_buffer is not None: self._p2p_store.unregister_named_tensors([h2d_buffer_name]) self.device_manager.device_module.empty_cache() @dataclass class MasterMetadata: zmq_ip: str zmq_port: int dist_ip: str dist_port: int class BroadcastOperation: """Async broadcast operation in separate thread. Args: rank (int): The rank of the current process. ranks_group (int): The process group's value. bucket (torch.Tensor): The tensor to broadcast. metadata (list[ParameterMeta]): The metadata of the tensor. """ def __init__( self, rank: int, ranks_group: int, bucket: torch.Tensor, metadata: list[ParameterMeta], ) -> None: self.rank = rank self.ranks_group = ranks_group self.bucket = bucket self.metadata = metadata loop = asyncio.get_running_loop() self._task = loop.run_in_executor(None, self._run) def _run(self): # broadcast tensor dist.broadcast(self.bucket, src=self.rank, group=self.ranks_group) async def wait_for_complete(self) -> list[ParameterMeta]: """Wait for the broadcast operation to complete. Returns: list[ParameterMeta]: The bucket meta after broadcast. """ await self._task return self.metadata @CheckpointEngineRegistry.register("kimi_ckpt_engine") class KIMICheckpointEngine(CheckpointEngine): """kimi checkpoint engine with collective communication. Args: bucket_size (int): Bucket size in bytes to transfer multiple weights at one time. Note that we use two buffer to send and recv weights at same time, so the device memory overhead is 2 * bucket_size. rebuild_group (bool): Whether to rebuild the process group in each update. Defaults to False. is_master (bool): Whether the current process is the master process. Defaults to False. rollout_dtype (torch.dtype): The dtype of the weights received from rollout workers. Defaults to torch.bfloat16. """ def __init__( self, bucket_size: int, rebuild_group: bool = False, is_master: bool = False, rollout_dtype: torch.dtype = torch.bfloat16, ) -> None: self.bucket_size = bucket_size self.rebuild_group = rebuild_group self.rollout_dtype = rollout_dtype self.is_master = is_master self.initialized = False self.checkpoint_name = "kimi_checkpoint_engine" def prepare(self) -> MasterMetadata: if self.is_master: self.ip = ray.util.get_node_ip_address().strip("[]") self.listen_port, _ = get_free_port(self.ip) return ( MasterMetadata(zmq_ip=None, zmq_port=None, dist_ip=self.ip, dist_port=self.listen_port) if self.is_master else None ) def finalize(self): """Destroy the ckpt engine process group if rebuild_group is True.""" if self.rebuild_group: dist.destroy_process_group() self.rank = None self.world_size = None self.initialized = False @classmethod def build_topology(cls, trainer_world_size: int, rollout_world_size: int, metadata: list[dict]): trainer_kwargs = { "method": ["init_process_group"] * trainer_world_size, "rank": list(range(0, trainer_world_size)), "trainer_world_size": [trainer_world_size] * trainer_world_size, "rollout_world_size": [rollout_world_size] * trainer_world_size, "master_metadata": [metadata[0]] * trainer_world_size, } rollout_kwargs = { "method": ["init_process_group"] * rollout_world_size, "rank": list(range(trainer_world_size, trainer_world_size + rollout_world_size)), "trainer_world_size": [trainer_world_size] * rollout_world_size, "rollout_world_size": [rollout_world_size] * rollout_world_size, "master_metadata": [metadata[0]] * rollout_world_size, } return trainer_kwargs, rollout_kwargs def init_process_group( self, rank: int, trainer_world_size: int, rollout_world_size: int, master_metadata: MasterMetadata, ): """Initialize the ckpt engine process group. Args: rank (int): The rank of the current process. world_size (int): The total number of processes. """ self.rank = rank self.trainer_world_size = trainer_world_size self.rollout_world_size = rollout_world_size self.world_size = trainer_world_size + rollout_world_size if not self.initialized: self.parameter_server = ParameterServer( rank=rank, world_size=self.world_size, auto_pg=False, master_addr=master_metadata.dist_ip, master_port=master_metadata.dist_port, ) self.parameter_server.receive_tensor = types.MethodType(receive_tensor, self.parameter_server) dist.use_backend(f"vllm_{get_nccl_backend()}") self.parameter_server.init_process_group() self.rollout_ranks = list(range(self.trainer_world_size, self.world_size)) self.rollout_group = dist.new_group(self.rollout_ranks) self.initialized = True @torch.no_grad() async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ def offload_cpu(name: str, tensor: torch.Tensor) -> tuple[str, torch.Tensor]: return name, tensor.to("cpu", non_blocking=True) start_time = time.time() named_tensors = {} for named_tensors_gpu in ckpt_get_named_tensor_buckets( weights, self.bucket_size, self.trainer_world_size, self.rank, self.rollout_dtype ): with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor: futures = [ executor.submit( offload_cpu, name, tensor, ) for name, tensor in named_tensors_gpu.items() ] for future in concurrent.futures.as_completed(futures): name, tensor_cpu = future.result() named_tensors[name] = tensor_cpu get_torch_device().synchronize() self.parameter_server.register_checkpoint(self.checkpoint_name, named_tensors=named_tensors) named_tensors = {} get_torch_device().empty_cache() logger.info(f"Rank {self.rank} offload and register, time cost: {time.time() - start_time:.2f}s") self.parameter_server.gather_metas(self.checkpoint_name) dist.barrier() self.parameter_server.unregister_checkpoint(self.checkpoint_name) logger.info(f"Rank {self.rank} send weights done, time cost: {time.time() - start_time:.2f}s") @torch.no_grad() async def receive_weights(self) -> AsyncGenerator[tuple[str, torch.Tensor], None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ self.parameter_server.gather_metas(self.checkpoint_name) start_time = time.time() total_bytes, total_params = 0, 0 async for name, tensor in self.parameter_server.receive_tensor( self.checkpoint_name, self.rollout_group, self.rollout_ranks, self.bucket_size ): total_bytes += tensor.element_size() * tensor.nelement() total_params += 1 yield name, tensor dist.barrier() time_cost = time.time() - start_time bandwidth = total_bytes / time_cost / (1024 * 1024 * 1024) logger.info( f"Rank {self.rank} receive weights done, total_params: {total_params}, " f"time cost: {time_cost:.2f}s, bandwidth: {bandwidth:.2f} GB/s" ) ================================================ FILE: verl/checkpoint_engine/mooncake_checkpoint_engine.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import gc import logging import os import time from typing import Any, AsyncGenerator, Generator import ray import torch from mooncake.engine import TransferEngine from vllm.distributed.utils import StatelessProcessGroup from verl.checkpoint_engine.base import CheckpointEngine, CheckpointEngineRegistry, TensorMeta from verl.utils.device import get_torch_device from verl.utils.net_utils import get_free_port logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "INFO")) @CheckpointEngineRegistry.register("mooncake") class MooncakeCheckpointEngine(CheckpointEngine): """Mooncake checkpoint engine with p2p communication using TransferEngine Args: bucket_size (int): Bucket size in bytes to transfer multiple weights at one time. device (str): The device to use for the checkpoint engine, "cpu" or "cuda". rollout_dtype (torch.dtype): The dtype of the weights received from rollout workers. device_name (str): Mooncake device name filter. """ def __init__( self, bucket_size: int, device: str = "cuda", rollout_dtype: torch.dtype = torch.bfloat16, device_name: str = "", is_master: bool = False, rebuild_group: bool = False, ): self.bucket_size = bucket_size self.device = device self.rollout_dtype = rollout_dtype self.is_master = is_master self.rebuild_group = rebuild_group rank = int(os.environ["RANK"]) device_count = get_torch_device().device_count() local_rank = rank % device_count get_torch_device().set_device(local_rank) self.engine = TransferEngine() hostname = ray.util.get_node_ip_address().strip("[]") ret = self.engine.initialize( hostname, "P2PHANDSHAKE", "ascend_direct" if self.device == "npu" else "rdma", device_name, ) assert ret == 0, f"TransferEngine initialize failed ret={ret}" rpc_port = self.engine.get_rpc_port() self.session_id = f"{hostname}:{rpc_port}" self.hostname = hostname self.buf = torch.empty(2 * self.bucket_size, dtype=torch.uint8, device=self.device) self.magic_buf = torch.empty(4 * 1024, dtype=torch.uint8, device=self.device) ret = self.engine.batch_register_memory( [self.buf.data_ptr(), self.magic_buf.data_ptr()], [2 * self.bucket_size, 4 * 1024], ) assert ret == 0, f"batch_register_memory failed ret={ret}" logger.info(f"__init__ session_id={self.session_id}") def prepare(self) -> dict[str, Any]: """Prepare send and recv buckets""" logger.info( f"prepare ptr={self.buf.data_ptr():#x} len={2 * self.bucket_size} " f"magic_buf_ptr={self.magic_buf.data_ptr():#x}" ) port, _ = get_free_port(self.hostname) return {"addr": self.hostname, "port": port} @classmethod def build_topology(cls, trainer_world_size: int, rollout_world_size: int, metadatas: list[dict]): trainer_kwargs = { "rank": [0] + [-1] * (trainer_world_size - 1), "world_size": [rollout_world_size + 1] * trainer_world_size, "metadata": [metadatas[0]] * trainer_world_size, } rollout_kwargs = { "rank": list(range(1, rollout_world_size + 1)), "world_size": [rollout_world_size + 1] * rollout_world_size, "metadata": [metadatas[0]] * rollout_world_size, } return trainer_kwargs, rollout_kwargs def init_process_group(self, rank: int, world_size: int, metadata: dict[str, Any]): self.rank = rank self.world_size = world_size if rank < 0: logger.info(f"init_process_group rank={rank}") return self.store = StatelessProcessGroup.create( host=metadata["addr"], port=metadata["port"], rank=rank, world_size=world_size, ) info = { "session_id": self.session_id, "ptr": self.buf.data_ptr(), } info_list = self.store.all_gather_obj(info) self.buffer_info = None if rank == 0 else info_list[rank - 1] logger.info(f"init_process_group rank={rank} world_size={world_size} buffer_info={self.buffer_info}") def finalize(self): """Cleanup communication and deregister memory""" self.store = None get_torch_device().empty_cache() gc.collect() logger.info(f"finalize rank={self.rank}") async def wait_for_complete(self, buf: torch.Tensor): magic = torch.tensor([0xAB, 0xDC, 0xEF, 0x88], dtype=torch.uint8, device=self.device) while True: if torch.equal(buf[:4], magic): break await asyncio.sleep(0) @torch.no_grad() async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send weights using Mooncake TransferEngine""" if self.rank < 0: for name, weight in weights: pass logger.info(f"send_weights rank={self.rank}") return total_bytes = 0 start_time = time.time() bucket_meta: dict[str, TensorMeta] = {} offset = 0 should_wait = False bufs = [self.buf[: self.bucket_size], self.buf[self.bucket_size :]] idx = 0 current = bufs[idx] for name, weight in weights: weight = weight.to(self.rollout_dtype) if offset + weight.nbytes > self.bucket_size: total_bytes += offset get_torch_device().synchronize() info = { "bucket_meta": bucket_meta, "ptr": current.data_ptr(), "len": offset, "is_last": False, } # send to rank 1 self.store.send_obj(info, 1) idx ^= 1 current = bufs[idx] bucket_meta = {} offset = 0 if should_wait: await self.wait_for_complete(current) should_wait = True assert offset + weight.nbytes <= self.bucket_size, ( f"Weight {name}({weight.shape}, {weight.dtype}) is too large to fit in the bucket." ) bucket_meta[name] = { "name": name, "shape": weight.shape, "dtype": weight.dtype, "offset": offset, } current[offset : offset + weight.nbytes].copy_(weight.view(-1).view(torch.uint8), non_blocking=True) offset += weight.nbytes get_torch_device().synchronize() info = { "bucket_meta": bucket_meta, "ptr": current.data_ptr(), "len": offset, "is_last": True, } self.store.send_obj(info, 1) await self.wait_for_complete(current) time_cost = time.time() - start_time bandwidth = total_bytes / time_cost / (1024 * 1024 * 1024) logger.info( f"Rank {self.rank} send weights done, " f"total bytes: {total_bytes} time cost: {time_cost:.2f}s bandwidth: {bandwidth:.2f} GB/s" ) @torch.no_grad() async def receive_weights(self) -> AsyncGenerator[tuple[str, torch.Tensor], None]: """Receive weights using Mooncake TransferEngine""" start_time = time.time() total_bytes = 0 bufs = [self.buf[: self.bucket_size], self.buf[self.bucket_size :]] idx = 0 current = bufs[idx] self.magic_buf[:4] = torch.tensor([0xAB, 0xDC, 0xEF, 0x88], dtype=torch.uint8, device=self.device) while True: # 1 receive info from previous rank info = self.store.recv_obj(self.rank - 1) if idx >= 2 and self.rank < self.world_size - 1: await self.wait_for_complete(current) ptr = info["ptr"] ret = self.engine.transfer_sync_read( self.buffer_info["session_id"], current.data_ptr(), ptr, info["len"], ) assert ret == 0, f"transfer_sync_read failed {ret}" total_bytes += info["len"] # 2 send info to next rank info["ptr"] = current.data_ptr() if self.rank < self.world_size - 1: self.store.send_obj(info, self.rank + 1) # 3 yield tensor from current buffer for name, meta in info["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = current[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor # 4 write magic data to previous rank ret = self.engine.transfer_sync_write( self.buffer_info["session_id"], self.magic_buf.data_ptr(), ptr, 4, ) assert ret == 0, f"transfer_sync_write failed {ret}" # 5 swap buffer idx += 1 current = bufs[idx % 2] get_torch_device().synchronize() if info["is_last"]: break time_cost = time.time() - start_time bandwidth = total_bytes / time_cost / (1024 * 1024 * 1024) logger.info( f"Rank {self.rank} receive weights done, time cost: {time_cost:.2f}s, bandwidth: {bandwidth:.2f} GB/s" ) ================================================ FILE: verl/checkpoint_engine/nccl_checkpoint_engine.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os import time from dataclasses import dataclass from typing import AsyncGenerator, Generator from unittest.mock import patch with patch("importlib.metadata.distributions", return_value=[]): import cupy as cp import ray import ray.util.collective as collective import torch import zmq from verl.checkpoint_engine.base import CheckpointEngine, CheckpointEngineRegistry, TensorMeta from verl.utils.net_utils import get_free_port, is_valid_ipv6_address logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @dataclass class MasterMetadata: zmq_ip: str zmq_port: int class BroadcastOperation: """Async broadcast operation with NCCL in separate thread. Args: rank (int): The rank of the current process. group_name (str): The name of the NCCL process group. bucket (cp.ndarray | torch.Tensor): The tensor to broadcast. metadata (dict[str, TensorMeta]): The metadata of the tensor. socket (zmq.Socket): The zeromq socket to communicate with master. topic (str): The topic to subscribe. """ def __init__( self, rank: int, group_name: str, bucket: cp.ndarray | torch.Tensor, metadata: dict[str, TensorMeta], socket: zmq.Socket, topic: str, ) -> None: self.rank = rank self.group_name = group_name self.bucket = bucket self.metadata = metadata self.socket = socket self.topic = topic loop = asyncio.get_running_loop() self._task = loop.run_in_executor(None, self._run) def _run(self): # broadcast tensor meta via zeromq PUB/SUB if self.rank == 0: self.socket.send_string(self.topic, flags=zmq.SNDMORE) self.socket.send_pyobj(self.metadata) else: self.socket.recv_string() self.metadata = self.socket.recv_pyobj() # broadcast tensor via NCCL collective.broadcast(self.bucket, src_rank=0, group_name=self.group_name) async def wait_for_complete(self) -> dict[str, TensorMeta]: """Wait for the broadcast operation to complete. Returns: dict[str, TensorMeta]: The bucket meta after broadcast. """ await self._task return self.metadata @CheckpointEngineRegistry.register("nccl") class NCCLCheckpointEngine(CheckpointEngine): """NCCL checkpoint engine with collective communication. Args: bucket_size (int): Bucket size in bytes to transfer multiple weights at one time. Note that we use two buffer to send and recv weights at same time, so the device memory overhead is 2 * bucket_size. group_name (str): The name of the NCCL process group. Defaults to "default". rebuild_group (bool): Whether to rebuild the NCCL process group in each update. Defaults to False. is_master (bool): Whether the current process is the master process. Defaults to False. rollout_dtype (torch.dtype): The dtype of the weights received from rollout workers. Defaults to torch.bfloat16. """ def __init__( self, bucket_size: int, group_name: str = "default", rebuild_group: bool = False, is_master: bool = False, rollout_dtype: torch.dtype = torch.bfloat16, ) -> None: self.bucket_size = bucket_size self.group_name = group_name self.rebuild_group = rebuild_group self.rollout_dtype = rollout_dtype # start zeromq server for broadcasting bucket tensor metadata self.is_master = is_master self.topic = "bucket_metadata" if self.is_master: self._start_zmq_server() def prepare(self) -> MasterMetadata: # For master process, use cupy instead of torch to avoid memory register error # when `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`. if self.is_master: self.send_buf = cp.zeros(self.bucket_size, dtype=cp.uint8) self.recv_buf = cp.zeros(self.bucket_size, dtype=cp.uint8) else: self.send_buf = torch.zeros(self.bucket_size, dtype=torch.uint8, device="cuda") self.recv_buf = torch.zeros(self.bucket_size, dtype=torch.uint8, device="cuda") return MasterMetadata(zmq_ip=self.ip, zmq_port=self.listen_port) if self.is_master else None def finalize(self): """Destroy the NCCL process group if rebuild_group is True.""" if self.rebuild_group: if self.rank >= 0: collective.destroy_collective_group(self.group_name) self.rank = None self.world_size = None self.send_buf = None self.recv_buf = None torch.cuda.empty_cache() @classmethod def build_topology(cls, trainer_world_size: int, rollout_world_size: int, metadata: list[dict]): trainer_kwargs = { "rank": [0] + [-1] * (trainer_world_size - 1), "world_size": [rollout_world_size + 1] * trainer_world_size, "master_metadata": [metadata[0]] * trainer_world_size, } rollout_kwargs = { "rank": list(range(1, rollout_world_size + 1)), "world_size": [rollout_world_size + 1] * rollout_world_size, "master_metadata": [metadata[0]] * rollout_world_size, } return trainer_kwargs, rollout_kwargs def _start_zmq_server(self): self.ip = ray.util.get_node_ip_address().strip("[]") self.listen_port, _ = get_free_port(self.ip) context = zmq.Context() self.socket = context.socket(zmq.PUB) if is_valid_ipv6_address(self.ip): address = f"tcp://[{self.ip}]:{self.listen_port}" self.socket.setsockopt(zmq.IPV6, 1) else: address = f"tcp://{self.ip}:{self.listen_port}" self.socket.bind(address) def _connect_zmq_client(self, metadata: MasterMetadata): assert not self.is_master, "Master process should not connect to other processes." context = zmq.Context() self.socket = context.socket(zmq.SUB) if is_valid_ipv6_address(metadata.zmq_ip): address = f"tcp://[{metadata.zmq_ip}]:{metadata.zmq_port}" self.socket.setsockopt(zmq.IPV6, 1) else: address = f"tcp://{metadata.zmq_ip}:{metadata.zmq_port}" self.socket.connect(address) self.socket.setsockopt_string(zmq.SUBSCRIBE, self.topic) def init_process_group(self, rank: int, world_size: int, master_metadata: MasterMetadata): """Initialize the NCCL process group. Args: rank (int): The rank of the current process. world_size (int): The total number of processes. """ # For trainer workers other than rank 0, their rank should be -1. if rank < 0: self.rank = rank self.world_size = world_size return if self.rebuild_group or not collective.is_group_initialized(self.group_name): collective.init_collective_group(world_size, rank, "nccl", self.group_name) self.rank = rank self.world_size = world_size else: assert self.rank == rank, f"rank {rank} is not equal to self.rank {self.rank}" assert self.world_size == world_size, ( f"world_size {world_size} is not equal to self.world_size {self.world_size}" ) if self.rank > 0: self._connect_zmq_client(master_metadata) collective.barrier(self.group_name) logger.info(f"init_process_group rank: {self.rank}, world_size: {self.world_size}") @torch.no_grad() async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ assert self.rank <= 0, "Trainer workers other than rank 0 should not send weights." # For trainer rank other than 0, consume weights without sending. if self.rank < 0: for name, weight in weights: pass return send_buf, recv_buf = self.send_buf, self.recv_buf broadcast_op = None start_time = time.time() bucket_meta: dict[str, TensorMeta] = {} offset = 0 for name, weight in weights: # fill the tensor bucket if offset + weight.nbytes > self.bucket_size: torch.cuda.synchronize() # wait previous broadcast op finish if broadcast_op is not None: await broadcast_op.wait_for_complete() broadcast_op = BroadcastOperation( rank=self.rank, group_name=self.group_name, bucket=send_buf, metadata={"bucket_meta": bucket_meta, "is_last": False}, socket=self.socket, topic=self.topic, ) # swap send_buf and recv_buf send_buf, recv_buf = recv_buf, send_buf bucket_meta = {} offset = 0 assert offset + weight.nbytes <= self.bucket_size, ( f"Weight {name}({weight.shape}, {weight.dtype}) is too large to fit in the bucket." ) bucket_meta[name] = { "name": name, "shape": weight.shape, "dtype": weight.dtype, "offset": offset, } send_buf[offset : offset + weight.nbytes] = cp.asarray(weight.view(-1).view(torch.uint8)) offset += weight.nbytes # broadcast last bucket torch.cuda.synchronize() if broadcast_op is not None: await broadcast_op.wait_for_complete() broadcast_op = BroadcastOperation( rank=self.rank, group_name=self.group_name, bucket=send_buf, metadata={"bucket_meta": bucket_meta, "is_last": True}, socket=self.socket, topic=self.topic, ) await broadcast_op.wait_for_complete() logger.info(f"Rank {self.rank} send weights done, time cost: {time.time() - start_time:.2f}s") @torch.no_grad() async def receive_weights(self) -> AsyncGenerator[tuple[str, torch.Tensor], None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ assert self.rank > 0, "Rank 0 should not receive weights." send_buf, recv_buf = self.send_buf, self.recv_buf total_bytes, total_params = 0, 0 # receive first bucket start_time = time.time() broadcast_op = BroadcastOperation( rank=self.rank, group_name=self.group_name, bucket=recv_buf, metadata=None, socket=self.socket, topic=self.topic, ) metadata = await broadcast_op.wait_for_complete() total_bytes += self.bucket_size total_params += len(metadata["bucket_meta"]) # swap send_buf and recv_buf send_buf, recv_buf = recv_buf, send_buf while not metadata["is_last"]: # 1. receive next bucket broadcast_op = BroadcastOperation( rank=self.rank, group_name=self.group_name, bucket=recv_buf, metadata=None, socket=self.socket, topic=self.topic, ) # 2. yield tensor from send_buf for name, meta in metadata["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = send_buf[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor # 3. wait for next bucket broadcast finish metadata = await broadcast_op.wait_for_complete() total_bytes += self.bucket_size total_params += len(metadata["bucket_meta"]) # 4. swap send_buf and recv_buf torch.cuda.synchronize() # sync non-blocking copy send_buf, recv_buf = recv_buf, send_buf # yield tensor from send_buf for name, meta in metadata["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = send_buf[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor time_cost = time.time() - start_time bandwidth = total_bytes / time_cost / (1024 * 1024 * 1024) logger.info( f"Rank {self.rank} receive weights done, total_params: {total_params}, " f"time cost: {time_cost:.2f}s, bandwidth: {bandwidth:.2f} GB/s" ) ================================================ FILE: verl/checkpoint_engine/nixl_checkpoint_engine.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os import time import uuid from collections import defaultdict, deque from dataclasses import dataclass from typing import AsyncGenerator, Generator from unittest.mock import patch with patch("importlib.metadata.distributions", return_value=[]): import cupy as cp import nixl._api as nixl_api import nixl._bindings as nixl_bindings import ray import torch import zmq import zmq.asyncio from verl.checkpoint_engine.base import CheckpointEngine, CheckpointEngineRegistry, TensorMeta from verl.utils.net_utils import get_free_port, is_valid_ipv6_address logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @dataclass class NixlAgentMetadata: agent_name: str agent_metadata: bytes zmq_ip: str zmq_port: int class NixlAgent: """This is a wrapper class for nixl_agent, the main purpose is to use ZeroMQ instead of `nixl_agent.send_notif` to send bucket tensor metadata. """ def __init__(self): self.agent_name = str(uuid.uuid4()) self.agent = nixl_api.nixl_agent(self.agent_name) self.notifications: dict[str, deque[bytes]] = defaultdict(deque) self.start_zmq_server() self.zmq_clients: dict[str, zmq.Socket] = {} self.messages: dict[str, deque[bytes]] = defaultdict(deque) def __getattr__(self, name): attr = getattr(self.agent, name) if callable(attr): def wrapper(*args, **kwargs): return attr(*args, **kwargs) return wrapper else: return attr def get_agent_metadata(self) -> NixlAgentMetadata: return NixlAgentMetadata( agent_name=self.agent_name, agent_metadata=self.agent.get_agent_metadata(), zmq_ip=self.ip, zmq_port=self.listen_port, ) def start_zmq_server(self): self.ip = ray.util.get_node_ip_address().strip("[]") self.listen_port, _ = get_free_port(self.ip) context = zmq.asyncio.Context() self.socket = context.socket(zmq.PULL) if is_valid_ipv6_address(self.ip): address = f"tcp://[{self.ip}]:{self.listen_port}" self.socket.setsockopt(zmq.IPV6, 1) else: address = f"tcp://{self.ip}:{self.listen_port}" self.socket.bind(address) def add_remote_agent(self, metadata: NixlAgentMetadata) -> str: agent_name = self.agent.add_remote_agent(metadata.agent_metadata).decode("utf-8") assert agent_name == metadata.agent_name, f"Agent name {agent_name} not equal to {metadata.agent_name}" context = zmq.Context() socket = context.socket(zmq.PUSH) if is_valid_ipv6_address(metadata.zmq_ip): address = f"tcp://[{metadata.zmq_ip}]:{metadata.zmq_port}" socket.setsockopt(zmq.IPV6, 1) else: address = f"tcp://{metadata.zmq_ip}:{metadata.zmq_port}" socket.connect(address) self.zmq_clients[agent_name] = socket return agent_name def remove_remote_agent(self, agent_name: str): self.agent.remove_remote_agent(agent_name) socket = self.zmq_clients.pop(agent_name) socket.close() def send_message(self, agent_name, message: dict): socket = self.zmq_clients[agent_name] socket.send_pyobj((self.agent_name, message), zmq.DONTWAIT) async def read_message(self, agent_name: str) -> dict: while len(self.messages[agent_name]) == 0: recv_agent_name, message = await self.socket.recv_pyobj() self.messages[recv_agent_name].append(message) return self.messages[agent_name].popleft() async def get_notification(self, remote_name: str) -> bytes: while len(self.notifications[remote_name]) == 0: notifs = self.agent.get_new_notifs() for remote_name, notif in notifs.items(): self.notifications[remote_name].extend(notif) await asyncio.sleep(0) return self.notifications[remote_name].popleft() class ReadableOperation: """Encapsulates a readable operation to remote agent. 1. send metadata to remote agent 2. wait until remote agent read complete. Args: agent (NixlAgent): The Nixl agent. remote_agent (str): The name of the remote agent. local_descs (nixl_bindings.nixlXferDList): The local transfer descriptors. metadata (dict): Metadata for the read operation. bucket_size (int): The size of the bucket in bytes. """ def __init__( self, agent: NixlAgent, remote_agent: str, local_descs: nixl_bindings.nixlXferDList, metadata: dict, ): self.agent = agent self.remote_agent = remote_agent self.local_descs = local_descs self.notify_key = uuid.uuid4().bytes message = {"notify_key": self.notify_key, "remote_descs": self.local_descs, **metadata} self.agent.send_message(self.remote_agent, message) async def wait_for_complete(self): """Block until remote agent read complete.""" notification = await self.agent.get_notification(self.remote_agent) assert self.notify_key == notification, f"Notify key {self.notify_key} not equal to {notification}" logger.debug(f"ReadableOperation to {self.remote_agent} complete") class ReadOperation: """Encapsulates a read operation from remote agent. 1. read medata from remote agent 2. start read transfer operation 3. wait until read complete Args: agent (NixlAgent): The Nixl agent. remote_agent (str): The name of the remote agent. local_descs (nixl_bindings.nixlXferDList): The local transfer descriptors. bucket_size (int): The size of the bucket in bytes. """ def __init__(self, agent: NixlAgent, remote_agent: str, local_descs: nixl_bindings.nixlXferDList, bucket_size: int): self.agent = agent self.remote_agent = remote_agent self.local_descs = local_descs self.remote_descs = None self.xfer_handle = None self.notify_key = None self.bucket_size = bucket_size self.start_time = None async def read_metadata(self) -> dict: """Block until the remote agent sends the metadata. Returns: dict: Metadata from the remote agent. """ metadata = await self.agent.read_message(self.remote_agent) self.remote_descs = metadata.pop("remote_descs") self.notify_key = metadata.pop("notify_key") return metadata def begin_read(self): """Start the read operation.""" assert self.remote_descs is not None and self.notify_key is not None self.xfer_handle = self.agent.initialize_xfer( "READ", self.local_descs, self.remote_descs, self.remote_agent, self.notify_key ) state = self.agent.transfer(self.xfer_handle) assert state != "ERR", f"Read from {self.remote_agent} got to {state} state." self.start_time = time.time() async def wait_for_complete(self): """Block until the read operation complete.""" while True: state = self.agent.check_xfer_state(self.xfer_handle) if state == "ERR": logger.error(f"Read from {self.remote_agent} got to {state} state.") exit(-1) elif state == "DONE": break else: await asyncio.sleep(0) self.agent.release_xfer_handle(self.xfer_handle) end_time = time.time() bandwidth = self.bucket_size / (end_time - self.start_time) / (1024 * 1024 * 1024) logger.debug(f"ReadOperation read data from {self.remote_agent} complete, bandwidth: {bandwidth:.2f} GB/s") @CheckpointEngineRegistry.register("nixl") class NIXLCheckpointEngine(CheckpointEngine): """NIXL checkpoint engine with p2p communication, support various backends: ucx, uccl, mooncacke, etc. For UCX backend, some environment variables need to be set: UCX_TLS, UCX_IB_GID_INDEX, UCX_IB_DEVICES, etc. Please refer to: https://openucx.readthedocs.io/en/master/faq.html Args: bucket_size (int): Bucket size in bytes to transfer multiple weights at one time. Note that we use two buffer to send and recv weights at same time, so the device memory overhead is 2 * bucket_size. device (str): The device to use for the checkpoint engine, "cpu" or "cuda". rollout_dtype (torch.dtype): The dtype of the weights received from rollout workers. Defaults to torch.bfloat16. """ def __init__( self, bucket_size: int, device: str = "cuda", rollout_dtype: torch.dtype = torch.bfloat16, is_master: bool = False, ): self.bucket_size = bucket_size self.device = device self.rollout_dtype = rollout_dtype self.agent = NixlAgent() self.is_master = is_master def prepare(self) -> NixlAgentMetadata: """Prepare send and recv bucket. Returns: NixlAgentMetadata: The metadata of the current nixl agent. """ # For master process, use cupy instead of torch to avoid memory register error # when `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`. if self.device == "cuda": send_buf = cp.zeros(self.bucket_size, dtype=cp.uint8) recv_buf = cp.zeros(self.bucket_size, dtype=cp.uint8) self.send_buf = torch.as_tensor(send_buf, dtype=torch.uint8) self.recv_buf = torch.as_tensor(recv_buf, dtype=torch.uint8) else: self.send_buf = torch.zeros(self.bucket_size, dtype=torch.uint8, device=self.device, pin_memory=True) self.recv_buf = torch.zeros(self.bucket_size, dtype=torch.uint8, device=self.device, pin_memory=True) self.send_reg_descs = self.agent.register_memory(self.send_buf) self.recv_reg_descs = self.agent.register_memory(self.recv_buf) self.send_descs = self.agent.get_xfer_descs(self.send_buf) self.recv_descs = self.agent.get_xfer_descs(self.recv_buf) return self.agent.get_agent_metadata() @classmethod def build_topology(cls, trainer_world_size: int, rollout_world_size: int, metadata: list[dict]): trainer_kwargs = { "method": ["init_process_group"] * trainer_world_size, "rank": [0] + [-1] * (trainer_world_size - 1), "world_size": [rollout_world_size + 1] * trainer_world_size, "prev_agent_metadata": [None] * trainer_world_size, "next_agent_metadata": [metadata[-rollout_world_size]] + [None] * (trainer_world_size - 1), } rollout_kwargs = { "method": ["init_process_group"] * rollout_world_size, "rank": list(range(1, rollout_world_size + 1)), "world_size": [rollout_world_size + 1] * rollout_world_size, "prev_agent_metadata": [metadata[0]] + metadata[-rollout_world_size:-1], "next_agent_metadata": metadata[-rollout_world_size + 1 :] + [None], } return trainer_kwargs, rollout_kwargs def init_process_group( self, rank: int, world_size: int, prev_agent_metadata: NixlAgentMetadata, next_agent_metadata: NixlAgentMetadata ): """Setup the communication with the previous and next agent. Args: rank (int): The rank of the current process. world_size (int): The total number of processes. prev_agent_metadata (NixlAgentMetadata): The metadata of the previous nixl agent. next_agent_metadata (NixlAgentMetadata): The metadata of the next nixl agent. """ if rank < 0: assert not prev_agent_metadata and not next_agent_metadata, ( f"rank {rank} should not have prev_agent_metadata or next_agent_metadata" ) elif rank == 0: assert not prev_agent_metadata and next_agent_metadata, f"rank {rank} should have next_agent_metadata" elif 0 < rank < world_size - 1: assert prev_agent_metadata and next_agent_metadata, ( f"rank {rank} should have prev_agent_metadata and next_agent_metadata" ) elif rank == world_size - 1: assert prev_agent_metadata and not next_agent_metadata, ( f"rank {rank} should have prev_agent_metadata and not next_agent_metadata" ) self.rank = rank self.world_size = world_size self.prev_agent = None self.next_agent = None if prev_agent_metadata is not None: self.prev_agent = self.agent.add_remote_agent(prev_agent_metadata) if next_agent_metadata is not None: self.next_agent = self.agent.add_remote_agent(next_agent_metadata) logger.info( f"init_process_group rank: {self.rank}, world_size: {self.world_size}, " f"prev_agent: {self.prev_agent}, next_agent: {self.next_agent}" ) def finalize(self): """Cleanup communication with the previous and next agent, and deregister the memory.""" if self.prev_agent: self.agent.remove_remote_agent(self.prev_agent) if self.next_agent: self.agent.remove_remote_agent(self.next_agent) self.agent.deregister_memory(self.send_reg_descs) self.agent.deregister_memory(self.recv_reg_descs) self.send_buf = None self.recv_buf = None self.send_reg_descs = None self.recv_reg_descs = None self.send_descs = None self.recv_descs = None self.rank = None self.world_size = None self.prev_agent = None self.next_agent = None @torch.no_grad() async def send_weights(self, weights: Generator[tuple[str, torch.Tensor], None, None]): """Send the weights of the model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ assert self.rank <= 0, "Trainer workers other than rank 0 should not send weights." # For trainer workers other than rank 0, just consume weights and do nothing. if self.rank < 0: for name, weight in weights: pass return assert self.next_agent is not None, "Next agent is not set." send_buf, recv_buf = self.send_buf, self.recv_buf send_descs, recv_descs = self.send_descs, self.recv_descs readable_op = None start_time = time.time() bucket_meta: dict[str, TensorMeta] = {} offset = 0 for name, weight in weights: # fill the tensor bucket if offset + weight.nbytes > self.bucket_size: torch.cuda.synchronize() # wait previous bucket to be received if readable_op is not None: await readable_op.wait_for_complete() # send bucket meta to next agent readable_op = ReadableOperation( self.agent, self.next_agent, send_descs, {"bucket_meta": bucket_meta, "is_last": False}, ) # swap send and recv buf send_buf, recv_buf = recv_buf, send_buf send_descs, recv_descs = recv_descs, send_descs bucket_meta = {} offset = 0 assert offset + weight.nbytes <= self.bucket_size, ( f"Weight {name}({weight.shape}, {weight.dtype}) is too large to fit in the bucket." ) bucket_meta[name] = { "name": name, "shape": weight.shape, "dtype": weight.dtype, "offset": offset, } send_buf[offset : offset + weight.nbytes].copy_(weight.view(-1).view(torch.uint8), non_blocking=True) offset += weight.nbytes # send last bucket meta to next agent torch.cuda.synchronize() if readable_op is not None: await readable_op.wait_for_complete() readable_op = ReadableOperation( self.agent, self.next_agent, send_descs, {"bucket_meta": bucket_meta, "is_last": True} ) await readable_op.wait_for_complete() logger.info(f"Rank {self.rank} send weights done, time cost: {time.time() - start_time:.2f}s") @torch.no_grad() async def receive_weights(self) -> AsyncGenerator[tuple[str, torch.Tensor], None]: """Receive the weights of the model. Yields: A tuple of the name of the weight tensor and the tensor itself. """ assert self.prev_agent is not None, "Previous agent is not set." send_buf, recv_buf = self.send_buf, self.recv_buf send_descs, recv_descs = self.send_descs, self.recv_descs total_bytes, total_params = 0, 0 # receive first bucket from previous agent start_time = time.time() read_op = ReadOperation(self.agent, self.prev_agent, recv_descs, self.bucket_size) metadata = await read_op.read_metadata() read_op.begin_read() await read_op.wait_for_complete() total_bytes += self.bucket_size total_params += len(metadata["bucket_meta"]) # swap send and recv buf send_buf, recv_buf = recv_buf, send_buf send_descs, recv_descs = recv_descs, send_descs while not metadata["is_last"]: # 1. send bucket to next agent readable_op = None if self.next_agent is not None: readable_op = ReadableOperation( self.agent, self.next_agent, send_descs, metadata, ) # 2. receive bucket from previous agent read_op = ReadOperation(self.agent, self.prev_agent, recv_descs, self.bucket_size) next_metadata = await read_op.read_metadata() read_op.begin_read() # 3. yield tensor from send_buf for name, meta in metadata["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = send_buf[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor # 4. wait for next agent read complete and read from previous agent complete if readable_op is not None: await readable_op.wait_for_complete() await read_op.wait_for_complete() total_bytes += self.bucket_size total_params += len(next_metadata["bucket_meta"]) # 5. swap send and recv buf torch.cuda.synchronize() # sync non-blocking copy metadata = next_metadata send_buf, recv_buf = recv_buf, send_buf send_descs, recv_descs = recv_descs, send_descs # send last bucket to next agent readable_op = None if self.next_agent is not None: readable_op = ReadableOperation( self.agent, self.next_agent, send_descs, metadata, ) # yield tensor from send_buf for name, meta in metadata["bucket_meta"].items(): dtype, shape = meta["dtype"], meta["shape"] size = dtype.itemsize * shape.numel() tensor = send_buf[meta["offset"] : meta["offset"] + size].view(dtype=dtype).view(shape) yield name, tensor # wait for next agent read complete if readable_op is not None: await readable_op.wait_for_complete() time_cost = time.time() - start_time bandwidth = total_bytes / time_cost / (1024 * 1024 * 1024) logger.info( f"Rank {self.rank} receive weights done, total_params: {total_params}, " f"time cost: {time_cost:.2f}s, bandwidth: {bandwidth:.2f} GB/s" ) ================================================ FILE: verl/experimental/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/agent_loop/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .agent_loop import AgentLoopBase, AgentLoopManager, AgentLoopWorker, AsyncLLMServerManager from .single_turn_agent_loop import SingleTurnAgentLoop from .tool_agent_loop import ToolAgentLoop _ = [SingleTurnAgentLoop, ToolAgentLoop] __all__ = ["AgentLoopBase", "AgentLoopManager", "AsyncLLMServerManager", "AgentLoopWorker"] ================================================ FILE: verl/experimental/agent_loop/agent_loop.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os import random from abc import ABC, abstractmethod from typing import Any, Optional from uuid import uuid4 import hydra import numpy as np import ray import torch from cachetools import LRUCache from omegaconf import DictConfig, OmegaConf from PIL import Image from pydantic import BaseModel, ConfigDict from tensordict import TensorDict from transformers import AutoProcessor, AutoTokenizer from verl.experimental.agent_loop.prometheus_utils import update_prometheus_config from verl.experimental.agent_loop.utils import resolve_config_path from verl.protocol import DataProto from verl.single_controller.ray.base import RayResourcePool, RayWorkerGroup from verl.utils.chat_template import apply_chat_template, initialize_system_prompt from verl.utils.config import omega_conf_to_dataclass from verl.utils.dataset.rl_dataset import RLHFDataset, get_dataset_class from verl.utils.model import compute_position_id_with_mask from verl.utils.ray_utils import auto_await, get_event_loop from verl.utils.rollout_trace import ( RolloutTraceConfig, rollout_trace_attr, rollout_trace_op, ) from verl.utils.tokenizer import normalize_token_ids from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.replica import TokenOutput, get_rollout_replica_class logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) DEFAULT_ROUTING_CACHE_SIZE = 10000 @ray.remote class GlobalRequestLoadBalancer: """Global sticky-session + in-flight load balancer shared by all AgentLoopWorkers.""" def __init__(self, server_actor_ids: list[str], max_cache_size: int = DEFAULT_ROUTING_CACHE_SIZE): if not server_actor_ids: raise ValueError("server_actor_ids must be non-empty") self._inflight_requests: dict[str, int] = {sid: 0 for sid in server_actor_ids} self._request_id_to_server: LRUCache = LRUCache(maxsize=max_cache_size) def acquire_server(self, request_id: str) -> str: """Acquire a server for the given request, reusing the same server for multi-turn conversations.""" # request-level sticky (multi-turn: same conversation -> same server) if request_id in self._request_id_to_server: server_id = self._request_id_to_server[request_id] self._inflight_requests[server_id] += 1 return server_id # new request: route to least loaded server server_id = min(self._inflight_requests, key=self._inflight_requests.get) self._request_id_to_server[request_id] = server_id self._inflight_requests[server_id] += 1 return server_id def release_server(self, server_id: str) -> None: """Release a server after a request completes, decrementing its inflight count.""" if server_id not in self._inflight_requests: raise ValueError(f"Invalid server_id for release: {server_id}") if self._inflight_requests[server_id] <= 0: raise ValueError(f"Release called with no inflight requests on server {server_id}") self._inflight_requests[server_id] -= 1 def _get_rollout_and_model_config(config: DictConfig) -> tuple[DictConfig, DictConfig]: # TODO: backward compatibility, remove this once we switch to new trainer. if config.get("actor_rollout_ref"): return config.actor_rollout_ref.rollout, config.actor_rollout_ref.model else: return config.rollout, config.model class AsyncLLMServerManager: """ A class to manage multiple OpenAI compatible LLM servers. This class provides - Load balance: least in-flight requests load balancing via global coordination - Sticky session: send multi-turn chat completions to same server for automatic prefix caching """ def __init__( self, config: DictConfig, servers: list[tuple[str, ray.actor.ActorHandle]], load_balancer_handle: ray.actor.ActorHandle, ): """Initialize the AsyncLLMServerManager. Args: config (DictConfig): whole config for main entrypoint. servers (list[tuple[str, ray.actor.ActorHandle]]): (address, handle) pairs for each LLM server. load_balancer_handle (ray.actor.ActorHandle): shared global load balancer actor. """ self.config = config self._load_balancer = load_balancer_handle self._server_id_to_handle: dict[str, ray.actor.ActorHandle] = dict(servers) async def _acquire_server(self, request_id: str) -> tuple[str, ray.actor.ActorHandle]: server_id = await self._load_balancer.acquire_server.remote(request_id=request_id) handle = self._server_id_to_handle.get(server_id) if handle is None: raise RuntimeError(f"Unknown server_id returned by load balancer: {server_id}") return server_id, handle def _release_server(self, server_id: str) -> None: # Fire-and-forget: release is just a counter decrement, no need to await. # Awaiting here risks blocking the finally clause if the LB actor is unresponsive. self._load_balancer.release_server.remote(server_id=server_id) @rollout_trace_op async def generate( self, request_id, *, prompt_ids: list[int], sampling_params: dict[str, Any], image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, ) -> TokenOutput: """Generate tokens from prompt ids. Args: request_id (str): request id for sticky session. prompt_ids (List[int]): List of prompt token ids. sampling_params (Dict[str, Any]): Sampling parameters for the chat completion. Returns: TokenOutput: token output """ server_id, server = await self._acquire_server(request_id) try: output: TokenOutput = await server.generate.remote( request_id=uuid4().hex, # use new request_id for each turn prompt_ids=prompt_ids, sampling_params=sampling_params, image_data=image_data, video_data=video_data, ) return output finally: self._release_server(server_id) class AgentLoopMetrics(BaseModel): """Agent loop performance metrics.""" generate_sequences: float = 0.0 tool_calls: float = 0.0 num_preempted: int = -1 # -1 means not available class AgentLoopOutput(BaseModel): """Agent loop output.""" prompt_ids: list[int] """Prompt token ids.""" response_ids: list[int] """Response token ids including LLM generated token, tool response token.""" response_mask: list[int] """Response mask, 1 for LLM generated token, 0 for tool response token.""" response_logprobs: Optional[list[float]] = None """Log probabilities for the response tokens.""" routed_experts: Optional[Any] = None """Routed experts for the total tokens.""" multi_modal_data: Optional[dict[str, Any]] = None """Multi-modal data for multi-modal tools.""" reward_score: Optional[float] = None """Reward score for the trajectory.""" num_turns: int = 0 """Number of chat turns, including user, assistant, tool.""" metrics: AgentLoopMetrics """Auxiliary performance metrics""" extra_fields: dict[str, Any] = {} """Extra fields for dynamic addition.""" class _InternalAgentLoopOutput(AgentLoopOutput): """Internal agent loop output with padded sequences.""" model_config = ConfigDict(arbitrary_types_allowed=True) prompt_ids: torch.Tensor """Padded prompt token ids.""" response_ids: torch.Tensor """Padded response token ids.""" input_ids: torch.Tensor """Padded input ids(prompt_ids + response_ids).""" position_ids: torch.Tensor """Padded position ids.""" response_mask: torch.Tensor """Padded response mask.""" attention_mask: torch.Tensor """Padded attention mask.""" response_logprobs: Optional[torch.Tensor] = None """Padded log probabilities for the response tokens.""" routed_experts: Optional[torch.Tensor] = None """Padded routed experts for the total tokens.""" multi_modal_inputs: Optional[dict[str, torch.Tensor]] = None """Multi-modal inputs for processors (e.g., pixel_values, image_grid_thw).""" extra_fields: dict[str, Any] = {} """Extra fields for dynamic addition.""" class DictConfigWrap: """Wrapper for DictConfig to avoid hydra.utils.instantiate recursive resolve.""" def __init__(self, config: DictConfig): self.config = config class AgentLoopBase(ABC): """An agent loop takes an input message, chat with OpenAI compatible LLM server and interact with various environments. Args: trainer_config (DictConfig): whole config for main entrypoint. server_manager (AsyncLLMServerManager): OpenAI compatible LLM server manager. tokenizer (AutoTokenizer): Tokenizer for tokenize messages. processor (AutoProcessor): Processor for process messages. dataset_cls (type[Dataset]): Dataset class for creating dataset, Defaults to RLHFDataset. data_config (DictConfigWrap): Dataset config. """ def __init__( self, trainer_config: DictConfigWrap, server_manager: AsyncLLMServerManager, tokenizer: AutoTokenizer, processor: AutoProcessor, dataset_cls: type[RLHFDataset], data_config: DictConfigWrap, **kwargs, ): self.config = trainer_config.config self.rollout_config, _ = _get_rollout_and_model_config(self.config) self.server_manager = server_manager self.tokenizer = tokenizer self.processor = processor self.dataset_cls = dataset_cls self.data_config = data_config.config self.apply_chat_template_kwargs = self.data_config.get("apply_chat_template_kwargs", {}) self.system_prompt = initialize_system_prompt(self.tokenizer, **self.apply_chat_template_kwargs) self.loop = get_event_loop() async def process_vision_info(self, messages: list[dict]) -> dict: """Extract images and videos from messages. Args: messages (list[dict]): Input messages. Returns: dict: Multi-modal data with keys "images" and "videos". """ multi_modal_data = {} if self.processor is not None: images, videos = await self.dataset_cls.process_vision_info( messages, image_patch_size=self.processor.image_processor.patch_size, config=self.data_config ) if images is not None: multi_modal_data["images"] = images if videos is not None: multi_modal_data["videos"] = videos return multi_modal_data async def apply_chat_template( self, messages: list[dict], tools: list[dict] = None, images: list[Image.Image] = None, videos: list[tuple[torch.Tensor, dict]] = None, remove_system_prompt: bool = False, ): """Apply chat template to messages with optional tools, images, and videos. Args: messages (list[dict]): Input messages. tools (list[dict], optional): Tools schemas. Defaults to None. images (list[Image.Image], optional): Input images. Defaults to None. videos (list[tuple[torch.Tensor, dict]], optional): Input videos. Defaults to None. remove_system_prompt (bool, optional): Whether to remove system prompt. Defaults to False. Returns: list[int]: Prompt token ids. """ if self.processor is not None: raw_prompt = await self.loop.run_in_executor( None, lambda: apply_chat_template( self.processor, messages, tools=tools, add_generation_prompt=True, tokenize=False, **self.apply_chat_template_kwargs, ), ) # split the videos and according metadatas if videos is not None: videos, video_metadatas = zip(*videos, strict=False) videos, video_metadatas = list(videos), list(video_metadatas) else: video_metadatas = None model_inputs = self.processor( text=[raw_prompt], images=images, videos=videos, video_metadata=video_metadatas, return_tensors="pt", do_sample_frames=False, ) prompt_ids = normalize_token_ids(model_inputs.pop("input_ids")) else: tokenized_prompt = await self.loop.run_in_executor( None, lambda: apply_chat_template( self.tokenizer, messages, tools=tools, add_generation_prompt=True, tokenize=True, **self.apply_chat_template_kwargs, ), ) prompt_ids = normalize_token_ids(tokenized_prompt) if remove_system_prompt: prompt_ids = prompt_ids[len(self.system_prompt) :] return prompt_ids @abstractmethod async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput: """Run agent loop to interact with LLM server and environment. Args: sampling_params (Dict[str, Any]): LLM sampling params. **kwargs: dataset fields from `verl.utils.dataset.RLHFDataset`. Returns: AgentLoopOutput: Agent loop output. """ raise NotImplementedError """Agent loop registry: key is agent_name, value is a dict of agent loop config used by hydra.utils.instantiate to initialize agent loop instance. https://hydra.cc/docs/advanced/instantiate_objects/overview/ """ _agent_loop_registry: dict[str, dict] = {} def register(agent_name: str): """Register agent loop class.""" def decorator(subclass: type[AgentLoopBase]) -> type[AgentLoopBase]: fqdn = f"{subclass.__module__}.{subclass.__qualname__}" _agent_loop_registry[agent_name] = {"_target_": fqdn} return subclass return decorator class AgentLoopWorker: """Agent loop worker takes a batch of messages and run each message in an agent loop. Args: config (DictConfig): whole config for main entrypoint. servers (list[tuple[str, ray.actor.ActorHandle]]): (address, handle) pairs for each LLM server. reward_loop_worker_handles (List[ray.actor.ActorHandle]): Actor handles for streaming reward computation. """ def __init__( self, config: DictConfig, servers: list[tuple[str, ray.actor.ActorHandle]], load_balancer_handle: ray.actor.ActorHandle, reward_loop_worker_handles: list[ray.actor.ActorHandle] = None, ): """Initialize agent loop manager. Args: config (DictConfig): YAML config. servers (list[tuple[str, ray.actor.ActorHandle]]): (address, handle) pairs for each LLM server. load_balancer_handle (ray.actor.ActorHandle): shared global load balancer actor. reward_loop_worker_handles (list[ray.actor.ActorHandle]): Actor handles for streaming reward computation. """ self.config = config rollout_config, model_config = _get_rollout_and_model_config(config) self.rollout_config: RolloutConfig = omega_conf_to_dataclass(rollout_config) self.model_config: HFModelConfig = omega_conf_to_dataclass(model_config) # for recipe to change if not hasattr(self, "server_manager"): self.server_manager = AsyncLLMServerManager( config, servers, load_balancer_handle=load_balancer_handle, ) self.dataset_cls = get_dataset_class(config.data) self.reward_loop_worker_handles = reward_loop_worker_handles self.tokenizer = self.model_config.tokenizer self.processor = self.model_config.processor agent_loop_config_path = self.rollout_config.agent.agent_loop_config_path if agent_loop_config_path: resolved_path = resolve_config_path(agent_loop_config_path) agent_loop_configs = OmegaConf.load(resolved_path) for agent_loop_config in agent_loop_configs: _agent_loop_registry[agent_loop_config.name] = agent_loop_config if self.model_config.get("custom_chat_template", None) is not None: if self.model_config.processor is not None: self.model_config.processor.chat_template = self.model_config.custom_chat_template self.model_config.tokenizer.chat_template = self.model_config.custom_chat_template trace_config = self.rollout_config.trace RolloutTraceConfig.init( self.rollout_config.trace.project_name, self.rollout_config.trace.experiment_name, trace_config.get("backend"), trace_config.get("token2text", False), trace_config.get("max_samples_per_step_per_worker", None), ) async def generate_sequences(self, batch: DataProto) -> DataProto: """Generate sequences from agent loop. Args: batch (DataProto): Input batch. Returns: DataProto: Output batch. - prompts: [bsz, prompt_length], prompt token ids from dataset. - responses: [bsz, response_length], output token ids include response tokens from LLM generation and observation tokens from tool_calls. - response_mask: [bsz, response_length], 1 for LLM generated tokens, 0 for observation/padding tokens. - input_ids: [bsz, prompt_length + response_length], whole sequence token ids, including prompt tokens and response tokens. - attention_mask: [bsz, prompt_length + response_length], 0 for padding tokens, 1 for other tokens. - position_ids: [bsz, prompt_length + response_length], incremental position ids. For multi-turn conversations: responses: |<- LLM generation ->|<- tool_calls ->|<- LLM generation ->|<- padding ->| response_mask: | 1, 1, 1, ..., 1, 1 | 0, 0, .., 0, 0 | 1, 1, 1, ..., 1, 1 | 0, 0, ..., 0| """ config = self.rollout_config sampling_params = dict( temperature=config.temperature, top_p=config.top_p, top_k=config.top_k, repetition_penalty=1.0, logprobs=config.calculate_log_probs, ) # override sampling params for validation if batch.meta_info.get("validate", False): sampling_params["top_p"] = config.val_kwargs.top_p sampling_params["top_k"] = config.val_kwargs.top_k sampling_params["temperature"] = config.val_kwargs.temperature # by default, we assume it's a single turn agent if "agent_name" not in batch.non_tensor_batch: default_agent_loop = config.agent.default_agent_loop batch.non_tensor_batch["agent_name"] = np.array([default_agent_loop] * len(batch), dtype=object) if "index" in batch.non_tensor_batch: index = batch.non_tensor_batch["index"] else: index = np.arange(len(batch)) max_samples_per_worker = RolloutTraceConfig.get_instance().max_samples_per_step_per_worker # For n rollouts per sample, we trace all n rollouts for selected samples # Note: This sampling happens per-worker, so total traces = max_samples_per_worker * num_workers * n if max_samples_per_worker is not None: unique_sample_indices = np.unique(index) if max_samples_per_worker < len(unique_sample_indices): selected_samples = set( np.random.choice(unique_sample_indices, max_samples_per_worker, replace=False).tolist() ) traced_indices = set(i for i in range(len(batch)) if index[i] in selected_samples) else: traced_indices = set(range(len(batch))) else: traced_indices = set(range(len(batch))) trajectory_info = await get_trajectory_info( batch.meta_info.get("global_steps", -1), index.tolist(), batch.meta_info.get("validate", False) ) tasks = [] for i in range(len(batch)): trace_this_sample = i in traced_indices kwargs = {k: v[i] for k, v in batch.non_tensor_batch.items()} tasks.append( asyncio.create_task( self._run_agent_loop(sampling_params, trajectory_info[i], trace=trace_this_sample, **kwargs) ) ) outputs = await asyncio.gather(*tasks) output = self._postprocess(outputs, input_non_tensor_batch=batch.non_tensor_batch) return output async def _run_agent_loop( self, sampling_params: dict[str, Any], trajectory: dict[str, Any], *, agent_name: str, trace: bool = True, **kwargs, ) -> _InternalAgentLoopOutput: with rollout_trace_attr( step=trajectory["step"], sample_index=trajectory["sample_index"], rollout_n=trajectory["rollout_n"], validate=trajectory["validate"], name="agent_loop", trace=trace, ): assert agent_name in _agent_loop_registry, ( f"Agent loop {agent_name} not registered, registered agent loops: {_agent_loop_registry.keys()}" ) agent_loop_config = _agent_loop_registry[agent_name] agent_loop = hydra.utils.instantiate( config=agent_loop_config, trainer_config=DictConfigWrap(config=self.config), server_manager=self.server_manager, tokenizer=self.tokenizer, processor=self.processor, dataset_cls=self.dataset_cls, data_config=DictConfigWrap(self.config.data), ) output: AgentLoopOutput = await agent_loop.run(sampling_params, **kwargs) return await self._agent_loop_postprocess(output, **kwargs) async def _agent_loop_postprocess(self, output, **kwargs) -> _InternalAgentLoopOutput: """Perform post-processing operations on the output of each individual agent loop.""" output.extra_fields["raw_prompt"] = kwargs["raw_prompt"] # Some AgentLoop may have already computed the reward score, e.g SWE-agent. # NOTE: consistent with the legacy batch version of generate_sequences that existed in the # deprecated vLLM SPMD rollout implementation. # prompt_ids: left padded with zeros (e.g., [0,0,0,0,1,2,3,4]) # response_ids: right padded with zeros (e.g., [5,6,7,8,0,0,0,0]) # input_ids: concatenation of prompt + response # Mask: # For example, if the prompt is [1,2,3,4] and the response is [5,6,7,(tool start)8,9(tool end),10,11,12] # - prompt_attention_mask: 0s for padding, 1s for tokens # e.g., [0,0,0,0,1,1,1,1] # - response_attention_mask: 0s for padding, 1s for tokens # e.g., [1,1,1,1,1,1,1,1,1,1,1,0,0,0,0] # attention_mask: concatenation of prompt_attention_mask and response_attention_mask # e.g., [0,0,0,0,1,1,1,1(prompt),1,1,1,1,1,1,1,1,1,1,1,0,0,0,0(response)] # - response_mask: 1s for LLM generated tokens, 0 for tool response/padding tokens # e.g., [1,1,1,1,1,1,1,(tool start),0,0(tool end),1,1,0,0,0,0] # - position_ids: sequential positions for tokens, starting at 0 # e.g., [0,0,0,0,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,0,0,0,0] # TODO(wuxibin): remove padding and use tensordict. self.tokenizer.padding_side = "left" prompt_output = self.tokenizer.pad( {"input_ids": output.prompt_ids}, padding="max_length", max_length=self.rollout_config.prompt_length, return_tensors="pt", return_attention_mask=True, ) if prompt_output["input_ids"].dim() == 1: prompt_output["input_ids"] = prompt_output["input_ids"].unsqueeze(0) prompt_output["attention_mask"] = prompt_output["attention_mask"].unsqueeze(0) self.tokenizer.padding_side = "right" response_output = self.tokenizer.pad( {"input_ids": output.response_ids}, padding="max_length", max_length=self.rollout_config.response_length, return_tensors="pt", return_attention_mask=True, ) if response_output["input_ids"].dim() == 1: response_output["input_ids"] = response_output["input_ids"].unsqueeze(0) response_output["attention_mask"] = response_output["attention_mask"].unsqueeze(0) response_mask_output = self.tokenizer.pad( {"input_ids": output.response_mask}, padding="max_length", max_length=self.rollout_config.response_length, return_tensors="pt", return_attention_mask=False, ) if response_mask_output["input_ids"].dim() == 1: response_mask_output["input_ids"] = response_mask_output["input_ids"].unsqueeze(0) response_logprobs = None if output.response_logprobs is not None: pad_size = self.rollout_config.response_length - len(output.response_logprobs) response_logprobs = torch.tensor(output.response_logprobs + [0.0] * pad_size).unsqueeze(0) response_mask = response_mask_output["input_ids"] * response_output["attention_mask"] attention_mask = torch.cat([prompt_output["attention_mask"], response_output["attention_mask"]], dim=1) input_ids = torch.cat([prompt_output["input_ids"], response_output["input_ids"]], dim=1) routed_experts = None if output.routed_experts is not None: total_length = input_ids.shape[1] length, layer_num, topk_num = output.routed_experts.shape if isinstance(output.routed_experts, np.ndarray): routed_experts_array = output.routed_experts if not routed_experts_array.flags.writeable: routed_experts_array = routed_experts_array.copy() experts_tensor = torch.from_numpy(routed_experts_array) elif isinstance(output.routed_experts, torch.Tensor): experts_tensor = output.routed_experts else: raise TypeError(f"Unsupported type for routed_experts: {type(output.routed_experts)}") routed_experts = torch.zeros(1, total_length, layer_num, topk_num, dtype=experts_tensor.dtype) # Calculate start position: left padding means original prompt starts at the end start_pos = prompt_output["input_ids"].shape[1] - len(output.prompt_ids) end_pos = min(start_pos + length, total_length) # Add boundary checks for robustness if start_pos < 0 or end_pos > total_length: raise ValueError( f"Invalid position range: start_pos={start_pos}, end_pos={end_pos}, total_length={total_length}" ) routed_experts[:, start_pos:end_pos] = experts_tensor.unsqueeze(0) multi_modal_inputs = self._compute_multi_modal_inputs(output, input_ids) position_ids = self._compute_position_ids(input_ids, attention_mask, multi_modal_inputs) await self._compute_score( output, prompts=prompt_output["input_ids"], responses=response_output["input_ids"], attention_mask=attention_mask, input_ids=input_ids, position_ids=position_ids, kwargs=kwargs, ) return _InternalAgentLoopOutput( prompt_ids=prompt_output["input_ids"], response_ids=response_output["input_ids"], input_ids=input_ids, position_ids=position_ids, response_mask=response_mask, attention_mask=attention_mask, response_logprobs=response_logprobs, routed_experts=routed_experts, multi_modal_inputs=multi_modal_inputs, multi_modal_data=output.multi_modal_data, reward_score=output.reward_score, num_turns=output.num_turns, metrics=output.metrics, extra_fields=output.extra_fields, ) def _compute_multi_modal_inputs(self, output, input_ids) -> dict[str, torch.Tensor]: """Compute multi-modal inputs with image and video.""" multi_modal_inputs = {} if self.processor is None: return multi_modal_inputs images = output.multi_modal_data.get("images") videos = output.multi_modal_data.get("videos") # split the videos and according metadatas if videos is not None: videos, video_metadatas = zip(*videos, strict=False) videos, video_metadatas = list(videos), list(video_metadatas) else: video_metadatas = None current_text = self.tokenizer.decode(input_ids.squeeze(0), skip_special_tokens=True) multi_modal_inputs = self.processor( text=[current_text], images=images, videos=videos, video_metadata=video_metadatas, return_tensors="pt", do_sample_frames=False, ) multi_modal_inputs.pop("input_ids", None) multi_modal_inputs.pop("attention_mask", None) # We must use dict(multi_modal_inputs) to convert BatchFeature values to a new dict # because np.array() only keeps the keys for BatchFeature. multi_modal_inputs = dict(multi_modal_inputs.convert_to_tensors("pt")) image_grid_thw = multi_modal_inputs.get("image_grid_thw") if image_grid_thw is not None: images_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]) multi_modal_inputs["images_seqlens"] = images_seqlens return multi_modal_inputs def _compute_position_ids(self, input_ids, attention_mask, multi_modal_inputs) -> torch.Tensor: """Compute position ids for multi-modal inputs.""" if self.processor is None: return compute_position_id_with_mask(attention_mask) # (1, seq_len) multi_modal_kwargs = { "image_grid_thw": multi_modal_inputs.get("image_grid_thw"), "video_grid_thw": multi_modal_inputs.get("video_grid_thw"), } # For transformers>=5.3.0, mm_token_type_ids is only used to calculate position ids. if multi_modal_inputs.pop("mm_token_type_ids", None) is not None: mm_token_type_ids = torch.zeros_like(input_ids) mm_token_type_ids[0][input_ids[0] == self.processor.image_token_id] = 1 mm_token_type_ids[0][input_ids[0] == self.processor.video_token_id] = 2 multi_modal_kwargs["mm_token_type_ids"] = mm_token_type_ids # Model's get_rope_index has been dynamically bind to the processor. vision_position_ids, _ = self.processor.get_rope_index( input_ids=input_ids, attention_mask=attention_mask, **multi_modal_kwargs, ) vision_position_ids = vision_position_ids.transpose(0, 1) # (3, 1, seq_len) => (1, 3, seq_len) valid_mask = attention_mask[0].bool() text_position_ids = torch.ones((1, len(input_ids[0])), dtype=torch.long) text_position_ids[0, valid_mask] = torch.arange(valid_mask.sum().item()) text_position_ids = text_position_ids.unsqueeze(0) position_ids = torch.cat((text_position_ids, vision_position_ids), dim=1) # (1, 4, seq_length) return position_ids async def _compute_score(self, output, prompts, responses, attention_mask, input_ids, position_ids, kwargs): """Compute reward score for single sample.""" enable_async_reward = self.reward_loop_worker_handles is not None if output.reward_score is None and enable_async_reward: batch = TensorDict( { "prompts": prompts, # [1, prompt_length] "responses": responses, # [1, response_length] "attention_mask": attention_mask, # [1, prompt_length + response_length] "input_ids": input_ids, # [1, prompt_length + response_length] "position_ids": position_ids, }, batch_size=1, ) non_tensor_batch = { **{k: np.array([v]) for k, v in kwargs.items()}, "__num_turns__": np.array([output.num_turns]), "tool_extra_fields": np.array([output.extra_fields], dtype=object), } data = DataProto( batch=batch, non_tensor_batch=non_tensor_batch, ) selected_reward_loop_worker_handle = random.choice(self.reward_loop_worker_handles) result = await selected_reward_loop_worker_handle.compute_score.remote(data) output.reward_score = result["reward_score"] output.extra_fields["reward_extra_info"] = result["reward_extra_info"] def _postprocess( self, inputs: list[_InternalAgentLoopOutput], input_non_tensor_batch: dict | None = None, ) -> DataProto: """Process the padded outputs from _run_agent_loop and combine them into a batch.""" # Convert lists back to tensors and stack them to create a batch. prompt_ids = torch.cat([input.prompt_ids for input in inputs], dim=0) response_ids = torch.cat([input.response_ids for input in inputs], dim=0) response_mask = torch.cat([input.response_mask for input in inputs], dim=0) attention_mask = torch.cat([input.attention_mask for input in inputs], dim=0) input_ids = torch.cat([input.input_ids for input in inputs], dim=0) position_ids = torch.cat([input.position_ids for input in inputs], dim=0) optional_outputs = {} if inputs[0].response_logprobs is not None: optional_outputs["rollout_log_probs"] = torch.cat([input.response_logprobs for input in inputs], dim=0) if inputs[0].routed_experts is not None: optional_outputs["routed_experts"] = torch.cat([input.routed_experts for input in inputs], dim=0) batch = TensorDict( { "prompts": prompt_ids, # [bsz, prompt_length] "responses": response_ids, # [bsz, response_length] "response_mask": response_mask, # [bsz, response_length] "input_ids": input_ids, # [bsz, prompt_length + response_length] "attention_mask": attention_mask, # [bsz, prompt_length + response_length] # position_ids: [bsz, 3, prompt_length + response_length] or [bsz, prompt_length + response_length] "position_ids": position_ids, **optional_outputs, }, batch_size=len(inputs), ) scores = [input.reward_score for input in inputs] if all(score is not None for score in scores): prompt_length = prompt_ids.size(1) response_length = attention_mask[:, prompt_length:].sum(dim=1) - 1 rm_scores = torch.zeros_like(response_mask, dtype=torch.float32) rm_scores[torch.arange(response_mask.size(0)), response_length] = torch.tensor(scores, dtype=torch.float32) batch["rm_scores"] = rm_scores non_tensor_batch = { "__num_turns__": np.array([input.num_turns for input in inputs], dtype=np.int32), } if self.reward_loop_worker_handles is None and input_non_tensor_batch: non_tensor_batch.update(input_non_tensor_batch) # add reward_extra_info to non_tensor_batch reward_extra_infos = [input.extra_fields.get("reward_extra_info", {}) for input in inputs] reward_extra_keys = list(reward_extra_infos[0].keys()) for key in reward_extra_keys: non_tensor_batch[key] = np.array([info[key] for info in reward_extra_infos]) # Add multi_modal_inputs to non_tensor_batch if any samples have them multi_modal_inputs_list = [input.multi_modal_inputs for input in inputs] if any(mmi is not None for mmi in multi_modal_inputs_list): non_tensor_batch["multi_modal_inputs"] = np.array(multi_modal_inputs_list, dtype=object) metrics = [input.metrics.model_dump() for input in inputs] # Collect extra fields from all inputs and convert them to np.ndarray # Keep a stable set of keys so downstream batch concat stays consistent across agent loops. extra_fields = {} default_extra_keys = { "turn_scores", "tool_rewards", "min_global_steps", "max_global_steps", "extras", } all_keys = set(key for input_item in inputs for key in input_item.extra_fields) | default_extra_keys for key in all_keys: temp_arr = np.empty(len(inputs), dtype=object) temp_arr[:] = [input.extra_fields.get(key) for input in inputs] extra_fields[key] = temp_arr non_tensor_batch.update(extra_fields) # Only include reward_extra_keys in meta_info if rm_scores is in batch # This avoids conflicts when reward_tensor is merged later in ray_trainer.py if "rm_scores" in batch.keys(): meta_info = {"metrics": metrics, "reward_extra_keys": reward_extra_keys} else: meta_info = {"metrics": metrics} return DataProto( batch=batch, non_tensor_batch=non_tensor_batch, meta_info=meta_info, ) async def get_trajectory_info(step, index, validate): """Get trajectory info. Args: step (int): global steps in the trainer. index (list): form datastore extra_info.index column. validate (bool): whether is a validate step. Returns: list: trajectory. """ trajectory_info = [] rollout_n = 0 for i in range(len(index)): if i > 0 and index[i - 1] == index[i]: rollout_n += 1 else: rollout_n = 0 trajectory_info.append({"step": step, "sample_index": index[i], "rollout_n": rollout_n, "validate": validate}) return trajectory_info class AgentLoopManager: """Agent loop manager that manages a group of agent loop workers. - if worker_group is not None, rollout server is in hybrid mode, share GPUs with training engine. - otherwise, rollout server is in standalone mode, use separate GPUs, e.g., one-step-off/fully async training. Args: config (DictConfig): whole config for main entrypoint. worker_group (RayWorkerGroup): ActorRolloutRef worker group for hybrid mode; None for standalone mode. rollout_resource_pool (RayResourcePool): Resource pool for hybrid mode, only used by TensorRT-LLM. reward_loop_worker_handles (List[ray.actor.ActorHandle]): Actor handles for streaming reward computation. """ def __init__( self, config: DictConfig, worker_group: RayWorkerGroup = None, rollout_resource_pool: RayResourcePool = None, reward_loop_worker_handles: list[ray.actor.ActorHandle] = None, ): self.config = config self.rollout_config, self.model_config = _get_rollout_and_model_config(config) self.worker_group = worker_group self.rollout_resource_pool = rollout_resource_pool self.reward_loop_worker_handles = reward_loop_worker_handles assert worker_group is not None or self.rollout_config.nnodes > 0, "nnodes must be > 0 in standalone mode" # for recipe to change if not hasattr(self, "rollout_replica_class"): self.rollout_replica_class = get_rollout_replica_class(self.rollout_config.name) if not hasattr(self, "agent_loop_workers_class"): self.agent_loop_workers_class = ray.remote(AgentLoopWorker) @classmethod @auto_await async def create( cls, config: DictConfig, worker_group: RayWorkerGroup = None, rollout_resource_pool: RayResourcePool = None, reward_loop_worker_handles: list[ray.actor.ActorHandle] = None, ): """Create agent loop manager.""" instance = cls(config, worker_group, rollout_resource_pool, reward_loop_worker_handles) await instance._initialize_llm_servers() await instance._init_global_load_balancer() await instance._init_agent_loop_workers() return instance async def _initialize_llm_servers(self): rollout_world_size = ( self.rollout_config.tensor_model_parallel_size * self.rollout_config.data_parallel_size * self.rollout_config.pipeline_model_parallel_size ) world_size = ( self.worker_group.world_size if self.worker_group else self.rollout_config.n_gpus_per_node * self.rollout_config.nnodes ) num_replicas = world_size // rollout_world_size self.rollout_replicas = [ self.rollout_replica_class( replica_rank=replica_rank, config=self.rollout_config, model_config=self.model_config, gpus_per_node=self.rollout_config.n_gpus_per_node, ) for replica_rank in range(num_replicas) ] if self.worker_group and self.rollout_config.name != "trtllm": await asyncio.gather(*[server.init_hybrid(self.worker_group) for server in self.rollout_replicas]) # TODO: unify trtllm to init_hybrid elif self.worker_group and self.rollout_config.name == "trtllm": await asyncio.gather( *[ server.init_hybrid_colocated(self.worker_group, self.rollout_resource_pool) for server in self.rollout_replicas ] ) else: await asyncio.gather(*[server.init_standalone() for server in self.rollout_replicas]) self.server_handles = [server._server_handle for server in self.rollout_replicas] self.server_addresses = [server._server_address for server in self.rollout_replicas] print(f"AgentLoopManager: {self.server_addresses}") # Update Prometheus configuration with server addresses if self.rollout_config.prometheus.enable: if self.rollout_config.disable_log_stats: raise ValueError("PROMETHEUS needs disable_log_stats==False, but it is currently True.") update_prometheus_config(self.rollout_config.prometheus, self.server_addresses, self.rollout_config.name) async def _init_agent_loop_workers(self): self.agent_loop_workers = [] num_workers = self.rollout_config.agent.num_workers load_balancer_handle = self.global_load_balancer servers = list(zip(self.server_addresses, self.server_handles, strict=True)) node_ids = [node["NodeID"] for node in ray.nodes() if node["Alive"] and node["Resources"].get("CPU", 0) > 0] for i in range(num_workers): # Round-robin scheduling over the all nodes node_id = node_ids[i % len(node_ids)] self.agent_loop_workers.append( self.agent_loop_workers_class.options( name=f"agent_loop_worker_{i}" + f"_{uuid4().hex[:8]}", scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy( node_id=node_id, soft=True ), ).remote( self.config, servers, load_balancer_handle, self.reward_loop_worker_handles, ) ) async def _init_global_load_balancer(self) -> None: self.global_load_balancer = GlobalRequestLoadBalancer.remote( server_actor_ids=self.server_addresses, max_cache_size=DEFAULT_ROUTING_CACHE_SIZE, ) @auto_await async def generate_sequences(self, prompts: DataProto) -> DataProto: """Split input batch and dispatch to agent loop workers. Args: prompts (DataProto): Input batch. Returns: DataProto: Output batch. """ chunkes = prompts.chunk(len(self.agent_loop_workers)) outputs = await asyncio.gather( *[ worker.generate_sequences.remote(chunk) for worker, chunk in zip(self.agent_loop_workers, chunkes, strict=True) ] ) output = DataProto.concat(outputs) # calculate performance metrics metrics = [output.meta_info.pop("metrics") for output in outputs] # List[List[Dict[str, str]]] timing = self._performance_metrics(metrics, output) output.meta_info = {"timing": timing, **outputs[0].meta_info} return output def _performance_metrics(self, metrics: list[list[dict[str, str]]], output: DataProto) -> dict[str, float]: timing = {} t_generate_sequences = np.array([metric["generate_sequences"] for chunk in metrics for metric in chunk]) t_tool_calls = np.array([metric["tool_calls"] for chunk in metrics for metric in chunk]) num_preempted = np.array([metric["num_preempted"] for chunk in metrics for metric in chunk]) timing["agent_loop/num_preempted/min"] = num_preempted.min() timing["agent_loop/num_preempted/max"] = num_preempted.max() timing["agent_loop/num_preempted/mean"] = num_preempted.mean() timing["agent_loop/generate_sequences/min"] = t_generate_sequences.min() timing["agent_loop/generate_sequences/max"] = t_generate_sequences.max() timing["agent_loop/generate_sequences/mean"] = t_generate_sequences.mean() timing["agent_loop/tool_calls/min"] = t_tool_calls.min() timing["agent_loop/tool_calls/max"] = t_tool_calls.max() timing["agent_loop/tool_calls/mean"] = t_tool_calls.mean() # batch sequence generation is bounded by the slowest sample slowest = np.argmax(t_generate_sequences + t_tool_calls) attention_mask = output.batch["attention_mask"][slowest] prompt_length = output.batch["prompts"].shape[1] timing["agent_loop/slowest/generate_sequences"] = t_generate_sequences[slowest] timing["agent_loop/slowest/tool_calls"] = t_tool_calls[slowest] timing["agent_loop/slowest/prompt_length"] = attention_mask[:prompt_length].sum().item() timing["agent_loop/slowest/response_length"] = attention_mask[prompt_length:].sum().item() timing["agent_loop/slowest/num_preempted"] = num_preempted[slowest] return timing @auto_await async def clear_kv_cache(self): """Clear all rollout kv cache, but don`t sleep.""" await asyncio.gather(*[replica.clear_kv_cache() for replica in self.rollout_replicas]) @auto_await async def start_profile(self, **kwargs): """Start profiling on all rollout replicas.""" await asyncio.gather(*[replica.start_profile(**kwargs) for replica in self.rollout_replicas]) @auto_await async def stop_profile(self): """Stop profiling on all rollout replicas.""" await asyncio.gather(*[replica.stop_profile() for replica in self.rollout_replicas]) ================================================ FILE: verl/experimental/agent_loop/prometheus_utils.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import ray import yaml from verl.workers.config.rollout import PrometheusConfig logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def update_prometheus_config(config: PrometheusConfig, server_addresses: list[str], rollout_name: str | None = None): """ Update Prometheus configuration file with server addresses and reload on first node. server_addresses: vllm or sglang server addresses rollout_name: name of the rollout backend (e.g., "vllm", "sglang") """ if not server_addresses: logger.warning("No server addresses available to update Prometheus config") return try: # Get Prometheus config file path from environment or use default prometheus_config_json = { "global": {"scrape_interval": "10s", "evaluation_interval": "10s"}, "scrape_configs": [ { "job_name": "ray", "file_sd_configs": [{"files": ["/tmp/ray/prom_metrics_service_discovery.json"]}], }, {"job_name": "rollout", "static_configs": [{"targets": server_addresses}]}, ], } # Write configuration file to all nodes @ray.remote(num_cpus=0) def write_config_file(config_data, config_path): os.makedirs(os.path.dirname(config_path), exist_ok=True) with open(config_path, "w") as f: yaml.dump(config_data, f, default_flow_style=False, indent=2) return True # Reload Prometheus on all nodes. Only master node should succeed, skip errors on other nodes. @ray.remote(num_cpus=0) def reload_prometheus(port): import socket import subprocess hostname = socket.gethostname() ip_address = socket.gethostbyname(hostname) reload_url = f"http://{ip_address}:{port}/-/reload" try: subprocess.run(["curl", "-X", "POST", reload_url], capture_output=True, text=True, timeout=10) print(f"Reloading Prometheus on node: {reload_url}") except Exception: # Skip errors on non-master nodes pass # Get all available nodes and schedule tasks on each node nodes = ray.nodes() alive_nodes = [node for node in nodes if node["Alive"]] # Write config files on all nodes write_tasks = [] for node in alive_nodes: node_ip = node["NodeManagerAddress"] task = write_config_file.options( resources={"node:" + node_ip: 0.001} # Schedule to specific node ).remote(prometheus_config_json, config.file) write_tasks.append(task) ray.get(write_tasks) server_type = rollout_name.upper() if rollout_name else "rollout" print(f"Updated Prometheus configuration at {config.file} with {len(server_addresses)} {server_type} servers") # Reload Prometheus on all nodes reload_tasks = [] for node in alive_nodes: node_ip = node["NodeManagerAddress"] task = reload_prometheus.options( resources={"node:" + node_ip: 0.001} # Schedule to specific node ).remote(config.port) reload_tasks.append(task) ray.get(reload_tasks) except Exception as e: logger.error(f"Failed to update Prometheus configuration: {e}") ================================================ FILE: verl/experimental/agent_loop/single_turn_agent_loop.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Any from uuid import uuid4 from verl.experimental.agent_loop.agent_loop import AgentLoopBase, AgentLoopOutput, register from verl.utils.profiler import simple_timer from verl.workers.rollout.replica import TokenOutput logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @register("single_turn_agent") class SingleTurnAgentLoop(AgentLoopBase): """Naive agent loop that only do single turn chat completion.""" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.prompt_length = self.rollout_config.prompt_length self.response_length = self.rollout_config.response_length async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput: messages = list(kwargs["raw_prompt"]) # 1. extract images and videos from messages multi_modal_data = await self.process_vision_info(messages) images = multi_modal_data.get("images") videos = multi_modal_data.get("videos") # 2. apply chat template and tokenize prompt_ids = await self.apply_chat_template( messages, images=images, videos=videos, ) # 3. generate sequences metrics = {} with simple_timer("generate_sequences", metrics): output: TokenOutput = await self.server_manager.generate( request_id=uuid4().hex, prompt_ids=prompt_ids, sampling_params=sampling_params, image_data=images, video_data=videos, ) if metrics.get("num_preempted") is None: metrics["num_preempted"] = output.num_preempted if output.num_preempted is not None else -1 response_mask = [1] * len(output.token_ids) output: AgentLoopOutput = AgentLoopOutput( prompt_ids=prompt_ids, response_ids=output.token_ids[: self.response_length], response_mask=response_mask[: self.response_length], response_logprobs=output.log_probs[: self.response_length] if output.log_probs else None, routed_experts=( output.routed_experts[: len(prompt_ids) + self.response_length] if output.routed_experts is not None else None ), multi_modal_data=multi_modal_data, num_turns=2, metrics=metrics, extra_fields=output.extra_fields, ) # keeping the schema consistent with tool_agent_loop output.extra_fields.update({"turn_scores": [], "tool_rewards": []}) return output ================================================ FILE: verl/experimental/agent_loop/tool_agent_loop.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import json import logging import os from enum import Enum from typing import Any, Optional from uuid import uuid4 import torch from PIL import Image from verl.experimental.agent_loop.agent_loop import ( AgentLoopBase, AgentLoopOutput, register, ) from verl.experimental.agent_loop.tool_parser import FunctionCall, ToolParser from verl.experimental.agent_loop.utils import build_gpt_oss_tool_response_text from verl.interactions.base import BaseInteraction from verl.interactions.utils.interaction_registry import initialize_interactions_from_config from verl.tools.schemas import ToolResponse from verl.tools.utils.tool_registry import initialize_tools_from_config from verl.utils.profiler import simple_timer from verl.utils.rollout_trace import rollout_trace_op from verl.workers.rollout.replica import TokenOutput logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class AgentState(Enum): PENDING = "pending" GENERATING = "generating" PROCESSING_TOOLS = "processing_tools" TERMINATED = "terminated" INTERACTING = "interacting" class AgentData: """Encapsulates all state variables for the agent loop. AgentData is passed to tool calling in case that tool may need to access full history state. User can store any tool session data in `extra_fields`.""" def __init__( self, messages: list[dict[str, Any]], image_data: list[Image.Image], video_data: list[tuple[torch.Tensor, dict[str, Any]]], metrics: dict[str, Any], request_id: str, tools_kwargs: dict[str, Any], interaction: Optional[BaseInteraction] = None, interaction_kwargs: Optional[dict[str, Any]] = None, ): self.messages = messages self.image_data = image_data self.video_data = video_data self.metrics = metrics self.request_id = request_id self.tools_kwargs = tools_kwargs self.interaction = interaction self.interaction_kwargs = interaction_kwargs or {} # State variables self.prompt_ids: list[int] = [] self.response_ids: list[int] = [] self.response_mask: list[int] = [] self.response_logprobs: list[float] = [] self.turn_scores: list[float] = [] self.tool_rewards: list[float] = [] self.user_turns = 0 self.assistant_turns = 0 # Temporary state for tool calls self.tool_calls: list[FunctionCall] = [] self.routed_experts = None # Extra fields for dynamic addition, e.g., tool session data self.extra_fields: dict[str, Any] = {} @register("tool_agent") class ToolAgentLoop(AgentLoopBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Initialize tools from config file self.max_user_turns = self.rollout_config.multi_turn.max_user_turns self.max_assistant_turns = self.rollout_config.multi_turn.max_assistant_turns self.max_parallel_calls = self.rollout_config.multi_turn.max_parallel_calls self.max_tool_response_length = self.rollout_config.multi_turn.max_tool_response_length self.tool_response_truncate_side = self.rollout_config.multi_turn.tool_response_truncate_side tool_config_path = self.rollout_config.multi_turn.tool_config_path tool_list = initialize_tools_from_config(tool_config_path) if tool_config_path else [] self.tools = {tool.name: tool for tool in tool_list} self.tool_schemas = [tool.tool_schema.model_dump(exclude_unset=True, exclude_none=True) for tool in tool_list] self.tool_parser = ToolParser.get_tool_parser(self.rollout_config.multi_turn.format, self.tokenizer) self.tool_parser_name = self.rollout_config.multi_turn.format self.prompt_length = self.rollout_config.prompt_length self.response_length = self.rollout_config.response_length # Initialize interactions from config file self.interaction_config_file = self.rollout_config.multi_turn.interaction_config_path if self.interaction_config_file: self.interaction_map: dict[str, BaseInteraction] = self._initialize_interactions( self.interaction_config_file ) @rollout_trace_op async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput: messages = list(kwargs["raw_prompt"]) # extract images and videos from messages multi_modal_data = await self.process_vision_info(messages) images = multi_modal_data.get("images") videos = multi_modal_data.get("videos") metrics = {} request_id = uuid4().hex tools_kwargs = kwargs.get("tools_kwargs", {}) # Initialize interaction if needed interaction = None interaction_kwargs = {} if self.interaction_config_file: interaction_kwargs = kwargs["extra_info"]["interaction_kwargs"] if "name" not in interaction_kwargs: raise ValueError("'name' key is required in interaction_kwargs") interaction_name = interaction_kwargs["name"] if interaction_name not in self.interaction_map: raise ValueError( f"Interaction '{interaction_name}' not found in interaction_map. Available interactions: " f"{list(self.interaction_map.keys())}" ) interaction = self.interaction_map[interaction_name] await interaction.start_interaction(request_id, **interaction_kwargs) # Create AgentData instance to encapsulate all state agent_data = AgentData( messages=messages, image_data=images, video_data=videos, metrics=metrics, request_id=request_id, tools_kwargs=tools_kwargs, interaction=interaction, interaction_kwargs=interaction_kwargs, ) # State machine loop state = AgentState.PENDING while state != AgentState.TERMINATED: if state == AgentState.PENDING: state = await self._handle_pending_state(agent_data, sampling_params) elif state == AgentState.GENERATING: state = await self._handle_generating_state(agent_data, sampling_params) elif state == AgentState.PROCESSING_TOOLS: state = await self._handle_processing_tools_state(agent_data) elif state == AgentState.INTERACTING: state = await self._handle_interacting_state(agent_data) else: logger.error(f"Invalid state: {state}") state = AgentState.TERMINATED # Finalize output response_ids = agent_data.prompt_ids[-len(agent_data.response_mask) :] prompt_ids = agent_data.prompt_ids[: len(agent_data.prompt_ids) - len(agent_data.response_mask)] multi_modal_data = {} if agent_data.image_data is not None: multi_modal_data["images"] = agent_data.image_data if agent_data.video_data is not None: multi_modal_data["videos"] = agent_data.video_data output: AgentLoopOutput = AgentLoopOutput( prompt_ids=prompt_ids, response_ids=response_ids[: self.response_length], response_mask=agent_data.response_mask[: self.response_length], multi_modal_data=multi_modal_data, response_logprobs=agent_data.response_logprobs[: self.response_length] if agent_data.response_logprobs else None, num_turns=agent_data.user_turns + agent_data.assistant_turns + 1, metrics=agent_data.metrics, routed_experts=agent_data.routed_experts, extra_fields=agent_data.extra_fields, ) output.extra_fields.update({"turn_scores": agent_data.turn_scores, "tool_rewards": agent_data.tool_rewards}) return output async def _handle_pending_state(self, agent_data: AgentData, sampling_params: dict[str, Any]) -> AgentState: """Handle the pending state: prepare the prompt and start generation.""" prompt_ids = await self.apply_chat_template( agent_data.messages, tools=self.tool_schemas, images=agent_data.image_data, videos=agent_data.video_data, ) agent_data.prompt_ids = prompt_ids return AgentState.GENERATING async def _handle_generating_state( self, agent_data: AgentData, sampling_params: dict[str, Any], ignore_termination: bool = False ) -> AgentState: """Handle the generating state: generate model response and check for tool calls.""" add_messages: list[dict[str, Any]] = [] with simple_timer("generate_sequences", agent_data.metrics): output: TokenOutput = await self.server_manager.generate( request_id=agent_data.request_id, prompt_ids=agent_data.prompt_ids, sampling_params=sampling_params, image_data=agent_data.image_data, video_data=agent_data.video_data, ) # first time to set num_preempted if agent_data.metrics.get("num_preempted") is None: agent_data.metrics["num_preempted"] = output.num_preempted if output.num_preempted is not None else -1 # then add num_preempted to the metrics else: agent_data.metrics["num_preempted"] += output.num_preempted if output.num_preempted is not None else 0 if not agent_data.extra_fields: agent_data.extra_fields.update(output.extra_fields) else: # Multi-round calls, only update the maximum max_global_steps. max_global_steps = output.extra_fields.get("max_global_steps", None) if max_global_steps: agent_data.extra_fields["max_global_steps"] = max_global_steps agent_data.assistant_turns += 1 agent_data.response_ids = output.token_ids agent_data.prompt_ids += agent_data.response_ids agent_data.response_mask += [1] * len(agent_data.response_ids) if output.log_probs: agent_data.response_logprobs += output.log_probs if output.routed_experts is not None: agent_data.routed_experts = output.routed_experts # Check termination conditions if not ignore_termination and len(agent_data.response_mask) >= self.response_length: return AgentState.TERMINATED if self.max_assistant_turns and agent_data.assistant_turns >= self.max_assistant_turns: return AgentState.TERMINATED if self.max_user_turns and agent_data.user_turns >= self.max_user_turns: return AgentState.TERMINATED # Extract tool calls tools = [tool.tool_schema for tool in self.tools.values()] _, agent_data.tool_calls = await self.tool_parser.extract_tool_calls(agent_data.response_ids, tools) # Handle interaction if needed if self.interaction_config_file: assistant_message = await self.loop.run_in_executor( None, lambda: self.tokenizer.decode(agent_data.response_ids, skip_special_tokens=True) ) add_messages.append({"role": "assistant", "content": assistant_message}) agent_data.messages.extend(add_messages) # Determine next state if agent_data.tool_calls: return AgentState.PROCESSING_TOOLS elif self.interaction_config_file: return AgentState.INTERACTING else: return AgentState.TERMINATED async def _handle_processing_tools_state(self, agent_data: AgentData) -> AgentState: """Handle the processing tools state: execute tool calls and prepare tool responses.""" add_messages: list[dict[str, Any]] = [] new_images_this_turn: list[Any] = [] # Local variable instead of agent_data attribute tasks = [] tool_call_names = [] for tool_call in agent_data.tool_calls[: self.max_parallel_calls]: tasks.append(self._call_tool(tool_call, agent_data.tools_kwargs, agent_data)) tool_call_names.append(tool_call.name) with simple_timer("tool_calls", agent_data.metrics): responses = await asyncio.gather(*tasks) # Process tool responses and update multi_modal_data # Removed: agent_data.new_images_this_turn = [] for tool_response, tool_reward, _ in responses: # Create message from tool response if tool_response.image or tool_response.video: # Multi-modal content with structured format if not getattr(self.processor, "image_processor", None): raise ValueError( "Multimedia data can only be processed by `processor`, but the processor is None. " "This error is often caused if you are using a LLM model but your tool returns multimodal " "data. Plase use a vlm as the base model." ) content = [] if tool_response.image: content.append({"type": "image"}) if tool_response.video: content.append({"type": "video"}) if tool_response.text: content.append({"type": "text", "text": tool_response.text}) message = {"role": "tool", "content": content} else: # Text-only content message = {"role": "tool", "content": tool_response.text or ""} add_messages.append(message) # Handle image data if tool_response.image: # Add new image data if isinstance(tool_response.image, list): # Ensure all elements in the list are valid image objects for img in tool_response.image: if img is not None: # Add a check to ensure the image is not None new_images_this_turn.append(img) # Using local variable else: # Ensure the image is not None if tool_response.image is not None: new_images_this_turn.append(tool_response.image) # Using local variable # Handle video data if tool_response.video: # Currently not supported, raise informative error logger.warning("Multimedia type 'video' is not currently supported. Only 'image' is supported.") raise NotImplementedError( "Multimedia type 'video' is not currently supported. Only 'image' is supported." ) if tool_reward is not None: agent_data.tool_rewards.append(tool_reward) agent_data.messages.extend(add_messages) if self.tool_parser_name == "gpt-oss": logger.info("manually format tool responses for gpt-oss") tool_response_text = build_gpt_oss_tool_response_text(add_messages, tool_call_names) response_ids = await self.loop.run_in_executor( None, lambda: self.tokenizer.encode(tool_response_text, add_special_tokens=False) ) else: # Note that we have to pass None to the images and videos if there are no new images / videos # to stay compatible with downstream image processing logic! images = new_images_this_turn if new_images_this_turn else None videos = None response_ids = await self.apply_chat_template( add_messages, images=images, videos=videos, remove_system_prompt=True, ) if len(agent_data.response_mask) + len(response_ids) >= self.response_length: return AgentState.TERMINATED # Update prompt_ids and response_mask if new_images_this_turn: if agent_data.image_data is None: agent_data.image_data = [] elif not isinstance(agent_data.image_data, list): agent_data.image_data = [agent_data.image_data] for img in new_images_this_turn: agent_data.image_data.append(img) agent_data.prompt_ids += response_ids agent_data.response_mask += [0] * len(response_ids) if agent_data.response_logprobs: agent_data.response_logprobs += [0.0] * len(response_ids) agent_data.user_turns += 1 return AgentState.GENERATING async def _handle_interacting_state(self, agent_data: AgentData) -> AgentState: """Handle the interacting state: get user input from interaction.""" ( should_terminate_sequence, interaction_responses, reward, metrics, ) = await agent_data.interaction.generate_response( agent_data.request_id, agent_data.messages, **agent_data.interaction_kwargs ) agent_data.user_turns += 1 add_messages: list[dict[str, Any]] = [{"role": "user", "content": interaction_responses}] agent_data.messages.extend(add_messages) if reward is not None: agent_data.turn_scores.append(reward) # Update prompt with user responses (similar to _handle_processing_tools_state) response_ids = await self.apply_chat_template( add_messages, remove_system_prompt=True, ) # Update prompt_ids and response_mask agent_data.prompt_ids += response_ids agent_data.response_mask += [0] * len(response_ids) if agent_data.response_logprobs: agent_data.response_logprobs += [0.0] * len(response_ids) # double check prompt # Check termination condition if should_terminate_sequence: return AgentState.TERMINATED else: return AgentState.GENERATING async def _call_tool( self, tool_call: FunctionCall, tools_kwargs: dict[str, Any], agent_data: AgentData ) -> tuple[ToolResponse, float, dict]: """Call tool and return tool response.""" tool, instance_id = None, None try: # TODO: append malformed tool_call to the prompt: invalid function name or arguments tool_name = tool_call.name tool_args = json.loads(tool_call.arguments) tool = self.tools[tool_name] kwargs = tools_kwargs.get(tool_name, {}) instance_id, _ = await tool.create(create_kwargs=kwargs.get("create_kwargs", {})) tool_execution_response, tool_reward, res = await tool.execute( instance_id, tool_args, agent_data=agent_data ) except Exception as e: logger.warning(f"Error when executing tool: {e}") return ( ToolResponse( text=f"Error when executing tool: {e}", ), 0.0, {}, ) finally: if tool and instance_id: await tool.release(instance_id) tool_response_text = tool_execution_response.text if tool_response_text and len(tool_response_text) > self.max_tool_response_length: if self.tool_response_truncate_side == "left": tool_response_text = tool_response_text[: self.max_tool_response_length] + "...(truncated)" elif self.tool_response_truncate_side == "right": tool_response_text = "(truncated)..." + tool_response_text[-self.max_tool_response_length :] else: length = self.max_tool_response_length // 2 tool_response_text = tool_response_text[:length] + "...(truncated)..." + tool_response_text[-length:] # Create ToolResponse from tool execution result tool_response_kwargs = {"text": tool_response_text} # Add multimedia data if present for attr_name in ["image", "video"]: if hasattr(tool_execution_response, attr_name): attr_value = getattr(tool_execution_response, attr_name) if attr_value is not None: tool_response_kwargs[attr_name] = attr_value return ToolResponse(**tool_response_kwargs), tool_reward, res def _initialize_interactions(self, interaction_config_file): """Initialize interactions from configuration. Returns: dict[str, BaseInteraction]: A dictionary mapping interaction names to interaction instances. """ if interaction_config_file is None: return {} interaction_map = initialize_interactions_from_config(interaction_config_file) return interaction_map ================================================ FILE: verl/experimental/agent_loop/tool_parser.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import os from abc import ABC, abstractmethod from typing import Any, Optional import regex from pydantic import BaseModel from verl.tools.schemas import OpenAIFunctionToolSchema from verl.utils.ray_utils import get_event_loop from verl.utils.rollout_trace import rollout_trace_op logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class FunctionCall(BaseModel): arguments: str """ The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. """ name: str """The name of the function to call.""" class ToolParser(ABC): _registry: dict[str, type["ToolParser"]] = {} def __init__(self, tokenizer) -> None: self.tokenizer = tokenizer @abstractmethod async def extract_tool_calls( self, responses_ids: list[int], tools: list[OpenAIFunctionToolSchema] = None ) -> tuple[str, list[FunctionCall]]: """Extract tool calls from the responses. Args: responses_ids (List[int]): The ids of the responses. tools (List[OpenAIFunctionToolSchema], optional): OpenAI function tool schema. Returns: Tuple[str, List[FunctionCall]]: Content and extracted tool calls. """ raise NotImplementedError @classmethod def get_tool_parser(cls, name: str, tokenizer): if name not in cls._registry: raise ValueError(f"Unknown tool parser: {name}") return cls._registry[name](tokenizer) @classmethod def register(cls, name: str): def decorator(subclass: type[ToolParser]) -> type[ToolParser]: cls._registry[name] = subclass return subclass return decorator @ToolParser.register("hermes") class HermesToolParser(ToolParser): """Adapted from https://github.com/vllm-project/vllm/blob/v0.9.1/vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py""" def __init__(self, tokenizer) -> None: super().__init__(tokenizer) self.tool_call_start_token: str = "" self.tool_call_end_token: str = "" self.tool_call_regex = regex.compile(r"(.*?)", regex.DOTALL) @rollout_trace_op async def extract_tool_calls( self, responses_ids: list[int], tools: list[OpenAIFunctionToolSchema] = None ) -> tuple[str, list[FunctionCall]]: loop = get_event_loop() text = await loop.run_in_executor(None, self.tokenizer.decode, responses_ids) if self.tool_call_start_token not in text or self.tool_call_end_token not in text: return text, [] matches = self.tool_call_regex.findall(text) function_calls = [] for match in matches: try: function_call = json.loads(match) name, arguments = function_call["name"], function_call["arguments"] function_calls.append(FunctionCall(name=name, arguments=json.dumps(arguments, ensure_ascii=False))) except Exception as e: logger.error(f"Failed to decode tool call: {e}") # remaing text exclude tool call tokens content = self.tool_call_regex.sub("", text) return content, function_calls @ToolParser.register("gpt-oss") class GptOssToolParser(ToolParser): """ Tool parser for gpt-oss model. Adapted from https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/function_call/gpt_oss_detector.py Args: tokenizer: The tokenizer to use. """ def __init__(self, tokenizer) -> None: super().__init__(tokenizer) # check https://cookbook.openai.com/articles/openai-harmony for more details. self.cot_pattern = regex.compile( r"<\|start\|>assistant<\|channel\|>analysis<\|message\|>.*?<\|end\|>", regex.DOTALL ) # <|start|>assistant may be pre-appended in prompts, so we need to remove it. self.partial_cot_pattern = regex.compile(r"<\|channel\|>analysis<\|message\|>(.*?)<\|end\|>", regex.DOTALL) self.tool_call_pattern = regex.compile( r"<\|start\|>assistant<\|channel\|>[^<]* to=functions\.([^<]+) " r"<\|constrain\|>json<\|message\|>(.*?)<\|call\|>", regex.DOTALL, ) @rollout_trace_op async def extract_tool_calls( self, responses_ids: list[int], tools: list[OpenAIFunctionToolSchema] = None ) -> tuple[str, list[FunctionCall]]: loop = get_event_loop() # We need to keep special tokens for gpt-oss model for better tool call extraction. text = await loop.run_in_executor(None, lambda: self.tokenizer.decode(responses_ids, skip_special_tokens=False)) # Need to remove padding tokens for better tool call extraction. text = text.replace(self.tokenizer.pad_token, "") # Need to reomve COT since COT may contain tool call tokens.But they are not valid tool calls. text = regex.sub(self.cot_pattern, "", text) text = regex.sub(self.partial_cot_pattern, "", text) # check if there are tool calls in the text by re.findall matches = regex.findall(self.tool_call_pattern, text) if not matches: return text, [] function_calls = [] for match in matches: try: name, arguments = match[0], match[1] # don't check if arguments is valid JSON and leave it to client function_calls.append(FunctionCall(name=name, arguments=arguments)) except Exception as e: logger.error(f"Failed to decode tool call: {e}") # remaing text exclude tool call tokens content = regex.sub(self.tool_call_pattern, "", text) return content, function_calls @ToolParser.register("qwen3_coder") class Qwen3XMLToolParser(ToolParser): """ Tool parser for qwen3_coder/qwen3.5 model. Adapted from https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/qwen3coder_tool_parser.py Args: tokenizer: The tokenizer to use. """ def __init__(self, tokenizer): super().__init__(tokenizer) self.tool_call_start_token: str = "" self.tool_call_end_token: str = "" self.tool_call_prefix: str = "(.*?)", regex.DOTALL) self.tool_call_regex = regex.compile(r"(.*?)|(.*?)$", regex.DOTALL) self.tool_call_function_regex = regex.compile(r"|| FunctionCall: def get_arguments_config(func_name: str) -> dict: for config in tools: if config.type == "function" and config.function.name == func_name: properties = config.function.parameters.properties return {k: v.model_dump() for k, v in properties.items()} logger.warning(f"Tool '{func_name}' is not defined in the tools list.") return {} def convert_param_value(param_value: str, param_name: str, param_config: dict, func_name: str) -> Any: # Handle null value for any type if param_value.lower() == "null": return None if param_name not in param_config: if param_config != {}: logger.warning( f"Parsed parameter '{param_name}' is not defined in the tool " f"parameters for tool '{func_name}', directly returning the string value." ) return param_value if isinstance(param_config[param_name], dict) and "type" in param_config[param_name]: param_type = str(param_config[param_name]["type"]).strip().lower() else: param_type = "string" if param_type in ["string", "str", "text", "varchar", "char", "enum"]: return param_value elif ( param_type.startswith("int") or param_type.startswith("uint") or param_type.startswith("long") or param_type.startswith("short") or param_type.startswith("unsigned") ): try: param_value = int(param_value) except Exception: logger.warning( f"Parsed value '{param_value}' of parameter '{param_name}' is not an integer in tool " f"'{func_name}', degenerating to string." ) return param_value elif param_type.startswith("num") or param_type.startswith("float"): try: float_param_value = float(param_value) param_value = ( float_param_value if float_param_value - int(float_param_value) != 0 else int(float_param_value) ) except Exception: logger.warning( f"Parsed value '{param_value}' of parameter '{param_name}' is not a float in tool " f"'{func_name}', degenerating to string." ) return param_value elif param_type in ["boolean", "bool", "binary"]: param_value = param_value.lower() if param_value not in ["true", "false"]: logger.warning( f"Parsed value '{param_value}' of parameter '{param_name}' is not a " f"boolean (`true` of `false`) in tool '{func_name}', degenerating to false." ) return param_value == "true" else: if param_type == "object" or param_type.startswith("dict"): try: param_value = json.loads(param_value) return param_value except Exception: logger.warning( f"Parsed value '{param_value}' of parameter '{param_name}' is not a valid " f"JSON object in tool '{func_name}', will try other methods to parse it." ) try: param_value = eval(param_value) except Exception: logger.warning( f"Parsed value '{param_value}' of parameter '{param_name}' cannot be converted " f"via Python `eval()` in tool '{func_name}', degenerating to string." ) return param_value # Extract function name end_index = function_call_str.index(">") function_name = function_call_str[:end_index] param_config = get_arguments_config(function_name) parameters = function_call_str[end_index + 1 :] param_dict = {} for match in self.tool_call_parameter_regex.findall(parameters): match_text = match[0] if match[0] else match[1] idx = match_text.index(">") param_name = match_text[:idx] param_value = str(match_text[idx + 1 :]) # Remove prefix and trailing \n if param_value.startswith("\n"): param_value = param_value[1:] if param_value.endswith("\n"): param_value = param_value[:-1] param_dict[param_name] = convert_param_value(param_value, param_name, param_config, function_name) return FunctionCall(name=function_name, arguments=json.dumps(param_dict, ensure_ascii=False)) def _get_function_calls(self, model_output: str) -> list[str]: # Find all tool calls matched_ranges = self.tool_call_regex.findall(model_output) raw_tool_calls = [match[0] if match[0] else match[1] for match in matched_ranges] # Back-off strategy if no tool_call tags found if len(raw_tool_calls) == 0: raw_tool_calls = [model_output] raw_function_calls = [] for tool_call in raw_tool_calls: raw_function_calls.extend(self.tool_call_function_regex.findall(tool_call)) function_calls = [match[0] if match[0] else match[1] for match in raw_function_calls] return function_calls @rollout_trace_op async def extract_tool_calls( self, responses_ids: list[int], tools: list[OpenAIFunctionToolSchema] = None ) -> tuple[str, list[FunctionCall]]: loop = get_event_loop() text = await loop.run_in_executor(None, self.tokenizer.decode, responses_ids) if self.tool_call_start_token not in text: return text, [] try: function_calls = self._get_function_calls(text) if len(function_calls) == 0: return text, [] tool_calls = [ self._parse_xml_function_call(function_call_str, tools) for function_call_str in function_calls ] # Extract content before tool calls content_index = text.find(self.tool_call_start_token) content_index = content_index if content_index >= 0 else text.find(self.tool_call_prefix) content = text[:content_index] # .rstrip() return content, tool_calls except Exception as e: logger.exception(f"Error in extracting tool call from response: {e}") return text, [] ================================================ FILE: verl/experimental/agent_loop/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from typing import Any def resolve_config_path(config_path: str) -> str: """Resolve agent loop configuration file path. In multi-node Ray training, relative paths may not resolve correctly because the working directory on remote nodes can differ from the driver node. This function resolves relative paths by checking multiple locations in order: 1. If already absolute, return as-is 2. Try current working directory 3. Try relative to verl package installation (project root) Args: config_path: Configuration file path (relative or absolute) Returns: Absolute path to the configuration file Raises: FileNotFoundError: If the configuration file cannot be found """ # Return absolute paths unchanged if os.path.isabs(config_path): return config_path # Try current working directory first cwd = os.path.abspath(os.getcwd()) cwd_path = os.path.abspath(os.path.join(cwd, config_path)) if (cwd_path == cwd or cwd_path.startswith(cwd + os.sep)) and os.path.exists(cwd_path): return cwd_path # Try relative to verl project root (where verl package is installed) try: import verl verl_package_dir = os.path.abspath(os.path.dirname(verl.__file__)) # Strategy 1: For development/editable installs. project_root = os.path.dirname(verl_package_dir) dev_path = os.path.abspath(os.path.join(project_root, config_path)) if (dev_path == project_root or dev_path.startswith(project_root + os.sep)) and os.path.exists(dev_path): return dev_path # Strategy 2: For standard package installations. install_path = os.path.abspath(os.path.join(verl_package_dir, config_path)) if (install_path == verl_package_dir or install_path.startswith(verl_package_dir + os.sep)) and os.path.exists( install_path ): return install_path except (ImportError, AttributeError): pass # verl not installed or __file__ not available # File not found - raise clear error raise FileNotFoundError( f"Agent loop configuration file not found: {config_path}. Tried current directory and verl project root." ) # tokenizer.apply_chat_template is not working properly for gpt-oss model. # Because the chat template requires tool call messages to parse tool response messages # so we need to format the tool response manually. def format_gpt_oss_tool_response_manually(tool_response: str, tool_call_name: str) -> str: """Format tool response for gpt-oss model. Args: tool_response: Tool response string tool_call_name: Name of the tool that was called Returns: Formatted tool response string """ return f"<|start|>functions.{tool_call_name} to=assistant<|channel|>commentary<|message|>{tool_response}<|end|>" def add_generation_prompt_for_gpt_oss(message_content: str) -> str: """Add generation prompt for gpt-oss model. Args: message_content: Message content string Returns: Message content string with generation prompt """ return message_content + "<|start|>assistant" def build_gpt_oss_tool_response_text(messages: list[dict[str, Any]], tool_call_names: list[str]) -> str: """Build gpt-oss tool response text (manual formatting + generation prompt).""" tool_response_texts: list[str] = [] for i, tool_msg in enumerate(messages): actual_tool_name = tool_call_names[i] formatted = format_gpt_oss_tool_response_manually(tool_msg["content"], actual_tool_name) tool_response_texts.append(formatted) return add_generation_prompt_for_gpt_oss("".join(tool_response_texts)) ================================================ FILE: verl/experimental/dataset/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/dataset/sampler.py ================================================ # Copyright 2025 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import abstractmethod from collections.abc import Sized from omegaconf import DictConfig from torch.utils.data import Sampler from verl import DataProto class AbstractSampler(Sampler[int]): """Abstract interface for custom samplers.""" @abstractmethod def __init__( self, data_source: Sized, data_config: DictConfig, ): pass class AbstractCurriculumSampler(AbstractSampler): """Experimental interface for curriculum learning samplers.""" @abstractmethod def update(self, batch: DataProto) -> None: pass ================================================ FILE: verl/experimental/dynamic_dataset/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/dynamic_dataset/dynamicgen_dataset.py ================================================ # Copyright 2025 Amazon.com Inc and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Dataset class that enables dynamic data generation strategies between iterations of training. This class extends RLHFDataset and uses an AbstractDataGen instance to generate data. This is especially useful in settings where proposer model generates new tasks based on rollout data. """ import logging from abc import ABC, abstractmethod from typing import Optional import datasets from omegaconf import DictConfig from torch.utils.data import Dataset from transformers import PreTrainedTokenizer, ProcessorMixin from verl import DataProto from verl.utils.dataset import RLHFDataset from verl.utils.import_utils import load_extern_object logger = logging.getLogger(__name__) class AbstractDataGenerator(ABC): def __init__(self, config: DictConfig): self.config = config @abstractmethod def generate(self, dataset: Dataset) -> datasets.Dataset: """ Generate method must be implemented by subclasses. Args: dataset: The dataset to generate from. Returns: Processed data or result as implemented by the subclass. """ pass class MockDataGenerator(AbstractDataGenerator): """ A noop data gen class that only reappends the first datapoint. This class is useful as a placeholder and testing. """ def __init__(self, config: DictConfig = None): super().__init__(config) def generate(self, dataset: Dataset) -> datasets.Dataset: print("MockDataGenerator: No operation performed on the dataset.") return dataset.dataframe.select([0]) class DynamicGenDataset(RLHFDataset): """ A dataset class that uses a data generation strategy to process data. This class extends RLHFDataset and uses an AbstractDataGen instance to generate data. """ def __init__( self, data_files: str | list[str], tokenizer: PreTrainedTokenizer, config: DictConfig, processor: Optional[ProcessorMixin] = None, ): super().__init__(data_files, tokenizer, config, processor) self.datagen: AbstractDataGenerator = config.datagen assert "datagen" in config and config.datagen.get("path", None) is not None, ( f"datagen path is not set in config: {config}" ) # Dynamically load the custom datagen class datagen_cls = load_extern_object(config.datagen.path, config.datagen.name) # Verify that the custom datagen class inherits from AbstractDataGenerator abs_cls = AbstractDataGenerator if not issubclass(datagen_cls, abs_cls): raise TypeError( f"The custom datagen class '{config.datagen.name}' from '{config.datagen.path}'" + " must inherit from {abs_cls}" ) self.data_generator = datagen_cls(config.datagen) self.on_batch_end() def append_dataframe(self, new_dataframe: datasets.Dataset): new_dataframe = self.maybe_filter_out_long_prompts(new_dataframe) self.dataframe = datasets.concatenate_datasets([self.dataframe, new_dataframe]) logger.info(f"new dataset len: {len(self.dataframe)}") def on_batch_end(self, batch: DataProto) -> None: """ Generate data using the provided data generation strategy. Note: This method is intended to change the dataset after each training batch. """ new_data = self.data_generator.generate(self) self.append_dataframe(new_data) ================================================ FILE: verl/experimental/fully_async_policy/README.md ================================================ # Recipe: Fully Async Policy Trainer **Author:** `https://github.com/meituan-search` Last updated: 02/05/2026. This document introduces a fully asynchronous PPO training system that completely decouples the Trainer and Rollouter, supporting asynchronous sample generation and training. Under this system, we achieved a 2.35x-2.67x performance improvement when training the Qwen2.5-7B model with 128 GPUs, without significantly affecting the results. ## Introduction ### Background The separated rollout and train architecture, compared to the colocate architecture, can allocate resources more flexibly and design more flexible training logic, thereby addressing issues such as low GPU utilization and training efficiency caused by long-tail problems. The one_step_off_policy alleviates the problem of long rollout times and achieves some gains in training efficiency by designing a separated architecture and performing asynchronous training between rollout and train for one round. However, it forcibly uses data from one round of asynchronous training, which is not flexible enough and cannot completely eliminate the impact of long-tail on training efficiency. In other frameworks such as AReaL, Magistral, StreamRL, and AsyncFlow, asynchronous training and streaming training have been implemented based on the separated architecture and have achieved gains. We borrow from their methods and implemented them in VERL. The fully_async_policy supports asynchronous, streaming, and partial rollout training. By reasonably setting parameters such as resource allocation and parameter synchronization frequency, fully_async_policy can significantly improve training efficiency. > Magistral https://arxiv.org/abs/2506.10910 > > AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language > Reasoning https://arxiv.org/abs/2505.24298 > > StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream > Generation https://arxiv.org/abs/2504.15930 > > AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training https://arxiv.org/abs/2507.01663 > ### Core Contributions * **Resource Isolation**: Unlike using hybrid_engine, Rollouter and Trainer use separate computing resources and need to specify the resources they occupy separately. * **Parallel Generation and Training**: While the Trainer is training, the Rollouter is generating new samples. * **Multi-step Asynchronous**: Compared to one step off policy, it supports asynchronous settings from 0.x steps to multiple steps, making the asynchronous solution more flexible. * **NCCL Parameter Synchronization**: Based on the nccl communication primitive, refer to [checkpoint-engine](https://github.com/MoonshotAI/checkpoint-engine) to achieve efficient parameter synchronization between Rollouter and Trainer. * **Stream Inference and Training**: Rollouter generates data sample by sample, and data transmission uses a single sample as the minimum transmission unit. * **Asynchronous Training and Freshness Control**: By setting the parameter async_training.staleness_threshold, it supports training with samples generated by old parameters. * **PartialRollout**: The Rollouter's inference process supports partial rollout logic. During parameter synchronization, by adding `sleep() and resume()` logic, it saves samples from ongoing rollouts and continues using them in the next rollout, reducing the time spent waiting for ongoing tasks to finish during parameter synchronization. Currently, the supported usage mode is megatron/fsdp+vllm. vllm must use the server mode based on AgentLoop. ## Design The overall architecture of fully_async_policy is shown in the figure below. fully_async_policy mainly consists of four parts: Rollouter, MessageQueue, Trainer, and ParameterSynchronizer. ![fully_async_policy_structure]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_structure.svg?raw=true) 1. Rollouter generates sequences sample by sample and puts the generated samples into the MessageQueue, with the production speed controlled by freshness. 2. MessageQueue is used to temporarily store samples generated by Rollouter. 3. Trainer fetches samples from MessageQueue sample by sample. After fetching `require_batches*ppo_mini_batch_size` samples, it will perform training. After training for async_training.trigger_parameter_sync_step rounds, it triggers a parameter synchronization with Rollouter. 4. ParameterSynchronizer implements the NCCL synchronous parameter synchronization capability. The source of benefits compared to the base scheme lies in the fact that in the colocate case, using more resources for rollout cannot solve the idleness caused by long-tail samples. After we perform resource isolation, the time for rollout and train may be longer than before (because fewer resources are used), but the overlap in their time consumption reduces the end-to-end time consumption. ![fully_async_policy_revenue]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_revenue.svg?raw=true) ## Usage ### Parameter Description | super params | implication | |------------------------------------------------------------------|------------------------------------------------------------------------------------------------| | `trainer.nnodes` | Number of nodes for Trainer | | `trainer.n_gpus_per_node` | Number of GPUs per node for Trainer | | `rollout.nnodes` | Number of nodes for Rollouter | | `rollout.n_gpus_per_node` | Number of GPUs per node for Rollouter | | `data.train_batch_size` | In the fully async strategy, this value is not effective (default is 0) | | `data.gen_batch_size` | In the fully async strategy, uses streaming sample production logic (default is 1) | | `rollout.total_rollout_steps` | Total number of rollout samples | | `rollout.test_freq` | How many times Rollouter updates parameters before performing a validation | | `actor_rollout_ref.actor.ppo_mini_batch_size` | The ppo_mini_batch_size is a global num across all workers/gpus | | `actor_rollout_ref.actor.use_rollout_log_probs=True` | Use log_probs generated by rollout | | `algorithm.rollout_correction.bypass_mode` | Whether to compute log_prob using the training model's parameters during the training phase. | | `async_training.require_batches` | Number of ppo_mini_batch_size that FullyAsyncTrainer fetches at once | | `async_training.trigger_parameter_sync_step` | Indicates how many local updates FullyAsyncTrainer performs before a parameter synchronization | | `async_training.staleness_threshold` | Freshness control | | `async_training.partial_rollout` | Whether to perform partial_rollout | | `async_training.use_trainer_do_validate` | Whether use trainer node to do validate process, default `False` | **Further Explanation:** * `rollout.total_rollout_steps` Compared to colocate, the quantity can be aligned by multiplying train_batch_size and step: `rollout.total_rollout_steps = data.train_batch_size * step`. * `async_training.trigger_parameter_sync_step` In the fully async strategy, it indicates how many local updates the Trainer performs (i.e., how many times it fetches `require_batches * ppo_mini_batch_size` samples) before a parameter synchronization with Rollouter. Between every two parameter synchronizations between Rollouter and Trainer, the Trainer will process `trigger_parameter_sync_step* require_batches*ppo_mini_batch_size` samples. To fairly compare speed with colocate, `trigger_parameter_sync_step` should be set to `data.train_batch_size / (require_batches * ppo_mini_batch_size)`. * `async_training.staleness_threshold` In the fully async strategy, it indicates the maximum proportion of stale samples allowed to be used. * `staleness_threshold`=0, indicates synchronous training. Rollouter will generate a fixed number of samples between two parameter updates, the sample count is: `rollout_num = (trigger_parameter_sync_step*require_batches*ppo_mini_batch_size)` * `staleness_threshold`>0, indicates asynchronous training, can be set to a decimal for more flexible asynchronous calls. Rollouter will generate at most the following number of samples between two parameter updates: `rollout_num = (1+staleness_threshold)*(trigger_parameter_sync_step*require_batches*ppo_mini_batch_size) - num_staleness_sample` `num_staleness_sample` represents the number of stale samples generated in excess during the last rollout. Since it's a streaming system, rollout continues to generate and trainer continues to consume. If rollouter is slower, trainer will trigger parameter synchronization earlier, and rollouter will not actually produce rollout_num samples. When rollout is fast enough, setting `staleness_threshold` to 1 is basically equivalent to one_step_off policy. To avoid too many expired samples affecting training accuracy, it is recommended to set this value to less than 1. * `async_training.partial_rollout` partial_rollout only actually takes effect when staleness_threshold>0. * `async_training.require_batches` In streaming training, require_batches should be set to 1, indicating that training is performed after producing enough ppo_mini_batch_size samples. In actual testing, we found that if fewer samples are issued at once, due to the order of data distribution, it can cause training instability and longer response lengths. Here, we additionally provide require_batches for streaming distribution and control the number of samples participating in training at once. * `actor_rollout_ref.actor.use_rollout_log_probs=True` In reinforcement learning algorithms, log_probs have implicit correlations with parameter versions and tokens. Due to the settings of algorithms like PPO/GRPO/DAPO, when calculating importance sampling, old_log_prob must use the log_probs corresponding to the rollout parameters and tokens to ensure algorithm correctness. In the fully async strategy, we default to old_log_prob being calculated by rollout rather than by trainer. * `algorithm.rollout_correction.bypass_mode` > algorithm.rollout_correction.bypass_mode default is True, using rollout log prob. During the training process, we observed that metrics and response lengths may become unstable in the later stages of training. To mitigate this issue, we can use the [Rollout Importance Sampling](https://verl.readthedocs.io/en/latest/advance/rollout_is.html) technique for importance sampling. To utilize Rollout Importance Sampling, we need to compute log_prob using the training engine, which requires enabling this switch. Additionally, when `algorithm.rollout_correction.bypass_mode=False` and Rollout Importance Sampling are enabled under mode d (async stream pipeline with partial rollout), our implementation approximates `Areal's Decoupled PPO`. * `async_training.use_trainer_do_validate` It controls whether to use the trainer's `do_validate` method for validation. If set to True, the trainer will perform validation after each parameter update. It can reduce the validation time overhead and trainer node idle time. If set to False, the trainer will not perform validation. ### Supported Modes 1. on policy pipeline: 1. **trigger_parameter_sync_step=1, staleness_threshold=0** 2. Rollouter produces `require_batches*ppo_mini_batch_size` samples at once, Trainer fetches these samples for training, and after training completes, Trainer and Rollouter perform a parameter synchronization; 3. During the rollout phase, if there are long-tail samples but few rollout samples, shorter samples cannot fill idle resources, causing some resource waste. 4. As shown in figure a; 2. stream off policy pipeline: 1. **trigger_parameter_sync_step>1, staleness_threshold=0** 2. Synchronous streaming training will be performed. Rollouter produces `require_batches*ppo_mini_batch_size*trigger_parameter_sync_step` samples at once, Trainer performs a local training every time it fetches `require_batches*ppo_mini_batch_size` samples, and after training trigger_parameter_sync_step times, Trainer and Rollouter perform a parameter synchronization; 3. Compared to a, since more samples are generated at once, resource idleness will be lower. 4. In one step training, there will be two periods of resource idleness: when fetching the first batch of samples, train waits for `require_batches*ppo_mini_batch_size` samples to be produced, and during the last parameter update, rollout waits for training to complete. 5. As shown in figure b; 3. async stream pipeline with stale samples: 1. **trigger_parameter_sync_step>=1, staleness_threshold>0, partial_rollout=False** 2. After each parameter update, Rollouter will plan to produce at most rollout_num samples (in practice, the number of samples generated may be less than this value depending on rollout speed). 3. If the rollout process is relatively fast, Rollouter will generate some additional samples num_stale_samples before parameter synchronization for immediate use by Trainer after synchronization. When triggering parameter synchronization, if Rollouter has ongoing tasks, it will wait for the tasks to complete and not add new tasks; 4. Compared to b, except for the first step training, subsequent training will not have the time to wait for the first batch rollout to finish, but will have the time to wait for active tasks to finish. 5. As shown in figure c; 4. async stream pipeline with partial rollout: 1. **trigger_parameter_sync_step>=1, staleness_threshold>0, partial_rollout=True** 2. Compared to c, when triggering parameter synchronization, if Rollouter has samples being produced, it will interrupt the rollout process and perform parameter synchronization. The interrupted samples will continue to be generated after synchronization. This reduces the time to wait for active tasks to finish. 3. As shown in figure d; ![fully_async_policy_mode]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_mode.svg?raw=true) ### Key Metrics | metrics | implication | |------------------------------------------------|--------------------------------------------------------------------------------------------------------| | `trainer/idle_ratio` | Trainer idle rate | | `rollouter/idle_ratio` | Rollouter idle rate | | `fully_async/count/stale_samples_processed` | Total number of old samples used in training | | `fully_async/count/stale_trajectory_processed` | Total number of old trajectories used in training (one sample produces rollout.n trajectories) | | `fully_async/partial/total_partial_num` | Number of partial samples processed by Trainer between two trigger_parameter_sync_step | | `fully_async/partial/partial_ratio` | Ratio of partial samples processed by Trainer between two trigger_parameter_sync_step | | `fully_async/partial/max_partial_span` | Maximum parameter span of partial samples processed by Trainer between two trigger_parameter_sync_step | ### Parameter Tuning Recommendations * Resource Allocation and Adjustment: * Reasonable resource allocation is the prerequisite for achieving good training efficiency. The ideal resource allocation should make the rollout time and train time close, thereby minimizing pipeline bubbles in the entire training process, avoiding resource idleness, and ensuring Trainer does not use old samples. In real training scenarios, resource allocation can be adjusted based on the idle time of rollout and train during actual training, which can be obtained from rollouter/idle_ratio and trainer/idle_ratio. If rollouter/idle_ratio is high and trainer/idle_ratio is low, Trainer resources should be increased and Rollouter resources should be reduced, and vice versa. * Key Parameters: * staleness_threshold: Setting it too high will cause more old samples to be used, affecting model performance. It is recommended to set it to less than 1. * require_batches: The closer to 1, the closer to a pure streaming process, the smaller the training bubbles, and the faster the acceleration effect that can be achieved in terms of speed, but it will affect the order of sample processing; * trigger_parameter_sync_step: The smaller the setting, the closer to on policy, but it will cause frequent parameter synchronization. Long-tail samples waste resources that cannot be filled by short samples, resulting in low resource utilization. The larger the setting, the higher the computational efficiency, but the accuracy will be affected by off policy. * rollout.test_freq: It will occupy Rollouter resources and is not recommended to be set too small. * Mode Selection: By adjusting different parameters, the Fully Async architecture supports optimization acceleration at different levels, suitable for tasks in different scenarios. * For small-scale tasks that need to ensure training stability and on-policy nature, and have low speed requirements, the on policy pipeline mode (Mode 1) can be tried. * For scenarios that need to improve training throughput but are sensitive to staleness, the stream off policy pipeline mode can be tried. That is, by setting trigger_parameter_sync_step>1 to improve training efficiency, but still maintaining the synchronization mechanism (staleness_threshold=0) (Mode 2). * For large-scale tasks with high training speed requirements and can tolerate a certain degree of off-policy and staleness, setting staleness_threshold> 0 and partial_rollout=True can improve training efficiency, using the async stream pipeline mode (Mode 3 or 4). ### Quick Start ```shell rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=10 staleness_threshold=0 trigger_parameter_sync_step=16 partial_rollout=False python -m recipe.fully_async_policy.fully_async_main \ train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.actor.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ rollout.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.partial_rollout="${partial_rollout}" ``` ## Experiments ### Asynchronous Training on 7B Model We used Qwen2.5-Math-7B to verify the benefits of the fully async strategy under long candidates and multiple resources. Using the `async stream pipeline with stale samples` strategy, we achieved about 2x performance improvement on 32 cards, 64 cards, and 128 cards without significantly affecting experimental results. * Machine: H20 * Model: Qwen2.5-Math-7B * Rollout length: max_response_length FSDP2: 28K tokens; * Algorithm: DAPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+FSDP2 * rollout.n: 16 * ppo_mini_batch_size: 32 * test_freq: 20 * colocate sync: * step: 400 * train_batch_size: 512 * fully_async_policy * total_rollout_steps: 512*400 * require_batches: 4 * trigger_parameter_sync_step: 4 * staleness_threshold: 0.5 * partial_rollout: True | training mode | resource allocation | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:--------------------:|:---------------------:|:--------:|:--------:|:--------------:|:---------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:-------------------------------:| | colocate sync | 32 | 790.10 | 357.41 | 107.71 | 269.80 | 13h 44m | 1d 3h 43m | 2d 9h 22m | 3d 17h 5m | max: 0.3313
last: 0.2448 | | fully_async_policy | 16:16 | 294.77 | 21.26 | \ | 313.81 | 7h 58m
(1.72x) | 16h 21m
(1.70x) | 1d 0h 53m
(2.31x) | 1d 9h 26m
(2.66x) | max: 0.3302
last: 0.2333 | | colocate sync | 64 | 365.28 | 150.72 | 70.26 | 133.41 | 10h 22m | 20h 45m | 1d 7h 6m | 1d 17h 32m | max: 0.3365
last: 0.2333 | | fully_async_policy | 32:32 | 189.26 | 28.46 | \ | 156.98 | 4h 57m
(2.09x) | 10h 14m
(2.03x) | 16h 58m
(1.83x) | 21h 40m
(1.92x) | max: 0.3677
last: 0.3406 | | colocate sync | 128 | 356.30 | 177.85 | 53.92 | 113.81 | 8h 36m | 17h 56m | 1d 5h 6m | 1d 16h 48m | max: 0.3573
last: 0.2958 | | fully_async_policy | 64:64 | 150.63 | 33.14 | \ | 113.16 | 3h 13m
(2.67x) | 6h 46m
(2.65x) | 10h 53m
(2.67x) | 17h 22m
(2.35x) | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-colocate_async?nw=nwuserhouzg ### 128-card 7B Asynchronous Mode Experiment We used Qwen2.5-Math-7B to verify the effects of various modes supported by fully async. We can see that the benefit brought by streaming is approximately 1.6x, and after combining staleness and partial_rollout, the benefit reaches 2.35x. | mode | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:-------------------------------------------------------------------------------------------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------------:| | colocate sync | 356.30 | 177.85 | 53.92 | 113.81 | 8h 36m | 17h 56m | 1d 5h 6m | 1d 16h 48m | max: 0.3573
last: 0.2958 | | `stream off policy pipeline`
(+fully async: trigger_parameter_sync_step= 4,
require_batches= 4) | 231.34 | 128.47 | \ | 98.77 | 4h 25m | 9h 41m | 15h 2m | 1d 1h 53m | max: 0.2844
last: 0.2604 | | `async stream pipeline with stale samples`
(+staleness_threshold=0.5) | | | | | | | | | | | `async stream pipeline with partial rollout`
(+partial_rollout=True) | 150.63 | 33.14 | \ | 113.16 | 3h 13m | 6h 46m | 10h 53m | 17h 22m | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-stream_stale_partial?nw=nwuserhouzg ### 128-card Stale Ablation Experiment Under the `async stream pipeline with partial rollout` mode, we verified the impact of staleness settings on training efficiency. We found that the larger the staleness, the more obvious the final gains. We also noticed that the times for staleness values of 0.3 and 0.5 are quite close, because as the training steps increase, the response length changes significantly, causing training instability. Further analysis and optimization are needed for this issue. | staleness_threshold | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:---------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:-----------------------------:| | 0 | 231.34 | 128.47 | \ | 98.77 | 4h 25m | 9h 41m | 15h 2m | 1d 1h 53m | max: 0.2844
last: 0.2604 | | 0.1 | 171.30 | 58.17 | \ | 109.12 | 3h 53m | 8h 37m | 14h 25m | 19h 59m | max: 0.3542
last: 0.2979 | | 0.3 | 146.11 | 38.88 | \ | 103.22 | 3h 18m | 6h 49m | 11h 40m | 17h 20m | max: 0.3469
last: 0.2865 | | 0.5 | 150.63 | 33.14 | \ | 113.16 | 3h 13m | 6h 46m | 10h 53m | 17h 22m | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-stream_stale_partial?nw=nwuserhouzg ### 128-card 7B require_batches Ablation Experiment In multiple tests, we found that the number of samples issued each time in streaming affects the response length during training, which in turn affects training time. We verified the impact on results by modifying `async_training.require_batches`. | require_batches | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | acc/mean@1 | |:-----------------:|:--------:|:-------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:-----------------------------:| | 1 | 203.47 | 30.88 | \ | 181.08 | 3h 31m | 8h 29m | 17h 36m | max: 0.349
last: 0.326 | | 2 | 158.72 | 26.32 | \ | 128.08 | 3h 35m | 7h 38m | 13h 57m | max: 0.351
last: 0.3406 | | 4 | 124.64 | 25.62 | \ | 95.06 | 3h 13m | 6h 46m | 10h 53m | max: 0.3521
last: 0.3521 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-ablation_require_batches?nw=nwuserhouzg ### 30B Model Mode Experiment We achieved a 1.7x performance improvement with `async stream pipeline with staleness samples` strategy on the Qwen3-30B-A3B-Base model compared to the colocate setup. It is worth noting that this is far from the upper limit of performance gains achievable through asynchrony. Firstly, the comparative experiments used a maximum response length of only 8k, which is much shorter than the 20k sequence length in previous experiments, resulting in a less pronounced rollout tail effect. Secondly, we adopted a highly skewed resource allocation, with rollout using 96 GPUs and trainer using 32 GPUs, which is not an optimal configuration. During the experiments, we observed that the current verl implementation imposes certain constraints, such as requiring data to be evenly divisible by the number of GPUs, making resource adjustment less flexible. Additionally, as asynchronous training and deployment accelerate, the performance gap is gradually narrowing. Therefore, enabling more flexible resource allocation and dynamic resource adjustment in the future will be our next focus. * Machine: H20 * Model: Qwen3-30B-A3B-Base * Rollout length: max_response_length : 8K tokens; * Algorithm: GRPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+Megatron * rollout.n: 16 * ppo_mini_batch_size: 128 * test_freq: 20 * colocate sync: * step:400 * train_batch_size: 512 * fully_async_policy * total_rollout_steps: 512*400 * trigger_parameter_sync_step: 512/128 = 4 * staleness_threshold: 0.5 * partial_rollout: True | Training Mode | Resource Allocation | Step | Gen | Old Log Prob | Ref | Update Actor | Total Time 100 Step | Total Time 200 Step | Total Time 300 Step | Total Time 400 Step | Acc/Mean@1 | |--------------------|---------------------|--------|--------|--------------|-------|--------------|---------------------|---------------------|---------------------|---------------------|-----------------------------| | Colocate Sync | 128 | 497.89 | 348.05 | 28.73 | 20.86 | 86.27 | 13h 36m | 1d 3h 48m | 1d 19h 4m | 2d 11h 39m | max: 0.3500
last: 0.3208 | | Fully Async Policy | 96:32 | 282.75 | 22.06 | \ | 50.05 | 206.63 | 6h 45m (2.01x) | 14h 48m (1.88x) | 1d 0h 9m (1.78x) | 1d 10h 41m (1.72x) | max: 0.3813
last: 0.3448 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-30B?nw=nwuserhouzg | | | ### checkpoint-engine Ablation Experiment We tested the single-step parameter synchronization time of the checkpoint-engine on three models: Qwen2.5-Math-7B, Qwen3-30B-A3B, and Qwen3-235B-A22B, using default checkpoint-engine configurations. All experiments were performed on H20 machines, and the Megatron engine was used for training. | model | trainer rank | rollout rank | checkpoint-engine | total sync time | |:---------------:|:--------------:|:-------------:|:-------------------:|:-----------------:| | Qwen2.5-Math-7B | 4 | 4 | False | 0.12s | | Qwen2.5-Math-7B | 4 | 4 | True | 0.02s | | Qwen3-30B-A3B | 16 | 16 | False | 15.76s | | Qwen3-30B-A3B | 16 | 16 | True | 4.38s | | Qwen3-235B-A22B | 64 | 64 | False | 58.57s | | Qwen3-235B-A22B | 64 | 64 | True | 23.70s | ### use_trainer_do_validate Experiment We tested the effect of setting `use_trainer_do_validate=True` on the training process. The results show that setting this parameter to True can reduce the validation time overhead and trainer node idle time. We used Qwen2.5-Math-7B to verify the benefits of `use_trainer_do_validate=True` on the training process, we achieved about 2x performance improvement on validation time, and the trainer node idle time is reduced by about 40%. * Machine: H20 * Model: Qwen2.5-Math-7B * Rollout length: max_response_length FSDP2: 10K tokens; * Algorithm: DAPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+FSDP2 * rollout.n: 16 * ppo_mini_batch_size: 32 * test_freq: 10 * fully_async_policy * total_rollout_steps: 512*400 * require_batches: 4 * trigger_parameter_sync_step: 4 * staleness_threshold: 0.5 * partial_rollout: True | training mode | resource allocation | step | gen | old_log_prob | update_actor | validate time | total time
50 step | acc/mean@2 | |:------------------:|:-------------------:|:-------:|:-------:|:------------:|:------------:|:-------------:|:---------------------:|:----------:| | colocate sync | 16 | 484.623 | 52.939 | 0 | 430.263 | 205.080 | 7h9m | 22.6 | | fully_async_policy | 8:8 | 489.953 | 52.622 | 0 | 435.874 | 95.699 | 7h2m | 21.0 | ## Multi-Turn Tool Calling Referencing **recipe/retool** and **ToolAgentLoop**, we implemented **AsyncPartialToolAgentLoop**, a multi-turn tool-calling loop that supports partial_rollout for **fully_async_policy**. ### Core Design `AsyncPartialToolAgentLoop` inherits from `ToolAgentLoop` and is adapted for the asynchronous training mode of `fully_async_policy`. When `partial_rollout=True`, the Rollouter interrupts ongoing generation tasks before synchronizing parameters with the Trainer. `AsyncPartialToolAgentLoop` is capable of: 1. **Interrupting Tasks**: Responding to an interrupt signal to save the current state. Currently, interruptions occur during the `GENERATING` process or after other states have completed. 2. **Resuming Tasks**: Resuming execution from the saved state after parameter synchronization is complete, rather than starting over. ### How to Use RL training with multi-turn tool calling in `fully_async_policy` is similar to `recipe/retool`. It is enabled by specifying `multi_turn` configurations in the config file. 1. **SFT Stage**: First, the model should undergo SFT to learn how to follow tool-calling format instructions. 2. **Multi-turn Configuration**: In the `fully_async_policy` training configuration, set the following parameters: ```yaml actor_rollout_ref: rollout: multi_turn: enable: True # AsyncPartialToolAgentLoop will be used by default in fully_async_policy mode # Other multi_turn related configurations ``` 3. **Async Parameters**: To improve efficiency, enable `partial_rollout` and `staleness_threshold` when using multi-turn tool calling: ```yaml async_training: partial_rollout: True staleness_threshold: 0.5 # Other async parameters ``` 4. **Example**: See `recipe/fully_async_policy/shell/dapo_7b_async_retool.sh`. ### Experimental Results To validate the performance of `fully_async_policy` on multi-turn tool-calling tasks, we compared it with the standard `colocate` synchronous mode. Key parameter settings are as follows. * **SFT Model**: Based on `Qwen2.5-7B-Instruct`, trained for 6 epochs on the `ReTool-SFT` dataset * **RL Algorithm**: DAPO * **Dataset**: * Train: `DAPO-Math-17k` * Test: `aime_2025` * **Resource and Mode Comparison**: * `colocate sync`: 32 H20 gpus * `fully_async_policy`: 16 gpus for Trainer + 16 gpus for Rollouter * **Key Configurations**: 1. **Tool Calling Configuration**: * `multi_turn.enable: True` * `multi_turn.max_user_turns: 16` * `multi_turn.max_assistant_turns: 16` * `multi_turn.tool_config_path: recipe/retool/sandbox_fusion_tool_config.yaml` 2. **`colocate sync` Configuration**: * `ppo_mini_batch_size: 16` * `train_batch_size: 64` 3. **`fully_async_policy` Configuration**: * `ppo_mini_batch_size: 16` * `trigger_parameter_sync_step: 4` * `require_batches: 1` * `staleness_threshold: 1` * `partial_rollout: True` | training mode | Resource allocation | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | aime_2025
acc/mean@30 | |:--------------------:|:---------------------:|:---------:|:---------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:-------------------------------:| | colocate | 32 | 375.47 | 228.03 | 35.19 | 111.84 | 9h 46m | 22h 28m | start:0.1078
last:0.2056 | | fully_async_policy | 16: 16 | 221.36 | 40.59 | \ | 179.58 | 6h 19m
(1.55x) | 14h 4m
(1.60x) | start:0.11
last:0.2044 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-multiturn-tool?nw=nwuserhouzg ## Future Plans * GRPO experiments * Megatron adaptation * SGLang integration * Transfer queue integration * Asynchronous parameter synchronization * AReaL asynchronous algorithm implementation * TPPO algorithm implementation * Multi-turn and Tool support ================================================ FILE: verl/experimental/fully_async_policy/README_zh.md ================================================ # Recipe: Fully Async Policy Trainer **Author:** `https://github.com/meituan-search` Last updated: 02/05/2026. 本文档介绍了完全异步PPO训练系统，该系统实现了 Trainer 和 Rollouter 的完全解耦，支持异步样本生成和训练。 在该系统下，我们使用128卡训练qwen2.5-7B模型取得了2.35x-2.67x的性能提升,同时效果没有显著受到影响。 ## Introduction ### Background rollout和train分离架构相较于colocate的架构能够更加灵活地分配资源，设计更加灵活的训练逻辑，从而处理长尾等问题带来的GPU利用率低，训练效率低的问题。 one_step_off_policy通过分离架构的设计并进行rollout和train一轮异步的训练方法，缓解了rollout时间过长的问题，并在训练效率上取得了一些收益， 但其强制使用一轮异步的数据，存在不够灵活等问题，而且并不能完全去除长尾对训练效率带来的的影响；在其他框架如areal、Magistral、streamrl、asyncflow上， 已经基于分离架构实现了异步训练、流式训练，并取得了收益；我们借鉴其方法，在verl上进行了实现。fully_async_policy支持异步、流式、partial rollout的训练， 通过合理设置资源分配情况、参数同步频率等参数，fully_async_policy能够显著提高训练效率。 > Magistral https://arxiv.org/abs/2506.10910 > > AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language > Reasoning https://arxiv.org/abs/2505.24298 > > StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream > Generation https://arxiv.org/abs/2504.15930 > > AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training https://arxiv.org/abs/2507.01663 > ### 核心贡献 * **资源隔离**：与使用hybrid_engine不同，Rollouter和Trainer使用分离的计算资源，需要分别指定所占用的资源。 * **生成与训练并行**：Trainer在训练的同时，Rollouter在生成新的样本。 * **多步异步**: 相比 one step off policy 支持0.x步到多步的异步设定，异步方案更加灵活。 * **nccl参数同步**：基于nccl通信原语，参考[checkpoint-engine](https://github.com/MoonshotAI/checkpoint-engine)实现Rollouter与Trainer间的高效参数同步。 * **Stream推理与训练**：Rollouter逐样本生成数据，同时数据传输以单个sample为最小传输单位。 * **异步训练与新鲜度控制**：通过设置参数async_training.staleness_threshold，支持使用旧参数生成的样本进行训练。 * **PartialRollout**: Rollouter推理过程支持partial rollout逻辑，通过参数同步时，添加`sleep()`和`resume()` 逻辑，保存进行中的rollout的样本，并在下一次rollout中继续使用，减少参数同步等待进行中的任务结束时间。 目前支持使用模式为 megatron/fsdp+vllm。vllm必须使用基于AgentLoop的server模式。 ## 设计 fully_async_policy的整体架构如下图所示，fully_async_policy主要由Rollouter、MessageQueue、Trainer、ParameterSynchronizer四部分组成。 ![fully_async_policy_structure]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_structure.svg?raw=true) 1. Rollouter逐样本生成序列，并将生成的sample放入MessageQueue中，生产的速度受新鲜度控制。 2. MessageQueue用于暂存Rollouter生成的sample。 3. Trainer逐样本从MessageQueue中获取，获取到`require_batches*ppo_mini_batch_size` 数量的样本后，就会进行训练，训练async_training.trigger_parameter_sync_step轮后，触发与Rollouter的一次参数同步。 4. ParameterSynchronizer 实现了Nccl的同步参数同步能力。 当前方案对比base的收益来源，在于colocate情况下，rollout使用更多的资源无法解决长尾样本带来的空闲， 当我们进行资源隔离后，rollout的时间和train的时间都可能相较于之前更长（因为使用的资源变少了）， 但是相互之间的耗时overlap，端到端的耗时反而有所缩减。 ![fully_async_policy_revenue]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_revenue.svg?raw=true) ## 使用方式 ### 参数说明 | super params | implication | |------------------------------------------------------------------|-----------------------------------------------------------------| | `trainer.nnodes` | Trainer的node数量 | | `trainer.n_gpus_per_node` | Trainer每个node上gpu的数量 | | `rollout.nnodes` | Rollouter的node数量 | | `rollout.n_gpus_per_node` | Rollouter每个node上gpu的数量 | | `data.train_batch_size` | 在fully async策略中，该值不生效（默认设置为0） | | `data.gen_batch_size` | 在fully async策略中，使用流式的样本生产逻辑（默认设置为1) | | `rollout.total_rollout_steps` | 总的rollout的sample数量 | | `rollout.test_freq` | Rollouter每更新多少次参数，进行一次validation | | `actor_rollout_ref.actor.ppo_mini_batch_size` | The ppo_mini_batch_size is a global num across all workers/gpus | | `actor_rollout_ref.actor.use_rollout_log_probs=True` | 使用rollout产生的log_probs | | `algorithm.rollout_correction.bypass_mode` | 是否在train阶段，使用train模型的参数计算token的 log_prob | | `async_training.require_batches` | FullyAsyncTrainer一次性获取的ppo_mini_batch_size的数量 | | `async_training.trigger_parameter_sync_step` | 表示FullyAsyncTrainer进行多少次本地更新后,进行一次参数同步 | | `async_training.staleness_threshold` | 新鲜度控制 | | `async_training.partial_rollout` | 是否进行partial_rollout | | `async_training.use_trainer_do_validate` | 是否使用Trainer的do_validate方法进行validation，默认值False | **进一步的解释：** * `rollout.total_rollout_steps` 与 colocate 相比，数量可以通过 train_batch_size 与 step 相乘对齐: `rollout.total_rollout_steps = data.train_batch_size * step`。 * `async_training.trigger_parameter_sync_step` 在fully async策略中，表示Trainer进行多少次本地更新后（也就是获取多少次`require_batches * ppo_mini_batch_size`数量样本）， 与Rollouter之间进行一次参数同步。 每两次Rollouter和Trainer参数同步之间，Trainer将会处理`trigger_parameter_sync_step* require_batches\ ppo_mini_batch_size`份sample。 如果为了与colocate在公平的情况下对比速度，trigger_parameter_sync_step应该设置为 `data.train_batch_size / ( require_batches * ppo_mini_batch_size)`。 * `async_training.staleness_threshold` 在fully async策略中，表示最大允许使用的staleness样本的比例。 * staleness_threshold=0，表示同步训练。 Rollouter两次参数更新之间将会生成固定数量的样本，样本数为： $$rollout\_num = (trigger\_parameter\_sync\_step*require\_batches*ppo\_mini\_batch\_size)$$ * staleness_threshold>0，表示异步训练， 可以设置为小数，支持更灵活的异步调用。 Rollouter两次参数更新之间将会最多生成的样本数为： $$rollout\_num = (1+staleness\_threshold)*(trigger\_parameter\_sync\_step*require\_batches*ppo\_mini\_batch\_size) - num\_staleness\_sample $$ num_staleness_sample 表示上一次rollout多生成的陈旧样本数。 由于是流式系统，rollout持续生成，trainer持续消费。如果rollouter较慢，trainer会更早触发参数同步，rollouter并不会实际生产rollout_num个样本。 当rollout 足够快时，staleness_threshold设置为1，基本上等价于one_step_off policy。 为了避免过期样本太多影响训练精度，建议该值设置小于1。 * `async_training.partial_rollout` partial_rollout只会在staleness_threshold>0时才实际上起作用。 * `actor_rollout_ref.actor.use_rollout_log_probs=True` 在强化学习算法中，log_probs与参数版本，token都存在隐性的相关性。由于PPO/GRPO/DAPO等算法的设定，我们在计算重要性采样时， 即 old_log_prob必须使用rollout参数及token所对应log_probs，才能保证算法的正确性。在fully async策略中，我们默认old_log_prob是由rollout所计算的，而不是由trainer所计算。 * `algorithm.rollout_correction.bypass_mode` algorithm.rollout_correction.bypass_mode 默认为 True, 直接使用rollout log prob。 我们在训练过程中，观测到随着训练的进行，训练后期指标和response长度可能会出现不稳定的情况， 这里我们可以使用 [Rollout Importance Sampling](https://verl.readthedocs.io/en/latest/advance/rollout_is.html) 的技术进行 重要性采样，缓解这一问题。为了使用 `Rollout Importance Sampling` 我们需要使用训练引擎使用当前的参数版本计算old_log_prob，此开关需要打开。 此外，在 mode d (async stream pipeline with partial rollout) 的情况下 `algorithm.rollout_correction.bypass_mode=False` 以及 `Rollout Importance Sampling` 后，我们的实现已近似Areal的 `Decoupled PPO`。 * `async_training.require_batches` 在流式训练中，require_batches 应该设置为1，表示生产够ppo_mini_batch_size样本后，就进行训练。 在实际测试中，我们发现，如果单次下发的样本较少，由于数据分发的顺序，会导致训练不稳定，response 长度变长。 在这里，我们额外提供 require_batches 进行流式分发，单次参与训练的样本数量控制。 * `async_training.use_trainer_do_validate` 控制是否使用trainer的`do_validate`方法进行validation。 如果设置为True，trainer会在每次参数更新后，调用`do_validate`方法进行validation。 如果设置为False，trainer不会调用`do_validate`方法。 ### 模式支持 1. on policy pipeline: 1. **trigger_parameter_sync_step=1，staleness_threshold=0** 2. Rollouter一次生产`require_batches*ppo_mini_batch_size` 的samples，Trainer获取这些samples后进行训练，训练完后Trainer和Rollouter之间进行一次参数同步; 3. 在rollout阶段，如果存在长尾的样本，但是rollout样本数较少时，较短的样本无法填充到空闲的资源中，会造成一定的资源浪费。 4. 如图a所示； 2. stream off policy pipeline: 1. **trigger_parameter_sync_step>1，staleness_threshold=0** 2. 将会进行同步的流式训练，Rollouter一次生产`require_batches*ppo_mini_batch_size*trigger_parameter_sync_step` 的samples，Trainer每获取`require_batches*ppo_mini_batch_size` 就进行一次本地训练，训练trigger_parameter_sync_step次后，Trainer和Rollouter之间进行一次参数同步; 3. 相较于a，由于一次生成的样本更多，资源的空闲会更低。 4. 在一次step训练中，会存在两次资源闲置的时间，分别是在第一次获取样本时，train等待`require_batches*ppo_mini_batch_size` 个样本生产，以及最后一次参数更新时，rollout等待训练完成。 5. 如图b所示； 3. async stream pipeline with staleness samples: 1. **trigger_parameter_sync_step>=1，staleness_threshold>0，partial_rollout=Flase** 2. Rollouter在每次参数更新后将计划最多生产rollout_num个样本（实际根据rollout速度，生成的样本可能会少与这个值）。 3. 如果rollout过程比较快，Rollouter将会在参数同步前额外生成一部分样本num_stale_samples，用于参数同步后立即给Trainer使用。 触发参数同步时，如果Rollouter有正在生产的任务，将会等待任务完成，同时不会添加新的任务； 4. 相较于b，除第一次step训练外，后续的训练都不会有wait first batch rollout finish的时间，但是会有wait active task finish的时间。 5. 如图c所示； 4. async stream pipeline with partial rollout: 1. **trigger_parameter_sync_step>=1，staleness_threshold>0，partial_rollout=True** 2. 相较于c，触发参数同步时，Rollouter如果有正在生产的sample，会打断rollout过程并进行参数同步，被中断的sample会在参数同步后继续生成。减少了wait active task finish的时间。 3. 如图d所示； ![fully_async_policy_mode]( https://github.com/ArronHZG/verl-community/blob/main/docs/fully_async_policy_mode.svg?raw=true) ### 关键指标 | metrics | implication | |------------------------------------------------|-----------------------------------------------------------| | `trainer/idle_ratio` | Trainer闲置率 | | `rollouter/idle_ratio` | Rollouter闲置率 | | `fully_async/count/stale_samples_processed` | 训练使用的旧sample总数 | | `fully_async/count/stale_trajectory_processed` | 训练使用的旧trajectory总数(一个sample会生产rollout.n条trajectory) | | `fully_async/partial/total_partial_num` | 两次trigger_parameter_sync_step之间Trainer处理的partial样本数 | | `fully_async/partial/partial_ratio` | 两次trigger_parameter_sync_step之间Trainer处理的partial样本的比例 | | `fully_async/partial/max_partial_span` | 两次trigger_parameter_sync_step之间Trainer处理的partial样本的最大参数跨度 | ### 调参建议 * 资源分配与调整: * 合理的资源分配是获得好的训练效率的前提。理想的资源分配情况应该是使得Rollout的时间和Train的时间接近，从而使得整个训练过程流水气泡最小， 避免资源闲置，同时Trainer不会使用旧样本。在真实训练场景下，可以根据实际训练过程中rollout和train的空闲时间调整资源分配， 可从rollouter/idle_ratio和trainer/idle_ratio获得，如果rollouter/idle_ratio较高trainer/idle_ratio较低， 应该增多Trainer的资源减少Rollouter的资源，反之亦然。 * 关键参数： * staleness_threshold: 设置太大会导致较多的旧样本使用，影响模型效果，建议设置小于1。 * require_batches：越接近1，越接近纯流式过程，训练过程中bubble越小，能够在速度上获得更快的加速效果，但会对样本的处理顺序产生影响； * trigger_parameter_sync_step: 设置的越小越接近on policy，但会导致频繁的参数同步，长尾样本浪费的资源无法被短样本填充，资源利用率低。 设置的越大有更高的计算效率，但是精度上会受到off policy的影响。 * rollout.test_freq: 会占用Rollouter资源，不建议设置太小。 * 模式选择：通过调整不同的参数，Fully Async架构支持不同程度上的优化加速，适用于不同场景的任务。 * 对于小规模任务，需要保证训练的稳定性和 on-policy 性，对速度要求不高的场景，可以尝试使用on policy pipeline的模式（模式1）。 * 对于需要提高训练吞吐量，但对 staleness 敏感的场景，可以尝试使用 stream off policy pipeline 的模式。即通过 设置trigger_parameter_sync_step>1 ，提高 训练效率，但仍保持同步机制 (staleness_threshold=0 )（模式2）。 * 对于大规模任务，对训练速度有较高要求，且可以容忍一定 off-policy 程度、staleness的场景，可以设置staleness_threshold> 0、partial_rollout=True提高训练效率，使用 async stream pipeline 模式（模式 3 或 4）。 ### 快速开始 ```shell rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=10 staleness_threshold=0 trigger_parameter_sync_step=16 partial_rollout=False python -m recipe.fully_async_policy.fully_async_main \ train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.actor.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ rollout.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.partial_rollout="${partial_rollout}" ``` ## 实验 ### 在7B模型上进行异步训练 我们使用 Qwen2.5-Math-7B 验证 fully async 策略在长候选下，多种资源下的收益情况。 使用`async stream pipeline with staleness samples` 策略，我们在32卡，64卡，128卡都取得2x左右的性能提升，同时没有显著影响实验效果。 * 机器：H20 * 模型：Qwen2.5-Math-7B * rollout长度：max_response_length FSDP2: 28K tokens; * 算法：DAPO * 数据集： TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * engine: vllm+FSDP2 * rollout.n: 16 * ppo_mini_batch_size: 32 * test_freq: 20 * colocate sync: * step: 400 * train_batch_size: 512 * fully_async_policy * total_rollout_steps: 512*400 * require_batches: 4 * trigger_parameter_sync_step: 4 * staleness_threshold: 0.5 * partial_rollout: True | training mode | resource allocation | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:--------------------:|:---------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:-------------------------------:| | colocate sync | 32 | 790.10 | 357.41 | 107.71 | 269.80 | 13h 44m | 1d 3h 43m | 2d 9h 22m | 3d 17h 5m | max: 0.3313
last: 0.2448 | | fully_async_policy | 16:16 | 294.77 | 21.26 | \ | 313.81 | 7h 58m
(1.72x) | 16h 21m
(1.70x) | 1d 0h 53m
(2.31x) | 1d 9h 26m
(2.66x) | max: 0.3302
last: 0.2333 | | colocate sync | 64 | 365.28 | 150.72 | 70.26 | 133.41 | 10h 22m | 20h 45m | 1d 7h 6m | 1d 17h 32m | max: 0.3365
last: 0.2333 | | fully_async_policy | 32:32 | 189.26 | 28.46 | \ | 156.98 | 4h 57m
(2.09x) | 10h 14m
(2.03x) | 16h 58m
(1.83x) | 21h 40m
(1.92x) | max: 0.3677
last: 0.3406 | | colocate sync | 128 | 356.30 | 177.85 | 53.92 | 113.81 | 8h 36m | 17h 56m | 1d 5h 6m | 1d 16h 48m | max: 0.3573
last: 0.2958 | | fully_async_policy | 64:64 | 150.63 | 33.14 | \ | 113.16 | 3h 13m
(2.67x) | 6h 46m
(2.65x) | 10h 53m
(2.67x) | 17h 22m
(2.35x) | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-colocate_async?nw=nwuserhouzg ### 128卡 7B 异步模式实验 我们使用 Qwen2.5-Math-7B 验证 fully async 所支持的各个模式的效果。 我们可以看到 stream 带来的收益大约1.6x，叠加 staleness 和 partial_rollout 后，收益为2.35x。 | mode | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:-------------------------------------------------------------------------------------------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------------:| | colocate sync | 356.30 | 177.85 | 53.92 | 113.81 | 8h 36m | 17h 56m | 1d 5h 6m | 1d 16h 48m | max: 0.3573
last: 0.2958 | | `stream off policy pipeline`
(+fully async: trigger_parameter_sync_step= 4,
require_batches= 4) | 231.34 | 128.47 | \ | 98.77 | 4h 25m | 9h 41m | 15h 2m | 1d 1h 53m | max: 0.2844
last: 0.2604 | | `async stream pipeline with staleness samples`
(+staleness_threshold=0.5) | | | | | | | | | | | `async stream pipeline with partial rollout`
(+partial_rollout=True) | 150.63 | 33.14 | \ | 113.16 | 3h 13m | 6h 46m | 10h 53m | 17h 22m | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-stream_stale_partial?nw=nwuserhouzg ### 128卡 stale 消融实验 在 `async stream pipeline with partial rollout` 模式下，我们验证 staleness 的设置对于训练效率的影响。 我们可以发现，staleness 越大，最终取得的收益越明显。 同时我们也注意到 staleness 取 0.3 和 0.5 的时间比较接近，原因是随着训练步数的增量，response 长度变化较大，训练出现了不稳定的问题。 后续还需要针对该问题进行进一步的分析和优化。 | staleness_threshold | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | total time
400 step | acc/mean@1 | |:---------------------:|:--------:|:--------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:-----------------------------:| | 0 | 231.34 | 128.47 | \ | 98.77 | 4h 25m | 9h 41m | 15h 2m | 1d 1h 53m | max: 0.2844
last: 0.2604 | | 0.1 | 171.30 | 58.17 | \ | 109.12 | 3h 53m | 8h 37m | 14h 25m | 19h 59m | max: 0.3542
last: 0.2979 | | 0.3 | 146.11 | 38.88 | \ | 103.22 | 3h 18m | 6h 49m | 11h 40m | 17h 20m | max: 0.3469
last: 0.2865 | | 0.5 | 150.63 | 33.14 | \ | 113.16 | 3h 13m | 6h 46m | 10h 53m | 17h 22m | max: 0.3521
last: 0.3094 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-ablation_stale?nw=nwuserhouzg ### 128卡 7B require_batches 消融实验 在多次测试下，我们发现流式每次下发样本的数量会影响训练的response长度，进而影响训练时长，我们通过修改 `async_training.require_batches` 验证对与结果的影响。 | require_batches | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | total time
300 step | acc/mean@1 | |:-----------------:|:--------:|:-------:|:--------------:|:--------------:|:------------------------:|:------------------------:|:------------------------:|:-----------------------------:| | 1 | 203.47 | 30.88 | \ | 181.08 | 3h 31m | 8h 29m | 17h 36m | max: 0.349
last: 0.326 | | 2 | 158.72 | 26.32 | \ | 128.08 | 3h 35m | 7h 38m | 13h 57m | max: 0.351
last: 0.3406 | | 4 | 124.64 | 25.62 | \ | 95.06 | 3h 13m | 6h 46m | 10h 53m | max: 0.3521
last: 0.3521 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-ablation_require_batches?nw=nwuserhouzg ### 30B模型模式实验 我们在 Qwen3-30B-A3B-Base 模型上通过`async stream pipeline with staleness samples` 策略，相比于 colocate 方案取得了 1.7 倍的性能提升。值得说明的是，这距离异步方式所能带来的性能提升上限还有很大空间。首先，对比实验中使用的最大响应长度仅为 8k，这远低于此前实验的 20k 序列长度，因此 rollout 的长尾效应并不明显。其次，我们采用了极为倾斜的资源分配方案，rollout 使用了 96 张 GPU，而 trainer 仅使用了 32 张 GPU，这并不是最优的配置。在实验过程中，我们观察到当前的 verl 实现存在一些限制，比如要求数据必须能被 GPU 数量整除，这使得资源调整的灵活性受到影响。此外，随着异步训练和部署的加速，性能差距也在逐渐缩小。因此，未来我们将重点关注如何实现更灵活的资源分配和动态调整资源。 * 机器：H20 * 模型：Qwen3-30B-A3B-Base * rollout长度：max_response_length : 8K tokens; * 算法： GRPO * 数据集： TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+Megatron * rollout.n: 16 * ppo_mini_batch_size: 128 * test_freq: 20 * colocate sync: * step:400 * train_batch_size: 512 * fully_async_policy * total_rollout_steps: 512*400 * trigger_parameter_sync_step: 512/128 = 4 * staleness_threshold: 0.5 * partial_rollout: True | Training Mode | Resource Allocation | Step | Gen | Old Log Prob | Ref | Update Actor | Total Time 100 Step | Total Time 200 Step | Total Time 300 Step | Total Time 400 Step | Acc/Mean@1 | |----------------------|--------------------|---------|--------|--------------|--------|--------------|---------------------|---------------------|---------------------|---------------------|-----------------------------| | Colocate Sync | 128 | 497.89 | 348.05 | 28.73 | 20.86 | 86.27 | 13h 36m | 1d 3h 48m | 1d 19h 4m | 2d 11h 39m | max: 0.3500
last: 0.3208 | | Fully Async Policy | 96:32 | 282.75 | 22.06 | \ | 50.05 | 206.63 | 6h 45m (2.01x) | 14h 48m (1.88x) | 1d 0h 9m (1.78x) | 1d 10h 41m (1.72x) | max: 0.3813
last: 0.3448 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-30B?nw=nwuserhouzg ### checkpoint-engine参数同步消融实验 我们在Qwen2.5-Math-7B，Qwen3-30B-A3B和Qwen3-235B-A22B三个模型上测试了checkpoint-engine参数同步的单步参数同步耗时，使用的参数均为默认参数配置。实验均在H20机器上完成，并使用megatron训练引擎。 | model | trainer rank | rollout rank | checkpoint-engine | total sync time | |:-----------------:|:--------:|:-------:|:--------------:|:--------------:| | Qwen2.5-Math-7B | 4 | 4 | False | 0.12s | | Qwen2.5-Math-7B | 4 | 4 | True | 0.02s | | Qwen3-30B-A3B | 16 | 16 | False | 15.76s | | Qwen3-30B-A3B | 16 | 16 | True | 4.38s | | Qwen3-235B-A22B | 64 | 64 | False | 58.57s | | Qwen3-235B-A22B | 64 | 64 | True | 23.70s | ### use_trainer_do_validate 实验测试 我们在Qwen2.5-Math-7B模型上测试了`use_trainer_do_validate`参数的影响。这个结果展示使用`use_trainer_do_validate=True`可以减少验证时间开销，并且训练器节点的空闲时间也减少了。 * Machine: H20 * Model: Qwen2.5-Math-7B * Rollout length: max_response_length FSDP2: 10K tokens; * Algorithm: DAPO * Dataset: TRAIN_FILE: dapo-math-17k.parquet TEST_FILE: aime-2024.parquet * Engine: vllm+FSDP2 * rollout.n: 16 * ppo_mini_batch_size: 32 * test_freq: 10 * fully_async_policy * total_rollout_steps: 512*400 * require_batches: 4 * trigger_parameter_sync_step: 4 * staleness_threshold: 0.5 * partial_rollout: True | training mode | resource allocation | step | gen | old_log_prob | update_actor | validate time | total time
50 step | acc/mean@2 | |:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:| | colocate sync | 16 | 484.623 | 52.939 | 0 | 430.263 | 205.080 | 7h9m | 22.6 | | fully_async_policy | 8:8 | 489.953 | 52.622 | 0 | 435.874 | 95.699 | 7h2m | 21.0 | ## 多轮工具调用 参考 **recipe/retool** 和 **ToolAgentLoop**，我们为 **fully_async_policy** 实现了支持partial rollout的多轮工具调用循环 * *AsyncPartialToolAgentLoop**。 ### 核心设计 `AsyncPartialToolAgentLoop` 继承自 `ToolAgentLoop`，其核心是适配了 `fully_async_policy` 的异步训练模式。当 `partial_rollout=True` 时，Rollouter 在与 Trainer 同步参数前会中断正在进行的生成任务。`AsyncPartialToolAgentLoop` 能够： 1. **中断任务**: 响应中断信号，保存当前的生成状态。目前，中断会发生在GENERATING过程中，或其他状态结束后； 2. **恢复任务**: 在参数同步完成后，从保存的状态恢复，继续执行，而不是从头开始。 ### 使用方法 `fully_async_policy`多轮与工具调用的RL训练与 `recipe/retool` 类似，通过在配置文件中指定 `multi_turn` 相关配置来启用。 1. **SFT 阶段**: 首先，需要对模型进行 SFT训练，使其具备遵循工具调用格式指令的能力。 2. **配置启用**: 在 `fully_async_policy` 的训练配置中，设置以下参数: ```yaml actor_rollout_ref: rollout: multi_turn: enable: True # 在fully_async_policy模式下将默认使用AsyncPartialToolAgentLoop # 其他 multi_turn 相关配置 ``` 3. **配置async参数**: 为提高效率，在启用多轮工具调用时，同时开启 `partial_rollout`和`staleness_threshold`： ```yaml async_training: partial_rollout: True staleness_threshold: 0.5 # 其他async参数 ``` 4. **example**: 参考`recipe/fully_async_policy/shell/dapo_7b_async_retool.sh` ### 实验结果 为验证 `fully_async_policy` 在多轮工具调用任务中的性能，我们将其与标准 `colocate` 同步模式进行了对比。实验具体设置如下。 * **SFT模型**: 实验基于 `Qwen2.5-7B-Instruct` 模型，使用`ReTool-SFT`数据集训练6个epoch； * **RL算法**: DAPO * **数据集**: * 训练集: `DAPO-Math-17k` * 测试集: `aime_2025` * **资源与模式对比**: * `colocate sync`: 32卡 H20 * `fully_async_policy`: 16卡 Trainer + 16卡 Rollouter * **关键配置**: 1. **工具调用配置**: * `multi_turn.enable: True` * `multi_turn.max_user_turns: 16` * `multi_turn.max_assistant_turns: 16` * `multi_turn.tool_config_path: recipe/retool/sandbox_fusion_tool_config.yaml` 2. **`colocate sync`配置**: * `ppo_mini_batch_size: 16` * `train_batch_size: 64` 3. **`fully_async_policy`配置**: * `ppo_mini_batch_size: 16` * `trigger_parameter_sync_step: 4` * `require_batches: 1` * `staleness_threshold: 1` * `partial_rollout: True` | training mode | Resource allocation | step | gen | old_log_prob | update_actor | total time
100 step | total time
200 step | aime_2025
acc/mean@30 | |:------------------: |:-------------------: |:-------: |:-------: |:------------: |:------------: |:----------------------: |:----------------------: |:---------------------------: | | colocate | 32 | 375.47 | 228.03 | 35.19 | 111.84 | 9h 46m | 22h 28m | start:0.1078
last:0.2056 | | fully_async_policy | 16: 16 | 221.36 | 40.59 | \ | 179.58 | 6h 19m
(1.55x) | 14h 4m
(1.60x) | start:0.11
last:0.2044 | > source data: https://wandb.ai/hou-zg-meituan/fully-async-policy-multiturn-tool?nw=nwuserhouzg ## 后续计划 * GRPO实验 * megatron 适配 * sglang 集成 * transfer queue 集成 * 异步参数同步 * Areal异步算法实现 * TPPO算法实现 * 多轮及Tool的支持 ================================================ FILE: verl/experimental/fully_async_policy/agent_loop/__init__.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .agent_loop import FullyAsyncAgentLoopManager __all__ = [FullyAsyncAgentLoopManager] ================================================ FILE: verl/experimental/fully_async_policy/agent_loop/agent_loop.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os from typing import Any, Optional import ray import torch from omegaconf import DictConfig from verl.experimental.agent_loop.agent_loop import ( AgentLoopManager, AgentLoopWorker, AsyncLLMServerManager, TokenOutput, ) from verl.protocol import DataProto from verl.single_controller.ray import RayResourcePool, RayWorkerGroup from verl.utils.ray_utils import auto_await from verl.utils.rollout_trace import ( rollout_trace_op, ) logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class FullyAsyncLLMServerManager(AsyncLLMServerManager): """FullyAsyncLLMServerManager supports resume generation on partial rollout, making rollout interruption invisible to the AgentLoop. """ @rollout_trace_op async def generate( self, request_id, *, prompt_ids: list[int], sampling_params: dict[str, Any], image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, ) -> TokenOutput: """Generate tokens from prompt ids. Args: request_id (str): request id for sticky session. prompt_ids (List[int]): List of prompt token ids. sampling_params (Dict[str, Any]): Sampling parameters for the chat completion. image_data (Optional[List[Any]]): Image data for the chat completion. video_data (Optional[List[Any]]): Video data for the chat completion. Returns: TokenOutput: token output """ limit_key = None if "max_tokens" in sampling_params: limit_key = "max_tokens" elif "max_new_tokens" in sampling_params: limit_key = "max_new_tokens" original_max_tokens = sampling_params.get(limit_key) if limit_key else None final_output = TokenOutput( token_ids=[], log_probs=[], num_preempted=0, ) min_global_steps, max_global_steps = None, None while True: # 1. generate tokens output = await super().generate( request_id=request_id, prompt_ids=prompt_ids + final_output.token_ids, sampling_params=sampling_params, image_data=image_data, video_data=video_data, ) # 2. merge output into final_output final_output.token_ids.extend(output.token_ids) if output.log_probs is not None: final_output.log_probs.extend(output.log_probs) if output.routed_experts is not None: if final_output.routed_experts is None: final_output.routed_experts = output.routed_experts else: final_output.routed_experts = torch.cat([final_output.routed_experts, output.routed_experts], dim=0) if output.num_preempted is not None: final_output.num_preempted += output.num_preempted final_output.stop_reason = output.stop_reason # update model weights version global_steps = output.extra_fields.get("global_steps", None) if min_global_steps is None: min_global_steps = global_steps max_global_steps = global_steps # 3. update max_new_tokens if original_max_tokens is not None: sampling_params[limit_key] = original_max_tokens - len(final_output.token_ids) if len(final_output.token_ids) >= original_max_tokens: final_output.stop_reason = "length" break # 4. check stop reason if output.stop_reason not in ("aborted", "abort") or not self.config.async_training.partial_rollout: break final_output.extra_fields["global_steps"] = global_steps final_output.extra_fields["min_global_steps"] = min_global_steps final_output.extra_fields["max_global_steps"] = max_global_steps return final_output @ray.remote class FullyAsyncAgentLoopWorker(AgentLoopWorker): def __init__( self, config: DictConfig, servers: list[tuple[str, ray.actor.ActorHandle]], load_balancer_handle: ray.actor.ActorHandle, reward_loop_worker_handles: list[ray.actor.ActorHandle] = None, ): self.server_manager = FullyAsyncLLMServerManager(config, servers, load_balancer_handle) super().__init__(config, servers, load_balancer_handle, reward_loop_worker_handles) class FullyAsyncAgentLoopManager(AgentLoopManager): def __init__( self, config: DictConfig, worker_group: RayWorkerGroup = None, rollout_resource_pool: RayResourcePool = None, reward_loop_worker_handles: list[ray.actor.ActorHandle] = None, ): self.agent_loop_workers_class = FullyAsyncAgentLoopWorker super().__init__(config, worker_group, rollout_resource_pool, reward_loop_worker_handles) @auto_await async def generate_sequences_single(self, prompts: DataProto) -> DataProto: """Split input batch and dispatch to agent loop workers. Args: prompts (DataProto): Input batch. Single sample data Returns: DataProto: Output batch. """ worker = self._select_best_worker() output_future = worker.generate_sequences.remote(prompts) return await asyncio.wrap_future(output_future.future()) def _select_best_worker(self): """Select the best worker, simple round-robin load balancing""" if not hasattr(self, "_worker_index"): self._worker_index = 0 worker = self.agent_loop_workers[self._worker_index] self._worker_index = (self._worker_index + 1) % len(self.agent_loop_workers) return worker ================================================ FILE: verl/experimental/fully_async_policy/config/fully_async_ppo_megatron_trainer.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_megatron_trainer - _self_ trainer: use_legacy_worker_impl: disable async_training: # Maximum samples staleness threshold staleness_threshold: 0.1 # Frequency of parameter synchronization between rollouter and trainer, # One step means trainer obtains a batch of required samples trigger_parameter_sync_step: 4 # The number of ppo_mini_batches that the FullyAsyncTrainer obtains once require_batches: 1 # When synchronizing parameters, Whether to resume generation when rollout is interrupted. # If True, AsyncLLMServerManager auto resume generation, making rollout interruption invisible to the AgentLoop. partial_rollout: True # whether to use trainer do_validate use_trainer_do_validate: False # Rollout config rollout: # Number of nodes used in the rollout nnodes: 1 # Number of GPUs per node n_gpus_per_node: 8 # number of responses (i.e. num sample times). > 1 for grpo n: 4 # total rollout samples # TODO rename to total_rollout_samples total_rollout_steps: 100 data: # Number of samples generated, currently only support 1 gen_batch_size: 1 actor_rollout_ref: rollout: # Must be enabled! Otherwise, log_probs cannot be calculated. calculate_log_probs: True checkpoint_engine: backend: "nccl" actor: # Must use rollout log probs for training use_rollout_log_probs: True model: # To use remove padding (thd) use_remove_padding: True # Only then will the use of log probs be correct. # And it can be used in conjunction with other rollout_correction algorithms. algorithm: rollout_correction: bypass_mode: True ================================================ FILE: verl/experimental/fully_async_policy/config/fully_async_ppo_trainer.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ trainer: use_legacy_worker_impl: disable async_training: # Maximum samples staleness threshold staleness_threshold: 0.1 # Frequency of parameter synchronization between rollouter and trainer, # One step means trainer obtains a batch of required samples trigger_parameter_sync_step: 4 # The number of ppo_mini_batches that the FullyAsyncTrainer obtains once require_batches: 1 # When synchronizing parameters, Whether to resume generation when rollout is interrupted. # If True, AsyncLLMServerManager auto resume generation, making rollout interruption invisible to the AgentLoop. partial_rollout: True # whether to use trainer do_validate use_trainer_do_validate: False # Rollout config rollout: # Number of nodes used in the rollout nnodes: 1 # Number of GPUs per node n_gpus_per_node: 8 # number of responses (i.e. num sample times). > 1 for grpo n: 4 # total rollout samples # TODO rename to total_rollout_samples total_rollout_steps: 100 data: # Number of samples generated, currently only support 1 gen_batch_size: 1 actor_rollout_ref: rollout: # Must be enabled! Otherwise, log_probs cannot be calculated. calculate_log_probs: True checkpoint_engine: backend: "nccl" actor: # Must use rollout log probs for training use_rollout_log_probs: True model: # To use remove padding (thd) use_remove_padding: True # Only then will the use of log probs be correct. # And it can be used in conjunction with other rollout_correction algorithms. algorithm: rollout_correction: bypass_mode: True ================================================ FILE: verl/experimental/fully_async_policy/detach_utils.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import time from collections import defaultdict from dataclasses import dataclass from typing import Any, Optional import numpy as np import torch from verl import DataProto from verl.trainer.ppo.ray_trainer import compute_response_mask @dataclass class RolloutSample: """Enhanced rollout sample containing both original batch info and AgentLoopOutput""" # Original batch information full_batch: Any # Metadata sample_id: str epoch: int # Processing metadata rollout_status: dict[str, Any] @dataclass class ValidateMetrics: """Metrics for validation""" timing_raw: dict[str, Any] metrics: Optional[dict[str, Any]] = None def prepare_single_generation_data(batch_dict, config) -> DataProto: """ Similar to the logic of ray_trainer._prepare_generate_batch, but for a single sample. Separate the data used for generation from the original data. Returns: tuple: (original_batch_dict, gen_data_for_single_sample) """ full_batch = DataProto.from_single_dict(batch_dict) batch_keys_to_pop = [] non_tensor_batch_keys_to_pop = [] existing_batch_keys = [k for k in batch_keys_to_pop if k in full_batch.batch.keys()] existing_non_tensor_keys = [k for k in non_tensor_batch_keys_to_pop if k in full_batch.non_tensor_batch.keys()] if existing_batch_keys or existing_non_tensor_keys: full_batch.pop( batch_keys=existing_batch_keys, non_tensor_batch_keys=existing_non_tensor_keys, ) # Setting selected agent, that supports partial if config.actor_rollout_ref.rollout.multi_turn.enable: full_batch.non_tensor_batch["agent_name"] = np.array(["tool_agent"] * len(full_batch), dtype=object) else: full_batch.non_tensor_batch["agent_name"] = np.array(["single_turn_agent"] * len(full_batch), dtype=object) # Add global step count to generated data full_batch = full_batch.repeat(repeat_times=config.actor_rollout_ref.rollout.n, interleave=True) return full_batch def addition_process(output: DataProto): """collect metirics""" metrics = output.meta_info.pop("metrics") # List[Dict[str, str]] processing_times_list = [item["generate_sequences"] for item in metrics] tool_calls_times_list = [item["tool_calls"] for item in metrics] output.non_tensor_batch["processing_times"] = processing_times_list output.non_tensor_batch["tool_calls_times"] = tool_calls_times_list return output def assemble_batch_from_rollout_samples( rollout_samples: list[RolloutSample], tokenizer, config, balance_batch=None ) -> DataProto: """ Assemble gen_batch_output from RolloutSample objects Assembles batches from RolloutSample objects, similar to the _post_generate_batch logic in ray_trainer. Args: rollout_samples: List of RolloutSample objects tokenizer: Tokenizer instance config: Configuration object containing trainer settings balance_batch: Whether to balance the batch (simplified version) Returns: DataProto: Assembled gen_batch_output Raises: ValueError: If rollout_samples is empty """ start_time = time.time() if not rollout_samples: raise ValueError("Empty rollout_samples provided for batch assembly") print(f"[BatchUtils] Assembling batch from {len(rollout_samples)} RolloutSample objects") rollout_samples_batch = [] rollout_status = rollout_samples[0].rollout_status # Add a prefix to all rollout_status keys rollout_status = {f"fully_async/{key}": value for key, value in rollout_status.items()} for rs in rollout_samples: batch = addition_process(rs.full_batch) rollout_samples_batch.append(batch) final_batch = DataProto.concat(rollout_samples_batch) # Calculate response_mask (if not present) if "response_mask" not in final_batch.batch.keys(): final_batch.batch["response_mask"] = compute_response_mask(final_batch) if balance_batch: balance_batch(final_batch, metrics={}) # Calculate the global valid token number if "attention_mask" in final_batch.batch: final_batch.meta_info["global_token_num"] = torch.sum(final_batch.batch["attention_mask"], dim=-1).tolist() processing_times = final_batch.non_tensor_batch["processing_times"] tool_calls = final_batch.non_tensor_batch["tool_calls_times"] # Collect statistics processing_time_stats = { "processing_time/avg": np.mean(processing_times), "processing_time/max": np.max(processing_times), "processing_time/min": np.min(processing_times), "processing_time/tp50": np.percentile(processing_times, 50), "processing_time/tp99": np.percentile(processing_times, 99), "processing_time/tp95": np.percentile(processing_times, 95), } tool_calls_stats = {} if len(tool_calls) > 0: tool_calls_stats = { "timing_s/agent_loop/tool_calls/max": np.max(tool_calls), "timing_s/agent_loop/tool_calls/min": np.min(tool_calls), "timing_s/agent_loop/tool_calls/mean": np.mean(tool_calls), } processing_time_stats = {f"fully_async/{key}": value for key, value in processing_time_stats.items()} param_version_start = final_batch.non_tensor_batch["min_global_steps"] param_version_end = final_batch.non_tensor_batch["max_global_steps"] param_version_diff = [abs(a - b) for a, b in zip(param_version_end, param_version_start, strict=False)] num_diff0 = param_version_diff.count(0) partial_stats = { "fully_async/partial/total_partial_num": len(param_version_diff) - num_diff0, "fully_async/partial/partial_ratio": (len(param_version_diff) - num_diff0) / len(param_version_diff), "fully_async/partial/max_partial_span": max(param_version_diff), } # add meta_info trajectory_param_versions = final_batch.non_tensor_batch["max_global_steps"] final_batch.meta_info.update( { "param_version_diversity": len(set(trajectory_param_versions)), "trajectory_param_versions": trajectory_param_versions, **processing_time_stats, **rollout_status, **partial_stats, **tool_calls_stats, } ) print(f"[BatchUtils] Batch assembly completed in {time.time() - start_time:.2f}s") return final_batch class MetricsAggregator: """Metrics aggregator, used to combine metrics from multiple training steps""" def __init__(self, total_gpus: int): # Store all values ​​for each metric self.metric_values: dict[str, list[float]] = defaultdict(list) # Store the number of samples at each step for weighted averaging self.sample_counts: list[int] = [] # Store the timestamp of each step for time-related calculations self.timestamps: list[float] = [] # Step Count self.step_count = 0 # total num gpus used self.total_gpus = total_gpus # Metric aggregation rule configuration self.aggregation_rules = self._init_aggregation_rules() def _init_aggregation_rules(self) -> dict[str, dict[str, list[str]]]: """Initialize metrics aggregation rules""" return { # Time-Based metrics, can add metrics here "time_sum": ["perf/time_per_step"], "min": ["timing_s/agent_loop/tool_calls/min"], "avg": ["timing_s/agent_loop/tool_calls/mean"], "max": ["timing_s/agent_loop/tool_calls/max"], "last": [ "fully_async/count/total_generated_samples", "fully_async/count/stale_samples_processed", "fully_async/count/stale_trajectory_processed", "fully_async/count/current_param_version", "fully_async/count/dropped_stale_samples", "training/global_step", # TODO change name to: total_step ], } def add_step_metrics(self, metrics: dict[str, Any], sample_count: int, timestamp: float = None): """Adding a single-step metrics""" if timestamp is None: timestamp = time.time() self.sample_counts.append(sample_count) self.timestamps.append(timestamp) self.step_count += 1 # Store all metrics values for key, value in metrics.items(): if isinstance(value, int | float | np.number): self.metric_values[key].append(float(value)) elif isinstance(value, torch.Tensor): self.metric_values[key].append(float(value.item())) def _get_aggregation_type(self, metric_name: str) -> str: """Determine the aggregation type based on the metric name""" for agg_type, metric_list in self.aggregation_rules.items(): if metric_name in metric_list: return agg_type metric_lower = metric_name.lower() if any(keyword in metric_lower for keyword in ["timing_s/"]): return "time_sum" if any(keyword in metric_lower for keyword in ["mean", "avg", "average"]): return "avg" if any(keyword in metric_lower for keyword in ["max", "maximum"]): return "max" if any(keyword in metric_lower for keyword in ["min", "minimum"]): return "min" if any(keyword in metric_lower for keyword in ["sum", "total"]): return "sum" if any(keyword in metric_lower for keyword in ["weighted_avg"]): return "weighted_avg" return "avg" def _aggregate_single_metric(self, metric_name: str, values: list[float]) -> float: """Aggregating a single metric""" if not values: return 0.0 agg_type = self._get_aggregation_type(metric_name) if agg_type == "last": return values[-1] elif agg_type == "weighted_avg": # Weighted average if len(values) != len(self.sample_counts): # If the lengths do not match, use a simple average return sum(values) / len(values) total_samples = sum(self.sample_counts) if total_samples == 0: return sum(values) / len(values) weighted_sum = sum(v * c for v, c in zip(values, self.sample_counts, strict=False)) return weighted_sum / total_samples elif agg_type == "sum" or agg_type == "time_sum": return sum(values) elif agg_type == "avg": return sum(values) / len(values) elif agg_type == "max": return max(values) elif agg_type == "min": return min(values) else: # Default average return sum(values) / len(values) def get_aggregated_metrics(self) -> dict[str, Any]: """aggregated metrics""" t = time.time() if self.step_count == 0: return {} aggregated = {} # Aggregate all metrics for metric_name, values in self.metric_values.items(): aggregated[metric_name] = self._aggregate_single_metric(metric_name, values) # Aggregate special metrics aggregated = self._special_metrics_aggergate(aggregated) print(f"aggregated metrics done. cost {time.time() - t:.4f} seconds.") return aggregated def _special_metrics_aggergate(self, aggregated: dict[str, Any]) -> dict[str, Any]: """calculate special metrics""" # global_seqlen/minmax_diff if "global_seqlen/minmax_diff" in aggregated.keys(): aggregated["global_seqlen/minmax_diff"] = aggregated["global_seqlen/max"] - aggregated["global_seqlen/min"] # perf/throughput REQUIRED_PERF_KEYS = {"perf/throughput", "perf/total_num_tokens", "perf/time_per_step"} if REQUIRED_PERF_KEYS.issubset(aggregated): aggregated["perf/throughput"] = aggregated["perf/total_num_tokens"] / ( aggregated["perf/time_per_step"] * self.total_gpus ) # trainer/idle_ratio if "timing_s/gen" in aggregated.keys() and "timing_s/step" in aggregated.keys(): aggregated["fully_async/trainer/idle_ratio"] = aggregated["timing_s/gen"] / aggregated["timing_s/step"] return aggregated def reset(self): """Reset Aggregator""" self.metric_values.clear() self.sample_counts.clear() self.timestamps.clear() self.step_count = 0 def get_current_stats(self) -> dict[str, Any]: """Get statistics about the current aggregation state (for debugging)""" return { "step_count": self.step_count, "metric_count": len(self.metric_values), "total_samples": sum(self.sample_counts), "metric_names": list(self.metric_values.keys()), } def task_exception_handler(task: asyncio.Task): """Handle task exceptions and log them""" try: task.result() except asyncio.CancelledError: pass # Task was cancelled, this is expected except Exception as e: print(f"Task {task.get_name()} failed with exception: {e}") raise e def safe_create_task(coro, name: str, task_set: set = None): """Safely create a task with exception handling Args: coro: The coroutine to run name: Name for the task task_set: Optional set to add the task to Returns: The created asyncio.Task """ task = asyncio.create_task(coro, name=name) task.add_done_callback(task_exception_handler) if task_set is not None: task_set.add(task) return task ================================================ FILE: verl/experimental/fully_async_policy/fully_async_main.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import socket import threading from pprint import pprint import hydra import ray from omegaconf import OmegaConf from verl.experimental.fully_async_policy.fully_async_rollouter import FullyAsyncRollouter from verl.experimental.fully_async_policy.fully_async_trainer import FullyAsyncTrainer from verl.experimental.fully_async_policy.message_queue import MessageQueue, MessageQueueClient from verl.experimental.separation.utils import create_resource_pool_manager, create_role_worker_mapping from verl.trainer.ppo.utils import Role from verl.utils.device import auto_set_device from verl.utils.fs import copy_to_local @ray.remote(num_cpus=1) class FullyAsyncTaskRunner: """ Ray remote class for executing distributed PPO training tasks. """ def __init__(self): self.running = False self.components = {} self.shutdown_event = threading.Event() def run(self, config): print("[ASYNC MAIN] Starting fully async PPO training...") self._initialize_components(config) self._run_training_loop() def _initialize_components(self, config) -> None: print(f"[ASYNC MAIN] TaskRunner hostname: {socket.gethostname()}, PID: {os.getpid()}") pprint(OmegaConf.to_container(config, resolve=True)) OmegaConf.resolve(config) print("[ASYNC MAIN] Initializing model and tokenizer...") local_path = copy_to_local( config.actor_rollout_ref.model.path, use_shm=config.actor_rollout_ref.model.get("use_shm", False) ) from verl.utils import hf_processor, hf_tokenizer trust_remote_code = config.data.get("trust_remote_code", False) tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) # Used for multimodal LLM, could be None processor = hf_processor(local_path, trust_remote_code=trust_remote_code, use_fast=True) self.components["tokenizer"] = tokenizer self.components["processor"] = processor self.components["config"] = config print("[ASYNC MAIN] Creating worker mapping and resource pools...") role_worker_mapping, ray_worker_group_cls = create_role_worker_mapping(config) self.components["role_worker_mapping"] = role_worker_mapping self.components["ray_worker_group_cls"] = ray_worker_group_cls from concurrent.futures import ThreadPoolExecutor print("[ASYNC MAIN] Creating FullyAsyncRollouter and FullyAsyncTrainer in parallel...") with ThreadPoolExecutor(max_workers=2) as executor: # Rollouter does not permit continuous allocation, so we allocate trainer first. trainer_future = executor.submit(self._create_trainer, config) trainer_future.result() rollouter_future = executor.submit(self._create_rollouter, config) rollouter_future.result() # sync total_train_steps between rollouter and trainer total_train_steps = ray.get(self.components["rollouter"].get_total_train_steps.remote()) print(f"total_train_steps {total_train_steps}") ray.get(self.components["trainer"].set_total_train_steps.remote(total_train_steps)) # max_queue_size max_queue_size = ray.get(self.components["rollouter"].get_max_queue_size.remote()) print(f"[ASYNC MAIN] Creating MessageQueue... max_queue_size {max_queue_size}") message_queue = MessageQueue.remote(config, max_queue_size) message_queue_client = MessageQueueClient(message_queue) self.components["message_queue"] = message_queue self.components["message_queue_client"] = message_queue_client ray.get(self.components["rollouter"].set_message_queue_client.remote(self.components["message_queue_client"])) ray.get(self.components["trainer"].set_message_queue_client.remote(self.components["message_queue_client"])) # param_version resume from ckpt or default 0 ray.get(self.components["trainer"].load_checkpoint.remote()) ray.get(self.components["rollouter"].load_checkpoint.remote()) print("[ASYNC MAIN] Setting up parameter synchronization...") ray.get(self.components["trainer"].set_rollouter.remote(self.components["rollouter"])) print("[ASYNC MAIN] Param sync before fit..") ray.get(self.components["trainer"]._fit_update_weights.remote()) if config.trainer.get("val_before_train", True): ray.get(self.components["trainer"]._fit_validate.remote(True)) print("[ASYNC MAIN] All components initialized successfully") def _create_rollouter(self, config) -> None: print("[ASYNC MAIN] Starting create rollouter...") rollouter = FullyAsyncRollouter.remote( config=config, tokenizer=self.components["tokenizer"], role_worker_mapping=None, resource_pool_manager=create_resource_pool_manager(config, roles=[Role.Rollout]), ray_worker_group_cls=self.components["ray_worker_group_cls"], processor=self.components["processor"], device_name=config.trainer.device, ) ray.get(rollouter.init_workers.remote()) ray.get(rollouter.set_max_required_samples.remote()) self.components["rollouter"] = rollouter print("[ASYNC MAIN] Rollouter created and initialized successfully") def _create_trainer(self, config) -> None: print("[ASYNC MAIN] Starting create trainer...") trainer_role_mapping = { role: worker_cls for role, worker_cls in self.components["role_worker_mapping"].items() if role != Role.Rollout } trainer = FullyAsyncTrainer.remote( config=config, tokenizer=self.components["tokenizer"], role_worker_mapping=trainer_role_mapping, resource_pool_manager=create_resource_pool_manager(config, roles=list(trainer_role_mapping.keys())), ray_worker_group_cls=self.components["ray_worker_group_cls"], processor=self.components["processor"], device_name=config.trainer.device, ) ray.get(trainer.init_workers.remote()) self.components["trainer"] = trainer print("[ASYNC MAIN] FullyAsyncTrainer created and initialized successfully") def _run_training_loop(self): self.running = True print("[ASYNC MAIN] Starting Rollouter and Trainer...") rollouter_future = self.components["rollouter"].fit.remote() trainer_future = self.components["trainer"].fit.remote() futures = [rollouter_future, trainer_future] try: while futures: # Use ray.wait to monitor all futures and return when any one is completed. done_futures, remaining_futures = ray.wait(futures, num_returns=1, timeout=None) for future in done_futures: try: ray.get(future) print("[ASYNC MAIN] One component completed successfully") except Exception as e: print(f"[ASYNC MAIN] Component failed with error: {e}") for remaining_future in remaining_futures: ray.cancel(remaining_future) raise e futures = remaining_futures except Exception as e: print(f"[ASYNC MAIN] Training failed: {e}") for future in futures: ray.cancel(future) raise finally: asyncio.run(self.components["message_queue_client"].clear_queue()) print("[ASYNC MAIN] Training completed or interrupted") @hydra.main(config_path="config", config_name="fully_async_ppo_trainer", version_base=None) def main(config): from verl.trainer.main_ppo import run_ppo # Ensure async training config exists if not hasattr(config, "async_training"): raise RuntimeError("must set async_training config") assert config.async_training.use_trainer_do_validate is False, "use_trainer_do_validate is not ready to use." from time import time start_time = time() auto_set_device(config) # TODO: unify rollout config with actor_rollout_ref config.actor_rollout_ref.rollout.nnodes = config.rollout.nnodes config.actor_rollout_ref.rollout.n_gpus_per_node = config.rollout.n_gpus_per_node run_ppo(config, task_runner_class=FullyAsyncTaskRunner) print(f"total time: {time() - start_time:.2f} seconds") if __name__ == "__main__": main() ================================================ FILE: verl/experimental/fully_async_policy/fully_async_rollouter.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import multiprocessing import os import time from concurrent.futures import ThreadPoolExecutor from pprint import pformat import numpy as np import ray import torch from verl.experimental.fully_async_policy.detach_utils import ( RolloutSample, ValidateMetrics, prepare_single_generation_data, safe_create_task, ) from verl.experimental.fully_async_policy.message_queue import MessageQueueClient from verl.experimental.separation.ray_trainer import SeparateRayPPOTrainer from verl.single_controller.ray import RayWorkerGroup from verl.trainer.ppo.ray_trainer import ResourcePoolManager from verl.trainer.ppo.utils import Role, WorkerType from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path from verl.utils.profiler import marked_timer from verl.utils.tracking import ValidationGenerationsLogger @ray.remote(num_cpus=10, max_concurrency=100) class FullyAsyncRollouter(SeparateRayPPOTrainer): """ Asynchronous sample generator, responsible for continuously generating training samples and putting them into MessageQueue Based on the mature implementation improvements of OneStepOffRayTrainer """ def __init__( self, config, tokenizer, role_worker_mapping: dict[Role, WorkerType], resource_pool_manager: ResourcePoolManager, ray_worker_group_cls: RayWorkerGroup = RayWorkerGroup, processor=None, device_name=None, ): # Store the tokenizer for text processing self.tokenizer = tokenizer self.processor = processor self.config = config self.hybrid_engine = config.actor_rollout_ref.hybrid_engine assert not self.hybrid_engine assert self.config.data.train_batch_size == 0, "train_batch_size must be zero" assert self.config.data.gen_batch_size == 1, "gen_batch_size must be one" assert self.config.async_training.staleness_threshold >= 0, "staleness_threshold must larger than 0" assert self.config.async_training.trigger_parameter_sync_step >= 1, ( "trigger_parameter_sync_step must larger or equal than 1" ) self.role_worker_mapping = role_worker_mapping self.resource_pool_manager = resource_pool_manager self.use_reference_policy = False self.use_rm = False self.use_critic = False self.ray_worker_group_cls = ray_worker_group_cls self.device_name = device_name if device_name else self.config.trainer.device self.validation_generations_logger = ValidationGenerationsLogger( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, ) self.ref_in_actor = False self.kl_ctrl_in_reward = False self.use_prefix_grouper = self.config.actor_rollout_ref.actor.get("use_prefix_grouper", False) self.use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") # ==================== fully async config ==================== print("[FullyAsyncRollouter] Creating datasets...") from verl.trainer.main_ppo import create_rl_dataset, create_rl_sampler from verl.utils.dataset.rl_dataset import collate_fn train_dataset = create_rl_dataset( config.data.train_files, config.data, tokenizer, processor, max_samples=config.data.get("train_max_samples", -1), ) val_dataset = create_rl_dataset( config.data.val_files, config.data, tokenizer, processor, max_samples=config.data.get("val_max_samples", -1), ) train_sampler = create_rl_sampler(config.data, train_dataset) self._validate_config() if self.config.async_training.use_trainer_do_validate: rollout_gpus = config.rollout.nnodes * config.rollout.n_gpus_per_node train_gpus = config.trainer.nnodes * config.trainer.n_gpus_per_node total_gpus = rollout_gpus + train_gpus print(f"[FullyAsyncRollouter] split before val_dataset total len: {len(val_dataset)}") split_dataset = val_dataset.split(total_gpus) rollout_val_dataset0 = split_dataset[:rollout_gpus] from torch.utils.data import ConcatDataset val_dataset = ConcatDataset(rollout_val_dataset0) print(f"[FullyAsyncRollouter] split after val_dataset total len: {len(val_dataset)}") print(f"[FullyAsyncRollouter] Rollouter _create_dataloader...\n{train_dataset}\n{val_dataset}") self._create_dataloader(train_dataset, val_dataset, collate_fn, train_sampler) self.total_rollout_steps = len(self.train_dataloader) * self.config.trainer.total_epochs if self.config.rollout.total_rollout_steps is not None: self.total_rollout_steps = min(self.config.rollout.total_rollout_steps, self.total_rollout_steps) print(f"[FullyAsyncRollouter] Total rollout steps: {self.total_rollout_steps}") self.total_train_steps = None # Rollouter parameter configuration self.message_queue_client = None # Worker groups: rollout_wg is same to actor_rollout_wg self.rollout_wg = None self.actor_rollout_wg = None self.async_rollout_manager = None # Config self.staleness_threshold: float = config.async_training.get("staleness_threshold", 1) # required_samples use ppo_mini_batch_size*require_batches as the minimum number of samples. self.require_batches = config.async_training.require_batches self.required_samples = config.actor_rollout_ref.actor.ppo_mini_batch_size * self.require_batches self.max_required_samples = None self.max_concurrent_samples = None # queue size self.max_queue_size = None # Statistics self.total_generated_samples = 0 self.staleness_samples = 0 self.dropped_stale_samples = 0 self.processed_sample_count = 0 # we start from step 1 self.global_steps = 1 self.idle_start_time = time.time() self.step_start_time = time.time() # Concurrency control # Modified by self.pause() or self._should_pause_generation() self.paused = False self.running = True # Add dataloader lock self.dataloader_lock = asyncio.Lock() # Initialize async queues self.pending_queue = asyncio.Queue(maxsize=128) self.active_tasks = set() cpu_cores = multiprocessing.cpu_count() # cpu case use cpu_cores; io case use cpu_cores*2 self.validate_executor = ThreadPoolExecutor(max_workers=cpu_cores) self.validate_task = None def _init_async_objects(self): # Initialize asyncio synchronization primitives. # We let asyncio.Condition create the Lock internally to ensure they share the same Event Loop. # This avoids 'ValueError: loop argument must agree with lock' which can occur in Ray environments # where the lock's captured loop (get_running_loop) differs from Condition's default loop check. # Explicitly passing the loop is deprecated/removed in Python 3.10+, so this reverse-initialization # is the most robust workaround. self.condition = asyncio.Condition() self.lock = self.condition._lock async def set_message_queue_client(self, message_queue_client: MessageQueueClient): """Set message queue client""" async with self.lock: self.message_queue_client = message_queue_client async def set_max_required_samples(self): async with self.lock: self.max_required_samples = int( self.required_samples * (self.staleness_threshold + 1) * self.config.async_training.trigger_parameter_sync_step ) self.total_train_steps = int( self.total_rollout_steps / (self.required_samples * self.config.async_training.trigger_parameter_sync_step) ) self.max_concurrent_samples = len(self.async_rollout_manager.server_handles) * 16 self.max_concurrent_samples = min(self.max_concurrent_samples, self.max_required_samples) self.max_queue_size = self.max_required_samples print( f"[FullyAsyncRollouter] required_samples : {self.required_samples} " f"max_required_samples: {self.max_required_samples} " f"max_queue_size: {self.max_queue_size} " f"total_train_steps: {self.total_train_steps} " f"total_rollout_steps: {self.total_rollout_steps} " f"max_concurrent_samples: {self.max_concurrent_samples} " ) def get_rollout_wg(self): """Get rollout worker group""" return self.rollout_wg def get_replicas(self): """Get rollout worker group""" return self.async_rollout_manager.rollout_replicas def get_max_queue_size(self): return self.max_queue_size def get_total_train_steps(self): return self.total_train_steps async def reset_staleness(self): """ Reset staleness samples after parameter update. Returns timing_raw dictionary for metrics. """ async with self.lock: self.paused = False self.condition.notify_all() # every time param change, reset staleness_samples self.staleness_samples = len(self.active_tasks) + await self.message_queue_client.get_queue_size() timing_raw = {} rollout_active_time = self.idle_start_time - self.step_start_time rollout_version_time = time.time() - self.step_start_time idle_ratio = 1 - rollout_active_time / rollout_version_time timing_raw["fully_async/rollouter/active_time"] = rollout_active_time timing_raw["fully_async/rollouter/version_time"] = rollout_version_time timing_raw["fully_async/rollouter/idle_ratio"] = idle_ratio print( f"[FullyAsyncRollouter][Public][reset_staleness] " f"reset staleness_samples to: {self.staleness_samples} " f"idle_ratio: {timing_raw['fully_async/rollouter/idle_ratio']:.4f}" ) self.step_start_time = time.time() return timing_raw def do_validate(self) -> ValidateMetrics: """Run validation and return metrics""" timing_raw = {} with marked_timer("rollouter/validate_time", timing_raw, color="green"): val_metrics: dict = self._validate() return ValidateMetrics(timing_raw=timing_raw, metrics=val_metrics) async def save_checkpoint(self, local_global_step_folder: str): # WARNING!: Due to the asynchronous nature, there are some in-flight samples # (pending/cancel/result queue and message queue). # Therefore, directly saving the state of the dataloader will result in losing these # samples when resuming training. # TODO: Implement dataloader recovery without losing in-flight samples. from verl.utils.fs import local_mkdir_safe # save dataloader local_mkdir_safe(local_global_step_folder) dataloader_local_path = os.path.join(local_global_step_folder, "data.pt") async with self.dataloader_lock: dataloader_state_dict = self.train_dataloader.state_dict() torch.save(dataloader_state_dict, dataloader_local_path) print(f"[FullyAsyncRollouter] Saved dataloader checkpoint to {dataloader_local_path}") def load_checkpoint(self): """Load checkpoint including dataloader state based on resume mode""" if self.config.trainer.resume_mode == "disable": print("[FullyAsyncRollouter] Resume mode is disabled, starting from scratch") return 0 # Determine checkpoint folder path if self.config.trainer.default_hdfs_dir is not None: raise NotImplementedError("[FullyAsyncRollouter] Load from hdfs is not implemented yet") else: checkpoint_folder = self.config.trainer.default_local_dir if not os.path.isabs(checkpoint_folder): working_dir = os.getcwd() checkpoint_folder = os.path.join(working_dir, checkpoint_folder) global_step_folder = find_latest_ckpt_path(checkpoint_folder) # Find and validate global_step_folder based on resume mode if self.config.trainer.resume_mode == "auto": if global_step_folder is None: print("[FullyAsyncRollouter] Training from scratch (no checkpoint found)") return 0 elif self.config.trainer.resume_mode == "resume_path": assert isinstance(self.config.trainer.resume_from_path, str), ( "[FullyAsyncRollouter] resume_from_path must be str type" ) assert "global_step_" in self.config.trainer.resume_from_path, ( "[FullyAsyncRollouter] resume_from_path must specify the global_steps" ) global_step_folder = self.config.trainer.resume_from_path if not os.path.isabs(global_step_folder): working_dir = os.getcwd() global_step_folder = os.path.join(working_dir, global_step_folder) else: raise ValueError(f"[FullyAsyncRollouter] Unknown resume_mode: {self.config.trainer.resume_mode}") print(f"[FullyAsyncRollouter] Loading checkpoint from: {global_step_folder}") # Extract and set global step trainer_global_steps = int(global_step_folder.split("global_step_")[-1]) self.global_steps = ( trainer_global_steps * self.required_samples * self.config.async_training.trigger_parameter_sync_step + 1 ) print(f"[FullyAsyncRollouter] Setting global_steps to {self.global_steps}") # Load dataloader state dataloader_local_path = os.path.join(global_step_folder, "data.pt") if os.path.exists(dataloader_local_path): dataloader_state_dict = torch.load(dataloader_local_path, weights_only=False) self.train_dataloader.load_state_dict(dataloader_state_dict) print(f"[FullyAsyncRollouter] Loaded dataloader state from {dataloader_local_path}") else: print( f"[FullyAsyncRollouter] Warning: No dataloader state found at {dataloader_local_path}, " f"will start from scratch" ) def _validate_config(self): # Validate asynchronous training configuration if not hasattr(self.config, "async_training"): raise ValueError("[FullyAsyncRollouter] Missing async_training configuration") assert self.config.actor_rollout_ref.rollout.calculate_log_probs, "must rollout calculate log_probs" async def init_workers(self): """Initialize distributed training workers using Ray backend. Creates: 1. Ray resource pools from configuration 2. Worker groups for each role (actor, critic, etc.) """ self._init_async_objects() self._create_worker_classes() self._init_reward_loop() await self._init_async_rollout_manager() def _create_actor_rollout_classes(self): # Skip rollout creation and let agentloop handle it pass def _init_models(self): self.rollout_wg = self.all_wg[str(Role.Rollout)] self.rollout_wg.init_model() self.actor_rollout_wg = self.rollout_wg def _create_continuous_iterator(self): """ Create a continuous data iterator across epoch """ for epoch in range(self.config.trainer.total_epochs): iterator = iter(self.train_dataloader) for batch_dict in iterator: yield epoch, batch_dict async def _init_async_rollout_manager(self): # infrastructure overview: https://verl.readthedocs.io/en/latest/advance/reward_loop.html#architecture-design # agent_reward_loop: streaming reward computation with actor rollout # two conditions satisfied: (1) no reward model, or (2) reward model with extra resource pool enable_agent_reward_loop = not self.use_rm or self.config.reward.reward_model.enable_resource_pool # if enable_agent_reward_loop, we directly pass reward_loop_workers to agent loop manager # to stream reward computation with actor rollout reward_loop_worker_handles = self.reward_loop_manager.reward_loop_workers if enable_agent_reward_loop else None # create async rollout manager and request scheduler assert self.config.actor_rollout_ref.rollout.mode == "async" from verl.experimental.fully_async_policy.agent_loop import FullyAsyncAgentLoopManager self.async_rollout_mode = True self.async_rollout_manager = await FullyAsyncAgentLoopManager.create( config=self.config, worker_group=self.rollout_wg, reward_loop_worker_handles=reward_loop_worker_handles ) # Add samples to the pending_queue async def _feed_samples(self): continuous_iterator = self._create_continuous_iterator() for epoch, batch_dict in continuous_iterator: # Similar to _prepare_generate_batch: Separate data full_batch = prepare_single_generation_data(batch_dict, self.config) sample_id = f"sample_{epoch}_{self.global_steps}" rollout_sample = RolloutSample( full_batch=full_batch, sample_id=sample_id, epoch=epoch, rollout_status={}, ) await self.pending_queue.put(rollout_sample) # Check if have reached the last step if self.global_steps >= self.total_rollout_steps: print( f"[FullyAsyncRollouter][Feed] " f"Maximum count has been reached, stop adding new samples: " f"{self.global_steps} >= {self.total_rollout_steps}" ) break self.global_steps += 1 # End signal await self.pending_queue.put(None) print(f"[FullyAsyncRollouter][Feed] Sample addition is complete, {self.global_steps} samples have been added") async def _processor_worker(self): """ Streaming worker coroutines, a sample is submitted for processing without waiting for batches """ while True: if self.paused or await self._should_pause_generation(): print( "[FullyAsyncRollouter][Processor] Received pause signal, waiting for remaining tasks to return..." ) async with self.lock: self.paused = True while self.active_tasks: async with self.lock: # After acquiring the lock, the number of active_tasks may change, need to be verified again if self.active_tasks: done_tasks, self.active_tasks = await asyncio.wait( self.active_tasks, return_when=asyncio.FIRST_COMPLETED ) for task in done_tasks: await task async with self.lock: while self.paused: self.idle_start_time = time.time() await self.condition.wait() continue # Get sample from appropriate queue and immediately mark task as done rollout_sample = await self.pending_queue.get() self.pending_queue.task_done() self.staleness_samples += 1 if rollout_sample is None: print( "[FullyAsyncRollouter][Processor] Received end signal, waiting for remaining tasks to complete..." ) while self.active_tasks: async with self.lock: if self.active_tasks: done_tasks, self.active_tasks = await asyncio.wait( self.active_tasks, return_when=asyncio.FIRST_COMPLETED ) for task in done_tasks: await task break # Check whether the number of concurrent tasks exceeds the limit while len(self.active_tasks) >= self.max_concurrent_samples: async with self.lock: if self.active_tasks: done_tasks, self.active_tasks = await asyncio.wait( self.active_tasks, return_when=asyncio.FIRST_COMPLETED ) for task in done_tasks: await task # Submit single sample processing async with self.lock: # After the pause is over, the lock is acquired and it is necessary # to determine whether it is the pause phase, otherwise continue to wait while self.paused: await self.condition.wait() task = safe_create_task( self._process_single_sample_streaming(rollout_sample), name=rollout_sample.sample_id, task_set=self.active_tasks, ) async def _process_single_sample_streaming(self, rollout_sample: RolloutSample): """Process a single sample streamingly""" # Calling asynchronous generation methods ret = await self.async_rollout_manager.generate_sequences_single(rollout_sample.full_batch) rollout_sample.full_batch = ret rollout_sample.full_batch.non_tensor_batch["uid"] = np.array( [f"uid_{rollout_sample.sample_id}"] * len(rollout_sample.full_batch), dtype=object ) rollout_sample.rollout_status = await self.get_statistics() success = await self.message_queue_client.put_sample( sample=ray.cloudpickle.dumps(rollout_sample), ) if success: self.total_generated_samples += 1 else: self.dropped_stale_samples += 1 self.processed_sample_count += 1 async def _streaming_generation_main(self): """The main entry method for stream processing""" if self.async_rollout_manager is None: await self._init_async_rollout_manager() # Start the streaming loop print(f"[FullyAsyncRollouter] Start streaming mode, maximum concurrent samples: {self.max_concurrent_samples}") # Start sample feed coroutine, streaming process coroutine self.feed_task = safe_create_task(self._feed_samples(), name="feed_task") self.processor_task = safe_create_task(self._processor_worker(), name="processor_task") try: # Wait for sample feed to complete # Use asyncio.wait to monitor all tasks. If processor exits early, # detect it instead of blocking on feed_task (it might be stuck on a full queue). done, pending = await asyncio.wait( [self.feed_task, self.processor_task], return_when=asyncio.FIRST_COMPLETED ) for task in done: if task.exception(): raise task.exception() if self.feed_task not in done: raise RuntimeError("Processor task exited prematurely") print("[FullyAsyncRollouter] Sample feed completed") # Wait for streaming to complete await self.processor_task print("[FullyAsyncRollouter] Streaming process completed") await self.pending_queue.join() print("[FullyAsyncRollouter] pending_queue joined") except Exception as e: print(f"[FullyAsyncRollouter] Streaming process exception: {e}") raise e finally: if self.feed_task and not self.feed_task.done(): self.feed_task.cancel() await asyncio.gather(self.feed_task, return_exceptions=True) if self.processor_task and not self.processor_task.done(): self.processor_task.cancel() await asyncio.gather(self.processor_task, return_exceptions=True) self.feed_task = None self.processor_task = None # Send a finish signal await self.message_queue_client.put_sample(sample=None) async with self.lock: self.running = False async def fit(self): """ Start the async rollouter - entry point that sets up and runs async tasks Main async fit method that coordinates all coroutines """ print("[FullyAsyncRollouter] Starting FullyAsyncRollouter...") if self.message_queue_client is None: raise ValueError("MessageQueue client not set. Call set_message_queue_client() first.") # Set the running status flag async with self.lock: self.paused = False self.running = True # Create the main asynchronous task generation_task = safe_create_task(self._streaming_generation_main(), name="generation_task") monitor_task = safe_create_task(self._async_monitor_loop(), name="monitor_task") try: # Run build and monitoring tasks concurrently await asyncio.gather(generation_task, monitor_task, return_exceptions=True) except Exception as e: print(f"[FullyAsyncRollouter] Asynchronous task execution error: {e}") finally: if not generation_task.done(): generation_task.cancel() if not monitor_task.done(): monitor_task.cancel() # Wait for the task to complete await asyncio.gather(generation_task, monitor_task, return_exceptions=True) print("[FullyAsyncRollouter] Rollouter fit completed") async def _async_monitor_loop(self): """ Async coroutine for monitoring: Function 1: Log information output Function 2: Trigger rollout recovery """ last_stats_time = time.time() stats_interval = 60.0 check_interval = 10.0 while True: async with self.lock: if not self.running: break await asyncio.sleep(check_interval) # Print statistics periodically current_time = time.time() if current_time - last_stats_time >= stats_interval: stats = await self.get_statistics() print(f"[FullyAsyncRollouter][MonitorLoop][Statistics] {pformat(stats)}") last_stats_time = current_time # Trigger rollout recovery if self.paused and not await self._should_pause_generation(): async with self.lock: self.paused = False print("[FullyAsyncRollouter][ShouldPause] notify all wait tasks.") self.condition.notify_all() async def _should_pause_generation(self) -> bool: """Determine whether the build should be paused""" queue_stats = self.message_queue_client.get_statistics_sync() queue_size = queue_stats["queue_size"] if queue_size >= self.max_queue_size: if not self.paused: print( f"[FullyAsyncRollouter][ShouldPause] " f"due to full queue: size={queue_size}, max={self.max_queue_size}" ) return True if self.staleness_samples >= self.max_required_samples: if not self.paused: print( "[FullyAsyncRollouter][ShouldPause] " f"due to " f"staleness_samples {self.staleness_samples} >= max_required_samples {self.max_required_samples} " ) return True return False async def get_statistics(self) -> dict: queue_stats = self.message_queue_client.get_statistics_sync() stats = { # monitor stats "monitor/active_tasks_size": len(self.active_tasks), "monitor/queue/pending_queue_size": self.pending_queue.qsize(), "monitor/queue/mq_queue_size": queue_stats["queue_size"], # counting stats "count/total_generated_samples": self.total_generated_samples, "count/staleness_samples": self.staleness_samples, "count/dropped_stale_samples": self.dropped_stale_samples, # static stats "static/max_required_samples": self.max_required_samples, "static/required_samples": self.required_samples, "static/staleness_threshold": self.staleness_threshold, "static/max_queue_size": self.max_queue_size, "static/max_concurrent_samples": self.max_concurrent_samples, } return stats ================================================ FILE: verl/experimental/fully_async_policy/fully_async_trainer.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import time from datetime import datetime from pprint import pprint from typing import Any import ray from omegaconf import OmegaConf, open_dict from tqdm import tqdm from verl import DataProto from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.fully_async_policy.detach_utils import ( MetricsAggregator, ValidateMetrics, assemble_batch_from_rollout_samples, ) from verl.experimental.fully_async_policy.message_queue import MessageQueueClient from verl.experimental.separation.ray_trainer import SeparateRayPPOTrainer from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup from verl.trainer.ppo import core_algos from verl.trainer.ppo.ray_trainer import ResourcePoolManager from verl.trainer.ppo.utils import Role, WorkerType, need_critic, need_reference_policy, need_reward_model from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path, should_save_ckpt_esi from verl.utils.config import omega_conf_to_dataclass from verl.utils.debug import marked_timer from verl.utils.tracking import Tracking logger = logging.getLogger(__name__) class TrainingStopException(Exception): """Exception raised to signal training should stop""" pass @ray.remote(num_cpus=10) class FullyAsyncTrainer(SeparateRayPPOTrainer): """ A fully asynchronous PPO trainer that obtains samples from a MessageQueue for training. Based on an improved implementation of OneStepOffRayTrainer """ def __init__( self, config, tokenizer, role_worker_mapping: dict[Role, WorkerType], resource_pool_manager: ResourcePoolManager, ray_worker_group_cls: RayWorkerGroup = RayWorkerGroup, processor=None, device_name=None, ): # ==================== RayPPOTrainer config ==================== # Store the tokenizer for text processing self.tokenizer = tokenizer self.processor = processor self.config = config self.hybrid_engine = config.actor_rollout_ref.hybrid_engine assert not self.hybrid_engine self.role_worker_mapping = role_worker_mapping self.resource_pool_manager = resource_pool_manager self.use_reference_policy = need_reference_policy(self.config) self.use_rm = need_reward_model(self.config) self.use_critic = need_critic(self.config) self.ray_worker_group_cls = ray_worker_group_cls self.device_name = device_name if device_name else self.config.trainer.device # if ref_in_actor is True, the reference policy will be actor without lora applied lora_rank = config.actor_rollout_ref.model.get("lora", {}).get("rank", 0) if lora_rank <= 0: lora_rank = config.actor_rollout_ref.model.get("lora_rank", 0) self.ref_in_actor = lora_rank > 0 or config.actor_rollout_ref.model.get("lora_adapter_path") is not None # define in-reward KL control # kl loss control currently not suppoorted if self.config.algorithm.use_kl_in_reward: self.kl_ctrl_in_reward = core_algos.get_kl_controller(self.config.algorithm.kl_ctrl) self.use_prefix_grouper = self.config.actor_rollout_ref.actor.get("use_prefix_grouper", False) self.use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") # ==================== SeparateRayPPOTrainer config ==================== self.global_steps = 0 self.epoch = 0 self.max_steps_duration = 0 self.progress_bar = None self.is_last_step = False self.prev_step_profile = False self.curr_step_profile = False self.next_step_profile = False self.last_val_metrics = {} self.metrics = {} self.timing_raw = {} # reward message self.future_reward = None self.reward_tensor = None self.reward_extra_infos_dict = {} self.logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) # ==================== fully async config ==================== self.message_queue_client = None # Statistics self.local_trigger_step = 1 self.processed_samples = 0 self.stale_trajectory_processed = 0 self.current_param_version = 0 self.total_train_steps = None self.progress_bar = None self.trigger_parameter_sync_step = config.async_training.trigger_parameter_sync_step self.last_ckpt_version = 0 self.train_role = Role.ActorRollout if config.async_training.use_trainer_do_validate else Role.Actor # required_samples use ppo_mini_batch_size*require_batches as the minimum number of samples. self.require_batches = config.async_training.require_batches self.required_samples = config.actor_rollout_ref.actor.ppo_mini_batch_size * self.require_batches total_gpus = ( config.trainer.nnodes * config.trainer.n_gpus_per_node + config.rollout.nnodes * config.rollout.n_gpus_per_node ) self.metrics_aggregator = MetricsAggregator(total_gpus=total_gpus) # use trainer to do validation if self.config.async_training.use_trainer_do_validate: from verl.trainer.main_ppo import create_rl_dataset from verl.utils.dataset.rl_dataset import collate_fn val_dataset = create_rl_dataset(config.data.val_files, config.data, tokenizer, processor) rollout_gpus = config.rollout.nnodes * config.rollout.n_gpus_per_node print(f"[FullyAsyncTrainer] split before val_dataset total len: {len(val_dataset)}") split_dataset = val_dataset.split(total_gpus) rollout_val_dataset0 = split_dataset[rollout_gpus:] from torch.utils.data import ConcatDataset val_dataset = ConcatDataset(rollout_val_dataset0) print(f"[FullyAsyncTrainer] split after val_dataset total len: {len(val_dataset)}") self.val_dataset = val_dataset # update val_dataloader val_batch_size = self.config.data.val_batch_size # Prefer config value if set if val_batch_size is None: val_batch_size = len(val_dataset) from torchdata.stateful_dataloader import StatefulDataLoader print(f"[FullyAsyncTrainer] create val_dataloader with batch_size: {val_batch_size}") self.val_dataloader = StatefulDataLoader( dataset=val_dataset, batch_size=val_batch_size, num_workers=self.config.data["dataloader_num_workers"], shuffle=self.config.data.get("validation_shuffle", True), drop_last=False, collate_fn=collate_fn, ) # Reference to rollouter for parameter synchronization self.rollouter = None self.checkpoint_manager = None # when use_trainer_do_validate == Ture, use colocate_checkpoint_manager to sync params self.colocate_checkpoint_manager = None def _setup_checkpoint_manager(self, rollouter): """Setup checkpoint manager after rollouter is initialized""" replicas = ray.get(rollouter.get_replicas.remote()) checkpoint_engine_config = omega_conf_to_dataclass(self.config.actor_rollout_ref.rollout.checkpoint_engine) self.checkpoint_manager = CheckpointEngineManager( config=checkpoint_engine_config, trainer=self.actor_wg, replicas=replicas ) print("[FullyAsyncTrainer] Checkpoint manager initialized") def set_message_queue_client(self, message_queue_client: MessageQueueClient): """Set message queue client""" self.message_queue_client = message_queue_client def set_rollouter(self, rollouter): """Set rollouter reference for parameter synchronization""" self.rollouter = rollouter # Setup checkpoint manager after rollouter is set self._setup_checkpoint_manager(rollouter) def set_total_train_steps(self, total_training_steps): self.total_train_steps = total_training_steps try: OmegaConf.set_struct(self.config, True) with open_dict(self.config): if OmegaConf.select(self.config, "actor_rollout_ref.actor.optim"): self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps if OmegaConf.select(self.config, "critic.optim"): self.config.critic.optim.total_training_steps = total_training_steps except Exception as e: print(f"Warning: Could not set total_training_steps in config. Structure missing? Error: {e}") self.progress_bar = tqdm(total=self.total_train_steps, initial=0, desc="Training Progress") def get_actor_wg(self): """Get actor worker group""" return self.actor_wg async def _get_samples_from_queue(self) -> tuple[None, None] | tuple[int, Any]: """ Get samples from message queue and compose gen_batch_output Uses a loop to continuously collect samples until enough are gathered Returns: tuple: (epoch, batch_dict, gen_batch_output) """ print( f"[FullyAsyncTrainer] Requesting {self.required_samples} samples from queue", flush=True, ) # Collect samples using a simple loop calling get_sample consumer_start = time.time() queue_samples = [] queue_len = 0 while len(queue_samples) < self.required_samples: # Get a single sample and wait until there is a sample or None is received sample, queue_len = self.message_queue_client.get_sample_sync() if sample is None: print( f"[FullyAsyncTrainer] Detected termination signal (None), stopping sample collection. " f"Collected {len(queue_samples)}/{self.required_samples} samples" ) break queue_samples.append(sample) if len(queue_samples) % 64 == 0: print( f"[FullyAsyncTrainer] Collected {len(queue_samples)}/{self.required_samples} samples. " f"mq_len: {queue_len}" ) consumer_end = time.time() if not queue_samples or len(queue_samples) < self.required_samples: print("[FullyAsyncTrainer] not enough samples collected after loop") return None, None total_wait_time = consumer_end - consumer_start print( f"[FullyAsyncTrainer] Loop collection completed: {len(queue_samples)}/{self.required_samples} samples, " f"total wait time: {total_wait_time:.2f} seconds. " f"mq_len: {queue_len}" ) queue_samples = [ray.cloudpickle.loads(x) for x in queue_samples] # Assemble batch - now working directly with RolloutSample objects if self.config.trainer.balance_batch: batch = assemble_batch_from_rollout_samples(queue_samples, self.tokenizer, self.config, self._balance_batch) else: batch = assemble_batch_from_rollout_samples(queue_samples, self.tokenizer, self.config, None) batch.meta_info["fully_async/total_wait_time"] = total_wait_time return 0, batch def _create_actor_rollout_classes(self): # create actor for role in [self.train_role]: resource_pool = self.resource_pool_manager.get_resource_pool(role) role_cls = RayClassWithInitArgs( cls=self.role_worker_mapping[role], config=self.config.actor_rollout_ref, role=str(role), ) self.resource_pool_to_cls[resource_pool][str(role)] = role_cls def _init_models(self): if self.use_critic: self.critic_wg = self.all_wg[str(Role.Critic)] self.critic_wg.init_model() if self.use_reference_policy and not self.ref_in_actor: self.ref_policy_wg = self.all_wg[str(Role.RefPolicy)] self.ref_policy_wg.init_model() if self.use_rm: self.rm_wg = self.all_wg[str(Role.RewardModel)] self.rm_wg.init_model() self.actor_wg = self.all_wg[str(self.train_role)] self.actor_wg.init_model() self.actor_rollout_wg = self.actor_wg # to be compatible with the functions that not be modified async def init_workers(self): """Initialize distributed training workers using Ray backend. Creates: 1. Ray resource pools from configuration 2. Worker groups for each role (actor, critic, etc.) """ self._init_resource_pools() self._create_worker_classes() self._init_worker_groups() self._init_models() self._init_reward_loop() await self._init_async_rollout_manager() def _init_reward_loop(self): if self.config.async_training.use_trainer_do_validate: print("[FullyAsyncTrainer] Init reward loop") super()._init_reward_loop() async def _init_async_rollout_manager(self): # use async rollout do validate print(f"[FullyAsyncTrainer] use_trainer_do_validate: {self.config.async_training.use_trainer_do_validate}") if self.config.async_training.use_trainer_do_validate: print("[FullyAsyncTrainer] Init async rollout manager") # infrastructure overview: https://verl.readthedocs.io/en/latest/advance/reward_loop.html#architecture-design # agent_reward_loop: streaming reward computation with actor rollout # two conditions satisfied: (1) no reward model, or (2) reward model with extra resource pool enable_agent_reward_loop = not self.use_rm or self.config.reward.reward_model.enable_resource_pool # if enable_agent_reward_loop, we directly pass reward_loop_workers to agent loop manager # to stream reward computation with actor rollout reward_loop_worker_handles = ( self.reward_loop_manager.reward_loop_workers if enable_agent_reward_loop else None ) # create async rollout manager and request scheduler assert self.config.actor_rollout_ref.rollout.mode == "async" self.async_rollout_mode = True from verl.experimental.agent_loop import AgentLoopManager self.async_rollout_manager = await AgentLoopManager.create( config=self.config, worker_group=self.actor_rollout_wg, reward_loop_worker_handles=reward_loop_worker_handles, ) print("[FullyAsyncTrainer] async_rollout_manager initialized") # Modify checkpoint_engine config to use naive backend checkpoint_engine_cfg = self.config.actor_rollout_ref.rollout.checkpoint_engine original_backend = checkpoint_engine_cfg.backend with open_dict(checkpoint_engine_cfg): checkpoint_engine_cfg.backend = "naive" checkpoint_engine_config = omega_conf_to_dataclass(checkpoint_engine_cfg) print(f"[FullyAsyncTrainer] checkpoint_engine_config: {checkpoint_engine_config}") self.colocate_checkpoint_manager = CheckpointEngineManager( config=checkpoint_engine_config, trainer=self.actor_rollout_wg, replicas=self.async_rollout_manager.rollout_replicas, ) # sleep all replicas to load checkpoint await self.colocate_checkpoint_manager.sleep_replicas() # Restore original backend value with open_dict(checkpoint_engine_cfg): checkpoint_engine_cfg.backend = original_backend print("[FullyAsyncTrainer] colocate_checkpoint_manager initialized") else: print("[FullyAsyncTrainer] Skip async rollout manager (use_trainer_do_validate=False)") async def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ print("[FullyAsyncTrainer] Starting FullyAsyncTrainer...") if self.message_queue_client is None: raise ValueError("MessageQueue client not set. Call set_message_queue_client() first.") if self.rollouter is None: raise ValueError("rollouter not set. Call set_rollouter() first.") self.max_steps_duration = 0 self.global_steps += 1 # Use queue mode, no need for traditional dataloader iterator # Initialize to get the first batch of data while True: try: await self.fit_step() except TrainingStopException: print("[FullyAsyncTrainer] Training stopped by queue termination signal") break self.progress_bar.close() if self.current_param_version % self.config.trainer.test_freq != 0 or self.local_trigger_step > 1: await self._fit_update_weights() await self._fit_validate() self._fit_save_checkpoint(force=True) async def fit_step(self, batch_dict: dict = None): """ Single-step training template method. Handles all logic for one training step. Flow: 1. Pre-step processing -> 2. Get batch -> 3. Generate sequences -> 4. Compute reward -> 5. Compute log_prob -> 6. Compute reward -> 7. Compute advantage -> 8. Update critic -> 9. Update actor -> 10. Post-step processing Args: batch_dict: Raw data dictionary """ self.metrics = {"training/global_step": self.global_steps, "training/epoch": self.epoch} self.timing_raw = {} # reward message self.future_reward = None self.reward_tensor = None self.reward_extra_infos_dict = {} self._fit_start_profile() with marked_timer("step", self.timing_raw): batch = await self._fit_generate(None) batch = self._fit_compute_reward(batch) batch = self._fit_compute_log_prob(batch) batch = self._fit_compute_ref_log_prob(batch) batch = self._fit_compute_critic(batch) batch = self._fit_compute_advantage(batch) batch = self._fit_update_critic(batch) batch = self._fit_update_actor(batch) self._fit_update_local_step() await self._fit_update_weights() self._fit_dump_data(batch) await self._fit_validate() self._fit_save_checkpoint() self._fit_stop_profile() self._fit_collect_metrics(batch) self._fit_torch_memory() self._fit_postprocess_step() async def _fit_generate(self, batch: DataProto = None) -> DataProto | None: metrics = self.metrics timing_raw = self.timing_raw with marked_timer("gen", timing_raw, color="red"): epoch, batch = await self._get_samples_from_queue() if batch is None: raise TrainingStopException("Training terminated: queue returned None") self._collect_metrics_from_samples(batch, metrics) batch.meta_info["temperature"] = self.config.actor_rollout_ref.rollout.temperature return batch def _compute_old_log_prob(self, batch: DataProto): """ If algorithm.rollout_correction.bypass_mode is False, use model engine and first version model params to re-calculate old_log_prob. If local_trigger_step == 1, load the training engine's parameters to the CPU and save a copy for subsequent MIS use. If local_trigger_step == 2, 3, ..., restore the parameters of version 1 to calculate the old_log_prob, then restore the parameters of the current version. """ if self.local_trigger_step == 1: self.actor_rollout_wg.save_model_to_cpu(1) old_log_prob, old_log_prob_mfu = super()._compute_old_log_prob(batch) else: self.actor_rollout_wg.save_model_to_cpu(self.local_trigger_step) self.actor_rollout_wg.restore_model_from_cpu(1) old_log_prob, old_log_prob_mfu = super()._compute_old_log_prob(batch) self.actor_rollout_wg.restore_model_from_cpu(self.local_trigger_step) self.actor_rollout_wg.clear_cpu_model(self.local_trigger_step) return old_log_prob, old_log_prob_mfu def _fit_update_local_step(self): time_str = datetime.now().strftime("%H:%M:%S.%f")[:-3] print( f"[FullyAsyncTrainer] global_steps: {self.global_steps} " f"local_trigger_step: {self.local_trigger_step} " f"trigger_parameter_sync_step: {self.trigger_parameter_sync_step} " f"{time_str}" ) if self.local_trigger_step < self.trigger_parameter_sync_step: self.local_trigger_step += 1 else: self.current_param_version += 1 self.local_trigger_step = 1 async def _fit_update_weights(self): if self.local_trigger_step != 1: return with marked_timer("timing_s/param_sync", self.timing_raw): await self.checkpoint_manager.update_weights(global_steps=self.current_param_version) print( f"[FullyAsyncTrainer] _fit_update_weights, " f"timing_s/param_sync: {self.timing_raw['timing_s/param_sync']:.4f} seconds " f"self.current_param_version: {self.current_param_version}" ) # Reset staleness in rollouter timing_raw = ray.get(self.rollouter.reset_staleness.remote()) self.logger.log( data=timing_raw, step=self.current_param_version, ) # Log aggregated training metrics self.logger.log( data=self.metrics_aggregator.get_aggregated_metrics(), step=self.current_param_version, ) self.metrics_aggregator.reset() async def _validate_process(self): """Run trainer-side validation using async rollout manager""" if self.config.async_training.use_trainer_do_validate: print("[FullyAsyncTrainer] _validate_process") from verl.utils.profiler import marked_timer # Wake up rollouter replicas and sync weights print("[FullyAsyncTrainer] wake up replicas before validation") await self.colocate_checkpoint_manager.update_weights(global_steps=self.current_param_version) with marked_timer("trainer/validate_time", self.timing_raw): train_val_metrics = self._validate(True) # Sleep rollouter replicas to free GPU memory for validation print("[FullyAsyncTrainer] sleep replicas after validation") await self.colocate_checkpoint_manager.sleep_replicas() print(f"[FullyAsyncTrainer] validate timing: {self.timing_raw['trainer/validate_time']}") return train_val_metrics else: print("[FullyAsyncTrainer] _validate_process without async_rollout_manager") return None async def _fit_validate(self, val_before_train=False): if self.local_trigger_step != 1: return # Check if validation is needed need_validate = ( self.config.trainer.test_freq > 0 and self.current_param_version % self.config.trainer.test_freq == 0 and self.current_param_version > 0 ) # Skip validation if not needed and not validation before training if not need_validate and not val_before_train: return # Trigger rollouter validation and get future val_future = self.rollouter.do_validate.remote() # Run trainer-side validation train_val_metrics = await self._validate_process() # Wait for rollouter validation result and log val_metrics: ValidateMetrics = ray.get(val_future) if train_val_metrics: # Merge trainer and rollouter validation results with marked_timer("timing_s/merge_val", self.timing_raw): new_metrics = self._merge_validation_results(train_val_metrics, val_metrics.metrics) if new_metrics: self.logger.log(data=new_metrics, step=self.current_param_version) pprint( f"[FullyAsyncTrainer] parameter version: {self.current_param_version} " f"Validation metrics: {new_metrics}, timing: {self.timing_raw['timing_s/merge_val']}" ) else: if val_metrics.metrics: self.logger.log(data=val_metrics.metrics, step=self.current_param_version) pprint( f"[FullyAsyncTrainer] parameter version: {self.current_param_version} " f"Validation metrics: {val_metrics.metrics}" ) self.logger.log(data=val_metrics.timing_raw, step=self.current_param_version) def _fit_save_checkpoint(self, force=False): if self.current_param_version == self.last_ckpt_version: return timing_raw = self.timing_raw # Check if the ESI (Elastic Server Instance)/training plan is close to expiration. esi_close_to_expiration = should_save_ckpt_esi( max_steps_duration=self.max_steps_duration, redundant_time=self.config.trainer.esi_redundant_time, ) # Check if the conditions for saving a checkpoint are met. # The conditions include a mandatory condition (1) and # one of the following optional conditions (2/3/4): # 1. The save frequency is set to a positive value. # 2. It's the last training step. # 3. The current step number is a multiple of the save frequency. # 4. The ESI(Elastic Server Instance)/training plan is close to expiration. if self.config.trainer.save_freq > 0 and ( force or self.current_param_version % self.config.trainer.save_freq == 0 or esi_close_to_expiration ): if esi_close_to_expiration: print("Force saving checkpoint: ESI instance expiration approaching.") with marked_timer("save_checkpoint", timing_raw, color="green"): # sleep replicas to avoid OOM during checkpoint saving self._save_checkpoint() self.last_ckpt_version = self.current_param_version def _fit_postprocess_step(self): self.global_steps += 1 self.metrics_aggregator.add_step_metrics( metrics=self.metrics, sample_count=self.required_samples, timestamp=time.time() ) if self.local_trigger_step == 1: self.progress_bar.update(1) def _save_checkpoint(self): # Warning: Currently, to align the training process and metrics of colocate, # we use current_param_version instead of global step. # This can be logically aligned with the original self.global_steps of colocate # and is used for metrics and ckpt. which means that the parameter synchronization # from trainer to rollouter will increase by 1 each time. # path: given_path + `/global_step_{global_steps}` + `/actor` local_global_step_folder = os.path.join( self.config.trainer.default_local_dir, f"global_step_{self.current_param_version}" ) print(f"[FullyAsyncTrainer] local_global_step_folder: {local_global_step_folder}") actor_local_path = os.path.join(local_global_step_folder, "actor") actor_remote_path = ( None if self.config.trainer.default_hdfs_dir is None else os.path.join( self.config.trainer.default_hdfs_dir, f"global_step_{self.current_param_version}", "actor" ) ) remove_previous_ckpt_in_save = self.config.trainer.get("remove_previous_ckpt_in_save", False) if remove_previous_ckpt_in_save: print( "[FullyAsyncTrainer] Warning: remove_previous_ckpt_in_save is deprecated," + " set max_actor_ckpt_to_keep=1 and max_critic_ckpt_to_keep=1 instead" ) max_actor_ckpt_to_keep = ( self.config.trainer.get("max_actor_ckpt_to_keep", None) if not remove_previous_ckpt_in_save else 1 ) max_critic_ckpt_to_keep = ( self.config.trainer.get("max_critic_ckpt_to_keep", None) if not remove_previous_ckpt_in_save else 1 ) self.actor_rollout_wg.save_checkpoint( actor_local_path, actor_remote_path, self.current_param_version, max_ckpt_to_keep=max_actor_ckpt_to_keep ) if self.use_critic: critic_local_path = os.path.join(local_global_step_folder, str(Role.Critic)) critic_remote_path = ( None if self.config.trainer.default_hdfs_dir is None else os.path.join( self.config.trainer.default_hdfs_dir, f"global_step_{self.current_param_version}", str(Role.Critic) ) ) self.critic_wg.save_checkpoint( critic_local_path, critic_remote_path, self.current_param_version, max_ckpt_to_keep=max_critic_ckpt_to_keep, ) ray.get(self.rollouter.save_checkpoint.remote(local_global_step_folder)) # latest checkpointed iteration tracker (for atomic usage) local_latest_checkpointed_iteration = os.path.join( self.config.trainer.default_local_dir, "latest_checkpointed_iteration.txt" ) with open(local_latest_checkpointed_iteration, "w") as f: f.write(str(self.current_param_version)) async def load_checkpoint(self): if self.config.trainer.resume_mode == "disable": return 0 # load from hdfs if self.config.trainer.default_hdfs_dir is not None: raise NotImplementedError("load from hdfs is not implemented yet") else: checkpoint_folder = self.config.trainer.default_local_dir # TODO: check path if not os.path.isabs(checkpoint_folder): working_dir = os.getcwd() checkpoint_folder = os.path.join(working_dir, checkpoint_folder) global_step_folder = find_latest_ckpt_path(checkpoint_folder) # None if no latest # find global_step_folder if self.config.trainer.resume_mode == "auto": if global_step_folder is None: return 0 else: if self.config.trainer.resume_mode == "resume_path": assert isinstance(self.config.trainer.resume_from_path, str), "resume ckpt must be str type" assert "global_step_" in self.config.trainer.resume_from_path, ( "resume ckpt must specify the global_steps" ) global_step_folder = self.config.trainer.resume_from_path if not os.path.isabs(global_step_folder): working_dir = os.getcwd() global_step_folder = os.path.join(working_dir, global_step_folder) print(f"[FullyAsyncTrainer] Load from checkpoint folder: {global_step_folder}") # set global step self.current_param_version = int(global_step_folder.split("global_step_")[-1]) self.global_steps = self.current_param_version * self.trigger_parameter_sync_step + 1 self.last_ckpt_version = self.current_param_version print( f"[FullyAsyncTrainer] Setting global step to {self.global_steps}, " f"current_param_version to {self.current_param_version}" ) print(f"[FullyAsyncTrainer] Resuming from {global_step_folder}") actor_path = os.path.join(global_step_folder, "actor") critic_path = os.path.join(global_step_folder, str(Role.Critic)) # load actor self.actor_rollout_wg.load_checkpoint( actor_path, del_local_after_load=self.config.trainer.del_local_ckpt_after_load ) # load critic if self.use_critic: self.critic_wg.load_checkpoint( critic_path, del_local_after_load=self.config.trainer.del_local_ckpt_after_load ) if self.colocate_checkpoint_manager: await self.colocate_checkpoint_manager.update_weights(self.current_param_version) await self.colocate_checkpoint_manager.sleep_replicas() return self.current_param_version def _collect_metrics_from_samples(self, batch, metrics): """ Collect metrics from samples """ if hasattr(batch, "meta_info") and batch.meta_info: trajectory_param_versions = batch.meta_info["trajectory_param_versions"] stale_traj_count = sum(1 for v in trajectory_param_versions if self.current_param_version - v >= 1) self.stale_trajectory_processed += stale_traj_count metrics.update( { "fully_async/count/stale_trajectory_processed": self.stale_trajectory_processed, "fully_async/count/current_param_version": self.current_param_version, } ) for key, value in batch.meta_info.items(): if key.startswith("fully_async") or key.startswith("timing_s"): metrics[key] = value ================================================ FILE: verl/experimental/fully_async_policy/message_queue.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging from collections import deque from typing import Any import ray from omegaconf import DictConfig logger = logging.getLogger(__name__) @ray.remote(num_cpus=2, max_concurrency=20) class MessageQueue: """ Simplified Ray-based asynchronous message queue for communication between Rollouter and Trainer """ def __init__(self, config: DictConfig, max_queue_size: int = 1000): self.config = config if max_queue_size is None: raise ValueError(f"max_queue_size cannot be None, got: {max_queue_size}") self.max_queue_size = int(max_queue_size) self.queue = deque(maxlen=self.max_queue_size) self.val_queue = deque() # Asyncio for message handling self.running = True # async safe self._lock = asyncio.Lock() self._consumer_condition = asyncio.Condition(self._lock) # statistic message self.total_produced = 0 self.total_consumed = 0 self.dropped_samples = 0 print(f"[MessageQueue] initialized with max_queue_size={max_queue_size}") async def put_sample(self, sample: Any) -> bool: """ Put a batch sample into the queue Args: sample: Sample data Returns: bool: Whether the sample was successfully put into the queue """ async with self._lock: # If queue is full, remove the oldest sample (rarely happens) is_drop = False if len(self.queue) >= self.max_queue_size: self.queue.popleft() self.dropped_samples += 1 is_drop = True logger.warning("Queue full, dropped sample") self.queue.append(sample) self.total_produced += 1 # Notify waiting consumers self._consumer_condition.notify_all() if self.total_produced % 100 == 0: print(f"MessageQueue stats: produced={self.total_produced}, queue_size={len(self.queue)}") if is_drop: return False return True async def get_sample(self) -> Any | None: """ Get a single sample from the queue, wait until one is available Returns: Any: Single sample data or None if queue is closed """ async with self._lock: while len(self.queue) == 0 and self.running: await self._consumer_condition.wait() # If queue is closed and empty, return None if not self.running and len(self.queue) == 0: return None # Get one sample data = self.queue.popleft() self.total_consumed += 1 return data, len(self.queue) async def get_queue_size(self) -> int: """Get current queue length""" async with self._lock: return len(self.queue) async def get_statistics(self) -> dict[str, Any]: """Get queue statistics""" async with self._lock: return { "queue_size": len(self.queue), "total_produced": self.total_produced, "total_consumed": self.total_consumed, "dropped_samples": self.dropped_samples, "max_queue_size": self.max_queue_size, } async def clear_queue(self): """Clear the queue""" async with self._lock: cleared_count = len(self.queue) self.queue.clear() logger.info(f"Cleared {cleared_count} samples from queue") async def shutdown(self): """Shutdown the message queue""" async with self._lock: self.running = False # Notify all waiting coroutines so they can exit self._consumer_condition.notify_all() logger.info("MessageQueue shutdown") async def get_memory_usage(self) -> dict: """Get memory usage statistics""" async with self._lock: # Estimate memory usage of samples in queue import sys total_size = 0 sample_count = len(self.queue) if sample_count > 0: # Estimate size of a single sample (simplified estimation) sample = list(self.queue)[0] try: sample_size = sys.getsizeof(sample) # Since we now store RolloutSample directly, estimate based on its components if hasattr(sample, "original_batch_dict") and sample.original_batch_dict: # Estimate batch data size batch_data = sample.original_batch_dict.get("batch", {}) sample_size += len(batch_data) * 1000 # Roughly estimate 1KB per batch entry if hasattr(sample, "agent_loop_output"): # Estimate AgentLoopOutput size sample_size += 5000 # Roughly estimate 5KB for AgentLoopOutput total_size = sample_size * sample_count except Exception: total_size = sample_count * 15000 # Roughly estimate 15KB per RolloutSample return { "queue_samples": sample_count, "estimated_memory_bytes": total_size, "estimated_memory_mb": total_size / (1024 * 1024), } async def put_validate(self, data): async with self._lock: self.val_queue.append(data) async def get_validate(self): async with self._lock: if self.val_queue: return self.val_queue.popleft() else: return None class MessageQueueClient: """Asyncio-compatible MessageQueue client for communicating with MessageQueue Actor""" def __init__(self, queue_actor: Any): self.queue_actor = queue_actor async def put_sample(self, sample: Any) -> bool: """Put batch into queue (async)""" future = self.queue_actor.put_sample.remote(sample) return await asyncio.wrap_future(future.future()) async def put_validate(self, data: Any) -> bool: future = self.queue_actor.put_validate.remote(data) return await asyncio.wrap_future(future.future()) def get_validate_sync(self) -> Any | None: return ray.get(self.queue_actor.get_validate.remote()) async def get_sample(self) -> Any | None: """Get single sample from queue, wait until one is available (async)""" future = self.queue_actor.get_sample.remote() return await asyncio.wrap_future(future.future()) async def get_queue_size(self) -> int: """Get queue size (async)""" future = self.queue_actor.get_queue_size.remote() return await asyncio.wrap_future(future.future()) async def get_statistics(self) -> dict[str, Any]: """Get statistics (async)""" future = self.queue_actor.get_statistics.remote() return await asyncio.wrap_future(future.future()) async def clear_queue(self): """Clear queue (async)""" future = self.queue_actor.clear_queue.remote() await asyncio.wrap_future(future.future()) async def shutdown(self): """Shutdown queue (async)""" future = self.queue_actor.shutdown.remote() await asyncio.wrap_future(future.future()) async def get_memory_usage(self) -> dict: """Get memory usage statistics (async)""" future = self.queue_actor.get_memory_usage.remote() return await asyncio.wrap_future(future.future()) def get_sample_sync(self) -> Any | None: """Get single sample from queue (sync - deprecated, use get_sample instead)""" return ray.get(self.queue_actor.get_sample.remote()) def get_statistics_sync(self) -> dict[str, Any]: """Get statistics (sync - deprecated, use get_statistics instead)""" return ray.get(self.queue_actor.get_statistics.remote()) ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_30b_a3b_base_math_fsdp.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO-Qwen3-30B-A3B-Base-Async' exp_name='Fsdp2-tp4sp4' # Ray RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} WORKING_DIR=${WORKING_DIR:-"${PWD}"} RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths DATA_PATH=${RAY_DATA_HOME:-"${HOME}/verl"} DATA_PATH=${DATA_PATH:-"/mnt/bn/${BYTENAS}"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${DATA_PATH}/shared/models/Qwen3-30B-A3B-Base"} CKPTS_DIR=${CKPTS_DIR:-"${DATA_PATH}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${DATA_PATH}/shared/data/dapo-math/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${DATA_PATH}/shared/data/dapo-math/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 20)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" enable_filter_groups=True filter_groups_metric=acc max_num_gen_batches=10 # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 NNODES=${NNODES:-4} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Fully async specific parameters n_gpus_rollout=8 n_gpus_training=8 n_nodes_rollout=2 n_nodes_train=2 # $((NNODES - n_nodes_rollout)) train_bsz=512 train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((train_bsz * 400))) test_freq=25 staleness_threshold=0.6 # 0 0.3 1 require_batches=1 total_train_gpus=$((n_gpus_training * n_nodes_train)) total_rollout_gpus=$((n_gpus_rollout * n_nodes_rollout)) trigger_parameter_sync_step=$((train_bsz / ( train_prompt_mini_bsz * require_batches))) # 8 16 32 partial_rollout=True enforce_eager=False nccl_timeout=72000 enable_sleep_mode=False # Performance Related Parameter sp_size=4 use_dynamic_bsz=True actor_ppo_max_token_len=$((max_prompt_length + max_response_length)) infer_ppo_max_token_len=$((max_prompt_length + max_response_length)) ref_offload=True actor_offload=False gen_tp=4 fsdp_size=-1 ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \ --working-dir "${WORKING_DIR}" \ --address "${RAY_ADDRESS}" \ -- python3 -m verl.experimental.fully_async_policy.fully_async_main \ --config-path=config \ --config-name='fully_async_dapo_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp \ critic.strategy=fsdp \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.nccl_timeout=${nccl_timeout} \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.50 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ +actor_rollout_ref.rollout.enable_sleep_mode=${enable_sleep_mode} \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.enforce_eager=${enforce_eager} \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','wandb'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}-i${total_rollout_gpus}_t${total_train_gpus}_s${staleness_threshold}" \ trainer.val_before_train=True \ trainer.test_freq="${test_freq}" \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${n_nodes_train}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${n_nodes_rollout}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq=${test_freq} \ async_training.require_batches=${require_batches} \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_async_retool.sh ================================================ set -x export VLLM_USE_V1=1 # ================= data/model/tool ================= HDFS_ROOT=${HDFS_ROOT:-$PWD} DATA_ROOT=${DATA_ROOT:-$PWD} dapo_math_17k=$DATA_ROOT/dataset/BytedTsinghua-SIA/DAPO-Math-17k aime_2024=$DATA_ROOT/dataset/Maxwell-Jia/AIME_2024 aime_2025=$DATA_ROOT/dataset/yentinglin/aime_2025 model_path=$HDFS_ROOT/checkpoint/multiturn-sft-qwen-2.5-7b-instruct/global_step_372 train_files="['$dapo_math_17k']" test_files="['$aime_2025', '$aime_2024']" # tool tool_config_path=recipe/retool/sandbox_fusion_tool_config.yaml retool_path=recipe/retool/retool.py # wandb / tensorboard project_name=retool experiment_name=qwen2.5-7b_dapo_async_tool default_local_dir=$DATA_ROOT/checkpoint/$experiment_name # ================= algorithm ================= adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_turns=16 max_prompt_length=2048 max_response_length=16384 actor_lr=1e-6 # ================= perfomance ================= infer_tp=4 # vllm train_sp=4 # train fsdp_size=4 # train offload=False actor_max_token_len_per_gpu=$(( (max_prompt_length + max_response_length) * 1 )) log_prob_max_token_len_per_gpu=$(( actor_max_token_len_per_gpu * 4 )) # ================= async policy ================= rollout_name="vllm" rollout_mode="async" NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=4 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) train_batch_size=0 ppo_mini_batch_size=16 gen_prompt_bsz=1 n_resp_per_prompt=16 n_resp_per_prompt_val=30 total_rollout_steps=$(((64*250))) test_freq=10 staleness_threshold=0.5 trigger_parameter_sync_step=4 require_batches=1 partial_rollout=True python3 -m verl.experimental.fully_async_policy.fully_async_main \ algorithm.adv_estimator=$adv_estimator \ algorithm.use_kl_in_reward=$use_kl_in_reward \ algorithm.kl_ctrl.kl_coef=$kl_coef \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.return_raw_chat=True \ data.train_batch_size=$train_batch_size \ data.max_prompt_length=$max_prompt_length \ data.max_response_length=$max_response_length \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.custom_cls.path=$retool_path \ data.custom_cls.name=CustomRLHFDataset \ reward.custom_reward_function.path=$retool_path \ reward.custom_reward_function.name=compute_score \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.model.path=$model_path \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.clip_ratio_low=$clip_ratio_low \ actor_rollout_ref.actor.clip_ratio_high=$clip_ratio_high \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.actor.optim.lr=$actor_lr \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$actor_max_token_len_per_gpu \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=$train_sp \ actor_rollout_ref.actor.fsdp_config.param_offload=$offload \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=$offload \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=$log_prob_max_token_len_per_gpu \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.tensor_model_parallel_size=$infer_tp \ actor_rollout_ref.rollout.multi_turn.enable=True \ actor_rollout_ref.rollout.multi_turn.max_user_turns=$max_turns \ actor_rollout_ref.rollout.multi_turn.max_assistant_turns=$max_turns \ actor_rollout_ref.rollout.multi_turn.tool_config_path=$tool_config_path \ actor_rollout_ref.rollout.multi_turn.format=hermes \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=$n_resp_per_prompt \ actor_rollout_ref.rollout.val_kwargs.top_p=0.6 \ actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \ actor_rollout_ref.rollout.val_kwargs.n=$n_resp_per_prompt_val \ actor_rollout_ref.rollout.calculate_log_probs=True \ trainer.logger=['console','tensorboard'] \ trainer.project_name=$project_name \ trainer.experiment_name=$experiment_name \ trainer.val_before_train=True \ trainer.log_val_generations=20 \ trainer.save_freq=-1 \ trainer.default_local_dir=$default_local_dir \ data.gen_batch_size=${gen_prompt_bsz} \ trainer.nnodes=$NNODES \ trainer.n_gpus_per_node=$n_gpus_training \ rollout.nnodes=$NNODES \ rollout.n_gpus_per_node=$n_gpus_rollout \ rollout.total_rollout_steps=$total_rollout_steps \ trainer.total_epochs=10 \ trainer.test_freq=$test_freq \ async_training.staleness_threshold=$staleness_threshold \ async_training.trigger_parameter_sync_step=$trigger_parameter_sync_step \ async_training.require_batches=$require_batches \ async_training.partial_rollout=$partial_rollout ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_16_16.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='dapo_qwen2-7B-math_28k_fsdp2_fully-async_16-16' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 28)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=4 sp_size=4 fsdp_size=8 # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-2} NNODES_TRAIN=${NNODES_TRAIN:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=20 staleness_threshold=0.1 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_32_32.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='dapo_qwen2-7B-math_28k_fsdp2_fully-async_32-32' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 28)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=4 sp_size=4 fsdp_size=8 # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-4} NNODES_TRAIN=${NNODES_TRAIN:-4} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=20 staleness_threshold=0.1 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_4_12.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-fully-async-4-12' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=1 sp_size=1 fsdp_size=2 # Fully async specific parameters NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*100))) test_freq=10 staleness_threshold=0.1 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.test_freq="${test_freq}" \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.test_freq="${test_freq}" \ trainer.total_epochs=10 \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_4_4.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-fully-async-4-4' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=1 sp_size=1 fsdp_size=2 # Fully async specific parameters NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=4 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*100))) test_freq=10 staleness_threshold=0.1 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_64_64.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='dapo_qwen2-7B-math_28k_fsdp2_fully-async_64-64' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 28)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=4 sp_size=4 fsdp_size=8 # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-8} NNODES_TRAIN=${NNODES_TRAIN:-8} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=20 staleness_threshold=0.5 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_64_64_mis.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='dapo_qwen2-7B-math_28k_fsdp2_fully-async_64-64' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 28)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=4 sp_size=4 fsdp_size=8 # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-8} NNODES_TRAIN=${NNODES_TRAIN:-8} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*400))) test_freq=20 staleness_threshold=0.5 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True # Rollout Correction rollout_is=token rollout_is_threshold=2.0 rollout_rs=seq_mean_k1 rollout_rs_threshold="0.99_1.001" python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" \ algorithm.rollout_correction.bypass_mode=False \ algorithm.rollout_correction.rollout_is=${rollout_is} \ algorithm.rollout_correction.rollout_is_threshold=${rollout_is_threshold} \ algorithm.rollout_correction.rollout_rs=${rollout_rs} \ algorithm.rollout_correction.rollout_rs_threshold=${rollout_rs_threshold} ================================================ FILE: verl/experimental/fully_async_policy/shell/dapo_7b_math_fsdp2_8_8.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-fully-async-8-8' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=1 sp_size=1 fsdp_size=2 # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-1} NNODES_TRAIN=${NNODES_TRAIN:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=32 total_rollout_steps=$(((512*100))) test_freq=10 staleness_threshold=0.1 trigger_parameter_sync_step=4 require_batches=4 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/geo3k_qwen25vl_7b_megatron_4_4.sh ================================================ set -x ENGINE=${1:-vllm} export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping HF_MODEL_PATH=${HF_MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-VL-7B-Instruct"} train_path=$HOME/data/geo3k/train.parquet test_path=$HOME/data/geo3k/test.parquet rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Fully async specific parameters NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=4 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=4 train_prompt_mini_bsz=128 total_rollout_steps=$(((512*100))) test_freq=5 staleness_threshold=0.1 trigger_parameter_sync_step=4 require_batches=2 partial_rollout=True total_epochs=200 python -m verl.experimental.fully_async_policy.fully_async_main \ --config-path=config \ --config-name='fully_async_ppo_megatron_trainer.yaml'\ algorithm.adv_estimator=grpo \ data.train_files="$train_path" \ data.val_files="$test_path" \ data.train_batch_size=${train_prompt_bsz} \ data.max_prompt_length=1024 \ data.max_response_length=2048 \ actor_rollout_ref.rollout.max_model_len=32768 \ actor_rollout_ref.rollout.max_num_batched_tokens=32768 \ data.filter_overlong_prompts=True \ data.truncation='error' \ data.gen_batch_size=${gen_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.model.path=$HF_MODEL_PATH \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_decay_steps=51200 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=1 \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=2 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.01 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=5120 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=5120 \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=5120 \ actor_rollout_ref.rollout.name=$ENGINE \ actor_rollout_ref.rollout.mode=${rollout_mode} \ +actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=1 \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=4 \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=1 \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.megatron.use_mbridge=True \ actor_rollout_ref.actor.megatron.param_offload=True \ actor_rollout_ref.actor.megatron.optimizer_offload=True \ actor_rollout_ref.actor.megatron.grad_offload=True \ actor_rollout_ref.ref.megatron.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger='["console","wandb"]' \ trainer.project_name='verl_grpo_example_geo3k' \ trainer.experiment_name='qwen2_5_vl_7b_megatron_async' \ trainer.test_freq="${test_freq}" \ trainer.total_epochs="${total_epochs}" \ trainer.val_before_train=False \ trainer.save_freq=-1 \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs="${total_epochs}" \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='GRPO-Qwen3-30b-Base-MATH' exp_name='GRPO-Qwen3-30b-Base-MATH-megatron-fully-async_96-32' RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 kl_loss_type=low_var_kl clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length))) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length))) offload=True train_ppo_micro_batch_size_per_gpu=2 infer_ppo_micro_batch_size_per_gpu=2 optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.} COMMON_PP=${COMMON_PP:-1} COMMON_VPP=${COMMON_VPP:-null} COMMON_CP=${COMMON_CP:-2} COMMON_TP=${COMMON_TP:-2} COMMON_EP=${COMMON_EP:-8} COMMON_ETP=${COMMON_ETP:-1} TRAIN_TP=${TRAIN_TP:-$COMMON_TP} INFER_TP=${INFER_TP:-4} ACTOR_PP=${ACTOR_PP:-$COMMON_PP} ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP} ACTOR_CP=${ACTOR_CP:-$COMMON_CP} ACTOR_TP=${ACTOR_TP:-$TRAIN_TP} ACTOR_EP=${ACTOR_EP:-$COMMON_EP} ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP} ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP} REF_PP=${REF_PP:-$COMMON_PP} REF_VPP=${REF_VPP:-$COMMON_VPP} REF_CP=${REF_CP:-$COMMON_CP} REF_TP=${REF_TP:-$TRAIN_TP} REF_EP=${REF_EP:-$COMMON_EP} REF_ETP=${REF_ETP:-$COMMON_ETP} CRITIC_PP=${CRITIC_PP:-$COMMON_PP} CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP} CRITIC_CP=${CRITIC_CP:-$COMMON_CP} CRITIC_TP=${CRITIC_TP:-$TRAIN_TP} CRITIC_EP=${CRITIC_EP:-$COMMON_EP} CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP} RM_PP=${RM_PP:-$COMMON_PP} RM_VPP=${RM_VPP:-$COMMON_VPP} RM_CP=${RM_CP:-$COMMON_CP} RM_TP=${RM_TP:-$TRAIN_TP} RM_EP=${RM_EP:-$COMMON_EP} RM_ETP=${RM_ETP:-$COMMON_ETP} # install mbridge # pip3 install git+https://github.com/ISEEKYAN/mbridge USE_MBRIDGE=True USE_DIST_CKPT=False # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-12} NNODES_TRAIN=${NNODES_TRAIN:-4} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=128 total_rollout_steps=$(((512*400))) test_freq=20 staleness_threshold=0.5 trigger_parameter_sync_step=4 require_batches=1 partial_rollout=True python -m verl.experimental.fully_async_policy.fully_async_main \ --config-path=config \ --config-name='fully_async_ppo_megatron_trainer.yaml'\ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ +actor_rollout_ref.model.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.model.use_fused_kernels=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.lr_decay_style='constant' \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.optim.lr_decay_steps=${total_rollout_steps} \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \ actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \ actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/grpo_30b_a3b_base_math_megatron_96_32_mis.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='GRPO-Qwen3-30b-Base-MATH' exp_name='GRPO-Qwen3-30b-Base-MATH-megatron-fully-async_96-32' RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-30B-A3B-Base"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} rollout_mode="async" rollout_name="vllm" # sglang or vllm if [ "$rollout_mode" = "async" ]; then export VLLM_USE_V1=1 return_raw_chat="True" fi # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=True kl_loss_coef=0.001 kl_loss_type=low_var_kl clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length))) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length))) offload=True train_ppo_micro_batch_size_per_gpu=2 infer_ppo_micro_batch_size_per_gpu=2 optimizer_offload_fraction=${OFFLOAD_FRACTION:-1.} COMMON_PP=${COMMON_PP:-1} COMMON_VPP=${COMMON_VPP:-null} COMMON_CP=${COMMON_CP:-2} COMMON_TP=${COMMON_TP:-2} COMMON_EP=${COMMON_EP:-8} COMMON_ETP=${COMMON_ETP:-1} TRAIN_TP=${TRAIN_TP:-$COMMON_TP} INFER_TP=${INFER_TP:-4} ACTOR_PP=${ACTOR_PP:-$COMMON_PP} ACTOR_VPP=${ACTOR_VPP:-$COMMON_VPP} ACTOR_CP=${ACTOR_CP:-$COMMON_CP} ACTOR_TP=${ACTOR_TP:-$TRAIN_TP} ACTOR_EP=${ACTOR_EP:-$COMMON_EP} ACTOR_ETP=${ACTOR_ETP:-$COMMON_ETP} ROLLOUT_TP=${ROLLOUT_TP:-$INFER_TP} REF_PP=${REF_PP:-$COMMON_PP} REF_VPP=${REF_VPP:-$COMMON_VPP} REF_CP=${REF_CP:-$COMMON_CP} REF_TP=${REF_TP:-$TRAIN_TP} REF_EP=${REF_EP:-$COMMON_EP} REF_ETP=${REF_ETP:-$COMMON_ETP} CRITIC_PP=${CRITIC_PP:-$COMMON_PP} CRITIC_VPP=${CRITIC_VPP:-$COMMON_VPP} CRITIC_CP=${CRITIC_CP:-$COMMON_CP} CRITIC_TP=${CRITIC_TP:-$TRAIN_TP} CRITIC_EP=${CRITIC_EP:-$COMMON_EP} CRITIC_ETP=${CRITIC_ETP:-$COMMON_ETP} RM_PP=${RM_PP:-$COMMON_PP} RM_VPP=${RM_VPP:-$COMMON_VPP} RM_CP=${RM_CP:-$COMMON_CP} RM_TP=${RM_TP:-$TRAIN_TP} RM_EP=${RM_EP:-$COMMON_EP} RM_ETP=${RM_ETP:-$COMMON_ETP} # install mbridge # pip3 install git+https://github.com/ISEEKYAN/mbridge USE_MBRIDGE=True USE_DIST_CKPT=False # Fully async specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-12} NNODES_TRAIN=${NNODES_TRAIN:-4} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=0 gen_prompt_bsz=1 n_resp_per_prompt=16 train_prompt_mini_bsz=128 total_rollout_steps=$(((512*400))) test_freq=20 staleness_threshold=0.5 trigger_parameter_sync_step=4 require_batches=1 partial_rollout=True # Rollout Importance Sampling rollout_is=null rollout_rs=seq_mean_k1 rollout_rs_threshold="0.999_1.001" python -m verl.experimental.fully_async_policy.fully_async_main \ --config-path=config \ --config-name='fully_async_ppo_megatron_trainer.yaml'\ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ data.return_raw_chat=${return_raw_chat} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ algorithm.rollout_correction.rollout_is=${rollout_is} \ algorithm.rollout_correction.rollout_rs=${rollout_rs} \ algorithm.rollout_correction.rollout_rs_threshold=${rollout_rs_threshold} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ +actor_rollout_ref.model.override_config.model_config.max_position_embeddings=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.model.use_fused_kernels=False \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=${train_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.lr_decay_style='constant' \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.optim.lr_decay_steps=${total_rollout_steps} \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_offload_fraction=${optimizer_offload_fraction} \ +actor_rollout_ref.actor.optim.override_optimizer_config.overlap_cpu_optimizer_d2h_h2d=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.use_precision_aware_optimizer=True \ +actor_rollout_ref.actor.optim.override_optimizer_config.optimizer_cpu_offload=True \ actor_rollout_ref.actor.megatron.use_mbridge=$USE_MBRIDGE \ actor_rollout_ref.actor.megatron.use_dist_checkpointing=$USE_DIST_CKPT \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${ACTOR_TP} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${ACTOR_PP} \ actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size=${ACTOR_VPP} \ actor_rollout_ref.actor.megatron.context_parallel_size=${ACTOR_CP} \ actor_rollout_ref.actor.megatron.expert_model_parallel_size=${ACTOR_EP} \ actor_rollout_ref.actor.megatron.expert_tensor_parallel_size=${ACTOR_ETP} \ +actor_rollout_ref.actor.megatron.override_transformer_config.apply_rope_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.masked_softmax_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_activation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.bias_dropout_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.gradient_accumulation_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.deallocate_pipeline_outputs=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.persist_layer_norm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_grouped_gemm=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_permute_fusion=True \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_token_dispatcher_type="flex" \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_router_dtype=fp32 \ +actor_rollout_ref.actor.megatron.override_transformer_config.moe_enable_deepep=True \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${INFER_TP} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=${rollout_name} \ actor_rollout_ref.rollout.mode=${rollout_mode} \ actor_rollout_ref.rollout.calculate_log_probs=True \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.free_cache_engine=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=${infer_ppo_micro_batch_size_per_gpu} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.megatron.use_dist_checkpointing=${USE_DIST_CKPT} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${REF_TP} \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${REF_PP} \ actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=${REF_VPP} \ actor_rollout_ref.ref.megatron.context_parallel_size=${REF_CP} \ actor_rollout_ref.ref.megatron.expert_model_parallel_size=${REF_EP} \ actor_rollout_ref.ref.megatron.expert_tensor_parallel_size=${REF_ETP} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.total_rollout_steps="${total_rollout_steps}" \ trainer.total_epochs=10 \ trainer.test_freq="${test_freq}" \ async_training.staleness_threshold="${staleness_threshold}" \ async_training.trigger_parameter_sync_step="${trigger_parameter_sync_step}" \ async_training.require_batches="${require_batches}" \ async_training.partial_rollout="${partial_rollout}" ================================================ FILE: verl/experimental/fully_async_policy/shell/runtime_env.yaml ================================================ env_vars: VLLM_USE_V1: "1" NCCL_DEBUG: "INFO" HYDRA_FULL_ERROR: "1" ================================================ FILE: verl/experimental/fully_async_policy/unittest/simple_streaming_demo.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import random import time class SimpleStreamingSystem: """Simplified streaming system demonstration""" def __init__(self, max_concurrent_tasks: int = 4): self.max_concurrent_tasks = max_concurrent_tasks self.data_queue = asyncio.Queue() self.result_queue = asyncio.Queue() self.consumer_count = 0 # Data stream coroutine async def data_stream(self): # Add initial data # Prepare test data test_data = [{"id": f"task_{i}", "content": f"data_{i}"} for i in range(8)] await self.add_data_stream(test_data) # Simulate subsequent data stream await asyncio.sleep(3) print("\nAdding second batch of data...") extra_data = [{"id": f"extra_{i}", "content": f"extra_data_{i}"} for i in range(5)] await self.add_data_stream(extra_data) # Send termination signal await asyncio.sleep(1) await self.data_queue.put("DONE") print("Sending termination signal") async def add_data_stream(self, data_list: list[dict]): """Simulate data stream""" print("Starting to add data stream...") for i, data_item in enumerate(data_list): await self.data_queue.put(data_item) print(f"Data {data_item['id']} added to pending queue") # Simulate interval between data streams if i < len(data_list) - 1: # Don't wait after the last item await asyncio.sleep(0.8) print("Initial data stream added successfully") async def _process_data_async(self, data_item: dict): """Asynchronously process a single data item""" data_id = data_item["id"] content = data_item["content"] # Simulate different processing times (1-3 seconds) processing_time = random.uniform(1, 3) print(f" Starting to process {data_id}, estimated time {processing_time:.1f}s") # Asynchronously wait for processing completion await asyncio.sleep(processing_time) result = { "id": data_id, "processed_content": f"Processed {content}", "processing_time": round(processing_time, 2), "completed_at": time.time(), } # Immediately put into result queue await self.result_queue.put(result) print(f" {data_id} processing completed! (took {processing_time:.1f}s) -> Added to result queue") async def _submit_worker(self): """Stream submission worker coroutine""" active_tasks = set() print("Stream submitter started...") while True: # Get data to process data_item = await self.data_queue.get() if data_item == "DONE": print("Received termination signal, waiting for remaining tasks to complete...") if active_tasks: await asyncio.gather(*active_tasks, return_exceptions=True) break # Check concurrent limit while len(active_tasks) >= self.max_concurrent_tasks: print(f"Reached maximum concurrency {self.max_concurrent_tasks}, waiting for tasks to complete...") done_tasks, active_tasks = await asyncio.wait(active_tasks, return_when=asyncio.FIRST_COMPLETED) # Clean up completed tasks for task in done_tasks: try: await task print(f"Task completed {task}") except Exception as e: print(f"Task execution failed: {e}") # Immediately submit new task task = asyncio.create_task(self._process_data_async(data_item), name=f"active {data_item}") active_tasks.add(task) print(f"Submitted task {data_item['id']}, current concurrency: {len(active_tasks)}") async def _consumer_worker(self): """Result consumer coroutine""" print("Consumer started...") while True: try: # Get processing result from result queue result = await asyncio.wait_for(self.result_queue.get(), timeout=2.0) self.consumer_count += 1 print( f"Consumed #{self.consumer_count}: {result['id']} " f"(processing time {result['processing_time']}s) - {result['processed_content']}" ) except asyncio.TimeoutError: print(" Consumer waiting...") await asyncio.sleep(0.5) async def run_demo(self): """Run demonstration""" print("=" * 60) print(f"Maximum concurrency: {self.max_concurrent_tasks}") print("=" * 60) # Start core coroutines stream_task = asyncio.create_task(self.data_stream()) submit_task = asyncio.create_task(self._submit_worker()) consumer_task = asyncio.create_task(self._consumer_worker()) try: # Wait for data stream to complete await stream_task print("Data stream completed") # Wait for processing to complete await submit_task print("All tasks processed") finally: # Cleanup submit_task.cancel() consumer_task.cancel() await asyncio.gather(submit_task, consumer_task, return_exceptions=True) print(f"\nFinal statistics: Consumed {self.consumer_count} results") async def main(): """Main function""" system = SimpleStreamingSystem(max_concurrent_tasks=3) await system.run_demo() if __name__ == "__main__": asyncio.run(main()) ================================================ FILE: verl/experimental/one_step_off_policy/README.md ================================================ # Recipe: One Step Off Policy Async Trainer **Author:** `https://github.com/meituan-search` Last updated: 07/17/2025. ## Introduction ### Background The current reinforcement learning training process implemented by verl is synchronous, adhering to the algorithmic workflows of established methods like PPO, GRPO, and DAPO. In each step, training samples are generated by the latest model, and the model is updated after training completes. While this approach aligns with off-policy reinforcement learning and stabilizes RL training, but it suffers from severe efficiency issues. Model updates must wait for the longest output in the generation phase to complete. During the generation of long-tail samples, GPUs remain idle, resulting in significant underutilization. The more severe the long-tail problem in sample generation, the lower the overall training efficiency. For example, in DAPO 32B training, the Rollout phase accounts for approximately 70% of the total time, and increasing resources does not reduce the Rollout duration. ![DAPO 32B Math Performance](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/dapo_32b_math.png) > source data: https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/workspace?nw=nwusertongyuxuan361 ### Solution We have implemented the **One Step Off Async Trainer** to help alleviate this issue. This approach parallelizes the generation and training processes, utilizing samples generated in the previous step for current training. It also involves appropriately partitioning resources, allocating dedicated resources for generation while automatically assigning the remainder to training. By reducing resources allocated to the generation phase, we mitigate GPU idle time during long-tail sample generation. Throughout this process, generation and training parameters maintain a one-step off policy. ![One Step Off Policy Diagram](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/one_step_off_policy.png) > reference: [AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning](https://arxiv.org/abs/2505.24298) > original work: [Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models](https://arxiv.org/abs/2410.18252) Our core contributions include: 1. **Parallel Generation and Training**: Samples for the next batch are asynchronously generated while the current batch is being trained. 2. **Resource Isolation**: Unlike `hybrid_engine`, this method requires explicit resource allocation for rollout, with remaining resources automatically assigned to training. 3. **NCCL Parameter Synchronization**: Employs NCCL communication primitives for seamless parameter transfer between generation and training modules. ### Experimental Results - **Machine Configuration**: 2 nodes with 16 H20 GPUs each - Generation: 4 GPUs - Training: 12 GPUs - **Model**: Qwen2.5-Math-7B - **Rollout Configuration**: - **Max Response Length**: FSDP2: 20,480 tokens; Megatron: 8,192 tokens - **Algorithm**: DAPO - **Rollout Engine**: vLLM | training mode | engine | step | gen | wait_prev_gen | generate_sequences | old_log_prob | update_actor | total time | acc/best@32/mean | acc/maj@32/mean | | ---------------------- | ------------- | ---- | --- | ------------- | ------------------ | ------------ | ------------ | -------------- | ---------------- | --------------- | | colocate sync | VLLM+FSDP2 | 749 | 321 | - | 247 | 88 | 286 | 19h18m | 0.5948 | 0.417 | | one-step-overlap async | VLLM+FSDP2 | 520 | - | 45 | 458 | 108 | 337 | 15h34m（+23%） | 0.6165 | 0.494 | | colocate sync | VLLM+Megatron | 699 | 207 | - | 162 | 119 | 344 | 18h21m | 0.605 | 0.4217 | | one-step-overlap async | VLLM+Megatron | 566 | - | 59 | 501 | 120 | 347 | 13h06m (+40%) | 0.6569 | 0.4038 | - colocate sync: step ≈ gen + old_log_prob + update_actor - one-step-overlap async: step ≈ wait_prev_gen + old_log_prob + update_actor ![One Step Off Megatron Performance](https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/docs/one_step_off_megatron.png) > source data: https://wandb.ai/hou-zg-meituan/one-step-off-policy?nw=nwuserhouzg ## Implementation ### One Step Off Policy Async Pipeline Our implemented **One Step Off Policy Async Pipeline** integrates seamlessly into existing training logic at minimal cost, eliminating the need for additional sample storage management. The core mechanism uses `async_gen_next_batch` for asynchronous rollout generation while maintaining continuous operation during epoch transitions via `create_continuous_iterator`. ```python # iterator generator, simplify one-step integration of the training process def _create_continuous_iterator(self): for epoch in range(self.config.trainer.total_epochs): iterator = iter(self.train_dataloader) for batch_dict in iterator: yield epoch, batch_dict # read next batch samples, parameters sync and launch asyn gen_seq def _async_gen_next_batch(self, continuous_iterator): # read train_data try: epoch, batch_dict = next(continuous_iterator) except StopIteration: return None batch = DataProto.from_single_dict(batch_dict) gen_batch = batch_pocess(batch) # sync weights from actor to rollout self.sync_rollout_weights() # async generation gen_batch_output = self.rollout_wg.async_generate_sequences(gen_batch) # future encapsulated return GenerationBatchFuture(epoch, batch, gen_batch_output) continuous_iterator = self._create_continuous_iterator() # run rollout first to achieve one-step-off batch_data_future = self._async_gen_next_batch(continuous_iterator) while batch_data_future is not None: # wait for the gen_seq result from the previous step batch = batch_data_future.get() # launch the next async call to generate sequences batch_data_future = self._async_gen_next_batch(continuous_iterator) # compute advantages batch = critic.compute_values(batch) batch = reference.compute_log_prob(batch) batch = reward.compute_reward(batch) batch = compute_advantages(batch) # model update critic_metrics = critic.update_critic(batch) actor_metrics = actor.update_actor(batch) ``` ### Parameter Synchronization The exciting point is that our nccl based weights updating for rollout model has great performance. At most of time, the latency is under 300ms, which is negligible for RLHF. > **sync_rollout_weights**：The time for synchronizing parameters from actor to rollout is extremely fast and can almost > be ignored because it is implemented with nccl. ```python class ActorRolloutRefWorker: # actor acquires the meta-info of model parameters for parameter sync @register(dispatch_mode=Dispatch.ONE_TO_ALL) def get_actor_weights_info(self): params = self._get_actor_params() ret = [] for key, tensor in params.items(): ret.append((key, tensor.size(), tensor.dtype)) self._weights_info = ret return ret # rollout sets the meta-info of model parameters for parameter sync @register(dispatch_mode=Dispatch.ONE_TO_ALL) def set_actor_weights_info(self, weights_info): self._weights_info = weights_info class AsyncRayPPOTrainer(RayPPOTrainer): def init_workers(self): ... # rollout obtains the meta-info of model parameters from the actor for parameter sync weights_info = self.actor_wg.get_actor_weights_info()[0] self.rollout_wg.set_actor_weights_info(weights_info) # Create an actor-rollout communication group for parameter sync actor_rollout_workers = self.actor_wg.workers + self.rollout_wg.workers collective.create_collective_group( actor_rollout_workers, len(actor_rollout_workers), list(range(0, len(actor_rollout_workers))), backend="nccl", group_name="actor_rollout" ) ``` ```python # drive process call the actor and rollout respectively to sync parameters by nccl def sync_rollout_weights(self): self.actor_wg.sync_rollout_weights() ray.get(self.rollout_wg.sync_rollout_weights()) # fsdp model parameter sync @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) def sync_rollout_weights(self): params = self._get_actor_params() if self._is_actor else None if self._is_rollout: inference_model = ( self.rollout.inference_engine.llm_engine.model_executor.driver_worker.worker.model_runner.model ) from verl.utils.vllm.patch import patch_vllm_moe_model_weight_loader patch_vllm_moe_model_weight_loader(inference_model) # Model parameters are broadcast tensor-by-tensor from actor to rollout for key, shape, dtype in self._weights_info: tensor = torch.empty(shape, dtype=dtype, device=get_torch_device().current_device()) if self._is_actor: assert key in params origin_data = params[key] if hasattr(origin_data, "full_tensor"): origin_data = origin_data.full_tensor() if torch.distributed.get_rank() == 0: tensor.copy_(origin_data) from ray.util.collective import collective collective.broadcast(tensor, src_rank=0, group_name="actor_rollout") if self._is_rollout: inference_model.load_weights([(key, tensor)]) ``` ### PPO Correctness To ensure the correctness of the PPO algorithm, we use rollout log_probs for PPO importance sampling. For the related algorithm details, please refer to: https://verl.readthedocs.io/en/latest/algo/rollout_corr_math.html The default mode is `bypass_ppo_clip`, but other modification strategies can also be explored. ### AgentLoop In the current implementation, we no longer provide SPMD model rollout mode. Instead, we have switched to AgentLoop mode, which also supports multi-turn tool calling. ## Usage ### FSDP2 Configuration Example ```shell python3 -m verl.experimental.one_step_off_policy.async_main_ppo \ --config-path=config \ --config-name='one_step_off_ppo_trainer.yaml' \ actor_rollout_ref.actor.strategy=fsdp2 \ # actor and rollout are placed separately actor_rollout_ref.hybrid_engine=False \ # actor and rollout resource trainer.nnodes=1 \ trainer.n_gpus_per_node=6 \ rollout.nnodes=1 \ rollout.n_gpus_per_node=2 ``` ### Megatron Configuration Example ```shell python3 -m verl.experimental.one_step_off_policy.async_main_ppo \ --config-path=config \ --config-name='one_step_off_ppo_megatron_trainer.yaml' \ actor_rollout_ref.actor.strategy=megatron \ # actor and rollout are placed separately actor_rollout_ref.hybrid_engine=False \ # actor and rollout resource trainer.nnodes=1 \ trainer.n_gpus_per_node=6 \ rollout.nnodes=1 \ rollout.n_gpus_per_node=2 ``` ### Configuration Guidelines 1. **Card Number Relationships** Maintain either of these relationships for optimal batch distribution: - `actor_rollout_ref.rollout.n` should be an integer divisor of: `trainer.n_gpus_per_node * trainer.nnodes` - `actor_rollout_ref.rollout.n * data.train_batch_size` should be evenly divisible by: `trainer.n_gpus_per_node * trainer.nnodes` > Rationale: Ensures training samples can be evenly distributed across training GPUs when using partial resources for > generation. 2. **Dynamic Resource Tuning** Adjust `trainer.nnodes` `trainer.n_gpus_per_node` `rollout.nnodes` `rollout.n_gpus_per_node` based on phase durations: - **Ideal state**: Rollout and training phases have comparable durations - **Diagnostic metrics**: - Monitor `wait_prev_gen` duration - Analyze `sequence_length` distribution - **Adjustment strategy**: - High `wait_prev_gen` + uniform sequence lengths → Increase rollout resources - High `wait_prev_gen` + long-tail sequences → Optimize stopping criteria (resource increase won't help) > **wait_prev_gen**：The time consumed waiting for the previous rollout to end (the part that is not fully > overlapped). > **Resource Configuration Strategies:** - **Resource-constrained scenario**: Optimize resource utilization by adjusting GPU allocation ratios, keeping the number of nodes equal to allow training and rollout to share nodes; - Configure `trainer.nnodes = rollout.nnodes` with `trainer.n_gpus_per_node + rollout.n_gpus_per_node = physical_gpus_per_node`. Control rollout resource allocation by adjusting `n_gpus_per_node`. - **Resource-abundant scenario**: Optimize performance by adjusting the number of nodes, keeping the number of GPUs per node equal to enable independent scaling of training and rollout parallelism. - Configure `trainer.n_gpus_per_node = rollout.n_gpus_per_node` and control rollout resource allocation by adjusting `trainer.nnodes` and `rollout.nnodes`to achieve optimal performance. > **Note**: The total number of nodes required by the system is not simply `trainer.nnodes + rollout.nnodes`. The > actual calculation depends on GPU capacity: > > - When `trainer.n_gpus_per_node + rollout.n_gpus_per_node <= physical_gpus_per_node`, > the required node count is `max(trainer.nnodes, rollout.nnodes)` > - When `trainer.n_gpus_per_node + rollout.n_gpus_per_node > physical_gpus_per_node`, > the required node count is `trainer.nnodes + rollout.nnodes` ## Functional Support | Category | Support Situation | | ------------------ | --------------------------------------------------------------------------------------------------------------- | | train engine | FSDP2
Megatron | | rollout engine | vLLM
SGLang | | AdvantageEstimator | GRPO
GRPO_PASSK
REINFORCE_PLUS_PLUS
RLOO
OPO
REINFORCE_PLUS_PLUS_BASELINE
GPG | | Reward | all | ================================================ FILE: verl/experimental/one_step_off_policy/config/one_step_off_ppo_megatron_trainer.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_megatron_trainer - _self_ trainer: use_legacy_worker_impl: disable # config for the rollout (only for resource isolation) rollout: # Number of nodes used in the rollout nnodes: 1 # Number of GPUs per node n_gpus_per_node: 8 # To adapt to the current logic of AgentLoopManager actor_rollout_ref: rollout: # Must be turned off! Otherwise, Parameter synchronization cannot be performed. free_cache_engine: False # Must be enabled! Otherwise, log_probs cannot be calculated. calculate_log_probs: True checkpoint_engine: backend: "nccl" # Only then will the use of log probs be correct. # And it can be used in conjunction with other rollout_correction algorithms. algorithm: rollout_correction: bypass_mode: True ================================================ FILE: verl/experimental/one_step_off_policy/config/one_step_off_ppo_trainer.yaml ================================================ hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ trainer: use_legacy_worker_impl: disable # config for the rollout (only for resource isolation) rollout: # Number of nodes used in the rollout nnodes: 1 # Number of GPUs per node n_gpus_per_node: 8 # To adapt to the current logic of AgentLoopManager actor_rollout_ref: rollout: # Must be turned off! Otherwise, Parameter synchronization cannot be performed. free_cache_engine: False # Must be enabled! Otherwise, log_probs cannot be calculated. calculate_log_probs: True checkpoint_engine: backend: "nccl" # Only then will the use of log probs be correct. # And it can be used in conjunction with other rollout_correction algorithms. algorithm: rollout_correction: bypass_mode: True ================================================ FILE: verl/experimental/one_step_off_policy/main_ppo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Note that we don't combine the main with ray_trainer as ray_trainer is used by other main. """ import asyncio import os import socket import hydra import ray from verl.experimental.one_step_off_policy.ray_trainer import OneStepOffRayTrainer from verl.experimental.separation.utils import create_resource_pool_manager, create_role_worker_mapping from verl.trainer.main_ppo import create_rl_dataset, create_rl_sampler from verl.trainer.ppo.utils import need_critic, need_reference_policy from verl.utils.config import validate_config from verl.utils.device import auto_set_device @ray.remote(num_cpus=10, max_concurrency=100) # please make sure main_task is not scheduled on head class OneStepTaskRunner: def run(self, config): # Print the initial configuration. `resolve=True` will evaluate symbolic values. from pprint import pprint from omegaconf import OmegaConf from verl.utils.fs import copy_to_local print(f"TaskRunner hostname: {socket.gethostname()}, PID: {os.getpid()}") pprint(OmegaConf.to_container(config, resolve=True)) OmegaConf.resolve(config) role_worker_mapping, ray_worker_group_cls = create_role_worker_mapping(config) # validate config validate_config( config=config, use_reference_policy=need_reference_policy(config), use_critic=need_critic(config), ) # Download the checkpoint from HDFS to the local machine. # `use_shm` determines whether to use shared memory, which could lead to faster model loading if turned on local_path = copy_to_local( config.actor_rollout_ref.model.path, use_shm=config.actor_rollout_ref.model.get("use_shm", False) ) # Instantiate the tokenizer and processor. from verl.utils import hf_processor, hf_tokenizer trust_remote_code = config.data.get("trust_remote_code", False) tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) # Used for multimodal LLM, could be None processor = hf_processor(local_path, trust_remote_code=trust_remote_code, use_fast=True) resource_pool_manager = create_resource_pool_manager(config, role_worker_mapping.keys()) from verl.utils.dataset.rl_dataset import collate_fn # Create training and validation datasets. train_dataset = create_rl_dataset( config.data.train_files, config.data, tokenizer, processor, max_samples=config.data.get("train_max_samples", -1), ) val_dataset = create_rl_dataset( config.data.val_files, config.data, tokenizer, processor, max_samples=config.data.get("val_max_samples", -1) ) train_sampler = create_rl_sampler(config.data, train_dataset) # Initialize the PPO trainer. trainer = OneStepOffRayTrainer( config=config, tokenizer=tokenizer, processor=processor, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, train_dataset=train_dataset, val_dataset=val_dataset, collate_fn=collate_fn, train_sampler=train_sampler, device_name=config.trainer.device, ) # Initialize the workers of the trainer. trainer.init_workers() # Start the training process. asyncio.run(trainer.fit()) @hydra.main(config_path="config", config_name="one_step_off_ppo_trainer", version_base=None) def main(config): from time import time from verl.trainer.main_ppo import run_ppo start_time = time() # Automatically set `config.trainer.device = npu` when running on Ascend NPU. auto_set_device(config) # TODO: unify rollout config with actor_rollout_ref config.actor_rollout_ref.rollout.nnodes = config.rollout.nnodes config.actor_rollout_ref.rollout.n_gpus_per_node = config.rollout.n_gpus_per_node run_ppo(config, task_runner_class=OneStepTaskRunner) print(f"total time: {time() - start_time:.2f} seconds") if __name__ == "__main__": main() ================================================ FILE: verl/experimental/one_step_off_policy/ray_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This trainer supports model-agonistic model initialization with huggingface """ import asyncio import uuid from pprint import pprint from typing import Optional import numpy as np import ray import torch from omegaconf import OmegaConf from torch.utils.data import Dataset, Sampler from tqdm import tqdm from verl import DataProto from verl.experimental.separation.ray_trainer import SeparateRayPPOTrainer from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup from verl.trainer.ppo import core_algos from verl.trainer.ppo.ray_trainer import ( ResourcePoolManager, compute_response_mask, ) from verl.trainer.ppo.reward import extract_reward from verl.trainer.ppo.utils import Role, WorkerType, need_critic, need_reference_policy, need_reward_model from verl.utils.debug import marked_timer from verl.utils.rollout_skip import RolloutSkip from verl.utils.tracking import ValidationGenerationsLogger class OneStepOffRayTrainer(SeparateRayPPOTrainer): def __init__( self, config, tokenizer, role_worker_mapping: dict[Role, WorkerType], resource_pool_manager: ResourcePoolManager, ray_worker_group_cls: type[RayWorkerGroup] = RayWorkerGroup, processor=None, train_dataset: Optional[Dataset] = None, val_dataset: Optional[Dataset] = None, collate_fn=None, train_sampler: Optional[Sampler] = None, device_name=None, ): """ Initialize distributed PPO trainer with Ray backend. Note that this trainer runs on the driver process on a single CPU/GPU node. Args: config: Configuration object containing training parameters. tokenizer: Tokenizer used for encoding and decoding text. role_worker_mapping (dict[Role, WorkerType]): Mapping from roles to worker classes. resource_pool_manager (ResourcePoolManager): Manager for Ray resource pools. ray_worker_group_cls (RayWorkerGroup, optional): Class for Ray worker groups. Defaults to RayWorkerGroup. processor: Optional data processor, used for multimodal data train_dataset (Optional[Dataset], optional): Training dataset. Defaults to None. val_dataset (Optional[Dataset], optional): Validation dataset. Defaults to None. collate_fn: Function to collate data samples into batches. train_sampler (Optional[Sampler], optional): Sampler for the training dataset. Defaults to None. device_name (str, optional): Device name for training (e.g., "cuda", "cpu"). Defaults to None. """ # Store the tokenizer for text processing self.tokenizer = tokenizer self.processor = processor self.config = config self.hybrid_engine = config.actor_rollout_ref.hybrid_engine assert not self.hybrid_engine # Skip rollout worker mapping and let agentloop create it. role_worker_mapping.pop(Role.Rollout, None) self.role_worker_mapping = role_worker_mapping self.resource_pool_manager = resource_pool_manager self.use_reference_policy = need_reference_policy(self.config) self.use_rm = need_reward_model(self.config) self.use_critic = need_critic(self.config) self.ray_worker_group_cls = ray_worker_group_cls self.device_name = device_name if device_name else self.config.trainer.device self.validation_generations_logger = ValidationGenerationsLogger( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, ) # if ref_in_actor is True, the reference policy will be actor without lora applied lora_rank = config.actor_rollout_ref.model.get("lora", {}).get("rank", 0) if lora_rank <= 0: lora_rank = config.actor_rollout_ref.model.get("lora_rank", 0) self.ref_in_actor = lora_rank > 0 or config.actor_rollout_ref.model.get("lora_adapter_path") is not None # define in-reward KL control # kl loss control currently not suppoorted if self.config.algorithm.use_kl_in_reward: self.kl_ctrl_in_reward = core_algos.get_kl_controller(self.config.algorithm.kl_ctrl) self.use_prefix_grouper = self.config.actor_rollout_ref.actor.get("use_prefix_grouper", False) self.use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") self._create_dataloader(train_dataset, val_dataset, collate_fn, train_sampler) # ==================== SeparateRayPPOTrainer config ==================== self.global_steps = 0 self.epoch = 0 self.max_steps_duration = 0 self.progress_bar = None self.logger = None self.is_last_step = False self.prev_step_profile = False self.curr_step_profile = False self.next_step_profile = False self.last_val_metrics = {} self.metrics = {} self.timing_raw = {} # reward message self.future_reward = None self.reward_tensor = None self.reward_extra_infos_dict = {} def _create_actor_rollout_classes(self): for role in [Role.Actor]: resource_pool = self.resource_pool_manager.get_resource_pool(role) role_cls = RayClassWithInitArgs( cls=self.role_worker_mapping[role], config=self.config.actor_rollout_ref, role=str(role), ) self.resource_pool_to_cls[resource_pool][str(role)] = role_cls def _init_models(self): if self.use_critic: self.critic_wg = self.all_wg[str(Role.Critic)] self.critic_wg.init_model() if self.use_reference_policy and not self.ref_in_actor: self.ref_policy_wg = self.all_wg[str(Role.RefPolicy)] self.ref_policy_wg.init_model() self.rm_wg = None if self.use_rm: self.rm_wg = self.all_wg[str(Role.RewardModel)] self.rm_wg.init_model() self.actor_wg = self.all_wg[str(Role.Actor)] self.actor_wg.init_model() self.actor_rollout_wg = self.actor_wg def _init_async_rollout_manager(self): # infrastructure overview: https://verl.readthedocs.io/en/latest/advance/reward_loop.html#architecture-design # agent_reward_loop: streaming reward computation with actor rollout # two conditions satisfied: (1) no reward model, or (2) reward model with extra resource pool enable_agent_reward_loop = not self.use_rm or self.config.reward.reward_model.enable_resource_pool # if enable_agent_reward_loop, we directly pass reward_loop_workers to agent loop manager # to stream reward computation with actor rollout reward_loop_worker_handles = self.reward_loop_manager.reward_loop_workers if enable_agent_reward_loop else None # create async rollout manager and request scheduler assert self.config.actor_rollout_ref.rollout.mode == "async" from verl.experimental.agent_loop import AgentLoopManager self.async_rollout_mode = True self.async_rollout_manager = AgentLoopManager.create( config=self.config, reward_loop_worker_handles=reward_loop_worker_handles ) def _create_continuous_iterator(self): """ Create a continuous data iterator across epoch """ for epoch in range(self.config.trainer.total_epochs): iterator = iter(self.train_dataloader) for batch_dict in iterator: yield epoch, batch_dict async def _async_gen_next_batch(self, continuous_iterator): """ Call parameter synchronization and asynchronous sequence generation. """ try: epoch, batch_dict = next(continuous_iterator) except StopIteration: return None except Exception as e: print(f"Error in async_gen_next_batch: {e}") return None metrics = {} timing_raw = {} # Create the initial batch from the data loader batch = DataProto.from_single_dict(batch_dict) # add uid to batch batch.non_tensor_batch["uid"] = np.array([str(uuid.uuid4()) for _ in range(len(batch.batch))], dtype=object) gen_batch = self._get_gen_batch(batch) # pass global_steps to trace gen_batch.meta_info["global_steps"] = self.global_steps gen_batch_output = gen_batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) # async generation with marked_timer("generate_async", timing_raw, color="purple"): gen_batch_output = await self.async_rollout_manager.generate_sequences(gen_batch_output) # repeat to align with repeated responses in rollout batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) batch = batch.union(gen_batch_output) if "response_mask" not in batch.batch.keys(): batch.batch["response_mask"] = compute_response_mask(batch) # Balance the number of valid tokens across DP ranks. # NOTE: This usually changes the order of data in the `batch`, # which won't affect the advantage calculation (since it's based on uid), # but might affect the loss calculation (due to the change of mini-batching). if self.config.trainer.balance_batch: self._balance_batch(batch, metrics=metrics) # compute global_valid tokens batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist() # Launch individual reward computations as each generation completes future_reward = None # Return the original, now-modified `batch` and the `future_reward` return metrics, timing_raw, epoch, batch, future_reward @staticmethod @ray.remote def _launch_individual_rewards(batch, config, tokenizer): reward_tensor, reward_extra_info = extract_reward(batch) return reward_tensor, reward_extra_info async def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ from verl.utils.tracking import Tracking self.logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) self.global_steps = 0 # load checkpoint and update weights before doing anything self._load_checkpoint() self._fit_update_weights() # perform validation before training # currently, we only support validation using the reward_function. if self.config.trainer.get("val_before_train", True): val_metrics = self._validate() assert val_metrics, f"{val_metrics=}" pprint(f"Initial validation metrics: {val_metrics}") self.logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get("val_only", False): return if self.config.actor_rollout_ref.rollout.get("skip_rollout", False): rollout_skip = RolloutSkip(self.config, self.actor_rollout_wg) rollout_skip.wrap_generate_sequences() # add tqdm self.progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress") # we start from step 1 self.global_steps += 1 self.last_val_metrics = None self.max_steps_duration = 0 self.prev_step_profile = False self.curr_step_profile = ( self.global_steps in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) self.next_step_profile = False # across epoch iterator continuous_iterator = self._create_continuous_iterator() # Start the first asynchronous generation task. batch_data_future = asyncio.create_task(self._async_gen_next_batch(continuous_iterator)) while batch_data_future is not None: batch_data_future = await self.fit_step(batch_data_future, continuous_iterator) if self.is_last_step: return async def fit_step(self, batch_data_future, continuous_iterator): """ Single-step training template method. Handles all logic for one training step. Flow: 1. Pre-step processing -> 2. Get batch -> 3. Generate sequences -> 4. Compute reward -> 5. Compute log_prob -> 6. Compute reward -> 7. Compute advantage -> 8. Update critic -> 9. Update actor -> 10. Post-step processing Args: batch_data_future: batch future """ self.metrics = {"training/global_step": self.global_steps, "training/epoch": self.epoch} self.timing_raw = {} # reward message self.future_reward = None self.reward_tensor = None self.reward_extra_infos_dict = {} self._fit_prepare_step() self._fit_start_profile() with marked_timer("step", self.timing_raw): batch, batch_data_future = await self._fit_generate(batch_data_future, continuous_iterator) # await asyncio.sleep(0) ensures: # Asynchronous tasks can start executing immediately # The event loop can handle other pending coroutines # Prevents computations in a certain phase from blocking the entire asynchronous workflow # # The purpose here is to ensure that after triggering # `self.async_rollout_manager.generate_sequences(gen_batch_output)`, # the subsequent relevant logic can proceed in a timely manner await asyncio.sleep(0) batch = self._fit_compute_reward(batch) await asyncio.sleep(0) batch = self._fit_compute_log_prob(batch) await asyncio.sleep(0) batch = self._fit_compute_ref_log_prob(batch) await asyncio.sleep(0) batch = self._fit_compute_critic(batch) await asyncio.sleep(0) batch = self._fit_compute_advantage(batch) await asyncio.sleep(0) batch = self._fit_update_critic(batch) await asyncio.sleep(0) batch = self._fit_update_actor(batch) await asyncio.sleep(0) self._fit_update_weights() await asyncio.sleep(0) self._fit_dump_data(batch) await asyncio.sleep(0) self._fit_validate() await asyncio.sleep(0) self._fit_save_checkpoint() await asyncio.sleep(0) self._fit_stop_profile() self._fit_collect_metrics(batch) self._fit_torch_memory() self._fit_experimental(batch) self._fit_postprocess_step() return batch_data_future async def _fit_generate(self, batch_data_future, continuous_iterator): metrics = self.metrics timing_raw = self.timing_raw with marked_timer("gen", timing_raw, color="red"): _metrics, _timing_raw, epoch, batch, future_reward = await batch_data_future batch.meta_info["temperature"] = self.config.actor_rollout_ref.rollout.temperature timing_raw.update(batch.meta_info["timing"]) timing_raw.update(_timing_raw) metrics.update(_metrics) batch.meta_info.pop("timing", None) # sync weights from actor to rollout with marked_timer("sync_rollout_weights", timing_raw, color="purple"): self._fit_update_weights() await self.async_rollout_manager.clear_kv_cache() # async next generation if not self.is_last_step: batch_data_future = asyncio.create_task(self._async_gen_next_batch(continuous_iterator)) await asyncio.sleep(0) else: batch_data_future = None return batch, batch_data_future ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_fsdp2_4_12.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-one-step-off-4-12' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=12 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=2 sp_size=4 fsdp_size=2 python3 -m verl.experimental.one_step_off_policy.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=100 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_fsdp2_64_64.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='dapo_qwen2-7B-math_28k_fsdp2_one_step_off_64-64' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 28)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=4 sp_size=4 fsdp_size=8 # one stepa specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-8} NNODES_TRAIN=${NNODES_TRAIN:-8} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=512 n_resp_per_prompt=16 train_prompt_mini_bsz=32 test_freq=20 python -m verl.experimental.one_step_off_policy.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.test_freq=20 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=400 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_fsdp2_64_64_ris.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='dapo_qwen2-7B-math_28k_fsdp2_one_step_off_64-64-ris' # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm parameters adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 # Response length parameters max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 28)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 # Training parameters loss_agg_mode="token-mean" # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=4 sp_size=4 fsdp_size=8 # one stepa specific parameters NNODES_ROLLOUT=${NNODES_ROLLOUT:-8} NNODES_TRAIN=${NNODES_TRAIN:-8} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} train_prompt_bsz=512 n_resp_per_prompt=16 train_prompt_mini_bsz=32 test_freq=20 # https://github.com/volcengine/verl/blob/main/docs/algo/rollout_corr.md # use decoupled_geo_rs #algorithm: # rollout_correction: # rollout_is: null # rollout_is_threshold=null # rollout_rs: seq_mean_k1 # rollout_rs_threshold: 0.999_1.001 # bypass_mode: false # Decoupled mode python -m verl.experimental.one_step_off_policy.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=400 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES_TRAIN}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ rollout.nnodes="${NNODES_ROLLOUT}" \ rollout.n_gpus_per_node="${NGPUS_PER_NODE}" \ algorithm.rollout_correction.rollout_is=null \ algorithm.rollout_correction.rollout_is_threshold=null \ algorithm.rollout_correction.rollout_rs=seq_mean_k1 \ algorithm.rollout_correction.rollout_rs_threshold="0.999_1.001" \ algorithm.rollout_correction.bypass_mode=false ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_fsdp2_colocate.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-colocate' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=12 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=2 sp_size=4 fsdp_size=2 # reference run wandb: https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/runs/ow47vvon?nw=nwusertongyuxuan361 python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=100 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_fsdp2_sglang_4_12.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-sglang-one-step-off-4-12' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=12 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=2 sp_size=4 fsdp_size=2 python3 -m verl.experimental.one_step_off_policy.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${actor_offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.ref.fsdp_config.param_offload=${ref_offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=100 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_fsdp2_sglang_colocate.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-sglang-colocate' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=12 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=2 sp_size=4 fsdp_size=2 # reference run wandb: https://wandb.ai/verl-org/DAPO%20Reproduction%20on%20verl/runs/ow47vvon?nw=nwusertongyuxuan361 python3 -m verl.trainer.main_ppo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.model.use_remove_padding=True \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.grad_clip=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node="${NGPUS_PER_NODE}" \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=100 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_megatron_4_12.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-megatron-one-step-off-4-12' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=12 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) ref_offload=True actor_offload=False gen_tp=2 train_tp=2 train_pp=2 # TODO: support dynamic_bsz for megatron # actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ # actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ # actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ # actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ # actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ # actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ python3 -m verl.experimental.one_step_off_policy.main_ppo \ --config-path=config \ --config-name='one_step_off_ppo_megatron_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.strategy=megatron \ critic.strategy=megatron \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ actor_rollout_ref.hybrid_engine=False \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.megatron.param_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.grad_offload=${actor_offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.param_offload=${ref_offload} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=100 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" ================================================ FILE: verl/experimental/one_step_off_policy/shell/dapo_7b_math_megatron_colocate.sh ================================================ #!/usr/bin/env bash set -xeuo pipefail project_name='DAPO' exp_name='DAPO-Qwen2.5-7b-MATH-0519a1-megatron-colocate' adv_estimator=grpo use_kl_in_reward=False kl_coef=0.0 use_kl_loss=False kl_loss_coef=0.0 clip_ratio_low=0.2 clip_ratio_high=0.28 max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 8)) enable_overlong_buffer=True overlong_buffer_len=$((1024 * 4)) overlong_penalty_factor=1.0 loss_agg_mode="token-mean" train_prompt_bsz=512 n_resp_per_prompt=16 train_prompt_mini_bsz=32 # Ray # RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"} # WORKING_DIR=${WORKING_DIR:-"${PWD}"} # RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"} NNODES=${NNODES:-2} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} # very important! please modify the max_position_embeddings in config.json to 32768 after downloading from huggingface MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-Math-7B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"} # Algorithm temperature=1.0 top_p=1.0 top_k=-1 # 0 for HF rollout, -1 for vLLM rollout val_top_p=0.7 # Performance Related Parameter use_dynamic_bsz=True actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3)) offload=True gen_tp=2 train_tp=2 train_pp=2 # TODO: support dynamic_bsz for megatron # actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ # actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ # actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ # actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ # actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ # actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ python3 -m verl.trainer.main_ppo \ --config-path=config \ --config-name='ppo_megatron_trainer.yaml' \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.prompt_key=prompt \ data.truncation='left' \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.train_batch_size=${train_prompt_bsz} \ actor_rollout_ref.rollout.n=${n_resp_per_prompt} \ algorithm.adv_estimator=${adv_estimator} \ algorithm.use_kl_in_reward=${use_kl_in_reward} \ algorithm.kl_ctrl.kl_coef=${kl_coef} \ actor_rollout_ref.actor.strategy=megatron \ critic.strategy=megatron \ actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \ actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \ actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \ actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \ actor_rollout_ref.actor.clip_ratio_c=10.0 \ +actor_rollout_ref.model.override_config.max_position_embeddings=32768 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.actor.optim.lr_warmup_steps=10 \ actor_rollout_ref.actor.optim.weight_decay=0.1 \ actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \ actor_rollout_ref.actor.megatron.param_offload=${offload} \ actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \ actor_rollout_ref.actor.megatron.grad_offload=${offload} \ actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.optim.clip_grad=1.0 \ actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \ actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \ actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \ actor_rollout_ref.rollout.enable_chunked_prefill=True \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.temperature=${temperature} \ actor_rollout_ref.rollout.top_p=${top_p} \ actor_rollout_ref.rollout.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \ actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \ actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \ actor_rollout_ref.rollout.val_kwargs.do_sample=True \ actor_rollout_ref.rollout.val_kwargs.n=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \ actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \ actor_rollout_ref.ref.megatron.param_offload=${offload} \ reward.reward_manager.name=dapo \ +reward.reward_kwargs.overlong_buffer_cfg.enable=${enable_overlong_buffer} \ +reward.reward_kwargs.overlong_buffer_cfg.len=${overlong_buffer_len} \ +reward.reward_kwargs.overlong_buffer_cfg.penalty_factor=${overlong_penalty_factor} \ +reward.reward_kwargs.overlong_buffer_cfg.log=False \ +reward.reward_kwargs.max_resp_len=${max_response_length} \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.n_gpus_per_node=8 \ trainer.nnodes="${NNODES}" \ trainer.val_before_train=True \ trainer.test_freq=10 \ trainer.save_freq=-1 \ trainer.total_epochs=10 \ trainer.total_training_steps=100 \ trainer.default_local_dir="${CKPTS_DIR}" \ trainer.resume_mode=auto \ trainer.log_val_generations=10 ================================================ FILE: verl/experimental/one_step_off_policy/shell/grpo_0.6b_gsm8k_fsdp2_2_6.sh ================================================ set -x project_name='GRPO' exp_name='GRPO-Qwen3-0.6b-gsm8k-fsdp2-one-step-off-2-6' # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-0.6B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"} NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) python3 -m verl.experimental.one_step_off_policy.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=1152 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=192 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.val_before_train=True \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=2 \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" $@ ================================================ FILE: verl/experimental/one_step_off_policy/shell/grpo_0.6b_gsm8k_fsdp2_sglang_2_6.sh ================================================ set -x project_name='GRPO' exp_name='GRPO-Qwen3-0.6b-gsm8k-fsdp2-sglang-one-step-off-2-6' # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-0.6B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"} NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) python3 -m verl.experimental.one_step_off_policy.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=1152 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=192 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=sglang \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.val_before_train=True \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=2 \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" $@ ================================================ FILE: verl/experimental/one_step_off_policy/shell/grpo_3b_gsm8k_fsdp2_2_6.sh ================================================ set -x project_name='GRPO' exp_name='GRPO-Qwen3-0.6b-gsm8k-fsdp2-one-step-off-2-6' # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen/Qwen2.5-3B-Instruct"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"} NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-8} n_gpus_rollout=2 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) python3 -m verl.experimental.one_step_off_policy.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=1152 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.actor.fsdp_config.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=192 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.rollout.tensor_model_parallel_size=2 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=5 \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.rollout.layered_summon=True \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.val_before_train=True \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=2 \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" $@ ================================================ FILE: verl/experimental/one_step_off_policy/shell/grpo_qwen3_8b_gsm8k_fsdp2_8_8_npu.sh ================================================ # The script has been validated on the Ascend Atlas 800T A3. set -x export HCCL_EXEC_TIMEOUT=60000 export HCCL_CONNECT_TIMEOUT=7200 project_name='GRPO' exp_name='GRPO-Qwen3-8b-gsm8k-fsdp2-one-step-off-8-8-npu' # Paths RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"} MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen/Qwen3-8B"} CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"} TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/BytedTsinghua-SIA/DAPO-Math-17k"} TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/BytedTsinghua-SIA/DAPO-Math-17k"} NNODES=${NNODES:-1} NGPUS_PER_NODE=${NGPUS_PER_NODE:-16} n_gpus_rollout=8 n_gpus_training=$((NGPUS_PER_NODE - n_gpus_rollout)) max_prompt_length=$((1024 * 2)) max_response_length=$((1024 * 32)) use_dynamic_bsz=True sp_size=8 fsdp_size=8 actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) / sp_size)) python3 -m verl.experimental.one_step_off_policy.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files="${TRAIN_FILE}" \ data.val_files="${TEST_FILE}" \ data.train_batch_size=32 \ data.max_prompt_length=${max_prompt_length} \ data.max_response_length=${max_response_length} \ data.filter_overlong_prompts=True \ data.filter_overlong_prompts_workers=64 \ data.truncation='error' \ actor_rollout_ref.actor.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.model.path="${MODEL_PATH}" \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.hybrid_engine=False \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=32 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \ actor_rollout_ref.actor.use_kl_loss=False \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.actor.use_torch_compile=False \ actor_rollout_ref.ref.use_torch_compile=False \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.rollout.tensor_model_parallel_size=4 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.mode=async \ actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \ actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \ actor_rollout_ref.rollout.n=8 \ actor_rollout_ref.rollout.enforce_eager=True \ actor_rollout_ref.rollout.load_format=safetensors \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.fsdp_size=${fsdp_size} \ actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \ actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \ actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \ algorithm.use_kl_in_reward=False \ actor_rollout_ref.nccl_timeout=14400 \ trainer.critic_warmup=0 \ trainer.val_before_train=False \ trainer.logger=['console','tensorboard'] \ trainer.project_name="${project_name}" \ trainer.experiment_name="${exp_name}" \ trainer.default_local_dir=${CKPTS_DIR} \ trainer.save_freq=10 \ trainer.test_freq=-1 \ trainer.total_epochs=15 \ trainer.resume_mode=auto \ trainer.nnodes="${NNODES}" \ trainer.n_gpus_per_node="${n_gpus_training}" \ rollout.nnodes="${NNODES}" \ rollout.n_gpus_per_node="${n_gpus_rollout}" $@ ================================================ FILE: verl/experimental/reward_loop/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .reward_loop import RewardLoopManager, RewardLoopWorker, migrate_legacy_reward_impl from .reward_model import RewardModelManager __all__ = ["RewardModelManager", "RewardLoopWorker", "RewardLoopManager", "migrate_legacy_reward_impl"] ================================================ FILE: verl/experimental/reward_loop/reward_loop.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os import aiohttp import numpy as np import ray import torch from omegaconf import DictConfig, open_dict from tensordict import TensorDict from verl.protocol import DataProto from verl.single_controller.ray.base import RayResourcePool from verl.trainer.ppo.reward import load_reward_manager from verl.utils import hf_tokenizer from verl.utils.fs import copy_to_local from .reward_model import RewardModelManager logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def migrate_legacy_reward_impl(config): """ Migrate the legacy reward model implementation to the new one. """ # 1. reward workers migration # config.reward_model.num_workers -> config.reward.num_workers if config.reward_model.num_workers is not None: config.reward.num_workers = config.reward_model.num_workers # 2. reward manager migration # config.reward_model.reward_manager -> config.reward.reward_manager if config.reward_model.reward_manager is not None: config.reward.reward_manager.name = config.reward_model.reward_manager if config.reward_model.reward_loop_source is not None: config.reward.reward_manager.source = config.reward_model.reward_loop_source config.reward.reward_manager.module.path = config.reward_model.reward_loop_module_path config.reward.reward_manager.module.name = config.reward_model.reward_loop_class_name # 3. custom reward function migration # config.custom_reward_function -> config.reward.custom_reward_function if not all(v is None for v in config.custom_reward_function.values()): config.reward.custom_reward_function = config.custom_reward_function # 4. reward model migration # config.reward_model -> config.reward.reward_model for key in ["enable", "enable_resource_pool", "n_gpus_per_node", "nnodes"]: if config.reward_model.get(key) is not None: config.reward.reward_model[key] = config.reward_model[key] if config.reward_model.model.path is not None: config.reward.reward_model.model_path = config.reward_model.model.path # config.reward_model.reward_kwargs -> config.reward.reward_kwargs (for dapo algo) if config.reward_model.get("reward_kwargs") is not None: with open_dict(config.reward): config.reward["reward_kwargs"] = config.reward_model["reward_kwargs"] # config.reward_model.rollout -> config.reward.reward_model.rollout legacy_rollout = config.reward_model.rollout for key in legacy_rollout.keys(): if legacy_rollout[key] is not None: config.reward.reward_model.rollout[key] = legacy_rollout[key] # 5. sandbox_fusion migration # config.sandbox_fusion -> reward.sandbox_fusion if not all(v is None for v in config.sandbox_fusion.values()): config.reward.sandbox_fusion = config.sandbox_fusion # 6. delete legacy config from configs with open_dict(config): del config.reward_model del config.custom_reward_function del config.sandbox_fusion return config class RewardLoopWorker: """ RewardLoopWork can tackle reward computation: (1) rule-based reward computation (2) reward model-based reward computation (both disrm and genrm) (3) high-flexible user-customized reward function (can access rm by posting requests to reward_model_router) Reward Computation Logic: - if user-customized reward function is provided: -> directly use user-customized reward function - if user-customized reward function is not provided: -> rm is not enabled: use default rule-based reward function -> rm is disrm: compute reward score using disrm -> rm is genrm: raise error (user-costomized reward func must be provided) """ def __init__(self, config: DictConfig, reward_router_address: str = None): """ Args: config: DictConfig, the config for reward loop worker. reward_router_address: str, the address of reward router. """ self.config = config self.reward_router_address = reward_router_address self._init_reward_fn() def _init_reward_fn(self): input_tokenizer_local_path = copy_to_local(self.config.actor_rollout_ref.model.path) self.input_tokenizer = hf_tokenizer(input_tokenizer_local_path, trust_remote_code=True) self.reward_model_tokenizer = None if self.config.reward.reward_model.enable: reward_model_tokenizer_local_path = copy_to_local(self.config.reward.reward_model.model_path) self.reward_model_tokenizer = hf_tokenizer(reward_model_tokenizer_local_path, trust_remote_code=True) self.reward_manager = load_reward_manager( self.config, self.input_tokenizer, reward_router_address=self.reward_router_address, reward_model_tokenizer=self.reward_model_tokenizer, ) async def compute_score_batch(self, data: DataProto) -> list[dict]: tasks = [] for i in range(len(data)): tasks.append(asyncio.create_task(self.compute_score(data[i : i + 1]))) outputs = await asyncio.gather(*tasks) return outputs async def compute_score(self, data: DataProto) -> dict: assert len(data) == 1, "RewardLoopWorker only support single data item" if self.config.reward.custom_reward_function.path is not None: # directly use user-customized reward function return await self.reward_manager.run_single(data) else: if self.config.reward.reward_model.enable: # we assume the rm is disrm # genrm must set custom_reward_function return await self.compute_score_disrm(data) else: return await self.reward_manager.run_single(data) async def _post_request(self, payload: dict, endpoint: str, max_retries: int = 16): url = f"http://{self.reward_router_address}/{endpoint}" last_exception = None for attempt in range(max_retries): try: # It's safer to have a timeout instead of None, which can hang indefinitely. timeout = aiohttp.ClientTimeout(total=None) async with aiohttp.ClientSession(timeout=timeout) as session: async with session.post(url, json=payload) as resp: resp.raise_for_status() return await resp.json() except aiohttp.ClientResponseError as e: # Do not retry on 4xx client errors, but retry on 5xx server errors. if 400 <= e.status < 500: logger.error(f"Request to {url} failed with client error HTTP {e.status}: {e}. Not retrying.") raise last_exception = e logger.warning( f"[Attempt {attempt + 1}/{max_retries}] Request to {url} failed with HTTP {e.status}: {e}. " "Retrying..." ) except (asyncio.TimeoutError, aiohttp.ClientConnectorError) as e: last_exception = e logger.warning(f"[Attempt {attempt + 1}/{max_retries}] Request to {url} failed: {e}. Retrying...") except Exception as e: last_exception = e logger.warning( f"[Attempt {attempt + 1}/{max_retries}] Request to {url} failed with unexpected error: {e}. " "Retrying..." ) if attempt < max_retries - 1: # Using exponential backoff is generally better than a fixed sleep. backoff_seconds = 2**attempt await asyncio.sleep(min(backoff_seconds, 30)) logger.error(f"Max retries ({max_retries}) reached for request to {url}.") if last_exception: raise last_exception async def _preprocess_reward_inputs(self, data: DataProto) -> str: assert len(data) == 1, "RewardLoopWorker only support single data item" data_item = data[0] assert "raw_prompt" in data_item.non_tensor_batch # extract raw prompt chat: list = list(data_item.non_tensor_batch["raw_prompt"]) # extract response response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode rollout_response = self.input_tokenizer.decode(valid_response_ids) # remove bos and eos rollout_response = rollout_response.replace(self.input_tokenizer.eos_token, "") chat.append({"role": "assistant", "content": rollout_response}) rm_prompt = self.reward_model_tokenizer.apply_chat_template( chat, add_generation_prompt=False, tokenize=False, ) # llama tokenizer will add bos token by default # will be removed in vllm >= 0.11.2, where we can add "add_special_tokens" = False if self.reward_model_tokenizer.bos_token is not None and rm_prompt.startswith( self.reward_model_tokenizer.bos_token ): rm_prompt = rm_prompt[len(self.reward_model_tokenizer.bos_token) :] return rm_prompt async def compute_score_disrm(self, data: DataProto) -> dict: disrm_prompt = await self._preprocess_reward_inputs(data) engine_name = self.config.reward.reward_model.rollout.name model_name = self.config.reward.reward_model.model_path if engine_name == "vllm": payloads = { "model": model_name, "input": disrm_prompt, "use_activation": False, } output = await self._post_request(payloads, "classify") rm_score = output["data"][-1]["probs"][-1] elif engine_name == "sglang": payloads = { "model": model_name, "input": disrm_prompt, } output = await self._post_request(payloads, "v1/embeddings") rm_score = output["data"][-1]["embedding"][-1] elif engine_name == "trtllm": # TODO: remove this once TRT-LLM switches to TorchSampler raise ValueError("TensorRT-LLM backend does not support reward models currently.") payloads = { "model": model_name, "prompt": disrm_prompt, "return_context_logits": True, } output = await self._post_request(payloads, "v1/completions") rm_score = output["choices"][0]["context_logits"] assert isinstance(rm_score, list) and len(rm_score) > 0, ( "TensorRT-LLM OpenAI server response for reward score is not in the expected format." ) rm_score = float(rm_score[0][0]) logger.debug(f"rm score: {rm_score}") else: raise NotImplementedError(f"RewardLoopManager does not support {engine_name}") return {"reward_score": rm_score} class RewardLoopManager: """ RewardLoopManager run in single controller. This class will create reward loop workers and manage them. """ def __init__(self, config: DictConfig, rm_resource_pool: RayResourcePool = None): self.config = config if self.config.reward.reward_model.enable: self.reward_model_manager = RewardModelManager(config.reward.reward_model, rm_resource_pool) self.reward_router_address = self.reward_model_manager.get_router_address() else: self.reward_model_manager = None self.reward_router_address = None self.reward_loop_workers_class = ray.remote(RewardLoopWorker) self._init_reward_loop_workers() def _init_reward_loop_workers(self): self.reward_loop_workers = [] num_workers = self.config.reward.num_workers node_ids = [node["NodeID"] for node in ray.nodes() if node["Alive"] and node["Resources"].get("CPU", 0) > 0] for i in range(num_workers): # Round-robin scheduling over the all nodes node_id = node_ids[i % len(node_ids)] self.reward_loop_workers.append( self.reward_loop_workers_class.options( name=f"reward_loop_worker_{i}", scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy( node_id=node_id, soft=True, ), ).remote(self.config, self.reward_router_address) ) def compute_rm_score(self, data: DataProto) -> DataProto: if self.reward_model_manager is not None: self.reward_model_manager.wake_up() chunks = data.chunk(len(self.reward_loop_workers)) outputs = ray.get( [ worker.compute_score_batch.remote(chunk) for worker, chunk in zip(self.reward_loop_workers, chunks, strict=True) ] ) outputs_flat = [item for sublist in outputs for item in sublist] # compute rm score scores = [item["reward_score"] for item in outputs_flat] prompt_length = data.batch["prompts"].size(1) valid_response_length = data.batch["attention_mask"][:, prompt_length:].sum(dim=1) rm_scores = torch.zeros_like(data.batch["responses"], dtype=torch.float32) rm_scores[torch.arange(rm_scores.size(0)), valid_response_length - 1] = torch.tensor( scores, dtype=torch.float32 ) batch = TensorDict({"rm_scores": rm_scores}, batch_size=len(data)) reward_extra_infos = [output.get("reward_extra_info", {}) for output in outputs_flat] reward_extra_keys = list(reward_extra_infos[0].keys()) non_tensor_batch = {} for key in reward_extra_keys: non_tensor_batch[key] = np.array([info[key] for info in reward_extra_infos]) if self.reward_model_manager is not None: self.reward_model_manager.sleep() return DataProto( batch=batch, non_tensor_batch=non_tensor_batch, meta_info={"reward_extra_keys": reward_extra_keys} ) def _run_all(self, tasks: list[asyncio.Task]): async def run_all(): return await asyncio.gather(*tasks) return asyncio.run(run_all()) ================================================ FILE: verl/experimental/reward_loop/reward_manager/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .registry import get_reward_manager_cls, register # noqa: I001 from .dapo import DAPORewardManager from .gdpo import GDPORewardManager from .naive import NaiveRewardManager from .limited import RateLimitedRewardManager from .remote import RemoteRewardManager __all__ = [ "DAPORewardManager", "GDPORewardManager", "NaiveRewardManager", "RateLimitedRewardManager", "RemoteRewardManager", "register", "get_reward_manager_cls", ] ================================================ FILE: verl/experimental/reward_loop/reward_manager/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from abc import ABC, abstractmethod from typing import Any, Callable from omegaconf import DictConfig from transformers import AutoTokenizer from verl import DataProto from verl.utils.ray_utils import get_event_loop logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) RawRewardFn = Callable[..., Any] | None class RewardManagerBase(ABC): _class_initialized = False def __init__(self, config: DictConfig, tokenizer: AutoTokenizer, compute_score: RawRewardFn): """Initialize reward manager. Args: config (DictConfig): YAML config. tokenizer (AutoTokenizer): Tokenizer for tokenize messages. """ self.config = config self.tokenizer = tokenizer self.compute_score = compute_score self.loop = get_event_loop() self.init_class(config, tokenizer) @classmethod def init_class(cls, config: DictConfig, tokenizer: AutoTokenizer): """Initialize class state shared across all instances.""" if cls._class_initialized: return cls._class_initialized = True @abstractmethod async def run_single(self, data: DataProto): raise NotImplementedError ================================================ FILE: verl/experimental/reward_loop/reward_manager/dapo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from verl import DataProto from verl.experimental.reward_loop.reward_manager import register from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase from verl.utils.reward_score import default_compute_score @register("dapo") class DAPORewardManager(RewardManagerBase): """DAPO Reward Manager.""" def __init__(self, config, tokenizer, compute_score, reward_router_address=None, reward_model_tokenizer=None): super().__init__(config, tokenizer, compute_score) self.compute_score = compute_score or default_compute_score self.is_async_reward_score = inspect.iscoroutinefunction(self.compute_score) # DAPO Reward Config overlong_buffer_cfg = config.reward.get("reward_kwargs", {}).get("overlong_buffer_cfg", None) self.overlong_buffer_cfg = overlong_buffer_cfg self.max_resp_len = config.reward.get("reward_kwargs", {}).get("max_resp_len", None) self.reward_router_address = reward_router_address self.reward_model_tokenizer = reward_model_tokenizer if self.overlong_buffer_cfg is not None: assert self.max_resp_len is not None, ( f"max_resp_len must be provided if {overlong_buffer_cfg=}, but got None" ) assert self.max_resp_len >= self.overlong_buffer_cfg.len, ( "max_resp_len must be larger than overlong_buffer.len" ) assert not self.overlong_buffer_cfg.enable or self.overlong_buffer_cfg.len > 0, ( "overlong_buffer.len must be positive when overlong penalty is enabled," f"but got {self.overlong_buffer_cfg.len}." "To disable the overlong penalty, set overlong_buffer.enable = False" ) async def run_single(self, data: DataProto) -> dict: assert len(data) == 1, "Only support single data item" data_item = data[0] response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] data_source = data_item.non_tensor_batch["data_source"] ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] extra_info = data_item.non_tensor_batch.get("extra_info", {}) response_str = await self.loop.run_in_executor( None, lambda: self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) ) extra_reward_kwargs = ( { "reward_router_address": self.reward_router_address, "reward_model_tokenizer": self.reward_model_tokenizer, } if self.reward_router_address is not None else {} ) if self.is_async_reward_score: result = await self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ) else: result = await self.loop.run_in_executor( None, lambda: self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ), ) reward_extra_info = {} score: float if isinstance(result, dict): score = result["score"] for key, value in result.items(): reward_extra_info[key] = value else: score = result reward_extra_info["acc"] = score reward = score if self.overlong_buffer_cfg is not None and self.overlong_buffer_cfg.enable: overlong_buffer_len = self.overlong_buffer_cfg.len expected_len = self.max_resp_len - overlong_buffer_len exceed_len = valid_response_length - expected_len overlong_penalty_factor = self.overlong_buffer_cfg.penalty_factor overlong_reward = min(-exceed_len / overlong_buffer_len * overlong_penalty_factor, 0) reward += overlong_reward if self.overlong_buffer_cfg.log: reward_extra_info["overlong_reward"] = overlong_reward reward_extra_info["overlong"] = overlong_reward < 0 return {"reward_score": reward, "reward_extra_info": reward_extra_info} ================================================ FILE: verl/experimental/reward_loop/reward_manager/gdpo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from verl import DataProto from verl.experimental.reward_loop.reward_manager import register from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase from verl.utils.reward_score import default_compute_score @register("gdpo") class GDPORewardManager(RewardManagerBase): """GDPO Reward Manager.""" def __init__(self, config, tokenizer, compute_score, reward_router_address=None, reward_model_tokenizer=None): super().__init__(config, tokenizer, compute_score) self.compute_score = compute_score or default_compute_score self.is_async_reward_score = inspect.iscoroutinefunction(self.compute_score) self.reward_router_address = reward_router_address self.reward_model_tokenizer = reward_model_tokenizer async def run_single(self, data: DataProto) -> dict: assert len(data) == 1, "Only support single data item" data_item = data[0] response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] data_source = data_item.non_tensor_batch["data_source"] ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] extra_info = data_item.non_tensor_batch.get("extra_info", {}) extra_info["experiment_name"] = self.config.trainer.experiment_name response_str = await self.loop.run_in_executor( None, lambda: self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) ) extra_reward_kwargs = ( { "reward_router_address": self.reward_router_address, "reward_model_tokenizer": self.reward_model_tokenizer, } if self.reward_router_address is not None else {} ) if self.is_async_reward_score: result = await self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ) else: result = await self.loop.run_in_executor( None, lambda: self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ), ) reward_extra_info = {} score: float if isinstance(result, dict): score = result["score"] for key, value in result.items(): reward_extra_info[key] = value else: score = result reward_extra_info["acc"] = score reward = score return {"reward_score": reward, "reward_extra_info": reward_extra_info} ================================================ FILE: verl/experimental/reward_loop/reward_manager/limited.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import inspect import logging from omegaconf import DictConfig from transformers import AutoTokenizer from verl import DataProto from verl.experimental.reward_loop.reward_manager import register as register_manager from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase from verl.utils.ray_utils import get_event_loop from verl.utils.reward_score import default_compute_score from verl.workers.reward_manager import register as register_manager_legacy logger = logging.getLogger(__file__) class AsyncTokenBucket: """Async token bucket for rate limiting with variable token consumption. The token bucket algorithm is a classic rate limiting technique that allows for burst traffic while maintaining an average rate limit. This implementation is async-first and thread-safe, designed for use in concurrent environments. The bucket starts full and refills at a constant rate (rate_limit tokens/second). When tokens are acquired, they are consumed from the bucket. If insufficient tokens are available, the acquire() method will sleep until enough tokens have been refilled. This implementation supports variable token consumption, making it suitable for rate limiting based on request size (e.g., API token usage). Args: rate_limit (float): The rate at which tokens are added to the bucket, in tokens per second. For example, rate_limit=10.0 means 10 tokens are added per second (or 600 per minute). max_tokens (float, optional): The maximum capacity of the token bucket. Defaults to rate_limit if not specified. This value determines the maximum burst size allowed. Attributes: rate_limit (float): Tokens added per second. max_tokens (float): Maximum bucket capacity. tokens (float): Current number of available tokens. last_update (float | None): Timestamp of last token update (from event loop). lock (asyncio.Lock): Async lock for thread-safe token operations. Example: >>> # Limit to 60 requests per minute (1 request per second) >>> rpm_limiter = AsyncTokenBucket(rate_limit=1.0, max_tokens=1.0) >>> await rpm_limiter.acquire(1.0) # Consumes 1 token >>> >>> # Limit to 10000 tokens per minute (~166.67 tokens per second) >>> tpm_limiter = AsyncTokenBucket(rate_limit=166.67, max_tokens=166.67) >>> await tpm_limiter.acquire(100.0) # Consumes 100 tokens Thread Safety: All operations are protected by an asyncio.Lock, making this class safe for concurrent use across multiple coroutines. Algorithm Details: 1. On each acquire(), calculate elapsed time since last update 2. Refill tokens: tokens += elapsed * rate_limit (capped at max_tokens) 3. If tokens >= num_tokens: consume tokens and return 4. Otherwise: calculate wait_time = tokens_needed / rate_limit, then sleep 5. Retry after sleep (loop back to step 1) """ def __init__(self, rate_limit: float, max_tokens: float = None): self.rate_limit = rate_limit self.max_tokens = max_tokens or rate_limit self.tokens = self.max_tokens self.last_update = None self.lock = asyncio.Lock() async def acquire(self, num_tokens: float = 1.0) -> None: """Acquire tokens from the bucket, waiting if necessary. This method will block (using asyncio.sleep) until sufficient tokens are available. It automatically refills tokens based on elapsed time and the configured rate_limit. For requests exceeding max_tokens, the method will wait for enough time to accumulate the required tokens at the configured rate_limit, allowing tokens to temporarily go negative. Args: num_tokens (float): Number of tokens to consume. Defaults to 1.0. Can be fractional for fine-grained rate limiting. Returns: None: Returns when tokens have been successfully acquired. Raises: No exceptions are raised. This method will wait indefinitely until tokens become available. Example: >>> bucket = AsyncTokenBucket(rate_limit=10.0) >>> await bucket.acquire(5.0) # Acquire 5 tokens >>> await bucket.acquire(1.0) # Acquire 1 more token Implementation Notes: - Uses event loop's time() for high-precision timestamps - Lock is released during sleep to allow other coroutines to proceed - Tokens are refilled continuously based on elapsed time - For requests > max_tokens, allows temporary negative balance """ # Handle requests larger than max_tokens separately if num_tokens > self.max_tokens: wait_time = 0.0 async with self.lock: loop = get_event_loop() now = loop.time() if self.last_update is None: self.last_update = now elapsed = now - self.last_update new_tokens = elapsed * self.rate_limit self.tokens = min(self.max_tokens, self.tokens + new_tokens) tokens_needed = num_tokens - self.tokens if tokens_needed > 0: wait_time = tokens_needed / self.rate_limit self.tokens -= num_tokens self.last_update = now if wait_time > 0: await asyncio.sleep(wait_time) return # Standard case: request <= max_tokens while True: wait_time = 0.0 async with self.lock: loop = get_event_loop() now = loop.time() if self.last_update is None: self.last_update = now elapsed = now - self.last_update new_tokens = elapsed * self.rate_limit self.tokens = min(self.max_tokens, self.tokens + new_tokens) self.last_update = now if self.tokens >= num_tokens: self.tokens -= num_tokens return tokens_needed = num_tokens - self.tokens wait_time = tokens_needed / self.rate_limit if wait_time > 0: await asyncio.sleep(wait_time) @register_manager("rate_limited") @register_manager_legacy("rate_limited") class RateLimitedRewardManager(RewardManagerBase): """Reward manager with rate limiting for API-based reward functions. This manager implements a sophisticated three-layer rate limiting system designed for LLM-as-judge scenarios where reward computation involves external API calls (e.g., OpenAI, Anthropic, Claude) that have rate limits. The three layers of rate limiting are: 1. **Concurrency limiting** (max_concurrent): Limits the number of simultaneous API requests using asyncio.Semaphore. This prevents overwhelming the API with too many parallel connections. 2. **Request rate limiting** (max_rpm): Limits requests per minute using AsyncTokenBucket. Each request consumes 1 token. Useful for APIs with per-minute request quotas. 3. **Token rate limiting** (max_tpm): Limits tokens per minute using AsyncTokenBucket. Each request consumes estimated_tokens_per_request tokens. Essential for APIs that bill or limit based on token usage (e.g., GPT-4 API). All rate limiters are **global class-level resources**, meaning they are shared across all instances of this manager. This ensures that rate limits are enforced consistently across multiple workers in distributed training. Rate Limiting Flow: When processing a reward request, the manager: 1. Acquires RPM token (if rpm_limiter enabled) 2. Acquires TPM tokens (if tpm_limiter enabled) 3. Acquires concurrency semaphore 4. Executes reward computation with timeout 5. Releases concurrency semaphore 6. Tokens are automatically refilled by the token buckets Args: config (DictConfig): Configuration object containing reward_model settings: - max_concurrent (int): Max parallel requests. Default: 1 - max_rpm (int | None): Max requests per minute. Default: None (unlimited) - max_tpm (int | None): Max tokens per minute. Default: None (unlimited) - estimated_tokens_per_request (int): Estimated tokens per request for TPM limiting. Default: 2000 - timeout (float): Timeout for reward computation in seconds. Default: 300 tokenizer (AutoTokenizer): HuggingFace tokenizer for decoding responses. compute_score (callable, optional): Custom reward scoring function. Can be sync or async. Defaults to default_compute_score. reward_router_address (str | None): Address for reward router service. reward_model_tokenizer (AutoTokenizer | None): Optional tokenizer for reward model. Class Attributes (Global State): _semaphore (asyncio.Semaphore): Global concurrency limiter _max_concurrent (int): Max concurrent requests _rpm_limiter (AsyncTokenBucket | None): Request rate limiter _max_rpm (int | None): Max requests per minute _tpm_limiter (AsyncTokenBucket | None): Token rate limiter _max_tpm (int | None): Max tokens per minute _estimated_tokens_per_request (int): Estimated tokens per request _class_initialized (bool): Whether class has been initialized Example Configuration: >>> config = DictConfig({ ... "reward": { ... "max_concurrent": 10, # 10 parallel requests ... "max_rpm": 500, # 500 requests/minute ... "max_tpm": 100000, # 100k tokens/minute ... "estimated_tokens_per_request": 2000, ... "timeout": 60.0, ... } ... }) >>> manager = RateLimitedRewardManager(config, tokenizer) Thread Safety: This class is designed for concurrent use. All rate limiting resources are protected by asyncio primitives (Lock, Semaphore). See Also: - AsyncTokenBucket: Token bucket implementation for rate limiting - RewardManagerBase: Base class for reward managers - verl.utils.reward_score.default_compute_score: Default scoring function """ # Class-level state for global rate limiting _semaphore = None _max_concurrent = None _rpm_limiter = None _max_rpm = None _tpm_limiter = None _max_tpm = None _estimated_tokens_per_request = None _class_initialized = False @classmethod def init_class(cls, config: DictConfig, tokenizer: AutoTokenizer): """Initialize class state shared across all instances.""" # Check if already initialized before calling parent. # # NOTE: This class owns a *global*, class-level set of rate limiters. Once the class has been # initialized, subsequent instantiations cannot change the shared limiters. This is by design, # but it can be surprising (and dangerous) when the first initialization happens with default # values (often "unlimited") and later code tries to apply limits. if cls._class_initialized: rm_cfg = config.get("reward") or {} incoming_max_rpm = rm_cfg.get("max_rpm", None) incoming_max_tpm = rm_cfg.get("max_tpm", None) # Warn when a caller is trying to change the global RPM/TPM limits after initialization. # This commonly happens if the first instance was created without a config (legacy signature), # which initializes the global limiters to their defaults and locks them in. if (incoming_max_rpm != cls._max_rpm) or (incoming_max_tpm != cls._max_tpm): if ( incoming_max_rpm is not None or incoming_max_tpm is not None or cls._max_rpm is not None or cls._max_tpm is not None ): logger.warning( "RateLimitedRewardManager has already been initialized and its rate limiters are shared " "globally across instances. The incoming (max_rpm/max_tpm) settings will be ignored. " "This can lead to unexpected behavior (e.g., exceeding API rate limits) if the first " "initialization used defaults (often unlimited). " f"Existing: max_rpm={cls._max_rpm}, max_tpm={cls._max_tpm}. " f"Incoming: max_rpm={incoming_max_rpm}, max_tpm={incoming_max_tpm}. " "To apply different limits, ensure the first RateLimitedRewardManager created in this " "process uses the desired configuration (or restart/reset the process)." ) return super().init_class(config, tokenizer) rm_cfg = config.get("reward") or {} # Concurrency limiter cls._max_concurrent = rm_cfg.get("max_concurrent", 1) cls._semaphore = asyncio.Semaphore(cls._max_concurrent) # Request rate limiter (RPM) cls._max_rpm = rm_cfg.get("max_rpm", None) if cls._max_rpm is not None: requests_per_second = cls._max_rpm / 60.0 cls._rpm_limiter = AsyncTokenBucket(rate_limit=requests_per_second, max_tokens=requests_per_second) else: cls._rpm_limiter = None # Token rate limiter (TPM) cls._max_tpm = rm_cfg.get("max_tpm", None) cls._estimated_tokens_per_request = rm_cfg.get("estimated_tokens_per_request", 2000) if cls._max_tpm is not None: tokens_per_second = cls._max_tpm / 60.0 cls._tpm_limiter = AsyncTokenBucket(rate_limit=tokens_per_second, max_tokens=tokens_per_second) else: cls._tpm_limiter = None log_msg = "Rate limiting configuration:\n" log_msg += f" - Concurrency limit: {cls._max_concurrent}\n" if cls._max_rpm is not None: log_msg += f" - Request rate limit: {cls._max_rpm} RPM ({cls._max_rpm / 60.0:.2f} RPS)\n" else: log_msg += " - Request rate limit: unlimited\n" if cls._max_tpm is not None: log_msg += f" - Token rate limit: {cls._max_tpm} TPM ({cls._max_tpm / 60.0:.2f} TPS)\n" log_msg += f" - Estimated tokens per request: {cls._estimated_tokens_per_request}\n" else: log_msg += " - Token rate limit: unlimited\n" log_msg += "All limiters are shared globally across all workers." logger.info(log_msg) cls._class_initialized = True def __init__( self, config, tokenizer, compute_score, reward_router_address=None, reward_model_tokenizer=None, # Legacy (AbstractRewardManager) kwargs for compatibility. Not used. num_examine: int | None = None, reward_fn_key: str | None = None, **kwargs, ): # When called via the legacy AbstractRewardManager signature, `config` may be absent. # In that case we fall back to an empty config so training can proceed. if config is None: config = DictConfig({"reward": {}}) if tokenizer is None: raise TypeError("RateLimitedRewardManager requires `tokenizer`.") super().__init__(config, tokenizer, compute_score) self.compute_score = compute_score or default_compute_score self.is_async_reward_score = inspect.iscoroutinefunction(self.compute_score) self.reward_router_address = reward_router_address self.reward_model_tokenizer = reward_model_tokenizer self.timeout = config.reward.get("timeout", 300.0) async def _compute_reward( self, data_source: str, solution_str: str, ground_truth: str, extra_info: dict ) -> dict | float: extra_reward_kwargs = ( { "reward_router_address": self.reward_router_address, "reward_model_tokenizer": self.reward_model_tokenizer, } if self.reward_router_address is not None else {} ) if self.is_async_reward_score: return await self.compute_score( data_source=data_source, solution_str=solution_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ) else: return await self.loop.run_in_executor( None, lambda: self.compute_score( data_source=data_source, solution_str=solution_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ), ) async def run_single(self, data: DataProto) -> dict: assert len(data) == 1, "Only support single data item" data_item = data[0] response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] data_source = data_item.non_tensor_batch["data_source"] ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] extra_info = data_item.non_tensor_batch.get("extra_info", {}) tool_extra_fields = data_item.non_tensor_batch.get("tool_extra_fields", None) if tool_extra_fields is not None: extra_info.update(tool_extra_fields.items()) response_str = await self.loop.run_in_executor( None, lambda: self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) ) reward_extra_info = {} # Apply rate limiting layers if self._rpm_limiter is not None: await self._rpm_limiter.acquire(1.0) if self._tpm_limiter is not None: estimated_tokens = self._estimated_tokens_per_request await self._tpm_limiter.acquire(estimated_tokens) async with self._semaphore: try: result = await asyncio.wait_for( self._compute_reward( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, ), timeout=self.timeout, ) score: float if isinstance(result, dict): score = result["score"] for key, value in result.items(): reward_extra_info[key] = value else: score = result reward_extra_info["acc"] = score reward = score except asyncio.TimeoutError: logger.warning( f"Reward computation timed out after {self.timeout}s for data_source={data_source}. " f"Response preview: {response_str[:100]}..." ) reward = 0.0 reward_extra_info["timeout"] = True reward_extra_info["acc"] = 0.0 except Exception as e: logger.error( f"Reward computation failed for data_source={data_source}: {e}. " f"Response preview: {response_str[:100]}..." ) reward = 0.0 reward_extra_info["error"] = str(e) reward_extra_info["acc"] = 0.0 return {"reward_score": reward, "reward_extra_info": reward_extra_info} def __call__(self, data: DataProto, return_dict: bool = False): """Make the manager callable like traditional reward managers. This method provides compatibility with the existing reward manager interface by wrapping the async run_single method in a synchronous call. Args: data (DataProto): Input data containing prompts and responses. return_dict (bool): If True, return a dict with reward_tensor and reward_extra_info. If False, return only the reward_tensor. Defaults to False. Returns: torch.Tensor | dict: If return_dict is False, returns a tensor of shape [batch_size, response_length] with rewards. If return_dict is True, returns a dict with: - reward_tensor: The reward tensor - reward_extra_info: Dict containing extra information about rewards """ from collections import defaultdict import torch # If there are pre-computed rm_scores, return them directly if "rm_scores" in data.batch.keys(): if return_dict: reward_extra_keys = data.meta_info.get("reward_extra_keys", []) reward_extra_info = {key: data.non_tensor_batch[key] for key in reward_extra_keys} return {"reward_tensor": data.batch["rm_scores"], "reward_extra_info": reward_extra_info} else: return data.batch["rm_scores"] # Initialize reward tensor reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) reward_extra_info = defaultdict(list) # Process each data item through the async event loop async def process_batch(): tasks = [] for i in range(len(data)): data_item = data[i : i + 1] # Get single item as DataProto slice tasks.append(self.run_single(data_item)) results = await asyncio.gather(*tasks) return results # Run the async processing using self.loop property which lazily gets/creates event loop # This ensures rate limiters and semaphores work correctly by using the same loop results = self.loop.run_until_complete(process_batch()) # Aggregate results into reward tensor and extra info for i, result in enumerate(results): data_item = data[i] response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() reward = result["reward_score"] reward_tensor[i, valid_response_length - 1] = reward # Collect extra info if "reward_extra_info" in result: for key, value in result["reward_extra_info"].items(): reward_extra_info[key].append(value) if return_dict: return { "reward_tensor": reward_tensor, "reward_extra_info": reward_extra_info, } else: return reward_tensor ================================================ FILE: verl/experimental/reward_loop/reward_manager/naive.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from verl import DataProto from verl.experimental.reward_loop.reward_manager import register from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase from verl.utils.reward_score import default_compute_score @register("naive") class NaiveRewardManager(RewardManagerBase): """The reward manager.""" def __init__(self, config, tokenizer, compute_score, reward_router_address=None, reward_model_tokenizer=None): super().__init__(config, tokenizer, compute_score) self.compute_score = compute_score or default_compute_score self.is_async_reward_score = inspect.iscoroutinefunction(self.compute_score) self.reward_router_address = reward_router_address self.reward_model_tokenizer = reward_model_tokenizer async def run_single(self, data: DataProto) -> dict: assert len(data) == 1, "Only support single data item" data_item = data[0] response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] data_source = data_item.non_tensor_batch["data_source"] ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] extra_info = data_item.non_tensor_batch.get("extra_info", {}) tool_extra_fields = data_item.non_tensor_batch.get("tool_extra_fields", None) if tool_extra_fields is not None: extra_info.update(tool_extra_fields.items()) num_turns = data_item.non_tensor_batch.get("__num_turns__", None) rollout_reward_scores = data_item.non_tensor_batch.get("reward_scores", {}) extra_info["num_turns"] = num_turns extra_info["rollout_reward_scores"] = rollout_reward_scores response_str = await self.loop.run_in_executor( None, lambda: self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) ) extra_reward_kwargs = ( { "reward_router_address": self.reward_router_address, "reward_model_tokenizer": self.reward_model_tokenizer, } if self.reward_router_address is not None else {} ) if self.is_async_reward_score: result = await self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ) else: result = await self.loop.run_in_executor( None, lambda: self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ), ) reward_extra_info = {} score: float if isinstance(result, dict): score = result["score"] for key, value in result.items(): reward_extra_info[key] = value else: score = result reward_extra_info["acc"] = score reward = score return {"reward_score": reward, "reward_extra_info": reward_extra_info} ================================================ FILE: verl/experimental/reward_loop/reward_manager/registry.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase __all__ = ["register", "get_reward_manager_cls"] REWARD_MANAGER: dict[str, type[RewardManagerBase]] = {} def register(name: str) -> Callable[[type[RewardManagerBase]], type[RewardManagerBase]]: """Decorator to register a reward manager class with a given name. Args: name: `(str)` The name of the reward manager. """ def decorator(cls: type[RewardManagerBase]) -> type[RewardManagerBase]: if name in REWARD_MANAGER and REWARD_MANAGER[name] != cls: raise ValueError(f"reward manager {name} has already been registered: {REWARD_MANAGER[name]} vs {cls}") REWARD_MANAGER[name] = cls return cls return decorator def get_reward_manager_cls(name: str) -> type[RewardManagerBase]: """Get the reward manager class with a given name. Args: name: `(str)` The name of the reward manager. Returns: `(type)`: The reward manager class. """ if name not in REWARD_MANAGER: raise ValueError(f"Unknown reward manager: {name}") return REWARD_MANAGER[name] ================================================ FILE: verl/experimental/reward_loop/reward_manager/remote.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import itertools import ray from verl import DataProto from verl.experimental.reward_loop.reward_manager import register from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase from verl.utils.reward_score import default_compute_score @ray.remote(num_cpus=1) class RewardComputeWorker: """ WARNING: This class cannot have async methods. """ def __init__(self, compute_score_fn): # since the reward function may not be pickleable, we need to init it in the worker self.compute_score_fn = compute_score_fn def compute_score(self, **kwargs) -> dict: return self.compute_score_fn(**kwargs) @register("remote") class RemoteRewardManager(RewardManagerBase): """ The reward manager. Some errors exist when using default thread pool to compute reward score, e.g., math-verify. https://github.com/volcengine/verl/issues/3407 To avoid the above issues, we use a separate process to compute reward score. Moreover, process may be more suitable for cpu-intensive requests. """ def __init__(self, config, tokenizer, compute_score, reward_router_address=None, reward_model_tokenizer=None): super().__init__(config, tokenizer, compute_score) self.compute_score = compute_score or default_compute_score self.is_async_reward_score = inspect.iscoroutinefunction(self.compute_score) assert not self.is_async_reward_score, "Async reward score is not supported in remote reward manager. " self.reward_router_address = reward_router_address self.reward_model_tokenizer = reward_model_tokenizer num_reward_workers = config.reward.num_workers # in the rollout & reward parallel mode # the sum of final reward workers will be agent_loop_workers * num_reward_workers self.reward_worker = [ # register the reward worker in the same node RewardComputeWorker.options( scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy( node_id=ray.get_runtime_context().get_node_id(), soft=True, ), ).remote(self.compute_score) for _ in range(num_reward_workers) ] self.reward_worker_pool = itertools.cycle(self.reward_worker) def choose_reward_worker(self): return next(self.reward_worker_pool) async def run_single(self, data: DataProto) -> dict: assert len(data) == 1, "Only support single data item" data_item = data[0] response_ids = data_item.batch["responses"] response_length = response_ids.shape[-1] valid_response_length = data_item.batch["attention_mask"][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] data_source = data_item.non_tensor_batch["data_source"] ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] extra_info = data_item.non_tensor_batch.get("extra_info", {}) tool_extra_fields = data_item.non_tensor_batch.get("tool_extra_fields", None) if tool_extra_fields is not None: extra_info.update(tool_extra_fields.items()) num_turns = data_item.non_tensor_batch.get("__num_turns__", None) rollout_reward_scores = data_item.non_tensor_batch.get("reward_scores", {}) extra_info["num_turns"] = num_turns extra_info["rollout_reward_scores"] = rollout_reward_scores response_str = await self.loop.run_in_executor( None, lambda: self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) ) extra_reward_kwargs = ( { "reward_router_address": self.reward_router_address, "reward_model_tokenizer": self.reward_model_tokenizer, } if self.reward_router_address is not None else {} ) reward_worker = self.choose_reward_worker() result = await reward_worker.compute_score.remote( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, **extra_reward_kwargs, ) reward_extra_info = {} score: float if isinstance(result, dict): score = result["score"] for key, value in result.items(): reward_extra_info[key] = value else: score = result reward_extra_info["acc"] = score reward = score return {"reward_score": reward, "reward_extra_info": reward_extra_info} ================================================ FILE: verl/experimental/reward_loop/reward_model.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os from verl.single_controller.ray.base import RayResourcePool, split_resource_pool from verl.workers.config import HFModelConfig, RewardModelConfig from verl.workers.rollout.replica import get_rollout_replica_class logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class RewardModelManager: """Reward model manager.""" def __init__( self, config: RewardModelConfig, resource_pool: RayResourcePool = None, ): """ Initialize the reward model manager. Args: config (RewardModelConfig): Reward model configuration. resource_pool (RayResourcePool, optional): Resource pool. Defaults to None. """ self.config = config self.resource_pool = resource_pool self._initialize_llm_servers() self._initialize_router() assert self.config.rollout.skip_tokenizer_init is False, "Reward model should not skip tokenizer init." if self.config.rollout.free_cache_engine: self.sleep() def _initialize_llm_servers(self): rollout_world_size = self.config.rollout.tensor_model_parallel_size world_size = ( self.resource_pool.world_size if self.resource_pool # colocate mode else self.config.n_gpus_per_node * self.config.nnodes # standalone mode ) num_replicas = world_size // rollout_world_size rollout_replica_class = get_rollout_replica_class(self.config.rollout.name) rollout_config = self.config.rollout model_config = HFModelConfig(path=self.config.model_path) self.tokenizer = model_config.get_processor() self.rollout_replicas = [ rollout_replica_class( replica_rank=replica_rank, config=rollout_config, model_config=model_config, gpus_per_node=self.config.n_gpus_per_node, is_reward_model=True, ) for replica_rank in range(num_replicas) ] if self.resource_pool: split_resource_pools = split_resource_pool(self.resource_pool, split_size=rollout_world_size) assert len(split_resource_pools) == len(self.rollout_replicas) self._run_all( [ server.init_colocated(resource_pool) for server, resource_pool in zip(self.rollout_replicas, split_resource_pools, strict=True) ] ) else: self._run_all([server.init_standalone() for server in self.rollout_replicas]) self.server_handles = [server._server_handle for server in self.rollout_replicas] self.server_addresses = [server._server_address for server in self.rollout_replicas] def _initialize_router(self): worker_urls = [f"http://{server_address}" for server_address in self.server_addresses] # TODO (dyy): sglang router is not ready yet. # if self.config.rollout.name == "sglang": # from .router.inner_sglang_router import launch_router_process # else: # from .router.naive_router import launch_router_process from .router.naive_router import launch_router_process self.router_address, _ = launch_router_process(worker_urls=worker_urls) def get_router_address(self): return self.router_address def wake_up(self): """Wake up all rollout replica instances.""" self._run_all([replica.wake_up() for replica in self.rollout_replicas]) def sleep(self): """Sleep all rollout replica instances.""" self._run_all([replica.sleep() for replica in self.rollout_replicas]) def _run_all(self, tasks: list[asyncio.Task]): async def run_all(): await asyncio.gather(*tasks) asyncio.run(run_all()) ================================================ FILE: verl/experimental/reward_loop/router/inner_sglang_router.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import multiprocessing import os import time import ray import requests from sglang_router.launch_server import RouterArgs, launch_router from verl.utils.net_utils import get_free_port, is_valid_ipv6_address logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def launch_router_process( worker_urls: list[str], request_timeout: int = 180, max_wait_time: int = 300, timeout: int = 30, ) -> str: router_ip = ray.util.get_node_ip_address().strip("[]") router_port, _ = get_free_port(router_ip) router_address = ( f"[{router_ip}]:{router_port}" if is_valid_ipv6_address(router_ip) else f"{router_ip}:{router_port}" ) router_args = RouterArgs( host=router_ip, port=router_port, worker_urls=worker_urls, balance_abs_threshold=0, log_level="warn", request_timeout_secs=request_timeout, ) router_process = multiprocessing.Process(target=launch_router, args=(router_args,)) router_process.daemon = True router_process.start() time.sleep(3) assert router_process.is_alive() # health check start_time = time.time() url = f"http://{router_address}/health" with requests.Session() as session: while time.time() - start_time < max_wait_time: try: response = session.get(url, timeout=timeout) if response.status_code == 200: break except requests.RequestException as e: logger.debug(f"Health check failed: {e}") time.sleep(2) else: router_process.terminate() raise RuntimeError(f"Router health check failed after {max_wait_time} seconds.") logger.info(f"Router is running on {router_address}") return router_address, router_process ================================================ FILE: verl/experimental/reward_loop/router/naive_router.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import multiprocessing import os import time from typing import Any import aiohttp import ray import uvicorn from fastapi import FastAPI, Request from fastapi.responses import JSONResponse from verl.utils.net_utils import get_free_port, is_valid_ipv6_address logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) async def _read_async_response(resp: aiohttp.ClientResponse) -> dict[str, Any]: if resp.status == 204 or (resp.content_length == 0): return {} try: return await resp.json(content_type=None) except Exception: try: text = await resp.text() except Exception: return {} return { "content_type": (resp.headers.get("Content-Type") or ""), "text": text, } def launch_router_process( worker_urls: list[str], ): router_ip = ray.util.get_node_ip_address().strip("[]") router_port, _ = get_free_port(router_ip) router_address = ( f"[{router_ip}]:{router_port}" if is_valid_ipv6_address(router_ip) else f"{router_ip}:{router_port}" ) router_process = multiprocessing.Process( target=run_router, args=( router_ip, router_port, worker_urls, ), ) router_process.daemon = True router_process.start() time.sleep(3) assert router_process.is_alive() logger.info(f"Router is running on {router_address}") return router_address, router_process def run_router(router_ip: str, router_port: int, worker_urls: list[str]): router = NaiveRouter(worker_urls=worker_urls, verbose=False) uvicorn.run(router.app, host=router_ip, port=router_port, log_level="warning") class NaiveRouter: def __init__( self, worker_urls: list[str], max_connections: int = 1024, timeout: int = 60, max_attempts: int = 3, retry_delay: float = 2.0, verbose: bool = False, ) -> None: """A minimal async load-balancing router.""" self.verbose = verbose self.app = FastAPI() self.worker_urls = worker_urls self.request_counts = {url: 0 for url in worker_urls} self.max_connections = max_connections self.timeout = timeout self.max_attempts = max_attempts self.retry_delay = retry_delay self.app = FastAPI() # Register startup / shutdown hooks self.app.on_event("startup")(self._on_startup) self.app.on_event("shutdown")(self._on_shutdown) # Catch-all proxy route self.app.api_route("/{endpoint:path}", methods=["GET", "POST"])(self._make_async_request) # Placeholder for aiohttp client self.client = None async def _on_startup(self): """Initialize aiohttp client safely inside the event loop""" connector = aiohttp.TCPConnector( limit=self.max_connections, limit_per_host=self.max_connections // 4, ttl_dns_cache=300, use_dns_cache=True, ) timeout = aiohttp.ClientTimeout(total=None) self.client = aiohttp.ClientSession(connector=connector, timeout=timeout) if self.verbose: logger.info(f"[router] aiohttp client initialized with max_connections={self.max_connections}") async def _on_shutdown(self): """Gracefully close aiohttp client""" if self.client and not self.client.closed: await self.client.close() if self.verbose: logger.info("[router] aiohttp client closed") async def _make_async_request(self, request: Request, endpoint: str): """Proxy single request to a worker URL.""" if not self.worker_urls: return JSONResponse(status_code=503, content={"error": "No available workers"}) worker_url = self._select_worker() target_url = f"{worker_url}/{endpoint}" if self.verbose: logger.debug(f"[router] Forwarding request → {target_url}") # Copy request data body = await request.body() headers = dict(request.headers) for attempt in range(self.max_attempts): # Send request to worker try: async with self.client.request(request.method, target_url, data=body, headers=headers) as response: response.raise_for_status() output = await _read_async_response(response) self._release_worker(worker_url) return output except asyncio.TimeoutError: logger.warning(f"Async request to {endpoint} timed out (attempt {attempt + 1})") except aiohttp.ClientConnectorError: logger.warning(f"Connection error for {endpoint} (attempt {attempt + 1})") except aiohttp.ClientResponseError as e: logger.error(f"HTTP error for {endpoint}: {e}") raise except Exception as e: logger.error(f"Unexpected error for {endpoint}: {e}") if attempt == self.max_attempts - 1: raise if attempt < self.max_attempts - 1: await asyncio.sleep(self.retry_delay * (2**attempt)) raise RuntimeError(f"Failed to complete async request to {endpoint} after {self.max_attempts} attempts") def _select_worker(self) -> str: """Select the least-loaded worker (simple round-robin by request count).""" url = min(self.request_counts, key=self.request_counts.get) self.request_counts[url] += 1 return url def _release_worker(self, url: str) -> None: """Mark worker as free after request completes.""" self.request_counts[url] = max(0, self.request_counts[url] - 1) ================================================ FILE: verl/experimental/separation/__init__.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/separation/engine_workers.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from omegaconf import DictConfig from verl.single_controller.base.decorator import Dispatch, register from verl.utils.device import ( get_device_name, ) from verl.workers.engine_workers import ActorRolloutRefWorker logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) device_name = get_device_name() __all__ = ["DetachActorWorker"] class DetachActorWorker(ActorRolloutRefWorker): """ A worker class that extends ActorRolloutRefWorker to support detaching and restoring the actor model. This worker facilitates saving the model state to CPU and restoring it, enabling efficient resource management and checkpointing in distributed training. It currently supports FSDP, FSDP2, and Megatron strategies. """ def __init__(self, config: DictConfig, role: str): """ Initialize the DetachActorWorker. Args: config: Configuration dictionary. role: The role of the worker (e.g., 'actor', 'rollout', 'ref'). """ ActorRolloutRefWorker.__init__(self, config, role) self._strategy_handlers = None self.copy_handler, self.restore_handler = self._get_strategy_handlers() def _get_strategy_handlers(self): """ Get the strategy-specific handlers for saving and restoring the model. Returns: tuple: A tuple containing (save_handler, restore_handler). Raises: NotImplementedError: If the strategy is not supported. """ if self._strategy_handlers is not None: return self._strategy_handlers strategy = self.config.actor.strategy if strategy in ["fsdp", "fsdp2"]: from verl.utils.fsdp_utils import ( fsdp2_sharded_load_from_cpu, fsdp2_sharded_save_to_cpu, ) self._strategy_handlers = (fsdp2_sharded_save_to_cpu, fsdp2_sharded_load_from_cpu) elif strategy == "megatron": from verl.utils.megatron_utils import ( copy_megatron_model_to_cpu, restore_megatron_model_from_cpu, ) self._strategy_handlers = (copy_megatron_model_to_cpu, restore_megatron_model_from_cpu) else: raise NotImplementedError(f"Unsupported strategy: {strategy}") return self._strategy_handlers @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_model_to_cpu(self, n): """ Save the current model state to CPU memory. Args: n: Identifier/Key for the saved model state. """ if not hasattr(self, "cpu_saved_models"): self.cpu_saved_models = {} self.cpu_saved_models[n] = self.copy_handler(self.actor.engine.module) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def restore_model_from_cpu(self, n): """ Restore the model state from CPU memory. Args: n: Identifier/Key for the saved model state to restore. """ if n in self.cpu_saved_models: strategy = self.config.actor.strategy if strategy in ["fsdp", "fsdp2"]: cpu_sharded_state, global_spec = self.cpu_saved_models[n] self.restore_handler(self.actor.engine.module, cpu_sharded_state, global_spec) else: self.restore_handler(self.actor.engine.module, self.cpu_saved_models[n]) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def clear_cpu_model(self, n): """ Clear the saved model state from CPU memory. Args: n: Identifier/Key for the saved model state to remove. """ if n in self.cpu_saved_models: del self.cpu_saved_models[n] ================================================ FILE: verl/experimental/separation/ray_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PPO Trainer with Ray-based single controller. This trainer supports model-agonistic model initialization with huggingface """ import uuid from copy import deepcopy from pprint import pprint from typing import Any, Optional import numpy as np import torch from omegaconf import OmegaConf from torch.utils.data import Dataset, Sampler from tqdm import tqdm from verl import DataProto from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.dataset.sampler import AbstractCurriculumSampler from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup, ResourcePoolManager from verl.single_controller.ray.base import create_colocated_worker_cls from verl.trainer.ppo.core_algos import AdvantageEstimator, agg_loss from verl.trainer.ppo.metric_utils import ( compute_data_metrics, compute_throughout_metrics, compute_timing_metrics, compute_variance_proxy_metrics, ) from verl.trainer.ppo.ray_trainer import RayPPOTrainer, apply_kl_penalty, compute_advantage, compute_response_mask from verl.trainer.ppo.reward import extract_reward from verl.trainer.ppo.utils import Role, WorkerType from verl.utils.checkpoint.checkpoint_manager import should_save_ckpt_esi from verl.utils.config import omega_conf_to_dataclass from verl.utils.debug import marked_timer from verl.utils.metric import reduce_metrics from verl.utils.rollout_skip import RolloutSkip class SeparateRayPPOTrainer(RayPPOTrainer): """ Support for the initialization and fit process of Ray Trainer in the resource-separated scenario: - Fully async policy - One-step off-policy """ def __init__( self, config, tokenizer, role_worker_mapping: dict[Role, WorkerType], resource_pool_manager: ResourcePoolManager, ray_worker_group_cls: type[RayWorkerGroup] = RayWorkerGroup, processor=None, reward_fn=None, val_reward_fn=None, train_dataset: Optional[Dataset] = None, val_dataset: Optional[Dataset] = None, collate_fn=None, train_sampler: Optional[Sampler] = None, device_name=None, ): super().__init__( config, tokenizer, role_worker_mapping, resource_pool_manager, ray_worker_group_cls, processor, train_dataset, val_dataset, collate_fn, train_sampler, device_name, ) self.global_steps = 0 self.epoch = 0 self.max_steps_duration = 0 self.progress_bar = None self.logger = None self.is_last_step = False self.prev_step_profile = False self.curr_step_profile = False self.next_step_profile = False self.last_val_metrics = {} self.metrics = {} self.timing_raw = {} # reward message self.reward_tensor = None self.reward_extra_infos_dict = {} self.checkpoint_manager = None def init_workers(self): """Initialize distributed training workers using Ray backend. Creates: 1. Ray resource pools from configuration 2. Worker groups for each role (actor, critic, etc.) """ self._init_resource_pools() self._create_worker_classes() self._init_worker_groups() self._init_models() self._init_reward_loop() self._init_async_rollout_manager() self.checkpoint_manager = CheckpointEngineManager( config=omega_conf_to_dataclass(self.config.actor_rollout_ref.rollout.checkpoint_engine), trainer=self.actor_rollout_wg, replicas=self.async_rollout_manager.rollout_replicas, ) def _init_resource_pools(self): self.resource_pool_manager.create_resource_pool() self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()} def _create_worker_classes(self): self._create_actor_rollout_classes() self._create_critic_class() self._create_reference_policy_class() self._create_reward_model_class() def _create_actor_rollout_classes(self): raise NotImplementedError def _create_critic_class(self): # create critic if self.use_critic: resource_pool = self.resource_pool_manager.get_resource_pool(Role.Critic) critic_cfg = omega_conf_to_dataclass(self.config.critic) if self.use_legacy_worker_impl == "disable": # convert critic_cfg into TrainingWorkerConfig from verl.workers.config import FSDPEngineConfig from verl.workers.engine_workers import TrainingWorkerConfig self.orig_critic_cfg = critic_cfg if self.orig_critic_cfg.strategy == "fsdp": engine_config: FSDPEngineConfig = self.orig_critic_cfg.model.fsdp_config engine_config.infer_max_token_len_per_gpu = critic_cfg.ppo_infer_max_token_len_per_gpu engine_config.max_token_len_per_gpu = critic_cfg.ppo_max_token_len_per_gpu else: raise NotImplementedError(f"Unknown strategy {self.orig_critic_cfg.strategy=}") critic_cfg = TrainingWorkerConfig( model_type="value_model", model_config=self.orig_critic_cfg.model_config, engine_config=engine_config, optimizer_config=self.orig_critic_cfg.optim, checkpoint_config=self.orig_critic_cfg.checkpoint, ) critic_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.Critic], config=critic_cfg) self.resource_pool_to_cls[resource_pool][str(Role.Critic)] = critic_cls def _create_reference_policy_class(self): # create reference policy if needed if self.use_reference_policy: resource_pool = self.resource_pool_manager.get_resource_pool(Role.RefPolicy) ref_policy_cls = RayClassWithInitArgs( self.role_worker_mapping[Role.RefPolicy], config=self.config.actor_rollout_ref, role=str(Role.RefPolicy), # profile_option=self.config.trainer.npu_profile.options, ) self.resource_pool_to_cls[resource_pool][str(Role.RefPolicy)] = ref_policy_cls def _create_reward_model_class(self): # create a reward model if reward_fn is None if self.use_rm: # we create a RM here resource_pool = self.resource_pool_manager.get_resource_pool(Role.RewardModel) rm_cls = RayClassWithInitArgs( self.role_worker_mapping[Role.RewardModel], config=self.config.reward.reward_model ) self.resource_pool_to_cls[resource_pool][str(Role.RewardModel)] = rm_cls def _init_worker_groups(self): # initialize WorkerGroup # NOTE: if you want to use a different resource pool for each role, which can support different parallel size, # you should not use `create_colocated_worker_cls`. # Instead, directly pass different resource pool to different worker groups. # See https://github.com/volcengine/verl/blob/master/examples/ray/tutorial.ipynb for more information. all_wg = {} wg_kwargs = {} # Setting up kwargs for RayWorkerGroup if OmegaConf.select(self.config.trainer, "ray_wait_register_center_timeout") is not None: wg_kwargs["ray_wait_register_center_timeout"] = self.config.trainer.ray_wait_register_center_timeout if OmegaConf.select(self.config.global_profiler, "steps") is not None: wg_kwargs["profile_steps"] = OmegaConf.select(self.config.global_profiler, "steps") # Only require nsight worker options when tool is nsys if OmegaConf.select(self.config.global_profiler, "tool") == "nsys": assert ( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") is not None ), "worker_nsight_options must be set when using nsys with profile_steps" wg_kwargs["worker_nsight_options"] = OmegaConf.to_container( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") ) wg_kwargs["device_name"] = self.device_name for resource_pool, class_dict in self.resource_pool_to_cls.items(): worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = self.ray_worker_group_cls( resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls, **wg_kwargs, ) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) self.all_wg = all_wg def _init_models(self): if self.use_critic: self.critic_wg = self.all_wg[str(Role.Critic)] if self.use_legacy_worker_impl == "disable": self.critic_wg.reset() # assign critic loss from functools import partial from verl.workers.utils.losses import value_loss value_loss_ = partial(value_loss, config=self.orig_critic_cfg) self.critic_wg.set_loss_fn(value_loss_) else: self.critic_wg.init_model() if self.use_reference_policy and not self.ref_in_actor: self.ref_policy_wg = self.all_wg[str(Role.RefPolicy)] self.ref_policy_wg.init_model() if self.use_rm: self.rm_wg = self.all_wg[str(Role.RewardModel)] self.rm_wg.init_model() # we should create rollout at the end so that vllm can have a better estimation of kv cache memory self.actor_rollout_wg = self.all_wg[str(Role.ActorRollout)] self.actor_rollout_wg.init_model() def _init_reward_loop(self): from verl.experimental.reward_loop import RewardLoopManager # initalize reward loop manager # reward model (colocate or standalone): get resource_pool # no reward model: resource_pool = None resource_pool = self.resource_pool_manager.get_resource_pool(Role.RewardModel) if self.use_rm else None self.reward_loop_manager = RewardLoopManager( config=self.config, rm_resource_pool=resource_pool, ) def _init_async_rollout_manager(self): pass def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. !!! The logic of fit is consistent with that of fit_refactor; if any modifications are made, apply them to both methods simultaneously. """ from omegaconf import OmegaConf from verl.utils.tracking import Tracking self.logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) self.global_steps = 0 # load checkpoint and update weights before doing anything self._load_checkpoint() self.checkpoint_manager.update_weights(self.global_steps) current_epoch = self.global_steps // len(self.train_dataloader) # perform validation before training # currently, we only support validation using the reward_function. if self.config.trainer.get("val_before_train", True): val_metrics = self._validate() assert val_metrics, f"{val_metrics=}" pprint(f"Initial validation metrics: {val_metrics}") self.logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get("val_only", False): return if self.config.actor_rollout_ref.rollout.get("skip_rollout", False): rollout_skip = RolloutSkip(self.config, self.actor_rollout_wg) rollout_skip.wrap_generate_sequences() # add tqdm self.progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress") # we start from step 1 self.global_steps += 1 self.last_val_metrics = None self.max_steps_duration = 0 self.prev_step_profile = False self.curr_step_profile = ( self.global_steps in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) self.next_step_profile = False for epoch in range(current_epoch, self.config.trainer.total_epochs): for batch_dict in self.train_dataloader: self.epoch = epoch self.fit_step(batch_dict) if self.is_last_step: return def fit_step(self, batch_dict: Any = None): """ Single-step training template method. Handles all logic for one training step. Flow: 1. Pre-step processing -> 2. Get batch -> 3. Generate sequences -> 4. Compute reward -> 5. Compute log_prob -> 6. Compute reward -> 7. Compute advantage -> 8. Update critic -> 9. Update actor -> 10. Post-step processing Args: batch_dict: Raw data dictionary """ self.metrics = {"training/global_step": self.global_steps, "training/epoch": self.epoch} self.timing_raw = {} # reward message self.reward_tensor = None self.reward_extra_infos_dict = {} self._fit_prepare_step() self._fit_start_profile() with marked_timer("step", self.timing_raw): batch = self._fit_get_batch(batch_dict) batch = self._fit_generate(batch) batch = self._fit_compute_reward(batch) batch = self._fit_compute_log_prob(batch) batch = self._fit_compute_ref_log_prob(batch) batch = self._fit_compute_critic(batch) batch = self._fit_compute_advantage(batch) batch = self._fit_update_critic(batch) batch = self._fit_update_actor(batch) self._fit_update_weights() self._fit_dump_data(batch) self._fit_validate() self._fit_save_checkpoint() self._fit_stop_profile() self._fit_collect_metrics(batch) self._fit_torch_memory() self._fit_experimental(batch) self._fit_postprocess_step() def _fit_prepare_step(self): if hasattr(self.actor_rollout_wg, "async_calls_finalize_fn_exec"): self.actor_rollout_wg.async_calls_finalize_fn_exec(blocking=False) self.is_last_step = self.global_steps >= self.total_training_steps def _fit_start_profile(self): timing_raw = self.timing_raw with marked_timer("start_profile", timing_raw): self._start_profiling( not self.prev_step_profile and self.curr_step_profile if self.config.global_profiler.profile_continuous_steps else self.curr_step_profile ) def _fit_get_batch(self, batch_dict: dict) -> DataProto: batch = DataProto.from_single_dict(batch_dict) batch.meta_info["temperature"] = self.config.actor_rollout_ref.rollout.temperature # add uid batch.non_tensor_batch["uid"] = np.array([str(uuid.uuid4()) for _ in range(len(batch.batch))], dtype=object) return batch def _fit_generate(self, batch: DataProto = None) -> DataProto: metrics = self.metrics timing_raw = self.timing_raw gen_batch = self._get_gen_batch(batch) # pass global_steps to trace gen_batch.meta_info["global_steps"] = self.global_steps gen_batch_output = gen_batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) with marked_timer("gen", timing_raw, color="red"): if self.curr_step_profile: self.async_rollout_manager.start_profile(global_step=self.global_steps) gen_batch_output = self.async_rollout_manager.generate_sequences(gen_batch_output) self.checkpoint_manager.sleep_replicas() if self.curr_step_profile: self.async_rollout_manager.stop_profile() timing_raw.update(gen_batch_output.meta_info["timing"]) gen_batch_output.meta_info.pop("timing", None) if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX: with marked_timer("gen_max", timing_raw, color="purple"): gen_baseline_batch = deepcopy(gen_batch) gen_baseline_batch.meta_info["do_sample"] = False if self.curr_step_profile: self.async_rollout_manager.start_profile() gen_baseline_output = self.async_rollout_manager.generate_sequences(gen_baseline_batch) self.checkpoint_manager.sleep_replicas() if self.curr_step_profile: self.async_rollout_manager.stop_profile() batch = batch.union(gen_baseline_output) # compute reward model score on batch rm_scores = None if self.use_rm and "rm_scores" not in batch.batch.keys(): batch_reward = self._compute_reward_colocate(batch) batch = batch.union(batch_reward) # Compute or extract reward for REMAX baseline reward_baseline_tensor = batch.batch["rm_scores"].sum(dim=-1) keys_to_pop = set(gen_baseline_output.batch.keys()) if rm_scores is not None: keys_to_pop.update(rm_scores.batch.keys()) batch.pop(batch_keys=list(keys_to_pop)) batch.batch["reward_baselines"] = reward_baseline_tensor del rm_scores, gen_baseline_batch, gen_baseline_output # repeat to align with repeated responses in rollout batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) batch = batch.union(gen_batch_output) if "response_mask" not in batch.batch.keys(): batch.batch["response_mask"] = compute_response_mask(batch) # Balance the number of valid tokens across DP ranks. # NOTE: This usually changes the order of data in the `batch`, # which won't affect the advantage calculation (since it's based on uid), # but might affect the loss calculation (due to the change of mini-batching). if self.config.trainer.balance_batch: self._balance_batch(batch, metrics=metrics) # compute global_valid tokens batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist() # get images_seqlens images_seqlens_all = [] for multi_modal_input in batch.non_tensor_batch["multi_modal_inputs"]: if "image_grid_thw" not in multi_modal_input.keys(): continue images_seqlens_all.extend(multi_modal_input["images_seqlens"].tolist()) batch.meta_info["images_seqlens"] = images_seqlens_all return batch def _fit_compute_reward(self, batch: DataProto) -> DataProto: timing_raw = self.timing_raw with marked_timer("reward", timing_raw, color="yellow"): # compute reward model score if self.use_rm and "rm_scores" not in batch.batch.keys(): batch_reward = self._compute_reward_colocate(batch) batch = batch.union(batch_reward) # Compute or extract reward_tensor and reward_extra_infos_dict for training reward_tensor, reward_extra_infos_dict = extract_reward(batch) self.reward_tensor = reward_tensor self.reward_extra_infos_dict = reward_extra_infos_dict return batch def _fit_compute_log_prob(self, batch: DataProto) -> DataProto: metrics = self.metrics timing_raw = self.timing_raw # Operating Mode Selection: # - Bypass mode: Sets old_log_probs = rollout_log_probs (2 policies: π_rollout, π_θ) # - Decoupled mode: Recomputes old_log_probs as proximal anchor (3 policies: π_rollout, π_old, π_θ) # Note: π_old computed once per data batch, serves as stable reference during mini-batch updates rollout_corr_config = self.config.algorithm.get("rollout_correction", None) bypass_recomputing_logprobs = rollout_corr_config and rollout_corr_config.get("bypass_mode", False) if bypass_recomputing_logprobs: # Use `rollout_log_probs` from verl.trainer.ppo.rollout_corr_helper import apply_bypass_mode apply_bypass_mode( batch=batch, rollout_corr_config=rollout_corr_config, policy_loss_config=self.config.actor_rollout_ref.actor.policy_loss, ) else: # Recompute old_log_probs with marked_timer("old_log_prob", timing_raw, color="blue"): old_log_prob, old_log_prob_mfu = self._compute_old_log_prob(batch) entropys = old_log_prob.batch["entropys"] response_masks = batch.batch["response_mask"] actor_config = self.config.actor_rollout_ref.actor entropy_agg = agg_loss( loss_mat=entropys, loss_mask=response_masks, loss_agg_mode=actor_config.loss_agg_mode, loss_scale_factor=actor_config.loss_scale_factor, ) old_log_prob_metrics = { "actor/entropy": entropy_agg.detach().item(), "perf/mfu/actor_infer": old_log_prob_mfu, } metrics.update(old_log_prob_metrics) old_log_prob.batch.pop("entropys") if "routed_experts" in batch.batch and "routed_experts" in old_log_prob.batch: router_mode = getattr(self.config.actor_rollout_ref.actor.router_replay, "mode", "disabled") if router_mode == "R2": batch.batch.pop("routed_experts") else: old_log_prob.batch.pop("routed_experts") batch = batch.union(old_log_prob) if "rollout_log_probs" in batch.batch.keys(): # TODO: we may want to add diff of probs too. from verl.utils.debug.metrics import calculate_debug_metrics metrics.update(calculate_debug_metrics(batch)) assert "old_log_probs" in batch.batch, f'"old_log_prob" not in {batch.batch.keys()=}' return batch def _fit_compute_ref_log_prob(self, batch: DataProto) -> DataProto: timing_raw = self.timing_raw if self.use_reference_policy: with marked_timer(str(Role.RefPolicy), timing_raw, color="olive"): ref_log_prob = self._compute_ref_log_prob(batch) batch = batch.union(ref_log_prob) return batch def _fit_compute_critic(self, batch: DataProto) -> DataProto: timing_raw = self.timing_raw if self.use_critic: with marked_timer("values", timing_raw, color="cyan"): values = self._compute_values(batch) batch = batch.union(values) return batch def _fit_compute_advantage(self, batch) -> DataProto: metrics = self.metrics timing_raw = self.timing_raw reward_tensor = self.reward_tensor reward_extra_infos_dict = self.reward_extra_infos_dict with marked_timer("adv", timing_raw, color="brown"): # we combine with rule-based rm reward_extra_infos_dict: dict[str, list] batch.batch["token_level_scores"] = reward_tensor if reward_extra_infos_dict: batch.non_tensor_batch.update({k: np.array(v) for k, v in reward_extra_infos_dict.items()}) # compute rewards. apply_kl_penalty if available if self.config.algorithm.use_kl_in_reward: batch, kl_metrics = apply_kl_penalty( batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty ) metrics.update(kl_metrics) else: batch.batch["token_level_rewards"] = batch.batch["token_level_scores"] # Compute rollout correction: IS weights, rejection sampling, and metrics # Only runs in decoupled mode (computes once per batch using stable π_old) # In bypass mode, this is skipped - actor computes metrics from evolving π_θ vs π_rollout rollout_corr_config = self.config.algorithm.get("rollout_correction", None) bypass_recomputing_logprobs = rollout_corr_config and rollout_corr_config.get("bypass_mode", False) if ( rollout_corr_config is not None and "rollout_log_probs" in batch.batch and not bypass_recomputing_logprobs # Only in decoupled mode ): from verl.trainer.ppo.rollout_corr_helper import compute_rollout_correction_and_add_to_batch # Compute IS weights, apply rejection sampling, compute metrics batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch, rollout_corr_config) # IS and off-policy metrics already have rollout_corr/ prefix metrics.update(is_metrics) # compute advantages, executed on the driver process norm_adv_by_std_in_grpo = self.config.algorithm.get( "norm_adv_by_std_in_grpo", True ) # GRPO adv normalization factor batch = compute_advantage( batch, adv_estimator=self.config.algorithm.adv_estimator, gamma=self.config.algorithm.gamma, lam=self.config.algorithm.lam, num_repeat=self.config.actor_rollout_ref.rollout.n, norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo, config=self.config.algorithm, ) return batch def _fit_update_critic(self, batch: DataProto) -> DataProto: metrics = self.metrics timing_raw = self.timing_raw if self.use_critic: with marked_timer("update_critic", timing_raw, color="pink"): critic_output = self._update_critic(batch) critic_output_metrics = reduce_metrics(critic_output.meta_info["metrics"]) metrics.update(critic_output_metrics) return batch def _fit_update_actor(self, batch: DataProto) -> DataProto: metrics = self.metrics timing_raw = self.timing_raw # implement critic warmup if self.config.trainer.critic_warmup <= self.global_steps: # update actor with marked_timer("update_actor", timing_raw, color="red"): actor_output = self._update_actor(batch) actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"]) metrics.update(actor_output_metrics) return batch def _fit_update_weights(self): timing_raw = self.timing_raw if self.config.trainer.critic_warmup <= self.global_steps: # update weights from trainer to rollout with marked_timer("update_weights", timing_raw, color="red"): self.checkpoint_manager.update_weights(self.global_steps) def _fit_dump_data(self, batch: DataProto): timing_raw = self.timing_raw reward_extra_infos_dict = self.reward_extra_infos_dict # Log rollout generations if enabled rollout_data_dir = self.config.trainer.get("rollout_data_dir", None) if rollout_data_dir: self._log_rollout_data(batch, reward_extra_infos_dict, timing_raw, rollout_data_dir) def _fit_validate(self): metrics = self.metrics timing_raw = self.timing_raw if self.config.trainer.test_freq > 0 and ( self.is_last_step or self.global_steps % self.config.trainer.test_freq == 0 ): with marked_timer("testing", timing_raw, color="green"): val_metrics: dict = self._validate() if self.is_last_step: self.last_val_metrics = val_metrics metrics.update(val_metrics) def _fit_save_checkpoint(self): timing_raw = self.timing_raw # Check if the ESI (Elastic Server Instance)/training plan is close to expiration. esi_close_to_expiration = should_save_ckpt_esi( max_steps_duration=self.max_steps_duration, redundant_time=self.config.trainer.esi_redundant_time, ) # Check if the conditions for saving a checkpoint are met. # The conditions include a mandatory condition (1) and # one of the following optional conditions (2/3/4): # 1. The save frequency is set to a positive value. # 2. It's the last training step. # 3. The current step number is a multiple of the save frequency. # 4. The ESI(Elastic Server Instance)/training plan is close to expiration. if self.config.trainer.save_freq > 0 and ( self.is_last_step or self.global_steps % self.config.trainer.save_freq == 0 or esi_close_to_expiration ): if esi_close_to_expiration: print("Force saving checkpoint: ESI instance expiration approaching.") with marked_timer("save_checkpoint", timing_raw, color="green"): # sleep replicas to avoid OOM during checkpoint saving # self.checkpoint_manager.sleep_replicas() self._save_checkpoint() # wake replicas to avoid OOM during checkpoint saving # TODO: Check separation is needed. # self.checkpoint_manager.update_weights() def _fit_stop_profile(self): timing_raw = self.timing_raw with marked_timer("stop_profile", timing_raw): self.next_step_profile = ( self.global_steps + 1 in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) self._stop_profiling( self.curr_step_profile and not self.next_step_profile if self.config.global_profiler.profile_continuous_steps else self.curr_step_profile ) self.prev_step_profile = self.curr_step_profile self.curr_step_profile = self.next_step_profile def _fit_collect_metrics(self, batch): metrics = self.metrics timing_raw = self.timing_raw # collect metrics metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic)) metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw)) # TODO: implement actual tflpo and theoretical tflpo n_gpus = self.resource_pool_manager.get_n_gpus() metrics.update(compute_throughout_metrics(batch=batch, timing_raw=timing_raw, n_gpus=n_gpus)) # compute variance proxy metrics gradient_norm = metrics.get("actor/grad_norm", None) metrics.update(compute_variance_proxy_metrics(batch=batch, gradient_norm=gradient_norm)) def _fit_torch_memory(self): if ( hasattr(self.config.actor_rollout_ref.actor, "profiler") and self.config.actor_rollout_ref.actor.profiler.tool == "torch_memory" ): self.actor_rollout_wg.dump_memory_snapshot( tag=f"post_update_step{self.global_steps}", sub_dir=f"step{self.global_steps}" ) def _fit_experimental(self, batch): # this is experimental and may be changed/removed in the future in favor of a general-purpose one if isinstance(self.train_dataloader.sampler, AbstractCurriculumSampler): self.train_dataloader.sampler.update(batch=batch) # this is experimental and may be changed/removed in the future # in favor of a general-purpose data buffer pool if hasattr(self.train_dataset, "on_batch_end"): # The dataset may be changed after each training batch self.train_dataset.on_batch_end(batch=batch) def _fit_postprocess_step(self): metrics = self.metrics timing_raw = self.timing_raw steps_duration = timing_raw["step"] self.max_steps_duration = max(self.max_steps_duration, steps_duration) # TODO: make a canonical logger that supports various backend self.logger.log(data=metrics, step=self.global_steps) self.progress_bar.update(1) self.global_steps += 1 if self.is_last_step: if hasattr(self.actor_rollout_wg, "async_calls_finalize_fn_exec"): self.actor_rollout_wg.async_calls_finalize_fn_exec(blocking=True) pprint(f"Final validation metrics: {self.last_val_metrics}") self.progress_bar.close() ================================================ FILE: verl/experimental/separation/utils.py ================================================ # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray from verl.trainer.ppo.ray_trainer import ResourcePoolManager from verl.trainer.ppo.utils import Role, need_reference_policy def create_resource_pool_manager(config, roles: list) -> ResourcePoolManager: """ Create resource pool manager Args: config: Configuration object roles: List of roles that need to create resource pools Returns: ResourcePoolManager: Resource pool manager """ resource_pool_spec = {} mapping = {} # Actor/Critic resource pool if any(role in roles for role in [Role.Actor, Role.ActorRollout, Role.Critic, Role.RefPolicy, Role.RewardModel]): assert config.trainer.n_gpus_per_node > 0, "config.trainer.n_gpus_per_node must be greater than 0" assert config.trainer.nnodes > 0, "config.trainer.nnodes must be greater than 0" trainer_pool = [config.trainer.n_gpus_per_node] * config.trainer.nnodes resource_pool_spec["trainer_pool"] = trainer_pool # Map training-related roles to the same resource pool for role in [Role.Actor, Role.ActorRollout, Role.Critic, Role.RefPolicy, Role.RewardModel]: if role in roles: mapping[role] = "trainer_pool" # Rollout resource pool if Role.Rollout in roles: assert config.rollout.n_gpus_per_node > 0, "config.rollout.n_gpus_per_node must be greater than 0" assert config.rollout.nnodes > 0, "config.rollout.nnodes must be greater than 0" return ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) def create_role_worker_mapping(config): """ Create mapping from roles to worker classes Args: config: Configuration object Returns: dict: Mapping from roles to worker classes """ # Select worker class based on strategy if config.trainer.get("use_legacy_worker_impl", "auto") != "disable": raise NotImplementedError( "Fully async policy or One step off policy does not support legacy worker implementation" ) from verl.experimental.separation.engine_workers import DetachActorWorker from verl.single_controller.ray import RayWorkerGroup from verl.workers.engine_workers import TrainingWorker ray_worker_group_cls = RayWorkerGroup train_role = Role.Actor if config.get("async_training", {}).get("use_trainer_do_validate", False): train_role = Role.ActorRollout role_worker_mapping = { train_role: ray.remote(DetachActorWorker), Role.Critic: ray.remote(TrainingWorker), } # Add reference policy (if KL loss or reward is required) if need_reference_policy(config): role_worker_mapping[Role.RefPolicy] = ray.remote(DetachActorWorker) return role_worker_mapping, ray_worker_group_cls ================================================ FILE: verl/experimental/vla/README.md ================================================ # [WIP] Experimental VLA RL Support This recipe introduces experimental support for training SimpleVLA-OFT, a VLA model. A key challenge in VLA RL training, which differs from standard LLM RL training, is that the environment/simulation phase has a higher computational overhead than the generation phase. To achieve high efficiency, RL in this context requires an effective environment scheduling mechanism in addition to verl's existing efficient training and inference scheduling. The goal is to reduce the inefficiency caused by the environment and the model's generation process waiting on each other. The core computational model of this PR is inspired by the pipeline parallelism design from RLinf. It aims to overlap the environment's execution time with the model's generation time, thereby maximizing environment utilization. This PR also proposes a future direction: creating a unified `Env` class. This class would encapsulate functionalities like tool calling, MCP, etc., under a single interface. The environment would manage its state internally, allowing the agent to communicate simply by calling `step(action)` to submit an action and receive an observation. Currently, this code is located independently within the `recipes` folder. Much of the design is tightly coupled with the SimpleVLA model and the Libero environment, serving as an initial version for demonstration and discussion. ## Supported Simulators | Simulator | Env Name | Difference | Benchmark data source | | --- | --- | --- | --- | | Mujoco | LiberoEnv | 1. init task from init_states in Libero dataset
2. each env can have different tasks | https://github.com/Lifelong-Robot-Learning/LIBERO | | IsaacSim | IsaacEnv | 1. init task from random states, which has more variety than init_states in dataset
2. each sim process must using the same task for its envs | https://huggingface.co/datasets/china-sae-robotics/IsaacLabPlayGround_Dataset | ## Hardware Requirements * Simulator GPU: NVIDIA L20 or L40 with 48GB memory and RT Cores Notes: 1. Mujoco can failback to CPU mode with degraded performance if no RT Cores is available 2. IsaacSim only support GPU with RT Cores 3. RTX GPU will be supported in the future release with remote deployment feature, but it can not work with colocated mode because of the limitation of GPU memory capacity. ## Docker image The Isaac Lab support for libero dataset depends on RobotLearningLab project from The Isaac Lab Project Developers team. The project is in the process of being public available and is currently build in this image with BSD-3-Clause license. `recipe/vla/run_simpleVLA_libero_grpo.sh` is the example of training SimpleVLA-OFT with this image: `vemlp-cn-shanghai.cr.volces.com/preset-images/verl_vla:preview_vla_0.1` ## Disaggregation Mode for Train-Rollout / Simulation Disaggregate Train-Rollout workers and Simulation workers into different nodes. To enable disaggregation mode for Train-Rollout nodes and Simulation nodes, we need to establish ray connection before running verl. * On Train-Rollout node (default main node): ```shell ray start --head --dashboard-host=0.0.0.0 --resources='{"train_rollout": 1}' ``` * On Simulation node: ```shell ray start --address=':6379' --resources='{"sim": 1}' ``` Then run verl on main node **only**. See `run_simpleVLA_isaac_disagg.sh` for example. - `env.disagg_sim.enable=True` enable disagg mode - `trainer.n_env_gpus_per_node` GPUs for simulaton per node - `trainer.n_rollout_gpus_per_node` GPUs for train-rollout node - `env.disagg_sim.nnodes` sim node num - `trainer.nnodes` train-rollout node num *Tips: you can run the following command on the sim node to check whether sim workers are scheduled up* ```shell python -c "import ray; ray.init(address=\":6379\"); print(ray._private.state.available_resources_per_node())" ``` *If you see output pattern like "'train_rollout': 0.9992" and "'sim': 0.9992", the sim workers are scheduled up successfully* *The actual value depends on your GPUs per node, usually <1 - 1e-4 * num_gpus>* **References:** * [https://github.com/PRIME-RL/SimpleVLA-RL](https://github.com/PRIME-RL/SimpleVLA-RL) * [https://github.com/RLinf/RLinf](https://github.com/RLinf/RLinf) ================================================ FILE: verl/experimental/vla/config/rob_ppo_trainer.yaml ================================================ # the rob_ppo config will override default ppo_trainer.yaml hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ env: rollout: pipeline_stage_num: 2 actor: model: num_action_chunks: 8 action_dim: 7 train: simulator_type: libero max_episode_steps: 512 reward_coef: 1.0 only_eval: False video_cfg: save_video: True video_base_dir: /tmp/videos num_envs: 16 seed: 42 task_suite_name: libero_10 init_params: camera_depths: False camera_heights: 256 camera_widths: 256 camera_names: - agentview - robot0_eye_in_hand # Profile the env worker profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # Profiling tool to use # options: nsys, npu, torch, torch_memory # Defaults to global_profiler.tool if set tool: ${oc.select:global_profiler.tool,null} # Whether to enable profiling for env worker enable: False # Whether to profile all ranks all_ranks: False # List of ranks to profile (empty means no specific ranks) ranks: [] # Path to save profiling results # Defaults to global_profiler.save_path if set save_path: ${oc.select:global_profiler.save_path,null} # Tool-specific configurations tool_config: # nsys tool config nsys: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NsightToolConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} # npu config npu: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NPUToolConfig # Contents to profile, can be empty # options: npu, cpu, memory, shapes, module, stack contents: [] # Collection level, optional values: level_none, level0, level1, level2. level: "level1" # Whether to automatically parse the data. analysis: True # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # torch profiler config torch: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchProfilerToolConfig # Contents to profile, can be empty # options: cuda, cpu, memory, shapes, stack contents: [] # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # torch memory profiler config torch_memory: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchMemoryToolConfig # Maximum number of memory allocation entries to track trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} # Stack trace depth for memory allocations stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} disagg_sim: enable: False nnodes: 1 actor_rollout_ref: actor: num_images_in_input: 1 traj_mini_batch_size: 16 fsdp_config: wrap_policy: transformer_layer_cls_to_wrap: - PrismaticProjector - LlamaDecoderLayer min_num_params: 0 param_offload: False optimizer_offload: False forward_prefetch: True fsdp_size: -1 rollout: mode: async_envloop prompt_length: 512 ================================================ FILE: verl/experimental/vla/config/rob_sac_trainer.yaml ================================================ # the rob_ppo config will override default ppo_trainer.yaml hydra: searchpath: - file://verl/trainer/config defaults: - ppo_trainer - _self_ env: rollout: pipeline_stage_num: 2 actor: model: num_action_chunks: 8 action_dim: 7 train: simulator_type: libero max_episode_steps: 512 reward_coef: 1.0 step_penalty: 0.001 only_eval: False video_cfg: save_video: True video_base_dir: /tmp/videos num_envs: 16 seed: 42 task_suite_name: libero_spatial init_params: camera_depths: False camera_heights: 256 camera_widths: 256 camera_names: - agentview - robot0_eye_in_hand disagg_sim: enable: False nnodes: 1 actor_rollout_ref: model: override_config: sac_enable: True flow_sde_enable: True flow_sde_noise_level: 0.065 flow_sde_rollout_noise_scale: 1.0 flow_sde_train_noise_scale: 1.0 flow_sde_initial_beta: 1.0 flow_sde_beta_min: 0.02 flow_sde_beta_schedule_T: 4000 actor: sac: gamma: 0.99 tau: 1.0 bc_loss_coef: 0 initial_alpha: 0 critic_replay_positive_sample_ratio: 0.5 actor_replay_positive_sample_ratio: 0.8 auto_entropy: False alpha_type: exp alpha_lr: 0.0003 target_entropy: -64.0 critic_lr: 0.0001 critic_weight_decay: 0 warm_rollout_steps: 23 critic_warmup_steps: 200 actor_update_interval: 1 actor_ema_enabled: true actor_ema_decay: 0.95 replay_pool_save_interval: 500 num_images_in_input: 1 traj_mini_batch_size: 16 replay_pool_single_size: 2000 replay_pool_save_dir: /tmp/replay_pools fsdp_config: wrap_policy: transformer_layer_cls_to_wrap: - PrismaticProjector - LlamaDecoderLayer min_num_params: 0 param_offload: False optimizer_offload: False forward_prefetch: True fsdp_size: -1 rollout: mode: async_envloop prompt_length: 512 ================================================ FILE: verl/experimental/vla/dp_rob.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Single Process Actor """ import logging import torch from tensordict.base import TensorDictBase from torch import nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP import verl.utils.torch_functional as verl_F from verl.protocol import DataProto from verl.trainer.ppo import core_algos from verl.utils.device import get_device_id, get_device_name from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import prepare_dynamic_batch, restore_dynamic_batch from verl.utils.torch_functional import logprobs_from_logits from verl.workers.actor import BasePPOActor logger = logging.getLogger(__name__) __all__ = ["RobDataParallelPPOActor"] class RobDataParallelPPOActor(BasePPOActor): def __init__( self, config, actor_module: nn.Module, actor_optimizer: torch.optim.Optimizer = None, ): """When optimizer is None, it is Reference Policy""" super().__init__(config) self.actor_module = actor_module self.actor_optimizer = actor_optimizer self.use_remove_padding = self.config.get("use_remove_padding", False) logger.info(f"Actor use_remove_padding={self.use_remove_padding}") logger.info(f"PRM use dynamic bsz={self.config.get('use_dynamic_bsz', False)}") self.ulysses_sequence_parallel_size = self.config.ulysses_sequence_parallel_size self.use_ulysses_sp = False # self.ulysses_sequence_parallel_size > 1 self.compute_entropy_from_logits = torch.compile(verl_F.entropy_from_logits, dynamic=True) def process_tensor(self, tensor, pad_id): mask = tensor != pad_id if not torch.all(mask == mask[0:1], dim=1).all(): raise ValueError("Padding error!") base_mask = mask[0] valid_len = base_mask.sum().item() return tensor[:, base_mask], valid_len def generate_traj_mask(self, end_step, traj_len): """ Args: end_step: (batch_size,), traj_len: Returns: mask: (batch_size, traj_len), """ steps = torch.arange(traj_len, device=end_step.device) # (traj_len,) steps_expanded = steps.unsqueeze(0).expand(end_step.size(0), -1) mask = steps_expanded < end_step.unsqueeze(1) # (batch_size, traj_len) return mask def apply_mask_with_grad_control(self, log_probs, entropy, mask): """ Args: log_probs: (batch_size, 7*8) entropy: (batch_size, 7*8) # mask: (batch_size, 8) mask: (batch_size, 7*8) Returns: log_probs_masked: entropy_masked: """ mask = mask.to(log_probs.device) log_probs_masked = torch.where(mask, log_probs, torch.zeros_like(log_probs, requires_grad=False)) entropy_masked = torch.where(mask, entropy, torch.zeros_like(entropy, requires_grad=False)) return log_probs_masked, entropy_masked def _forward_micro_batch(self, micro_batch, temperature) -> tuple[torch.Tensor, torch.Tensor]: """ micro_batch: Returns: entropy: # (bs, response_len) log_probs: # (bs, response_len) """ with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): input_ids = micro_batch["input_ids"] attention_mask = micro_batch["attention_mask"] pixel_values = micro_batch["pixel_values"] responses = micro_batch["responses"] input_ids_unpad, _ = self.process_tensor(input_ids, self.pad_token_id) attention_mask_unpad, _ = self.process_tensor(attention_mask, 0) logits = self.actor_module( input_ids=input_ids_unpad, attention_mask=attention_mask_unpad, pixel_values=pixel_values, ) # prevent model thinks we are generating assert self.actor_module.vocab_size == 32000 start_index = self.actor_module.vocab_size - 256 logits = logits[..., -256 - 64 : -64] # Shape: [batch_size, seq_len, 256] responses = responses - start_index # assert (0<=responses<=255).all() logits = logits.div(temperature) log_probs = logprobs_from_logits(logits, responses.to(logits.device)) entropy = verl_F.entropy_from_logits(logits) # (bsz, response_length) # assert len(log_probs.shape) == 2 and len(entropy.shape) == 2 # TODO(caiyunke.astra): check here mask = micro_batch["response_mask"] log_probs, entropy = self.apply_mask_with_grad_control(log_probs, entropy, mask) return entropy, log_probs def _forward_micro_batch_update( self, input_ids, attention_mask, pixel_values, responses, temperature ) -> tuple[torch.Tensor, torch.Tensor]: with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): input_ids_unpad, _ = self.process_tensor(input_ids, self.pad_token_id) attention_mask_unpad, _ = self.process_tensor(attention_mask, 0) logits = self.actor_module( input_ids=input_ids_unpad, attention_mask=attention_mask_unpad, pixel_values=pixel_values, ) assert logits.requires_grad assert self.actor_module.vocab_size == 32000 start_index = self.actor_module.vocab_size - 256 logits = logits[..., -256 - 64 : -64] # Shape: [batch_size, seq_len, 256] responses = responses - start_index logits = logits.div(temperature) log_probs = logprobs_from_logits(logits, responses) entropy = verl_F.entropy_from_logits(logits) # (bsz, response_length) return entropy, log_probs def _optimizer_step(self): assert self.config.grad_clip is not None if isinstance(self.actor_module, FSDP): grad_norm = self.actor_module.clip_grad_norm_(max_norm=self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.actor_module.parameters(), max_norm=self.config.grad_clip) self.actor_optimizer.step() return grad_norm def compute_log_prob(self, data: DataProto, calculate_entropy=False) -> torch.Tensor: """Compute the log probability of the responses given input_ids, attention_mask and position_ids Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``. ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64. ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Returns: torch.Tensor: the log_prob tensor """ self.actor_module.eval() micro_batch_size = data.meta_info["micro_batch_size"] # 256 temperature = data.meta_info[ "temperature" ] # temperature must be in the data.meta_info to avoid slient error # 1 use_dynamic_bsz = data.meta_info["use_dynamic_bsz"] # trues self.pad_token_id = data.meta_info["pad_token_id"] select_keys = ["responses", "input_ids", "attention_mask", "pixel_values", "response_mask"] data = data.select(batch_keys=select_keys).batch if use_dynamic_bsz: max_token_len = data.meta_info["max_token_len"] * self.ulysses_sequence_parallel_size micro_batches, batch_idx_list = prepare_dynamic_batch(data, max_token_len=max_token_len) else: micro_batches = data.split(micro_batch_size) log_probs_lst = [] entropy_lst = [] for micro_batch in micro_batches: with torch.no_grad(): entropy, log_probs = self._forward_micro_batch(micro_batch, temperature=temperature) log_probs_lst.append(log_probs) if calculate_entropy: entropy_lst.append(entropy) log_probs = torch.concat(log_probs_lst, dim=0) entropys = None if calculate_entropy: entropys = torch.concat(entropy_lst, dim=0) if use_dynamic_bsz: log_probs = restore_dynamic_batch(log_probs, batch_idx_list) if calculate_entropy: entropys = restore_dynamic_batch(entropys, batch_idx_list) return {"log_probs": log_probs, "entropys": entropys} def update_policy(self, data: DataProto): self.actor_module.train() assert self.config.ppo_mini_batch_size % self.config.ppo_micro_batch_size_per_gpu == 0 self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu temperature = data.meta_info["temperature"] # temperature must be in the data.meta_info to avoid slient error select_keys = [ "responses", "response_mask", "input_ids", "attention_mask", "pixel_values", "old_log_probs", "advantages", ] batch = data.select(batch_keys=select_keys).batch self.pad_token_id = data.meta_info["pad_token_id"] # TODO(caiyunke.astra): check here # assert self.config.ppo_micro_batch_size_per_gpu == 1 # Split to make minibatch iterator for updating the actor # See PPO paper for details. https://arxiv.org/abs/1707.06347 mini_batches = batch.split(self.config.ppo_mini_batch_size) metrics = {} for batch_idx, mini_batch in enumerate(mini_batches): if self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, _ = prepare_dynamic_batch(mini_batch, max_token_len=max_token_len) else: self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu) self.actor_optimizer.zero_grad() for _, micro_batch in enumerate[DataProto | TensorDictBase](micro_batches): micro_batch = micro_batch.to(get_device_id()) # actor device is cpu when using offload responses = micro_batch["responses"] response_mask = micro_batch["response_mask"] # (batch_size, traj_len) old_log_prob = micro_batch["old_log_probs"] advantages = micro_batch["advantages"] # clip_ratio = self.config.clip_ratio clip_ratio_high = self.config.clip_ratio_high clip_ratio_low = self.config.clip_ratio_low input_ids = micro_batch["input_ids"] attention_mask = micro_batch["attention_mask"] pixel_values = micro_batch["pixel_values"] responses = micro_batch["responses"] loss_info = { "actor/pg_loss": 0, "actor/pg_clipfrac": 0, "actor/ppo_kl": 0, "actor/pg_clipfrac_lower": 0, } _, log_prob = self._forward_micro_batch_update( input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values, responses=responses, temperature=temperature, ) pg_loss, pg_clipfrac, ppo_kl, pg_clipfrac_lower = core_algos.compute_policy_loss( old_log_prob=old_log_prob, log_prob=log_prob, advantages=advantages, response_mask=response_mask, cliprange_high=clip_ratio_high, cliprange_low=clip_ratio_low, ) loss = pg_loss / self.gradient_accumulation loss.backward() loss_info["actor/pg_loss"] = loss_info["actor/pg_loss"] + pg_loss.detach().item() loss_info["actor/pg_clipfrac"] = loss_info["actor/pg_clipfrac"] + pg_clipfrac.detach().item() loss_info["actor/ppo_kl"] = loss_info["actor/ppo_kl"] + ppo_kl.detach().item() loss_info["actor/pg_clipfrac_lower"] = ( loss_info["actor/pg_clipfrac_lower"] + pg_clipfrac_lower.detach().item() ) append_to_dict(metrics, loss_info) grad_norm = self._optimizer_step() mini_batch_metrics = {"actor/grad_norm": grad_norm.detach().item()} append_to_dict(metrics, mini_batch_metrics) self.actor_optimizer.zero_grad() return metrics ================================================ FILE: verl/experimental/vla/env_loop.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os import numpy as np import torch from omegaconf import DictConfig from verl import DataProto from verl.single_controller.ray import RayWorkerGroup logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class EnvLoop: """An env loop manages interactions between models and vectorized environments. It's designed for computationally intensive environments, such as robotics simulators.""" def __init__(self, env_wg: RayWorkerGroup, rollout_wg: RayWorkerGroup, config: DictConfig): """ Initialize the EnvLoop. Args: env_wg (RayWorkerGroup): Environment worker group. rollout_wg (RayWorkerGroup): Rollout worker group for model inference. config (DictConfig): YAML config. """ self.env_wg = env_wg self.rollout_wg = rollout_wg self.config = config # Extract relevant configuration self.max_interactions = config.env.train.max_episode_steps // config.env.actor.model.num_action_chunks self.stage_num = config.env.rollout.pipeline_stage_num self.num_envs_per_worker = config.env.train.num_envs self.action_dim = config.env.actor.model.action_dim self.num_action_chunks = config.env.actor.model.num_action_chunks # Derived properties self.total_envs = self.env_wg.world_size * self.num_envs_per_worker if self.total_envs % self.stage_num != 0: raise ValueError(f"Total envs ({self.total_envs}) must be divisible by stage_num ({self.stage_num})") self.envs_per_stage = self.total_envs // self.stage_num self.env_wg.init_worker() self.env_wg.init_simulator() def generate_sequences(self, prompts: DataProto, reset_future: asyncio.Future) -> DataProto: """Split input batch and dispatch to env loop workers. Args: prompts (DataProto): Input batch. Returns: DataProto: Output batch. """ reset_results = reset_future.get() loop = asyncio.get_event_loop() self.rollout_wg.switch_to_rollout() output = loop.run_until_complete(self.run(prompts, reset_results)) self.rollout_wg.switch_to_train() # TODO(caiyunke.astra): add timing metrics return output async def run(self, prompts: DataProto, reset_results: DataProto) -> DataProto: """ Run the environment interaction loop. This method orchestrates a pipelined process: 1. Resets environments to specified initial states. 2. In a loop, it gets actions from the rollout workers and applies them to the environments. 3. Collects all trajectory data (observations, actions, rewards, dones). 4. Formats and returns the collected trajectories as a single batch. Args: prompts (DataProto): Contains initial state IDs and other settings. - 'non_tensor_batch.state_ids': A numpy array of state IDs to reset envs. Returns: DataProto: A batch containing the complete trajectories. """ initial_state_ids = prompts.non_tensor_batch["state_ids"] staged_obs = self._restructure_obs_data(reset_results) # --- Pipeline state --- trajectories = {i: [] for i in range(self.stage_num)} # To store (obs, action, rew, done) tuples rollout_futures = {} # is_complete = torch.zeros((self.total_envs,), dtype=torch.bool) for stage_id in range(self.stage_num): # trajectories[stage_id].append({'obs': staged_obs[stage_id]}) trajectories[stage_id].append({}) vla_input = staged_obs[stage_id] vla_input.meta_info = prompts.meta_info # Pass along rollout config rollout_futures[stage_id] = self.rollout_wg.generate_sequences(vla_input) async def _stage_loop(stage_id: int): for step_idx in range(self.max_interactions): if stage_id == 0: logger.info(f"[{step_idx}/{self.max_interactions - 1}] rollout step") action_result: DataProto = await asyncio.to_thread(rollout_futures[stage_id].get) trajectories[stage_id][-1]["action"] = action_result action_data = DataProto.from_dict( non_tensors={ "actions": action_result.batch["action"].cpu().numpy(), "critic_values": action_result.batch["critic_value"].cpu().numpy(), }, meta_info={"stage_id": stage_id}, ) env_ref = self.env_wg.env_interact_step(action_data) env_result: DataProto = await asyncio.to_thread(env_ref.get) trajectories[stage_id][-1]["rew"] = env_result.batch["rews"] trajectories[stage_id][-1]["done"] = env_result.batch["terminations"] next_obs = DataProto( batch=env_result.batch.select("full_image", "wrist_image", "state"), non_tensor_batch={"task_descriptions": env_result.non_tensor_batch["task_descriptions"]}, ) if step_idx < self.max_interactions - 1: trajectories[stage_id].append({}) vla_input = next_obs vla_input.meta_info = prompts.meta_info rollout_futures[stage_id] = self.rollout_wg.generate_sequences(vla_input) await asyncio.gather(*[asyncio.create_task(_stage_loop(sid)) for sid in range(self.stage_num)]) self.env_wg.finish_rollout() return self._collate_trajectories(trajectories, initial_state_ids, meta_info=prompts.meta_info) def _restructure_obs_data(self, data_proto: DataProto) -> list[DataProto]: """Reshapes flat observation data from env_wg into a list of per-stage DataProto objects.""" # env_wg returns a flat batch ordered by [worker0_stage0, worker0_stage1, ..., # worker1_stage0, worker1_stage1, ...] # First, un-flatten by worker, then by stage num_workers = self.env_wg.world_size staged_data = [[] for _ in range(self.stage_num)] chunks = data_proto.chunk(num_workers) for worker_chunk in chunks: stage_chunks = worker_chunk.chunk(self.stage_num) for stage_id, data in enumerate(stage_chunks): staged_data[stage_id].append(data) # Concatenate data from all workers for each stage return [DataProto.concat(data_list) for data_list in staged_data] def _collate_trajectories(self, trajectories: dict, initial_state_ids: np.ndarray, meta_info) -> DataProto: """ Collates the collected trajectory data into the final batch format. """ flat_trajs = [{} for _ in range(len(trajectories[0]))] for stage_id in range(self.stage_num): for step_idx, step_data in enumerate(trajectories[stage_id]): if not flat_trajs[step_idx]: # if dict is empty flat_trajs[step_idx] = step_data else: # Concatenate DataProto objects for key, value in step_data.items(): if isinstance(value, DataProto): flat_trajs[step_idx][key] = DataProto.concat([flat_trajs[step_idx][key], value]) elif isinstance(value, torch.Tensor): flat_trajs[step_idx][key] = torch.cat([flat_trajs[step_idx][key], value], dim=0) # iterate all action batch keys (e.g., action, images, pixel_values, input_ids, ...) batch_dict = {} action_batch_keys = list(flat_trajs[0]["action"].batch.keys()) for key in action_batch_keys: per_step_values = [step["action"].batch[key] for step in flat_trajs] batch_dict[key] = torch.stack(per_step_values, dim=1) batch_dict["complete"] = torch.stack([step["done"] for step in flat_trajs], dim=1).squeeze(-1) batch_dict["env_state_id"] = torch.from_numpy(initial_state_ids.astype(int)) return DataProto.from_single_dict(batch_dict, meta_info=meta_info) ================================================ FILE: verl/experimental/vla/envs/__init__.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/vla/envs/action_utils.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from io import BytesIO from typing import Any, Optional import imageio import numpy as np import torch import torchvision.transforms.functional as F from PIL import Image, ImageDraw, ImageFont def prepare_actions_simplevla( raw_chunk_actions, ) -> torch.Tensor: from verl.experimental.vla.envs.libero_env.utils import invert_gripper_action, normalize_gripper_action normalized_action = normalize_gripper_action(raw_chunk_actions, binarize=True) inverted_action = invert_gripper_action(normalized_action) return inverted_action def prepare_actions( simulator_type, raw_chunk_actions, num_action_chunks, action_dim, action_scale: float = 1.0, policy: str = "widowx_bridge", ) -> torch.Tensor: # TODO: prepare_actions according to simulator_type chunk_actions = prepare_actions_simplevla( raw_chunk_actions=raw_chunk_actions, ) return chunk_actions def to_tensor(array: dict | torch.Tensor | np.ndarray | list | Any, device: str = "cpu") -> dict | torch.Tensor: """ Copied from ManiSkill! Maps any given sequence to a torch tensor on the CPU/GPU. If physx gpu is not enabled then we use CPU, otherwise GPU, unless specified by the device argument Args: array: The data to map to a tensor device: The device to put the tensor on. By default this is None and to_tensor will put the device on the GPU if physx is enabled and CPU otherwise """ if isinstance(array, (dict)): return {k: to_tensor(v, device=device) for k, v in array.items()} elif isinstance(array, torch.Tensor): ret = array.to(device) elif isinstance(array, np.ndarray): if array.dtype == np.uint16: array = array.astype(np.int32) elif array.dtype == np.uint32: array = array.astype(np.int64) ret = torch.tensor(array).to(device) else: if isinstance(array, list) and isinstance(array[0], np.ndarray): array = np.array(array) ret = torch.tensor(array, device=device) if ret.dtype == torch.float64: ret = ret.to(torch.float32) return ret def tile_images(images: list[np.ndarray | torch.Tensor], nrows: int = 1) -> np.ndarray | torch.Tensor: """ Copied from maniskill https://github.com/haosulab/ManiSkill Tile multiple images to a single image comprised of nrows and an appropriate number of columns to fit all the images. The images can also be batched (e.g. of shape (B, H, W, C)), but give images must all have the same batch size. if nrows is 1, images can be of different sizes. If nrows > 1, they must all be the same size. """ # Sort images in descending order of vertical height batched = False if len(images[0].shape) == 4: batched = True if nrows == 1: images = sorted(images, key=lambda x: x.shape[0 + batched], reverse=True) columns: list[list[np.ndarray | torch.Tensor]] = [] if batched: max_h = images[0].shape[1] * nrows cur_h = 0 cur_w = images[0].shape[2] else: max_h = images[0].shape[0] * nrows cur_h = 0 cur_w = images[0].shape[1] # Arrange images in columns from left to right column = [] for im in images: if cur_h + im.shape[0 + batched] <= max_h and cur_w == im.shape[1 + batched]: column.append(im) cur_h += im.shape[0 + batched] else: columns.append(column) column = [im] cur_h, cur_w = im.shape[0 + batched : 2 + batched] columns.append(column) # Tile columns total_width = sum(x[0].shape[1 + batched] for x in columns) is_torch = False if torch is not None: is_torch = isinstance(images[0], torch.Tensor) output_shape = (max_h, total_width, 3) if batched: output_shape = (images[0].shape[0], max_h, total_width, 3) if is_torch: output_image = torch.zeros(output_shape, dtype=images[0].dtype) else: output_image = np.zeros(output_shape, dtype=images[0].dtype) cur_x = 0 for column in columns: cur_w = column[0].shape[1 + batched] next_x = cur_x + cur_w if is_torch: column_image = torch.concatenate(column, dim=0 + batched) else: column_image = np.concatenate(column, axis=0 + batched) cur_h = column_image.shape[0 + batched] output_image[..., :cur_h, cur_x:next_x, :] = column_image cur_x = next_x return output_image def put_text_on_image(image: np.ndarray, lines: list[str], max_width: int = 200) -> np.ndarray: """ Put text lines on an image with automatic line wrapping. Args: image: Input image as numpy array lines: List of text lines to add max_width: Maximum width for text wrapping """ assert image.dtype == np.uint8, image.dtype image = image.copy() image = Image.fromarray(image) draw = ImageDraw.Draw(image) font = ImageFont.load_default(size=20) new_lines = [] for line in lines: words = line.split() current_line = [] for word in words: test_line = " ".join(current_line + [word]) test_width = font.getlength(test_line) if test_width <= max_width: current_line.append(word) else: new_lines.append(" ".join(current_line)) current_line = [word] if current_line: new_lines.append(" ".join(current_line)) y = -10 for line in new_lines: bbox = draw.textbbox((0, 0), text=line) textwidth = bbox[2] - bbox[0] textheight = bbox[3] - bbox[1] y += textheight + 10 x = 10 pad = 2 draw.rectangle( [(x - pad, y - pad), (x + textwidth + pad, y + textheight + pad)], fill=(0, 0, 0), ) draw.text((x, y), text=line, fill=(255, 255, 255)) return np.array(image) def put_info_on_image( image: np.ndarray, info: dict[str, float], extras: Optional[list[str]] = None, overlay: bool = True, ) -> np.ndarray: """ Put information dictionary and extra lines on an image. Args: image: Input image info: Dictionary of key-value pairs to display extras: Additional text lines to display overlay: Whether to overlay text on image """ lines = [f"{k}: {v:.3f}" if isinstance(v, float) else f"{k}: {v}" for k, v in info.items()] if extras is not None: lines.extend(extras) return put_text_on_image(image, lines) def list_of_dict_to_dict_of_list( list_of_dict: list[dict[str, Any]], ) -> dict[str, list[Any]]: """ Convert a list of dictionaries to a dictionary of lists. Args: list_of_dict: List of dictionaries with same keys Returns: Dictionary where each key maps to a list of values """ if len(list_of_dict) == 0: return {} keys = list_of_dict[0].keys() output = {key: [] for key in keys} for data in list_of_dict: for key, item in data.items(): assert key in output output[key].append(item) return output def save_rollout_video(rollout_images: list[np.ndarray], output_dir: str, video_name: str, fps: int = 30) -> None: """ Saves an MP4 replay of an episode. Args: rollout_images: List of images from the episode output_dir: Directory to save the video video_name: Name of the output video file fps: Frames per second for the video """ os.makedirs(output_dir, exist_ok=True) mp4_path = os.path.join(output_dir, f"{video_name}.mp4") video_writer = imageio.get_writer(mp4_path, fps=fps) for img in rollout_images: video_writer.append_data(img) video_writer.close() def resize_image(img: np.ndarray, resize_size: tuple[int, int]) -> np.ndarray: """ Takes numpy array corresponding to a single image and returns resized image as numpy array. Args: img: Input image as numpy array resize_size: Target size for resizing Returns: Resized image as numpy array """ assert isinstance(resize_size, tuple), "resize_size must be a tuple" assert isinstance(img, np.ndarray), "img must be a numpy array" # Convert numpy array to PIL Image pil_img = Image.fromarray(img) # Encode as JPEG, as done in RLDS dataset builder buffer = BytesIO() pil_img.save(buffer, format="JPEG") buffer.seek(0) # Immediately decode back img = Image.open(buffer) img = img.resize(resize_size, Image.Resampling.LANCZOS) img = np.array(img) img = np.clip(np.round(img), 0, 255).astype(np.uint8) return img def center_crop_image(image: Image.Image) -> Image.Image: crop_scale = 0.9 orig_w, orig_h = image.size image_tensor = F.to_tensor(image) crop_h = int(orig_h * crop_scale) crop_w = int(orig_w * crop_scale) image_tensor = F.center_crop(image_tensor, (crop_h, crop_w)) image_tensor = F.resize(image_tensor, (orig_h, orig_w)) final_image = F.to_pil_image(image_tensor) final_image = final_image.convert("RGB") return final_image ================================================ FILE: verl/experimental/vla/envs/isaac_env/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .isaac_env import IsaacEnv __all__ = ["IsaacEnv"] ================================================ FILE: verl/experimental/vla/envs/isaac_env/isaac_env.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Optional import gymnasium as gym import numpy as np import omni import torch from verl.experimental.vla.envs.action_utils import ( put_info_on_image, save_rollout_video, tile_images, to_tensor, ) logger = logging.getLogger(__name__) class IsaacEnv(gym.Env): def __init__(self, cfg, rank, world_size): self.rank = rank self.cfg = cfg self.world_size = world_size self.seed = self.cfg.seed + rank self.num_envs = self.cfg.num_envs self.action_dim = self.cfg.get("action_dim", 7) self.device = self.cfg.get("device", "cuda:0") self._generator = np.random.default_rng(seed=self.seed) self.task_suite_name = self.cfg.task_suite_name self.env = None self.prev_step_reward = np.zeros(self.num_envs) self.use_rel_reward = False self._init_metrics() self._elapsed_steps = np.zeros(self.num_envs, dtype=np.int32) self.max_episode_steps = cfg.max_episode_steps self.video_cfg = cfg.video_cfg self.render_images = [] self.video_cnt = 0 self.camera_name = cfg.init_params.camera_names # sys env must be set before import isaaclab from isaaclab.app import AppLauncher launch_args = {"headless": True, "enable_cameras": True} app_launcher = AppLauncher(**launch_args) self.app = app_launcher.app # force franka registration import isaaclab_playground.tasks.manipulation.libero.config.franka # noqa def _init_env(self, task_id=0): """Initializes the Isaac Sim environment.""" self.task_name = self.cfg.get("task_name") self.task_id = task_id # FIXME since isaac use env to set task id, all env have to use the same task id if self.task_suite_name.startswith("libero"): os.environ["LIBERO_TASK_SUITE"] = self.task_suite_name os.environ["LIBERO_TASK_ID"] = str(task_id) os.environ["LIBERO_OSC_TYPE"] = "pose_rel" if not self.task_name: self.task_name = "Isaac-Libero-Franka-OscPose-v0" from isaaclab_tasks.utils import parse_env_cfg self.env_cfg = parse_env_cfg(self.task_name, num_envs=self.num_envs) self.env_cfg.env_name = self.cfg.get("env_name", str(self.task_id)) self.env_cfg.sim.device = self.device self.env_cfg.sim.physx.enable_ccd = True self.env_cfg.terminations.time_out = None self.env_cfg.observations.policy.concatenate_terms = False # create environment from loaded config if self.env: self.env.close() omni.usd.get_context().new_stage() self.env = gym.make(self.task_name, cfg=self.env_cfg).unwrapped if self.cfg.video_cfg.save_video: video_dir = os.path.join(self.cfg.video_cfg.video_base_dir, f"rank_{self.rank}") os.makedirs(video_dir, exist_ok=True) self.action_space = self.env.action_space self.observation_space = self.env.observation_space # TODO support other task suite if self.task_suite_name.startswith("libero"): self.task_descriptions = self.env.cfg.libero_config.task_info["language_instruction"] assert self.env_cfg.osc_type == "pose_rel", ( f"Only pose_rel osc type is supported for libero. Received: {self.env_cfg.osc_type}" ) else: raise ValueError(f"Task suite {self.task_suite_name} is not supported.") logger.info("Isaac Sim environment initialized") def _init_metrics(self): self.success_once = np.zeros(self.num_envs, dtype=bool) self.returns = np.zeros(self.num_envs) def _reset_metrics(self, env_idx=None): if env_idx is not None: mask = np.zeros(self.num_envs, dtype=bool) mask[env_idx] = True self.prev_step_reward[mask] = 0.0 self.success_once[mask] = False self.returns[mask] = 0 self._elapsed_steps[env_idx] = 0 else: self.prev_step_reward[:] = 0 self.success_once[:] = False self.returns[:] = 0.0 self._elapsed_steps[:] = 0 def _record_metrics(self, step_reward, terminations, infos): episode_info = {} self.returns += step_reward # Ensure terminations is a numpy array before the bitwise OR if isinstance(terminations, torch.Tensor): terminations = terminations.cpu().numpy() self.success_once = self.success_once | terminations episode_info["success_once"] = self.success_once.copy() episode_info["return"] = self.returns.copy() episode_info["episode_len"] = self.elapsed_steps.copy() if any(self.elapsed_steps > 0): episode_info["reward"] = episode_info["return"] / self.elapsed_steps else: episode_info["reward"] = 0 infos["episode"] = to_tensor(episode_info) return infos def reset(self, env_idx: Optional[int | list[int] | np.ndarray] = None, options: Optional[dict] = None): if env_idx is None: env_idx = np.arange(self.num_envs) raw_obs, infos = self.env.reset() obs = self._wrap_obs(raw_obs) self._reset_metrics(env_idx) return obs, infos def step(self, actions=None, critic_values=None): if actions is None: # isaac should start with reset_envs_to_initial_state # do nothing for None return (None, None, None, None, None) truncations = self.elapsed_steps >= self.max_episode_steps # _actions = torch.zeros(self.action_space.shape) if isinstance(actions, np.ndarray): actions = torch.from_numpy(actions) self._elapsed_steps += 1 raw_obs, _reward, terminations, _, infos = self.env.step(actions) self.last_obs = raw_obs self.last_infos = infos obs = self._wrap_obs(raw_obs) step_reward = self._calc_step_reward(_reward.cpu().numpy()) if self.video_cfg.save_video: plot_infos = { "rewards": step_reward, "terminations": terminations, "task": self.task_descriptions, } if critic_values is not None: plot_infos["critic_value"] = np.asarray(critic_values, dtype=np.float32) self.add_new_frames(obs, plot_infos) infos = self._record_metrics(step_reward, terminations, infos) return ( obs, to_tensor(step_reward), to_tensor(terminations), to_tensor(truncations), infos, ) def chunk_step(self, chunk_actions, chunk_values=None): # chunk_actions: [num_envs, chunk_step, action_dim] chunk_size = chunk_actions.shape[1] chunk_rewards = [] raw_chunk_terminations = [] raw_chunk_truncations = [] for i in range(chunk_size): actions = chunk_actions[:, i] step_values = None if chunk_values is not None: if len(chunk_values.shape) == 1: step_values = chunk_values elif len(chunk_values.shape) == 2: step_values = chunk_values[:, i] extracted_obs, step_reward, terminations, truncations, infos = self.step(actions, critic_values=step_values) chunk_rewards.append(step_reward) raw_chunk_terminations.append(terminations) raw_chunk_truncations.append(truncations) chunk_rewards = torch.stack(chunk_rewards, dim=1) # [num_envs, chunk_steps] raw_chunk_terminations = torch.stack(raw_chunk_terminations, dim=1) # [num_envs, chunk_steps] raw_chunk_truncations = torch.stack(raw_chunk_truncations, dim=1) # [num_envs, chunk_steps] chunk_terminations = raw_chunk_terminations.clone() chunk_truncations = raw_chunk_truncations.clone() return ( extracted_obs, chunk_rewards, chunk_terminations, chunk_truncations, infos, ) def _calc_step_reward(self, reward): if self.use_rel_reward: reward_diff = reward - self.prev_step_reward self.prev_step_reward = reward return reward_diff else: return reward def _wrap_obs(self, raw_obs): images_and_states = self._extract_image_and_state(raw_obs) obs = { "images_and_states": to_tensor(images_and_states), "task_descriptions": [self.task_descriptions] * self.num_envs, } return obs def _extract_image_and_state(self, obs): # TODO support multiple camera camera_name = self.camera_name[0] for key in self.env.unwrapped.scene.keys(): if key.startswith(camera_name): cam = self.env.unwrapped.scene[key] break assert cam is not None, f"camera {camera_name} not found in scene" rgb = cam.data.output["rgb"] full_image = rgb.cpu().numpy() return { "full_image": full_image, "state": np.concatenate( [ obs["policy"]["eef_pose"].cpu(), # quat2axisangle(obs["robot0_eef_quat"]), # isaac do not return robot0_eef_quat # obs["policy"]["gripper_pos"].cpu(), ], axis=-1, ), } def add_new_frames(self, obs, plot_infos): images = [] for env_id, img in enumerate(obs["images_and_states"]["full_image"]): info_item = {k: v if np.size(v) == 1 else v[env_id] for k, v in plot_infos.items()} img = put_info_on_image(img.cpu().numpy(), info_item) images.append(img) full_image = tile_images(images, nrows=int(np.sqrt(self.num_envs))) self.render_images.append(full_image) def flush_video(self, video_sub_dir: Optional[str] = None): output_dir = os.path.join(self.video_cfg.video_base_dir, f"rank_{self.rank}") if video_sub_dir is not None: output_dir = os.path.join(output_dir, f"{video_sub_dir}") save_rollout_video( self.render_images, output_dir=output_dir, video_name=f"{self.video_cnt}", ) self.video_cnt += 1 self.render_images = [] def close(self): if self.env is not None: self.env.close() self.app.close() def load_state(self, state_buffer: bytes): self.env.load_state(state_buffer) def get_state(self): return None def reset_envs_to_state_ids(self, state_ids_list, task_ids_list): logger.info(f"IsaacEnv reset_envs_to_state_ids task_ids_list: {task_ids_list}") assert len(set(task_ids_list)) == 1, "Isaac env only support single task" self._init_env(task_ids_list[0]) # In Isaac, reset to random status in groups to have more test coverage # TODO support reset in group with options = {"group": len(set(state_ids_list))} raw_obs, infos = self.env.reset() env_idx = np.arange(self.num_envs) self._reset_metrics(env_idx) self.elapsed_steps = np.zeros(self.num_envs, dtype=np.int32) # stablize the environment for _ in range(10): zero_actions = torch.zeros((self.num_envs, self.action_dim), device=self.device) raw_obs, _, _, _, infos = self.env.step(zero_actions) obs = self._wrap_obs(raw_obs) return obs, infos ================================================ FILE: verl/experimental/vla/envs/libero_env/__init__.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/vla/envs/libero_env/libero_env.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Optional import gymnasium as gym import numpy as np import torch from libero.libero import get_libero_path from libero.libero.benchmark import Benchmark, get_benchmark from libero.libero.envs import OffScreenRenderEnv from omegaconf.omegaconf import OmegaConf from verl.experimental.vla.envs.action_utils import ( list_of_dict_to_dict_of_list, put_info_on_image, save_rollout_video, tile_images, to_tensor, ) from verl.experimental.vla.envs.libero_env.utils import get_libero_image, get_libero_wrist_image, quat2axisangle from verl.experimental.vla.envs.libero_env.venv import ReconfigureSubprocEnv logger = logging.getLogger(__name__) def patched_get_task_init_states(self, i): init_states_path = os.path.join( get_libero_path("init_states"), self.tasks[i].problem_folder, self.tasks[i].init_states_file, ) init_states = torch.load(init_states_path, weights_only=False) return init_states Benchmark.get_task_init_states = patched_get_task_init_states class LiberoEnv(gym.Env): def __init__(self, cfg, rank, world_size, stage_id: int = 0): self.rank = rank self.stage_id = stage_id self.cfg = cfg self.world_size = world_size self.seed = int(self.cfg.seed) self.rollout_id = 0 self.num_envs = self.cfg.num_envs self.ignore_terminations = False self._generator = np.random.default_rng(seed=self._compose_seed(env_id=0, rollout_id=0, stream_id=0)) self._generator_ordered = np.random.default_rng(seed=self._compose_seed(env_id=0, rollout_id=0, stream_id=1)) self.start_idx = 0 self.task_suite: Benchmark = get_benchmark(cfg.task_suite_name)() self._compute_total_num_group_envs() self.reset_state_ids_all = self.get_reset_state_ids_all() self.reset_state_ids = self._get_ordered_reset_state_ids(self.num_envs) self._init_task_and_trial_ids() self._init_env() self.prev_step_reward = np.zeros(self.num_envs) self.use_rel_reward = False self._init_metrics() self._elapsed_steps = np.zeros(self.num_envs, dtype=np.int32) self.video_cfg = cfg.video_cfg self.video_cnt = 0 self.render_images = [] def _compose_seed(self, env_id: int, rollout_id: Optional[int] = None, stream_id: int = 0) -> int: if rollout_id is None: rollout_id = self.rollout_id mixed_seed = ( self.seed * 1000003 + self.rank * 10007 + self.stage_id * 1009 + int(rollout_id) * 97 + int(env_id) + int(stream_id) * 53 ) return int(mixed_seed % (2**31 - 1)) @property def elapsed_steps(self): return self._elapsed_steps def get_all_state_ids(self): """Returns all possible state IDs from the entire benchmark.""" return np.arange(self.total_num_group_envs) # (total_num_states,) def _init_env(self): env_fns = self.get_env_fns() self.env = ReconfigureSubprocEnv(env_fns) def get_env_fns(self): env_fn_params = self.get_env_fn_params() env_fns = [] for env_fn_param in env_fn_params: def env_fn(param=env_fn_param): seed = param.pop("seed") env = OffScreenRenderEnv(**param) env.seed(seed) return env env_fns.append(env_fn) return env_fns def get_env_fn_params(self, env_idx=None): env_fn_params = [] raw_base_env_args = OmegaConf.to_container(self.cfg.init_params, resolve=True) if raw_base_env_args is None: base_env_args = {} elif isinstance(raw_base_env_args, dict): base_env_args = raw_base_env_args else: raise TypeError(f"Expected init_params to be a mapping, got {type(raw_base_env_args)}") task_descriptions = [] if env_idx is None: env_idx = np.arange(self.cfg.num_envs) for env_id in range(self.cfg.num_envs): if env_id not in env_idx: task_descriptions.append(self.task_descriptions[env_id]) continue task = self.task_suite.get_task(self.task_ids[env_id]) task_bddl_file = os.path.join(get_libero_path("bddl_files"), task.problem_folder, task.bddl_file) env_fn_params.append( { **base_env_args, "bddl_file_name": task_bddl_file, "seed": self._compose_seed(env_id=env_id), } ) task_descriptions.append(task.language) self.task_descriptions = task_descriptions return env_fn_params def _compute_total_num_group_envs(self): self.total_num_group_envs = 0 self.trial_id_bins = [] for task_id in range(self.task_suite.get_num_tasks()): task_num_trials = len(self.task_suite.get_task_init_states(task_id)) self.trial_id_bins.append(task_num_trials) self.total_num_group_envs += task_num_trials self.cumsum_trial_id_bins = np.cumsum(self.trial_id_bins) def _init_task_and_trial_ids(self): self.task_ids, self.trial_ids = self._get_task_and_trial_ids_from_reset_state_ids(self.reset_state_ids) def _get_random_reset_state_ids(self, num_reset_states): reset_state_ids = self._generator.integers(low=0, high=self.total_num_group_envs, size=(num_reset_states,)) return reset_state_ids def get_reset_state_ids_all(self): reset_state_ids = np.arange(self.total_num_group_envs) valid_size = len(reset_state_ids) - (len(reset_state_ids) % self.world_size) if not self.cfg.only_eval: self._generator_ordered.shuffle(reset_state_ids) reset_state_ids = reset_state_ids[:valid_size] reset_state_ids = reset_state_ids.reshape(self.world_size, -1) return reset_state_ids def _get_ordered_reset_state_ids(self, num_reset_states): reset_state_ids = self.reset_state_ids_all[self.rank][self.start_idx : self.start_idx + num_reset_states] self.start_idx = self.start_idx + num_reset_states if self.start_idx >= len(self.reset_state_ids_all[0]): self.reset_state_ids_all = self.get_reset_state_ids_all() self.start_idx = 0 return reset_state_ids def _get_task_and_trial_ids_from_reset_state_ids(self, reset_state_ids): task_ids = [] trial_ids = [] # get task id and trial id from reset state ids for reset_state_id in reset_state_ids: start_pivot = 0 for task_id, end_pivot in enumerate(self.cumsum_trial_id_bins): if reset_state_id < end_pivot and reset_state_id >= start_pivot: task_ids.append(task_id) trial_ids.append(reset_state_id - start_pivot) break start_pivot = end_pivot logger.debug( "get task and trial id", self.cumsum_trial_id_bins, reset_state_ids, task_ids, trial_ids, ) return np.array(task_ids), np.array(trial_ids) def _get_reset_states(self, env_idx): if env_idx is None: env_idx = np.arange(self.num_envs) init_state = [ self.task_suite.get_task_init_states(self.task_ids[env_id])[self.trial_ids[env_id]] for env_id in env_idx ] return init_state def _init_metrics(self): self.success_once = np.zeros(self.num_envs, dtype=bool) self.fail_once = np.zeros(self.num_envs, dtype=bool) self.returns = np.zeros(self.num_envs) def _reset_metrics(self, env_idx=None): if env_idx is not None: mask = np.zeros(self.num_envs, dtype=bool) mask[env_idx] = True self.prev_step_reward[mask] = 0.0 self.success_once[mask] = False self.fail_once[mask] = False self.returns[mask] = 0 self._elapsed_steps[env_idx] = 0 else: self.prev_step_reward[:] = 0 self.success_once[:] = False self.fail_once[:] = False self.returns[:] = 0.0 self._elapsed_steps[:] = 0 def _record_metrics(self, step_reward, terminations, infos): episode_info = {} self.returns += step_reward self.success_once = self.success_once | terminations episode_info["success_once"] = self.success_once.copy() episode_info["return"] = self.returns.copy() episode_info["episode_len"] = self.elapsed_steps.copy() episode_info["reward"] = episode_info["return"] / episode_info["episode_len"] infos["episode"] = to_tensor(episode_info) return infos def _extract_image_and_state(self, obs): return { "full_image": get_libero_image(obs), "wrist_image": get_libero_wrist_image(obs), "state": np.concatenate( [ obs["robot0_eef_pos"], quat2axisangle(obs["robot0_eef_quat"]), obs["robot0_gripper_qpos"], ] ), } def _wrap_obs(self, obs_list): images_and_states_list = [] for obs in obs_list: images_and_states = self._extract_image_and_state(obs) images_and_states_list.append(images_and_states) obs = { "images_and_states": to_tensor(list_of_dict_to_dict_of_list(images_and_states_list)), "task_descriptions": self.task_descriptions, } return obs def _reconfigure(self, reset_state_ids, env_idx): reconfig_env_idx = [] task_ids, trial_ids = self._get_task_and_trial_ids_from_reset_state_ids(reset_state_ids) for j, env_id in enumerate(env_idx): if self.task_ids[env_id] != task_ids[j]: reconfig_env_idx.append(env_id) self.task_ids[env_id] = task_ids[j] self.trial_ids[env_id] = trial_ids[j] if reconfig_env_idx: env_fn_params = self.get_env_fn_params(reconfig_env_idx) self.env.reconfigure_env_fns(env_fn_params, reconfig_env_idx) seed_list = [self._compose_seed(env_id=int(env_id)) for env_id in env_idx] self.env.seed(seed_list) self.env.reset(id=env_idx) init_state = self._get_reset_states(env_idx=env_idx) self.env.set_init_state(init_state=init_state, id=env_idx) def reset( self, env_idx: Optional[int | list[int] | np.ndarray] = None, reset_state_ids=None, options: Optional[dict] = None, ): self.rollout_id += 1 if env_idx is None: env_idx = np.arange(self.num_envs) if reset_state_ids is None: num_reset_states = len(env_idx) reset_state_ids = self._get_random_reset_state_ids(num_reset_states) self._reconfigure(reset_state_ids, env_idx) for _ in range(10): zero_actions = np.zeros((self.num_envs, 7)) raw_obs, _reward, terminations, info_lists = self.env.step(zero_actions) obs = self._wrap_obs(raw_obs) if env_idx is not None: self._reset_metrics(env_idx) else: self._reset_metrics() infos = {} return obs, infos def step(self, actions=None, critic_values=None): if actions is None: obs, infos = self.reset(reset_state_ids=self.reset_state_ids) terminations = np.zeros(self.num_envs, dtype=bool) truncations = np.zeros(self.num_envs, dtype=bool) return obs, None, to_tensor(terminations), to_tensor(truncations), infos if isinstance(actions, torch.Tensor): actions = actions.detach().cpu().numpy() self._elapsed_steps += 1 raw_obs, _reward, terminations, info_lists = self.env.step(actions) infos = list_of_dict_to_dict_of_list(info_lists) truncations = self.elapsed_steps >= self.cfg.max_episode_steps obs = self._wrap_obs(raw_obs) step_reward = self._calc_step_reward(terminations) if self.video_cfg.save_video: plot_infos = { "rewards": step_reward, "terminations": terminations, "critic_value": np.asarray(critic_values, dtype=np.float32), } plot_infos["task"] = self.task_descriptions self.add_new_frames(raw_obs, plot_infos) infos = self._record_metrics(step_reward, terminations, infos) return ( obs, to_tensor(step_reward), to_tensor(terminations), to_tensor(truncations), infos, ) def chunk_step(self, chunk_actions, chunk_values=None): # chunk_actions: [num_envs, chunk_step, action_dim] chunk_size = chunk_actions.shape[1] chunk_rewards = [] raw_chunk_terminations = [] raw_chunk_truncations = [] for i in range(chunk_size): actions = chunk_actions[:, i] if len(chunk_values.shape) == 1: step_values = chunk_values elif len(chunk_values.shape) == 2: step_values = chunk_values[:, i] extracted_obs, step_reward, terminations, truncations, infos = self.step(actions, critic_values=step_values) chunk_rewards.append(step_reward) raw_chunk_terminations.append(terminations) raw_chunk_truncations.append(truncations) chunk_rewards = torch.stack(chunk_rewards, dim=1) # [num_envs, chunk_steps] raw_chunk_terminations = torch.stack(raw_chunk_terminations, dim=1) # [num_envs, chunk_steps] raw_chunk_truncations = torch.stack(raw_chunk_truncations, dim=1) # [num_envs, chunk_steps] chunk_terminations = raw_chunk_terminations.clone() chunk_truncations = raw_chunk_truncations.clone() return ( extracted_obs, chunk_rewards, chunk_terminations, chunk_truncations, infos, ) def _calc_step_reward(self, terminations): reward = self.cfg.reward_coef * terminations reward_diff = reward - self.prev_step_reward self.prev_step_reward = reward if self.use_rel_reward: return reward_diff else: return reward def add_new_frames(self, raw_obs, plot_infos): images = [] for env_id, raw_single_obs in enumerate(raw_obs): info_item = {k: v if np.size(v) == 1 else v[env_id] for k, v in plot_infos.items()} img = raw_single_obs["agentview_image"][::-1, ::-1] img = put_info_on_image(img, info_item) images.append(img) full_image = tile_images(images, nrows=int(np.sqrt(self.num_envs))) self.render_images.append(full_image) def flush_video(self, video_sub_dir: Optional[str] = None): output_dir = os.path.join(self.video_cfg.video_base_dir, f"rank_{self.rank}") if video_sub_dir is not None: output_dir = os.path.join(output_dir, f"{video_sub_dir}") save_rollout_video( self.render_images, output_dir=output_dir, video_name=f"{self.video_cnt}", ) self.video_cnt += 1 self.render_images = [] def reset_envs_to_state_ids(self, state_ids_list, task_ids_list): """Reset environments to specified state IDs. Args: state_ids_list: List of state IDs to reset environments to """ env_idx = np.arange(len(state_ids_list)) obs, infos = self.reset(env_idx=env_idx, reset_state_ids=state_ids_list) return obs, infos def load_state(self, state_buffer: bytes): self.env.load_state(state_buffer) ================================================ FILE: verl/experimental/vla/envs/libero_env/utils.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utils for evaluating policies in LIBERO simulation environments.""" import math import numpy as np def get_libero_image(obs: dict[str, np.ndarray]) -> np.ndarray: """ Extracts image from observations and preprocesses it. Args: obs: Observation dictionary from LIBERO environment Returns: Preprocessed image as numpy array """ img = obs["agentview_image"] img = img[::-1, ::-1] # IMPORTANT: rotate 180 degrees to match train preprocessing return img def get_libero_wrist_image(obs: dict[str, np.ndarray]) -> np.ndarray: """ Extracts wrist camera image from observations and preprocesses it. Args: obs: Observation dictionary from LIBERO environment Returns: Preprocessed wrist camera image as numpy array """ img = obs["robot0_eye_in_hand_image"] img = img[::-1, ::-1] # IMPORTANT: rotate 180 degrees to match train preprocessing return img def quat2axisangle(quat: np.ndarray) -> np.ndarray: """ Copied from robosuite: https://github.com/ARISE-Initiative/robosuite/blob/eafb81f54ffc104f905ee48a16bb15f059176ad3/robosuite/utils/transform_utils.py#L490C1-L512C55 Converts quaternion to axis-angle format. Returns a unit vector direction scaled by its angle in radians. Args: quat (np.array): (x,y,z,w) vec4 float angles Returns: np.array: (ax,ay,az) axis-angle exponential coordinates """ # clip quaternion if quat[3] > 1.0: quat[3] = 1.0 elif quat[3] < -1.0: quat[3] = -1.0 den = np.sqrt(1.0 - quat[3] * quat[3]) if math.isclose(den, 0.0): # This is (close to) a zero degree rotation, immediately return return np.zeros(3) return (quat[:3] * 2.0 * math.acos(quat[3])) / den def normalize_gripper_action(action: np.ndarray, binarize: bool = True) -> np.ndarray: """ Normalize gripper action from [0,1] to [-1,+1] range. This is necessary for some environments because the dataset wrapper standardizes gripper actions to [0,1]. Note that unlike the other action dimensions, the gripper action is not normalized to [-1,+1] by default. Normalization formula: y = 2 * (x - orig_low) / (orig_high - orig_low) - 1 Args: action: Action array with gripper action in the last dimension binarize: Whether to binarize gripper action to -1 or +1 Returns: np.ndarray: Action array with normalized gripper action """ # Create a copy to avoid modifying the original normalized_action = action.copy() # Normalize the last action dimension to [-1,+1] orig_low, orig_high = 0.0, 1.0 normalized_action[..., -1] = 2 * (normalized_action[..., -1] - orig_low) / (orig_high - orig_low) - 1 if binarize: # Binarize to -1 or +1 normalized_action[..., -1] = np.sign(normalized_action[..., -1]) return normalized_action def invert_gripper_action(action: np.ndarray) -> np.ndarray: """ Flip the sign of the gripper action (last dimension of action vector). This is necessary for environments where -1 = open, +1 = close, since the RLDS dataloader aligns gripper actions such that 0 = close, 1 = open. Args: action: Action array with gripper action in the last dimension Returns: np.ndarray: Action array with inverted gripper action """ # Create a copy to avoid modifying the original inverted_action = action.copy() # Invert the gripper action inverted_action[..., -1] = inverted_action[..., -1] * -1.0 return inverted_action ================================================ FILE: verl/experimental/vla/envs/libero_env/venv.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from multiprocessing import Pipe, connection from multiprocessing.context import Process from typing import Any, Callable, Optional import gymnasium as gym import numpy as np from libero.libero.envs import OffScreenRenderEnv from libero.libero.envs.venv import ( BaseVectorEnv, CloudpickleWrapper, EnvWorker, ShArray, SubprocEnvWorker, SubprocVectorEnv, _setup_buf, ) def _worker( parent: connection.Connection, p: connection.Connection, env_fn_wrapper: CloudpickleWrapper, obs_bufs: Optional[dict | tuple | ShArray] = None, ) -> None: def _encode_obs(obs: dict | tuple | np.ndarray, buffer: dict | tuple | ShArray) -> None: if isinstance(obs, np.ndarray) and isinstance(buffer, ShArray): buffer.save(obs) elif isinstance(obs, tuple) and isinstance(buffer, tuple): for o, b in zip(obs, buffer, strict=False): _encode_obs(o, b) elif isinstance(obs, dict) and isinstance(buffer, dict): for k in obs.keys(): _encode_obs(obs[k], buffer[k]) return None parent.close() env = env_fn_wrapper.data() try: while True: try: cmd, data = p.recv() except EOFError: # the pipe has been closed p.close() break if cmd == "step": env_return = env.step(data) if obs_bufs is not None: _encode_obs(env_return[0], obs_bufs) env_return = (None, *env_return[1:]) p.send(env_return) elif cmd == "reset": retval = env.reset(**data) reset_returns_info = ( isinstance(retval, (tuple | list)) and len(retval) == 2 and isinstance(retval[1], dict) ) if reset_returns_info: obs, info = retval else: obs = retval if obs_bufs is not None: _encode_obs(obs, obs_bufs) obs = None if reset_returns_info: p.send((obs, info)) else: p.send(obs) elif cmd == "close": p.send(env.close()) p.close() break elif cmd == "render": p.send(env.render(**data) if hasattr(env, "render") else None) elif cmd == "seed": if hasattr(env, "seed"): p.send(env.seed(data)) else: env.reset(seed=data) p.send(None) elif cmd == "getattr": p.send(getattr(env, data) if hasattr(env, data) else None) elif cmd == "setattr": setattr(env.unwrapped, data["key"], data["value"]) elif cmd == "check_success": p.send(env.check_success()) elif cmd == "get_segmentation_of_interest": p.send(env.get_segmentation_of_interest(data)) elif cmd == "get_sim_state": p.send(env.get_sim_state()) elif cmd == "set_init_state": obs = env.set_init_state(data) p.send(obs) elif cmd == "reconfigure": env.close() seed = data.pop("seed") env = OffScreenRenderEnv(**data) env.seed(seed) p.send(None) else: p.close() raise NotImplementedError except KeyboardInterrupt: p.close() class ReconfigureSubprocEnvWorker(SubprocEnvWorker): def __init__(self, env_fn: Callable[[], gym.Env], share_memory: bool = False): self.parent_remote, self.child_remote = Pipe() self.share_memory = share_memory self.buffer: Optional[dict | tuple | ShArray] = None if self.share_memory: dummy = env_fn() obs_space = dummy.observation_space dummy.close() del dummy self.buffer = _setup_buf(obs_space) args = ( self.parent_remote, self.child_remote, CloudpickleWrapper(env_fn), self.buffer, ) self.process = Process(target=_worker, args=args, daemon=True) self.process.start() self.child_remote.close() EnvWorker.__init__(self, env_fn) def reconfigure_env_fn(self, env_fn_param): self.parent_remote.send(["reconfigure", env_fn_param]) return self.parent_remote.recv() class ReconfigureSubprocEnv(SubprocVectorEnv): def __init__(self, env_fns: list[Callable[[], gym.Env]], **kwargs: Any) -> None: def worker_fn(fn: Callable[[], gym.Env]) -> ReconfigureSubprocEnvWorker: return ReconfigureSubprocEnvWorker(fn, share_memory=False) BaseVectorEnv.__init__(self, env_fns, worker_fn, **kwargs) def reconfigure_env_fns(self, env_fns, id=None): self._assert_is_not_closed() id = self._wrap_id(id) if self.is_async: self._assert_id(id) for j, i in enumerate(id): self.workers[i].reconfigure_env_fn(env_fns[j]) ================================================ FILE: verl/experimental/vla/fsdp_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The main entry point to run the PPO algorithm """ import asyncio import contextlib import logging import os import torch import torch.distributed from packaging import version from torch.distributed.device_mesh import init_device_mesh from torch.distributed.fsdp import FSDPModule from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp._unshard_param_utils import _get_module_fsdp_state, _unshard_params_for_summon from torch.distributed.fsdp.api import FullStateDictConfig, ShardedStateDictConfig, StateDictType from verl import DataProto from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import get_device_id, get_device_name, get_torch_device, set_expandable_segments from verl.utils.flops_counter import FlopsCounter from verl.utils.fsdp_utils import fsdp_version, set_reshard_after_forward from verl.utils.import_utils import import_external_libs from verl.utils.memory_utils import aggressive_empty_cache from verl.utils.profiler import DistProfiler, log_gpu_memory_usage, simple_timer from verl.utils.profiler.performance import reduce_timing, topk_reduce_ratio_min_max from verl.workers.config import HFModelConfig from verl.workers.fsdp_workers import ActorRolloutRefWorker logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) device_name = get_device_name() class RobActorRolloutRefWorker(ActorRolloutRefWorker): """ This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on the config.rollout """ fsdp_unshard_exit_stack = contextlib.ExitStack() def _build_rollout(self, trust_remote_code=False): self.base_sync_done = False world_size = torch.distributed.get_world_size() dp = world_size infer_tp = self.config.rollout.tensor_model_parallel_size rollout_device_mesh = init_device_mesh( device_name, mesh_shape=(dp, infer_tp), mesh_dim_names=["dp", "infer_tp"] ) # 3. init trainer and rollout random states self.torch_random_states = get_torch_device().get_rng_state() gen_dp_rank = rollout_device_mesh["dp"].get_local_rank() get_torch_device().manual_seed(gen_dp_rank + 1000) # make sure all tp ranks have the same random states self.gen_random_states = get_torch_device().get_rng_state() get_torch_device().set_rng_state(self.torch_random_states) fsdp_ver = fsdp_version(self.actor_module_fsdp) if torch.distributed.get_world_size() == 1 and fsdp_ver == 1: FSDP.set_state_dict_type( self.actor_module_fsdp, state_dict_type=StateDictType.FULL_STATE_DICT, state_dict_config=FullStateDictConfig(), ) elif fsdp_ver == 1: FSDP.set_state_dict_type( self.actor_module_fsdp, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig(), ) elif fsdp_ver == 2: # FSDP2 already handles state dict logic via torch.distributed.checkpoint APIs. pass else: raise NotImplementedError(f"Unsupported fsdp version {fsdp_ver}") self._register_dispatch_collect_info("rollout", dp_rank=self.rank, is_collect=True) if self.config.get("algorithm", "grpo") == "sac": from verl.experimental.vla.sac.naive_rollout_pi05 import PI0RolloutRob self.rollout = PI0RolloutRob( module=self.actor_module_fsdp, model_config=self.config.model, tokenizer=self.tokenizer ) else: from verl.experimental.vla.naive_rollout_rob import NaiveRolloutRob self.rollout = NaiveRolloutRob(module=self.actor_module_fsdp, model_config=self.config.model) model_config: HFModelConfig = omega_conf_to_dataclass(self.config.model, dataclass_type=HFModelConfig) self.model_config = model_config @register(dispatch_mode=Dispatch.ONE_TO_ALL) def switch_to_rollout(self): loop = asyncio.get_event_loop() loop.run_until_complete(self.rollout_mode()) log_gpu_memory_usage("After switch to rollout mode", logger=logger) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def switch_to_train(self): loop = asyncio.get_event_loop() loop.run_until_complete(self.trainer_mode()) log_gpu_memory_usage("After switch to trainer mode", logger=logger) async def rollout_mode(self): """Context switch hybridengine to rollout mode.""" self.actor_module_fsdp.eval() aggressive_empty_cache(force_sync=True) self.base_sync_done = True # important: need to manually set the random states of each tp to be identical. self.torch_random_states = get_torch_device().get_rng_state() get_torch_device().set_rng_state(self.gen_random_states) if fsdp_version(self.actor_module_fsdp) == 1: fsdp_unshard_exit_stack = contextlib.ExitStack() optional_state = _get_module_fsdp_state(self.actor_module_fsdp) if optional_state is None: self.fsdp_unshard_exit_stack = fsdp_unshard_exit_stack states_and_modules = ([optional_state], [self.actor_module_fsdp]) for state, fsdp_module in zip(*states_and_modules, strict=False): fsdp_unshard_exit_stack.enter_context( _unshard_params_for_summon( module=fsdp_module, state=state, writeback=False, rank0_only=False, offload_to_cpu=False, with_grads=False, ) ) self.fsdp_unshard_exit_stack = fsdp_unshard_exit_stack elif fsdp_version(self.actor_module_fsdp) == 2: self.actor_module_fsdp.unshard() for m in self.actor_module_fsdp.modules(): if isinstance(m, FSDPModule) or hasattr(m, "unshard"): m.unshard() if version.parse(torch.__version__) < version.parse("2.8"): set_reshard_after_forward(self.actor_module_fsdp, False) else: self.actor_module_fsdp.set_reshard_after_forward(False) else: raise NotImplementedError(f"Unsupported fsdp version {fsdp_version(self.actor_module_fsdp)}") logger.info("rollout mode") async def trainer_mode(self): """Context switch hybridengine to trainer mode.""" self.actor_module_fsdp.train() # add empty cache after each compute aggressive_empty_cache(force_sync=True) set_expandable_segments(True) # restore random states self.gen_random_states = get_torch_device().get_rng_state() get_torch_device().set_rng_state(self.torch_random_states) if fsdp_version(self.actor_module_fsdp) == 1: if self.fsdp_unshard_exit_stack is not None: self.fsdp_unshard_exit_stack.close() self.fsdp_unshard_exit_stack = None elif fsdp_version(self.actor_module_fsdp) == 2: self.actor_module_fsdp.reshard() for m in self.actor_module_fsdp.modules(): if isinstance(m, FSDPModule) or hasattr(m, "reshard"): m.reshard() if version.parse(torch.__version__) < version.parse("2.8"): set_reshard_after_forward(self.actor_module_fsdp, True) else: self.actor_module_fsdp.set_reshard_after_forward(True) else: raise NotImplementedError(f"Unsupported fsdp version {fsdp_version(self.actor_module_fsdp)}") logger.info("trainer mode") @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="rollout"), blocking=False) @DistProfiler.annotate(color="red", role="rollout_generate") def generate_sequences(self, prompts: DataProto): # Support all hardwares assert self._is_rollout prompts = prompts.to(get_device_id()) meta_info = { "eos_token_id": self.model_config.generation_config.eos_token_id if self.model_config.generation_config is not None else self.model_config.tokenizer.eos_token_id, "pad_token_id": self.model_config.generation_config.pad_token_id if self.model_config.generation_config is not None else self.model_config.tokenizer.pad_token_id, } prompts.meta_info.update(meta_info) timing_generate = {} with simple_timer("generate_sequences", timing_generate): output = self.rollout.generate_sequences(prompts=prompts) timing_generate_topk_ratio, timing_generate_min, timing_generate_max = topk_reduce_ratio_min_max( timing_generate["generate_sequences"] ) timing_generate = reduce_timing(timing_generate) timing_generate.update( { "generation_timing/max": timing_generate_max, "generation_timing/min": timing_generate_min, "generation_timing/topk_ratio": timing_generate_topk_ratio, } ) output.meta_info["metrics"] = timing_generate output = output.to("cpu") # clear kv cache get_torch_device().empty_cache() return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get("external_lib", None)) # Initialize QAT config before _build_model_optimizer self._init_qat_config() from omegaconf import OmegaConf override_model_config = OmegaConf.to_container(self.config.model.get("override_config", OmegaConf.create())) from verl.experimental.vla.models import register_vla_models register_vla_models() from transformers import AutoProcessor self.processor = AutoProcessor.from_pretrained(self.config.model.path, trust_remote_code=True) if self._is_actor or self._is_rollout: # we need the model for actor and rollout if self._is_actor: optim_config = self.config.actor.optim fsdp_config = self.config.actor.fsdp_config else: optim_config = None fsdp_config = OmegaConf.create() self.actor_module_fsdp, self.actor_optimizer, self.actor_lr_scheduler, self.actor_model_config = ( self._build_model_optimizer( model_path=self.config.model.path, fsdp_config=fsdp_config, optim_config=optim_config, override_model_config=override_model_config, enable_gradient_checkpointing=self.config.model.get("enable_gradient_checkpointing", False), trust_remote_code=self.config.model.get("trust_remote_code", False), ) ) if fsdp_version(self.actor_module_fsdp) == 1: # get the original unwrapped module self.actor_module = self.actor_module_fsdp._fsdp_wrapped_module if self._is_actor: OmegaConf.set_struct(self.config.actor, True) if self.config.get("algorithm") == "sac": from verl.experimental.vla.sac.sac_actor import RobDataParallelSACActor self.actor = RobDataParallelSACActor( config=self.config.actor, actor_module=self.actor_module_fsdp, actor_optimizer=self.actor_optimizer, tokenizer=self.tokenizer, ) else: from verl.experimental.vla.dp_rob import RobDataParallelPPOActor self.actor = RobDataParallelPPOActor( config=self.config.actor, actor_module=self.actor_module_fsdp, actor_optimizer=self.actor_optimizer ) if self._is_rollout: self._build_rollout(trust_remote_code=self.config.model.get("trust_remote_code", False)) if self._is_actor: self.flops_counter = FlopsCounter(self.actor_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.actor_module_fsdp, optimizer=self.actor.actor_optimizer, lr_scheduler=self.actor_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer, checkpoint_config=self.config.actor.checkpoint, trust_remote_code=self.config.model.trust_remote_code, ) torch.distributed.barrier() ================================================ FILE: verl/experimental/vla/main_ppo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import datasets import hydra import ray import torch from omegaconf import OmegaConf from verl import DataProto from verl.trainer.constants_ppo import get_ppo_ray_runtime_env from verl.trainer.ppo.ray_trainer import ResourcePoolManager from verl.trainer.ppo.utils import Role from verl.utils.device import is_cuda_available from .rob_ray_trainer import RobRayPPOTrainer logger = logging.getLogger(__name__) def calculate_reward(data: DataProto, return_dict: bool = False) -> torch.Tensor: complete_tensor = data.batch["complete"] batch_size, num_steps = complete_tensor.shape[:2] traj_has_complete = torch.any(complete_tensor, dim=(1, 2)) # shape: [batch_size] reward_per_traj = traj_has_complete.float() reward_per_step = reward_per_traj.unsqueeze(1).expand(batch_size, num_steps) if return_dict: return {"reward_tensor": reward_per_step} else: return reward_per_step @hydra.main(config_path="config", config_name="rob_ppo_trainer", version_base=None) def main(config): if not ray.is_initialized(): default_runtime_env = get_ppo_ray_runtime_env() ray_init_kwargs = config.ray_kwargs.get("ray_init", {}) runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {}) runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs) ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env}) logger.info(f"ray init kwargs: {ray_init_kwargs}") ray.init(**OmegaConf.to_container(ray_init_kwargs)) # Apply controller nsight profiling if configured if ( is_cuda_available and config.global_profiler.tool == "nsys" and config.global_profiler.get("steps") is not None and len(config.global_profiler.get("steps", [])) > 0 ): from verl.utils.import_utils import is_nvtx_available assert is_nvtx_available(), "nvtx is not available in CUDA platform. Please 'pip3 install nvtx'" nsight_options = OmegaConf.to_container( config.global_profiler.global_tool_config.nsys.controller_nsight_options ) main_task_with_options = main_task.options(runtime_env={"nsight": nsight_options}) ray.get(main_task_with_options.remote(config)) else: ray.get(main_task.remote(config)) # [Optional] get the path of the timeline trace file from the configuration, default to None # This file is used for performance analysis timeline_json_file = config.ray_kwargs.get("timeline_json_file", None) if timeline_json_file: ray.timeline(filename=timeline_json_file) @ray.remote def main_task(config): # print initial config from pprint import pprint from omegaconf import OmegaConf from verl.utils.fs import copy_local_path_from_hdfs pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) # download the checkpoint from hdfs local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path) # instantiate tokenizer from verl.utils import hf_tokenizer tokenizer = hf_tokenizer(local_path) # define worker classes if config.actor_rollout_ref.actor.strategy in ["fsdp", "fsdp2"]: assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.experimental.vla.workers.env.env_worker import EnvWorker from verl.single_controller.ray import RayWorkerGroup from .fsdp_workers import RobActorRolloutRefWorker ray_worker_group_cls = RayWorkerGroup else: raise NotImplementedError role_worker_mapping = { # Role.Critic: ray.remote(RobActorRolloutRefWorker), Role.ActorRollout: ray.remote(RobActorRolloutRefWorker), # Role.RefPolicy: ray.remote(RobActorRolloutRefWorker), Role.Env: ray.remote(EnvWorker), } train_rollout_pool_id = "train_rollout_pool" num_nodes_actor_rollout = config.trainer.nnodes train_rollout_gpu_num = config.trainer.n_rollout_gpus_per_node env_gpu_num = config.trainer.n_env_gpus_per_node if config.env.disagg_sim.enable: # disaggregated sim and actor rollout num_nodes_sim = config.env.disagg_sim.nnodes else: # colocated sim and actor rollout num_nodes_sim = config.trainer.nnodes resource_pool_spec = { train_rollout_pool_id: [train_rollout_gpu_num] * num_nodes_actor_rollout, "env_gpu_pool": [env_gpu_num] * num_nodes_sim, } mapping = { Role.ActorRollout: train_rollout_pool_id, # Role.Critic: global_pool_id, # Role.RefPolicy: global_pool_id, Role.Env: "env_gpu_pool", } reward_fn = calculate_reward val_reward_fn = calculate_reward resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) # Create training and validation datasets. train_dataset = datasets.load_dataset("parquet", data_files=config.data.train_files)["train"] val_dataset = datasets.load_dataset("parquet", data_files=config.data.val_files)["train"] trainer = RobRayPPOTrainer( config=config, tokenizer=tokenizer, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, reward_fn=reward_fn, val_reward_fn=val_reward_fn, train_dataset=train_dataset, val_dataset=val_dataset, ) trainer.init_workers() trainer.fit() if __name__ == "__main__": main() ================================================ FILE: verl/experimental/vla/main_sac.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from pprint import pprint import datasets import hydra import ray import torch from omegaconf import OmegaConf from verl import DataProto from verl.experimental.vla.sac.sac_ray_trainer import RobRaySACTrainer from verl.trainer.constants_ppo import get_ppo_ray_runtime_env from verl.trainer.ppo.ray_trainer import ResourcePoolManager from verl.trainer.ppo.utils import Role from verl.utils import hf_tokenizer from verl.utils.fs import copy_local_path_from_hdfs logger = logging.getLogger(__name__) def calculate_reward(data: DataProto, return_dict: bool = False) -> torch.Tensor: complete_tensor = data.batch["complete"] reward_per_step = complete_tensor.float() if return_dict: return {"reward_tensor": reward_per_step} else: return reward_per_step @hydra.main(config_path="config", config_name="rob_sac_trainer", version_base=None) def main(config): if not ray.is_initialized(): default_runtime_env = get_ppo_ray_runtime_env() ray_init_kwargs = config.ray_kwargs.get("ray_init", {}) runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {}) runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs) ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env}) logger.info(f"ray init kwargs: {ray_init_kwargs}") ray.init(**OmegaConf.to_container(ray_init_kwargs)) ray.get(main_task.remote(config)) @ray.remote def main_task(config): # print initial config pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) # download the checkpoint from hdfs local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path) # instantiate tokenizer tokenizer = hf_tokenizer(local_path) # define worker classes if config.actor_rollout_ref.actor.strategy in ["fsdp", "fsdp2"]: assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.experimental.vla.workers.env.env_worker import EnvWorker from verl.single_controller.ray import RayWorkerGroup from .fsdp_workers import RobActorRolloutRefWorker ray_worker_group_cls = RayWorkerGroup else: raise NotImplementedError role_worker_mapping = { Role.ActorRollout: ray.remote(RobActorRolloutRefWorker), Role.Env: ray.remote(EnvWorker), } # setup resource pool manager train_rollout_gpu_num = config.trainer.n_rollout_gpus_per_node train_rollout_nodes_num = config.trainer.nnodes env_gpu_num = config.trainer.n_env_gpus_per_node env_nodes_num = config.env.disagg_sim.nnodes if config.env.disagg_sim.enable else config.trainer.nnodes resource_pool_spec = { "train_rollout_pool": [train_rollout_gpu_num] * train_rollout_nodes_num, "env_gpu_pool": [env_gpu_num] * env_nodes_num, } mapping = { Role.ActorRollout: "train_rollout_pool", Role.Env: "env_gpu_pool", } resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) # create datasets train_dataset = datasets.load_dataset("parquet", data_files=config.data.train_files)["train"] val_dataset = datasets.load_dataset("parquet", data_files=config.data.val_files)["train"] # instantiate trainer and start training trainer = RobRaySACTrainer( config=config, tokenizer=tokenizer, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, train_dataset=train_dataset, val_dataset=val_dataset, ) trainer.init_workers() trainer.fit() if __name__ == "__main__": main() ================================================ FILE: verl/experimental/vla/models/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .register_vla_models import register_vla_models __all__ = [ "register_vla_models", ] ================================================ FILE: verl/experimental/vla/models/modules/mlp.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch.nn as nn import torch.nn.init as init class MLP(nn.Module): """ A configurable Multi-Layer Perceptron (MLP) module. It supports dynamic layer construction, multiple activation functions, and various weight initialization strategies. Attributes: input_dim (int): The number of input features. hidden_dims (list of int): List containing the number of units in each hidden layer. output_dim (int): The number of output units. activation (str): The non-linear activation function to use. Options: 'relu', 'tanh', 'sigmoid', 'leaky_relu', 'elu', 'selu', 'none'. init_method (str): The weight initialization strategy. Options: 'kaiming', 'xavier', 'normal', 'orthogonal'. output_init_scale (float): Scale for uniform initialization of output layer weights. """ def __init__( self, input_dim: int, hidden_dims: list[int], output_dim: int, activation: str = "relu", init_method: str = "kaiming", output_init_scale: float = 3e-3, ): super().__init__() self.input_dim = input_dim self.hidden_dims = hidden_dims self.output_dim = output_dim self.activation_name = activation.lower() self.init_method = init_method.lower() self.output_init_scale = float(output_init_scale) layers = [] current_dim = input_dim for h_dim in hidden_dims: layers.append(nn.Linear(current_dim, h_dim)) act_layer = self._get_activation(self.activation_name) if act_layer is not None: layers.append(act_layer) current_dim = h_dim layers.append(nn.Linear(current_dim, output_dim)) self.network = nn.Sequential(*layers) self.apply(self.init_weights) def _get_activation(self, name: str): """ Factory method to return a *fresh* activation layer instance based on string name. Available options: 'relu', 'tanh', 'sigmoid', 'leaky_relu', 'elu', 'selu', 'none'. """ name = name.lower() if name == "relu": return nn.ReLU() if name == "tanh": return nn.Tanh() if name == "sigmoid": return nn.Sigmoid() if name == "leaky_relu": return nn.LeakyReLU(0.2) if name == "elu": return nn.ELU() if name == "selu": return nn.SELU() if name == "none": return None return nn.ReLU() def init_weights(self, m: nn.Module): """ Initialize weights for Linear layers. Hidden layers follow init_method. Output layer uses small uniform init (±output_init_scale) to keep initial outputs near 0. """ if not isinstance(m, nn.Linear): return # Identify the output layer by matching out_features to the requested output_dim # (works because only the last Linear has out_features == self.output_dim in this MLP) is_output_layer = m.out_features == self.output_dim if is_output_layer: init.uniform_(m.weight, -self.output_init_scale, self.output_init_scale) else: if self.init_method == "kaiming": if self.activation_name == "leaky_relu": init.kaiming_normal_(m.weight, a=0.2, nonlinearity="leaky_relu") else: init.kaiming_normal_(m.weight, nonlinearity="relu") elif self.init_method == "xavier": init.xavier_normal_(m.weight) elif self.init_method == "normal": init.normal_(m.weight, mean=0.0, std=0.02) elif self.init_method == "orthogonal": init.orthogonal_(m.weight) if m.bias is not None: init.constant_(m.bias, 0.0) def forward(self, x): return self.network(x) ================================================ FILE: verl/experimental/vla/models/openvla_oft/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/vla/models/openvla_oft/configuration_prismatic.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from https://github.com/PRIME-RL/SimpleVLA-RL/blob/main/verl/utils/vla_utils/openvla_oft/ # form https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero10-trajall/blob/main/ """ configuration_prismatic.py HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`. Default configuration specifies `siglip-224px+7b`. """ from typing import Any, Optional from transformers import PretrainedConfig from transformers.models.auto import CONFIG_MAPPING # === Utilities for Mapping Prismatic names to HF names === # fmt: off VISION_BACKBONE_TO_RESOLUTION: dict[str, list[int]] = { "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224], "clip-vit-l-336px": [336], "siglip-vit-so400m-384px": [384], "dinoclip-vit-l-336px": [336, 336], "dinosiglip-vit-so-224px": [224, 224], "dinosiglip-vit-so-384px": [384, 384], } VISION_BACKBONE_TO_TIMM_ID: dict[str, list[str]] = { "clip-vit-l": ["vit_large_patch14_clip_224.openai"], "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"], "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"], "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"], "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"], "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"], "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"], "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"], "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"], } TIMM_OVERRIDE_ACT_LAYER: dict[str, list[Optional[str]]] = { "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"], "dinov2-vit-l": [None], "in1k-vit-l": [None], "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None], "dinoclip-vit-l-336px": [None, "quick_gelu"], "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None] } LLM_BACKBONE_TO_HF_PATH = { "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf", "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf", "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5", "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1", "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1", "phi-2-3b": "microsoft/phi-2", } LLM_BACKBONE_TO_HF_METACLASS = { "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama", "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama", "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral", "phi-2-3b": "phi", } VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys()) VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH) # fmt: on class PrismaticConfig(PretrainedConfig): model_type: str = "prismatic" is_composition: bool = False def __init__( self, vision_backbone_id: str = "siglip-vit-so400m", llm_backbone_id: str = "vicuna-v15-7b", arch_specifier: str = "no-align+gelu-mlp", use_fused_vision_backbone: Optional[bool] = None, image_resize_strategy: str = "letterbox", text_config: Optional[dict[str, Any]] = None, llm_max_length: int = 2048, pad_token_id: int = 32000, pad_to_multiple_of: int = 64, output_projector_states: bool = False, **kwargs: str, ) -> None: if vision_backbone_id not in VALID_VISION_BACKBONES: raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }") if llm_backbone_id not in VALID_LLM_BACKBONES: raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }") # Set Prismatic Configuration Fields self.vision_backbone_id = vision_backbone_id self.llm_backbone_id = llm_backbone_id self.arch_specifier = arch_specifier self.output_projector_states = output_projector_states # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing self.use_fused_vision_backbone = ( use_fused_vision_backbone if use_fused_vision_backbone is not None else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"]) ) self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id] self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id] self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id] self.image_resize_strategy = image_resize_strategy self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id] self.llm_max_length = llm_max_length self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming! self.text_config = ( CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config) if text_config is not None else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]() ) # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well... super().__init__(pad_token_id=pad_token_id, **kwargs) class OpenVLAConfig(PrismaticConfig): model_type: str = "openvla" def __init__( self, norm_stats: Optional[dict[str, dict[str, dict[str, dict[str, list[float]]]]]] = None, n_action_bins: int = 256, **kwargs: str, ) -> None: self.norm_stats, self.n_action_bins = norm_stats, n_action_bins super().__init__(**kwargs) ================================================ FILE: verl/experimental/vla/models/openvla_oft/constants.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from https://github.com/PRIME-RL/SimpleVLA-RL/blob/main/verl/utils/vla_utils/openvla_oft/ """ Important constants for VLA training and evaluation. Attempts to automatically identify the correct constants to set based on the Python command used to launch training or evaluation. If it is unclear, defaults to using the LIBERO simulation benchmark constants. """ import sys from enum import Enum # Llama 2 token constants IGNORE_INDEX = -100 ACTION_TOKEN_BEGIN_IDX = 31743 STOP_INDEX = 2 # '' # Defines supported normalization schemes for action and proprioceptive state. class NormalizationType(str, Enum): # fmt: off NORMAL = "normal" # Normalize to Mean = 0, Stdev = 1 BOUNDS = "bounds" # Normalize to Interval = [-1, 1] BOUNDS_Q99 = "bounds_q99" # Normalize [quantile_01, ..., quantile_99] --> [-1, ..., 1] # fmt: on # Define constants for each robot platform LIBERO_CONSTANTS = { "NUM_ACTIONS_CHUNK": 8, "ACTION_DIM": 7, "PROPRIO_DIM": 8, "ACTION_PROPRIO_NORMALIZATION_TYPE": NormalizationType.BOUNDS_Q99, } ALOHA_CONSTANTS = { "NUM_ACTIONS_CHUNK": 25, "ACTION_DIM": 14, "PROPRIO_DIM": 14, "ACTION_PROPRIO_NORMALIZATION_TYPE": NormalizationType.BOUNDS, } BRIDGE_CONSTANTS = { "NUM_ACTIONS_CHUNK": 5, "ACTION_DIM": 7, "PROPRIO_DIM": 7, "ACTION_PROPRIO_NORMALIZATION_TYPE": NormalizationType.BOUNDS_Q99, } # Function to detect robot platform from command line arguments def detect_robot_platform(): cmd_args = " ".join(sys.argv).lower() if "libero" in cmd_args: return "LIBERO" elif "aloha" in cmd_args: return "ALOHA" elif "bridge" in cmd_args: return "BRIDGE" else: # Default to LIBERO if unclear return "LIBERO" # Determine which robot platform to use ROBOT_PLATFORM = detect_robot_platform() # Set the appropriate constants based on the detected platform if ROBOT_PLATFORM == "LIBERO": constants = LIBERO_CONSTANTS elif ROBOT_PLATFORM == "ALOHA": constants = ALOHA_CONSTANTS elif ROBOT_PLATFORM == "BRIDGE": constants = BRIDGE_CONSTANTS # Assign constants to global variables NUM_ACTIONS_CHUNK = constants["NUM_ACTIONS_CHUNK"] ACTION_DIM = constants["ACTION_DIM"] PROPRIO_DIM = constants["PROPRIO_DIM"] ACTION_PROPRIO_NORMALIZATION_TYPE = constants["ACTION_PROPRIO_NORMALIZATION_TYPE"] # Print which robot platform constants are being used (for debugging) print(f"Using {ROBOT_PLATFORM} constants:") print(f" NUM_ACTIONS_CHUNK = {NUM_ACTIONS_CHUNK}") print(f" ACTION_DIM = {ACTION_DIM}") print(f" PROPRIO_DIM = {PROPRIO_DIM}") print(f" ACTION_PROPRIO_NORMALIZATION_TYPE = {ACTION_PROPRIO_NORMALIZATION_TYPE}") print("If needed, manually set the correct constants in `prismatic/vla/constants.py`!") ================================================ FILE: verl/experimental/vla/models/openvla_oft/modeling_prismatic.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from https://github.com/PRIME-RL/SimpleVLA-RL/blob/main/verl/utils/vla_utils/openvla_oft/ # form https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero10-trajall/blob/main/ """ modeling_prismatic.py Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions. Inherits from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the logic in `prismatic.models.vlms.prismatic.py`. """ import logging from dataclasses import dataclass from functools import partial from typing import Any, Callable, ClassVar, Optional import numpy as np import timm import tokenizers import torch import torch.nn as nn import transformers from timm.models.vision_transformer import LayerScale from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel from transformers.modeling_outputs import ModelOutput from .configuration_prismatic import OpenVLAConfig, PrismaticConfig from .constants import ( ACTION_DIM, ACTION_PROPRIO_NORMALIZATION_TYPE, ACTION_TOKEN_BEGIN_IDX, IGNORE_INDEX, NUM_ACTIONS_CHUNK, STOP_INDEX, NormalizationType, ) from .train_utils import ( get_current_action_mask, get_next_actions_mask, ) # Set up logger logger = logging.getLogger(__name__) # === Utility Functions for Monkey-Patching === def unpack_tuple(fn: Callable[[Any], tuple[Any]]) -> Callable[[Any], Any]: def wrapper(*args: Any, **kwargs: Any) -> Any: result = fn(*args, **kwargs) return result[0] if isinstance(result, tuple) else result return wrapper # HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale. # =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109 # =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960 def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor: return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor def ls_apply_patch(ls_module: LayerScale): ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone()) ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale) del ls_module.gamma # === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) === class PrismaticVisionBackbone(nn.Module): """ Vision backbone for Prismatic models that handles image feature extraction. Supports both single backbone (e.g., SigLIP) and fused backbone (e.g., SigLIP + DINOv2) configurations. For fused backbones, features from both models are concatenated along the feature dimension. """ def __init__( self, use_fused_vision_backbone: bool, image_sizes: list[int], timm_model_ids: list[str], timm_override_act_layers: list[Optional[str]], ) -> None: """ Initialize the vision backbone. Args: use_fused_vision_backbone: Whether to use two backbones and fuse their features image_sizes: List of image sizes for each backbone timm_model_ids: List of TIMM model IDs to use for each backbone timm_override_act_layers: List of activation layer overrides for each backbone """ super().__init__() self.use_fused_vision_backbone = use_fused_vision_backbone self.num_images_in_input = 1 # Default value, can be overridden later # Validate number of (fused) vision backbones if len(timm_model_ids) > 2: raise ValueError("Prismatic models only support up to 2 (fused) vision backbones!") # Create primary featurizer self.featurizer = self._create_featurizer( model_id=timm_model_ids[0], img_size=image_sizes[0], act_layer=timm_override_act_layers[0] ) self.embed_dim = self.featurizer.embed_dim # Create secondary featurizer if using fused backbone if self.use_fused_vision_backbone: self.fused_featurizer = self._create_featurizer( model_id=timm_model_ids[1], img_size=image_sizes[1], act_layer=timm_override_act_layers[1] ) self.embed_dim += self.fused_featurizer.embed_dim # Patch LayerScale modules for HF compatibility self._patch_layer_scales() def _create_featurizer(self, model_id: str, img_size: int, act_layer: Optional[str]) -> nn.Module: """ Create a TIMM-based featurizer model with appropriate configurations. Args: model_id: The TIMM model ID to load img_size: Input image size for the model act_layer: Override for the activation layer type Returns: A configured featurizer model """ featurizer = timm.create_model( model_id, pretrained=False, num_classes=0, img_size=img_size, act_layer=act_layer, ) # Monkey-patch the forward function to extract the second-to-last layer features num_blocks = len(featurizer.blocks) featurizer.forward = unpack_tuple(partial(featurizer.get_intermediate_layers, n={num_blocks - 2})) return featurizer def _patch_layer_scales(self) -> None: """ Patch all LayerScale modules to be compatible with HF's parameter naming. HF Transformers overwrites parameters with names containing 'gamma', so we need to rename and modify the forward method. """ # Patch primary featurizer for module in self.featurizer.modules(): if isinstance(module, LayerScale): ls_apply_patch(module) # Patch secondary featurizer if it exists if self.use_fused_vision_backbone: for module in self.fused_featurizer.modules(): if isinstance(module, LayerScale): ls_apply_patch(module) def get_num_patches(self) -> int: """ Returns the number of vision patches output by the vision backbone. Returns: Number of patches per image """ return self.featurizer.patch_embed.num_patches def get_num_images_in_input(self) -> int: """ Returns the number of input images for the vision backbone. Returns: Number of images expected in the input """ return self.num_images_in_input def set_num_images_in_input(self, num_images_in_input: int) -> None: """ Sets the number of input images for the vision backbone. Args: num_images_in_input: Number of images to expect in the input """ self.num_images_in_input = num_images_in_input def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: """ Implements the forward pass for the vision backbone. If `self.use_fused_vision_backbone == True`, uses both SigLIP and DINOv2 transformers to extract visual features (otherwise uses SigLIP only). Allows multi-image inputs (but only for fused vision backbone). Args: pixel_values (torch.Tensor): Pixels for input image(s), (B, C, H, W). """ if self.num_images_in_input == 1: if not self.use_fused_vision_backbone: return self.featurizer(pixel_values) # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack img, img_fused = torch.split(pixel_values, [3, 3], dim=1) patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused) return torch.cat([patches, patches_fused], dim=2) else: assert self.use_fused_vision_backbone, "Multi-image inputs require using fused backbone!" # Split `pixel_values` into individual images (each with 6 channels: 3 for SigLIP + 3 for DINOv2) images = torch.split(pixel_values, [6] * self.num_images_in_input, dim=1) # Process each image and collect patches all_patches = [] for img in images: # Split each image further into two stacks of channels (each with 3 channels) img_regular, img_fused = torch.split(img, [3, 3], dim=1) # Get patches from both SigLIP and DINOv2 vision transformers patches = self.featurizer(img_regular) patches_fused = self.fused_featurizer(img_fused) # Concatenate SigLIP and DINOv2 patches along the hidden dimension combined_patches = torch.cat([patches, patches_fused], dim=2) all_patches.append(combined_patches) # Concatenate all patches along the patch dimension return torch.cat(all_patches, dim=1) # === Prismatic Projector (nn.Module) Definitions === class PrismaticProjector(nn.Module): def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None: super().__init__() self.use_fused_vision_backbone = use_fused_vision_backbone self.vision_dim, self.llm_dim = vision_dim, llm_dim # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors! if not self.use_fused_vision_backbone: self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True) self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True) self.act_fn1 = nn.GELU() else: initial_projection_dim = 4 * vision_dim self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True) self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True) self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True) self.act_fn1 = nn.GELU() self.act_fn2 = nn.GELU() def forward(self, img_patches: torch.Tensor) -> torch.Tensor: if not self.use_fused_vision_backbone: projected_features = self.fc1(img_patches) projected_features = self.act_fn1(projected_features) projected_features = self.fc2(projected_features) else: projected_features = self.fc1(img_patches) projected_features = self.act_fn1(projected_features) projected_features = self.fc2(projected_features) projected_features = self.act_fn2(projected_features) projected_features = self.fc3(projected_features) return projected_features # === Main HF Class Definitions === @dataclass class PrismaticCausalLMOutputWithPast(ModelOutput): """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features.""" loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None attentions: Optional[tuple[torch.FloatTensor]] = None # Additions for VLMs projector_features: Optional[torch.FloatTensor] = None class PrismaticPreTrainedModel(PreTrainedModel): config_class: PretrainedConfig = PrismaticConfig base_model_prefix: str = "model" supports_gradient_checkpointing: bool = True _no_split_modules: ClassVar[list[str]] = ["PrismaticProjector"] _skip_keys_device_placement: str = "past_key_values" _supports_flash_attn_2: bool = True def _init_weights(self, module: nn.Module) -> None: # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning! # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at # https://github.com/TRI-ML/prismatic-vlms std = ( self.config.initializer_range if hasattr(self.config, "initializer_range") else self.config.text_config.initializer_range ) if hasattr(module, "class_embedding"): module.class_embedding.data.normal_(mean=0.0, std=std) if isinstance(module, (nn.Linear | nn.Conv2d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() @property def _supports_sdpa(self) -> bool: """Check LLM supports SDPA Attention""" return self.language_model._supports_sdpa class PrismaticForConditionalGeneration(PrismaticPreTrainedModel): def __init__(self, config: PrismaticConfig) -> None: super().__init__(config) # [Validation] Lightweight Validate on `config` Fields + Dependency Versions if config.use_fused_vision_backbone is None: raise ValueError("Missing config field `use_fused_vision_backbone`") if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}: raise NotImplementedError( "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue " "if you urgently need support for latest TIMM versions." ) if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"): logger.warning( f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got " f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; " f"there might be inference-time regressions due to dependency changes. If in doubt, please" f"use the above versions." ) # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone) self.vision_backbone = PrismaticVisionBackbone( config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers ) # Create Multimodal Projector self.projector = PrismaticProjector( config.use_fused_vision_backbone, vision_dim=self.vision_backbone.embed_dim, llm_dim=config.text_config.hidden_size, ) # Instantiate LLM Backbone self.language_model = AutoModelForCausalLM.from_config( config.text_config, attn_implementation=config._attn_implementation ) self.vocab_size = config.text_config.vocab_size self.pad_token_id = config.pad_token_id self.llm_dim = config.text_config.hidden_size # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing self.post_init() # === `PreTrainedModel` Boilerplate === def get_input_embeddings(self) -> nn.Module: return self.language_model.get_input_embeddings() def set_input_embeddings(self, value: nn.Module) -> None: self.language_model.set_input_embeddings(value) def get_output_embeddings(self) -> nn.Module: return self.language_model.get_output_embeddings() def set_output_embeddings(self, new_embeddings: nn.Module) -> None: self.language_model.set_output_embeddings(new_embeddings) def get_decoder(self) -> nn.Module: return self.language_model.get_decoder() def set_decoder(self, decoder: nn.Module) -> None: self.language_model.set_decoder(decoder) def tie_weights(self) -> None: self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op) def resize_token_embeddings( self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None ) -> nn.Embedding: updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) # Update config/instance variables self.config.text_config.vocab_size = updated_embeddings.num_embeddings self.vocab_size = updated_embeddings.num_embeddings return updated_embeddings def _replace_input_embeddings(self, input_embeddings, all_actions_mask, noisy_action_features): """ Replace embeddings in input_embeddings at positions where all_actions_mask is True with embeddings from noisy_action_features, using vectorized operations. Args: input_embeddings: Tensor of shape (B, S, D) all_actions_mask: Boolean tensor of shape (B, S) noisy_action_features: Tensor of shape (B, K, D) where K is the number of True values in mask per sample Returns: Modified input_embeddings tensor """ # Clone input to avoid modifying the original tensor new_input_embeddings = input_embeddings.clone() # Create a tensor with the same shape of input_embeddings to hold the noisy action features repositioned_noisy_action_features = torch.zeros_like(input_embeddings) # Create batch indices for splicing batch_indices = torch.arange(input_embeddings.shape[0], device=input_embeddings.device) batch_indices = batch_indices.unsqueeze(1).expand(-1, noisy_action_features.shape[1]) # Get indices where mask is True for each sample masked_indices = torch.stack([torch.where(mask)[0] for mask in all_actions_mask]) # Move the noisy action features into their correct positions repositioned_noisy_action_features[batch_indices, masked_indices] = noisy_action_features # Combine original input embeddings and noisy action embeddings using the mask new_input_embeddings = torch.where( all_actions_mask.unsqueeze(-1), repositioned_noisy_action_features, new_input_embeddings ) return new_input_embeddings def _process_action_masks(self, labels): """Helper to get action masks from labels""" current_action_mask = get_current_action_mask(labels) next_actions_mask = get_next_actions_mask(labels) all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len) return all_actions_mask def _process_vision_features(self, pixel_values, language_embeddings=None, use_film=False): """Process vision features with optional FiLM conditioning""" if use_film: # FiLM: Infuse language inputs into visual features patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D) else: patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D) # Project patch embeddings into language embedding space return self.projector(patch_features) def _process_proprio_features(self, projected_patch_embeddings, proprio, proprio_projector): """Process proprioceptive features and append to vision features""" if proprio_projector is not None and proprio is not None: # projected_patch_embeddings: (bsz, num_patches * num_images, llm_dim) # proprio: (bsz, proprio_dim) or (propro_dim,) proprio = proprio.reshape(projected_patch_embeddings.shape[0], -1) # (bsz, proprio_dim) proprio_features = proprio_projector(proprio) # (bsz, llm_dim) proprio_features = proprio_features.unsqueeze(dim=1) # (bsz, 1, llm_dim) # For simplicity, just append proprio token to the end of projected vision patch tokens return torch.cat((projected_patch_embeddings, proprio_features), dim=1) return projected_patch_embeddings def _build_multimodal_attention(self, input_embeddings, projected_patch_embeddings, attention_mask): """Build multimodal embeddings and attention mask""" # Update attention mask projected_patch_attention_mask = None if attention_mask is not None: projected_patch_attention_mask = torch.full( (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]), fill_value=True, dtype=attention_mask.dtype, device=attention_mask.device, ) # Build multimodal embeddings & attention mask; insert embeddings after token (1:) multimodal_embeddings = torch.cat( [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1 ) multimodal_attention_mask = None if attention_mask is not None: multimodal_attention_mask = torch.cat( [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1 ) return multimodal_embeddings, multimodal_attention_mask def _build_multimodal_labels(self, labels, projected_patch_embeddings): """Build multimodal labels with IGNORE_INDEX for patch embeddings""" if labels is not None: projected_patch_labels = torch.full( (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]), fill_value=IGNORE_INDEX, dtype=labels.dtype, device=labels.device, ) return torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1) return None # === Core Prismatic VLM `forward()` Logic === # def forward( # self, # input_ids: Optional[torch.LongTensor] = None, # attention_mask: Optional[torch.Tensor] = None, # pixel_values: Optional[torch.FloatTensor] = None, # labels: Optional[torch.LongTensor] = None, # inputs_embeds: Optional[torch.FloatTensor] = None, # past_key_values: Optional[List[torch.FloatTensor]] = None, # use_cache: Optional[bool] = None, # output_attentions: Optional[bool] = None, # output_hidden_states: Optional[bool] = None, # output_projector_features: Optional[bool] = None, # return_dict: Optional[bool] = None, # proprio=None, # proprio_projector=None, # noisy_actions=None, # noisy_action_projector=None, # diffusion_timestep_embeddings=None, # use_film: bool = False, # ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]: # """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance.""" # output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions # output_hidden_states = ( # output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states # ) # output_projector_features = output_projector_features if output_projector_features is not None else False # return_dict = return_dict if return_dict is not None else self.config.use_return_dict # # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off) # use_cache = use_cache and not self.training # # Instantiate Placeholder for Projector Features # projected_patch_embeddings = None # # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` === # if input_ids.shape[1] == 1: # assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!" # assert past_key_values is not None, "You must provide `past_key_values` during cached generation!" # assert labels is None, "Unexpected key `labels` provided during cached generation!" # language_model_output = self.language_model( # input_ids=input_ids, # attention_mask=None, # position_ids=None, # past_key_values=past_key_values, # inputs_embeds=None, # labels=None, # use_cache=use_cache, # output_attentions=output_attentions, # output_hidden_states=output_hidden_states, # return_dict=return_dict, # ) # # === Handle Unimodal Forward === # elif pixel_values is None: # assert (input_ids is not None) and (inputs_embeds is None), \ # "Missing `input_ids` in language-only forward!" # assert past_key_values is None, \ # "Unexpected key `past_key_values` provided during language-only forward!" # language_model_output = self.language_model( # input_ids=input_ids, # attention_mask=attention_mask, # position_ids=None, # past_key_values=None, # inputs_embeds=None, # labels=labels, # use_cache=use_cache, # output_attentions=output_attentions, # output_hidden_states=output_hidden_states, # return_dict=return_dict, # ) # # === Handle Multimodal Forward === # elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]): # assert past_key_values is None, "Unexpected key `past_key_values` provided during multimodal forward!" # #test # # #test end # # Get input embeddings (from language model embeddings) # input_embeddings = self.get_input_embeddings()(input_ids) # (B, seq_len, D) # # Extract action masks # all_actions_mask = self._process_action_masks(labels) # # Extract the language portion of the input embeddings (i.e. remove the action tokens portion) # language_embeddings = input_embeddings[~all_actions_mask].reshape( # input_embeddings.shape[0], -1, input_embeddings.shape[2] # ) # (B, lang_seq_len, llm_dim) # # Get visual features # projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film) # # Add proprioceptive state if provided # projected_patch_embeddings = self._process_proprio_features( # projected_patch_embeddings, proprio, proprio_projector # ) # # [Diffusion] Add diffusion timestep embedding if provided # if diffusion_timestep_embeddings is not None: # # For simplicity, just append diffusion timestep embedding to the end of projected vision patch tokens # projected_patch_embeddings = torch.cat( # (projected_patch_embeddings, diffusion_timestep_embeddings), dim=1 # ) # # Process action embeddings # if noisy_actions is not None: # # Get mask corresponding to all action tokens # all_actions_mask = self._process_action_masks(labels) # # Reshape noisy actions into individual action tokens # # noisy_actions: (B, chunk_len, action_dim) -> (B, chunk_len * action_dim, 1) # B = noisy_actions.shape[0] # noisy_actions = noisy_actions.reshape(B, -1).unsqueeze(-1) # # Project noisy action tokens into language model embedding space # noisy_action_features = noisy_action_projector(noisy_actions) # (B, chunk_len * action_dim, llm_dim) # # Replace embeddings of the action tokens with noisy action embeddings # input_embeddings = self._replace_input_embeddings( # input_embeddings, all_actions_mask, noisy_action_features # ) # else: # # Replace the embeddings of the action tokens with zeros # # (Later on, the positional embeddings will be added to them) # all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1) # input_embeddings = input_embeddings * ~all_actions_mask # # Build multimodal embeddings & attention mask # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( # input_embeddings, projected_patch_embeddings, attention_mask # ) # # Build labels for multimodal sequence if needed # multimodal_labels = self._build_multimodal_labels(labels, projected_patch_embeddings) # # Dispatch to language model # language_model_output = self.language_model( # input_ids=None, # attention_mask=multimodal_attention_mask, # position_ids=None, # past_key_values=None, # inputs_embeds=multimodal_embeddings, # labels=multimodal_labels, # use_cache=use_cache, # output_attentions=output_attentions, # output_hidden_states=output_hidden_states, # return_dict=return_dict, # ) # # === Otherwise =>> Assume Invalid! === # elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]): # raise ValueError("Non-homogenous batch of (text, image) input \ # -- forward() does not support mixed batches!") # else: # raise ValueError( # "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n" # f"=> `input_ids` = {input_ids is not None}\n" # f"=> `attention_mask` = {attention_mask is not None}\n" # f"=> `pixel_values` = {pixel_values is not None}\n" # f"=> `labels` = {labels is not None}\n" # f"=> `input_embeds` = {inputs_embeds is not None}\n" # f"=> `past_key_values` = {past_key_values is not None}\n" # f"=> `use_cache` = {use_cache}" # ) # # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`) # if not return_dict: # if output_projector_features and (projected_patch_embeddings is not None): # return *language_model_output, projected_patch_embeddings # return language_model_output # return PrismaticCausalLMOutputWithPast( # loss=language_model_output.loss, # logits=language_model_output.logits, # past_key_values=language_model_output.past_key_values, # hidden_states=language_model_output.hidden_states, # attentions=language_model_output.attentions, # projector_features=projected_patch_embeddings, # ) # === GenerationMixin Methods === def prepare_inputs_for_generation( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[list[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, **kwargs: str, ) -> dict[str, torch.Tensor]: """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic.""" if ((input_ids is not None) and (input_ids.shape[0] > 1)) or ( (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1) ): raise ValueError("Generation with batch size > 1 is not currently supported!") # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens if past_key_values is not None: input_ids = input_ids[:, -1:] # If `input_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"input_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} # Make sure `pixel_values` are preserved in `model_inputs` model_inputs.update( { "attention_mask": attention_mask, "pixel_values": pixel_values, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), } ) return model_inputs # Defer to Language Model (all handle this differently, with different return types) def _reorder_cache(self, *args, **kwargs) -> Any: return self.language_model._reorder_cache(*args, **kwargs) def _prepare_input_for_action_prediction_verl(self, input_ids, attention_mask): """Prepares input for action prediction by adding necessary tokens""" # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens placeholder_action_token_ids = ( torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype) ) input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1) # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time) stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX input_ids = torch.cat([input_ids, stop_token_id], dim=-1) # Extend the attention mask to fit the new shape of input # Note: Only batch size == 1 supported right now mask_extension = ( torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1])) .to(attention_mask.device) .to(attention_mask.dtype) ) attention_mask = torch.cat([attention_mask, mask_extension], dim=-1) return input_ids, attention_mask def _prepare_labels_for_action_prediction_verl(self, labels, input_ids): """Creates labels tensor for action prediction if not provided""" # Extend labels tensor with fake action labels ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1 labels_extension = ( torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype) * ARBITRARY_ACTION_TOKEN_IDX ) labels = torch.cat([labels, labels_extension], dim=-1) # Replace last label token with stop token labels[:, -1] = STOP_INDEX return labels def _verl_discrete_compute_logits( self, input_embeddings, all_actions_mask, projected_patch_embeddings, attention_mask, labels, NUM_PATCHES, NUM_PROMPT_TOKENS, action_head=None, ): # contintue!!!!! """Run L1 regression-based continuous action prediction or discrete action tokens prediction.""" # Zero out action token embeddings all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1) input_embeddings = input_embeddings * ~all_actions_mask # Build multimodal embeddings and attention mask multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( input_embeddings, projected_patch_embeddings, attention_mask ) # Forward pass through language model language_model_output = self.language_model( input_ids=None, attention_mask=multimodal_attention_mask, position_ids=None, past_key_values=None, inputs_embeds=multimodal_embeddings, labels=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ) # Extract hidden states for action tokens # last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D) # actions_hidden_states = last_hidden_states[ # :, # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, # :, # ] # (B, act_chunk_len, D) # Handle different prediction methods # if action_head is not None: # # L1 regression prediction # normalized_actions = action_head.predict_action(actions_hidden_states) # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM) # normalized_actions = normalized_actions.float().cpu().detach().numpy() # else: # Discrete token-based prediction compute_logits = language_model_output.logits[ :, NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, ] return compute_logits # def forward( # self, # input_ids: Optional[torch.LongTensor] = None, # unnorm_key: Optional[str] = None, # proprio=None, # proprio_projector=None, # action_head=None, # noisy_action_projector=None, # use_film: bool = False, # **kwargs: str, # ) : # """Predict actions from input sequence, with options for different prediction methods. # Args: # input_ids: Input token ids # unnorm_key: Key for unnormalization statistics # proprio: Proprioceptive features # proprio_projector: Projector for proprioceptive features # action_head: Optional head for L1 regression or diffusion-based prediction # noisy_action_projector: Projector for noisy actions in diffusion-based prediction # use_film: Whether to use FiLM conditioning # **kwargs: Additional arguments including pixel_values and attention_mask # Returns: # Tuple of (unnormalized_actions, action_hidden_states) # """ # # If the special empty token ('') does not already appear after the colon (':') token in the prompt # # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time # # if not torch.all(input_ids[:, -1] == 29871): # # input_ids = torch.cat( # # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1 # # ) # #print("!!!!!!!!!!!!!!Entering forward!!!!!!!!!!") # pixel_values = kwargs["pixel_values"] # attention_mask = kwargs["attention_mask"] # # Create fake labels tensor (needed for action mask) # labels = input_ids.clone() # labels[:] = IGNORE_INDEX # # Get number of tokens in prompt (excluding the start token) # NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token # # Prepare inputs by adding necessary tokens # #input_ids, attention_mask = self._prepare_input_for_action_prediction_verl(input_ids, attention_mask) # #test # placeholder_action_token_ids = ( # torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype) # ) # input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1) # # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time) # stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX # input_ids = torch.cat([input_ids, stop_token_id], dim=-1) # # Extend the attention mask to fit the new shape of input # # Note: Only batch size == 1 supported right now # mask_extension = ( # torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1])) # .to(attention_mask.device) # .to(attention_mask.dtype) # ) # attention_mask = torch.cat([attention_mask, mask_extension], dim=-1) # #return input_ids, attention_mask # #test end # # Update labels tensor for action mask computation later # #labels = self._prepare_labels_for_action_prediction_verl(labels, input_ids) # #test # ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1 # labels_extension = ( # torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype) # * ARBITRARY_ACTION_TOKEN_IDX # ) # labels = torch.cat([labels, labels_extension], dim=-1) # # Replace last label token with stop token # labels[:, -1] = STOP_INDEX # #return labels # #test ed # # Get input embeddings and action masks # input_embeddings = self.get_input_embeddings()(input_ids) # #all_actions_mask = self._process_action_masks(labels) # #test # #current_action_mask = get_current_action_mask(labels) # newline_positions = labels != IGNORE_INDEX # # Calculate cumulative sum to identify regions between newlines # cumsum = torch.cumsum(newline_positions, dim=1) # # Create the mask # mask = (1 <= cumsum) & (cumsum <= ACTION_DIM) # # Extract the action part only # action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX # current_action_mask = action_tokens_only_mask * mask # #next_actions_mask = get_next_actions_mask(labels) # newline_positions = labels != IGNORE_INDEX # # Calculate cumulative sum to identify regions between newlines # cumsum = torch.cumsum(newline_positions, dim=1) # # Create the mask # mask = cumsum > ACTION_DIM # # Extract the action part only # action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX # next_actions_mask = action_tokens_only_mask * mask # all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len) # #test end # # Extract language embeddings # language_embeddings = input_embeddings[~all_actions_mask].reshape( # input_embeddings.shape[0], -1, input_embeddings.shape[2] # ) # # Process vision features # #projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film) # #test # if use_film: # # FiLM: Infuse language inputs into visual features # raise ValueError # patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D) # else: # patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D) # projected_patch_embeddings = self.projector(patch_features) # #test end # # Add proprioceptive features if provided # use_proprio = proprio_projector is not None and proprio is not None # if use_proprio: # proprio = torch.Tensor(proprio).to(projected_patch_embeddings.device, # dtype=projected_patch_embeddings.dtype) # projected_patch_embeddings = self._process_proprio_features( # projected_patch_embeddings, proprio, proprio_projector # ) # # Use diffusion if provided, otherwise use regression or discrete prediction # use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler") # # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present) # NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input() # if use_proprio: # NUM_PATCHES += 1 # if use_diffusion: # NUM_PATCHES += 1 # if use_diffusion: # raise ValueError # # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion # noise = torch.randn( # size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype # ) # # Run diffusion-based prediction # normalized_actions, actions_hidden_states = self._run_diffusion_prediction( # input_embeddings, # all_actions_mask, # noise, # action_head, # projected_patch_embeddings, # labels, # attention_mask, # NUM_PATCHES, # NUM_PROMPT_TOKENS, # noisy_action_projector, # ) # else: # # Run regression or discrete token-based prediction # # compute_logits = self._verl_discrete_compute_logits( # # input_embeddings, # # all_actions_mask, # # projected_patch_embeddings, # # attention_mask, # # labels, # # NUM_PATCHES, # # NUM_PROMPT_TOKENS, # # action_head, # # ) # #test # all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1) # input_embeddings = input_embeddings * ~all_actions_mask # # Build multimodal embeddings and attention mask # # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( # # input_embeddings, projected_patch_embeddings, attention_mask # # ) # #test # projected_patch_attention_mask = None # if attention_mask is not None: # projected_patch_attention_mask = torch.full( # (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]), # fill_value=True, # dtype=attention_mask.dtype, # device=attention_mask.device, # ) # # Build multimodal embeddings & attention mask; insert embeddings after token (1:) # multimodal_embeddings = torch.cat( # [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1 # ) # multimodal_attention_mask = None # if attention_mask is not None: # multimodal_attention_mask = torch.cat( # [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1 # ) # #return multimodal_embeddings, multimodal_attention_mask # #test end # # Forward pass through language model # language_model_output = self.language_model( # input_ids=None, # attention_mask=multimodal_attention_mask, # position_ids=None, # past_key_values=None, # inputs_embeds=multimodal_embeddings, # labels=None, # use_cache=None, # output_attentions=False, # output_hidden_states=False, # return_dict=True, # ) # compute_logits = language_model_output.logits[ # :, # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + \ # NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, # ] # #test end # return compute_logits def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values=None, attention_mask=None, # labels=None, proprio=None, proprio_projector=None, action_head=None, noisy_action_projector=None, use_film: bool = False, **kwargs: str, ): """Predict actions from input sequence, with options for different prediction methods. Args: input_ids: Input token ids unnorm_key: Key for unnormalization statistics proprio: Proprioceptive features proprio_projector: Projector for proprioceptive features action_head: Optional head for L1 regression or diffusion-based prediction noisy_action_projector: Projector for noisy actions in diffusion-based prediction use_film: Whether to use FiLM conditioning **kwargs: Additional arguments including pixel_values and attention_mask Returns: Tuple of (unnormalized_actions, action_hidden_states) """ # If the special empty token ('') does not already appear after the colon (':') token in the prompt # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time # if not torch.all(input_ids[:, -1] == 29871): # input_ids = torch.cat( # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1 # ) # pixel_values = kwargs["pixel_values"] # attention_mask = kwargs["attention_mask"] # Create fake labels tensor (needed for action mask) labels = input_ids.clone() labels[:] = IGNORE_INDEX # # Get number of tokens in prompt (excluding the start token) NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token # # Prepare inputs by adding necessary tokens # #input_ids, attention_mask = self._prepare_input_for_action_prediction_verl(input_ids, attention_mask) # #test placeholder_action_token_ids = ( torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype) ) input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1) # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time) stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX input_ids = torch.cat([input_ids, stop_token_id], dim=-1) # Extend the attention mask to fit the new shape of input # Note: Only batch size == 1 supported right now mask_extension = ( torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1])) .to(attention_mask.device) .to(attention_mask.dtype) ) attention_mask = torch.cat([attention_mask, mask_extension], dim=-1) ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1 labels_extension = ( torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype) * ARBITRARY_ACTION_TOKEN_IDX ) labels = torch.cat([labels, labels_extension], dim=-1) # # Replace last label token with stop token labels[:, -1] = STOP_INDEX # Get input embeddings and action masks # NUM_PROMPT_TOKENS = kwargs["num_prompt_tokens"] input_embeddings = self.get_input_embeddings()(input_ids) # all_actions_mask = self._process_action_masks(labels) # test # current_action_mask = get_current_action_mask(labels) newline_positions = labels != IGNORE_INDEX # Calculate cumulative sum to identify regions between newlines cumsum = torch.cumsum(newline_positions, dim=1) # Create the mask mask = (1 <= cumsum) & (cumsum <= ACTION_DIM) # Extract the action part only action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX current_action_mask = action_tokens_only_mask * mask # next_actions_mask = get_next_actions_mask(labels) newline_positions = labels != IGNORE_INDEX # Calculate cumulative sum to identify regions between newlines cumsum = torch.cumsum(newline_positions, dim=1) # Create the mask mask = cumsum > ACTION_DIM # Extract the action part only action_tokens_only_mask = labels > ACTION_TOKEN_BEGIN_IDX next_actions_mask = action_tokens_only_mask * mask all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len) # test end # Extract language embeddings language_embeddings = input_embeddings[~all_actions_mask].reshape( input_embeddings.shape[0], -1, input_embeddings.shape[2] ) # Process vision features # projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film) # test if use_film: # FiLM: Infuse language inputs into visual features raise ValueError patch_features = self.vision_backbone(pixel_values, language_embeddings) # (bsz, 256 * num_images, D) else: patch_features = self.vision_backbone(pixel_values) # (bsz, 256 * num_images, D) projected_patch_embeddings = self.projector(patch_features) # test end # Add proprioceptive features if provided use_proprio = proprio_projector is not None and proprio is not None if use_proprio: proprio = torch.Tensor(proprio).to( projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype ) projected_patch_embeddings = self._process_proprio_features( projected_patch_embeddings, proprio, proprio_projector ) # Use diffusion if provided, otherwise use regression or discrete prediction use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler") # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present) NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input() if use_proprio: NUM_PATCHES += 1 if use_diffusion: NUM_PATCHES += 1 if use_diffusion: raise ValueError # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion noise = torch.randn( size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype ) # Run diffusion-based prediction normalized_actions, actions_hidden_states = self._run_diffusion_prediction( input_embeddings, all_actions_mask, noise, action_head, projected_patch_embeddings, labels, attention_mask, NUM_PATCHES, NUM_PROMPT_TOKENS, noisy_action_projector, ) else: # Run regression or discrete token-based prediction # compute_logits = self._verl_discrete_compute_logits( # input_embeddings, # all_actions_mask, # projected_patch_embeddings, # attention_mask, # labels, # NUM_PATCHES, # NUM_PROMPT_TOKENS, # action_head, # ) # test all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1) input_embeddings = input_embeddings * ~all_actions_mask # Build multimodal embeddings and attention mask # multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( # input_embeddings, projected_patch_embeddings, attention_mask # ) # test projected_patch_attention_mask = None if attention_mask is not None: projected_patch_attention_mask = torch.full( (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]), fill_value=True, dtype=attention_mask.dtype, device=attention_mask.device, ) # Build multimodal embeddings & attention mask; insert embeddings after token (1:) multimodal_embeddings = torch.cat( [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1 ) multimodal_attention_mask = None if attention_mask is not None: multimodal_attention_mask = torch.cat( [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1 ) # return multimodal_embeddings, multimodal_attention_mask # test end # Forward pass through language model language_model_output = self.language_model( input_ids=None, attention_mask=multimodal_attention_mask, position_ids=None, past_key_values=None, inputs_embeds=multimodal_embeddings, labels=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ) compute_logits = language_model_output.logits[ :, NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, ] # test end return compute_logits class OpenVLAForActionPrediction(PrismaticForConditionalGeneration): config_class: PretrainedConfig = OpenVLAConfig _supports_sdpa = True def __init__(self, config: OpenVLAConfig) -> None: super().__init__(config) self.norm_stats = config.norm_stats # Compute action bins self.bins = np.linspace(-1, 1, config.n_action_bins) self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0 # Compute vocab size for de-tokenization -- revert added "multiple of" self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of def _prepare_input_for_action_prediction(self, input_ids, attention_mask): """Prepares input for action prediction by adding necessary tokens""" # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens placeholder_action_token_ids = ( torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype) ) input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1) # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time) stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * STOP_INDEX input_ids = torch.cat([input_ids, stop_token_id], dim=-1) # Extend the attention mask to fit the new shape of input # Note: Only batch size == 1 supported right now mask_extension = ( torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1])) .to(attention_mask.device) .to(attention_mask.dtype) ) attention_mask = torch.cat([attention_mask, mask_extension], dim=-1) return input_ids, attention_mask def _prepare_labels_for_action_prediction(self, labels, input_ids): """Creates labels tensor for action prediction if not provided""" # Extend labels tensor with fake action labels ARBITRARY_ACTION_TOKEN_IDX = ACTION_TOKEN_BEGIN_IDX + 1 labels_extension = ( torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype) * ARBITRARY_ACTION_TOKEN_IDX ) labels = torch.cat([labels, labels_extension], dim=-1) # Replace last label token with stop token labels[:, -1] = STOP_INDEX return labels def _unnormalize_actions(self, normalized_actions, unnorm_key=None): """Unnormalize actions using dataset statistics""" action_norm_stats = self.get_action_stats(unnorm_key) if ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS: mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["min"], dtype=bool)) action_high, action_low = np.array(action_norm_stats["max"]), np.array(action_norm_stats["min"]) elif ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS_Q99: mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool)) action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"]) else: raise ValueError("Unsupported action/proprio normalization type detected!") actions = np.where( mask, 0.5 * (normalized_actions + 1) * (action_high - action_low + 1e-8) + action_low, normalized_actions, ) return actions def _run_diffusion_prediction( self, input_embeddings, all_actions_mask, noise, action_head, projected_patch_embeddings, labels, attention_mask, NUM_PATCHES, NUM_PROMPT_TOKENS, noisy_action_projector, ): """Run diffusion-based action prediction""" # Set diffusion timestep values action_head.noise_scheduler.set_timesteps(action_head.num_diffusion_steps) # Clone embedding for reuse in each timestep orig_projected_patch_embeddings = projected_patch_embeddings.clone() curr_noisy_actions = noise # Reverse diffusion: Iteratively denoise to generate action prediction for t in action_head.noise_scheduler.timesteps: # Get diffusion model's noise prediction (conditioned on VLA latent embedding, current noisy action # embedding, and diffusion timestep embedding) timesteps = torch.Tensor([t]).to(labels.device) diffusion_timestep_embeddings = ( action_head.time_encoder(timesteps).to(curr_noisy_actions.dtype).to(curr_noisy_actions.device) ) # (B, llm_dim) diffusion_timestep_embeddings = diffusion_timestep_embeddings.unsqueeze(1) # (B, 1, llm_dim) # [Diffusion] Replace the embeddings of the action tokens with noisy actions # (Later on, the positional embeddings will be added to them) # For simplicity, append diffusion timestep embedding to the end of projected vision tokens projected_patch_embeddings = torch.cat( (orig_projected_patch_embeddings, diffusion_timestep_embeddings), dim=1 ) # Reshape and project noisy actions into language embedding space B = curr_noisy_actions.shape[0] orig_curr_noisy_actions_shape = curr_noisy_actions.shape curr_noisy_actions = curr_noisy_actions.reshape(B, -1).unsqueeze(-1) noisy_action_features = noisy_action_projector(curr_noisy_actions) curr_noisy_actions = curr_noisy_actions.reshape(orig_curr_noisy_actions_shape) # Replace action token embeddings with noisy action embeddings input_embeddings = self._replace_input_embeddings( input_embeddings.clone(), all_actions_mask, noisy_action_features ) # Build multimodal embeddings and attention mask multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( input_embeddings, projected_patch_embeddings, attention_mask ) # Forward pass through language model language_model_output = self.language_model( input_ids=None, attention_mask=multimodal_attention_mask, position_ids=None, past_key_values=None, inputs_embeds=multimodal_embeddings, labels=None, use_cache=None, output_attentions=False, output_hidden_states=True, return_dict=True, ) # Extract hidden states for action portion of response last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D) actions_hidden_states = last_hidden_states[ :, NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, :, ] # (B, act_chunk_len, D) # Predict noise and update noisy actions: x_t -> x_{t-1} noise_pred = action_head.predict_noise(actions_hidden_states) curr_noisy_actions = action_head.noise_scheduler.step(noise_pred, t, curr_noisy_actions).prev_sample curr_noisy_actions = curr_noisy_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM) # Return final actions return curr_noisy_actions.float().cpu().detach().numpy(), actions_hidden_states def _regression_or_discrete_prediction( self, input_embeddings, all_actions_mask, projected_patch_embeddings, attention_mask, labels, NUM_PATCHES, NUM_PROMPT_TOKENS, action_head=None, ): """Run L1 regression-based continuous action prediction or discrete action tokens prediction.""" # Zero out action token embeddings all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1) input_embeddings = input_embeddings * ~all_actions_mask # Build multimodal embeddings and attention mask multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( input_embeddings, projected_patch_embeddings, attention_mask ) # Forward pass through language model language_model_output = self.language_model( input_ids=None, attention_mask=multimodal_attention_mask, position_ids=None, past_key_values=None, inputs_embeds=multimodal_embeddings, labels=None, use_cache=None, output_attentions=False, output_hidden_states=True, return_dict=True, ) # Extract hidden states for action tokens last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D) actions_hidden_states = last_hidden_states[ :, NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, :, ] # (B, act_chunk_len, D) # Handle different prediction methods if action_head is not None: # L1 regression prediction normalized_actions = action_head.predict_action(actions_hidden_states) normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM) normalized_actions = normalized_actions.float().cpu().detach().numpy() else: # Discrete token-based prediction predicted_action_token_ids = ( language_model_output.logits[ :, NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, ] .argmax(dim=2) .cpu() .numpy() ) discretized_actions = self.vocab_size - predicted_action_token_ids discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1) normalized_actions = self.bin_centers[discretized_actions] normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM) return normalized_actions, actions_hidden_states def _verl_discrete_prediction( self, input_embeddings, all_actions_mask, projected_patch_embeddings, attention_mask, labels, NUM_PATCHES, NUM_PROMPT_TOKENS, action_head=None, do_sample=True, temperature=1, ): """Run L1 regression-based continuous action prediction or discrete action tokens prediction.""" # Zero out action token embeddings all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1) input_embeddings = input_embeddings * ~all_actions_mask # Build multimodal embeddings and attention mask multimodal_embeddings, multimodal_attention_mask = self._build_multimodal_attention( input_embeddings, projected_patch_embeddings, attention_mask ) # Forward pass through language model language_model_output = self.language_model( input_ids=None, attention_mask=multimodal_attention_mask, position_ids=None, past_key_values=None, inputs_embeds=multimodal_embeddings, labels=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ) # Extract hidden states for action tokens # last_hidden_states = language_model_output.hidden_states[-1] # (B, seq_len, D) # actions_hidden_states = last_hidden_states[ # :, # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, # :, # ] # (B, act_chunk_len, D) # Handle different prediction methods # if action_head is not None: # # L1 regression prediction # normalized_actions = action_head.predict_action(actions_hidden_states) # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM) # normalized_actions = normalized_actions.float().cpu().detach().numpy() # else: # Discrete token-based prediction # test # NUM_PROMPT_TOKENS = NUM_PROMPT_TOKENS + NUM_PATCHES # j = torch.arange(language_model_output.logits.shape[1], device=NUM_PROMPT_TOKENS.device) # start = NUM_PROMPT_TOKENS.unsqueeze(1) # end = start + ACTION_DIM * NUM_ACTIONS_CHUNK # mask_2d = (j >= start) & (j < end) # mask = mask_2d.unsqueeze(-1) # actions_masks = mask.expand_as(language_model_output.logits) NUM_PROMPT_TOKENS = NUM_PROMPT_TOKENS + NUM_PATCHES batch_size = language_model_output.logits.shape[0] device = language_model_output.logits.device start_indices = NUM_PROMPT_TOKENS.unsqueeze(1) # [batch_size, 1] position_offsets = torch.arange(ACTION_DIM * NUM_ACTIONS_CHUNK, device=device).unsqueeze(0) # [1, seq_length] seq_indices = start_indices + position_offsets # [batch_size, ACTION_DIM*NUM_ACTIONS_CHUNK] # test end # test add # print("language_model_output",language_model_output.logits.shape[-1]) # print("self.vocab_size",self.vocab_size) 32000 # topk_values, topk_indices = torch.topk(language_model_output.logits, k=256, dim=-1) # print(topk_indices) # assert language_model_output.logits.shape[-1] == self.vocab_size # test add if not do_sample: # org # reponse_ids = language_model_output.logits[ # :, # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES +\ # NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, # ].argmax(dim=2) # reponse_ids = language_model_output.logits[actions_masks].argmax(dim=2) # org end # padding # reponse_ids = language_model_output.logits[ # torch.arange(batch_size, device=device).unsqueeze(-1), # seq_indices, # : # ].argmax(dim=2) # padding end # padding + only get last 256 token reponse_ids_logits = language_model_output.logits[ torch.arange(batch_size, device=device).unsqueeze(-1), seq_indices, : ] start_index = self.vocab_size - 256 response_last256 = reponse_ids_logits[..., -256 - 64 : -64] # Shape: [batch_size, seq_len, 256] last256_argmax = response_last256.argmax(dim=-1) # Shape: [batch_size, seq_len] reponse_ids = last256_argmax + start_index # Shape: [batch_size, seq_len] # padding + only get last 256 token end predicted_action_token_ids = reponse_ids.cpu().numpy() else: assert temperature > 0 # org # action_logits = language_model_output.logits[ # :, # NUM_PATCHES + NUM_PROMPT_TOKENS : NUM_PATCHES + \ # NUM_PROMPT_TOKENS + ACTION_DIM * NUM_ACTIONS_CHUNK, # ] # action_logits = language_model_output.logits[actions_masks] # org end action_logits = language_model_output.logits[ torch.arange(batch_size, device=device).unsqueeze(-1), seq_indices, : ] # padding # scaled_logits = action_logits / temperature # probs = torch.softmax(scaled_logits, dim=-1) # probs_flat = probs.reshape(-1, probs.shape[-1]) # (B*act_chunk_len, vocab_size) # sampled_indices_flat = torch.multinomial(probs_flat, num_samples=1) # (B*act_chunk_len, 1) # reponse_ids = sampled_indices_flat.view(action_logits.shape[0], -1) # padding end # padding + only get last 256 token action_logits_last256 = action_logits[..., -256 - 64 : -64] scaled_logits = action_logits_last256 / temperature probs = torch.softmax(scaled_logits, dim=-1) assert probs.shape[-1] == 256 probs_flat = probs.reshape(-1, probs.shape[-1]) sampled_indices_flat = torch.multinomial(probs_flat, num_samples=1) original_ids_flat = sampled_indices_flat + (self.vocab_size - 256) reponse_ids = original_ids_flat.view(action_logits.shape[0], -1) # padding + only get last 256 token end predicted_action_token_ids = reponse_ids.cpu().numpy() discretized_actions = self.vocab_size - predicted_action_token_ids discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1) normalized_actions = self.bin_centers[discretized_actions] # normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM) normalized_actions = normalized_actions.reshape(-1, ACTION_DIM) return normalized_actions, reponse_ids # return normalized_actions, actions_hidden_states def predict_action( self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, proprio=None, proprio_projector=None, action_head=None, noisy_action_projector=None, use_film: bool = False, **kwargs: str, ) -> np.ndarray: """Predict actions from input sequence, with options for different prediction methods. Args: input_ids: Input token ids unnorm_key: Key for unnormalization statistics proprio: Proprioceptive features proprio_projector: Projector for proprioceptive features action_head: Optional head for L1 regression or diffusion-based prediction noisy_action_projector: Projector for noisy actions in diffusion-based prediction use_film: Whether to use FiLM conditioning **kwargs: Additional arguments including pixel_values and attention_mask Returns: Tuple of (unnormalized_actions, action_hidden_states) """ # If the special empty token ('') does not already appear after the colon (':') token in the prompt # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time if not torch.all(input_ids[:, -1] == 29871): input_ids = torch.cat( (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1 ) pixel_values = kwargs["pixel_values"] attention_mask = kwargs["attention_mask"] # Create fake labels tensor (needed for action mask) labels = input_ids.clone() labels[:] = IGNORE_INDEX # Get number of tokens in prompt (excluding the start token) NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token # Prepare inputs by adding necessary tokens input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask) # Update labels tensor for action mask computation later labels = self._prepare_labels_for_action_prediction(labels, input_ids) # Get input embeddings and action masks input_embeddings = self.get_input_embeddings()(input_ids) all_actions_mask = self._process_action_masks(labels) # Extract language embeddings language_embeddings = input_embeddings[~all_actions_mask].reshape( input_embeddings.shape[0], -1, input_embeddings.shape[2] ) # Process vision features projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film) # Add proprioceptive features if provided use_proprio = proprio_projector is not None and proprio is not None if use_proprio: proprio = torch.Tensor(proprio).to( projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype ) projected_patch_embeddings = self._process_proprio_features( projected_patch_embeddings, proprio, proprio_projector ) # Use diffusion if provided, otherwise use regression or discrete prediction use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler") # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present) NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input() if use_proprio: NUM_PATCHES += 1 if use_diffusion: NUM_PATCHES += 1 if use_diffusion: # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion noise = torch.randn( size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype ) # Run diffusion-based prediction normalized_actions, actions_hidden_states = self._run_diffusion_prediction( input_embeddings, all_actions_mask, noise, action_head, projected_patch_embeddings, labels, attention_mask, NUM_PATCHES, NUM_PROMPT_TOKENS, noisy_action_projector, ) else: # Run regression or discrete token-based prediction normalized_actions, actions_hidden_states = self._regression_or_discrete_prediction( input_embeddings, all_actions_mask, projected_patch_embeddings, attention_mask, labels, NUM_PATCHES, NUM_PROMPT_TOKENS, action_head, ) # Unnormalize predicted actions actions = self._unnormalize_actions(normalized_actions, unnorm_key) return actions, actions_hidden_states def generate_action_verl( self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, proprio=None, proprio_projector=None, action_head=None, noisy_action_projector=None, use_film: bool = False, **kwargs: str, ) -> np.ndarray: """Predict actions from input sequence, with options for different prediction methods. Args: input_ids: Input token ids unnorm_key: Key for unnormalization statistics proprio: Proprioceptive features proprio_projector: Projector for proprioceptive features action_head: Optional head for L1 regression or diffusion-based prediction noisy_action_projector: Projector for noisy actions in diffusion-based prediction use_film: Whether to use FiLM conditioning **kwargs: Additional arguments including pixel_values and attention_mask Returns: Tuple of (unnormalized_actions, action_hidden_states) """ # If the special empty token ('') does not already appear after the colon (':') token in the prompt # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time # if not torch.all(input_ids[:, -1] == 29871): # input_ids = torch.cat( # (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1 # ) pixel_values = kwargs["pixel_values"] attention_mask = kwargs["attention_mask"] do_sample = kwargs["do_sample"] temperature = kwargs["temperature"] # Create fake labels tensor (needed for action mask) labels = input_ids.clone() labels[:] = IGNORE_INDEX # Get number of tokens in prompt (excluding the start token) # NUM_PROMPT_TOKENS = input_ids.shape[-1] - 1 # Subtract action tokens and stop token # test padding_idx = kwargs["padding_idx"] num_prompt_tokens = input_ids.ne(padding_idx).sum(dim=1) - 1 # test end # Prepare inputs by adding necessary tokens input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask) # Update labels tensor for action mask computation later labels = self._prepare_labels_for_action_prediction(labels, input_ids) # here to convert padding from before to last # test padding_mask = input_ids.ne(padding_idx) assert torch.all(padding_mask == attention_mask.ne(0)) # print("in predict_action padding_mask:", padding_mask) padding_mask = padding_mask.int() sorted_indices = torch.argsort(padding_mask, dim=1, descending=True, stable=True) input_ids = torch.gather(input_ids, 1, sorted_indices) attention_mask = torch.gather(attention_mask, 1, sorted_indices) labels = torch.gather(labels, 1, sorted_indices) assert not use_film # test end # Get input embeddings and action masks input_embeddings = self.get_input_embeddings()(input_ids) all_actions_mask = self._process_action_masks(labels) # Extract language embeddings language_embeddings = input_embeddings[~all_actions_mask].reshape( input_embeddings.shape[0], -1, input_embeddings.shape[2] ) # Process vision features projected_patch_embeddings = self._process_vision_features(pixel_values, language_embeddings, use_film) # Add proprioceptive features if provided use_proprio = proprio_projector is not None and proprio is not None if use_proprio: proprio = torch.Tensor(proprio).to( projected_patch_embeddings.device, dtype=projected_patch_embeddings.dtype ) projected_patch_embeddings = self._process_proprio_features( projected_patch_embeddings, proprio, proprio_projector ) # Use diffusion if provided, otherwise use regression or discrete prediction use_diffusion = noisy_action_projector is not None and hasattr(action_head, "noise_scheduler") # Calculate number of patches (including proprio token and/or diffusion timestep embedding if present) NUM_PATCHES = self.vision_backbone.get_num_patches() * self.vision_backbone.get_num_images_in_input() if use_proprio: NUM_PATCHES += 1 if use_diffusion: NUM_PATCHES += 1 if use_diffusion: raise ValueError # # Sample random noise with shape equal to output action, used as the starting state for reverse diffusion # noise = torch.randn( # size=(1, NUM_ACTIONS_CHUNK, ACTION_DIM), device=input_embeddings.device, dtype=input_embeddings.dtype # ) # # Run diffusion-based prediction # normalized_actions, actions_hidden_states = self._run_diffusion_prediction( # input_embeddings, # all_actions_mask, # noise, # action_head, # projected_patch_embeddings, # labels, # attention_mask, # NUM_PATCHES, # NUM_PROMPT_TOKENS, # noisy_action_projector, # ) else: # Run regression or discrete token-based prediction normalized_actions, reponse_ids = self._verl_discrete_prediction( input_embeddings, all_actions_mask, projected_patch_embeddings, attention_mask, labels, NUM_PATCHES, num_prompt_tokens, action_head, do_sample=do_sample, temperature=temperature, ) # Unnormalize predicted actions actions = self._unnormalize_actions(normalized_actions, unnorm_key) # verl add! actions = actions.reshape(-1, NUM_ACTIONS_CHUNK, ACTION_DIM) # return actions, reponse_ids @staticmethod def _check_unnorm_key(norm_stats: dict[str, dict[str, Any]], unnorm_key: Optional[str]) -> str: """Validate and resolve the unnormalization key for action statistics""" if unnorm_key is None: assert len(norm_stats) == 1, ( f"Your model was trained on more than one dataset, " f"please pass a `unnorm_key` from the following options to choose the statistics " f"used for un-normalizing actions: {norm_stats.keys()}" ) unnorm_key = next(iter(norm_stats.keys())) assert unnorm_key in norm_stats, ( f"The `unnorm_key` you chose is not in the set of available dataset statistics, " f"please choose from: {norm_stats.keys()}" ) return unnorm_key def get_action_dim(self, unnorm_key: Optional[str] = None) -> int: """Get the dimensionality of the policy's action space.""" unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key) return len(self.norm_stats[unnorm_key]["action"]["min"]) def get_action_stats(self, unnorm_key: Optional[str] = None) -> dict[str, Any]: """Get all the logged statistics for the given dataset.""" unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key) return self.norm_stats[unnorm_key]["action"] ================================================ FILE: verl/experimental/vla/models/openvla_oft/processing_prismatic.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from https://github.com/PRIME-RL/SimpleVLA-RL/blob/main/verl/utils/vla_utils/openvla_oft/ # form https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero10-trajall/blob/main/ """ processing_prismatic.py HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration specifies `siglip-224px+7b`. """ from typing import Any, ClassVar, Optional import timm.data import torch import torchvision.transforms.functional as TVF from PIL import Image from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor from transformers import PreTrainedTokenizerBase from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin from transformers.processing_utils import ProcessorMixin from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from transformers.utils import TensorType # === Image Processing === def letterbox_pad_transform(image: Image.Image, padding_fill_value: tuple[int, int, int]) -> Image.Image: """Given a PIL.Image, pad to square by adding a symmetric border around the height/width.""" (w, h), max_wh = image.size, max(image.size) horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2) padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad) return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant") class PrismaticImageProcessor(ImageProcessingMixin): model_input_names: ClassVar[list[str]] = ["pixel_values"] def __init__( self, use_fused_vision_backbone: bool = False, image_resize_strategy: str = "letterbox", input_sizes: Optional[list[tuple[int, int, int]]] = None, interpolations: Optional[list[str]] = None, means: Optional[list[tuple[float, float, float]]] = None, stds: Optional[list[tuple[float, float, float]]] = None, **kwargs: str, ) -> None: """ Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be created by TIMM, and edited to follow our custom `image_resize_strategy` logic. @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox > @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height) @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic") @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`) @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`) """ self.use_fused_vision_backbone = use_fused_vision_backbone self.image_resize_strategy = image_resize_strategy # Handle `None` default values input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes means = [(0.5, 0.5, 0.5)] if means is None else means stds = [(0.5, 0.5, 0.5)] if stds is None else stds # TIMM `data_cfg` Parameters self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values! self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], [] self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None for idx in range(len(input_sizes)): transform = timm.data.create_transform( input_size=self.input_sizes[idx], interpolation=self.interpolations[idx], mean=self.means[idx], std=self.stds[idx], crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`) crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0` is_training=False, # No image augmentations when loading the transform! ) # [Validation] Ensure appropriate transform structure, expected sizes if not ( isinstance(transform, Compose) and (len(transform.transforms) == 4) and isinstance(transform.transforms[0], Resize) and isinstance(transform.transforms[1], CenterCrop) and isinstance(transform.transforms[2], ToTensor) and isinstance(transform.transforms[3], Normalize) and (transform.transforms[0].size == self.input_sizes[idx][-1]) and (transform.transforms[1].size == self.input_sizes[idx][-2:]) ): raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`") # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute. # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`) resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3] self.tvf_resize_params.append( { "size": resize_t.size, "interpolation": TVF.pil_modes_mapping[resize_t.interpolation], "max_size": None, "antialias": True, } ) self.tvf_crop_params.append({"output_size": crop_t.size}) self.tvf_normalize_params.append( { "mean": norm_t.mean.float().numpy().tolist(), "std": norm_t.std.float().numpy().tolist(), "inplace": False, } ) self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None # Handle Prismatic `image_resize_strategy` if self.image_resize_strategy == "resize-naive": self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size) elif self.image_resize_strategy == "letterbox": self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]]) elif self.image_resize_strategy == "resize-crop": pass else: raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!") # Dispatch **kwargs to super() super().__init__(**kwargs) def apply_transform(self, img: Image.Image) -> torch.Tensor: """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])""" if self.tvf_do_letterbox: img = letterbox_pad_transform(img, self.tvf_letterbox_fill) # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side! imgs_t = [] for idx in range(len(self.input_sizes)): img_idx = TVF.resize(img, **self.tvf_resize_params[idx]) img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx]) img_idx_t = TVF.to_tensor(img_idx) img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx]) imgs_t.append(img_idx_t) # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0 img_t = torch.vstack(imgs_t) return img_t def preprocess( self, images: Image.Image | list[Image.Image], return_tensors: Optional[str | TensorType] = None, **_: str, ) -> BatchFeature: """ Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we explicitly only handle PIL.Image.Image instances for simplicity. @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values" """ if not isinstance(images, list): images = [images] # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images]) # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors) def __call__(self, images: Image.Image | list[Image.Image], **kwargs) -> BatchFeature: return self.preprocess(images, **kwargs) # === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer === # =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py class PrismaticProcessor(ProcessorMixin): attributes: ClassVar[list[str]] = ["image_processor", "tokenizer"] image_processor_class: str = "AutoImageProcessor" tokenizer_class: str = "AutoTokenizer" def __init__( self, image_processor: Optional[ImageProcessingMixin] = None, tokenizer: Optional[PreTrainedTokenizerBase] = None, ) -> None: super().__init__(image_processor, tokenizer) def __call__( self, text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput], images: Image.Image | list[Image.Image], padding: bool | str | PaddingStrategy = False, truncation: Optional[bool | str | TruncationStrategy] = None, max_length: Optional[int] = None, return_tensors: Optional[str | TensorType] = TensorType.PYTORCH, ) -> BatchFeature: """ Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer, forwards images to PrismaticImageProcessor. @param text: The (batch) of text to encode; must be a string or list of strings. @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False > @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified @param max_length: Maximum length (in tokens) to truncate @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH) @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`. """ pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"] text_inputs = self.tokenizer( text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length ) # [Validate] Need same number of images and text inputs! if pixel_values.shape[0] != text_inputs.input_ids.shape[0]: raise ValueError("Batch is malformed; expected same number of images and text inputs!") return BatchFeature(data={**text_inputs, "pixel_values": pixel_values}) # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation === def batch_decode( self, sequences: list[int] | list[list[int]] | torch.Tensor | Any, # `Any` = np.ndarray | tf.Tensor skip_special_tokens: bool = False, clean_up_tokenization_spaces: Optional[bool] = None, **kwargs: str, ) -> list[str]: return self.tokenizer.batch_decode( sequences=sequences, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) def decode( self, token_ids: int | list[int] | torch.Tensor | Any, # `Any` = np.ndarray | tf.Tensor skip_special_tokens: bool = False, clean_up_tokenization_spaces: Optional[bool] = None, **kwargs: str, ) -> str: return self.tokenizer.decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) @property def model_input_names(self) -> list[str]: tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) ================================================ FILE: verl/experimental/vla/models/openvla_oft/train_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from https://github.com/PRIME-RL/SimpleVLA-RL/blob/main/verl/utils/vla_utils/openvla_oft/ """Utils for training/fine-tuning scripts.""" import torch from .constants import ACTION_DIM, ACTION_TOKEN_BEGIN_IDX, IGNORE_INDEX def get_current_action_mask(token_ids): # Create a tensor marking positions of IGNORE_INDEX newline_positions = token_ids != IGNORE_INDEX # Calculate cumulative sum to identify regions between newlines cumsum = torch.cumsum(newline_positions, dim=1) # Create the mask mask = (1 <= cumsum) & (cumsum <= ACTION_DIM) # Extract the action part only action_tokens_only_mask = token_ids > ACTION_TOKEN_BEGIN_IDX mask = action_tokens_only_mask * mask return mask def get_next_actions_mask(token_ids): # Create a tensor marking positions of IGNORE_INDEX newline_positions = token_ids != IGNORE_INDEX # Calculate cumulative sum to identify regions between newlines cumsum = torch.cumsum(newline_positions, dim=1) # Create the mask mask = cumsum > ACTION_DIM # Extract the action part only action_tokens_only_mask = token_ids > ACTION_TOKEN_BEGIN_IDX mask = action_tokens_only_mask * mask return mask def compute_token_accuracy(predicted_token_ids, ground_truth_token_ids, mask): correct_preds = (predicted_token_ids == ground_truth_token_ids) & mask accuracy = correct_preds.sum().float() / mask.sum().float() return accuracy def compute_actions_l1_loss(action_tokenizer, predicted_token_ids, ground_truth_token_ids, mask): pred_continuous_actions = torch.tensor( action_tokenizer.decode_token_ids_to_actions(predicted_token_ids[mask].cpu().numpy()) ) true_continuous_actions = torch.tensor( action_tokenizer.decode_token_ids_to_actions(ground_truth_token_ids[mask].cpu().numpy()) ) l1_loss = torch.nn.functional.l1_loss(pred_continuous_actions, true_continuous_actions) return l1_loss ================================================ FILE: verl/experimental/vla/models/pi0_torch/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .configuration_pi0_torch import PI0TorchConfig from .modeling_pi0_torch import PI0ForActionPrediction __all__ = ["PI0TorchConfig", "PI0ForActionPrediction"] ================================================ FILE: verl/experimental/vla/models/pi0_torch/configuration_pi0_torch.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from transformers import PretrainedConfig class PI0TorchConfig(PretrainedConfig): model_type = "pi0_torch" def __init__(self, **kwargs): super().__init__(**kwargs) self.state_norm_stats = kwargs.get("state_norm_stats", {}) self.action_norm_stats = kwargs.get("action_norm_stats", {}) self.pi05_enabled = kwargs.get("pi05_enabled", False) ================================================ FILE: verl/experimental/vla/models/pi0_torch/model/modeling_pi0.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright 2025 Giga Team. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # from https://github.com/open-gigaai/giga-models import math import torch import torch.nn.functional as F # noqa: N812 from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin from torch import Tensor, nn from .paligemma_with_expert import PaliGemmaWithExpertModel def get_safe_dtype(dtype: torch.dtype, device: str | torch.device) -> torch.dtype: """Mps is currently not compatible with float64.""" if isinstance(device, torch.device): device = device.type if device == "mps" and dtype == torch.float64: return torch.float32 else: return dtype def create_sinusoidal_pos_embedding( time: torch.Tensor, dimension: int, min_period: float, max_period: float, device: str | torch.device = "cpu" ) -> Tensor: """Computes sine-cosine positional embedding vectors for scalar positions.""" if dimension % 2 != 0: raise ValueError(f"dimension ({dimension}) must be divisible by 2") if time.ndim != 1: raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.") dtype = get_safe_dtype(torch.float64, device) fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device) period = min_period * (max_period / min_period) ** fraction # Compute the outer product scaling_factor = 1.0 / period * 2 * math.pi sin_input = scaling_factor[None, :] * time[:, None] pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1) return pos_emb def make_att_2d_masks(pad_masks: torch.Tensor, att_masks: torch.Tensor) -> torch.Tensor: """Copied from big_vision. Tokens can attend to valid inputs tokens which have a cumulative mask_ar smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to setup several types of attention, for example: [[1 1 1 1 1 1]]: pure causal attention. [[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between themselves and the last 3 tokens have a causal attention. The first entry could also be a 1 without changing behaviour. [[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a block can attend all previous blocks and all tokens on the same block. Args: pad_masks: bool[B, N] indicating valid (true) vs. padding (false) tokens. att_masks: int[B, N] defining attention type. A `1` at a position indicates the start of a new causal block. Returns: A 2D boolean attention mask of shape (B, N, N). """ if att_masks.ndim != 2: raise ValueError(att_masks.ndim) if pad_masks.ndim != 2: raise ValueError(pad_masks.ndim) cumsum = torch.cumsum(att_masks, dim=1) att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None] pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None] att_2d_masks = att_2d_masks & pad_2d_masks return att_2d_masks class PI0Model(ModelMixin, ConfigMixin): """pi0: A Vision-Language-Action Flow Model for General Robot Control. [Paper](https://www.physicalintelligence.company/download/pi0.pdf) [Jax code](https://github.com/Physical-Intelligence/openpi) ┌──────────────────────────────┐ │ actions │ │ ▲ │ │ ┌┴─────┐ │ │ kv cache │Gemma │ │ │ ┌──────────►│Expert│ │ │ │ │ │ │ │ ┌┴────────┐ │x 10 │ │ │ │ │ └▲──▲──┘ │ │ │PaliGemma│ │ │ │ │ │ │ │ robot state │ │ │ │ noise │ │ └▲──▲─────┘ │ │ │ │ │ │ │ image(s) │ │ language tokens │ └──────────────────────────────┘ """ @register_to_config def __init__( self, max_state_dim: int = 32, max_action_dim: int = 32, proj_width: int = 1024, n_action_steps: int = 50, num_steps: int = 10, use_cache: bool = True, pi05_enabled: bool = False, ): super().__init__() # Store the parameters self.max_state_dim = max_state_dim self.max_action_dim = max_action_dim self.proj_width = proj_width self.n_action_steps = n_action_steps self.num_steps = num_steps self.use_cache = use_cache self.pi05_enabled = pi05_enabled self.paligemma_with_expert = PaliGemmaWithExpertModel( pi05_enabled=pi05_enabled, ) # Projections are float32 if self.pi05_enabled: self.time_mlp_in = nn.Linear(self.proj_width, self.proj_width, dtype=torch.float32) self.time_mlp_out = nn.Linear(self.proj_width, self.proj_width, dtype=torch.float32) else: self.state_proj = nn.Linear(self.max_state_dim, self.proj_width, dtype=torch.float32) self.action_time_mlp_in = nn.Linear(self.proj_width * 2, self.proj_width, dtype=torch.float32) self.action_time_mlp_out = nn.Linear(self.proj_width, self.proj_width, dtype=torch.float32) self.action_in_proj = nn.Linear(self.max_action_dim, self.proj_width, dtype=torch.float32) self.action_out_proj = nn.Linear(self.proj_width, self.max_action_dim, dtype=torch.float32) def forward( self, images: list[torch.Tensor], img_masks: list[torch.Tensor], lang_tokens: torch.Tensor, lang_masks: torch.Tensor, state: torch.Tensor, x_t: torch.Tensor, timestep: torch.Tensor, ) -> Tensor: """Full forward pass for one diffusion denoising step. Args: images: List of image tensors, each shaped (B, C, H, W) after batching. img_masks: List of boolean masks corresponding to images, each (B,). lang_tokens: Language token ids (B, L). lang_masks: Language attention mask (B, L) with True for valid tokens. state: State tensor (B, state_dim) if pi05 is disabled else ignored. x_t: Noisy action tokens (B, n_action_steps, action_dim). timestep: Diffusion timestep as float tensor (B,). Returns: Predicted v_t with shape (B, n_action_steps, action_dim). """ prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(images, img_masks, lang_tokens, lang_masks) suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, timestep) pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1) att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1) att_2d_masks = make_att_2d_masks(pad_masks, att_masks) position_ids = torch.cumsum(pad_masks, dim=1) - 1 (_, suffix_out), _ = self.paligemma_with_expert.forward( attention_mask=att_2d_masks, position_ids=position_ids, past_key_values=None, inputs_embeds=[prefix_embs, suffix_embs], use_cache=False, fill_kv_cache=False, adarms_cond=[None, adarms_cond], ) suffix_out = suffix_out[:, -self.n_action_steps :] # Original openpi code, upcast attention output suffix_out = suffix_out.to(dtype=self.action_out_proj.weight.dtype) v_t = self.action_out_proj(suffix_out) return v_t def sample_noise(self, shape: tuple[int, ...], device: torch.device | str) -> torch.Tensor: """Generate Gaussian noise for the action trajectory. Args: shape: Desired output shape, typically (B, n_action_steps, action_dim). device: Target device string or torch.device. Returns: A float32 tensor of standard normal samples with the given shape. """ noise = torch.normal( mean=0.0, std=1.0, size=shape, dtype=torch.float32, device=device, ) return noise def embed_prefix( self, images: list[torch.Tensor], img_masks: list[torch.Tensor], lang_tokens: torch.Tensor, lang_masks: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Embed visual and language inputs as the transformer prefix. Args: images: List of (B, C, H, W) tensors. img_masks: List of (B,) boolean masks for image presence. lang_tokens: (B, L) token ids. lang_masks: (B, L) boolean mask; True indicates valid tokens. Returns: A tuple of (embs, pad_masks, att_masks): - embs: (B, Np, D) concatenated image and language embeddings - pad_masks: (B, Np) valid token mask - att_masks: (B, Np) attention mask scheme selector """ # Optimize: batch process images and pre-allocate tensors num_images = len(images) # Stack images and masks for batch processing images_stacked = torch.stack(images, dim=0) # (num_images, bsize, ...) img_masks_stacked = torch.stack(img_masks, dim=0) # (num_images, bsize) # Batch embed all images at once # Reshape to (num_images * bsize, ...) orig_shape = images_stacked.shape images_flat = images_stacked.reshape(-1, *orig_shape[2:]) img_embs_flat = self.paligemma_with_expert.embed_image(images_flat) # Reshape back to (num_images, bsize, num_img_embs, emb_dim) bsize = orig_shape[1] img_embs = img_embs_flat.reshape(num_images, bsize, *img_embs_flat.shape[1:]) # Normalize image embeddings img_emb_dim = img_embs.shape[-1] num_img_embs = img_embs.shape[2] # Expand masks: (num_images, bsize) -> (num_images, bsize, num_img_embs) img_masks_expanded = img_masks_stacked[:, :, None].expand(num_images, bsize, num_img_embs) # Reshape to (bsize, num_images * num_img_embs, emb_dim) img_embs_concat = img_embs.transpose(0, 1).reshape(bsize, num_images * num_img_embs, img_emb_dim) img_masks_concat = img_masks_expanded.transpose(0, 1).reshape(bsize, num_images * num_img_embs) # Process language embeddings lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens) lang_emb_dim = lang_emb.shape[-1] lang_emb = lang_emb * math.sqrt(lang_emb_dim) lang_emb = lang_emb.to(dtype=img_embs_concat.dtype) num_lang_embs = lang_emb.shape[1] total_seq_len = num_images * num_img_embs + num_lang_embs # Pre-allocate final tensors embs = torch.empty( bsize, total_seq_len, img_emb_dim, dtype=img_embs_concat.dtype, device=img_embs_concat.device ) pad_masks = torch.empty(bsize, total_seq_len, dtype=torch.bool, device=img_embs_concat.device) # Fill pre-allocated tensors embs[:, : num_images * num_img_embs] = img_embs_concat embs[:, num_images * num_img_embs :] = lang_emb pad_masks[:, : num_images * num_img_embs] = img_masks_concat pad_masks[:, num_images * num_img_embs :] = lang_masks # Create attention masks (all zeros for full attention between image and language) att_masks = torch.zeros(total_seq_len, dtype=torch.bool, device=pad_masks.device) att_masks = att_masks[None, :].expand(bsize, total_seq_len) return embs, pad_masks, att_masks def embed_suffix( self, state: torch.Tensor, noisy_actions: torch.Tensor, timestep: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: """Embed state, action and time tokens as the transformer suffix. Args: state: (B, state_dim) robot state; ignored when pi05 is enabled. noisy_actions: (B, n_action_steps, action_dim) current x_t. timestep: (B,) diffusion time in [0, 1]. Returns: (embs, pad_masks, att_masks, adarms_cond) where: - embs: (B, Ns, D) suffix embeddings - pad_masks: (B, Ns) valid mask - att_masks: (B, Ns) causal scheme for suffix - adarms_cond: (B, D) AdaRMS conditioning or None """ embs = [] pad_masks = [] att_masks = [] action_emb = self.action_in_proj(noisy_actions) bsize = action_emb.shape[0] dtype = action_emb.dtype device = action_emb.device # Embed state if not self.pi05_enabled: state_emb = self.state_proj(state) embs.append(state_emb[:, None, :]) state_mask = torch.ones(bsize, 1, dtype=torch.bool, device=device) pad_masks.append(state_mask) # Set attention masks so that image and language inputs do not attend to state or actions att_masks += [1] # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1] time_emb = create_sinusoidal_pos_embedding( timestep, self.proj_width, min_period=4e-3, max_period=4.0, device=device ) time_emb = time_emb.type(dtype=dtype) if self.pi05_enabled: # time MLP (for adaRMS) time_emb = self.time_mlp_in(time_emb) time_emb = F.silu(time_emb) time_emb = self.time_mlp_out(time_emb) time_emb = F.silu(time_emb) action_expert_emb = action_emb adarms_cond = time_emb else: # Fuse timestep + action information using an MLP time_emb = time_emb[:, None, :].expand_as(action_emb) action_time_emb = torch.cat([action_emb, time_emb], dim=2) action_time_emb = self.action_time_mlp_in(action_time_emb) action_time_emb = F.silu(action_time_emb) # swish == silu action_time_emb = self.action_time_mlp_out(action_time_emb) action_expert_emb = action_time_emb adarms_cond = None # Add to input tokens embs.append(action_expert_emb) bsize, action_time_dim = action_expert_emb.shape[:2] action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=device) pad_masks.append(action_time_mask) # Set attention masks so that image, language and state inputs do not attend to action tokens att_masks += [1] + ([0] * (self.n_action_steps - 1)) embs = torch.cat(embs, dim=1) pad_masks = torch.cat(pad_masks, dim=1) att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device) att_masks = att_masks[None, :].expand(bsize, len(att_masks)) return embs, pad_masks, att_masks, adarms_cond @torch.no_grad() def sample_actions( self, images: list[torch.Tensor], img_masks: list[torch.Tensor], lang_tokens: torch.Tensor, lang_masks: torch.Tensor, state: torch.Tensor, noise: Tensor | None = None, ) -> Tensor: """Run the full inference loop to predict an action trajectory. Args: images: List of (B, C, H, W) image tensors. img_masks: List of (B,) boolean masks. lang_tokens: (B, L) token ids. lang_masks: (B, L) boolean mask for tokens. state: (B, state_dim) robot state. noise: Optional initial noise; if None, generated internally. Returns: Predicted actions with shape (B, n_action_steps, action_dim). """ bsize = lang_tokens.shape[0] device = lang_tokens.device if noise is None: actions_shape = (bsize, self.n_action_steps, self.max_action_dim) noise = self.sample_noise(actions_shape, device) prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(images, img_masks, lang_tokens, lang_masks) prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks) prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1 # Compute image and language key value cache _, past_key_values = self.paligemma_with_expert.forward( attention_mask=prefix_att_2d_masks, position_ids=prefix_position_ids, past_key_values=None, inputs_embeds=[prefix_embs, None], use_cache=self.use_cache, fill_kv_cache=True, adarms_cond=[None, None], ) x_t = noise dt = -1.0 / self.num_steps timesteps = torch.arange(1.0, -dt / 2, dt, dtype=torch.float32, device=device) for timestep in timesteps: v_t = self.denoise_step( state, prefix_pad_masks, past_key_values, x_t, timestep.expand(bsize), ) x_t += dt * v_t return x_t def denoise_step( self, state: torch.Tensor, prefix_pad_masks: torch.Tensor, past_key_values: dict, x_t: torch.Tensor, timestep: torch.Tensor, ) -> torch.Tensor: """Apply one denoising step of the noise x_t at a given timestep. Args: state: (B, state_dim) robot state. prefix_pad_masks: (B, Np) prefix pad masks computed from embed_prefix. past_key_values: KV cache dict for the prefix (images+language). x_t: (B, n_action_steps, action_dim) current noisy actions. timestep: (B,) current time in [0, 1]. Returns: v_t prediction with shape (B, n_action_steps, action_dim). """ suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, timestep) suffix_len = suffix_pad_masks.shape[1] batch_size = prefix_pad_masks.shape[0] prefix_len = prefix_pad_masks.shape[1] prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len) suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks) full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2) prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None] position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1 outputs_embeds, _ = self.paligemma_with_expert.forward( attention_mask=full_att_2d_masks, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=[None, suffix_embs], use_cache=self.use_cache, fill_kv_cache=False, adarms_cond=[None, adarms_cond], ) suffix_out = outputs_embeds[1] suffix_out = suffix_out[:, -self.n_action_steps :] suffix_out = suffix_out.to(dtype=self.action_out_proj.weight.dtype) v_t = self.action_out_proj(suffix_out) return v_t ================================================ FILE: verl/experimental/vla/models/pi0_torch/model/paligemma_with_expert.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright 2025 Giga Team. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # from https://github.com/open-gigaai/giga-models from typing import Optional import torch import torch.nn.functional as F from torch import nn from transformers.activations import ACT2FN from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from transformers.models.auto import CONFIG_MAPPING from transformers.models.siglip.configuration_siglip import SiglipVisionConfig from transformers.models.siglip.modeling_siglip import ( SiglipEncoder, SiglipMultiheadAttentionPoolingHead, SiglipVisionEmbeddings, ) from transformers.utils import can_return_tuple from verl.utils.device import get_device_name def get_transformers_siglip_vision_config() -> SiglipVisionConfig: return CONFIG_MAPPING["siglip_vision_model"]( hidden_size=1152, intermediate_size=4304, num_channels=3, num_attention_heads=16, num_hidden_layers=27, num_image_tokens=256, patch_size=14, projection_dim=2048, projector_hidden_act="gelu_fast", torch_dtype="float32", vision_use_head=False, ) class GemmaRMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-6, use_ada_rms_norm: bool = False): super().__init__() self.eps = eps self.use_ada_rms_norm = use_ada_rms_norm if use_ada_rms_norm: self.dense = nn.Linear(dim, dim * 3, bias=True) nn.init.zeros_(self.dense.weight) else: self.weight = nn.Parameter(torch.zeros(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x, cond: torch.Tensor | None = None): normed_inputs = self._norm(x.float()) if self.use_ada_rms_norm: modulation = self.dense(cond) scale, shift, gate = torch.chunk(modulation.unsqueeze(1), 3, dim=-1) normed_inputs = normed_inputs.float() * (1.0 + scale.float()) + shift.float() return normed_inputs.type_as(x), gate.type_as(x) # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16) # See https://github.com/huggingface/transformers/pull/29402 output = normed_inputs * (1.0 + self.weight.float()) return output.type_as(x) def extra_repr(self): if self.use_ada_rms_norm: return f"{tuple(self.dense.weight.shape)}, eps={self.eps}, use_ada_rms_norm=True" else: return f"{tuple(self.weight.shape)}, eps={self.eps}" class SiglipVisionTransformer(nn.Module): def __init__(self, config: SiglipVisionConfig): super().__init__() self.config = config self.config._attn_implementation = "sdpa" embed_dim = config.hidden_size self.embeddings = SiglipVisionEmbeddings(config) self.encoder = SiglipEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head if self.use_head: self.head = SiglipMultiheadAttentionPoolingHead(config) @can_return_tuple # @auto_docstring def forward( self, pixel_values, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False, ) -> BaseModelOutputWithPooling: """Forward pass of the SigLIP vision encoder. Args: pixel_values: Image tensor expected by SigLIP (B, C, H, W). output_attentions: Whether to return attention maps. output_hidden_states: Whether to return hidden states. interpolate_pos_encoding: Enable pos-encoding interpolation for different sizes. Returns: BaseModelOutputWithPooling with last_hidden_state and optionally pooled output. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) hidden_states = hidden_states.to(dtype=torch.bfloat16) with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): encoder_outputs: BaseModelOutput = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) last_hidden_state = encoder_outputs.last_hidden_state last_hidden_state = self.post_layernorm(last_hidden_state) pooler_output = self.head(last_hidden_state) if self.use_head else None return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooler_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaMultiModalProjector class PaliGemmaMultiModalProjector(nn.Module): def __init__(self, vision_hidden_size: int = 1152, projection_dim: int = 2048): super().__init__() self.linear = nn.Linear(vision_hidden_size, projection_dim, bias=True) def forward(self, image_features: torch.Tensor) -> torch.Tensor: """Project vision features to the transformer hidden size.""" hidden_states = self.linear(image_features) return hidden_states class RoPEEmbedding(nn.Module): """Precomputed RoPE embeddings for improved performance. This implementation precomputes sin/cos values for a maximum sequence length, avoiding redundant trigonometric calculations during forward passes. """ def __init__(self, dim: int, max_wavelength: int = 10_000, max_seq_len: int = 8192): super().__init__() self.dim = dim self.max_wavelength = max_wavelength self.max_seq_len = max_seq_len # Precompute frequency exponents and inverse frequencies d_half = dim // 2 freq_exponents = (2.0 / dim) * torch.arange(d_half, dtype=torch.float32) inv_freq = 1.0 / (max_wavelength**freq_exponents) # Precompute sin and cos for all positions up to max_seq_len # Shape: [max_seq_len, d_half] positions = torch.arange(max_seq_len, dtype=torch.float32) freqs = torch.outer(positions, inv_freq) # [max_seq_len, d_half] # Precompute sin and cos values # We expand to [max_seq_len, 1, d_half] for broadcasting in forward cos_cached = torch.cos(freqs).unsqueeze(1) # [max_seq_len, 1, d_half] sin_cached = torch.sin(freqs).unsqueeze(1) # [max_seq_len, 1, d_half] # Register as buffers so they automatically move to the correct device with the model self.register_buffer("cos_cached", cos_cached, persistent=False) self.register_buffer("sin_cached", sin_cached, persistent=False) def forward(self, x: torch.Tensor, positions: torch.LongTensor) -> torch.Tensor: """Applies RoPE positions [B, L] to x [B, L, H, D]. Args: x: Input tensor of shape [B, L, H, D] positions: Position indices of shape [B, L] Returns: Rotated tensor of shape [B, L, H, D] """ dtype = x.dtype x = x.to(torch.float32) # Index precomputed sin/cos values using positions # positions: [B, L] -> cos/sin: [B, L, 1, d_half] cos = self.cos_cached[positions] # [B, L, 1, d_half] sin = self.sin_cached[positions] # [B, L, 1, d_half] # Apply rotary embeddings d_half = self.dim // 2 x1, x2 = x.split(d_half, dim=-1) # Each: [B, L, H, d_half] # Rotate: out1 = x1 * cos - x2 * sin, out2 = x2 * cos + x1 * sin res = torch.empty_like(x) res[..., :d_half] = x1 * cos - x2 * sin res[..., d_half:] = x2 * cos + x1 * sin return res.to(dtype) class GemmaAttentionWithExpert(nn.Module): def __init__( self, layer_idx: int, # PaliGemma params paligemma_hidden_size: int = 2048, paligemma_num_attention_heads: int = 8, paligemma_num_key_value_heads: int = 1, paligemma_head_dim: int = 256, paligemma_attention_bias: bool = False, # Expert params expert_hidden_size: int = 1024, expert_num_attention_heads: int = 8, expert_num_key_value_heads: int = 1, expert_head_dim: int = 256, expert_attention_bias: bool = False, # RoPE params rope_max_wavelength: int = 10_000, rope_max_seq_len: int = 8192, ): super().__init__() self.layer_idx = layer_idx self.q_proj = nn.ModuleList( [ nn.Linear( paligemma_hidden_size, paligemma_num_attention_heads * paligemma_head_dim, bias=paligemma_attention_bias, ), nn.Linear(expert_hidden_size, expert_num_attention_heads * expert_head_dim, bias=expert_attention_bias), ] ) self.k_proj = nn.ModuleList( [ nn.Linear( paligemma_hidden_size, paligemma_num_key_value_heads * paligemma_head_dim, bias=paligemma_attention_bias, ), nn.Linear(expert_hidden_size, expert_num_key_value_heads * expert_head_dim, bias=expert_attention_bias), ] ) self.v_proj = nn.ModuleList( [ nn.Linear( paligemma_hidden_size, paligemma_num_key_value_heads * paligemma_head_dim, bias=paligemma_attention_bias, ), nn.Linear(expert_hidden_size, expert_num_key_value_heads * expert_head_dim, bias=expert_attention_bias), ] ) self.o_proj = nn.ModuleList( [ nn.Linear( paligemma_num_attention_heads * paligemma_head_dim, paligemma_hidden_size, bias=paligemma_attention_bias, ), nn.Linear(expert_num_attention_heads * expert_head_dim, expert_hidden_size, bias=expert_attention_bias), ] ) self.paligemma_num_attention_heads = paligemma_num_attention_heads self.paligemma_num_key_value_heads = paligemma_num_key_value_heads self.paligemma_head_dim = paligemma_head_dim self.expert_num_attention_heads = expert_num_attention_heads self.expert_num_key_value_heads = expert_num_key_value_heads self.expert_head_dim = expert_head_dim assert paligemma_head_dim == expert_head_dim assert paligemma_num_attention_heads == expert_num_attention_heads assert paligemma_num_key_value_heads == expert_num_key_value_heads self.rope_embedding = RoPEEmbedding( dim=paligemma_head_dim, max_wavelength=rope_max_wavelength, max_seq_len=rope_max_seq_len ) def forward( self, inputs_embeds: list[Optional[torch.Tensor]], position_ids: torch.LongTensor, attention_mask: torch.Tensor, use_cache: bool, past_key_values: Optional[dict] = None, fill_kv_cache: bool = False, ) -> list[Optional[torch.Tensor]]: """Multi-source attention over PaliGemma and Expert streams. Args: inputs_embeds: [paligemma_embeds, expert_embeds]. Each is (B, L, D) or None. position_ids: (B, L) rotary positions. attention_mask: (B, L, L) attention mask. use_cache: Whether to use KV cache. past_key_values: Optional cache dict per layer. fill_kv_cache: If True, fill cache; otherwise, append to it. Returns: List[Optional[Tensor]]: outputs per stream aligned to inputs order. """ query_states = [] key_states = [] value_states = [] if inputs_embeds[0] is not None: # PaliGemma hidden_states = inputs_embeds[0] input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.paligemma_head_dim) query_states.append(self.q_proj[0](hidden_states).view(hidden_shape)) key_states.append(self.k_proj[0](hidden_states).view(hidden_shape)) value_states.append(self.v_proj[0](hidden_states).view(hidden_shape)) if inputs_embeds[1] is not None: # Expert hidden_states = inputs_embeds[1] input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.expert_head_dim) query_states.append(self.q_proj[1](hidden_states).view(hidden_shape)) key_states.append(self.k_proj[1](hidden_states).view(hidden_shape)) value_states.append(self.v_proj[1](hidden_states).view(hidden_shape)) query_states = torch.cat(query_states, dim=1) key_states = torch.cat(key_states, dim=1) value_states = torch.cat(value_states, dim=1) query_states = self.rope_embedding(query_states, position_ids) key_states = self.rope_embedding(key_states, position_ids) if use_cache: if fill_kv_cache: past_key_values[self.layer_idx] = { "key_states": key_states, "value_states": value_states, } else: key_states = torch.cat([past_key_values[self.layer_idx]["key_states"], key_states], dim=1) value_states = torch.cat([past_key_values[self.layer_idx]["value_states"], value_states], dim=1) num_att_heads = self.paligemma_num_attention_heads # Assume same for both num_key_value_heads = self.paligemma_num_key_value_heads head_dim = self.paligemma_head_dim batch_size = query_states.shape[0] query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if num_key_value_heads != num_att_heads: # key_states: (B, num_kv_heads, L, D) -> (B, num_att_heads, L, D) key_states = torch.repeat_interleave(key_states, num_att_heads // num_key_value_heads, dim=1) value_states = torch.repeat_interleave(value_states, num_att_heads // num_key_value_heads, dim=1) att_output = F.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask[:, None, :, :], is_causal=False, ) att_output = att_output.permute(0, 2, 1, 3) att_output = att_output.reshape(batch_size, -1, num_att_heads * head_dim) outputs_embeds = [] start = 0 if inputs_embeds[0] is not None: hidden_states = inputs_embeds[0] end = start + hidden_states.shape[1] if att_output.dtype != self.o_proj[0].weight.dtype: att_output_i = att_output[:, start:end].to(self.o_proj[0].weight.dtype) else: att_output_i = att_output[:, start:end] out_emb = self.o_proj[0](att_output_i) outputs_embeds.append(out_emb) start = end else: outputs_embeds.append(None) if inputs_embeds[1] is not None: hidden_states = inputs_embeds[1] end = start + hidden_states.shape[1] if att_output.dtype != self.o_proj[1].weight.dtype: att_output_i = att_output[:, start:end].to(self.o_proj[1].weight.dtype) else: att_output_i = att_output[:, start:end] out_emb = self.o_proj[1](att_output_i) outputs_embeds.append(out_emb) else: outputs_embeds.append(None) return outputs_embeds class GemmaMLP(nn.Module): def __init__(self, hidden_size: int = 1024, intermediate_size: int = 4096, hidden_act: str = "gelu_pytorch_tanh"): super().__init__() self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[hidden_act] def forward(self, x: torch.Tensor) -> torch.Tensor: """Gated MLP block used in both streams.""" down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class GemmaDecoderLayerWithExpert(nn.Module): def __init__( self, layer_idx: int, pi05_enabled: bool, # PaliGemma params paligemma_hidden_size: int = 2048, paligemma_num_attention_heads: int = 8, paligemma_num_key_value_heads: int = 1, paligemma_head_dim: int = 256, paligemma_attention_bias: bool = False, paligemma_intermediate_size: int = 16384, paligemma_hidden_act: str = "gelu_pytorch_tanh", paligemma_rms_norm_eps: float = 1e-6, # Expert params expert_hidden_size: int = 1024, expert_num_attention_heads: int = 8, expert_num_key_value_heads: int = 1, expert_head_dim: int = 256, expert_attention_bias: bool = False, expert_intermediate_size: int = 4096, expert_hidden_act: str = "gelu_pytorch_tanh", expert_rms_norm_eps: float = 1e-6, # RoPE params rope_max_wavelength: int = 10_000, rope_max_seq_len: int = 8192, ): super().__init__() self.self_attn = GemmaAttentionWithExpert( layer_idx, paligemma_hidden_size, paligemma_num_attention_heads, paligemma_num_key_value_heads, paligemma_head_dim, paligemma_attention_bias, expert_hidden_size, expert_num_attention_heads, expert_num_key_value_heads, expert_head_dim, expert_attention_bias, rope_max_wavelength, rope_max_seq_len, ) self.mlps = nn.ModuleList( [ GemmaMLP(paligemma_hidden_size, paligemma_intermediate_size, paligemma_hidden_act), GemmaMLP(expert_hidden_size, expert_intermediate_size, expert_hidden_act), ] ) self.input_layernorms = nn.ModuleList( [ GemmaRMSNorm(paligemma_hidden_size, eps=paligemma_rms_norm_eps), GemmaRMSNorm(expert_hidden_size, eps=expert_rms_norm_eps, use_ada_rms_norm=pi05_enabled), ] ) self.post_attention_layernorms = nn.ModuleList( [ GemmaRMSNorm(paligemma_hidden_size, eps=paligemma_rms_norm_eps), GemmaRMSNorm(expert_hidden_size, eps=expert_rms_norm_eps, use_ada_rms_norm=pi05_enabled), ] ) self.pi05_enabled = pi05_enabled def gated_residual(self, x, y, gate): if x is None or y is None: return None if gate is None: return x + y return x + y * gate def forward( self, inputs_embeds: list[Optional[torch.Tensor]], adarms_cond: list[Optional[torch.Tensor]], position_ids: torch.LongTensor, attention_mask: torch.Tensor, use_cache: bool, past_key_values: Optional[dict] = None, fill_kv_cache: bool = False, ) -> list[Optional[torch.Tensor]]: """Decoder layer with dual-stream attention and optional AdaRMS modulation. Args: inputs_embeds: [paligemma, expert] embeds. adarms_cond: Optional conditioning vectors for AdaRMS. position_ids: (B, L) positions for RoPE. attention_mask: (B, L, L) attention mask. use_cache: Whether to use KV cache. past_key_values: Optional cache dict. fill_kv_cache: Whether to fill or reuse KV cache. Returns: List[Optional[Tensor]]: Updated hidden states per stream. """ residuals = list(inputs_embeds) normed_embeds = [] attn_gates = [] for i, hidden_states in enumerate(inputs_embeds): if hidden_states is not None: if self.pi05_enabled and adarms_cond[i] is not None: normed_h, attn_gate = self.input_layernorms[i](hidden_states, adarms_cond[i]) normed_embeds.append(normed_h) attn_gates.append(attn_gate) else: normed_embeds.append(self.input_layernorms[i](hidden_states)) attn_gates.append(None) else: normed_embeds.append(None) attn_gates.append(None) attn_outputs = self.self_attn( normed_embeds, position_ids, attention_mask, use_cache, past_key_values, fill_kv_cache ) after_attn_embeds = [] for i, (residual, attn_output, attn_gate) in enumerate(zip(residuals, attn_outputs, attn_gates, strict=False)): if residual is not None: after_attn_embeds.append(self.gated_residual(residual, attn_output, attn_gate)) else: after_attn_embeds.append(None) outputs = [] for i, hidden_states in enumerate(after_attn_embeds): if hidden_states is not None: residual = hidden_states if self.pi05_enabled and adarms_cond[i] is not None: normed_h, mlp_gate = self.post_attention_layernorms[i](hidden_states, adarms_cond[i]) else: normed_h = self.post_attention_layernorms[i](hidden_states) mlp_gate = None mlp_out = self.mlps[i](normed_h) outputs.append(self.gated_residual(residual, mlp_out, mlp_gate)) else: outputs.append(None) return outputs, past_key_values class PaliGemmaWithExpertModel(nn.Module): def __init__( self, pi05_enabled: bool = False, # Paligemma params paligemma_vocab_size: int = 257152, paligemma_pad_token_id: int = 0, paligemma_num_hidden_layers: int = 18, paligemma_hidden_size: int = 2048, paligemma_num_attention_heads: int = 8, paligemma_num_key_value_heads: int = 1, paligemma_attention_bias: bool = False, paligemma_intermediate_size: int = 16384, paligemma_hidden_act: str = "gelu_pytorch_tanh", paligemma_rms_norm_eps: float = 1e-6, # Expert params expert_hidden_size: int = 1024, expert_num_attention_heads: int = 8, expert_num_key_value_heads: int = 1, expert_head_dim: int = 256, expert_attention_bias: bool = False, expert_intermediate_size: int = 4096, expert_hidden_act: str = "gelu_pytorch_tanh", expert_rms_norm_eps: float = 1e-6, # RoPE params rope_max_wavelength: int = 10_000, rope_max_seq_len: int = 8192, ): super().__init__() self.pi05_enabled = pi05_enabled siglip_vision_config = get_transformers_siglip_vision_config() # Vision and projection self.vision_tower = SiglipVisionTransformer(siglip_vision_config) self.multi_modal_projector = PaliGemmaMultiModalProjector( vision_hidden_size=siglip_vision_config.hidden_size, projection_dim=siglip_vision_config.projection_dim ) self.paligemma_hidden_size = paligemma_hidden_size # Language embed self.embed_tokens = nn.Embedding(paligemma_vocab_size, paligemma_hidden_size, paligemma_pad_token_id) # Decoder layers self.layers = nn.ModuleList( [ GemmaDecoderLayerWithExpert( layer_idx=i, pi05_enabled=pi05_enabled, paligemma_hidden_size=paligemma_hidden_size, paligemma_num_attention_heads=paligemma_num_attention_heads, paligemma_num_key_value_heads=paligemma_num_key_value_heads, paligemma_head_dim=paligemma_hidden_size // paligemma_num_attention_heads, paligemma_attention_bias=paligemma_attention_bias, # gemma default paligemma_intermediate_size=paligemma_intermediate_size, paligemma_hidden_act=paligemma_hidden_act, paligemma_rms_norm_eps=paligemma_rms_norm_eps, # gemma default expert_hidden_size=expert_hidden_size, expert_num_attention_heads=expert_num_attention_heads, expert_num_key_value_heads=expert_num_key_value_heads, expert_head_dim=expert_head_dim, expert_attention_bias=expert_attention_bias, expert_intermediate_size=expert_intermediate_size, expert_hidden_act=expert_hidden_act, expert_rms_norm_eps=expert_rms_norm_eps, rope_max_wavelength=rope_max_wavelength, rope_max_seq_len=rope_max_seq_len, ) for i in range(paligemma_num_hidden_layers) ] ) # Final norms self.norms = nn.ModuleList( [ GemmaRMSNorm(paligemma_hidden_size, eps=1e-6), GemmaRMSNorm(expert_hidden_size, eps=expert_rms_norm_eps, use_ada_rms_norm=pi05_enabled), ] ) def embed_image(self, image: torch.Tensor) -> torch.Tensor: """Encode images with SigLIP and project to hidden size.""" image_outputs = self.vision_tower(image) selected_image_feature = image_outputs.last_hidden_state image_features = self.multi_modal_projector(selected_image_feature) return image_features def embed_language_tokens(self, tokens: torch.Tensor) -> torch.Tensor: """Embed token ids into continuous vectors.""" return self.embed_tokens(tokens) def forward( self, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[dict] = None, inputs_embeds: list[torch.FloatTensor] = None, use_cache: Optional[bool] = None, fill_kv_cache: Optional[bool] = None, adarms_cond: list[torch.FloatTensor] = None, ) -> tuple[list[Optional[torch.Tensor]], dict]: """Run the stacked dual-stream decoder with optional caching and AdaRMS. Args: attention_mask: (B, L, L) attention mask for both streams. position_ids: (B, L) RoPE positions. past_key_values: Optional KV cache dict to reuse. inputs_embeds: [paligemma_embeds, expert_embeds]. use_cache: Whether to use KV cache. fill_kv_cache: If True, populate cache from inputs. adarms_cond: Optional per-stream modulation vectors for AdaRMS. Returns: (outputs_embeds, past_key_values): outputs per stream and the KV cache. """ inputs_embeds = [ input_embed.to(dtype=torch.bfloat16) if input_embed is not None else None for input_embed in inputs_embeds ] with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): if use_cache and past_key_values is None: past_key_values = {} hidden_states_list = inputs_embeds for layer in self.layers: # FSDP will make a copy of the "past_key_values" dictionary, which needs to be reassigned. hidden_states_list, past_key_values = layer( hidden_states_list, adarms_cond=adarms_cond, position_ids=position_ids, attention_mask=attention_mask, use_cache=use_cache, past_key_values=past_key_values, fill_kv_cache=fill_kv_cache, ) outputs_embeds = [] for i, hidden_states in enumerate(hidden_states_list): if hidden_states is not None: if self.pi05_enabled and adarms_cond[i] is not None: out_emb, _ = self.norms[i](hidden_states, adarms_cond[i]) else: out_emb = self.norms[i](hidden_states) outputs_embeds.append(out_emb) else: outputs_embeds.append(None) return outputs_embeds, past_key_values ================================================ FILE: verl/experimental/vla/models/pi0_torch/modeling_pi0_torch.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import math from typing import Literal import torch from onnx_ir import Tensor from torch import nn from torch.distributed.fsdp import register_fsdp_forward_method from transformers import PreTrainedModel from typing_extensions import override from verl.protocol import DataProto from verl.utils.device import get_device_name from ...sac.base import SupportSACTraining from ..modules.mlp import MLP from .configuration_pi0_torch import PI0TorchConfig from .model.modeling_pi0 import PI0Model, make_att_2d_masks from .pi0_utils import ( ImageTransform, Normalize, PromptTokenizerTransform, Unnormalize, ) from .policy.base import Pi0Output def beta_schedule(step, beta0, beta_min, T): progress = min(step / T, 1.0) beta = beta_min + (beta0 - beta_min) * 0.5 * (1 + math.cos(math.pi * progress)) return beta class PI0ForActionPrediction(PreTrainedModel, SupportSACTraining): config_class = PI0TorchConfig base_model_prefix = "pi0_torch" def __init__(self, config: PI0TorchConfig): super().__init__(config) self.model: PI0Model = None self.state_norm_stats = config.state_norm_stats self.action_norm_stats = config.action_norm_stats self.pi05_enabled = config.pi05_enabled assert self.state_norm_stats, "state_norm_stats must be provided in PI0TorchConfig" assert self.action_norm_stats, "action_norm_stats must be provided in PI0TorchConfig" assert isinstance(self.pi05_enabled, bool), "pi05_enabled must be provided in PI0TorchConfig" # Input transforms self.state_normalize_transform = Normalize(self.state_norm_stats, use_quantiles=self.pi05_enabled) self.action_normalize_transform = Normalize(self.action_norm_stats, use_quantiles=self.pi05_enabled) self.image_transform = ImageTransform(resize_imgs_with_padding=(224, 224), enable_image_aug=False) max_length = 200 if self.pi05_enabled else 48 self.prompt_tokenizer_transform = PromptTokenizerTransform(max_length=max_length, discrete_state_input=False) # Output transforms self.state_unnormalize_transform = Unnormalize(self.state_norm_stats, use_quantiles=self.pi05_enabled) self.action_unnormalize_transform = Unnormalize(self.action_norm_stats, use_quantiles=self.pi05_enabled) # Flow SDE parameters self._to(get_device_name()) self.flow_sde_enable = bool(getattr(config, "flow_sde_enable", True)) self.flow_sde_noise_level = float(getattr(config, "flow_sde_noise_level", 0.5)) self.flow_sde_rollout_noise_scale = float(getattr(config, "flow_sde_rollout_noise_scale", 1.0)) self.flow_sde_train_noise_scale = float(getattr(config, "flow_sde_train_noise_scale", 1.0)) self.flow_sde_initial_beta = float(getattr(config, "flow_sde_initial_beta", 1.0)) self.flow_sde_beta_min = float(getattr(config, "flow_sde_beta_min", 0.02)) self.flow_sde_beta_schedule_T = int(getattr(config, "flow_sde_beta_schedule_T", 2000)) self.register_buffer("flow_sde_step", torch.zeros((), dtype=torch.long)) ##### SAC Algorithm Support ##### if getattr(self.config, "sac_enable", False): head_num = int(getattr(self.config, "critic_head_num", 10)) attn_heads = int(getattr(self.config, "critic_prefix_attn_heads", 8)) self.critic_state_token = nn.Parameter(torch.zeros(1, 1, 2048)) self.target_state_token = nn.Parameter(torch.zeros(1, 1, 2048)) nn.init.normal_(self.critic_state_token, mean=0.0, std=0.02) self.target_state_token.data.copy_(self.critic_state_token.data) self.critic_prefix_cross_attn = nn.MultiheadAttention( embed_dim=2048, num_heads=attn_heads, batch_first=True, ) self.target_prefix_cross_attn = nn.MultiheadAttention( embed_dim=2048, num_heads=attn_heads, batch_first=True, ) self.critic_heads = nn.ModuleList( [ MLP( input_dim=2150, hidden_dims=[2048, 1024, 256], output_dim=1, activation="relu", init_method="kaiming", ) for _ in range(head_num) ] ) self.target_network_heads = nn.ModuleList( [ MLP( input_dim=2150, hidden_dims=[2048, 1024, 256], output_dim=1, activation="relu", init_method="kaiming", ) for _ in range(head_num) ] ) self.target_network_heads.load_state_dict(self.critic_heads.state_dict()) self.target_prefix_cross_attn.load_state_dict(self.critic_prefix_cross_attn.state_dict()) def _to(self, device: torch.device | str): self.state_normalize_transform.to(device) self.state_unnormalize_transform.to(device) self.action_normalize_transform.to(device) self.action_unnormalize_transform.to(device) return self def forward( self, images: list[torch.Tensor], img_masks: list[torch.Tensor], lang_tokens: torch.Tensor, lang_masks: torch.Tensor, state: torch.Tensor, x_t: torch.Tensor, timestep: torch.Tensor, ) -> Tensor: """Full forward pass for one diffusion denoising step. Args: images: List of image tensors, each shaped (B, C, H, W) after batching. img_masks: List of boolean masks corresponding to images, each (B,). lang_tokens: Language token ids (B, L). lang_masks: Language attention mask (B, L) with True for valid tokens. state: State tensor (B, state_dim) if pi05 is disabled else ignored. x_t: Noisy action tokens (B, n_action_steps, action_dim). timestep: Diffusion timestep as float tensor (B,). Returns: Predicted v_t with shape (B, n_action_steps, action_dim). """ if self.model is None: raise RuntimeError("PI0ForActionPrediction.model is not initialized. Did from_pretrained() run?") return self.model( images, img_masks, lang_tokens, lang_masks, state, x_t, timestep, ) @torch.no_grad() def sample_actions( self, env_obs: DataProto, tokenizer, validate: bool = False, ) -> tuple[Pi0Output, dict, dict]: """Run one forward pass from raw inputs to final action sequence. Args: env_obs: The environment observations as DataProto. tokenizer: The tokenizer used for prompt tokenization. Returns: A tuple of (pi0_output, s, a): - pi0_output: The Pi0Output containing the predicted actions. - s: Dictionary of tensors representing the states, with keys - "images": torch.Tensor of shape (B, n_images, C, H, W) - "image_masks": torch.Tensor of shape (B, n_images) - "lang_tokens": torch.Tensor of shape (B, L) - "lang_masks": torch.Tensor of shape (B, L) - "states": torch.Tensor of shape (B, state_dim) - a: Dictionary of tensors representing actions, with key: - "full_action": torch.Tensor of shape (B, action_steps, action_dim) """ from .policy.libero_policy import LiberoPi0Input pi0_input = LiberoPi0Input.from_env_obs(env_obs) # Input transforms state = self.state_normalize_transform(pi0_input.state) images, _ = self.image_transform.call_batch(pi0_input.images) lang_tokens, lang_masks = self.prompt_tokenizer_transform.call_batch( {"task": pi0_input.task, "observation.state": state}, tokenizer ) if self.flow_sde_enable and not validate: prefix_features = self.model.embed_prefix( images=images, img_masks=pi0_input.img_masks, lang_tokens=lang_tokens, lang_masks=lang_masks, ) pred_action, rollout_log_probs = self._sample_actions_flow_sde( state_features=(prefix_features, state), noise_scale=self.flow_sde_rollout_noise_scale, requires_grad=False, return_log_prob=True, ) else: pred_action = self.model.sample_actions(images, pi0_input.img_masks, lang_tokens, lang_masks, state=state) rollout_log_probs = torch.zeros(pred_action.shape[0], device=pred_action.device, dtype=torch.float32) # Output transforms from .policy.libero_policy import LiberoPi0Output pi0_output = LiberoPi0Output.from_model_output({"full_action": self.action_unnormalize_transform(pred_action)}) s = { "states": state, "images": torch.stack(images, dim=1), "image_masks": torch.stack(pi0_input.img_masks, dim=1), "lang_tokens": lang_tokens, "lang_masks": lang_masks, } a = { "full_action": pred_action, "log_probs": rollout_log_probs, } return pi0_output, s, a @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): config = kwargs.pop("config", None) if config is None: config = PI0TorchConfig.from_pretrained(pretrained_model_name_or_path) policy = cls(config) policy.model = PI0Model.from_pretrained(pretrained_model_name_or_path) return policy # def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False): # filtered_state_dict = { # key: value # for key, value in state_dict.items() # if key.startswith("model.") # } # return super().load_state_dict(filtered_state_dict, strict=False, assign=assign) def freeze_vision_tower(self) -> None: """Freeze the vision tower parameters.""" if self.model is None: raise RuntimeError("PI0ForActionPrediction.model is not initialized. Did from_pretrained() run?") vision_tower = self.model.paligemma_with_expert.vision_tower vision_tower.requires_grad_(False) vision_tower.eval() def bc_loss( self, state_features: tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor], actions: dict[str, torch.Tensor], valids: torch.Tensor, ) -> torch.Tensor: """Calculate the BC loss for the actor.""" prefix_features, states = state_features _, prefix_pad_masks, _ = prefix_features action_tensor = actions["full_action"] batch_size = action_tensor.shape[0] device = action_tensor.device noise = self.model.sample_noise(action_tensor.shape, device=device) gamma1 = torch.empty((batch_size,), device=device).uniform_(0, 1).pow(1 / 1.5) gamma2 = torch.empty((batch_size,), device=device).uniform_(0, 1).pow(1 / 1.0) time = (gamma1 / (gamma1 + gamma2)) * 0.999 + 0.001 time = time.to(dtype=torch.float32, device=device) time_expanded = time[:, None, None] x_t = time_expanded * noise + (1.0 - time_expanded) * action_tensor u_t = noise - action_tensor past_key_values = self._build_kv_cache_from_prefix(prefix_features) model_pred = self.model.denoise_step( states, prefix_pad_masks, past_key_values, x_t, time, ) loss = torch.nn.functional.mse_loss(u_t, model_pred, reduction="none").mean(dim=-1).mean(dim=-1) valid_f = valids.float().to(loss.device) return (loss * valid_f).sum() / valid_f.sum().clamp_min(1.0) # --- SAC Algorithm Support --- def _multi_heads_value( self, value_heads: nn.ModuleList, input_tensor: torch.Tensor, method: Literal["cat", "min"] = "cat" ) -> torch.Tensor: q_values = [head(input_tensor) for head in value_heads] if method == "cat": q_values = torch.cat(q_values, dim=-1) elif method == "min": q_values = torch.min(torch.cat(q_values, dim=-1), dim=-1).values else: raise ValueError(f"Unknown method: {method}") return q_values def _cross_attention_pool_prefix( self, prefix_embs: torch.Tensor, prefix_pad_masks: torch.Tensor, use_target_network: bool, ) -> torch.Tensor: cross_attn = self.target_prefix_cross_attn if use_target_network else self.critic_prefix_cross_attn state_token = self.target_state_token if use_target_network else self.critic_state_token batch_size = prefix_embs.shape[0] query = state_token.expand(batch_size, -1, -1) key_padding_mask = ~prefix_pad_masks.to(dtype=torch.bool) pooled, _ = cross_attn( query=query, key=prefix_embs, value=prefix_embs, key_padding_mask=key_padding_mask, need_weights=False, ) return pooled.squeeze(1) def _gaussian_log_prob( self, sample: torch.Tensor, mean: torch.Tensor, std: torch.Tensor, ) -> torch.Tensor: std_safe = std.clamp_min(1e-6) log_prob = -0.5 * (((sample - mean) / std_safe) ** 2 + 2.0 * torch.log(std_safe) + math.log(2.0 * math.pi)) return log_prob.mean(dim=(-1, -2)) def flow_sde_beta(self) -> torch.Tensor: beta = beta_schedule( int(self.flow_sde_step.item()), beta0=self.flow_sde_initial_beta, beta_min=self.flow_sde_beta_min, T=self.flow_sde_beta_schedule_T, ) return torch.tensor(beta, device=self.flow_sde_step.device, dtype=torch.float32) def _sample_actions_flow_sde( self, state_features: tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor], noise_scale: float, requires_grad: bool, return_log_prob: bool, ) -> tuple[torch.Tensor, torch.Tensor | None]: """ add noise to the action sampling process using Flow-SDE method. see https://arxiv.org/abs/2510.25889 """ prefix_features, states = state_features prefix_embs, prefix_pad_masks, _ = prefix_features batch_size = prefix_embs.shape[0] device = prefix_embs.device beta = self.flow_sde_beta().to(device=device, dtype=prefix_embs.dtype) past_key_values = self._build_kv_cache_from_prefix(prefix_features) actions_shape = (batch_size, self.model.n_action_steps, self.model.max_action_dim) x_t = torch.randn(actions_shape, device=device, dtype=prefix_embs.dtype) timesteps = torch.linspace(1.0, 0.0, self.model.num_steps + 1, dtype=torch.float32, device=device) step_log_probs: list[torch.Tensor] = [] for idx in range(self.model.num_steps): t_cur = timesteps[idx] t_next = timesteps[idx + 1] delta = (t_cur - t_next).clamp_min(1e-6) if requires_grad: v_t = self.model.denoise_step( states, prefix_pad_masks, past_key_values, x_t, t_cur.expand(batch_size), ) else: with torch.no_grad(): v_t = self.model.denoise_step( states, prefix_pad_masks, past_key_values, x_t, t_cur.expand(batch_size), ) t_cur_safe = t_cur.clamp(min=1e-4, max=1.0 - 1e-4) t_cur_exp = t_cur_safe.view(1, 1, 1) t_next_exp = t_next.view(1, 1, 1) delta_exp = delta.view(1, 1, 1) x0_pred = x_t - v_t * t_cur_exp x1_pred = x_t + v_t * (1.0 - t_cur_exp) if noise_scale > 0: sigma_schedule = self.flow_sde_noise_level * noise_scale * torch.sqrt(t_cur_safe / (1.0 - t_cur_safe)) sigma = beta * sigma_schedule sigma_exp = sigma.view(1, 1, 1) x0_weight = 1.0 - t_next_exp x1_weight = t_next_exp - sigma_exp.pow(2) * delta_exp / (2.0 * t_cur_exp) x_mean = x0_pred * x0_weight + x1_pred * x1_weight sigma_t = torch.sqrt(delta_exp) * sigma_exp eps = torch.randn_like(x_t) x_prev = x_mean + sigma_t * eps else: x0_weight = 1.0 - t_next_exp x1_weight = t_next_exp x_mean = x0_pred * x0_weight + x1_pred * x1_weight sigma_t = torch.zeros_like(x_mean) x_prev = x_mean if return_log_prob: step_log_probs.append(self._gaussian_log_prob(x_prev, x_mean, sigma_t)) x_t = x_prev if return_log_prob: log_probs = torch.stack(step_log_probs, dim=1).sum(dim=1) else: log_probs = None return x_t, log_probs def _build_kv_cache_from_prefix( self, prefix_features: tuple[torch.Tensor, torch.Tensor, torch.Tensor], ): """Build KV cache for prefix. No grad needed.""" prefix_embs, prefix_pad_masks, prefix_att_masks = prefix_features prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks) prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1 with torch.no_grad(): _, past_key_values = self.model.paligemma_with_expert.forward( attention_mask=prefix_att_2d_masks, position_ids=prefix_position_ids, past_key_values=None, inputs_embeds=[prefix_embs, None], use_cache=self.model.use_cache, fill_kv_cache=True, adarms_cond=[None, None], ) return past_key_values @override def sac_init(self): """Initialize SAC-related components.""" self.freeze_vision_tower() register_fsdp_forward_method(self, "bc_loss") register_fsdp_forward_method(self, "sac_forward_critic") register_fsdp_forward_method(self, "sac_forward_actor") register_fsdp_forward_method(self, "sac_update_target_network") register_fsdp_forward_method(self, "sac_forward_state_features") @override def sac_forward_actor( self, state_features: tuple[ tuple[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor, ], is_first_micro_batch: bool = False, ) -> tuple[torch.Tensor, torch.Tensor | None, dict[str, float]]: actions, log_probs = self._sample_actions_flow_sde( state_features=state_features, noise_scale=self.flow_sde_train_noise_scale, requires_grad=True, return_log_prob=True, ) if is_first_micro_batch: self.flow_sde_step.add_(1) actor_metrics: dict[str, float] = {} if self.flow_sde_enable: actor_metrics = { "flow_sde_beta": float(self.flow_sde_beta().item()), "flow_sde_step": float(self.flow_sde_step.item()), } return actions, log_probs, actor_metrics @override def sac_forward_critic( self, a: dict[str, torch.Tensor], state_features: tuple[ tuple[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor, ], *, use_target_network: bool = False, method: Literal["cat", "min"] = "cat", requires_grad: bool = False, ): critic_head = self.target_network_heads if use_target_network else self.critic_heads for p in critic_head.parameters(): p.requires_grad_(requires_grad) prefix_cross_attn = self.target_prefix_cross_attn if use_target_network else self.critic_prefix_cross_attn for p in prefix_cross_attn.parameters(): p.requires_grad_(requires_grad) if use_target_network: self.target_state_token.requires_grad_(requires_grad) else: self.critic_state_token.requires_grad_(requires_grad) prefix_features, states = state_features prefix_embs, prefix_pad_masks, _ = prefix_features pooled_prefix_embs = self._cross_attention_pool_prefix( prefix_embs=prefix_embs, prefix_pad_masks=prefix_pad_masks, use_target_network=use_target_network, ) # (B, 2048) actions = a["full_action"][:, :10, :7] # (B, 10, 7) flattened_actions = actions.reshape(actions.shape[0], -1) # (B, 70) critic_input = torch.cat([pooled_prefix_embs, states, flattened_actions], dim=-1) q_values = self._multi_heads_value(critic_head, critic_input, method=method) return q_values @override def sac_get_critic_parameters(self) -> list[torch.nn.Parameter]: critic_head_params = [p for head in self.critic_heads for p in head.parameters()] critic_prefix_cross_attn_params = list(self.critic_prefix_cross_attn.parameters()) return critic_head_params + critic_prefix_cross_attn_params + [self.critic_state_token] @override def sac_get_named_actor_parameters(self) -> list[tuple[str, torch.nn.Parameter]]: named_parameters = [(name, param) for name, param in self.model.named_parameters() if param.requires_grad] return named_parameters @override def sac_forward_state_features( self, s: dict[str, torch.Tensor] ) -> tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]: with torch.no_grad(): prefix_features = self.model.embed_prefix( images=s["images"].unbind(dim=1), img_masks=s["image_masks"].unbind(dim=1), lang_tokens=s["lang_tokens"], lang_masks=s["lang_masks"], ) return (prefix_features, s["states"]) @override @torch.no_grad() def sac_update_target_network(self, tau: float): for t_head, head in zip(self.target_network_heads, self.critic_heads, strict=True): t_sd = t_head.state_dict() h_sd = head.state_dict() for k in t_sd.keys(): t_sd[k].mul_(1.0 - tau).add_(h_sd[k], alpha=tau) t_head.load_state_dict(t_sd, strict=True) t_cross_attn_sd = self.target_prefix_cross_attn.state_dict() cross_attn_sd = self.critic_prefix_cross_attn.state_dict() for k in t_cross_attn_sd.keys(): t_cross_attn_sd[k].mul_(1.0 - tau).add_(cross_attn_sd[k], alpha=tau) self.target_prefix_cross_attn.load_state_dict(t_cross_attn_sd, strict=True) self.target_state_token.data.mul_(1.0 - tau).add_(self.critic_state_token.data, alpha=tau) ================================================ FILE: verl/experimental/vla/models/pi0_torch/pi0_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright 2025 Giga Team. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # from https://github.com/open-gigaai/giga-models from typing import Any import torch import torch.nn.functional as F from torchvision import transforms class Normalize: """Normalize robot state vectors using mean/std or quantiles. Args: stats: A dict containing either {'mean', 'std'} or {'q01', 'q99'}. use_quantiles: If True, use quantile based normalization. """ def __init__(self, stats: dict[str, Any], *, use_quantiles: bool = False) -> None: self.EPSILON = 1e-6 self.stats = stats self.use_quantiles = use_quantiles required_attrs = ["mean", "std"] if self.use_quantiles: required_attrs = ["q01", "q99"] for attr in required_attrs: if attr not in stats: raise AttributeError(f"stats object is missing the following attribute: {attr}") if self.use_quantiles: self.q01 = torch.tensor(stats["q01"], dtype=torch.float32) self.q99 = torch.tensor(stats["q99"], dtype=torch.float32) else: self.mean = torch.tensor(stats["mean"], dtype=torch.float32) self.std = torch.tensor(stats["std"], dtype=torch.float32) def to(self, device: torch.device | str) -> None: if self.use_quantiles: self.q01 = self.q01.to(device) self.q99 = self.q99.to(device) else: self.mean = self.mean.to(device) self.std = self.std.to(device) def __call__(self, x: torch.Tensor) -> torch.Tensor: x_dim = x.shape[-1] if self.use_quantiles: return (x - self.q01[..., :x_dim]) / ( self.q99[..., :x_dim] - self.q01[..., :x_dim] + self.EPSILON ) * 2.0 - 1.0 else: return (x - self.mean[..., :x_dim]) / (self.std[..., :x_dim] + self.EPSILON) class Unnormalize: def __init__(self, stats, *, use_quantiles: bool = False): self.EPSILON = 1e-6 self.stats = stats self.use_quantiles = use_quantiles if self.use_quantiles: self.q01 = torch.tensor(stats["q01"], dtype=torch.float32) self.q99 = torch.tensor(stats["q99"], dtype=torch.float32) else: self.mean = torch.tensor(stats["mean"], dtype=torch.float32) self.std = torch.tensor(stats["std"], dtype=torch.float32) def to(self, device: torch.device | str) -> None: if self.use_quantiles: self.q01 = self.q01.to(device) self.q99 = self.q99.to(device) else: self.mean = self.mean.to(device) self.std = self.std.to(device) def __call__(self, x: torch.Tensor) -> torch.Tensor: x_dim = x.shape[-1] if self.use_quantiles: return (x + 1.0) / 2.0 * (self.q99[..., :x_dim] - self.q01[..., :x_dim] + self.EPSILON) + self.q01[ ..., :x_dim ] else: return x * (self.std[..., :x_dim] + self.EPSILON) + self.mean[..., :x_dim] class DeltaActions: """Repacks absolute actions into delta action space.""" def __init__(self): # If the robot has mobile base, masks of base action are False and it doesn't need to be specified explicitly. self.mask = torch.tensor([True, True, True, True, True, True, False, True, True, True, True, True, True, False]) def to(self, device: torch.device | str) -> None: self.mask = self.mask.to(device) def __call__(self, data: dict[str, Any]) -> dict[str, Any]: if "action" not in data or "observation.state" not in data: return data state, action = data["observation.state"], data["action"] dims = self.mask.shape[-1] action[..., :dims] -= torch.where(self.mask, state[..., :dims], torch.zeros_like(state[..., :dims])).unsqueeze( -2 ) data["action"] = action return data class AbsoluteActions: """Repacks delta actions into absolute action space.""" def __init__(self): # If the robot has mobile base, masks of base action are False and it doesn't need to be specified explicitly. self.mask = torch.tensor([True, True, True, True, True, True, False, True, True, True, True, True, True, False]) def to(self, device: torch.device | str) -> None: self.mask = self.mask.to(device) def __call__(self, data: dict[str, Any]) -> dict[str, Any]: if "action" not in data or "observation.state" not in data: return data state, action = data["observation.state"], data["action"] dims = self.mask.shape[-1] action[..., :dims] += torch.where(self.mask, state[..., :dims], torch.zeros_like(state[..., :dims])).unsqueeze( -2 ) data["action"] = action return data class AlohaInputs: """Inputs for the Aloha policy.""" def __init__(self, adapt_to_pi: bool = True) -> None: self.joint_flip_mask = torch.tensor([1, -1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1]) self.adapt_to_pi = adapt_to_pi def to(self, device: torch.device | str) -> None: self.joint_flip_mask = self.joint_flip_mask.to(device) def _gripper_from_angular_inv(self, value: torch.Tensor) -> torch.Tensor: # Directly inverts the gripper_from_angular function. value = _unnormalize(value, min_val=-0.6213, max_val=1.4910) return value - 0.5476 def _gripper_to_angular(self, value: torch.Tensor) -> torch.Tensor: # Aloha transforms the gripper positions into a linear space. The following code # reverses this transformation to be consistent with pi0 which is pretrained in # angular space. # # These values are coming from the Aloha code: # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED value = _unnormalize(value, min_val=0.01844, max_val=0.05800) # This is the inverse of the angular to linear transformation inside the Interbotix code. def linear_to_radian(linear_position, arm_length, horn_radius): value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position) return torch.arcsin(torch.clip(value, -1.0, 1.0)) # The constants are taken from the Interbotix code. value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022) # pi0 gripper data is normalized (0, 1) between encoder counts (2405, 3110). # There are 4096 total encoder counts and aloha uses a zero of 2048. # Converting this to radians means that the normalized inputs are between (0.5476, 1.6296) return _normalize(value, min_val=0.5476, max_val=1.6296) def _encode_actions_inv(self, actions: torch.Tensor) -> torch.Tensor: if self.adapt_to_pi: actions[:, :14] = self.joint_flip_mask * actions[:, :14] actions[:, [6, 13]] = self._gripper_from_angular_inv(actions[:, [6, 13]]) return actions def _decode_state(self, state: torch.Tensor) -> torch.Tensor: if self.adapt_to_pi: # Flip the joints. state[:14] = self.joint_flip_mask * state[:14] # Reverse the gripper transformation that is being applied by the Aloha runtime. state[[6, 13]] = self._gripper_to_angular(state[[6, 13]]) return state def _decode_aloha(self, state: torch.Tensor) -> torch.Tensor: # state is [left_arm_joint_angles, left_arm_gripper, right_arm_joint_angles, right_arm_gripper] # dim sizes: [6, 1, 6, 1] state = self._decode_state(state) return state def __call__(self, data: dict[str, Any]) -> dict[str, Any]: """Decode Aloha-specific input formats into the pi0 training/runtime format.""" state = self._decode_aloha(data["observation.state"]) data["observation.state"] = state # Actions are only available during training. if "action" in data: actions = data["action"] actions = self._encode_actions_inv(actions) data["action"] = actions return data # VeRL: Batch Inference def _encode_actions_inv_batch(self, actions: torch.Tensor) -> torch.Tensor: if self.adapt_to_pi: actions[..., :14] = self.joint_flip_mask * actions[..., :14] actions[..., [6, 13]] = self._gripper_from_angular_inv(actions[..., [6, 13]]) return actions def _decode_state_batch(self, state: torch.Tensor) -> torch.Tensor: if self.adapt_to_pi: state[..., :14] = self.joint_flip_mask * state[..., :14] state[..., [6, 13]] = self._gripper_to_angular(state[..., [6, 13]]) return state def call_batch(self, data: dict[str, Any]) -> dict[str, Any]: state = self._decode_state_batch(data["observation.state"]) data["observation.state"] = state if "action" in data: actions = data["action"] actions = self._encode_actions_inv_batch(actions) data["action"] = actions return data class AlohaOutputs: """Outputs for the Aloha policy.""" def __init__(self, original_action_dim: int, adapt_to_pi: bool = True): """ Args: original_action_dim: int. The original action dimension of the policy. dual-arm robot has 14 dims and mobile dual-arm robot has 16 dims. adapt_to_pi: bool. If true, this will convert the joint and gripper values from the standard Aloha space to the space used by the pi internal runtime which was used to train the base model. """ self.joint_flip_mask = torch.tensor([1, -1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1]) self.original_action_dim = original_action_dim self.adapt_to_pi = adapt_to_pi def to(self, device: torch.device | str) -> None: self.joint_flip_mask = self.joint_flip_mask.to(device) def _gripper_from_angular(self, value: torch.Tensor) -> torch.Tensor: # Convert from the gripper position used by pi0 to the gripper position that is used by Aloha. # Note that the units are still angular but the range is different. # We do not scale the output since the trossen model predictions are already in radians. # See the comment in _gripper_to_angular for a derivation of the constant value = value + 0.5476 # These values are coming from the Aloha code: # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE return _normalize(value, min_val=-0.6213, max_val=1.4910) def _encode_actions(self, actions: torch.Tensor) -> torch.Tensor: if self.adapt_to_pi: # Flip the joints. actions[:, :14] = self.joint_flip_mask * actions[:, :14] actions[:, [6, 13]] = self._gripper_from_angular(actions[:, [6, 13]]) return actions def __call__(self, data: dict[str, Any]) -> dict[str, Any]: actions = data["action"][:, : self.original_action_dim] return {"action": self._encode_actions(actions)} # VeRL: Batch Inference def _encode_actions_batch(self, actions: torch.Tensor) -> torch.Tensor: if self.adapt_to_pi: actions[..., :14] = self.joint_flip_mask * actions[..., :14] actions[..., [6, 13]] = self._gripper_from_angular(actions[..., [6, 13]]) return actions def call_batch(self, data: dict[str, Any]) -> dict[str, Any]: actions = data["action"][..., : self.original_action_dim] return {"action": self._encode_actions_batch(actions)} class PadStatesAndActions: """Zero-pads states and actions to the model action dimension.""" def __init__(self, action_dim: int) -> None: self.action_dim = action_dim def _pad_to_dim(self, x: torch.Tensor, target_dim: int, axis: int = -1) -> torch.Tensor: """Pad an array to the target dimension with zeros along the specified axis.""" current_dim = x.shape[axis] if current_dim < target_dim: shape = list(x.shape) shape[-1] = target_dim new_vector = torch.zeros(*shape, dtype=x.dtype, device=x.device) new_vector[..., :current_dim] = x x = new_vector return x def __call__(self, data: dict[str, Any]) -> dict[str, Any]: data["observation.state"] = self._pad_to_dim(data["observation.state"], self.action_dim, axis=-1) if "action" in data: data["action"] = self._pad_to_dim(data["action"], self.action_dim, axis=-1) return data def _normalize(x: torch.Tensor, min_val: float, max_val: float) -> torch.Tensor: return (x - min_val) / (max_val - min_val) def _unnormalize(x: torch.Tensor, min_val: float, max_val: float) -> torch.Tensor: return x * (max_val - min_val) + min_val def resize_with_pad(img: torch.Tensor, width: int, height: int, pad_value: float = -1.0) -> torch.Tensor: """Resize an image to fit inside the given (width, height) while preserving aspect ratio, then pad with the specified value so that the final image exactly matches the target size. Args: img: Input image, shape (C, H, W), with values typically in [0, 1]. width: Target width (W). height: Target height (H). pad_value: Value to use for padding, defaults to -1. Returns: A torch.Tensor of shape (C, height, width). """ # Validate input dimensions if img.ndim != 3: raise ValueError(f"(C,H,W) expected, but got {img.shape}") cur_height, cur_width = img.shape[1:] ratio = max(cur_width / width, cur_height / height) resized_height = int(cur_height / ratio) resized_width = int(cur_width / ratio) resized_img = F.interpolate( img.unsqueeze(0), size=(resized_height, resized_width), mode="bilinear", align_corners=False ).squeeze(0) pad_height = max(0, int(height - resized_height)) pad_width = max(0, int(width - resized_width)) pad_top = pad_height // 2 pad_bottom = pad_height - pad_top pad_left = pad_width // 2 pad_right = pad_width - pad_left padded_img = F.pad(resized_img, (pad_left, pad_right, pad_top, pad_bottom), value=pad_value) return padded_img.squeeze(0) class ImageTransform: def __init__( self, resize_imgs_with_padding: tuple[int, int], present_img_keys: list[str] | None = None, enable_image_aug: bool = False, ) -> None: self.resize_imgs_with_padding = resize_imgs_with_padding self.present_img_keys = present_img_keys if self.present_img_keys is None: self.present_img_keys = [ "observation.images.cam_high", "observation.images.cam_left_wrist", "observation.images.cam_right_wrist", ] self.enable_image_aug = enable_image_aug self.width, self.height = resize_imgs_with_padding if self.enable_image_aug: self.color_jitter_transform = transforms.ColorJitter( brightness=0.3, contrast=0.4, saturation=0.5, ) self.pose_transform = transforms.Compose( [ transforms.RandomCrop(int(self.width * 0.95), int(self.height * 0.95)), transforms.Resize((self.width, self.height)), transforms.RandomRotation((-5, 5)), ] ) def __call__(self, data: dict[str, torch.Tensor]) -> tuple[list[torch.Tensor], list[torch.Tensor]]: """Preprocesses input images: optionally scales and pads to a fixed size, then maps the pixel range from [0,1] to [-1,1]. Returns two lists: images: The processed image arrays (C, H, W). img_masks: A list of boolean masks of the same length as images, currently fixed to True. """ images = [] img_masks = [] for key in self.present_img_keys: if key not in data: raise ValueError( f"{key} not found in data. Please check the present_img_keys in the config or the dataset." ) img = data[key] # [C, H, W] -> preprocess if self.resize_imgs_with_padding is not None: original_height, original_width = img.shape[1:] target_height, target_width = self.resize_imgs_with_padding if original_height != target_height or original_width != target_width: img = resize_with_pad(img, *self.resize_imgs_with_padding, pad_value=0) if self.enable_image_aug: if "wrist" not in key: img = self.pose_transform(img) img = self.color_jitter_transform(img) # Normalize pixel values to [-1, 1] img = img * 2.0 - 1.0 images.append(img) img_masks.append(torch.tensor(True, dtype=torch.bool, device=img.device)) return images, img_masks # VeRL: Batch Inference def call_batch(self, data: dict[str, torch.Tensor]) -> tuple[list[torch.Tensor], list[torch.Tensor]]: images = [] img_masks = [] for key in self.present_img_keys: if key not in data: raise ValueError( f"{key} not found in data. Please check the present_img_keys in the config or the dataset." ) img = data[key] if img.ndim != 4: raise ValueError(f"(B,C,H,W) expected, but got {img.shape}") if self.resize_imgs_with_padding is not None: original_height, original_width = img.shape[2:] target_height, target_width = self.resize_imgs_with_padding if original_height != target_height or original_width != target_width: ratio = max(original_width / target_width, original_height / target_height) resized_height = int(original_height / ratio) resized_width = int(original_width / ratio) img = F.interpolate(img, size=(resized_height, resized_width), mode="bilinear", align_corners=False) pad_height = max(0, int(target_height - resized_height)) pad_width = max(0, int(target_width - resized_width)) pad_top = pad_height // 2 pad_bottom = pad_height - pad_top pad_left = pad_width // 2 pad_right = pad_width - pad_left img = F.pad(img, (pad_left, pad_right, pad_top, pad_bottom), value=0) if self.enable_image_aug: imgs = [] for sample in img: if "wrist" not in key: sample = self.pose_transform(sample) sample = self.color_jitter_transform(sample) imgs.append(sample) img = torch.stack(imgs, dim=0) img = img / 255.0 * 2.0 - 1.0 # pi05 libero images.append(img) img_masks.append(torch.ones((img.shape[0],), dtype=torch.bool, device=img.device)) return images, img_masks class PromptTokenizerTransform: def __init__(self, max_length: int, discrete_state_input: bool = False) -> None: # self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_path) self.tokenizer_max_length = max_length self.discrete_state_input = discrete_state_input def __call__(self, data: dict[str, Any], tokenizer) -> tuple[torch.Tensor, torch.Tensor]: """Tokenize the text input. Args: data: Dict containing 'task' string and optionally 'observation.state' tensor to infer device. Returns: A tuple of (lang_tokens, lang_masks), both as torch tensors on the inferred device. """ task = data["task"].strip().replace("_", " ").replace("\n", " ") # Infer device from observation.state if available device = data["observation.state"].device if "observation.state" in data else torch.device("cpu") if self.discrete_state_input: assert "observation.state" in data, "discrete_state_input is True, but observation.state is not found." discretized_state = ( torch.bucketize(data["observation.state"], torch.linspace(-1, 1, 256 + 1, device=device)[:-1]) - 1 ) state_values = " ".join([str(int(x)) for x in discretized_state.tolist()]) task = f"Task: {task}, State: {state_values};\nAction: " else: # PaliGemma prompt has to end with a new line in Pi0 task = f"{task}\n" tokenized_prompt = tokenizer( task, padding="max_length", padding_side="right", max_length=self.tokenizer_max_length, return_tensors="pt", ) lang_tokens = tokenized_prompt["input_ids"][0].to(dtype=torch.int32, device=device) lang_masks = tokenized_prompt["attention_mask"][0].to(dtype=torch.bool, device=device) return lang_tokens, lang_masks # VeRL: Batch Inference def call_batch(self, data: dict[str, Any], tokenizer) -> tuple[torch.Tensor, torch.Tensor]: task = data["task"] if hasattr(task, "tolist") and not isinstance(task, str): tasks = task.tolist() else: tasks = list(task) tasks = [str(t).strip().replace("_", " ").replace("\n", " ") for t in tasks] device = data["observation.state"].device if "observation.state" in data else torch.device("cpu") if self.discrete_state_input: assert "observation.state" in data, "discrete_state_input is True, but observation.state is not found." state = data["observation.state"] discretized_state = torch.bucketize(state, torch.linspace(-1, 1, 256 + 1, device=device)[:-1]) - 1 state_values = [" ".join([str(int(x)) for x in row.tolist()]) for row in discretized_state] tasks = [ f"Task: {task_item}, State: {state_value};\nAction: " for task_item, state_value in zip(tasks, state_values, strict=False) ] else: tasks = [f"{task_item}\n" for task_item in tasks] tokenized_prompt = tokenizer( tasks, padding="max_length", padding_side="right", max_length=self.tokenizer_max_length, return_tensors="pt", ) lang_tokens = tokenized_prompt["input_ids"].to(dtype=torch.int32, device=device) lang_masks = tokenized_prompt["attention_mask"].to(dtype=torch.bool, device=device) return lang_tokens, lang_masks ================================================ FILE: verl/experimental/vla/models/pi0_torch/policy/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/experimental/vla/models/pi0_torch/policy/base.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod import torch class Pi0Input(ABC): def __init__(self): # three images for pi0 input with keys: # [ # 'observation.images.cam_high', # 'observation.images.cam_left_wrist', # 'observation.images.cam_right_wrist', # ], # each with shape (B, C, H, W) self.images: dict[str, torch.Tensor] = {} # image masks corresponding to the images, each with shape (B,) self.img_masks: list[torch.Tensor] = [] # task description as a list of strings self.task: list[str] = [] # robot state with shape (B, state_dim) self.state: torch.Tensor = None @classmethod @abstractmethod def from_env_obs(cls, env_obs) -> "Pi0Input": ... class Pi0Output: def __init__(self): self.action: torch.Tensor = None @classmethod @abstractmethod def from_model_output(cls, model_output) -> "Pi0Output": ... ================================================ FILE: verl/experimental/vla/models/pi0_torch/policy/libero_policy.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from typing_extensions import override from verl.protocol import DataProto from .base import Pi0Input, Pi0Output PI0_MAX_STATE_DIM = 32 PI0_ACTION_CHUNK_SIZE = 10 LIBERO_ACTION_DIM = 7 class LiberoPi0Input(Pi0Input): @override @classmethod def from_env_obs(cls, env_obs: DataProto) -> "LiberoPi0Input": input = cls() # Process images images = env_obs.batch["full_image"] wrist_images = env_obs.batch["wrist_image"] batch_size = images.shape[0] cam_high = images.permute(0, 3, 1, 2) left_wrist = wrist_images.permute(0, 3, 1, 2) # (B, H, W, C) -> (B, C, H, W) empty_images = torch.zeros( (batch_size, 3, cam_high.shape[2], cam_high.shape[3]), device=env_obs.batch.device, dtype=torch.bfloat16, ) input.images = { "observation.images.cam_high": cam_high.to(torch.bfloat16), "observation.images.cam_left_wrist": left_wrist.to(torch.bfloat16), "observation.images.cam_right_wrist": empty_images, } input.img_masks = [ torch.ones((batch_size,), device=env_obs.batch.device, dtype=torch.bool), torch.ones((batch_size,), device=env_obs.batch.device, dtype=torch.bool), torch.zeros((batch_size,), device=env_obs.batch.device, dtype=torch.bool), ] # Process other data input.task = list(env_obs.non_tensor_batch["task_descriptions"]) state = env_obs.batch["state"] input.state = torch.nn.functional.pad( state, (0, max(0, PI0_MAX_STATE_DIM - state.shape[-1])), "constant", 0 ).to(env_obs.batch.device, dtype=torch.float32) return input class LiberoPi0Output(Pi0Output): @override @classmethod def from_model_output(cls, model_output: dict) -> "LiberoPi0Output": output = cls() output.action = model_output["full_action"][:, :PI0_ACTION_CHUNK_SIZE, :LIBERO_ACTION_DIM] return output ================================================ FILE: verl/experimental/vla/models/register_vla_models.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utility helpers to register custom VLA models with Hugging Face Auto classes.""" from transformers import AutoConfig, AutoImageProcessor, AutoProcessor from verl.utils.transformers_compat import get_auto_model_for_vision2seq from .openvla_oft.configuration_prismatic import OpenVLAConfig from .openvla_oft.modeling_prismatic import OpenVLAForActionPrediction from .openvla_oft.processing_prismatic import PrismaticImageProcessor, PrismaticProcessor from .pi0_torch import PI0ForActionPrediction, PI0TorchConfig _REGISTERED_MODELS = { "openvla_oft": False, "pi0_torch": False, } AutoModelForVision2Seq = get_auto_model_for_vision2seq() def register_openvla_oft() -> None: """Register the OpenVLA OFT model and processors.""" if _REGISTERED_MODELS["openvla_oft"]: return AutoConfig.register("openvla", OpenVLAConfig) AutoImageProcessor.register(OpenVLAConfig, PrismaticImageProcessor) AutoProcessor.register(OpenVLAConfig, PrismaticProcessor) AutoModelForVision2Seq.register(OpenVLAConfig, OpenVLAForActionPrediction) _REGISTERED_MODELS["openvla_oft"] = True def register_pi0_torch_model() -> None: """Register the PI0 wrapper with the HF auto classes.""" if _REGISTERED_MODELS["pi0_torch"]: return AutoConfig.register("pi0_torch", PI0TorchConfig) AutoModelForVision2Seq.register(PI0TorchConfig, PI0ForActionPrediction) _REGISTERED_MODELS["pi0_torch"] = True def register_vla_models() -> None: """Register all custom VLA models with Hugging Face.""" register_openvla_oft() register_pi0_torch_model() ================================================ FILE: verl/experimental/vla/naive_rollout_rob.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ In single GPU rollout, the sequences are generated directly by sampling from the model. The output will contain 1. output_ids 2. attention_masks (left padding) 3. eos_masks 4. log_probs """ import json import logging import os import torch from PIL import Image from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.nn.utils.rnn import pad_sequence from verl import DataProto from verl.experimental.vla.envs.action_utils import center_crop_image, resize_image from verl.experimental.vla.models.openvla_oft.modeling_prismatic import OpenVLAForActionPrediction from verl.experimental.vla.models.openvla_oft.processing_prismatic import PrismaticProcessor from verl.utils.device import get_device_id, get_device_name, get_torch_device from verl.workers.rollout.base import BaseRollout logger = logging.getLogger(__name__) __all__ = ["NaiveRolloutRob"] def pad_sequence_to_length(tensors, max_seq_len, pad_token_id, left_pad=False): """ pad a 2D tensors (e.g. responses, logprobs) in the last dim to max_seq_length. input shape: [bs, seq_length] output shape: [bs, max_seq_length] (0, max_seq_len - tensors.shape[-1]) means right pad to max_seq_length and no left pad """ if tensors.shape[-1] >= max_seq_len: return tensors pad_tuple = (max_seq_len - tensors.shape[-1], 0) if left_pad else (0, max_seq_len - tensors.shape[-1]) return torch.nn.functional.pad(tensors, pad_tuple, "constant", pad_token_id) def process_input(task_descriptions, images_and_states, processor): batchdata = {"input_ids": [], "attention_mask": [], "pixel_values": []} for i in range(len(task_descriptions)): task_description = task_descriptions[i] image = resize_image(images_and_states["full_image"][i].cpu().numpy(), (224, 224)) image = Image.fromarray(image).convert("RGB") image = center_crop_image(image) prompt = f"In: What action should the robot take to {task_description.lower()}?\nOut:" batch_feature = processor(prompt, image) input_ids = batch_feature["input_ids"] attention_mask = batch_feature["attention_mask"] pixel_values = batch_feature["pixel_values"] if not torch.all(input_ids[:, -1] == 29871): input_ids = torch.cat( (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1 ) attention_mask = torch.cat( (attention_mask, torch.unsqueeze(torch.Tensor([True]).bool(), dim=0).to(attention_mask.device)), dim=1 ) batchdata["input_ids"].append(input_ids) batchdata["attention_mask"].append(attention_mask) batchdata["pixel_values"].append(pixel_values) device = get_device_id() batchdata["input_ids"] = [x.transpose(0, 1) for x in batchdata["input_ids"]] batchdata["attention_mask"] = [x.transpose(0, 1) for x in batchdata["attention_mask"]] batchdata["input_ids"] = ( pad_sequence(batchdata["input_ids"], batch_first=True, padding_value=processor.tokenizer.pad_token_id) .squeeze(-1) .to(device) ) batchdata["attention_mask"] = ( pad_sequence(batchdata["attention_mask"], batch_first=True, padding_value=0).squeeze(-1).to(device) ) padding_mask = batchdata["input_ids"].ne(processor.tokenizer.pad_token_id) assert torch.all(padding_mask == batchdata["attention_mask"].ne(0)) padding_mask = ~padding_mask padding_mask = padding_mask.int() sorted_indices = torch.argsort(padding_mask, dim=1, descending=True, stable=True) batchdata["input_ids"] = torch.gather(batchdata["input_ids"], 1, sorted_indices) batchdata["attention_mask"] = torch.gather(batchdata["attention_mask"], 1, sorted_indices) batchdata["pixel_values"] = torch.cat(batchdata["pixel_values"], dim=0).to(device) assert torch.all(batchdata["attention_mask"].ne(0) == batchdata["input_ids"].ne(processor.tokenizer.pad_token_id)) return batchdata class NaiveRolloutRob(BaseRollout): def __init__( self, model_config: dict, module: torch.nn.Module = None, ): self.model_config = model_config if module is not None: self.module = module else: self.module = OpenVLAForActionPrediction.from_pretrained(model_config["path"], trust_remote_code=True) self.module.vision_backbone.set_num_images_in_input(1) self.processor = PrismaticProcessor.from_pretrained(model_config["path"], trust_remote_code=True) dataset_statistics_path = os.path.join(model_config["path"], "dataset_statistics.json") if os.path.isfile(dataset_statistics_path): with open(dataset_statistics_path) as f: norm_stats = json.load(f) if isinstance(self.module, FSDP): self.module.module.norm_stats = norm_stats else: self.module.norm_stats = norm_stats self.module.eval() @torch.no_grad() def _generate_one_step(self, prompts: dict, do_sample, temperature, max_prompt_length): idx = prompts["input_ids"] # (bs, prompt_length) attention_mask = prompts["attention_mask"] # left-padded attention_mask pixel_values = prompts["pixel_values"] with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): actions, response = self.module.generate_action_verl( input_ids=idx, pixel_values=pixel_values, attention_mask=attention_mask, padding_idx=self.processor.tokenizer.pad_token_id, do_sample=do_sample, unnorm_key="libero_10_no_noops", temperature=temperature, ) assert self.processor.tokenizer.pad_token_id is not None assert idx.ndim == 2 idx = pad_sequence_to_length( idx, max_seq_len=max_prompt_length, pad_token_id=self.processor.tokenizer.pad_token_id, left_pad=True ) assert attention_mask.ndim == 2 attention_mask = pad_sequence_to_length( attention_mask, max_seq_len=max_prompt_length, pad_token_id=0, left_pad=True ) device_type = get_device_name() assert idx.device.type == device_type assert response.device.type == device_type assert attention_mask.device.type == device_type assert pixel_values.device.type == device_type batch = { "responses": response, "input_ids": idx, "attention_mask": attention_mask, "pixel_values": pixel_values, "action": actions, } return batch # @conditional_profiler(name="generate_sequences", path="traces/rollout", max_steps=5) @torch.no_grad() def generate_sequences(self, prompts: DataProto) -> DataProto: """Generate sequences""" # make sampling args can be overriden by inputs do_sample = prompts.meta_info["do_sample"] temperature = prompts.meta_info["temperature"] max_prompt_length = prompts.meta_info["prompt_length"] # TODO: split into micro-batches task_descriptions = prompts.non_tensor_batch["task_descriptions"] images_and_states = {"full_image": prompts.batch["full_image"]} vla_input = process_input(task_descriptions, images_and_states, self.processor) vla_output = self._generate_one_step(vla_input, do_sample, temperature, max_prompt_length) # batch = TensorDict(vla_output) batch = DataProto.from_dict(tensors=vla_output) return batch async def update_weights(self, weights_iterator, **kwargs): prefix = "_fsdp_wrapped_module." target_state_dict = self.module.state_dict() loaded_tensors_count = 0 for name, param in weights_iterator: cleaned_name = name.replace(prefix, "") if cleaned_name in target_state_dict: target_tensor = target_state_dict[cleaned_name] try: target_tensor.copy_(param, non_blocking=True) loaded_tensors_count += 1 except Exception as e: logger.warning(f"Warning: Failed to copy tensor '{cleaned_name}'. Error: {e}") else: logger.warning(f"Warning: Failed to copy tensor '{cleaned_name}'. Model has no such key.") logger.info(f"Rollout model weights updated. Loaded {loaded_tensors_count} tensors one by one.") async def release(self): if self.module.device.type == get_device_name(): logger.info("Releasing rollout model to CPU.") self.module.cpu() self.device = torch.device("cpu") get_torch_device().empty_cache() async def resume(self, **kwargs): if self.module.device.type == "cpu": target_device = get_device_name() logger.info(f"Resuming rollout model to device: {target_device}.") self.module.to(target_device) self.device = torch.device(target_device) ================================================ FILE: verl/experimental/vla/prepare_libero_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocess the Geometry3k dataset to parquet format """ import argparse import os import random import numpy as np import torch from datasets import Dataset from libero.libero import get_libero_path from libero.libero.benchmark import Benchmark, get_benchmark, get_benchmark_dict def patched_get_task_init_states(self, i): init_states_path = os.path.join( get_libero_path("init_states"), self.tasks[i].problem_folder, self.tasks[i].init_states_file, ) init_states = torch.load(init_states_path, weights_only=False) return init_states Benchmark.get_task_init_states = patched_get_task_init_states def compute_total_num_group_envs(task_suite: Benchmark): total_num_group_envs = 0 trial_id_bins = [] for task_id in range(task_suite.get_num_tasks()): task_num_trials = len(task_suite.get_task_init_states(task_id)) trial_id_bins.append(task_num_trials) total_num_group_envs += task_num_trials cumsum_trial_id_bins = np.cumsum(trial_id_bins) return total_num_group_envs, cumsum_trial_id_bins def build_dataset_for_suite(task_suite_name: str, local_save_dir: str): task_suite = get_benchmark(task_suite_name)() total_num_group_envs, cumsum_trial_id_bins = compute_total_num_group_envs(task_suite) print(f"\n[Suite: {task_suite_name}]") print(f"Total number of group envs: {total_num_group_envs}") print(f"Cumsum trial id bins: {cumsum_trial_id_bins}") def get_state_ids_for_task(task_id): start_id = 0 if task_id == 0 else cumsum_trial_id_bins[task_id - 1] end_id = cumsum_trial_id_bins[task_id] return list(range(start_id, end_id)) all_task_ids = list(range(task_suite.get_num_tasks())) if len(all_task_ids) > 1: train_task_num = max(1, len(all_task_ids) - 1) train_task_ids = sorted(random.sample(all_task_ids, train_task_num)) ood_test_task_ids = sorted(list(set(all_task_ids) - set(train_task_ids))) else: train_task_ids = all_task_ids ood_test_task_ids = [] print("\n[Data Split Plan]") print(f"Training Task IDs: {train_task_ids}") print(f"OOD Test Task IDs: {ood_test_task_ids}") train_metadata = [] test_metadata = [] # Train split + ID test split for task_id in train_task_ids: all_trials = get_state_ids_for_task(task_id) random.shuffle(all_trials) train_count = int(len(all_trials) * 0.8) train_count = min(train_count, 40) selected_train_trials = all_trials[:train_count] selected_id_test_trials = all_trials[train_count:] for state_id in selected_train_trials: train_metadata.append({"task_id": task_id, "state_id": state_id, "data_source": "train"}) for state_id in selected_id_test_trials[:10]: test_metadata.append({"task_id": task_id, "state_id": state_id, "data_source": "test_in_distribution"}) # OOD split for ood_task_id in ood_test_task_ids: ood_all_trials = get_state_ids_for_task(ood_task_id) random.shuffle(ood_all_trials) selected_ood_trials = ood_all_trials[:20] for state_id in selected_ood_trials: test_metadata.append( {"task_id": ood_task_id, "state_id": state_id, "data_source": "test_out_of_distribution"} ) print(f"Generated {len(train_metadata)} training samples.") print(f"Generated {len(test_metadata)} testing samples.") print("-" * 20) train_ds_meta = Dataset.from_list(train_metadata) test_ds_meta = Dataset.from_list(test_metadata) def map_and_process(example, idx): task_id = example["task_id"] state_id = example["state_id"] data_source = example["data_source"] split = "train" if data_source == "train" else "test" task = task_suite.get_task(task_id) data = { "data_source": data_source, "prompt": task.language, "state_ids": state_id, "task_ids": task_id, "ability": "robot", "extra_info": { "split": split, "state_ids": state_id, "index": idx, "task": task, "task_ids": task_id, "task_suite_name": task_suite_name, }, } return data print("Mapping and processing training dataset...") train_dataset = train_ds_meta.map(map_and_process, with_indices=True, num_proc=8) print("Mapping and processing test dataset...") test_dataset = test_ds_meta.map(map_and_process, with_indices=True, num_proc=8) suite_save_dir = os.path.join(local_save_dir, task_suite_name) os.makedirs(suite_save_dir, exist_ok=True) print(f"Saving training dataset to {os.path.join(suite_save_dir, 'train.parquet')}") train_dataset.to_parquet(os.path.join(suite_save_dir, "train.parquet")) print(f"Saving test dataset to {os.path.join(suite_save_dir, 'test.parquet')}") test_dataset.to_parquet(os.path.join(suite_save_dir, "test.parquet")) print("\nDataset generation complete!") print("\n--- Verification ---") print("Train dataset data sources:", train_dataset.unique("data_source")) print("Test dataset data sources:", test_dataset.unique("data_source")) print("Train dataset length:", len(train_dataset)) print("Test dataset length:", len(test_dataset)) def resolve_task_suites(task_suite_name: str) -> list[str]: benchmark_dict = get_benchmark_dict() available_suites = sorted( [name for name in benchmark_dict.keys() if name.startswith("libero_") and name != "libero_100"] ) requested = task_suite_name.strip().lower() if requested == "all": return available_suites suites = [suite.strip().lower() for suite in requested.split(",") if suite.strip()] invalid = [suite for suite in suites if suite not in available_suites] if invalid: raise ValueError( f"Unknown task_suite_name: {invalid}. Available suites: {available_suites}. " "You can also pass --task_suite_name all" ) return suites if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--task_suite_name", default="libero_spatial", help="Task suite name. Support single suite (e.g. libero_spatial), multiple suites split by comma, or all.", ) parser.add_argument( "--local_save_dir", default="~/data/libero_rl", help="The save directory for the preprocessed dataset." ) args = parser.parse_args() random.seed(42) np.random.seed(42) local_save_dir = os.path.expanduser(args.local_save_dir) os.makedirs(local_save_dir, exist_ok=True) task_suites = resolve_task_suites(args.task_suite_name) print(f"Will process task suites: {task_suites}") for task_suite_name in task_suites: build_dataset_for_suite(task_suite_name=task_suite_name, local_save_dir=local_save_dir) ================================================ FILE: verl/experimental/vla/requirements_vla.txt ================================================ # libero timm<1.0.0 imageio draccus==0.8.0 einops huggingface_hub json-numpy jsonlines matplotlib rich sentencepiece==0.1.99 # dlimp @ git+https://github.com/moojink/dlimp_openvla diffusers==0.30.3 imageio uvicorn fastapi json-numpy wandb==0.19.11 protobuf==3.20.3 ================================================ FILE: verl/experimental/vla/rob_ray_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PPO Trainer with Ray-based single controller. This trainer supports model-agonistic model initialization with huggingface """ import asyncio import uuid from collections import defaultdict from pprint import pprint import numpy as np import torch from omegaconf import OmegaConf from tqdm import tqdm from verl import DataProto from verl.experimental.dataset.sampler import AbstractCurriculumSampler from verl.protocol import pad_dataproto_to_divisor, unpad_dataproto from verl.single_controller.ray import RayClassWithInitArgs from verl.single_controller.ray.base import create_colocated_worker_cls from verl.trainer.ppo.core_algos import agg_loss from verl.trainer.ppo.metric_utils import ( compute_data_metrics, compute_throughout_metrics, compute_timing_metrics, process_validation_metrics, ) from verl.trainer.ppo.ray_trainer import RayPPOTrainer, apply_kl_penalty, compute_advantage from verl.trainer.ppo.reward import compute_reward from verl.trainer.ppo.utils import Role from verl.utils.checkpoint.checkpoint_manager import should_save_ckpt_esi from verl.utils.debug import marked_timer from verl.utils.metric import reduce_metrics def compute_response_mask(config, data: DataProto) -> torch.Tensor: """Compute the attention mask for the response part of the sequence. This function extracts the portion of the attention mask that corresponds to the model's response, which is used for masking computations that should only apply to response tokens. Args: data (DataProto): The data containing batched model outputs and inputs. Returns: torch.Tensor: The attention mask for the response tokens. """ complete = data.batch["complete"] # shape: [batch_size, num_steps, chunk_size] complete_traj = complete.view(complete.shape[0], -1) # # shape: [batch_size, num_steps * chunk_size] batch_size, action_steps = complete_traj.shape step_indices = torch.arange(action_steps, device=complete.device).unsqueeze(0).expand(batch_size, -1) first_true_idx_approx = torch.argmax(complete_traj.long(), dim=1) has_any_true = complete_traj.any(dim=1) final_first_true_idx = torch.where( has_any_true, first_true_idx_approx, torch.tensor(action_steps - 1, device=complete.device) ) mask_traj = step_indices <= final_first_true_idx.unsqueeze(1) mask = mask_traj.view(complete.shape) # shape: [batch_size, num_steps, chunk_size] mask = mask.repeat_interleave(config.env.actor.model.action_dim, dim=-1) # eapand to action dim return mask def flatten_trajectories(data: DataProto) -> DataProto: batch_size, num_steps = data.batch["action"].shape[:2] new_batch_fields = {} for key, tensor in data.batch.items(): if len(tensor.shape) >= 2 and tensor.shape[0] == batch_size and tensor.shape[1] == num_steps: # (B, S, H, W) -> (B*S, H, W) new_shape = (batch_size * num_steps, *tensor.shape[2:]) new_batch_fields[key] = tensor.reshape(new_shape) elif len(tensor.shape) == 1 and tensor.shape[0] == batch_size: # [e1, e2] -> [e1, e1, ..., e2, e2, ...] (S times each) new_batch_fields[key] = tensor.repeat_interleave(num_steps) else: new_batch_fields[key] = tensor new_data = DataProto.from_dict(tensors=new_batch_fields, meta_info=data.meta_info) return new_data # def filter_by_acc(data: DataProto, accuracy_lower_bound, accuracy_upper_bound) -> torch.Tensor: class RobRayPPOTrainer(RayPPOTrainer): """Distributed PPO trainer using Ray for scalable reinforcement learning. This trainer orchestrates distributed PPO training across multiple nodes and GPUs, managing actor rollouts, critic training, and reward computation with Ray backend. Supports various model architectures including FSDP, Megatron, vLLM, and SGLang integration. """ def _start_profiling(self, do_profile: bool) -> None: """Start profiling for all worker groups including env workers.""" super()._start_profiling(do_profile) if do_profile and hasattr(self, "env_wg"): self.env_wg.start_profile(role="env", profile_step=self.global_steps) def _stop_profiling(self, do_profile: bool) -> None: """Stop profiling for all worker groups including env workers.""" super()._stop_profiling(do_profile) if do_profile and hasattr(self, "env_wg"): self.env_wg.stop_profile() def init_workers(self): self.resource_pool_manager.create_resource_pool() if self.config.env.disagg_sim.enable: # pin EnvWorker to Simulator GPU nodes self.resource_pool_manager.get_resource_pool(Role.Env).accelerator_type = "sim" self.resource_pool_manager.get_resource_pool(Role.ActorRollout).accelerator_type = "train_rollout" self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()} resource_pool = self.resource_pool_manager.get_resource_pool(Role.ActorRollout) actor_rollout_cls = RayClassWithInitArgs( cls=self.role_worker_mapping[Role.ActorRollout], config=self.config.actor_rollout_ref, role="actor_rollout", ) self.resource_pool_to_cls[resource_pool]["actor_rollout"] = actor_rollout_cls assert Role.Env in self.role_worker_mapping if Role.Env in self.role_worker_mapping: resource_pool = self.resource_pool_manager.get_resource_pool(Role.Env) env_cls = RayClassWithInitArgs(self.role_worker_mapping[Role.Env], config=self.config.env) self.resource_pool_to_cls[resource_pool]["env"] = env_cls # initialize WorkerGroup # NOTE: if you want to use a different resource pool for each role, which can support different parallel size, # you should not use `create_colocated_worker_cls`. # Instead, directly pass different resource pool to different worker groups. # See https://github.com/volcengine/verl/blob/master/examples/ray/tutorial.ipynb for more information. all_wg = {} wg_kwargs = {} # Setting up kwargs for RayWorkerGroup if OmegaConf.select(self.config.trainer, "ray_wait_register_center_timeout") is not None: wg_kwargs["ray_wait_register_center_timeout"] = self.config.trainer.ray_wait_register_center_timeout if OmegaConf.select(self.config.global_profiler, "steps") is not None: wg_kwargs["profile_steps"] = OmegaConf.select(self.config.global_profiler, "steps") # Only require nsight worker options when tool is nsys if OmegaConf.select(self.config.global_profiler, "tool") == "nsys": assert ( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") is not None ), "worker_nsight_options must be set when using nsys with profile_steps" wg_kwargs["worker_nsight_options"] = OmegaConf.to_container( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") ) wg_kwargs["device_name"] = self.device_name for resource_pool, class_dict in self.resource_pool_to_cls.items(): worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = self.ray_worker_group_cls( resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls, **wg_kwargs, ) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) # we should create rollout at the end so that vllm can have a better estimation of kv cache memory self.actor_rollout_wg = all_wg["actor_rollout"] self.actor_rollout_wg.init_model() self.env_wg = all_wg["env"] # create async rollout manager and request scheduler self.async_rollout_mode = False if self.config.actor_rollout_ref.rollout.mode == "async_envloop": from verl.experimental.vla.env_loop import EnvLoop self.async_rollout_mode = True self.async_rollout_manager = EnvLoop( config=self.config, rollout_wg=self.actor_rollout_wg, env_wg=self.env_wg ) def _get_gen_batch(self, batch: DataProto) -> DataProto: # pop those keys for generation batch_keys_to_pop = [] non_tensor_batch_keys_to_pop = set(batch.non_tensor_batch.keys()) gen_batch = batch.pop( batch_keys=batch_keys_to_pop, non_tensor_batch_keys=list(non_tensor_batch_keys_to_pop), ) return gen_batch def _reset_envs(self, gen_batch: DataProto) -> asyncio.Future: initial_state_ids = gen_batch.non_tensor_batch["state_ids"] task_ids = gen_batch.non_tensor_batch["task_ids"] reset_prompts = DataProto.from_dict(non_tensors={"state_ids": initial_state_ids, "task_ids": task_ids}) reset_future = self.env_wg.reset_envs_to_state_ids(reset_prompts) return reset_future def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ from omegaconf import OmegaConf from verl.utils.tracking import Tracking logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) self.global_steps = 0 # load checkpoint before doing anything self._load_checkpoint() # perform validation before training # currently, we only support validation using the reward_function. if self.val_reward_fn is not None and self.config.trainer.get("val_before_train", True): val_metrics = self._validate() assert val_metrics, f"{val_metrics=}" pprint(f"Initial validation metrics: {val_metrics}") logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get("val_only", False): return # add tqdm progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress") # we start from step 1 self.global_steps += 1 last_val_metrics = None self.max_steps_duration = 0 prev_step_profile = False curr_step_profile = ( self.global_steps in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) next_step_profile = False for epoch in range(self.config.trainer.total_epochs): train_iter = iter(self.train_dataloader) next_batch_dict = next(train_iter) need_validate = False dataloader_len = len(self.train_dataloader) print(f"Starting epoch {epoch}, dataloader length: {dataloader_len}") for step_idx in range(dataloader_len): batch_dict = next_batch_dict try: next_batch_dict = next(train_iter) except StopIteration: next_batch_dict = None metrics = {} timing_raw = {} with marked_timer("start_profile", timing_raw): self._start_profiling( not prev_step_profile and curr_step_profile if self.config.global_profiler.profile_continuous_steps else curr_step_profile ) batch: DataProto = DataProto.from_single_dict(batch_dict) # add uid to batch batch.non_tensor_batch["uid"] = np.array([str(uuid.uuid4()) for _ in range(len(batch))], dtype=object) gen_batch = self._get_gen_batch(batch) # pass global_steps to trace gen_batch.meta_info["global_steps"] = self.global_steps # pass generation config to gen_batch gen_batch.meta_info["do_sample"] = True gen_batch.meta_info["temperature"] = self.config.actor_rollout_ref.rollout.temperature gen_batch.meta_info["prompt_length"] = self.config.actor_rollout_ref.rollout.prompt_length gen_batch.meta_info["eos_token_id"] = self.tokenizer.eos_token_id gen_batch.meta_info["n_samples"] = self.config.actor_rollout_ref.rollout.n gen_batch.meta_info["pad_token_id"] = self.tokenizer.pad_token_id gen_batch = gen_batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) is_last_step = self.global_steps >= self.total_training_steps if step_idx == 0 or need_validate: # reset env workers in first step # if validation on last step, the reset was not executed and need to be done here reset_future = self._reset_envs(gen_batch) need_validate = ( self.val_reward_fn is not None and self.config.trainer.test_freq > 0 and (is_last_step or self.global_steps % self.config.trainer.test_freq == 0) ) with marked_timer("step", timing_raw): # generate a batch with marked_timer("gen", timing_raw, color="red"): gen_batch_output = self.async_rollout_manager.generate_sequences(gen_batch, reset_future) # prepare for next batch's env reset if step_idx != dataloader_len - 1 and not need_validate: next_batch: DataProto = DataProto.from_single_dict(next_batch_dict) next_gen_batch = self._get_gen_batch(next_batch) next_gen_batch = next_gen_batch.repeat( repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True ) reset_future = self._reset_envs(next_gen_batch) # repeat to align with repeated responses in rollout batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) batch = gen_batch_output if "response_mask" not in batch.batch.keys(): batch.batch["response_mask"] = compute_response_mask(self.config, batch) with marked_timer("reward", timing_raw, color="yellow"): # compute reward model score reward_tensor, reward_extra_infos_dict = compute_reward(batch, self.reward_fn) batch.batch["reward_tensor"] = reward_tensor batch = flatten_trajectories(batch) batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist() # recompute old_log_probs with marked_timer("old_log_prob", timing_raw, color="blue"): old_log_prob = self.actor_rollout_wg.compute_log_prob(batch) entropys = old_log_prob.batch["entropys"] response_masks = batch.batch["response_mask"] actor_config = self.config.actor_rollout_ref.actor entropy_agg = agg_loss( loss_mat=entropys, loss_mask=response_masks, loss_agg_mode=actor_config.loss_agg_mode, loss_scale_factor=actor_config.loss_scale_factor, ) old_log_prob_metrics = {"actor/entropy": entropy_agg.detach().item()} metrics.update(old_log_prob_metrics) old_log_prob.batch.pop("entropys") batch = batch.union(old_log_prob) if "rollout_log_probs" in batch.batch.keys(): # TODO: we may want to add diff of probs too. from verl.utils.debug.metrics import calculate_debug_metrics metrics.update(calculate_debug_metrics(batch)) if self.use_reference_policy: # compute reference log_prob with marked_timer("ref", timing_raw, color="olive"): if not self.ref_in_actor: ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch) else: ref_log_prob = self.actor_rollout_wg.compute_ref_log_prob(batch) batch = batch.union(ref_log_prob) # compute values if self.use_critic: with marked_timer("values", timing_raw, color="cyan"): values = self.critic_wg.compute_values(batch) batch = batch.union(values) with marked_timer("adv", timing_raw, color="brown"): # we combine with rule-based rm reward_extra_infos_dict: dict[str, list] = None token_level_scores = torch.zeros_like(response_masks, dtype=torch.float32) flipped_mask = response_masks.flip(dims=[1]) indices_in_flipped = torch.argmax(flipped_mask.long(), dim=1) last_true_indices = response_masks.shape[-1] - 1 - indices_in_flipped rows_with_response = response_masks.any(dim=1) effective_rewards = batch.batch["reward_tensor"] * rows_with_response.to( batch.batch["reward_tensor"].dtype ) row_indices = torch.arange(response_masks.shape[0], device=token_level_scores.device) token_level_scores[row_indices, last_true_indices] = effective_rewards batch.batch["token_level_scores"] = token_level_scores if reward_extra_infos_dict: batch.non_tensor_batch.update({k: np.array(v) for k, v in reward_extra_infos_dict.items()}) # compute rewards. apply_kl_penalty if available if self.config.algorithm.use_kl_in_reward: batch, kl_metrics = apply_kl_penalty( batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty ) metrics.update(kl_metrics) else: batch.batch["token_level_rewards"] = batch.batch["token_level_scores"] # compute advantages, executed on the driver process norm_adv_by_std_in_grpo = self.config.algorithm.get( "norm_adv_by_std_in_grpo", True ) # GRPO adv normalization factor batch = compute_advantage( batch, adv_estimator=self.config.algorithm.adv_estimator, gamma=self.config.algorithm.gamma, lam=self.config.algorithm.lam, num_repeat=self.config.actor_rollout_ref.rollout.n, norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo, config=self.config.algorithm, ) # update critic if self.use_critic: with marked_timer("update_critic", timing_raw, color="pink"): critic_output = self.critic_wg.update_critic(batch) critic_output_metrics = reduce_metrics(critic_output.meta_info["metrics"]) metrics.update(critic_output_metrics) # implement critic warmup if self.config.trainer.critic_warmup <= self.global_steps: # update actor with marked_timer("update_actor", timing_raw, color="red"): batch.meta_info["multi_turn"] = self.config.actor_rollout_ref.rollout.multi_turn.enable actor_output = self.actor_rollout_wg.update_actor(batch) actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"]) metrics.update(actor_output_metrics) # Log rollout generations if enabled rollout_data_dir = self.config.trainer.get("rollout_data_dir", None) if rollout_data_dir: with marked_timer("dump_rollout_generations", timing_raw, color="green"): inputs = self.tokenizer.batch_decode(batch.batch["prompts"], skip_special_tokens=True) outputs = self.tokenizer.batch_decode(batch.batch["responses"], skip_special_tokens=True) scores = batch.batch["token_level_scores"].sum(-1).cpu().tolist() sample_gts = [ item.non_tensor_batch.get("reward_model", {}).get("ground_truth", None) for item in batch ] if "request_id" in batch.non_tensor_batch: reward_extra_infos_dict.setdefault( "request_id", batch.non_tensor_batch["request_id"].tolist(), ) self._dump_generations( inputs=inputs, outputs=outputs, gts=sample_gts, scores=scores, reward_extra_infos_dict=reward_extra_infos_dict, dump_path=rollout_data_dir, ) # validate if need_validate: with marked_timer("testing", timing_raw, color="green"): val_metrics: dict = self._validate() if is_last_step: last_val_metrics = val_metrics metrics.update(val_metrics) # Check if the ESI (Elastic Server Instance)/training plan is close to expiration. esi_close_to_expiration = should_save_ckpt_esi( max_steps_duration=self.max_steps_duration, redundant_time=self.config.trainer.esi_redundant_time, ) # Check if the conditions for saving a checkpoint are met. # The conditions include a mandatory condition (1) and # one of the following optional conditions (2/3/4): # 1. The save frequency is set to a positive value. # 2. It's the last training step. # 3. The current step number is a multiple of the save frequency. # 4. The ESI(Elastic Server Instance)/training plan is close to expiration. if self.config.trainer.save_freq > 0 and ( is_last_step or self.global_steps % self.config.trainer.save_freq == 0 or esi_close_to_expiration ): if esi_close_to_expiration: print("Force saving checkpoint: ESI instance expiration approaching.") with marked_timer("save_checkpoint", timing_raw, color="green"): self._save_checkpoint() with marked_timer("stop_profile", timing_raw): next_step_profile = ( self.global_steps + 1 in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) self._stop_profiling( curr_step_profile and not next_step_profile if self.config.global_profiler.profile_continuous_steps else curr_step_profile ) prev_step_profile = curr_step_profile curr_step_profile = next_step_profile steps_duration = timing_raw["step"] self.max_steps_duration = max(self.max_steps_duration, steps_duration) # training metrics metrics.update( { "training/global_step": self.global_steps, "training/epoch": epoch, } ) # collect metrics metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic)) metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw)) # TODO: implement actual tflpo and theoretical tflpo n_gpus = self.resource_pool_manager.get_n_gpus() metrics.update(compute_throughout_metrics(batch=batch, timing_raw=timing_raw, n_gpus=n_gpus)) # this is experimental and may be changed/removed in the future in favor of a general-purpose one if isinstance(self.train_dataloader.sampler, AbstractCurriculumSampler): self.train_dataloader.sampler.update(batch=batch) # TODO: make a canonical logger that supports various backend logger.log(data=metrics, step=self.global_steps) progress_bar.update(1) self.global_steps += 1 if ( hasattr(self.config.actor_rollout_ref.actor, "profiler") and self.config.actor_rollout_ref.actor.profiler.tool == "torch_memory" ): self.actor_rollout_wg.dump_memory_snapshot( tag=f"post_update_step{self.global_steps}", sub_dir=f"step{self.global_steps}" ) if is_last_step: pprint(f"Final validation metrics: {last_val_metrics}") progress_bar.close() return # this is experimental and may be changed/removed in the future # in favor of a general-purpose data buffer pool if hasattr(self.train_dataset, "on_batch_end"): # The dataset may be changed after each training batch self.train_dataset.on_batch_end(batch=batch) def _validate(self): data_source_lst = [] reward_extra_infos_dict: dict[str, list] = defaultdict(list) # Lists to collect samples for the table sample_scores = [] sample_turns = [] sample_uids = [] for test_data in self.val_dataloader: test_batch = DataProto.from_single_dict(test_data) if len(test_batch) < self.config.data.val_batch_size: print(f"drop last batch in val_dataloader, len {len(test_batch)}") break if "uid" not in test_batch.non_tensor_batch: test_batch.non_tensor_batch["uid"] = np.array( [str(uuid.uuid4()) for _ in range(len(test_batch))], dtype=object ) test_gen_batch = self._get_gen_batch(test_batch) test_gen_batch.meta_info = { "eos_token_id": self.tokenizer.eos_token_id, "pad_token_id": self.tokenizer.pad_token_id, "prompt_length": self.config.actor_rollout_ref.rollout.prompt_length, "recompute_log_prob": False, "do_sample": self.config.actor_rollout_ref.rollout.val_kwargs.do_sample, "temperature": self.config.actor_rollout_ref.rollout.temperature, "n_samples": self.config.actor_rollout_ref.rollout.n, "validate": True, "global_steps": self.global_steps, } test_gen_batch = test_gen_batch.repeat( repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True ) sample_uids.extend(test_gen_batch.non_tensor_batch["uid"]) # pad to be divisible by dp_size size_divisor = self.config.env.train.num_envs * self.config.env.rollout.pipeline_stage_num test_gen_batch_padded, pad_size = pad_dataproto_to_divisor(test_gen_batch, size_divisor) reset_future = self._reset_envs(test_gen_batch_padded) test_output_gen_batch_padded = self.async_rollout_manager.generate_sequences( test_gen_batch_padded, reset_future ) # unpad test_output_gen_batch = unpad_dataproto(test_output_gen_batch_padded, pad_size=pad_size) print("validation generation end") test_batch = test_output_gen_batch test_batch.meta_info["validate"] = True # evaluate using reward_function if self.val_reward_fn is None: raise ValueError("val_reward_fn must be provided for validation.") result = self.val_reward_fn(test_batch, return_dict=True) reward_tensor = result["reward_tensor"] scores = reward_tensor.sum(-1).cpu().tolist() sample_scores.extend(scores) reward_extra_infos_dict["reward"].extend(scores) print(f"len reward_extra_infos_dict['reward']: {len(reward_extra_infos_dict['reward'])}") if "reward_extra_info" in result: for key, lst in result["reward_extra_info"].items(): reward_extra_infos_dict[key].extend(lst) print(f"len reward_extra_infos_dict['{key}']: {len(reward_extra_infos_dict[key])}") # collect num_turns of each prompt if "__num_turns__" in test_batch.non_tensor_batch: sample_turns.append(test_batch.non_tensor_batch["__num_turns__"]) data_source_lst.append(test_batch.non_tensor_batch.get("data_source", ["unknown"] * reward_tensor.shape[0])) for key_info, lst in reward_extra_infos_dict.items(): assert len(lst) == 0 or len(lst) == len(sample_scores), f"{key_info}: {len(lst)=}, {len(sample_scores)=}" data_sources = np.concatenate(data_source_lst, axis=0) data_src2var2metric2val = process_validation_metrics(data_sources, sample_uids, reward_extra_infos_dict) metric_dict = {} for data_source, var2metric2val in data_src2var2metric2val.items(): core_var = "acc" if "acc" in var2metric2val else "reward" for var_name, metric2val in var2metric2val.items(): n_max = max([int(name.split("@")[-1].split("/")[0]) for name in metric2val.keys()]) for metric_name, metric_val in metric2val.items(): if ( (var_name == core_var) and any(metric_name.startswith(pfx) for pfx in ["mean", "maj", "best"]) and (f"@{n_max}" in metric_name) ): metric_sec = "val-core" else: metric_sec = "val-aux" pfx = f"{metric_sec}/{data_source}/{var_name}/{metric_name}" metric_dict[pfx] = metric_val if len(sample_turns) > 0: sample_turns = np.concatenate(sample_turns) metric_dict["val-aux/num_turns/min"] = sample_turns.min() metric_dict["val-aux/num_turns/max"] = sample_turns.max() metric_dict["val-aux/num_turns/mean"] = sample_turns.mean() return metric_dict ================================================ FILE: verl/experimental/vla/run_pi05_libero_sac.sh ================================================ set -x libero_train_path=$HOME/data/libero_rl/train.parquet libero_test_path=$HOME/data/libero_rl/test.parquet train_files=$libero_train_path test_files=$libero_test_path OUTPUT_DIR=${MLP_MODEL_OUTPUT:-"$HOME/models/vla_libero_grpo"} VIDEO_OUTPUT=${MLP_MODEL_OUTPUT:-"$HOME"}/video SFT_MODEL_PATH=${SFT_MODEL_PATH:-"$HOME/data/pi05_libero_torch"} TOKENIZER_PATH="$SFT_MODEL_PATH" # Physical Node Config NUM_NODES=1 # number of nodes NUM_GPUS=4 # total number of gpus per node # Role Config NUM_ENV_GPUS=2 # number of gpus for env workers per node NUM_ROLLOUT_GPUS=$((NUM_GPUS - NUM_ENV_GPUS)) # number of gpus for rollout workers per node # Rollout Config # NOTE: TRAIN_BATCH_SIZE * ROLLOUT_N == NUM_ENV_GPUS * NUM_STAGE * NUM_ENV TRAIN_BATCH_SIZE=32 # batch size for dataloaders per step ROLLOUT_N=1 # response number for each prompt (for GRPO) NUM_STAGE=2 # number of pipeline stages NUM_ENV=8 # number of envs per env worker NUM_ACTION_CHUNKS=10 # number of action chunks MAX_EPISODE_STEPS=512 # max episode steps for each env # max_interactions = MAX_EPISODE_STEPS / num_action_chunks # Training Config MINI_BATCH_SIZE=1024 # mini batch size (batch size per GPU, automatically multiplied by ROLLOUT_N) MICRO_BATCH_SIZE=8 # micro batch size (per GPU, for gradient accumulation, should divide MINI_BATCH_SIZE) # isaac or libero # libero means original libero benchmark with mujoco sim # isaac means libero benchmark using isaac sim SIM_TYPE=${SIM_TYPE:-"libero"} PROJECT_NAME="vla_libero_RL" EXPERIMENT_NAME="${SIM_TYPE}_reinforce_plus_plus" ISSC_PYTHON="/workspace/isaaclab/_isaac_sim/python.sh" PYTHON=python if [ -f "$ISSC_PYTHON" ]; then PYTHON=$ISSC_PYTHON fi # avoiding warnings mkdir /root/LIBERO/libero/libero/../datasets gpu_name=$(nvidia-smi --query-gpu=name --format=csv,noheader,nounits | head -n 1) # force osmesa in Hopper if echo "$gpu_name" | grep "NVIDIA H"; then echo "enable MUJOCO_GL=osmesa in Hopper" export MUJOCO_GL=osmesa fi export VERL_LOGGING_LEVEL=INFO $PYTHON -m verl.experimental.vla.main_sac \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=$TRAIN_BATCH_SIZE \ data.val_batch_size=4 \ actor_rollout_ref.rollout.n=$ROLLOUT_N \ env.train.num_envs=$NUM_ENV \ data.max_prompt_length=256 \ data.max_response_length=128 \ env.rollout.pipeline_stage_num=$NUM_STAGE \ env.train.simulator_type=$SIM_TYPE \ env.actor.model.num_action_chunks=$NUM_ACTION_CHUNKS \ env.actor.model.action_dim=7 \ env.train.only_eval=False \ env.train.max_episode_steps=$MAX_EPISODE_STEPS \ env.train.video_cfg.save_video=True \ env.train.video_cfg.video_base_dir=${VIDEO_OUTPUT} \ env.train.seed=42 \ actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \ actor_rollout_ref.actor.fsdp_config.wrap_policy.transformer_layer_cls_to_wrap=[SiglipEncoderLayer,GemmaDecoderLayerWithExpert] \ actor_rollout_ref.model.path=$SFT_MODEL_PATH \ actor_rollout_ref.model.tokenizer_path=$TOKENIZER_PATH \ actor_rollout_ref.rollout.mode=async_envloop \ actor_rollout_ref.actor.optim.lr=5e-6 \ actor_rollout_ref.actor.optim.warmup_style=constant \ actor_rollout_ref.actor.ppo_mini_batch_size=$MINI_BATCH_SIZE \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=$MICRO_BATCH_SIZE \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.grad_clip=1 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.num_images_in_input=1 \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.model.trust_remote_code=False \ +actor_rollout_ref.model.override_config.attn_implementation=eager \ actor_rollout_ref.actor.entropy_coeff=0. \ actor_rollout_ref.rollout.temperature=1.6 \ actor_rollout_ref.rollout.prompt_length=512 \ actor_rollout_ref.rollout.log_prob_micro_batch_size=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=hf \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.ref.log_prob_micro_batch_size=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ +actor_rollout_ref.algorithm='sac' \ algorithm.kl_ctrl.kl_coef=0.00 \ trainer.logger=['console'] \ trainer.project_name=$PROJECT_NAME \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.default_local_dir=$OUTPUT_DIR \ trainer.n_gpus_per_node=$NUM_GPUS \ +trainer.n_env_gpus_per_node=$NUM_ENV_GPUS \ +trainer.n_rollout_gpus_per_node=$NUM_ROLLOUT_GPUS \ +trainer.rollout_interval=20 \ trainer.nnodes=$NUM_NODES \ trainer.save_freq=300 \ trainer.test_freq=-1 \ trainer.total_epochs=100 \ trainer.val_only=False \ trainer.val_before_train=False ================================================ FILE: verl/experimental/vla/run_pi05_libero_sac_disagg.sh ================================================ set -x libero_train_path=$HOME/data/libero_rl/libero_spatial/train.parquet libero_test_path=$HOME/data/libero_rl/libero_spatial/test.parquet train_files=$libero_train_path test_files=$libero_test_path OUTPUT_DIR=${MLP_MODEL_OUTPUT:-"$HOME/models/vla_libero_grpo"} VIDEO_OUTPUT=${MLP_MODEL_OUTPUT:-"$HOME"}/video SFT_MODEL_PATH=${SFT_MODEL_PATH:-"$HOME/data/pi05_libero_torch"} TOKENIZER_PATH="$SFT_MODEL_PATH" # Physical Node Config NUM_GPUS=8 # total number of gpus per node # Role Config NUM_NODES=1 # number of nodes for rollout SIM_NODES=1 # number of nodes for sim NUM_ENV_GPUS=4 # number of gpus for env workers per node NUM_ROLLOUT_GPUS=8 # number of gpus for rollout workers per node # Rollout Config # NOTE: TRAIN_BATCH_SIZE * ROLLOUT_N == NUM_ENV_GPUS * NUM_STAGE * NUM_ENV TRAIN_BATCH_SIZE=64 # batch size for dataloaders per step ROLLOUT_N=1 # response number for each prompt (for GRPO) NUM_STAGE=2 # number of pipeline stages NUM_ENV=8 # number of envs per env worker NUM_ACTION_CHUNKS=10 # number of action chunks MAX_EPISODE_STEPS=220 # max episode steps for each env # max_interactions = MAX_EPISODE_STEPS / num_action_chunks # Training Config MINI_BATCH_SIZE=1024 # mini batch size (batch size per GPU, automatically multiplied by ROLLOUT_N) MICRO_BATCH_SIZE=16 # micro batch size (per GPU, for gradient accumulation, should divide MINI_BATCH_SIZE) # isaac or libero # libero means original libero benchmark with mujoco sim # isaac means libero benchmark using isaac sim SIM_TYPE=${SIM_TYPE:-"libero"} PROJECT_NAME="pi05-libero-sac" EXPERIMENT_NAME="${SIM_TYPE}_reinforce_plus_plus" ISSC_PYTHON="/workspace/isaaclab/_isaac_sim/python.sh" PYTHON=python if [ -f "$ISSC_PYTHON" ]; then PYTHON=$ISSC_PYTHON fi # avoiding warnings mkdir /root/LIBERO/libero/libero/../datasets gpu_name=$(nvidia-smi --query-gpu=name --format=csv,noheader,nounits | head -n 1) # force osmesa in Hopper if echo "$gpu_name" | grep "NVIDIA H"; then echo "enable MUJOCO_GL=osmesa in Hopper" export MUJOCO_GL=osmesa fi export VERL_LOGGING_LEVEL=INFO $PYTHON -m verl.experimental.vla.main_sac \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=$TRAIN_BATCH_SIZE \ data.val_batch_size=$TRAIN_BATCH_SIZE \ actor_rollout_ref.rollout.n=$ROLLOUT_N \ env.train.num_envs=$NUM_ENV \ data.max_prompt_length=256 \ data.max_response_length=128 \ env.disagg_sim.enable=True \ env.disagg_sim.nnodes=$SIM_NODES \ env.rollout.pipeline_stage_num=$NUM_STAGE \ env.train.simulator_type=$SIM_TYPE \ env.actor.model.num_action_chunks=$NUM_ACTION_CHUNKS \ env.actor.model.action_dim=7 \ env.train.only_eval=False \ env.train.max_episode_steps=$MAX_EPISODE_STEPS \ env.train.video_cfg.save_video=True \ env.train.video_cfg.video_base_dir=${VIDEO_OUTPUT} \ env.train.seed=42 \ actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \ actor_rollout_ref.actor.fsdp_config.wrap_policy.transformer_layer_cls_to_wrap=[SiglipEncoderLayer,GemmaDecoderLayerWithExpert] \ actor_rollout_ref.model.path=$SFT_MODEL_PATH \ actor_rollout_ref.model.tokenizer_path=$TOKENIZER_PATH \ actor_rollout_ref.rollout.mode=async_envloop \ actor_rollout_ref.actor.optim.lr=5e-6 \ actor_rollout_ref.actor.optim.warmup_style=constant \ actor_rollout_ref.actor.ppo_mini_batch_size=$MINI_BATCH_SIZE \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=$MICRO_BATCH_SIZE \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.strategy=fsdp2 \ critic.strategy=fsdp2 \ actor_rollout_ref.actor.grad_clip=1 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.num_images_in_input=1 \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.model.trust_remote_code=False \ +actor_rollout_ref.model.override_config.attn_implementation=eager \ actor_rollout_ref.actor.entropy_coeff=0. \ actor_rollout_ref.rollout.temperature=1.6 \ actor_rollout_ref.rollout.prompt_length=512 \ actor_rollout_ref.rollout.log_prob_micro_batch_size=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=hf \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.ref.log_prob_micro_batch_size=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ +actor_rollout_ref.algorithm='sac' \ algorithm.kl_ctrl.kl_coef=0.00 \ trainer.logger=['console'] \ trainer.project_name=$PROJECT_NAME \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.default_local_dir=$OUTPUT_DIR \ trainer.n_gpus_per_node=$NUM_GPUS \ +trainer.n_env_gpus_per_node=$NUM_ENV_GPUS \ +trainer.n_rollout_gpus_per_node=$NUM_ROLLOUT_GPUS \ +trainer.rollout_interval=20 \ trainer.nnodes=$NUM_NODES \ trainer.save_freq=500 \ trainer.test_freq=10 \ trainer.total_epochs=1000 \ trainer.val_only=False \ algorithm.adv_estimator=reinforce_plus_plus \ trainer.val_before_train=False ================================================ FILE: verl/experimental/vla/run_simpleVLA_isaac_disagg.sh ================================================ #!/bin/bash set -x echo "remember to set ray param < --resources='{\"sim\"/\"actor_rollout\":1}' > if using disagg sim" libero_train_path=$HOME/data/libero_rl/train.parquet libero_test_path=$HOME/data/libero_rl/test.parquet train_files=$libero_train_path test_files=$libero_test_path OUTPUT_DIR=${MLP_MODEL_OUTPUT:-"$HOME/models/vla_libero_grpo"} VIDEO_OUTPUT=${MLP_MODEL_OUTPUT:-"$HOME"}/video SFT_MODEL_PATH=${SFT_MODEL_PATH:-"$HOME/data/Openvla-oft-SFT-libero10-trajall"} # for rollout and train NUM_NODES=1 # for simulator SIM_NODES=1 NUM_ENV_GPUS=8 NUM_ROLLOUT_GPUS=8 STAGE_NUM=2 BATCH_SIZE=16 # rollout.n should equal to num_envs for isaac env ROLLOUT_N=8 # 512 is required for libero benchmark, but you can reduce it in debugging to run faster MAX_EPISODE_STEPS=512 # isaac or libero # libero means original libero benchmark with mujoco sim # isaac means libero benchmark using isaac sim SIM_TYPE=${SIM_TYPE:-"isaac"} PROJECT_NAME="vla-disagg-issac" EXPERIMENT_NAME="${SIM_TYPE}_rl" ISSC_PYTHON="/workspace/isaaclab/_isaac_sim/python.sh" PYTHON=python if [ -f "$ISSC_PYTHON" ]; then PYTHON=$ISSC_PYTHON fi # avoiding warnings mkdir /root/LIBERO/libero/libero/../datasets $PYTHON -m verl.experimental.vla.main_ppo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=${BATCH_SIZE} \ data.val_batch_size=${BATCH_SIZE} \ actor_rollout_ref.rollout.n=$ROLLOUT_N \ env.train.num_envs=$ROLLOUT_N \ data.max_prompt_length=256 \ data.max_response_length=128 \ env.rollout.pipeline_stage_num=$STAGE_NUM \ env.train.simulator_type=$SIM_TYPE \ env.actor.model.num_action_chunks=8 \ env.actor.model.action_dim=7 \ env.train.only_eval=False \ env.train.max_episode_steps=$MAX_EPISODE_STEPS \ env.train.video_cfg.save_video=True \ env.train.video_cfg.video_base_dir=${VIDEO_OUTPUT} \ env.train.seed=42 \ env.disagg_sim.enable=True \ env.disagg_sim.nnodes=$SIM_NODES \ +actor_rollout_ref.algorithm='grpo' \ actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \ actor_rollout_ref.model.path=$SFT_MODEL_PATH \ actor_rollout_ref.rollout.mode=async_envloop \ actor_rollout_ref.actor.optim.lr=5e-6 \ actor_rollout_ref.actor.optim.warmup_style=constant \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.grad_clip=1 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.num_images_in_input=1 \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.model.trust_remote_code=False \ actor_rollout_ref.actor.entropy_coeff=0. \ actor_rollout_ref.rollout.temperature=1.6 \ actor_rollout_ref.rollout.prompt_length=512 \ actor_rollout_ref.rollout.log_prob_micro_batch_size=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=hf \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.ref.log_prob_micro_batch_size=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=0.00 \ trainer.logger=['console'] \ trainer.project_name=$PROJECT_NAME \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.default_local_dir=$OUTPUT_DIR \ trainer.n_gpus_per_node=$NUM_ROLLOUT_GPUS \ +trainer.n_env_gpus_per_node=$NUM_ENV_GPUS \ +trainer.n_rollout_gpus_per_node=$NUM_ROLLOUT_GPUS \ trainer.nnodes=$NUM_NODES \ trainer.save_freq=30 \ trainer.test_freq=-1 \ trainer.total_epochs=20 \ trainer.val_only=False \ trainer.total_training_steps=10000 \ algorithm.adv_estimator=reinforce_plus_plus \ trainer.val_before_train=False $@ ================================================ FILE: verl/experimental/vla/run_simpleVLA_libero_grpo.sh ================================================ set -x libero_train_path=$HOME/data/libero_rl/train.parquet libero_test_path=$HOME/data/libero_rl/test.parquet train_files=$libero_train_path test_files=$libero_test_path OUTPUT_DIR=${MLP_MODEL_OUTPUT:-"$HOME/models/vla_libero_grpo"} VIDEO_OUTPUT=${MLP_MODEL_OUTPUT:-"$HOME"}/video SFT_MODEL_PATH=${SFT_MODEL_PATH:-"$HOME/data/Openvla-oft-SFT-libero10-trajall"} NUM_NODES=1 NUM_GPUS=8 NUM_ENV_GPUS=4 # rollout.n should equal to num_envs for isaac env ROLLOUT_N=8 # isaac or libero # libero means original libero benchmark with mujoco sim # isaac means libero benchmark using isaac sim SIM_TYPE=${SIM_TYPE:-"isaac"} PROJECT_NAME="vla_libero_RL" EXPERIMENT_NAME="${SIM_TYPE}_reinforce_plus_plus" ISSC_PYTHON="/workspace/isaaclab/_isaac_sim/python.sh" PYTHON=python if [ -f "$ISSC_PYTHON" ]; then PYTHON=$ISSC_PYTHON fi # avoiding warnings mkdir /root/LIBERO/libero/libero/../datasets gpu_name=$(nvidia-smi --query-gpu=name --format=csv,noheader,nounits | head -n 1) # force osmesa in Hopper if echo "$gpu_name" | grep "NVIDIA H"; then echo "enable MUJOCO_GL=osmesa in Hopper" export MUJOCO_GL=osmesa fi $PYTHON -m verl.experimental.vla.main_ppo \ data.train_files="$train_files" \ data.val_files="$test_files" \ data.train_batch_size=8 \ data.val_batch_size=8 \ actor_rollout_ref.rollout.n=$ROLLOUT_N \ env.train.num_envs=$ROLLOUT_N \ data.max_prompt_length=256 \ data.max_response_length=128 \ env.rollout.pipeline_stage_num=2 \ env.train.simulator_type=$SIM_TYPE \ env.actor.model.num_action_chunks=8 \ env.actor.model.action_dim=7 \ env.train.only_eval=False \ env.train.max_episode_steps=512 \ env.train.video_cfg.save_video=True \ env.train.video_cfg.video_base_dir=${VIDEO_OUTPUT} \ env.train.seed=42 \ +actor_rollout_ref.algorithm='grpo' \ actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \ actor_rollout_ref.model.path=$SFT_MODEL_PATH \ actor_rollout_ref.rollout.mode=async_envloop \ actor_rollout_ref.actor.optim.lr=5e-6 \ actor_rollout_ref.actor.optim.warmup_style=constant \ actor_rollout_ref.actor.ppo_mini_batch_size=128 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \ actor_rollout_ref.actor.use_dynamic_bsz=False \ actor_rollout_ref.actor.grad_clip=1 \ actor_rollout_ref.actor.clip_ratio_high=0.28 \ actor_rollout_ref.actor.clip_ratio_low=0.2 \ actor_rollout_ref.actor.num_images_in_input=1 \ actor_rollout_ref.model.enable_gradient_checkpointing=False \ actor_rollout_ref.model.use_remove_padding=False \ actor_rollout_ref.model.trust_remote_code=False \ actor_rollout_ref.actor.entropy_coeff=0. \ actor_rollout_ref.rollout.temperature=1.6 \ actor_rollout_ref.rollout.prompt_length=512 \ actor_rollout_ref.rollout.log_prob_micro_batch_size=16 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=hf \ actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \ actor_rollout_ref.rollout.free_cache_engine=False \ actor_rollout_ref.ref.log_prob_micro_batch_size=16 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=0.00 \ trainer.logger=['console'] \ trainer.project_name=$PROJECT_NAME \ trainer.experiment_name=$EXPERIMENT_NAME \ trainer.default_local_dir=$OUTPUT_DIR \ trainer.n_gpus_per_node=$NUM_GPUS \ +trainer.n_env_gpus_per_node=$NUM_ENV_GPUS \ +trainer.n_rollout_gpus_per_node=$((NUM_GPUS - NUM_ENV_GPUS)) \ trainer.nnodes=$NUM_NODES \ trainer.save_freq=30 \ trainer.test_freq=30 \ trainer.total_epochs=20 \ trainer.val_only=False \ trainer.total_training_steps=10000 \ algorithm.adv_estimator=reinforce_plus_plus \ trainer.val_before_train=False $@ ================================================ FILE: verl/experimental/vla/sac/base.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from typing import Any, Literal, Optional import torch from verl import DataProto class SupportSACTraining: """ Base class for Soft Actor-Critic (SAC). Subclasses implement a Policy that can be plugged directly into SAC training. This implementation requires the actor and critic to be integrated within a single model instance, e.g., sharing a backbone with an additional MLP head that outputs critic values (Q/V) alongside the actor's action distribution. Note: This class intentionally does NOT inherit from `abc.ABC`. The root model may be wrapped or transformed by FSDP (Fully Sharded Data Parallel), which performs runtime class substitution; using `ABCMeta` can break FSDP's class rewriting mechanism. """ def sac_init(self): raise NotImplementedError("Subclasses must implement sac_init method.") def sac_get_critic_parameters(self) -> list[torch.nn.Parameter]: """Get the parameters of the critic head for optimization. Returns: A list of torch.nn.Parameter objects representing the critic head parameters. """ raise NotImplementedError("Subclasses must implement sac_get_critic_parameters method.") def sac_get_named_actor_parameters(self) -> list[tuple[str, torch.nn.Parameter]]: """Get named actor parameters for optimization/EMA updates. Returns: A list of (name, parameter) tuples representing actor-side trainable parameters. """ raise NotImplementedError("Subclasses must implement sac_get_named_actor_parameters method.") def sac_forward_critic( self, a: dict[str, torch.Tensor], state_features: Any, *, use_target_network: bool = False, method: Literal["cat", "min"] = "cat", requires_grad: bool = False, ) -> torch.Tensor: """Compute Q-values for given state-action pairs. Args: a: Dictionary of tensors representing actions, with key: - "full_action": torch.Tensor of shape (B, action_steps, action_dim) state_features: Any data structure representing the processed state features. use_target_network: Whether to use the target critic network heads. method: Method to combine multiple heads' outputs ("cat" or "min"). requires_grad: Whether to enable gradients for the critic head parameters. Returns: q_values: torch.Tensor of shape (B, num_heads) if method is "cat", or (B, 1) if method is "min", representing the computed Q-values """ raise NotImplementedError("Subclasses must implement sac_forward_critic method.") def sac_forward_actor( self, state_features: Any, is_first_micro_batch: bool = False, ) -> tuple[torch.Tensor, Optional[torch.Tensor], dict[str, float]]: """Compute actions and their log probabilities from state features. Args: state_features: Any data structure representing the processed state features. is_first_micro_batch: Whether the current forward corresponds to the first micro batch of the actor update step. Returns: actions: torch.Tensor of shape (B, n_action_steps, action_dim), sampled actions. log_probs: Optional torch.Tensor of shape (B,), log probabilities of sampled actions. Can be None when SAC is configured to train without entropy/log-prob terms. metrics: Scalar metrics produced by actor forward, used by outer trainer for logging. """ raise NotImplementedError("Subclasses must implement sac_forward_actor method.") def sac_forward_state_features(self, s: dict[str, torch.Tensor]) -> Any: """Compute state features needed for SAC actor and critic. Args: s: Dictionary of tensors representing the states, with keys - "images": torch.Tensor of shape (B, n_images, C, H, W) - "image_masks": torch.Tensor of shape (B, n_images) - "lang_tokens": torch.Tensor of shape (B, L) - "lang_masks": torch.Tensor of shape (B, L) - "states": torch.Tensor of shape (B, state_dim) Returns: state_features: Any data structure representing the processed state features. """ raise NotImplementedError("Subclasses must implement sac_forward_state_features method.") def bc_loss( self, state_features: Any, actions: dict[str, torch.Tensor], valids: torch.Tensor, ) -> torch.Tensor: """Compute behavior cloning loss for actor regularization.""" raise NotImplementedError("Subclasses must implement bc_loss method.") def sac_update_target_network(self, tau: float): """Update the target network heads using Polyak averaging. Args: tau: The interpolation parameter for Polyak averaging. """ raise NotImplementedError("Subclasses must implement sac_update_target_network method.") class BaseSACActor(ABC): @abstractmethod def update_policy(self, data: DataProto) -> dict: """ Update the policy using the provided data batch. Args: data: DataProto containing the following entries in `data.batch`: - "a0.full_action": Tensor of shape (B, action_steps, action_dim), representing the current action chunk for each sample. - "a1.full_action": Tensor of shape (B, action_steps, action_dim), representing the next action chunk for each sample. - "s0.states": Tensor of shape (B, state_dim), representing the current environment or agent state. - "s1.states": Tensor of shape (B, state_dim), representing the next environment or agent state. - "s0.images": Tensor of shape (B, n_images, C, H, W), containing current visual observations. - "s1.images": Tensor of shape (B, n_images, C, H, W), containing next-step visual observations. - "s0.image_masks": Tensor of shape (B, n_images), indicating valid images per sample. - "s1.image_masks": Tensor of shape (B, n_images), indicating valid images per sample. - "s0.lang_tokens": Tensor of shape (B, max_seq_len), tokenized language instructions. - "s1.lang_tokens": Tensor of shape (B, max_seq_len), tokenized language instructions for the next step. - "s0.lang_masks": Tensor of shape (B, max_seq_len), attention masks for language tokens. - "s1.lang_masks": Tensor of shape (B, max_seq_len), attention masks for language tokens for the next step. - "rewards": Tensor of shape (B,), chunk-level scalar rewards aligned to the next step. - "response_mask": Tensor of shape (B, action_steps), mask indicating whether each sample has a valid response. """ raise NotImplementedError("Subclasses must implement update_policy method.") ================================================ FILE: verl/experimental/vla/sac/naive_rollout_pi05.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ In single GPU rollout, the sequences are generated directly by sampling from the model. The output will contain 1. output_ids 2. attention_masks (left padding) 3. eos_masks 4. log_probs """ import logging from typing import Any import torch from verl import DataProto from verl.experimental.vla.naive_rollout_rob import NaiveRolloutRob from verl.utils.device import get_device_id, get_device_name logger = logging.getLogger(__name__) __all__ = ["PI0RolloutRob"] class PI0RolloutRob(NaiveRolloutRob): def __init__( self, model_config: dict, module: torch.nn.Module, tokenizer: Any, ): self.model_config = model_config self.module = module self.tokenizer = tokenizer from torch.distributed.fsdp import register_fsdp_forward_method register_fsdp_forward_method(self.module, "sample_actions") register_fsdp_forward_method(self.module, "sac_forward_state_features") register_fsdp_forward_method(self.module, "sac_forward_critic") @torch.no_grad() def generate_sequences(self, prompts: DataProto) -> DataProto: """Generate sequences""" with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): prompts.to(get_device_id()) validate = bool(prompts.meta_info.get("validate", False)) output, s, a = self.module.sample_actions( prompts, tokenizer=self.tokenizer, validate=validate, ) state_features = self.module.sac_forward_state_features(s) critic_value = ( self.module.sac_forward_critic( {"full_action": a["full_action"]}, state_features, use_target_network=False, method="min", requires_grad=False, ) .detach() .float() .reshape(-1) ) tensor_batch = { "action": output.action, "full_action": a["full_action"], "images": s["images"], "image_masks": s["image_masks"], "lang_tokens": s["lang_tokens"], "lang_masks": s["lang_masks"], "states": s["states"], "critic_value": critic_value, } ret = DataProto.from_dict(tensor_batch) return ret ================================================ FILE: verl/experimental/vla/sac/replay_pool.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from dataclasses import dataclass from typing import Any, Optional, Sequence import torch from tensordict import TensorDict logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @dataclass class _DualPoolState: positive_pool: Optional[TensorDict] = None negative_pool: Optional[TensorDict] = None positive_size: int = 0 negative_size: int = 0 positive_position: int = 0 negative_position: int = 0 class SACReplayPool: """Task-aware SAC Replay Pool. For each task_id we maintain two independent pools: - positive pool - negative pool `single_pool_capacity` is the size of each single pool. """ def __init__( self, single_pool_capacity: int, pool_device: str = "cpu", sample_device: str = "cpu", ): self.single_pool_capacity = int(single_pool_capacity) self.pool_device = pool_device self.sample_device = sample_device self.task_pools: dict[str, _DualPoolState] = {} self.size = 0 self.positive_size = 0 self.negative_size = 0 self.rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 def add_batch(self, batch: TensorDict, task_ids: Sequence[Any]): """Add a batch of samples into task-specific positive/negative pools.""" if batch.batch_size[0] == 0: return if len(task_ids) != batch.batch_size[0]: raise ValueError(f"task_ids length ({len(task_ids)}) must match batch size ({batch.batch_size[0]}).") valid_mask = batch["valids"].to(torch.bool) valid_indices = torch.nonzero(valid_mask, as_tuple=False).squeeze(-1) if valid_indices.numel() == 0: return batch = self._index_select_batch(batch, valid_indices.to(batch.device)) selected = valid_indices.cpu().tolist() task_ids = [task_ids[i] for i in selected] positive_mask = self._extract_positive_mask(batch) grouped_indices: dict[str, dict[str, list[int]]] = {} for idx in range(batch.batch_size[0]): task_key = self._normalize_task_id(task_ids[idx]) if task_key not in grouped_indices: grouped_indices[task_key] = {"positive": [], "negative": []} if bool(positive_mask[idx].item()): grouped_indices[task_key]["positive"].append(idx) else: grouped_indices[task_key]["negative"].append(idx) for task_key, groups in grouped_indices.items(): pool_state = self._get_or_create_task_pool(task_key, batch) if groups["positive"]: positive_idx = torch.tensor(groups["positive"], device=batch.device, dtype=torch.long) positive_batch = self._index_select_batch(batch, positive_idx) self._insert_block_to_pool(pool_state, positive_batch, is_positive_pool=True) if groups["negative"]: negative_idx = torch.tensor(groups["negative"], device=batch.device, dtype=torch.long) negative_batch = self._index_select_batch(batch, negative_idx) self._insert_block_to_pool(pool_state, negative_batch, is_positive_pool=False) self._refresh_global_stats() def sample_batch( self, batch_size: int, positive_sample_ratio: float = 0.5, return_sample_info: bool = False, ) -> TensorDict | tuple[TensorDict, dict]: """Sample a batch from all task-specific pools.""" if self.size == 0: raise ValueError("Replay pool is empty, unable to sample.") positive_sample_ratio = max(0.0, min(1.0, float(positive_sample_ratio))) target_positive = int(round(batch_size * positive_sample_ratio)) target_negative = batch_size - target_positive sampled_positive = min(target_positive, self.positive_size) sampled_negative = min(target_negative, self.negative_size) deficit = batch_size - sampled_positive - sampled_negative if deficit > 0: remaining_positive = self.positive_size - sampled_positive remaining_negative = self.negative_size - sampled_negative if remaining_positive >= remaining_negative: extra_positive = min(deficit, remaining_positive) sampled_positive += extra_positive deficit -= extra_positive extra_negative = min(deficit, remaining_negative) sampled_negative += extra_negative deficit -= extra_negative else: extra_negative = min(deficit, remaining_negative) sampled_negative += extra_negative deficit -= extra_negative extra_positive = min(deficit, remaining_positive) sampled_positive += extra_positive deficit -= extra_positive sampled_parts = [] if sampled_positive > 0: sampled_parts.append(self._sample_from_task_pools(sampled_positive, is_positive_pool=True)) if sampled_negative > 0: sampled_parts.append(self._sample_from_task_pools(sampled_negative, is_positive_pool=False)) sampled_count = sampled_positive + sampled_negative if len(sampled_parts) == 1: sampled_batch = sampled_parts[0] else: sampled_batch = TensorDict( {key: torch.cat([part[key] for part in sampled_parts], dim=0) for key in sampled_parts[0].keys()}, batch_size=[sampled_count], device=self.sample_device, ) if sampled_count < batch_size: sampled_batch = self._pad_sampled_batch(sampled_batch, target_batch_size=batch_size) else: sampled_batch = TensorDict( {key: value for key, value in sampled_batch.items()}, batch_size=[batch_size], device=self.sample_device, ) shuffle_idx = torch.randperm(batch_size, device=self.sample_device) sampled_batch = TensorDict( {key: value.index_select(0, shuffle_idx) for key, value in sampled_batch.items()}, batch_size=[batch_size], device=self.sample_device, ) if not return_sample_info: return sampled_batch sample_info = { "actual_positive_sample_ratio": sampled_positive / max(batch_size, 1), "positive_size": self.positive_size, "negative_size": self.negative_size, "task_count": len(self.task_pools), } return sampled_batch, sample_info def insert_and_resample( self, source: TensorDict, task_ids: Sequence[Any], ) -> TensorDict: """Insert source into replay pool and sample a batch with the same size.""" self.add_batch(source, task_ids=task_ids) return self.sample_batch(source.batch_size[0]) def save(self, directory: str): """Save the replay pool to a directory.""" os.makedirs(directory, exist_ok=True) filepath = f"{directory}/sac_replay_pool_rank_{self.rank}.pt" tasks_payload: dict[str, dict[str, Any]] = {} for task_id, pool_state in self.task_pools.items(): assert pool_state.positive_pool is not None assert pool_state.negative_pool is not None tasks_payload[task_id] = { "positive_pool": pool_state.positive_pool.cpu(), "negative_pool": pool_state.negative_pool.cpu(), "positive_size": pool_state.positive_size, "negative_size": pool_state.negative_size, "positive_position": pool_state.positive_position, "negative_position": pool_state.negative_position, } payload = { "meta_info": { "version": 3, "single_pool_capacity": self.single_pool_capacity, "pool_device": self.pool_device, "sample_device": self.sample_device, "size": self.size, "positive_size": self.positive_size, "negative_size": self.negative_size, "task_count": len(self.task_pools), }, "tasks": tasks_payload, } torch.save(payload, filepath) logger.info( f"[Rank {self.rank}] Task replay pool saved to {filepath} with \ size={self.size}, tasks={len(self.task_pools)}" ) def load(self, directory: str): """Load the replay pool from a directory.""" filepath = f"{directory}/sac_replay_pool_rank_{self.rank}.pt" if not os.path.exists(filepath): return False payload = torch.load(filepath, weights_only=False) meta_info = payload["meta_info"] tasks_payload = payload["tasks"] self.single_pool_capacity = int(meta_info["single_pool_capacity"]) self.task_pools = {} for task_id, task_payload in tasks_payload.items(): pool_state = _DualPoolState( positive_pool=task_payload["positive_pool"].to(self.pool_device), negative_pool=task_payload["negative_pool"].to(self.pool_device), positive_size=int(task_payload["positive_size"]), negative_size=int(task_payload["negative_size"]), positive_position=int(task_payload["positive_position"]), negative_position=int(task_payload["negative_position"]), ) self.task_pools[task_id] = pool_state self._refresh_global_stats() logger.info( f"[Rank {self.rank}] Task replay pool loaded from {filepath} with \ size={self.size}, tasks={len(self.task_pools)}" ) return True @classmethod def from_path( cls, directory: str, ) -> "SACReplayPool": """Load a replay pool from a file.""" rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 filepath = f"{directory}/sac_replay_pool_rank_{rank}.pt" payload = torch.load(filepath, weights_only=False) meta_info = payload["meta_info"] replay_pool = cls( single_pool_capacity=int(meta_info["single_pool_capacity"]), pool_device=meta_info["pool_device"], sample_device=meta_info["sample_device"], ) replay_pool.rank = rank loaded = replay_pool.load(directory) if not loaded: raise RuntimeError(f"Failed to load replay pool from {filepath}.") return replay_pool def _insert_block_to_pool( self, pool_state: _DualPoolState, source: TensorDict, is_positive_pool: bool, ): """Insert a block of data from source into one task pool.""" source_size = source.batch_size[0] if source_size == 0: return length = min(source_size, self.single_pool_capacity) idx = torch.arange(length, device=self.pool_device) if is_positive_pool: assert pool_state.positive_pool is not None idx = (pool_state.positive_position + idx) % self.single_pool_capacity for key in source.keys(): pool_state.positive_pool[key].index_copy_(0, idx, source[key][:length].to(self.pool_device)) pool_state.positive_position = (pool_state.positive_position + length) % self.single_pool_capacity pool_state.positive_size = min(pool_state.positive_size + length, self.single_pool_capacity) else: assert pool_state.negative_pool is not None idx = (pool_state.negative_position + idx) % self.single_pool_capacity for key in source.keys(): pool_state.negative_pool[key].index_copy_(0, idx, source[key][:length].to(self.pool_device)) pool_state.negative_position = (pool_state.negative_position + length) % self.single_pool_capacity pool_state.negative_size = min(pool_state.negative_size + length, self.single_pool_capacity) def _get_or_create_task_pool(self, task_id: str, sample: TensorDict) -> _DualPoolState: if task_id in self.task_pools: return self.task_pools[task_id] logger.info( f"Initializing replay pools for task_id={task_id} with single_pool_capacity={self.single_pool_capacity}" ) pool_template = TensorDict( { key: torch.zeros( (self.single_pool_capacity, *value.shape[1:]), dtype=value.dtype, device=self.pool_device, ) for key, value in sample.items() }, batch_size=[self.single_pool_capacity], device=self.pool_device, ) pool_state = _DualPoolState( positive_pool=pool_template.clone(), negative_pool=pool_template.clone(), positive_size=0, negative_size=0, positive_position=0, negative_position=0, ) self.task_pools[task_id] = pool_state return pool_state def _extract_positive_mask(self, batch: TensorDict) -> torch.Tensor: positive_mask = batch["positive_sample_mask"].to(torch.bool) if positive_mask.ndim == 1: return positive_mask return positive_mask.reshape(positive_mask.shape[0], -1).any(dim=1) def _pad_sampled_batch(self, sampled_batch: TensorDict, target_batch_size: int) -> TensorDict: current_size = sampled_batch.batch_size[0] if current_size >= target_batch_size: return sampled_batch pad_size = target_batch_size - current_size pad_idx = torch.zeros(pad_size, dtype=torch.long, device=self.sample_device) padded_batch = TensorDict( {key: torch.cat([value, value.index_select(0, pad_idx)], dim=0) for key, value in sampled_batch.items()}, batch_size=[target_batch_size], device=self.sample_device, ) valid_tensor = padded_batch["valids"].clone() if valid_tensor.dtype == torch.bool: valid_tensor[current_size:] = False else: valid_tensor[current_size:] = 0 padded_batch["valids"] = valid_tensor return padded_batch def _index_select_batch(self, batch: TensorDict, idx: torch.Tensor) -> TensorDict: length = int(idx.numel()) return TensorDict( {key: value.index_select(0, idx) for key, value in batch.items()}, batch_size=[length], device=batch.device, ) def _sample_from_task_pools(self, batch_size: int, is_positive_pool: bool) -> TensorDict: task_sizes = { task_id: (pool_state.positive_size if is_positive_pool else pool_state.negative_size) for task_id, pool_state in self.task_pools.items() if (pool_state.positive_size if is_positive_pool else pool_state.negative_size) > 0 } allocation = self._allocate_counts_across_tasks(task_sizes, batch_size) sampled_parts = [] for task_id, count in allocation.items(): if count == 0: continue sampled_parts.append(self._sample_from_single_task_pool(self.task_pools[task_id], count, is_positive_pool)) if len(sampled_parts) == 1: return sampled_parts[0] return TensorDict( {key: torch.cat([part[key] for part in sampled_parts], dim=0) for key in sampled_parts[0].keys()}, batch_size=[batch_size], device=self.sample_device, ) def _sample_from_single_task_pool( self, pool_state: _DualPoolState, batch_size: int, is_positive_pool: bool, ) -> TensorDict: pool = pool_state.positive_pool if is_positive_pool else pool_state.negative_pool size = pool_state.positive_size if is_positive_pool else pool_state.negative_size assert pool is not None idx = torch.randperm(size, device=self.pool_device)[:batch_size] return TensorDict( {key: value.index_select(0, idx).to(self.sample_device) for key, value in pool.items()}, batch_size=[batch_size], device=self.sample_device, ) def _allocate_counts_across_tasks(self, task_sizes: dict[str, int], total_count: int) -> dict[str, int]: total_available = sum(task_sizes.values()) if total_count > total_available: raise ValueError(f"Requested {total_count} samples but only {total_available} available across task pools.") allocation: dict[str, int] = {task_id: 0 for task_id in task_sizes} task_order = list(task_sizes.keys()) remaining = total_count while remaining > 0: progressed = False for task_id in task_order: if allocation[task_id] < task_sizes[task_id]: allocation[task_id] += 1 remaining -= 1 progressed = True if remaining == 0: break if not progressed: raise RuntimeError("No eligible task pool left while allocation is still remaining.") return allocation def _refresh_global_stats(self): self.positive_size = sum(state.positive_size for state in self.task_pools.values()) self.negative_size = sum(state.negative_size for state in self.task_pools.values()) self.size = self.positive_size + self.negative_size def _normalize_task_id(self, task_id: Any) -> str: if isinstance(task_id, torch.Tensor): task_id = task_id.item() return str(task_id) def __repr__(self): return ( f"SACReplayPool(single_pool_capacity={self.single_pool_capacity}, size={self.size}, " f"positive_size={self.positive_size}, negative_size={self.negative_size}, " f"task_count={len(self.task_pools)}, pool_device={self.pool_device}, sample_device={self.sample_device})" ) def __len__(self): return self.size ================================================ FILE: verl/experimental/vla/sac/sac_actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Single Process Actor """ import logging import os from typing import Optional import numpy as np import torch import torch.nn.functional as F from tensordict import TensorDict from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from typing_extensions import override from verl.experimental.vla.sac.replay_pool import SACReplayPool from verl.protocol import DataProto from verl.utils.device import get_device_id, get_device_name from .base import BaseSACActor, SupportSACTraining logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def get_dict_from_prefix(tensordict: TensorDict, prefix: str) -> dict: """Extract a sub-dictionary from a TensorDict based on a given prefix. Args: tensordict: The input TensorDict containing various keys. prefix: The prefix string to filter keys. Returns: A dictionary containing key-value pairs from the TensorDict where the keys start with the specified prefix. The prefix is removed from the keys in the resulting dictionary. """ result = {} prefix_length = len(prefix) for key in tensordict.keys(): if key.startswith(prefix): new_key = key[prefix_length:] result[new_key] = tensordict[key] return result def merge_nested_dicts_or_tuples(a: dict | tuple, b: dict | tuple) -> dict | tuple: """Merge two nested structures (dictionaries or tuples) by concatenating tensors along the first dimension. """ if isinstance(a, dict) and isinstance(b, dict): merged = {} for key in a.keys(): merged[key] = merge_nested_dicts_or_tuples(a[key], b[key]) return merged elif isinstance(a, tuple) and isinstance(b, tuple): merged = [] for item_a, item_b in zip(a, b, strict=False): merged.append(merge_nested_dicts_or_tuples(item_a, item_b)) return tuple(merged) else: return torch.cat([a, b], dim=0) def split_nested_dicts_or_tuples(data: dict | tuple, split_num: int) -> list[dict | tuple]: """Split a nested structure (dictionary or tuple) into smaller chunks along the first dimension.""" if isinstance(data, torch.Tensor): split_tensors = torch.chunk(data, split_num, dim=0) return list(split_tensors) elif isinstance(data, dict): split_dicts = [dict() for _ in range(split_num)] for key, value in data.items(): split_values = split_nested_dicts_or_tuples(value, split_num) for i in range(split_num): split_dicts[i][key] = split_values[i] return split_dicts elif isinstance(data, tuple): split_tuples = [list() for _ in range(split_num)] for item in data: split_items = split_nested_dicts_or_tuples(item, split_num) for i in range(split_num): split_tuples[i].append(split_items[i]) return [tuple(split_tuple) for split_tuple in split_tuples] else: raise TypeError("Input data must be a torch.Tensor, dict, or tuple.") def valid_mean(x: torch.Tensor, valid: torch.Tensor) -> torch.Tensor: """Compute the mean of tensor `x` over valid entries indicated by `valid` mask. Args: x: Tensor of shape (B, ...) containing values to average. valid: Tensor of shape (B,) indicating valid entries (1 for valid, 0 for invalid). Returns: Scalar tensor (mean over valid samples only) """ x = x.squeeze(-1) valid_f = valid.float().to(x.device) denom = valid_f.sum().clamp_min(1.0) return (x * valid_f).sum() / denom class RobDataParallelSACActor(BaseSACActor): def __init__( self, config, actor_module: SupportSACTraining, actor_optimizer: torch.optim.Optimizer, tokenizer=None, ): super().__init__() self.config = config self.sac_config = config.sac self.device = get_device_name() self.actor_optimizer = actor_optimizer self.actor_module = actor_module self.actor_module.sac_init() self.tokenizer = tokenizer self.replay_pool = SACReplayPool( single_pool_capacity=self.config.replay_pool_single_size, sample_device=self.device, ) self.replay_pool.load(self.config.replay_pool_save_dir) self._init_alpha() self._init_critic() self.actor_ema_enabled = bool(self.config.get("actor_ema_enabled", True)) self.actor_ema_decay = float(self.config.get("actor_ema_decay", 0.995)) self.actor_ema_shadow: dict[str, torch.Tensor] = {} self.actor_ema_initialized = False self.bc_loss_coef = float(self.sac_config.get("bc_loss_coef", 0.5)) def _init_critic(self): """Initialize the critic optimizer.""" self.critic_optimizer = torch.optim.Adam( self.actor_module.sac_get_critic_parameters(), lr=self.config.critic_lr, weight_decay=self.config.critic_weight_decay, ) self.critic_scheduler = torch.optim.lr_scheduler.ConstantLR(self.critic_optimizer, factor=1.0) def _init_alpha(self): """Initialize the alpha optimizer for automatic entropy tuning.""" self.auto_entropy = self.sac_config.get("auto_entropy", False) if self.auto_entropy: self.target_entropy = torch.tensor(float(self.sac_config.get("target_entropy", -32.0)), device=self.device) # Initialize raw_alpha parameter self.alpha_type = self.sac_config.get("alpha_type", "softplus") if self.alpha_type == "exp": self.raw_alpha = torch.nn.Parameter( np.log(np.exp(self.sac_config.get("initial_alpha", 1))) * torch.ones(1, device=self.device), requires_grad=True, ) elif self.alpha_type == "softplus": self.raw_alpha = torch.nn.Parameter( np.log(np.exp(self.sac_config.get("initial_alpha", 0.01)) - 1) * torch.ones(1, device=self.device), requires_grad=True, ) else: return NotImplementedError(f"Unsupported alpha_type: {self.alpha_type}") # build alpha optimizer and scheduler self.alpha_optimizer = torch.optim.Adam([self.raw_alpha], lr=self.sac_config.get("alpha_lr", 3e-4)) self.alpha_scheduler = torch.optim.lr_scheduler.ConstantLR(self.alpha_optimizer, factor=1.0) def _init_actor_ema(self): if self.actor_ema_initialized: return self.actor_ema_shadow = {} if not self.actor_ema_enabled: self.actor_ema_initialized = True return for name, param in self.actor_module.sac_get_named_actor_parameters(): self.actor_ema_shadow[name] = param.detach().clone().to(dtype=torch.float32) self.actor_ema_initialized = True @torch.no_grad() def _update_actor_ema(self): if not self.actor_ema_enabled: return one_minus_decay = 1.0 - self.actor_ema_decay for name, param in self.actor_module.sac_get_named_actor_parameters(): shadow = self.actor_ema_shadow[name] shadow.mul_(self.actor_ema_decay).add_(param.detach().to(dtype=torch.float32), alpha=one_minus_decay) @torch.no_grad() def _apply_actor_ema_to_actor_module(self): if not self.actor_ema_enabled: return for name, param in self.actor_module.sac_get_named_actor_parameters(): shadow = self.actor_ema_shadow[name] param.copy_(shadow.to(device=param.device, dtype=param.dtype)) def _get_alpha(self) -> torch.Tensor: if self.auto_entropy: if self.alpha_type == "exp": return self.raw_alpha.exp() elif self.alpha_type == "softplus": return torch.nn.functional.softplus(self.raw_alpha) else: return NotImplementedError(f"Unsupported alpha_type: {self.alpha_type}") else: return torch.tensor(float(self.sac_config.get("initial_alpha", 0.2)), device=self.device) def _calculate_actor_loss( self, log_probs: Optional[torch.Tensor], q_values: torch.Tensor, valids: torch.Tensor, ) -> torch.Tensor: """Calculate actor loss using the SAC loss function. Args: log_probs: Tensor of shape (B,) representing the log probabilities of actions. q_values: Tensor of shape (B,) representing the Q-values for the actions. valids: Tensor of shape (B,) indicating valid samples (1 for valid, 0 for invalid). Returns: Tensor of shape (1,) representing the actor loss. """ alpha = self._get_alpha() if log_probs is None: loss = -q_values else: loss = alpha * log_probs - q_values actor_loss = (loss * valids).sum() / (valids.sum().clamp_min(1.0)) return actor_loss def _calculate_alpha_loss(self, log_probs: Optional[torch.Tensor], valids: torch.Tensor) -> torch.Tensor: """Calculate alpha loss for automatic entropy tuning. Args: log_probs: Tensor of shape (B,) representing the log probabilities of actions. valids: Tensor of shape (B,) indicating valid samples (1 for valid, 0 for invalid). Returns: Tensor of shape (1,) representing the alpha loss. """ if log_probs is None: return torch.tensor(0.0, device=valids.device) alpha_loss = -self._get_alpha() * (log_probs.detach() + self.target_entropy) alpha_loss = (alpha_loss * valids).sum() / (valids.sum().clamp_min(1.0)) return alpha_loss def _calculate_critic_loss( self, q_predict: torch.Tensor, q_target: torch.Tensor, rewards: torch.Tensor, dones: torch.Tensor, next_log_prob: Optional[torch.Tensor], valids: torch.Tensor, ) -> torch.Tensor: """Calculate critic loss using the SAC loss function. Args: q_predict: Tensor of shape (B, critic_num) representing predicted Q-values. q_target: Tensor of shape (B,) representing target Q-values. rewards: Tensor of shape (B,) representing rewards. dones: Tensor of shape (B,) representing done flags. next_log_prob: Tensor of shape (B,) representing log probabilities of next actions. valids: Tensor of shape (B,) indicating valid samples (1 for valid, 0 for invalid). Returns: Tensor of shape (1,) representing the critic loss. """ gamma = self.sac_config.gamma alpha = self._get_alpha() with torch.no_grad(): if next_log_prob is None: y = rewards + gamma * (1.0 - dones) * q_target else: y = rewards + gamma * (1.0 - dones) * (q_target - alpha * next_log_prob) y = y.unsqueeze(1).expand_as(q_predict) # (B, critic_num) valid_mask = valids.unsqueeze(1) mse = F.mse_loss(q_predict, y, reduction="none") per_critic = (mse * valid_mask).sum(dim=0) / valid_mask.sum().clamp_min(1.0) critic_loss = per_critic.sum() return critic_loss def _forward_critic( self, micro_batch: TensorDict, resample=True ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: s0 = get_dict_from_prefix(micro_batch, "s0.") s1 = get_dict_from_prefix(micro_batch, "s1.") a0 = get_dict_from_prefix(micro_batch, "a0.") a1 = get_dict_from_prefix(micro_batch, "a1.") with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): with torch.no_grad(): s = merge_nested_dicts_or_tuples(s0, s1) state_features = self.actor_module.sac_forward_state_features(s) s0_state_features, s1_state_features = split_nested_dicts_or_tuples(state_features, 2) if resample: a1_actions, log_probs_1, _ = self.actor_module.sac_forward_actor( s1_state_features, is_first_micro_batch=False, ) a1 = {"full_action": a1_actions} else: log_probs_1 = None q_values_0 = self.actor_module.sac_forward_critic( a0, s0_state_features, use_target_network=False, method="cat", requires_grad=True, ) q_values_1 = self.actor_module.sac_forward_critic( a1, s1_state_features, use_target_network=True, method="min", requires_grad=False, ) critic_loss = self._calculate_critic_loss( q_predict=q_values_0, q_target=q_values_1, rewards=micro_batch["rewards"], dones=micro_batch["dones"], next_log_prob=log_probs_1, valids=micro_batch["valids"], ) return critic_loss, q_values_0, q_values_1 def _forward_actor( self, micro_batch: TensorDict, is_first_micro_batch: bool, ) -> tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor, dict[str, float]]: micro_batch = micro_batch.to(get_device_id()) s0 = get_dict_from_prefix(micro_batch, "s0.") with torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): s0_state_features = self.actor_module.sac_forward_state_features(s0) a0_actions, log_probs_0, actor_forward_metrics = self.actor_module.sac_forward_actor( s0_state_features, is_first_micro_batch=is_first_micro_batch, ) q_values_0 = self.actor_module.sac_forward_critic( {"full_action": a0_actions}, s0_state_features, use_target_network=False, method="min", requires_grad=False, ) sac_loss = self._calculate_actor_loss( log_probs=log_probs_0, q_values=q_values_0, valids=micro_batch["valids"], ) if self.bc_loss_coef > 0: bc_loss = self.actor_module.bc_loss( state_features=s0_state_features, actions={"full_action": a0_actions}, valids=micro_batch["valids"], ) actor_loss = sac_loss + self.bc_loss_coef * bc_loss else: actor_loss = sac_loss return actor_loss, log_probs_0, q_values_0, actor_forward_metrics def _force_set_lr(self, opt: torch.optim.Optimizer, lr: float): for pg in opt.param_groups: pg["lr"] = lr @override def update_policy(self, data: DataProto): if not self.actor_ema_initialized: self._init_actor_ema() # self._force_set_lr(self.actor_optimizer, 5e-6) # self._force_set_lr(self.critic_optimizer, 1e-4) if "empty_batch" not in data.meta_info: task_ids = data.batch["task_ids"] self.replay_pool.add_batch( data.select( [ "a0.full_action", "a1.full_action", "s0.states", "s1.states", "s0.images", "s1.images", "s0.image_masks", "s1.image_masks", "s0.lang_tokens", "s1.lang_tokens", "s0.lang_masks", "s1.lang_masks", "rewards", "dones", "valids", "positive_sample_mask", ] ).batch, task_ids=task_ids, ) replay_positive_sample_ratio = float(self.sac_config.get("critic_replay_positive_sample_ratio", 0.5)) critic_batch, critic_replay_sample_info = self.replay_pool.sample_batch( self.config.ppo_mini_batch_size, positive_sample_ratio=replay_positive_sample_ratio, return_sample_info=True, ) micro_batches = critic_batch.split(self.config.ppo_micro_batch_size_per_gpu) global_steps = data.meta_info["global_steps"] grad_accum_steps = len(micro_batches) * torch.distributed.get_world_size() actor_logprobs_list, actor_qvalues_list = [], [] critic_qvalues_0_list, critic_qvalues_1_list = [], [] actor_loss_list, critic_loss_list, alpha_loss_list = [], [], [] actor_forward_metrics: dict[str, float] = {} # Training critic self.critic_optimizer.zero_grad() for batch_idx, micro_batch in enumerate(micro_batches): logger.info(f"[{batch_idx + 1}/{len(micro_batches)}] critic micro batch ") micro_batch = micro_batch.to(get_device_id()) raw_critic_loss, q_values_0, q_values_1 = self._forward_critic(micro_batch, resample=True) (raw_critic_loss / grad_accum_steps).backward() critic_loss_list.append(raw_critic_loss.detach().item()) critic_qvalues_0_list.append(q_values_0.mean(dim=-1).detach()) critic_qvalues_1_list.append(q_values_1.detach()) critic_grad_norm = torch.nn.utils.clip_grad_norm_( self.actor_module.sac_get_critic_parameters(), max_norm=self.config.grad_clip ) self.critic_optimizer.step() self.critic_scheduler.step() update_actor = ( global_steps >= self.config.critic_warmup_steps and global_steps % self.config.actor_update_interval == 0 ) if update_actor: replay_positive_sample_ratio = float(self.sac_config.get("actor_replay_positive_sample_ratio", 0.5)) actor_batch, actor_replay_sample_info = self.replay_pool.sample_batch( self.config.ppo_mini_batch_size, positive_sample_ratio=replay_positive_sample_ratio, return_sample_info=True, ) micro_batches = actor_batch.split(self.config.ppo_micro_batch_size_per_gpu) # Training actor self.actor_optimizer.zero_grad() for batch_idx, micro_batch in enumerate(micro_batches): logger.info(f"[{batch_idx + 1}/{len(micro_batches)}] actor micro batch ") micro_batch = micro_batch.to(get_device_id()) raw_actor_loss, log_probs, q_values, actor_forward_metrics_mb = self._forward_actor( micro_batch, is_first_micro_batch=(batch_idx == 0), ) (raw_actor_loss / grad_accum_steps).backward() actor_loss_list.append(raw_actor_loss.detach().item()) if log_probs is not None: actor_logprobs_list.append(log_probs.detach()) actor_qvalues_list.append(q_values.detach()) actor_forward_metrics.update(actor_forward_metrics_mb) actor_grad_norm = self._optimizer_step() self._update_actor_ema() self._apply_actor_ema_to_actor_module() # Training alpha # NOTE: We reuse the log-probabilities computed during the actor forward pass # to update the entropy temperature (alpha), instead of re-forwarding # the actor after the policy update (saving compute). if self.auto_entropy and actor_logprobs_list: self.alpha_optimizer.zero_grad() for micro_batch, log_probs in zip(micro_batches, actor_logprobs_list, strict=False): micro_batch = micro_batch.to(get_device_id()) raw_alpha_loss = self._calculate_alpha_loss(log_probs, micro_batch["valids"]) (raw_alpha_loss / grad_accum_steps).backward() alpha_loss_list.append(raw_alpha_loss.detach().item()) torch.distributed.all_reduce(self.raw_alpha.grad, op=torch.distributed.ReduceOp.SUM) alpha_grad_norm = torch.nn.utils.clip_grad_norm_(self.raw_alpha, max_norm=self.config.grad_clip) self.alpha_optimizer.step() self.alpha_scheduler.step() # Update target networks self.actor_module.sac_update_target_network(self.sac_config.tau) # Save replay pool if global_steps % self.config.replay_pool_save_interval == 0: self.replay_pool.save(self.config.replay_pool_save_dir) # Log metrics positive_qvalue_mean = ( torch.cat(critic_qvalues_0_list)[ (critic_batch["positive_sample_mask"].to(torch.bool) & critic_batch["valids"].to(torch.bool)).to( torch.cat(critic_qvalues_0_list).device ) ] .mean() .detach() .item() if critic_qvalues_0_list and (critic_batch["positive_sample_mask"].to(torch.bool) & critic_batch["valids"].to(torch.bool)).any() else 0.0 ) negative_qvalue_mean = ( torch.cat(critic_qvalues_0_list)[ (~critic_batch["positive_sample_mask"].to(torch.bool) & critic_batch["valids"].to(torch.bool)).to( torch.cat(critic_qvalues_0_list).device ) ] .mean() .detach() .item() if critic_qvalues_0_list and (~critic_batch["positive_sample_mask"].to(torch.bool) & critic_batch["valids"].to(torch.bool)).any() else 0.0 ) metrics = { "data/reward_mean": valid_mean(critic_batch["rewards"], critic_batch["valids"]).detach().item(), "data/valid_ratio": critic_batch["valids"].float().mean().item(), "sac/critic_replay_sampled_ratio": critic_replay_sample_info["actual_positive_sample_ratio"], "sac/actor_replay_sampled_ratio": actor_replay_sample_info["actual_positive_sample_ratio"] if update_actor else 0.0, "sac/replay_pool_positive_size": critic_replay_sample_info["positive_size"], "sac/replay_pool_negative_size": critic_replay_sample_info["negative_size"], "sac/replay_task_count": critic_replay_sample_info["task_count"], "sac/alpha": self._get_alpha().detach().item(), "sac/actor_ema_enabled": float(self.actor_ema_enabled), "sac/actor_ema_decay": self.actor_ema_decay, "sac/replay_pool_size": len(self.replay_pool), "critic/loss": sum(critic_loss_list) / len(critic_loss_list) if critic_loss_list else 0.0, "critic/lr": self.critic_optimizer.param_groups[0]["lr"], "critic/grad_norm": critic_grad_norm.detach().item(), "critic/qvalue0_mean": ( valid_mean(torch.cat(critic_qvalues_0_list), critic_batch["valids"]).detach().item() if critic_qvalues_0_list else 0.0 ), "critic/qvalue1_mean": ( valid_mean(torch.cat(critic_qvalues_1_list), critic_batch["valids"]).detach().item() if critic_qvalues_1_list else 0.0 ), "critic/positive_qvalue_mean": positive_qvalue_mean, "critic/negative_qvalue_mean": negative_qvalue_mean, "critic/diff_pos_neg_qvalue_mean": positive_qvalue_mean - negative_qvalue_mean, } if update_actor: metrics.update( { "actor/loss": sum(actor_loss_list) / len(actor_loss_list), "actor/lr": self.actor_optimizer.param_groups[0]["lr"], "actor/grad_norm": actor_grad_norm.detach().item(), "actor/logprob_mean": ( valid_mean(torch.cat(actor_logprobs_list), actor_batch["valids"]).detach().item() if actor_logprobs_list else 0.0 ), "actor/qvalue_mean": valid_mean(torch.cat(actor_qvalues_list), actor_batch["valids"]) .detach() .item(), "sac/alpha_lr": self.alpha_optimizer.param_groups[0]["lr"] if self.auto_entropy and actor_logprobs_list else 0.0, "sac/alpha_loss": sum(alpha_loss_list) / len(alpha_loss_list) if self.auto_entropy and alpha_loss_list else 0.0, "sac/alpha_grad_norm": alpha_grad_norm.detach().item() if self.auto_entropy and actor_logprobs_list else 0.0, } ) metrics.update({f"actor/{k}": v for k, v in actor_forward_metrics.items()}) return metrics def _optimizer_step(self) -> torch.Tensor: assert self.config.grad_clip is not None if isinstance(self.actor_module, FSDP): grad_norm = self.actor_module.clip_grad_norm_(max_norm=self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.actor_module.parameters(), max_norm=self.config.grad_clip) self.actor_optimizer.step() return grad_norm ================================================ FILE: verl/experimental/vla/sac/sac_ray_trainer.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio from pprint import pprint from typing import Optional import numpy as np import torch from omegaconf import OmegaConf from tqdm import tqdm from verl import DataProto from verl.single_controller.ray import RayClassWithInitArgs from verl.single_controller.ray.base import create_colocated_worker_cls from verl.trainer.ppo.ray_trainer import RayPPOTrainer from verl.trainer.ppo.utils import Role from verl.utils.checkpoint.checkpoint_manager import should_save_ckpt_esi from verl.utils.debug import marked_timer from verl.utils.metric import reduce_metrics def compute_avg_positive_trajectory_length(batch: DataProto) -> float: dones = batch.batch["dones"].bool() # (B, T) positive_mask = batch.batch["positive_sample_mask"] # (B, T) positive_traj = positive_mask.any(dim=1) # (B,) if positive_traj.sum() == 0: return 0.0 B, T = dones.shape done_idx = torch.argmax(dones.int(), dim=1) # (B,) traj_lens = done_idx + 1 return traj_lens[positive_traj].float().mean().item() def flatten_trajectories(data: DataProto) -> DataProto: batch_size, num_steps = data.batch["action"].shape[:2] new_batch_fields = {} for key, tensor in data.batch.items(): if len(tensor.shape) >= 2 and tensor.shape[0] == batch_size and tensor.shape[1] == num_steps: # (B, S, H, W) -> (B*S, H, W) new_shape = (batch_size * num_steps, *tensor.shape[2:]) new_batch_fields[key] = tensor.reshape(new_shape) elif len(tensor.shape) == 1 and tensor.shape[0] == batch_size: # [e1, e2] -> [e1, e1, ..., e2, e2, ...] (S times each) new_batch_fields[key] = tensor.repeat_interleave(num_steps) else: new_batch_fields[key] = tensor new_data = DataProto.from_dict(tensors=new_batch_fields, meta_info=data.meta_info) return new_data def add_transition_prefixes(data: DataProto) -> DataProto: batch = data.batch step_key = "action" if "action" in batch else "full_action" if step_key not in batch: return data num_steps = batch[step_key].shape[1] if num_steps <= 1: return data def drop_last(tensor: torch.Tensor) -> torch.Tensor: return tensor[:, :-1, ...] def shift_next(tensor: torch.Tensor) -> torch.Tensor: return tensor[:, 1:, ...] state_keys = ["states", "images", "image_masks", "lang_tokens", "lang_masks"] action_keys = ["full_action", "action"] for key in state_keys: if key in batch: batch[f"s0.{key}"] = drop_last(batch[key]) batch[f"s1.{key}"] = shift_next(batch[key]) for key in action_keys: if key in batch: batch[f"a0.{key}"] = drop_last(batch[key]) batch[f"a1.{key}"] = shift_next(batch[key]) batch_size = batch[step_key].shape[0] for key, tensor in list(batch.items()): if tensor.ndim >= 2 and tensor.shape[0] == batch_size and tensor.shape[1] == num_steps: batch[key] = drop_last(tensor) return data class RobRaySACTrainer(RayPPOTrainer): def _start_profiling(self, do_profile: bool) -> None: """Start profiling for all worker groups including env workers.""" super()._start_profiling(do_profile) if do_profile and hasattr(self, "env_wg"): self.env_wg.start_profile(role="env", profile_step=self.global_steps) def _stop_profiling(self, do_profile: bool) -> None: """Stop profiling for all worker groups including env workers.""" super()._stop_profiling(do_profile) if do_profile and hasattr(self, "env_wg"): self.env_wg.stop_profile() def init_workers(self): self.resource_pool_manager.create_resource_pool() if self.config.env.disagg_sim.enable: # pin EnvWorker to Simulator GPU nodes self.resource_pool_manager.get_resource_pool(Role.Env).accelerator_type = "sim" self.resource_pool_manager.get_resource_pool(Role.ActorRollout).accelerator_type = "train_rollout" self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()} resource_pool = self.resource_pool_manager.get_resource_pool(Role.ActorRollout) actor_rollout_cls = RayClassWithInitArgs( cls=self.role_worker_mapping[Role.ActorRollout], config=self.config.actor_rollout_ref, role="actor_rollout", ) self.resource_pool_to_cls[resource_pool]["actor_rollout"] = actor_rollout_cls assert Role.Env in self.role_worker_mapping if Role.Env in self.role_worker_mapping: resource_pool = self.resource_pool_manager.get_resource_pool(Role.Env) env_cls = RayClassWithInitArgs(self.role_worker_mapping[Role.Env], config=self.config.env) self.resource_pool_to_cls[resource_pool]["env"] = env_cls # initialize WorkerGroup # NOTE: if you want to use a different resource pool for each role, which can support different parallel size, # you should not use `create_colocated_worker_cls`. # Instead, directly pass different resource pool to different worker groups. # See https://github.com/volcengine/verl/blob/master/examples/ray/tutorial.ipynb for more information. all_wg = {} wg_kwargs = {} # Setting up kwargs for RayWorkerGroup if OmegaConf.select(self.config.trainer, "ray_wait_register_center_timeout") is not None: wg_kwargs["ray_wait_register_center_timeout"] = self.config.trainer.ray_wait_register_center_timeout if OmegaConf.select(self.config.global_profiler, "steps") is not None: wg_kwargs["profile_steps"] = OmegaConf.select(self.config.global_profiler, "steps") # Only require nsight worker options when tool is nsys if OmegaConf.select(self.config.global_profiler, "tool") == "nsys": assert ( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") is not None ), "worker_nsight_options must be set when using nsys with profile_steps" wg_kwargs["worker_nsight_options"] = OmegaConf.to_container( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") ) wg_kwargs["device_name"] = self.device_name for resource_pool, class_dict in self.resource_pool_to_cls.items(): worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = self.ray_worker_group_cls( resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls, **wg_kwargs, ) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) # we should create rollout at the end so that vllm can have a better estimation of kv cache memory self.actor_rollout_wg = all_wg["actor_rollout"] self.actor_rollout_wg.init_model() self.env_wg = all_wg["env"] # create async rollout manager and request scheduler self.async_rollout_mode = False if self.config.actor_rollout_ref.rollout.mode == "async_envloop": from verl.experimental.vla.env_loop import EnvLoop self.async_rollout_mode = True self.async_rollout_manager = EnvLoop( config=self.config, rollout_wg=self.actor_rollout_wg, env_wg=self.env_wg ) def _get_gen_batch(self, batch: DataProto) -> DataProto: # pop those keys for generation batch_keys_to_pop = [] non_tensor_batch_keys_to_pop = set(batch.non_tensor_batch.keys()) gen_batch = batch.pop( batch_keys=batch_keys_to_pop, non_tensor_batch_keys=list(non_tensor_batch_keys_to_pop), ) return gen_batch def _reset_envs(self, gen_batch: DataProto) -> asyncio.Future: initial_state_ids = gen_batch.non_tensor_batch["state_ids"] task_ids = gen_batch.non_tensor_batch["task_ids"] reset_prompts = DataProto.from_dict(non_tensors={"state_ids": initial_state_ids, "task_ids": task_ids}) reset_future = self.env_wg.reset_envs_to_state_ids(reset_prompts) return reset_future def _next_rollout_batch(self, train_iter) -> Optional[DataProto]: try: batch_dict = next(train_iter) except StopIteration: return None rollout_batch = DataProto.from_single_dict(batch_dict) rollout_batch = self._get_gen_batch(rollout_batch) rollout_batch = rollout_batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) rollout_batch.meta_info["task_ids"] = np.asarray(rollout_batch.non_tensor_batch["task_ids"], dtype=np.int64) rollout_batch.meta_info["global_steps"] = self.global_steps return rollout_batch def _prepare_actor_input(self, rollout_output: Optional[DataProto]) -> DataProto: # dones complete_any = rollout_output.batch["complete"].any(dim=-1) # (B, T) dones_step = complete_any.clone() dones_step[:, -2] = True rollout_output.batch["dones"] = dones_step.float() # reward (sparse reward with step penalty) sparse_rewards = complete_any.float() rollout_output.batch["valids"] = (~rollout_output.batch["complete"]).any(dim=-1).float() step_penalty = float(self.config.env.train.get("step_penalty", 0.0)) rollout_output.batch["rewards"] = sparse_rewards - step_penalty * rollout_output.batch["valids"] rollout_output.batch["rewards"][:, -2] = -1.0 # mark samples in successful trajectories as positive samples rollout_output.batch["positive_sample_mask"] = ( sparse_rewards.any(dim=-1).unsqueeze(-1).repeat_interleave(rollout_output.batch["action"].shape[1], dim=-1) ) # task id rollout_output.batch["task_ids"] = torch.as_tensor( rollout_output.meta_info["task_ids"], dtype=torch.long, device=rollout_output.batch["action"].device, ) rollout_output.meta_info["global_token_num"] = [0] rollout_output.meta_info["data/trajectory_avg_reward"] = ( sparse_rewards.any(dim=-1).mean(dtype=torch.float32).item() ) rollout_output.meta_info["data/avg_positive_trajectory_length"] = compute_avg_positive_trajectory_length( rollout_output ) rollout_output = add_transition_prefixes(rollout_output) rollout_output = flatten_trajectories(rollout_output) return rollout_output def fit(self): from omegaconf import OmegaConf from verl.utils.tracking import Tracking logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) self.global_steps = 0 # load checkpoint before doing anything self._load_checkpoint() # perform validation before training # currently, we only support validation using the reward_function. if self.config.trainer.get("val_before_train", True): val_metrics = self._validate() assert val_metrics, f"{val_metrics=}" pprint(f"Initial validation metrics: {val_metrics}") logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get("val_only", False): return # add tqdm self.total_training_steps = ( self.config.trainer.total_epochs * len(self.train_dataloader) * self.config.trainer.rollout_interval ) progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress") self.global_steps += 1 last_val_metrics = None self.max_steps_duration = 0 prev_step_profile = False curr_step_profile = ( self.global_steps in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) next_step_profile = False for epoch in range(self.config.trainer.total_epochs): train_iter = iter(self.train_dataloader) reset_future = None next_rollout_batch = self._next_rollout_batch(train_iter) if next_rollout_batch is None: continue print(f"Starting epoch {epoch}, dataloader length: {len(self.train_dataloader)}") while next_rollout_batch is not None: for training_step in range(self.config.trainer.rollout_interval): metrics = {} timing_raw = {} # === start profiling === with marked_timer("start_profile", timing_raw): self._start_profiling( not prev_step_profile and curr_step_profile if self.config.global_profiler.profile_continuous_steps else curr_step_profile ) with marked_timer("step", timing_raw): # === rollout === # Determine whether to perform rollout: # enable at start and early warmup, disable during critic warmup phase warm_rollout_steps = int(getattr(self.config.actor_rollout_ref.actor, "warm_rollout_steps", 0)) need_rollout = (training_step == 0) or self.global_steps < warm_rollout_steps if ( warm_rollout_steps <= self.global_steps < self.config.actor_rollout_ref.actor.critic_warmup_steps ): need_rollout = False if need_rollout and next_rollout_batch is None: break actor_input = None if need_rollout: with marked_timer("rollout", timing_raw): # execute rollout rollout_batch = next_rollout_batch assert rollout_batch is not None if reset_future is None: reset_future = self._reset_envs(rollout_batch) with marked_timer("generate", timing_raw, color="red"): rollout_output = self.async_rollout_manager.generate_sequences( rollout_batch, reset_future ) # prepare for next batch's env reset next_rollout_batch = self._next_rollout_batch(train_iter) if next_rollout_batch is not None: reset_future = self._reset_envs(next_rollout_batch) # compute rewards and other metrics, and prepare for actor update actor_input = self._prepare_actor_input(rollout_output) # === update policy === with marked_timer("update_actor", timing_raw, color="red"): if actor_input is not None: actor_output = self.actor_rollout_wg.update_actor(actor_input) else: actor_output = self.actor_rollout_wg.update_actor( DataProto( meta_info={ "empty_batch": True, "global_steps": self.global_steps, "global_token_num": [0], } ) ) actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"]) metrics.update(actor_output_metrics) # === validate === is_last_step = self.global_steps >= self.total_training_steps if ( self.config.trainer.test_freq > 0 and (is_last_step or self.global_steps % self.config.trainer.test_freq == 0) and self.global_steps >= self.config.actor_rollout_ref.actor.critic_warmup_steps ): with marked_timer("testing", timing_raw, color="green"): val_metrics: dict = self._validate() if is_last_step: last_val_metrics = val_metrics metrics.update(val_metrics) reset_future = None # === save checkpoint === # Check if the ESI (Elastic Server Instance)/training plan is close to expiration. esi_close_to_expiration = should_save_ckpt_esi( max_steps_duration=self.max_steps_duration, redundant_time=self.config.trainer.esi_redundant_time, ) # Check if the conditions for saving a checkpoint are met. # The conditions include a mandatory condition (1) and # one of the following optional conditions (2/3/4): # 1. The save frequency is set to a positive value. # 2. It's the last training step. # 3. The current step number is a multiple of the save frequency. # 4. The ESI(Elastic Server Instance)/training plan is close to expiration. if self.config.trainer.save_freq > 0 and ( is_last_step or self.global_steps % self.config.trainer.save_freq == 0 or esi_close_to_expiration ): if esi_close_to_expiration: print("Force saving checkpoint: ESI instance expiration approaching.") with marked_timer("save_checkpoint", timing_raw, color="green"): self._save_checkpoint() # === stop profiling === with marked_timer("stop_profile", timing_raw): next_step_profile = ( self.global_steps + 1 in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) self._stop_profiling( curr_step_profile and not next_step_profile if self.config.global_profiler.profile_continuous_steps else curr_step_profile ) prev_step_profile = curr_step_profile curr_step_profile = next_step_profile steps_duration = timing_raw["step"] self.max_steps_duration = max(self.max_steps_duration, steps_duration) # === training metrics === metrics.update( { "training/global_step": self.global_steps, "training/epoch": epoch, } ) metrics.update({f"timing_s/{name}": value for name, value in timing_raw.items()}) if actor_input is not None: metrics["data/trajectory_avg_reward"] = actor_input.meta_info["data/trajectory_avg_reward"] metrics["data/avg_positive_trajectory_length"] = actor_input.meta_info[ "data/avg_positive_trajectory_length" ] logger.log(data=metrics, step=self.global_steps) progress_bar.update(1) self.global_steps += 1 if ( hasattr(self.config.actor_rollout_ref.actor, "profiler") and self.config.actor_rollout_ref.actor.profiler.tool == "torch_memory" ): self.actor_rollout_wg.dump_memory_snapshot( tag=f"post_update_step{self.global_steps}", sub_dir=f"step{self.global_steps}" ) if is_last_step: pprint(f"Final validation metrics: {last_val_metrics}") progress_bar.close() return def _validate(self) -> dict: metric_list = [] val_iter = iter(self.val_dataloader) test_batch = self._next_rollout_batch(val_iter) while test_batch is not None: if len(test_batch) < self.config.data.val_batch_size: print(f"drop last batch in val_dataloader, len {len(test_batch)}") break test_batch.meta_info["validate"] = True reset_future = self._reset_envs(test_batch) rollout_output = self.async_rollout_manager.generate_sequences(test_batch, reset_future) self._prepare_actor_input(rollout_output) test_batch = self._next_rollout_batch(val_iter) actor_input = self._prepare_actor_input(rollout_output) metric_list.append( { "val/avg_reward": actor_input.meta_info["data/trajectory_avg_reward"], "val/avg_positive_trajectory_length": actor_input.meta_info["data/avg_positive_trajectory_length"], } ) metrics = {} if metric_list: metrics["val/avg_reward"] = np.mean([m["val/avg_reward"] for m in metric_list]) metrics["val/avg_positive_trajectory_length"] = np.mean( [m["val/avg_positive_trajectory_length"] for m in metric_list] ) return metrics ================================================ FILE: verl/experimental/vla/workers/env/env_loop_wg_test.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import ray from omegaconf import OmegaConf from verl import DataProto from verl.experimental.vla.naive_rollout_rob import NaiveRolloutRob # from verl.workers.env.env_worker import EnvWorker from verl.experimental.vla.workers.env.env_worker import EnvWorker from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup if not ray.is_initialized(): ray.init() # for debugging # ray.init( # runtime_env={ # "env_vars": {"RAY_DEBUG_POST_MORTEM": "1"}, # } # ) ENV_WORKERS_NUM = 1 STAGE_NUM = 1 # NUM_ENVS_PER_ITER = 32 # NUM_ENVS_PER_STAGE = 8 # NUM_ENVS_PER_ITER = STAGE_NUM * NUM_ENVS_PER_STAGE # NUM_ENVS_PER_ITER = 8 # NUM_ENVS_PER_ITER = 32 NUM_ENVS_PER_ITER = 2 NUM_ENVS_PER_WORKER = NUM_ENVS_PER_ITER // ENV_WORKERS_NUM # NUM_ENVS_PER_WORKER_PER_STAGE = NUM_ENVS_PER_STAGE // ENV_WORKERS_NUM GROUP_SIZE = 2 # real group size = GROUP_SIZE * STAGE_NUM GROUP_NUM_PER_ITER = NUM_ENVS_PER_ITER * STAGE_NUM // GROUP_SIZE BATCH_SIZE_PER_GPU = 2 NUM_ACTS_CHUNKS = 8 MAX_EPISODE_STEPS = 32 MAX_INFER_STEPS = MAX_EPISODE_STEPS // NUM_ACTS_CHUNKS cfg_dict = { "rollout": {"pipeline_stage_num": STAGE_NUM}, "train": { "use_fixed_reset_state_ids": False, "ignore_terminations": False, # "auto_reset": True, "auto_reset": False, "max_episode_steps": MAX_EPISODE_STEPS, "use_rel_reward": False, "reward_coef": 1.0, "only_eval": False, "use_ordered_reset_state_ids": False, # "num_images_in_input": 1, "init_params": { "camera_depths": False, "camera_heights": 256, "camera_widths": 256, "camera_names": ["agentview", "robot0_eye_in_hand"], }, "video_cfg": { "save_video": True, "video_base_dir": "/tmp/videos", }, "task_suite_name": "libero_10", "num_envs": NUM_ENVS_PER_WORKER, "simulator_type": "isaac", "seed": 0, }, "enable_offload": False, "actor": {"model": {"num_action_chunks": NUM_ACTS_CHUNKS, "action_dim": 7}}, "runner": {"only_eval": False}, } env_cfg = OmegaConf.create(cfg_dict) gpu_pool = RayResourcePool([ENV_WORKERS_NUM], use_gpu=True) # RayEnvWorker = ray.remote(num_gpus=1)(EnvWorker) ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(EnvWorker), config=env_cfg) env_wg = RayWorkerGroup(gpu_pool, ray_cls_with_init) def restructure_data_proto(data_proto: DataProto) -> list[DataProto]: total_batch_size = len(data_proto) tensors = data_proto.batch non_tensors = data_proto.non_tensor_batch full_image_tensor = tensors["full_image"] state_tensor = tensors["state"] task_descriptions_np = non_tensors["task_descriptions"] if total_batch_size != ENV_WORKERS_NUM * STAGE_NUM * NUM_ENVS_PER_WORKER: raise ValueError( f"Total batch size {total_batch_size} does not match the expected size " f"ENV_WORKERS_NUM * STAGE_NUM * NUM_ENVS_PER_WORKER = " f"{ENV_WORKERS_NUM * STAGE_NUM * NUM_ENVS_PER_WORKER}" ) image_rest_shape = (ENV_WORKERS_NUM, STAGE_NUM, NUM_ENVS_PER_WORKER) + full_image_tensor.shape[1:] state_rest_shape = (ENV_WORKERS_NUM, STAGE_NUM, NUM_ENVS_PER_WORKER) + state_tensor.shape[1:] reshaped_full_image = full_image_tensor.view(image_rest_shape) reshaped_state = state_tensor.view(state_rest_shape) reshaped_task_descriptions = task_descriptions_np.reshape(ENV_WORKERS_NUM, STAGE_NUM, NUM_ENVS_PER_WORKER) stages_data_list = [] for stage_idx in range(STAGE_NUM): stage_images = reshaped_full_image[:, stage_idx, :] stage_states = reshaped_state[:, stage_idx, :] stage_tasks = reshaped_task_descriptions[:, stage_idx, :] final_images = stage_images.reshape(ENV_WORKERS_NUM * NUM_ENVS_PER_WORKER, *full_image_tensor.shape[1:]) final_states = stage_states.reshape(ENV_WORKERS_NUM * NUM_ENVS_PER_WORKER, *state_tensor.shape[1:]) final_tasks = stage_tasks.flatten().tolist() stage_dp = DataProto.from_dict( tensors={"full_image": final_images, "state": final_states}, non_tensors={"task_descriptions": final_tasks}, meta_info={"do_sample": True, "temperature": 1.6, "prompt_length": 512}, ) stages_data_list.append(stage_dp) return stages_data_list async def run(): # breakpoint() env_wg.init_worker() env_wg.init_simulator() reset_state_ids_tensordict = DataProto.from_dict( non_tensors={"state_ids": [0] * NUM_ENVS_PER_ITER * STAGE_NUM, "task_ids": [0] * NUM_ENVS_PER_ITER * STAGE_NUM} ) reset_result = env_wg.reset_envs_to_state_ids(reset_state_ids_tensordict) print(f"reset_envs_to_state_ids result: {reset_result}") stages_data_list = restructure_data_proto(reset_result) RayNaiveRolloutRob = ray.remote(num_gpus=1)(NaiveRolloutRob) model_config = {"path": "Haozhan72/Openvla-oft-SFT-libero10-trajall"} rollout_workers = RayNaiveRolloutRob.remote(model_config) env_obs_refs = {} rollout_refs = {} traj = [[], []] for _ in range(MAX_INFER_STEPS): for stage_id in range(STAGE_NUM): if _ == 0: rollout_refs[stage_id] = rollout_workers.generate_sequences.remote(stages_data_list[stage_id]) else: # env_batch = env_obs_refs[stage_id] env_batch: DataProto = env_obs_refs[stage_id].get() env_batch_traj = env_batch.select(batch_keys=["rews", "terminations", "truncations"]) traj[stage_id][-1].update({"env": env_batch_traj}) obs = env_batch obs.meta_info.update({"do_sample": True, "temperature": 1.6, "prompt_length": 512}) rollout_refs[stage_id] = rollout_workers.generate_sequences.remote(obs) for stage_id in range(STAGE_NUM): batch: DataProto = ray.get(rollout_refs[stage_id]) traj[stage_id].append({"model": batch}) action = batch.batch["action"] action = action.cpu().numpy() # already in env data = DataProto.from_dict(non_tensors={"actions": action}, meta_info={"stage_id": stage_id}) env_obs_refs[stage_id] = env_wg.env_interact_step(data) env_wg.finish_rollout() asyncio.run(run()) ray.timeline(filename="2stage_pipeline_timeline_wg.json") ================================================ FILE: verl/experimental/vla/workers/env/env_manager.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gc import logging import os import subprocess from typing import Optional import torch import torch.multiprocessing as mp from verl.utils.device import get_torch_device logger = logging.getLogger(__name__) def cleanup_device_tensors(): gc.collect() get_torch_device().empty_cache() def get_gpu_numa_node(gpu_id: int) -> int: try: try: import pynvml pynvml.nvmlInit() handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id) # Get PCI bus info pci_info = pynvml.nvmlDeviceGetPciInfo(handle) pci_bus_id = pci_info.busId except ImportError: # Fallback to nvidia-smi result = subprocess.run( [ "nvidia-smi", "--query-gpu=pci.bus_id", "--format=csv,noheader,nounits", f"--id={gpu_id}", ], capture_output=True, text=True, check=True, ) pci_bus_id = result.stdout.strip() # Extract bus number from PCI bus ID (format: 0000:XX:YY.Z) bus_number = pci_bus_id.split(":")[1] # Get NUMA node from sysfs numa_node_path = f"/sys/bus/pci/devices/0000:{bus_number}:00.0/numa_node" if os.path.exists(numa_node_path): with open(numa_node_path) as f: numa_node = int(f.read().strip()) if numa_node >= 0: return numa_node # Fallback: try to get from lscpu result = subprocess.run(["lscpu"], capture_output=True, text=True, check=True) numa_nodes = 0 for line in result.stdout.split("\n"): if "NUMA node(s):" in line: numa_nodes = int(line.split(":")[1].strip()) break # If we can't determine the exact NUMA node, distribute evenly return gpu_id % numa_nodes if numa_nodes > 0 else 0 except Exception as e: logger.error(f"Warning: Could not determine NUMA node for GPU {gpu_id}: {e}") return 0 def get_numa_cpus(numa_node: int) -> list: try: # Read from sysfs cpulist_path = f"/sys/devices/system/node/node{numa_node}/cpulist" if os.path.exists(cpulist_path): with open(cpulist_path) as f: cpulist = f.read().strip() # Parse CPU list (e.g., "0-7,16-23" or "0,1,2,3") cpus = [] for part in cpulist.split(","): if "-" in part: start, end = map(int, part.split("-")) cpus.extend(range(start, end + 1)) else: cpus.append(int(part)) return cpus except Exception as e: logger.error(f"Warning: Could not get CPU list for NUMA node {numa_node}: {e}") # Fallback: return all available CPUs return list(range(os.cpu_count() or 1)) def set_process_numa_affinity(gpu_id: int) -> None: try: numa_node = get_gpu_numa_node(gpu_id) cpus = get_numa_cpus(numa_node) if not cpus: logger.error(f"Warning: No CPUs found for NUMA node {numa_node}") return os.sched_setaffinity(0, cpus) try: subprocess.run( ["numactl", "--membind", str(numa_node), "--"], check=False, capture_output=True, ) except FileNotFoundError: pass # numactl not available, that's ok except Exception as e: logger.error(f"Warning: Could not set NUMA affinity for GPU {gpu_id}: {e}") def recursive_to_own(obj): if isinstance(obj, torch.Tensor): return obj.clone() if obj.is_shared() else obj elif isinstance(obj, list): return [recursive_to_own(elem) for elem in obj] elif isinstance(obj, tuple): return tuple(recursive_to_own(elem) for elem in obj) elif isinstance(obj, dict): return {k: recursive_to_own(v) for k, v in obj.items()} else: return obj class EnvManager: def __init__(self, cfg, rank, world_size, env_cls, stage_id: int = 0): self.cfg = cfg self.rank = rank self.world_size = world_size self.stage_id = stage_id self.process: Optional[mp.Process] = None self.command_queue: Optional[mp.Queue] = None self.result_queue: Optional[mp.Queue] = None self.state_buffer: Optional[bytes] = None self.env_cls = env_cls def start_simulator(self): """Start simulator process with shared memory queues""" if self.process: logger.info(f"Simulator process already running for rank {self.rank}") return self.context = mp.get_context("spawn") # Create shared memory queues self.command_queue = self.context.Queue() self.result_queue = self.context.Queue() # Start simulator process self.process = self.context.Process( target=_simulator_worker, args=( self.cfg, self.rank, self.world_size, self.stage_id, self.env_cls, self.command_queue, self.result_queue, self.state_buffer, True, ), ) self.process.start() # Wait for initialization result = self.result_queue.get(timeout=180) if result["status"] != "ready": raise RuntimeError(f"Simulator initialization failed: {result}") def stop_simulator(self): if not self.process: return # Request state save self.command_queue.put({"method": "get_state", "args": [], "kwargs": {}}) # Get saved state result = self.result_queue.get(timeout=180) if result["status"] == "success": self.state_buffer = result["data"] self.command_queue.put({"method": "shutdown"}) self.command_queue.close() self.result_queue.close() self.command_queue = None self.result_queue = None self.process.join(timeout=5) self.command_queue = None self.result_queue = None if self.process.is_alive(): self.process.terminate() self.process.join() self.process = None def __getattr__(self, name): if name in [ "cfg", "rank", "world_size", "stage_id", "process", "command_queue", "result_queue", "state_buffer", "env_cls", "context", ]: return super().__getattr__(name) def method_proxy(*args, **kwargs): if self.process is None or not self.process.is_alive(): raise RuntimeError("Simulator not running") args = recursive_to_own(args) kwargs = recursive_to_own(kwargs) self.command_queue.put({"method": name, "args": args, "kwargs": kwargs}) result = self.result_queue.get() result = recursive_to_own(result) if result["status"] == "error": raise Exception(result["error"]) return result["data"] return method_proxy def get_all_state_ids(self): """Get all available state IDs from the environment.""" if self.process is None or not self.process.is_alive(): raise RuntimeError("Simulator not running") self.command_queue.put({"method": "get_all_state_ids", "args": [], "kwargs": {}}) result = self.result_queue.get() result = recursive_to_own(result) if result["status"] == "error": raise Exception(result["error"]) return result["data"] def reset_envs_to_state_ids(self, state_ids_list, task_ids_list): """Reset environments to specified state IDs.""" if self.process is None or not self.process.is_alive(): raise RuntimeError("Simulator not running") state_ids_list = recursive_to_own(state_ids_list) task_ids_list = recursive_to_own(task_ids_list) self.command_queue.put( { "method": "reset_envs_to_state_ids", "args": [state_ids_list, task_ids_list], "kwargs": {}, } ) result = self.result_queue.get() result = recursive_to_own(result) if result["status"] == "error": raise Exception(result["error"]) return result["data"] def __setattr__(self, name, value): # Handle special attributes that should be set on self if name in [ "cfg", "rank", "world_size", "stage_id", "process", "command_queue", "result_queue", "state_buffer", "env_cls", "context", ]: super().__setattr__(name, value) return if self.process is None or not self.process.is_alive(): raise RuntimeError(f"Simulator not running to set attribute {name} to {value}") value = recursive_to_own(value) self.command_queue.put( { "method": "__setattr__", "args": [name, value], "kwargs": {}, } ) result = self.result_queue.get() result = recursive_to_own(result) if result["status"] == "error": raise Exception(result["error"]) def _simulator_worker( cfg, rank, world_size, stage_id, env_cls, command_queue, result_queue, state_buffer, bind_numa=True, ): """Worker process for simulator""" # Set NUMA affinity for the process to match the GPU rank import logging import os pid = os.getpid() logger = logging.getLogger(f"simulator_worker_{rank}_{pid}") if bind_numa: set_process_numa_affinity(rank) try: try: env = env_cls(cfg, rank, world_size, stage_id=stage_id) except TypeError: env = env_cls(cfg, rank, world_size) if state_buffer: env.load_state(state_buffer) # Signal ready result_queue.put({"status": "ready"}) # Main command processing loop while True: try: command = command_queue.get() logger.debug(f"Received command method: {command['method']}") if command["method"] == "shutdown": env.close() break method_name = command["method"] args = command.get("args", []) kwargs = command.get("kwargs", {}) if method_name == "__setattr__": # Handle attribute setting attr_name, attr_value = args setattr(env, attr_name, attr_value) result_queue.put({"status": "success", "data": None}) elif hasattr(env, method_name): method = getattr(env, method_name) assert callable(method), f"Method {method_name} is not callable" result = method(*args, **kwargs) result_queue.put({"status": "success", "data": result}) else: logger.error(f"Method '{method_name}' not found") result_queue.put( { "status": "error", "error": f"Method '{method_name}' not found", } ) except Exception as e: logger.exception(e) result_queue.put({"status": "error", "error": str(e)}) except Exception as e: logger.exception(e) result_queue.put({"status": "error", "error": str(e)}) finally: command_queue.close() result_queue.close() ================================================ FILE: verl/experimental/vla/workers/env/env_worker.py ================================================ # Copyright 2025 The RLinf Authors. # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import torch from omegaconf import DictConfig from torch.distributed.device_mesh import init_device_mesh from verl import DataProto from verl.experimental.vla.workers.env.env_manager import EnvManager from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import ( get_device_name, ) from verl.utils.distributed import initialize_global_process_group_ray from verl.utils.profiler import DistProfiler, DistProfilerExtension, ProfilerConfig def put_tensor_cpu(data_dict): for key, value in data_dict.items(): if isinstance(value, dict): data_dict[key] = put_tensor_cpu(value) if isinstance(value, torch.Tensor): data_dict[key] = value.cpu().contiguous() return data_dict def create_env_batch(obs, rews, dones, infos, meta=None): ret_dict = {"obs": obs, "rews": rews, "dones": dones, "infos": infos} if meta is not None: ret_dict.update(meta=meta) ret_dict = put_tensor_cpu(ret_dict) return ret_dict def create_env_batch_dataproto(obs, rews, terminations, truncations, infos, meta=None): ret_dict = {"obs": obs, "rews": rews, "terminations": terminations, "truncations": truncations, "infos": infos} if meta is not None: ret_dict.update(meta=meta) ret_dict = put_tensor_cpu(ret_dict) tensor_batch = { "full_image": ret_dict["obs"]["images_and_states"]["full_image"], "wrist_image": ret_dict["obs"]["images_and_states"]["wrist_image"], "state": ret_dict["obs"]["images_and_states"]["state"], "rews": ret_dict["rews"], "terminations": ret_dict["terminations"], "truncations": ret_dict["truncations"], } non_tensor_batch = {"task_descriptions": obs["task_descriptions"]} output = DataProto.from_dict(tensors=tensor_batch, non_tensors=non_tensor_batch) return output class EnvWorker(Worker, DistProfilerExtension): def __init__(self, config: DictConfig): Worker.__init__(self) self.cfg = config self.train_video_cnt = 0 self.eval_video_cnt = 0 self.simulator_list = [] self.last_obs_list = [] self.last_dones_list = [] self.eval_simulator_list = [] self.stage_num = self.cfg.rollout.pipeline_stage_num initialize_global_process_group_ray(timeout_second=None) device_name = get_device_name() env_device_mesh = init_device_mesh(device_name, mesh_shape=(self.world_size, 1), mesh_dim_names=["dp", "tp"]) self._register_dispatch_collect_info("env", dp_rank=env_device_mesh["dp"].get_local_rank(), is_collect=True) # Initialize profiler omega_profiler_config = config.train.get("profiler", {}) profiler_config = omega_conf_to_dataclass(omega_profiler_config, dataclass_type=ProfilerConfig) if omega_profiler_config.get("tool", None) in ["npu", "nsys", "torch", "torch_memory"]: tool_config = omega_conf_to_dataclass( omega_profiler_config.get("tool_config", {}).get(omega_profiler_config.get("tool")) ) else: tool_config = None DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=profiler_config, tool_config=tool_config) ) @register(dispatch_mode=Dispatch.ONE_TO_ALL) @DistProfiler.annotate(color="green", role="env_init") def init_worker(self): if self.cfg.train.simulator_type == "libero": from verl.experimental.vla.envs.libero_env.libero_env import LiberoEnv for stage_id in range(self.stage_num): self.simulator_list.append( EnvManager( self.cfg.train, rank=self._rank, world_size=self._world_size, env_cls=LiberoEnv, stage_id=stage_id, ) ) elif self.cfg.train.simulator_type == "isaac": from verl.experimental.vla.envs.isaac_env.isaac_env import IsaacEnv for stage_id in range(self.stage_num): self.simulator_list.append( EnvManager( self.cfg.train, rank=self._rank, world_size=self._world_size, env_cls=IsaacEnv, stage_id=stage_id, ) ) else: raise NotImplementedError(f"Simulator type {self.cfg.train.simulator_type} not implemented") @register(dispatch_mode=Dispatch.ONE_TO_ALL) @DistProfiler.annotate(color="green", role="env_init_simulator") def init_simulator(self): for i in range(self.stage_num): self.simulator_list[i].start_simulator() return @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="env"), blocking=False) @DistProfiler.annotate(color="red", role="env_interact_step") def env_interact_step(self, data: DataProto) -> dict: """ This function is used to interact with the environment. """ chunk_actions: torch.Tensor = data.non_tensor_batch["actions"] chunk_values = data.non_tensor_batch["critic_values"] stage_id: int = data.meta_info["stage_id"] # Pi0.5 Libero is not required # TODO: prepare actions according to simulator type # chunk_actions = prepare_actions( # simulator_type=self.cfg.train.simulator_type, # raw_chunk_actions=chunk_actions, # num_action_chunks=self.cfg.actor.model.num_action_chunks, # action_dim=self.cfg.actor.model.action_dim, # ) env_info_list = {} extracted_obs, chunk_rewards, chunk_terminations, chunk_truncations, infos = self.simulator_list[ stage_id ].chunk_step(chunk_actions, chunk_values=chunk_values) chunk_dones = torch.logical_or(chunk_terminations, chunk_truncations) if chunk_dones.any(): if "final_info" in infos: final_info = infos["final_info"] for key in final_info["episode"]: env_info_list[key] = final_info["episode"][key][chunk_dones[:, -1]].cpu() env_batch = create_env_batch_dataproto( obs=extracted_obs, rews=chunk_rewards, terminations=chunk_terminations, truncations=chunk_truncations, infos=infos, meta=env_info_list, ) return env_batch @register(dispatch_mode=Dispatch.ONE_TO_ALL) def get_all_state_ids(self): """Get all available state IDs from the environment.""" state_ids = self.simulator_list[0].get_all_state_ids() return state_ids @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="env"), blocking=False) @DistProfiler.annotate(color="blue", role="env_reset_envs_to_state_ids") def reset_envs_to_state_ids(self, data: DataProto): """Reset environments to specified state IDs. Args: state_ids: State IDs to reset environments to """ state_ids_list = list(data.non_tensor_batch["state_ids"]) task_ids_list = list(data.non_tensor_batch["task_ids"]) assert len(state_ids_list) == self.cfg.train.num_envs * self.stage_num, ( f"state_ids_list length is {len(state_ids_list)}, but should be {self.cfg.train.num_envs * self.stage_num}" ) result_list = [] for stage_id in range(self.stage_num): if self.cfg.train.simulator_type == "isaac": assert ( len( set( state_ids_list[ stage_id * self.cfg.train.num_envs : (stage_id + 1) * self.cfg.train.num_envs ] ) ) == 1 ), "rollout.n should equal to num_envs for isaac" result = self.simulator_list[stage_id].reset_envs_to_state_ids( state_ids_list[stage_id * self.cfg.train.num_envs : (stage_id + 1) * self.cfg.train.num_envs], task_ids_list[stage_id * self.cfg.train.num_envs : (stage_id + 1) * self.cfg.train.num_envs], ) result_list.append(result) output_tensor_dict = {} output_non_tensor_dict = {} # Handle nested 'images_and_states' images_and_states_list = [d[0]["images_and_states"] for d in result_list] if images_and_states_list: # Assuming all dicts in the list have the same keys for k in images_and_states_list[0].keys(): if isinstance(images_and_states_list[0][k], torch.Tensor): output_tensor_dict[k] = torch.cat([d[k] for d in images_and_states_list]) # Handle 'task_descriptions' task_descriptions_list = [d[0]["task_descriptions"] for d in result_list] output_non_tensor_dict["task_descriptions"] = list(itertools.chain.from_iterable(task_descriptions_list)) output = DataProto.from_dict(tensors=output_tensor_dict, non_tensors=output_non_tensor_dict) return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) @DistProfiler.annotate(color="gray", role="env_finish_rollout") def finish_rollout(self, mode="train"): # reset if mode == "train": if self.cfg.train.video_cfg.save_video: for i in range(self.stage_num): self.simulator_list[i].flush_video(video_sub_dir=f"stage_{i}") ================================================ FILE: verl/interactions/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/interactions/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Optional from uuid import uuid4 class BaseInteraction: def __init__(self, config: dict[str, Any]): self.config = config self.name: str = config.get("name", "interaction_agent") # More general agent default role name async def start_interaction(self, instance_id: Optional[str] = None, **kwargs) -> str: """Create a tool instance. Args: instance_id: The instance id of the tool. Returns: The instance id of the tool. """ if instance_id is None: return str(uuid4()) else: return instance_id async def generate_response( self, instance_id: str, messages: list[dict[str, Any]], **kwargs ) -> tuple[bool, str, float, dict[str, Any]]: # More clear response generation method """ Generates a response for the current turn of interaction. Returns a tuple containing: - should_terminate_sequence (bool): True if the interaction sequence should end. - response_content (str): The textual content of the response. - current_turn_score (float): The score for this specific turn/response. - additional_data (dict): Any extra information or metadata. """ should_terminate_sequence: bool = False # if True, end rollout response_content: str = "Your current result seems acceptable." current_turn_score: float = 0.8 additional_data: dict[str, Any] = {} return should_terminate_sequence, response_content, current_turn_score, additional_data async def calculate_score(self) -> float: # More clear score calculation method """ Calculates a score for the interaction, potentially considering aspects like partial exposure & in-context task switching. should be invoke at turn-level """ # ...implement the logic to calculate turn-level score... score = 0.0 return score async def finalize_interaction(self) -> None: # More clear interaction end and resource release method """ Finalizes the interaction session and releases any associated state or resources. Simulates: release state """ # ...implement the logic to release state... pass ================================================ FILE: verl/interactions/gsm8k_interaction.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Any, Optional from uuid import uuid4 from verl.utils.reward_score import gsm8k from .base import BaseInteraction logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class Gsm8kInteraction(BaseInteraction): """A demo interaction for calculating the reward of gsm8k. - `start_interaction`: start a interaction instance for a trajectory. - `generate_response`: generate the response of the assistant. - `calculate_score`: calculate the score of the interaction. - `finalize_interaction`: finalize the interaction instance. """ def __init__(self, config: dict): super().__init__(config) self._instance_dict = {} async def start_interaction( self, instance_id: Optional[str] = None, ground_truth: Optional[str] = None, **kwargs ) -> str: if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "ground_truth": ground_truth, "reward": 0.0, } return instance_id async def generate_response( self, instance_id: str, messages: list[dict[str, Any]], **kwargs ) -> tuple[bool, str, float, dict]: content = "" for i in range(len(messages) - 1, -1, -1): item = messages[i] if item.get("role") == "assistant": content = item.get("content") break self._instance_dict[instance_id]["response"] = content reward = await self.calculate_score(instance_id) if reward == 1.0: response = "Your response is correct!" should_terminate_sequence = True else: response = "Your response is incorrect! You need to reflect on your answer and try again." should_terminate_sequence = False return should_terminate_sequence, response, reward, {} async def calculate_score(self, instance_id: str, **kwargs) -> float: return gsm8k.compute_score( self._instance_dict[instance_id]["response"], self._instance_dict[instance_id]["ground_truth"], method="strict", format_score=0.0, score=1.0, ) async def finalize_interaction(self, instance_id: str, **kwargs) -> None: del self._instance_dict[instance_id] ================================================ FILE: verl/interactions/utils/__init__.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/interactions/utils/interaction_registry.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib.util import logging import os import sys from omegaconf import OmegaConf logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def get_interaction_class(cls_name): """Dynamically import and return the interaction class.""" module_name, class_name = cls_name.rsplit(".", 1) if module_name not in sys.modules: spec = importlib.util.find_spec(module_name) module = importlib.util.module_from_spec(spec) sys.modules[module_name] = module spec.loader.exec_module(module) else: module = sys.modules[module_name] interaction_cls = getattr(module, class_name) return interaction_cls def initialize_interactions_from_config(interaction_config_file): """Initialize interactions from configuration file. Args: interaction_config_file: Path to the interaction configuration file. Returns: dict: A dictionary mapping interaction names to BaseInteraction instances. """ interaction_config = OmegaConf.load(interaction_config_file) interaction_map = {} for interaction_item in interaction_config.interaction: cls_name = interaction_item.class_name interaction_cls = get_interaction_class(cls_name) # Extract config and name config = OmegaConf.to_container(interaction_item.config, resolve=True) # Get the interaction name - either from config or derive from class name name = interaction_item.get("name", None) if name is None: # If no name is specified, use the class name as default class_simple_name = cls_name.split(".")[-1] # Remove "Interaction" suffix if present, otherwise use full class name if class_simple_name.endswith("Interaction"): name = class_simple_name[:-11].lower() # Remove "Interaction" (11 chars) else: name = class_simple_name.lower() # Check for duplicate names if name in interaction_map: raise ValueError(f"Duplicate interaction name '{name}' found. Each interaction must have a unique name.") # Inject the name into the config config["name"] = name # Create the interaction instance interaction = interaction_cls(config=config) interaction_map[name] = interaction logger.info(f"Initialized interaction '{name}' with class '{cls_name}'") return interaction_map ================================================ FILE: verl/interactions/weather_interaction.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Any, Optional from uuid import uuid4 from .base import BaseInteraction logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class WeatherInteraction(BaseInteraction): """A demo interaction for handling weather-related queries. - `start_interaction`: start a interaction instance for a trajectory. - `generate_response`: generate the response of the assistant. - `calculate_score`: calculate the score of the interaction. - `finalize_interaction`: finalize the interaction instance. """ def __init__(self, config: dict): super().__init__(config) self._instance_dict = {} async def start_interaction( self, instance_id: Optional[str] = None, ground_truth: Optional[str] = None, **kwargs ) -> str: if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "ground_truth": ground_truth, "reward": 0.0, } return instance_id async def generate_response( self, instance_id: str, messages: list[dict[str, Any]], **kwargs ) -> tuple[bool, str, float, dict]: content = "no tool call" for i in range(len(messages) - 1, -1, -1): item = messages[i] if item.get("role") == "tool": content = item.get("content") break self._instance_dict[instance_id]["response"] = content reward = await self.calculate_score(instance_id) if reward == 1.0: response = "Thank you for your weather query!" should_terminate_sequence = True else: response = "Please use the weather tool to get the weather information." should_terminate_sequence = True return should_terminate_sequence, response, reward, {} async def calculate_score(self, instance_id: str, **kwargs) -> float: # For weather interaction, we can implement a more complex scoring logic # For now, we'll just return a default score of 1.0 if self._instance_dict[instance_id]["response"] == "no tool call": return 0.0 return 1.0 async def finalize_interaction(self, instance_id: str, **kwargs) -> None: del self._instance_dict[instance_id] ================================================ FILE: verl/model_merger/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/model_merger/__main__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This module is used to merge huggingface model and test verl checkpoints from FSDP and Megatron backends. To merge FSDP checkpoints: ```sh python -m verl.model_merger merge \ --backend fsdp \ --local_dir checkpoints/verl_fsdp_gsm8k_examples/qwen2_5_0b5_fsdp_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model ``` To merge Megatron checkpoints: ```sh python -m verl.model_merger merge \ --backend megatron \ --tie-word-embedding \ --local_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/actor \ --target_dir /path/to/merged_hf_model ``` or use distribtued merge for large models like dpskv3 671B ```sh torchrun --nproc_per_node 1 --nnodes 8 --node_rank ${RANK} -m verl.model_merger merge\ --backend megatron \ --local_dir ./checkpoints/global_step_1/actor \ --target_dir /path/to/merged_hf_model ``` For more details, please refer to documentation: https://verl.readthedocs.io/en/latest/advance/checkpoint.html#convert-fsdp-and-megatron-checkpoints-to-huggingface-format-model """ from .base_model_merger import generate_config_from_args, parse_args def main(): args = parse_args() config = generate_config_from_args(args) print(f"config: {config}") if config.backend == "fsdp": from .fsdp_model_merger import FSDPModelMerger merger = FSDPModelMerger(config) elif config.backend == "megatron": from .megatron_model_merger import MegatronModelMerger merger = MegatronModelMerger(config) else: raise NotImplementedError(f"Unknown backend: {config.backend}") merger.merge_and_save() merger.cleanup() if __name__ == "__main__": main() ================================================ FILE: verl/model_merger/base_model_merger.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import warnings from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Optional import torch from accelerate import init_empty_weights from transformers import ( AutoConfig, AutoModelForCausalLM, AutoModelForTokenClassification, GenerationConfig, ) from verl.utils import hf_processor, hf_tokenizer from verl.utils.transformers_compat import get_auto_model_for_vision2seq AutoModelForVision2Seq = get_auto_model_for_vision2seq() def parse_args(): parser = argparse.ArgumentParser(description="verl model merger") subparsers = parser.add_subparsers(dest="operation", required=True, help="Specify 'merge' or 'test' operation.") base_op_parser = argparse.ArgumentParser(add_help=False) base_op_parser.add_argument( "--backend", type=str, required=True, choices=["fsdp", "megatron"], help="The backend of the model" ) base_op_parser.add_argument("--local_dir", type=str, default=None, help="Path to the saved model checkpoints.") base_op_parser.add_argument( "--tie-word-embedding", action="store_true", help="Whether to tie word embedding weights (currently only Megatron supported)", ) base_op_parser.add_argument("--trust-remote-code", action="store_true", help="Whether to trust remote code") base_op_parser.add_argument( "--is-value-model", action="store_true", help="Whether the model is a value model (currently only Megatron supported)", ) base_op_parser.add_argument( "--use_cpu_initialization", action="store_true", help="Whether to use CPU initialization for the model. This is useful for large models that cannot " "fit into GPU memory during initialization.", ) merge_parser = subparsers.add_parser("merge", parents=[base_op_parser], help="Merge model checkpoints and save.") merge_parser.add_argument( "--target_dir", default="tmp", type=str, help="Directory to save the merged huggingface model" ) merge_parser.add_argument( "--hf_upload_path", default=None, type=str, help="Hugging Face repository ID to upload the model" ) merge_parser.add_argument( "--private", action="store_true", help="Whether to upload the model to a private Hugging Face repository" ) test_parser = subparsers.add_parser( "test", parents=[base_op_parser], help="Test merged model against a reference Hugging Face model" ) test_parser.add_argument( "--test_hf_dir", type=str, required=True, help="Path to the reference Hugging Face model directory for testing" ) args = parser.parse_args() return args @dataclass class ModelMergerConfig: """Configuration for model merger operations. Args: operation (str): Operation type - 'merge' or 'test'. backend (str): Backend type for the model ('fsdp' or 'megatron'). target_dir (Optional[str]): Directory to save the merged huggingface model. Defaults to "tmp". hf_upload_path (Optional[str]): Hugging Face repository ID to upload the model. Defaults to None. private (bool): Whether to upload the model to a private Hugging Face repository. Defaults to False. test_hf_dir (Optional[str]): Path to the reference Hugging Face model directory for testing. Defaults to None. tie_word_embedding (bool): Whether to tie word embedding weights (currently only Megatron supported). Defaults to False. trust_remote_code (bool): Whether to trust remote code. Defaults to False. is_value_model (bool): Whether the model is a value model (currently only Megatron supported). Defaults to False. local_dir (Optional[str]): Path to the saved model checkpoints. Defaults to None. hf_model_config_path (Optional[str]): Path to HuggingFace model configuration files. Defaults to None. hf_upload (bool): Whether to upload to HuggingFace (computed automatically). Not for initialization. use_cpu_initialization (bool): Whether to use CPU initialization for large models. Defaults to False. """ operation: str # 'merge' or 'test' backend: str target_dir: Optional[str] = "tmp" hf_upload_path: Optional[str] = None private: bool = False test_hf_dir: Optional[str] = None tie_word_embedding: bool = False trust_remote_code: bool = False is_value_model: bool = False local_dir: Optional[str] = None hf_model_config_path: Optional[str] = None hf_upload: bool = field(init=False) use_cpu_initialization: bool = False def __post_init__(self): self.hf_upload = self.operation == "merge" and bool(self.hf_upload_path) if self.operation == "test": self.target_dir = None self.hf_upload_path = None self.private = False def generate_config_from_args(args: argparse.Namespace) -> ModelMergerConfig: common_config_args = { "operation": args.operation, "backend": args.backend, "tie_word_embedding": args.tie_word_embedding, "trust_remote_code": args.trust_remote_code, "is_value_model": args.is_value_model, "local_dir": args.local_dir, "hf_model_config_path": os.path.join(args.local_dir, "huggingface"), "use_cpu_initialization": args.use_cpu_initialization, } if args.operation == "merge": config = ModelMergerConfig( **common_config_args, target_dir=args.target_dir, hf_upload_path=args.hf_upload_path, private=args.private, test_hf_dir=None, ) os.makedirs(config.target_dir, exist_ok=True) elif args.operation == "test": config = ModelMergerConfig( **common_config_args, test_hf_dir=args.test_hf_dir, # the following args are not used by test operation target_dir=None, hf_upload_path=None, private=False, ) else: raise NotImplementedError(f"Unknown operation: {args.operation}") return config class BaseModelMerger(ABC): """ Abstract base class for merging distributed model checkpoints into HuggingFace format. This class provides common functionality for converting model checkpoints from different distributed training backends (FSDP, Megatron) into standard HuggingFace format that can be easily loaded and used for inference or further training. The merger supports two main operations: - merge: Convert and save checkpoints to HuggingFace format - test: Validate merged checkpoints against a reference model Args: config (ModelMergerConfig): Configuration object containing paths, backend type, and operation parameters. Attributes: config (ModelMergerConfig): The configuration object passed during initialization. hf_model_config_path (str): Path to the HuggingFace model configuration files. model_config (PretrainedConfig): Loaded HuggingFace model configuration. """ def __init__(self, config: ModelMergerConfig): self.config = config self.hf_model_config_path = config.hf_model_config_path self.model_config = AutoConfig.from_pretrained( self.hf_model_config_path, trust_remote_code=self.config.trust_remote_code ) def get_transformers_auto_model_class(self): has_remote_code = hasattr(self.model_config, "auto_map") and any( self.model_config.architectures[0] in val for val in self.model_config.auto_map.values() ) if has_remote_code: auto_class = next( k for k, v in self.model_config.auto_map.items() if self.model_config.architectures[0] in v ) match auto_class: case "AutoModelForCausalLM": return AutoModelForCausalLM case "AutoModelForTokenClassification": return AutoModelForTokenClassification case "AutoModelForVision2Seq": return AutoModelForVision2Seq case "AutoModelForImageTextToText": return AutoModelForVision2Seq case _: raise NotImplementedError(f"Unknown auto class {auto_class}") else: if "ForTokenClassification" in self.model_config.architectures[0]: return AutoModelForTokenClassification elif "ForCausalLM" in self.model_config.architectures[0]: return AutoModelForCausalLM elif "ForConditionalGeneration" in self.model_config.architectures[0]: return AutoModelForVision2Seq raise NotImplementedError(f"Unknown architecture {self.model_config.architectures}") def patch_model_generation_config(self, model): """ The generation_config created from model config may be different to the pretrained model, this may lead to error when generating: https://github.com/volcengine/verl/issues/1246 This function patch the generation_config created from model config to the pretrained model. """ if model.can_generate(): try: model.generation_config = GenerationConfig.from_pretrained(self.hf_model_config_path) except OSError: print( f"Warning: Generation config file not found in {self.hf_model_config_path}, using a " f"generation config created from the model config." ) return model def _load_lora_train_meta(self) -> Optional[dict[str, object]]: if not self.config.local_dir: return None meta_path = os.path.join(self.config.local_dir, "lora_train_meta.json") if not os.path.exists(meta_path): return None import json try: with open(meta_path, encoding="utf-8") as f: lora_meta = json.load(f) except Exception as e: warnings.warn(f"Failed to read LoRA metadata from {meta_path}: {e}", stacklevel=2) return None result = {} if "r" in lora_meta: try: result["r"] = int(lora_meta["r"]) except (TypeError, ValueError): warnings.warn(f"Invalid LoRA rank in {meta_path}: {lora_meta['r']}", stacklevel=2) if "lora_alpha" in lora_meta: try: result["lora_alpha"] = int(lora_meta["lora_alpha"]) except (TypeError, ValueError): warnings.warn(f"Invalid lora_alpha in {meta_path}: {lora_meta['lora_alpha']}", stacklevel=2) if "task_type" in lora_meta: task_type = lora_meta["task_type"] if task_type is None: pass elif isinstance(task_type, str): result["task_type"] = task_type else: warnings.warn(f"Invalid task_type in {meta_path}: {task_type}", stacklevel=2) return result if len(result) > 0 else None def save_lora_adapter(self, state_dict: dict[str, torch.Tensor]): """ Save lora adapter to safetensors. Returns: lora_path: str, the path to the lora adapter. None if no lora adapter found. Note: This function change the 'state_dict' in place. """ lora_params_names = [name for name in state_dict.keys() if "lora_" in name] if len(lora_params_names) == 0: return None import json from typing import OrderedDict import peft from safetensors.torch import save_file lora_params = OrderedDict() target_modules = set() lora_key = None for name in lora_params_names: lora_key = name.replace(".default.weight", ".weight") target_modules.add(lora_key.split(".")[-3]) lora_params[lora_key] = state_dict.pop(name) inferred_lora_rank = min(lora_params[lora_key].shape[0], lora_params[lora_key].shape[1]) lora_meta = self._load_lora_train_meta() lora_rank = inferred_lora_rank lora_alpha = 0 task_type = None if lora_meta is not None: meta_rank = lora_meta.get("r") if meta_rank is not None and meta_rank > 0: if meta_rank != inferred_lora_rank: warnings.warn( f"LoRA rank mismatch between metadata ({meta_rank}) and adapter weights " f"({inferred_lora_rank}); using metadata rank.", stacklevel=2, ) lora_rank = meta_rank meta_alpha = lora_meta.get("lora_alpha") if meta_alpha is not None: lora_alpha = meta_alpha meta_task_type = lora_meta.get("task_type") if meta_task_type is not None: task_type = meta_task_type if lora_alpha == 0: warnings.warn( "LoRA alpha metadata is missing or equals 0; falling back to lora_alpha=0. " "Please verify checkpoint LoRA metadata (lora_train_meta.json).", stacklevel=2, ) peft_dict = { "r": lora_rank, "lora_alpha": lora_alpha, "target_modules": list(target_modules), } if task_type is not None: peft_dict["task_type"] = task_type peft_config = peft.LoraConfig(**peft_dict).to_dict() peft_config["task_type"] = peft_config["task_type"].value if peft_config["task_type"] else None peft_config["peft_type"] = peft_config["peft_type"].value if peft_config["peft_type"] else None peft_config["target_modules"] = list(peft_config["target_modules"]) lora_path = os.path.join(self.config.target_dir, "lora_adapter") os.makedirs(lora_path, exist_ok=True) with open(os.path.join(lora_path, "adapter_config.json"), "w", encoding="utf-8") as f: json.dump(peft_config, f, ensure_ascii=False, indent=4) save_file(lora_params, os.path.join(lora_path, "adapter_model.safetensors")) for name in list(state_dict.keys()): key = ( name.replace("base_model.model.", "") .replace(".base_layer.weight", ".weight") .replace(".base_layer.bias", ".bias") ) state_dict[key] = state_dict.pop(name) return lora_path def save_hf_model_and_tokenizer(self, state_dict: dict[str, torch.Tensor]): auto_model_class = self.get_transformers_auto_model_class() with init_empty_weights(): model = auto_model_class.from_config( self.model_config, torch_dtype=torch.bfloat16, trust_remote_code=self.config.trust_remote_code ) model.to_empty(device="cpu") model = self.patch_model_generation_config(model) lora_path = self.save_lora_adapter(state_dict) if lora_path: print(f"Saving lora adapter to {lora_path}") print(f"Saving model to {self.config.target_dir}") model.save_pretrained(self.config.target_dir, state_dict=state_dict) del state_dict del model processor = hf_processor(self.hf_model_config_path, trust_remote_code=self.config.trust_remote_code) tokenizer = hf_tokenizer(self.hf_model_config_path, trust_remote_code=self.config.trust_remote_code) if processor is not None: print(f"Saving processor to {self.config.target_dir}") processor.save_pretrained(self.config.target_dir) if tokenizer is not None: print(f"Saving tokenizer to {self.config.target_dir}") tokenizer.save_pretrained(self.config.target_dir) def upload_to_huggingface(self): import requests from huggingface_hub import HfApi from huggingface_hub.utils import HfHubHTTPError, RepositoryNotFoundError api = HfApi() try: # Attempt to create repository api.create_repo(repo_id=self.config.hf_upload_path, private=self.config.private, exist_ok=True) except HfHubHTTPError as e: # Handle authentication/API errors if e.response.status_code == 401: raise PermissionError( "Hugging Face authentication failed. Verify your token is valid and has write permissions." ) from e elif e.response.status_code == 404: raise RepositoryNotFoundError(f"Repository path not found: {self.config.hf_upload_path}") from e else: raise ConnectionError(f"Failed to create repository ({e.response.status_code}): {e}") from e except requests.exceptions.ConnectionError as e: raise ConnectionError("Network connection failed. Check your internet connection.") from e try: # Attempt folder upload api.upload_folder(folder_path=self.config.target_dir, repo_id=self.config.hf_upload_path, repo_type="model") except HfHubHTTPError as e: if e.response.status_code == 401: raise PermissionError("Authentication failed during upload. Token may have expired.") from e else: raise RuntimeError(f"Upload failed ({e.response.status_code}): {e}") from e except requests.exceptions.ConnectionError as e: raise ConnectionError("Network interruption during upload. Try again with stable connection.") from e except OSError as e: raise FileNotFoundError(f"Local folder error: {self.config.target_dir} - {str(e)}") from e except Exception as e: raise RuntimeError(f"Unexpected error during upload: {str(e)}") from e @abstractmethod def merge_and_save(self): raise NotImplementedError("Subclasses should implement this method") @abstractmethod def cleanup(self): raise NotImplementedError("Subclasses should implement this method to clean up resources if needed") ================================================ FILE: verl/model_merger/fsdp_model_merger.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from concurrent.futures import ThreadPoolExecutor from pathlib import Path import numpy as np import torch from torch.distributed._tensor import Placement, Shard try: # for torch 2.5+ from torch.distributed.tensor import DTensor except ImportError: from torch.distributed._tensor import DTensor from tqdm import tqdm from .base_model_merger import BaseModelMerger class FSDPModelMerger(BaseModelMerger): """ Model merger for FSDP (Fully Sharded Data Parallel) checkpoints. This class handles the conversion of FSDP distributed checkpoints into HuggingFace format. FSDP shards model parameters across multiple processes, and this merger reconstructs the full model by loading and concatenating the sharded parameters from all ranks. The merger supports various FSDP configurations including: - Pure FSDP (single dimension sharding) - FSDP + DDP (data parallel + fully sharded data parallel) - DTensor-based sharding with custom device meshes Key features: - Automatic detection of world size from checkpoint filenames - Support for DTensor and non-DTensor checkpoints - Parallel loading of checkpoint shards for efficiency - Validation against reference HuggingFace models Example: To merge FSDP checkpoints: ```python config = ModelMergerConfig( operation="merge", backend="fsdp", local_dir="path/to/fsdp/checkpoints", target_dir="path/to/output" ) merger = FSDPModelMerger(config) merger.merge_and_save() ``` """ def _get_world_size(self) -> int: """_summary_ From FSDP json config file, extract the world size. Returns: int: world size """ config_path = Path(self.config.local_dir) / "fsdp_config.json" if not config_path.exists(): raise FileNotFoundError(f"Config file {config_path} does not exist.") with open(config_path) as f: config = json.load(f) # Extract world size from the config world_size = config.get("world_size", None) if world_size is None: raise ValueError("World size not found in the config file.") return world_size def _load_rank_zero_state_dict(self, world_size: int) -> dict: return torch.load( Path(self.config.local_dir) / f"model_world_size_{world_size}_rank_0.pt", map_location="cpu", weights_only=False, ) def _extract_device_mesh_info(self, state_dict: dict, world_size: int) -> tuple[np.ndarray, tuple[str, ...]]: """ Retrieves sharding information (device_mesh, mesh_dim_names) from a DTensor in the state_dict. If no DTensor is found, infers a simple FSDP mesh based on world_size. """ pivot_key = sorted(list(state_dict.keys()))[0] weight = state_dict[pivot_key] if isinstance(weight, DTensor): # get sharding info device_mesh = weight.device_mesh mesh = device_mesh.mesh mesh_dim_names = device_mesh.mesh_dim_names else: # for non-DTensor mesh = np.array([world_size], dtype=np.int64) mesh_dim_names = ("fsdp",) return mesh, mesh_dim_names def _calculate_shard_configuration( self, mesh: np.ndarray, mesh_dim_names: tuple[str, ...] ) -> tuple[int, tuple[int, ...]]: """Calculates the total number of shards and the shape of the device mesh.""" assert mesh_dim_names in (("fsdp",), ("ddp", "fsdp")), f"Unsupported mesh_dim_names {mesh_dim_names}" if "tp" in mesh_dim_names: # TODO: "tp" is not supported yet due to the above assert total_shards = mesh.shape[-1] * mesh.shape[-2] mesh_shape = (mesh.shape[-2], mesh.shape[-1]) else: total_shards = mesh.shape[-1] mesh_shape = (mesh.shape[-1],) return total_shards, mesh_shape def _merge_by_placement(self, tensors: list[torch.Tensor], placement: Placement) -> torch.Tensor: """Merges a list of tensors based on their DTensor placement""" if placement.is_replicate(): return tensors[0] elif placement.is_partial(): raise NotImplementedError("Partial placement is not supported yet") elif placement.is_shard(): return torch.cat(tensors, dim=placement.dim).contiguous() raise NotImplementedError(f"Unsupported placement: {placement}") def _load_and_merge_state_dicts( self, world_size: int, total_shards: int, mesh_shape: tuple[int, ...], mesh_dim_names: tuple[str, ...] ) -> dict[str, torch.Tensor]: model_state_dict_lst = [None] * total_shards def process_one_shard(rank: int, model_state_dict_lst: list): model_path = Path(self.config.local_dir) / f"model_world_size_{world_size}_rank_{rank}.pt" state_dict = torch.load(model_path, map_location="cpu", weights_only=False) model_state_dict_lst[rank] = state_dict return state_dict with ThreadPoolExecutor(max_workers=min(32, os.cpu_count())) as executor: futures = [executor.submit(process_one_shard, rank, model_state_dict_lst) for rank in range(total_shards)] for future in tqdm(futures, desc=f"Loading {total_shards} FSDP shards", total=total_shards): future.result() # Merge state dicts from all shards state_dict = {} param_placements: dict[str, list] = {} for key in set(model_state_dict_lst[0].keys()): state_dict[key] = [] for model_state_shard in model_state_dict_lst: # add tensor shard in order of rank to state_dict[key] tensor = model_state_shard.pop(key) if isinstance(tensor, DTensor): state_dict[key].append(tensor._local_tensor.bfloat16()) placements = tuple(tensor.placements) # replicated placement at dp dimension can be discarded if mesh_dim_names[0] in ("dp", "ddp"): placements = placements[1:] if key not in param_placements: param_placements[key] = placements else: assert param_placements[key] == placements else: state_dict[key].append(tensor.bfloat16()) del model_state_dict_lst # Merge tensors for key in sorted(state_dict): if not isinstance(state_dict[key], list): print(f"No need to merge key {key}") continue if key in param_placements: # merge shards placements: tuple[Shard] = param_placements[key] if len(mesh_shape) == 1: # 1-D list, FSDP without TP assert len(placements) == 1 shards = state_dict[key] state_dict[key] = self._merge_by_placement(shards, placements[0]) else: # 2-D list, FSDP + TP raise NotImplementedError("FSDP + TP is not supported yet") else: state_dict[key] = torch.cat(state_dict[key], dim=0) return state_dict def merge_and_save(self): world_size = self._get_world_size() rank_zero_state_dict = self._load_rank_zero_state_dict(world_size) mesh, mesh_dim_names = self._extract_device_mesh_info(rank_zero_state_dict, world_size) print(f"Got device mesh {mesh}, mesh_dim_names {mesh_dim_names}") total_shards, mesh_shape = self._calculate_shard_configuration(mesh, mesh_dim_names) print(f"Processing model shards with {total_shards} {mesh_shape} in total") merged_state_dict = self._load_and_merge_state_dicts(world_size, total_shards, mesh_shape, mesh_dim_names) if self.config.operation == "test": if not self.config.test_hf_dir: raise ValueError("test_hf_dir must be provided for test operation") self._validate_state_dict(merged_state_dict) elif self.config.operation == "merge": self.save_hf_model_and_tokenizer(merged_state_dict) if self.config.hf_upload: self.upload_to_huggingface() else: raise ValueError(f"Unknown operation: {self.config.operation}") def _validate_state_dict(self, state_dict: dict[str, torch.Tensor]): auto_model_class = self.get_transformers_auto_model_class() hf_model = auto_model_class.from_pretrained(self.config.test_hf_dir, torch_dtype=torch.bfloat16) hf_state_dict = hf_model.state_dict() del hf_model hf_model_keys = set(hf_state_dict.keys()) collected_keys = set(state_dict.keys()) missing_keys = hf_model_keys - collected_keys assert len(missing_keys) == 0, f"Missing keys in collected state dict: {list(sorted(missing_keys))}" extra_keys = collected_keys - hf_model_keys assert len(extra_keys) == 0, f"Extra keys in collected state dict: {list(sorted(extra_keys))}" for key in hf_model_keys: hf_shape = hf_state_dict[key].shape collected_shape = state_dict[key].shape assert hf_shape == collected_shape, ( f"Shape mismatch for key '{key}': original {hf_shape} vs collected {collected_shape}" ) hf_dtype = hf_state_dict[key].dtype collected_dtype = state_dict[key].dtype assert hf_dtype == collected_dtype, ( f"Dtype mismatch for key '{key}': original {hf_dtype} vs collected {collected_dtype}" ) torch.testing.assert_close(hf_state_dict[key], state_dict[key], atol=1e-6, rtol=1e-6) print("FSDP checks passed: The merged state_dict matches the hf model saved by FSDPCheckpointManager.") def cleanup(self): """Cleanup temporary files if needed.""" # FSDP merger does not create temporary files, so no cleanup is needed. pass ================================================ FILE: verl/model_merger/megatron_model_merger.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import warnings from contextlib import contextmanager from pathlib import Path from typing import Any, Callable, ContextManager import numpy as np import torch import torch.distributed as dist try: # NPU patch import mindspeed.megatron_adaptor # noqa: F401 except ImportError: pass from accelerate import init_empty_weights from megatron.core import mpu from megatron.core.models.gpt.gpt_model import ModelType from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from safetensors.torch import load_file from transformers import ( AutoConfig, PretrainedConfig, ) from verl.models.mcore import hf_to_mcore_config from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device from verl.utils.distributed import set_numa_affinity from verl.utils.megatron.dist_checkpointing import load_dist_checkpointing from verl.utils.megatron_utils import get_model from verl.utils.tokenizer import hf_processor, hf_tokenizer from .base_model_merger import BaseModelMerger, ModelMergerConfig @contextmanager def noop_context() -> Any: yield def get_dynamic_pipeline_shards(layer_num: int, pp_size: int) -> list[int]: """Calculate the pipeline sharding configuration for Megatron-LM. Args: layer_num: Total number of layers in the model. pp_size: Number of pipeline parallel ranks. Returns: layer number of each pp rank. Make the sharding of the pipeline as uniform as possible. """ if layer_num < pp_size: raise ValueError(f"layer_num {layer_num} must be greater than pp_size {pp_size}.") if pp_size < 1: raise ValueError(f"pp_size must be at least 1, got {pp_size}.") if pp_size == 1: return [layer_num] if pp_size == 2: return [ layer_num // 2, layer_num - layer_num // 2, ] middle_size = pp_size - 2 shards_strategy = [] for middle_layer_num in range(layer_num): first_last_layer_num = layer_num - middle_layer_num * middle_size first_layer_num = first_last_layer_num // 2 last_layer_num = first_last_layer_num - first_last_layer_num // 2 if 0 < first_layer_num <= middle_layer_num and 0 < last_layer_num <= middle_layer_num: shards_strategy.append( ( [first_layer_num] + [middle_layer_num] * middle_size + [last_layer_num], abs(first_layer_num - middle_layer_num), ) ) # sort by diff of layer_num, to make it as uniform as possible res = sorted(shards_strategy, key=lambda x: x[1])[0][0] assert sum(res) == layer_num, f"sum(res)={sum(res)} != layer_num={layer_num}, pp_size={pp_size}" return res class MegatronModelMerger(BaseModelMerger): """ Model merger for Megatron-LM distributed checkpoints. This class handles the conversion of Megatron-LM distributed checkpoints into HuggingFace format. Megatron-LM uses tensor parallelism, pipeline parallelism, and data parallelism to distribute large language models across multiple GPUs. This merger reconstructs the full model by loading distributed checkpoints and applying the necessary transformations. Key features: - Support for tensor parallel, pipeline parallel, and data parallel configurations - Automatic parameter name mapping from Megatron to HuggingFace conventions - Handling of QKV and gate-up tensor splitting/merging - Support for tied word embeddings and value models - Integration with Megatron's distributed checkpointing system The merger handles various model architectures and configurations: - Standard transformer models (GPT-style) - Models with tied word embeddings - Value models for reinforcement learning - Multi-layer attention (MLA) architectures - Mixture of Experts (MoE) models Args: config (ModelMergerConfig): Configuration object with Megatron-specific settings including tie_word_embedding and is_value_model flags. Example: To merge Megatron checkpoints: ```python config = ModelMergerConfig( operation="merge", backend="megatron", local_dir="path/to/megatron/checkpoints", target_dir="path/to/output", tie_word_embedding=True ) merger = MegatronModelMerger(config) merger.merge_and_save() ``` """ def __init__(self, config: ModelMergerConfig): super().__init__(config) # Currently we use only 1 rank to merge the dist_ckpt, we will move to multi-process save shortly afterwards if "WORLD_SIZE" not in os.environ: os.environ["RANK"] = "0" os.environ["LOCAL_RANK"] = "0" os.environ["WORLD_SIZE"] = "1" os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" set_numa_affinity() torch.distributed.init_process_group(get_nccl_backend()) self.rank = torch.distributed.get_rank() self.world_size = torch.distributed.get_world_size() local_rank = os.environ.get("LOCAL_RANK", 0) get_torch_device().set_device(f"{get_device_name()}:{local_rank}") mpu.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=self.world_size, virtual_pipeline_model_parallel_size=None, context_parallel_size=1, expert_model_parallel_size=1, ) model_parallel_cuda_manual_seed(0) self.hf_config = AutoConfig.from_pretrained( self.config.hf_model_config_path, trust_remote_code=self.config.trust_remote_code ) print(self.hf_config, flush=True) self.params_mapping = { # megatron core gpt model name, huggingface model name # NOTICE: It's a little bit tricky, when 2 keys have the same prefix, we need to make sure the # longer key within the containing relationship is processed first. "embedding.word_embeddings": "model.embed_tokens", # input layer norm for dpskv3 "input_layernorm.weight": "input_layernorm.weight", "input_layernorm.bias": "input_layernorm.bias", # attn "self_attention.linear_qkv.layer_norm_weight": "input_layernorm.weight", "self_attention.linear_qkv.layer_norm_bias": "input_layernorm.bias", "self_attention.linear_qkv": "self_attn.qkv_proj", "self_attention.q_layernorm": "self_attn.q_norm", "self_attention.k_layernorm": "self_attn.k_norm", "self_attention.linear_proj": "self_attn.o_proj", # mla "self_attention.linear_q_proj": "self_attn.q_proj", "self_attention.linear_q_down_proj": "self_attn.q_a_proj", "self_attention.linear_q_up_proj.layer_norm_weight": "self_attn.q_a_layernorm.weight", "self_attention.linear_q_up_proj": "self_attn.q_b_proj", "self_attention.linear_kv_down_proj": "self_attn.kv_a_proj_with_mqa", "self_attention.linear_kv_up_proj.layer_norm_weight": "self_attn.kv_a_layernorm.weight", "self_attention.linear_kv_up_proj": "self_attn.kv_b_proj", # mlp "pre_mlp_layernorm": "post_attention_layernorm", "mlp.linear_fc1.layer_norm_weight": "post_attention_layernorm.weight", "mlp.linear_fc1.layer_norm_bias": "post_attention_layernorm.bias", "mlp.linear_fc1": "mlp.gate_up_proj", "mlp.linear_fc2": "mlp.down_proj", # moe "mlp.router.expert_bias": "mlp.gate.e_score_correction_bias", "mlp.router": "mlp.gate", "mlp.shared_experts.linear_fc1": "mlp.shared_experts.gate_up_proj", "mlp.shared_experts.linear_fc2": "mlp.shared_experts.down_proj", "linear_fc1": "gate_up_proj", "linear_fc2": "down_proj", # output "final_layernorm": "norm", "output_layer": "lm_head", } if "Qwen2MoeForCausalLM" in self.hf_config.architectures: self.params_mapping["mlp.shared_experts.linear_fc1"] = "mlp.shared_expert.gate_up_proj" self.params_mapping["mlp.shared_experts.linear_fc2"] = "mlp.shared_expert.down_proj" self.params_mapping["mlp.shared_experts.gate_weight"] = "mlp.shared_expert_gate.weight" def _load_state_dicts(self, model_ckpt_path: str) -> dict[str, Any]: """_summary_ Use Megatron dist_checkpointing to load the model state dicts from the checkpoint directory. Args: model_ckpt_path (str): Path to the model checkpoint directory. Returns: State dict containing the model parameters. """ # init hf config self.pipeline_shards = get_dynamic_pipeline_shards(self.hf_config.num_hidden_layers, self.world_size) print(f"Pipeline shards: {self.pipeline_shards}, total layers: {sum(self.pipeline_shards)}") tf_config = hf_to_mcore_config( self.hf_config, torch.bfloat16, num_layers_in_first_pipeline_stage=self.pipeline_shards[0] if len(self.pipeline_shards) > 1 else None, num_layers_in_last_pipeline_stage=self.pipeline_shards[-1] if len(self.pipeline_shards) > 2 else None, ) tf_config.use_cpu_initialization = self.config.use_cpu_initialization tie_word_embeddings = getattr(self.hf_config, "tie_word_embeddings", False) # init megatron model def megatron_model_provider(pre_process, post_process): from verl.models.mcore import init_mcore_model parallel_model = init_mcore_model( tf_config, self.hf_config, pre_process, post_process, share_embeddings_and_output_weights=tie_word_embeddings, value=False, ) return parallel_model context: Callable[..., ContextManager] = ( init_empty_weights if self.config.use_cpu_initialization else noop_context ) with context(): whole_model = get_model( model_provider_func=megatron_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=False, transformer_config=tf_config, ) if self.config.use_cpu_initialization: # convert meta device to empty tensor so it can use `copy_` function whole_model[0].module = whole_model[0].module.to_empty(device="cpu") # load state dicts sharded_state_dict = {} for vpp_rank, model in enumerate(whole_model): key = f"model{vpp_rank}" if len(whole_model) > 1 else "model" mpu.set_virtual_pipeline_model_parallel_rank(vpp_rank) sharded_state_dict[key] = model.sharded_state_dict() model_state_dict = load_dist_checkpointing(sharded_state_dict, model_ckpt_path) model_state_dict_list = [] for vpp_rank, model in enumerate(whole_model): key = f"model{vpp_rank}" if len(whole_model) > 1 else "model" mpu.set_virtual_pipeline_model_parallel_rank(vpp_rank) model_state_dict_list.append(model_state_dict[key]) return model_state_dict_list def _check_megatron_state_key(self, key: str) -> bool: """ Checks if the key is a valid Megatron state key. Now the model merger only supports keys that start with "decoder/embedding/output_layer" in TransformerLayer. Shall not use key starts with "model." """ if key.startswith("model."): raise ValueError( f"Invalid key {key} in Megatron state_dict. Expected keys to start with " f"'decoder/embedding/output_layer' in TransformerLayer." ) skip_checking_keys = ["embedding.word_embeddings", "output_layer"] for skip_key in skip_checking_keys: if skip_key in key: print(f"skip checking key {key}") return # Exclude extra state keys if not key.startswith("decoder"): raise ValueError( f"Invalid key {key} in Megatron state_dict. Expected keys to start with 'decoder' in TransformerLayer." ) def _split_tensors( self, key: str, tensor: torch.Tensor, config: PretrainedConfig, is_value_model: bool = False ) -> list[torch.Tensor]: """ Splits a tensor into multiple tensors based on the name. This is used to handle qkv and gate_up tensors. """ if "linear_fc1.weight" in key: # if the tensor is gate and proj gate_lst = [] up_lst = [] gate, up = tensor.chunk(2) gate_lst.append(gate) up_lst.append(up) gate = torch.cat(gate_lst, dim=0) up = torch.cat(up_lst, dim=0) return [gate, up] elif "self_attention.linear_qkv." in key and "layer_norm" not in key: # if the tensor is qkv, for each param on tp, split into q, k, v # concat q, k, v separately. q_lst, k_lst, v_lst = [], [], [] assert config.num_attention_heads % config.num_key_value_heads == 0 num_q_per_kv = config.num_attention_heads // config.num_key_value_heads assert tensor.shape[0] % (num_q_per_kv + 2) == 0, ( f"Tensor shape {tensor.shape} is not divisible by {num_q_per_kv + 2}" ) kv_size = tensor.shape[0] // (num_q_per_kv + 2) split_size = [kv_size * num_q_per_kv, kv_size, kv_size] num_query_groups_per_partition = config.num_key_value_heads for chunk in tensor.chunk(num_query_groups_per_partition): split_size = [ kv_size * num_q_per_kv // num_query_groups_per_partition, kv_size // num_query_groups_per_partition, kv_size // num_query_groups_per_partition, ] q, k, v = chunk.split(split_size) q_lst.append(q) k_lst.append(k) v_lst.append(v) return [torch.cat(q_lst, dim=0), torch.cat(k_lst, dim=0), torch.cat(v_lst, dim=0)] else: return [tensor] def _merge_state_dicts(self, model_state_dict_list: list[dict[str, Any]]) -> dict[str, torch.Tensor]: state_dict = {} layers_cum = 0 if self.world_size > 1: pipeline_cumsum = np.cumsum(self.pipeline_shards) layers_cum = 0 if self.rank == 0 else pipeline_cumsum[self.rank - 1] print(f"{layers_cum=}") for model_state_dict in model_state_dict_list: layers_handled = 0 keys = model_state_dict.keys() for key in keys: if "extra_state" in key: continue if self.config.tie_word_embedding and ("output_layer" in key): print("skip lm_head and reward_head loading because of tie_word_embeddings") continue self._check_megatron_state_key(key) hf_name = self._replace_name(key, self.params_mapping) assert hf_name is not None, f"Failed to convert layer name [{key}] from megatron to huggingface." if "model.layers." in hf_name: local_layer_no = int(hf_name.split(".")[2]) layers_handled = max(local_layer_no, layers_handled) global_layer_no = local_layer_no + layers_cum new_key_list = hf_name.split(".") new_key_list[2] = str(global_layer_no) hf_name = ".".join(new_key_list) else: warnings.warn(f"hf_name {hf_name} will not be fixed with layer number", stacklevel=2) if "mlp.experts." in hf_name and ".weight" in hf_name: name_prefix, expert_id = hf_name.split(".weight") for proj in ["gate_up", "down"]: if f"{proj}_proj" in hf_name: hf_name = hf_name.replace( f"mlp.experts.{proj}_proj.weight{expert_id}", f"mlp.experts.{expert_id}.{proj}_proj.weight", ) tensor = model_state_dict[key] split_tensor = self._split_tensors( key, tensor, self.hf_config, is_value_model=self.config.is_value_model ) if len(split_tensor) == 1: state_dict[hf_name] = split_tensor[0] elif len(split_tensor) == 3: # split qkv for n, d in zip(["q", "k", "v"], split_tensor, strict=True): state_dict[hf_name.replace("qkv", n)] = d elif len(split_tensor) == 2: # split gate up state_dict[hf_name.replace("gate_up", "gate")] = split_tensor[0] state_dict[hf_name.replace("gate_up", "up")] = split_tensor[1] shape_info = ( split_tensor.shape if isinstance(split_tensor, torch.Tensor) else [t.shape for t in split_tensor] ) print(f"converted {key} to {hf_name} with shape {shape_info}") layers_cum += layers_handled + 1 # zero based return state_dict def save_hf_model_and_tokenizer(self, merged_state_dict): if self.world_size == 1: return super().save_hf_model_and_tokenizer(merged_state_dict) from safetensors.torch import save_file layer_num = self.hf_config.num_hidden_layers # FIXME: make configurable saves_per_layer = 1 if layer_num < 30 else 2 saves_total = saves_per_layer * layer_num saves_indexes = {} # calculate the layer start index and key chunks layer_this_rank = self.pipeline_shards[self.rank] pipeline_cumsum = np.cumsum(self.pipeline_shards) layer_start = 0 if self.rank == 0 else pipeline_cumsum[self.rank - 1] keys = list(merged_state_dict.keys()) keys_chunk = np.array_split(np.array(keys), layer_this_rank * saves_per_layer) numel = 0 assert len(keys_chunk) == layer_this_rank * saves_per_layer, ( f"Expected {len(keys_chunk)} chunks, but got {layer_this_rank * saves_per_layer} for rank {self.rank}." ) # save to model shards manually target_dir = Path(self.config.target_dir) for i, keys in enumerate(keys_chunk): sd_to_save = {k: merged_state_dict[k] for k in keys} numel += sum([sd_to_save[i].numel() for i in sd_to_save]) save_idx = layer_start * saves_per_layer + i save_path = target_dir / f"model-{save_idx + 1:05d}-of-{saves_total:05d}.safetensors" save_file(sd_to_save, save_path) for k in keys: saves_indexes[k] = str(save_path.name) tensor = torch.tensor([numel]).to(get_device_name()) dist.all_reduce(tensor, op=dist.ReduceOp.SUM) numel = tensor.cpu().item() all_save_indexes = [{} for _ in range(self.world_size)] dist.all_gather_object(all_save_indexes, saves_indexes) saves_indexes = {k: v for i in all_save_indexes for k, v in i.items()} if self.rank == 0: with open(target_dir / "model.safetensors.index.json", "w") as f: json.dump( { "metadata": { "total_size": numel, }, "weight_map": saves_indexes, }, f, indent=4, ) print(f"model saved to {target_dir} with {numel=}") self.model_config.save_pretrained(self.config.target_dir) processor = hf_processor(self.hf_model_config_path, trust_remote_code=self.config.trust_remote_code) tokenizer = hf_tokenizer(self.hf_model_config_path, trust_remote_code=self.config.trust_remote_code) if processor is not None: print(f"Saving processor to {self.config.target_dir}") processor.save_pretrained(self.config.target_dir) if tokenizer is not None: print(f"Saving tokenizer to {self.config.target_dir}") tokenizer.save_pretrained(self.config.target_dir) def merge_and_save(self): from verl.utils.megatron_utils import get_dist_checkpoint_path model_ckpt_path = get_dist_checkpoint_path(self.config.local_dir) model_state_dict = self._load_state_dicts(model_ckpt_path) merged_state_dict = self._merge_state_dicts(model_state_dict) del model_state_dict if self.config.operation == "test": if not self.config.test_hf_dir: raise ValueError("test_hf_dir must be provided for test operation") self._validate_state_dict(merged_state_dict) elif self.config.operation == "merge": self.save_hf_model_and_tokenizer(merged_state_dict) if self.config.hf_upload: self.upload_to_huggingface() else: raise ValueError(f"Unknown operation: {self.config.operation}") def _validate_state_dict(self, state_dict: dict[str, torch.Tensor]): """ Compares the merged Megatron state_dict against a reference safetensors model. Applies necessary name mappings from Megatron to Hugging Face conventions using _replace_name. """ ref_state_dict = load_file(Path(self.config.test_hf_dir) / "model.safetensors") for name, loaded_weight in state_dict.items(): # name = self._replace_name(original_name, self.params_mapping) if not name or name.endswith(".bias") and name not in ref_state_dict: continue if "rotary_emb.inv_freq" in name: continue if "lm_head.weight" in name: if self.config.is_value_model or self.config.tie_word_embedding: continue if name not in ref_state_dict: raise RuntimeError(f"key: {name} not exist in state_dict") param = ref_state_dict[name] assert loaded_weight.dtype == param.dtype torch.testing.assert_close(loaded_weight.to("cpu"), param, atol=1e-2, rtol=5e-2) def _replace_name(self, megatron_name: str, name_mapping: dict[str, str]) -> str: for m_name, v_name in name_mapping.items(): if m_name not in megatron_name: continue megatron_name = megatron_name.replace("decoder", "model") param_name = megatron_name.replace(m_name, v_name) return param_name return None # Return None if no mapping found def cleanup(self): torch.distributed.destroy_process_group() ================================================ FILE: verl/models/README.md ================================================ # Models Common modelzoo such as huggingface/transformers stuggles when using Pytorch native model parallelism. Following the design principle of vLLM, we keep a simple, parallelizable, highly-optimized with packed inputs in verl. ## Adding a New Huggingface Model ### Step 1: Copy the model file from HF to verl - Add a new file under verl/models/hf - Copy ONLY the model file from huggingface/transformers/models to verl/models/hf ### Step 2: Modify the model file to use packed inputs - Remove all the code related to inference (kv cache) - Modify the inputs to include only - input_ids (total_nnz,) - cu_seqlens (total_nnz + 1,) - max_seqlen_in_batch: int - Note that this requires using flash attention with causal mask. ### Step 2.5: Add tests - Add a test to compare this version and the huggingface version - Following the infrastructure and add tests to tests/models/hf ### Step 3: Add a function to apply tensor parallelism - Please follow - https://pytorch.org/docs/stable/distributed.tensor.parallel.html - https://pytorch.org/tutorials/intermediate/TP_tutorial.html - General comments - Tensor Parallelism in native Pytorch is NOT auto-parallelism. The way it works is to specify how model parameters and input/output reshards using configs. These configs are then registered as hooks to perform input/output resharding before/after model forward. ### Step 4: Add a function to apply data parallelism - Please use FSDP2 APIs - See demo here https://github.com/pytorch/torchtitan/blob/main/torchtitan/parallelisms/parallelize_llama.py#L413 ### Step 5: Add a function to apply pipeline parallelism - Comes in Pytorch 2.4 - Currently only in alpha in nightly version - Check torchtitan for more details ================================================ FILE: verl/models/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/llama/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/llama/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .modeling_llama_megatron import ( ParallelLlamaForCausalLM, # rmpad with megatron ParallelLlamaForCausalLMRmPad, # rmpad with megatron and pipeline parallelism ParallelLlamaForCausalLMRmPadPP, ParallelLlamaForValueRmPad, ParallelLlamaForValueRmPadPP, # original model with megatron ParallelLlamaModel, ) __all__ = [ "ParallelLlamaForCausalLM", "ParallelLlamaForCausalLMRmPad", "ParallelLlamaForCausalLMRmPadPP", "ParallelLlamaForValueRmPad", "ParallelLlamaForValueRmPadPP", "ParallelLlamaModel", ] ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/llama_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from verl.utils.device import get_device_id, get_torch_device def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu print(f"get megatron data parallel size: {mpu.get_data_parallel_world_size()}") pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_llama( state_dict, wrapped_models, config, params_dtype, is_value_model=False, tie_word_embeddings=False ): """Load merged state_dict to sharded Megatron module in training.""" from megatron.core import DistributedDataParallel as LocalDDP from megatron.core import mpu from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model start_time = time.time() def _get_gpt_model(model): return model def fetch_params(module): for param in module.parameters(): torch.distributed.fetch( param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group() ) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers, ( f"num_layers_per_model: {num_layers_per_model} * pp_size: {pp_size} * virtual_pp_size " f"{virtual_pp_size} != config.num_hidden_layers: {config.num_hidden_layers}" ) models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.model.layers) == num_layers_per_model def _fetch_tensor(tensor, name) -> torch.Tensor: """fetch tensor""" nonlocal state_dict if tensor is not None: tensor.data.copy_(state_dict[name]) def _fetch_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """fetch tensor in tp shards""" nonlocal state_dict tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) if tensor is not None: tensor.data.copy_(tensor_chunk[tp_rank]) else: print(f"tp_shard tensor:[{name}] not in state_dict, skip loading") def _fetch_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """fetch tensor in tp shards""" nonlocal state_dict tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) if tensor is not None: tensor.data.copy_(tensor_chunk[tp_rank]) else: print(f"tp_shard tensor:[{name}] not in state_dict, skip loading") def _fetch_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """fetch gate_up tensor in tp shards""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if gate_name in state_dict and up_name in state_dict: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty( config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0) ) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) if tensor is not None: tensor.data.copy_(tensor_chunk[tp_rank]) else: print(f"tp_shard tensor:[{gate_name}, {up_name}] not in state_dict, skip loading") def _fetch_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name) -> torch.Tensor: """fetch tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() assert q_name in state_dict and k_name in state_dict and v_name in state_dict full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp : (i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp : (i + 1) * kv_size_tp] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) if tensor is not None: tensor.data.copy_(tensor_chunk[tp_rank]) # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) embed_tokens_weight = None if pp_rank == 0: embed_tokens_weight = gpt_model_module.model.embed_tokens.weight _fetch_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() num_layer_per_pp = config.num_hidden_layers // pp_size vpp_size = mpu.get_virtual_pipeline_model_parallel_world_size() layer_list = [] if vpp_size is not None: for vpp_rank in range(vpp_size): num_layer_vpp_chunk = num_layer_per_pp // vpp_size num_layer_this_model = num_layer_vpp_chunk offset = vpp_rank * (config.num_hidden_layers // mpu.get_virtual_pipeline_model_parallel_world_size()) + ( mpu.get_pipeline_model_parallel_rank() * num_layer_vpp_chunk ) layer_list.extend(list(range(offset, offset + num_layer_this_model))) else: num_layer_this_model = num_layer_per_pp offset = pp_rank * num_layer_per_pp layer_list.extend(list(range(offset, offset + num_layer_this_model))) for layer in layer_list: print_rank_0(f"loading layer #{layer}...") layer_name = f"model.layers.{layer}" dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[dst_layer_idx] _fetch_tensor( sync_layer.input_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) _fetch_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) _fetch_tp_shard_tensor( sync_layer.self_attn.o_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _fetch_tensor( sync_layer.post_attention_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _fetch_tp_shard_tensor_gate_up( sync_layer.mlp.gate_up_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", ) _fetch_tp_shard_tensor( sync_layer.mlp.down_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _fetch_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", ) print_rank_0("loading lm_head...") if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.lm_head.weight if is_value_model: if "lm_head.weight" in state_dict and state_dict["lm_head.weight"].shape[0] == 1: _fetch_tensor(lm_head_weight, "lm_head.weight") print_rank_0("load lm_head weight") elif "reward_head.weight" in state_dict and state_dict["reward_head.weight"].shape[0] == 1: _fetch_tensor(lm_head_weight, "reward_head.weight") print_rank_0("load lm_head from value_head weight") else: _fetch_tensor(None, "lm_head.weight") print_rank_0("fail to match lm_head in value_model") else: _fetch_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() get_torch_device().empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/llama_loader_depracated.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from verl.utils.device import get_device_id, get_torch_device def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu print(f"get megatron data parallel size: {mpu.get_data_parallel_world_size()}") pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_llama( state_dict, wrapped_models, config, params_dtype, is_value_model=False, tie_word_embeddings=False ): """Load merged state_dict to sharded Megatron module in training.""" from megatron.core import DistributedDataParallel as LocalDDP from megatron.core import mpu from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model start_time = time.time() def _get_gpt_model(model): return model def broadcast_params(module): for param in module.parameters(): torch.distributed.broadcast( param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group() ) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers, ( f"num_layers_per_model: {num_layers_per_model} * pp_size: {pp_size} * virtual_pp_size " f"{virtual_pp_size} != config.num_hidden_layers: {config.num_hidden_layers}" ) models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.model.layers) == num_layers_per_model def _broadcast_tensor(tensor, name) -> torch.Tensor: """broadcast tensor from rank0 across mp_group""" nonlocal state_dict nonlocal mp_group if torch.distributed.get_rank() == 0: if name in state_dict: weight = state_dict[name] tensor_shape = weight.shape else: tensor_shape = None else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not in state_dict, skip load") return if tensor is None: tensor = torch.empty( tensor_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) if torch.distributed.get_rank() == 0: tensor.data.copy_(weight) dist.broadcast(tensor, src=0, group=mp_group) def _broadcast_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty( config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0) ) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank() == 0:} tensor {gate_name, up_name} shape " f"{tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: assert q_name in state_dict and k_name in state_dict and v_name in state_dict full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp : (i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp : (i + 1) * kv_size_tp] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_( torch.cat([q_part, k_part, v_part], dim=0) ) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_( torch.cat([q_part, k_part, v_part], dim=0) ) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name, k_name, v_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {q_name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) if dp_rank == 0: # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) embed_tokens_weight = None if pp_rank == 0: embed_tokens_weight = gpt_model_module.model.embed_tokens.weight _broadcast_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"loading layer #{layer}...") layer_name = f"model.layers.{layer}" dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[dst_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _broadcast_tp_shard_tensor_gate_up( sync_layer.mlp.gate_up_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", ) _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", ) print_rank_0("loading lm_head...") lm_head_weight = None if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.lm_head.weight if is_value_model: if "lm_head.weight" in state_dict and state_dict["lm_head.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "lm_head.weight") print_rank_0("load lm_head weight") elif "reward_head.weight" in state_dict and state_dict["reward_head.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "reward_head.weight") print_rank_0("load lm_head from value_head weight") else: _broadcast_tensor(None, "lm_head.weight") print_rank_0("fail to match lm_head in value_model") else: _broadcast_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() # Broadcast weights inside data parallel groups for wrapped_model in wrapped_models: broadcast_params(wrapped_model) get_torch_device().empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/llama_saver.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from megatron.core import mpu from megatron.core.distributed import DistributedDataParallel as LocalDDP from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.device import get_device_id, get_torch_device from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model def _megatron_calc_global_rank(tp_rank: int = 0, dp_rank: int = 0, pp_rank: int = 0): """given TP,DP,PP rank to get the global rank.""" tp_size = mpu.get_tensor_model_parallel_world_size() dp_size = mpu.get_data_parallel_world_size() pp_size = mpu.get_pipeline_model_parallel_world_size() assert tp_size * dp_size * pp_size == torch.distributed.get_world_size(), ( f"{tp_size} x {dp_size} x {pp_size} != {torch.distributed.get_world_size()}" ) # We only support TP-DP-PP grouping, for correctness when resharding return (pp_rank * dp_size + dp_rank) * tp_size + tp_rank def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def merge_megatron_ckpt_llama(wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False): """Merge sharded parameters of a Megatron module into a merged checkpoint. Args: wrapped_models (list of megatron.core.distributed.DistributedDataParallel): The local DDP wrapped megatron modules. config (str or None): HF config for model dtype: model params type is_value_model: if model is value model tie_word_embeddings: tie_word_embeddings, not used in llama, only to keep same interface with qwen2 Returns: state_dict (dict): The merged state_dict in rank 0, and an empty dictionary in other ranks. """ start_time = time.time() def _get_gpt_model(model): return model dp_rank = mpu.get_data_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() pp_rank = mpu.get_pipeline_model_parallel_rank() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if dist.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) assert len(models[i].model.layers) == num_layers_per_model, ( "len model layers {} not equal to num_layers_per_model {}".format( len(models[i].model.layers), num_layers_per_model ) ) state_dict = dict() def _get_cpu_tensor(tensor: torch.Tensor): if tensor is None: return None if tensor.device == torch.device("cpu"): return tensor.detach().clone() return tensor.detach().cpu() def _broadcast_tensor(tensor, name, src_pp_rank) -> torch.Tensor: """broadcast tensor across mp_group""" nonlocal state_dict nonlocal mp_group src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: if tensor is None: weight = None tensor_shape = None else: weight = tensor tensor_shape = weight.shape else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not exist, skip collect") return if weight is None: weight = torch.empty( tensor_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) dist.broadcast(weight, src=src_rank, group=mp_group) if torch.distributed.get_rank() == 0: state_dict[name] = _get_cpu_tensor(weight) def _broadcast_tp_shard_tensor(tensor, name, src_pp_rank, concat_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=concat_dim) if mutate_func is not None: full_tensor = mutate_func(full_tensor) state_dict[name] = full_tensor def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name, src_pp_rank) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) intermediate_size_tp = config.intermediate_size // tp_size gate_weight_list = [] up_weight_list = [] for i in range(tp_size): gate_up_weight_tp = full_tensor[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)] gate_weight_tp = gate_up_weight_tp[:intermediate_size_tp] up_weight_tp = gate_up_weight_tp[intermediate_size_tp:] gate_weight_list.append(gate_weight_tp) up_weight_list.append(up_weight_tp) state_dict[gate_name] = torch.cat(gate_weight_list, dim=0) state_dict[up_name] = torch.cat(up_weight_list, dim=0) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, src_pp_rank): """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) q_weight_list = [] k_weight_list = [] v_weight_list = [] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size : (i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp : q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp : total_size] q_weight_list.append(q_part) k_weight_list.append(k_part) v_weight_list.append(v_part) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size : (i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp : q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp : total_size] q_weight_list.append(q_part) if i * config.num_key_value_heads % tp_size == 0: k_weight_list.append(k_part) v_weight_list.append(v_part) state_dict[q_name] = torch.cat(q_weight_list, dim=0) state_dict[k_name] = torch.cat(k_weight_list, dim=0) state_dict[v_name] = torch.cat(v_weight_list, dim=0) # empty cache before collecting weights get_torch_device().empty_cache() # Embeddings # ------------------- if dp_rank == 0: # Embeddings # ------------------- print_rank_0("collecting embeddings...") gpt_model_module = _get_gpt_model(models[0]) _broadcast_tp_shard_tensor( gpt_model_module.model.embed_tokens.weight if pp_rank == 0 else None, "model.embed_tokens.weight", src_pp_rank=0, ) # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"collecting layer #{layer}...") layer_name = f"model.layers.{layer}" src_pp_rank, src_virtual_pp_rank, src_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[src_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[src_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight, f"{layer_name}.input_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight, f"{layer_name}.self_attn.o_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight, f"{layer_name}.post_attention_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_gate_up( sync_layer.mlp.gate_up_proj.weight, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight, f"{layer_name}.mlp.down_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) # Final Layernorm # ------------------- print_rank_0("collecting final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", src_pp_rank=pp_size - 1, ) print_rank_0("collecting lm_head...") if is_value_model: if pp_rank == pp_size - 1: print(f"gpt_model_module.lm_head.weight: {gpt_model_module.lm_head.weight.shape}") _broadcast_tensor( gpt_model_module.lm_head.weight if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) _broadcast_tensor( gpt_model_module.reward_head.weight if pp_rank == pp_size - 1 and getattr(gpt_model_module, "reward_weight", None) is not None else None, "reward_head.weight", src_pp_rank=pp_size - 1, ) else: _broadcast_tp_shard_tensor( getattr(gpt_model_module.lm_head, "weight", None) if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) dist.barrier() get_torch_device().empty_cache() if torch.distributed.get_rank() == 0: if dtype not in [torch.float16, torch.bfloat16, torch.float32]: print(f'Unknown/unsupported dtype to save: {dtype}"') exit(1) for k, v in state_dict.items(): if dtype != v.dtype: state_dict[k] = v.to(dtype) print_rank_0(f"merge megatron ckpt done, time elapsed {time.time() - start_time}s") return state_dict ================================================ FILE: verl/models/llama/megatron/layers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .parallel_attention import ParallelLlamaAttention from .parallel_decoder import ParallelLlamaDecoderLayer, ParallelLlamaDecoderLayerRmPad from .parallel_linear import ( LinearForLastLayer, MergedColumnParallelLinear, QKVParallelLinear, ) from .parallel_mlp import ParallelLlamaMLP from .parallel_rmsnorm import ParallelLlamaRMSNorm __all__ = [ "LinearForLastLayer", "MergedColumnParallelLinear", "QKVParallelLinear", "ParallelLlamaAttention", "ParallelLlamaDecoderLayer", "ParallelLlamaDecoderLayerRmPad", "ParallelLlamaMLP", "ParallelLlamaRMSNorm", ] ================================================ FILE: verl/models/llama/megatron/layers/parallel_attention.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Optional import torch import torch.nn.functional as F from einops import rearrange from flash_attn.layers.rotary import apply_rotary_emb from megatron.core import ModelParallelConfig, tensor_parallel from megatron.core import parallel_state as mpu from torch import nn from transformers import LlamaConfig from transformers.utils import is_flash_attn_2_available from verl.models.llama.megatron.layers.parallel_linear import QKVParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class LlamaRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) class LlamaLlama3ScalingRotaryEmbedding(LlamaRotaryEmbedding): def __init__(self, dim, config, max_position_embeddings=2048, base=10000, device=None): super().__init__(dim, max_position_embeddings, base, device) self.factor = config.rope_scaling["factor"] # `8` in the original implementation self.high_freq_factor = config.rope_scaling["high_freq_factor"] # `1` in the original implementation self.low_freq_factor = config.rope_scaling["low_freq_factor"] # `4` in the original implementation self.old_context_len = config.rope_scaling[ "original_max_position_embeddings" ] # `8192` in the original implementation low_freq_wavelen = self.old_context_len / self.low_freq_factor high_freq_wavelen = self.old_context_len / self.high_freq_factor wavelen = 2 * math.pi / self.inv_freq # wavelen < high_freq_wavelen: do nothing; wavelen > low_freq_wavelen: divide by factor inv_freq_llama = torch.where(wavelen > low_freq_wavelen, self.inv_freq / self.factor, self.inv_freq) # otherwise: interpolate between the two, using a smooth factor smooth_factor = (self.old_context_len / wavelen - self.low_freq_factor) / ( self.high_freq_factor - self.low_freq_factor ) smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / self.factor + smooth_factor * inv_freq_llama is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen) inv_freq = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class ParallelLlamaAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta # assign values after tp tp_size = mpu.get_tensor_model_parallel_world_size() assert self.num_heads % tp_size == 0, ( f"num_head must be divisible by tp_size. Got num_head={self.num_heads}, tp_size={tp_size}" ) assert self.num_key_value_heads % tp_size == 0, ( f"num_key_value_heads must be divisible by tp_size. Got num_key_value_heads=" f"{self.num_key_value_heads}, tp_size={tp_size}" ) self.num_heads_per_tp = self.num_heads // tp_size self.num_key_value_heads_per_tp = self.num_key_value_heads // tp_size self.hidden_size_per_tp = self.hidden_size // tp_size if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and " f"`num_heads`: {self.num_heads})." ) column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" assert row_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) # [self.q_size, self.k_size, self.v_size] self.qkv_proj = QKVParallelLinear( input_size=self.hidden_size, num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads, head_dim=self.head_dim, bias=config.attention_bias, gather_output=False, skip_bias_add=False, **column_kwargs, ) self.q_size = self.num_heads_per_tp * self.head_dim self.k_size = self.num_key_value_heads_per_tp * self.head_dim self.v_size = self.num_key_value_heads_per_tp * self.head_dim self.o_proj = tensor_parallel.RowParallelLinear( input_size=self.num_heads * self.head_dim, output_size=self.hidden_size, bias=config.attention_bias, input_is_parallel=True, skip_bias_add=False, **row_kwargs, ) self._init_rope() def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = LlamaRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) else: rope_type_key = "type" if "type" in self.config.rope_scaling else "rope_type" scaling_type = self.config.rope_scaling[rope_type_key] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = LlamaLinearScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "dynamic": self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "llama3": self.rotary_emb = LlamaLlama3ScalingRotaryEmbedding( self.head_dim, self.config, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1) query_states = query_states.view(bsz, q_len, self.num_heads_per_tp, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads_per_tp, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads_per_tp, q_len, kv_seq_len)}, " f"but is {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads_per_tp, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads_per_tp, q_len, self.head_dim)}, " f"but is {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size_per_tp) attn_output = self.o_proj(attn_output)[0] return attn_output """ Remove padding Attention - Using Flash-attn 2 - Compatible with sequence parallel """ if is_flash_attn_2_available(): from flash_attn import flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa: F401 def apply_rotary_pos_emb_rmpad(q, k, cos, sin, position_ids, indices, sequence_length): batch_size = position_ids.shape[0] q = pad_input(q, indices, batch_size, sequence_length) # (batch_size, seqlen, num_head, head_dim) k = pad_input(k, indices, batch_size, sequence_length) cos = cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] sin = sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) q_embed = index_first_axis(rearrange(q_embed, "b s ... -> (b s) ..."), indices) k_embed = index_first_axis(rearrange(k_embed, "b s ... -> (b s) ..."), indices) return q_embed, k_embed # use flash-attn rotary embeddings with rmpad # cos/sin shoudl be: (seq_length, rotary_dim / 2) def apply_rotary_pos_emb_rmpad_flash(q, k, cos, sin, cu_seqlens, max_seqlen): q_embed = apply_rotary_emb( q, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen ) k_embed = apply_rotary_emb( k, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen ) return q_embed, k_embed class ParallelLlamaAttentionRmPad(ParallelLlamaAttention): def forward( self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: torch.Tensor = None, max_seqlen_in_batch: int = None, ): total_nnz, _, _ = hidden_states.size() # This is the total_nnz padded after sequence parallel if self.megatron_config.sequence_parallel: total_nnz = total_nnz * mpu.get_tensor_model_parallel_world_size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split( [self.q_size, self.k_size, self.v_size], dim=-1 ) # (total_nnz, 1, hidden_size) if self.megatron_config.sequence_parallel: sequence_parallel_pad = total_nnz - cu_seqlens[-1] total_nnz = cu_seqlens[-1] # total_nnz before sp padding query_states = query_states[:total_nnz] key_states = key_states[:total_nnz] value_states = value_states[:total_nnz] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dime x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(total_nnz, self.num_heads_per_tp, self.head_dim) key_states = key_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) value_states = value_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) cos, sin = self.rotary_emb(value_states, seq_len=sequence_length) cos, sin = cos[:, : cos.shape[1] // 2], sin[:, : sin.shape[1] // 2] # flash attn only needs half query_states, key_states = apply_rotary_pos_emb_rmpad_flash( query_states, key_states, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen_in_batch ) # query_states, key_states = apply_rotary_pos_emb_rmpad(query_states, key_states, cos, sin, # position_ids, indices, # TODO: llama does not have dropout in the config?? # It is recommended to use dropout with FA according to the docs # when training. dropout_rate = 0.0 # if not self.training else self.attn_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: query_states = query_states.to(torch.float16) key_states = key_states.to(torch.float16) value_states = value_states.to(torch.float16) attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens, cu_seqlens_k=cu_seqlens, max_seqlen_q=max_seqlen_in_batch, max_seqlen_k=max_seqlen_in_batch, dropout_p=dropout_rate, softmax_scale=None, causal=True, ) attn_output_unpad = attn_output_unpad.to(input_dtype) attn_output_unpad = attn_output_unpad.reshape(total_nnz, 1, self.hidden_size_per_tp).contiguous() # sequence parallel reduce_scatter is performed inside RowColumnParallel if enabled # Here we need to repad if self.megatron_config.sequence_parallel: attn_output_unpad = F.pad(attn_output_unpad, pad=(0, 0, 0, 0, 0, sequence_parallel_pad)) attn_output_unpad = self.o_proj(attn_output_unpad)[0] return attn_output_unpad ================================================ FILE: verl/models/llama/megatron/layers/parallel_decoder.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional import torch from megatron.core import ModelParallelConfig from torch import nn from transformers import LlamaConfig from verl.utils.megatron_utils import TransformerConfig, convert_config from .parallel_attention import ParallelLlamaAttention, ParallelLlamaAttentionRmPad from .parallel_mlp import ParallelLlamaMLP from .parallel_rmsnorm import ParallelLlamaRMSNorm class ParallelLlamaDecoderLayer(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig, layer_idx: int): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.self_attn = ParallelLlamaAttention(config=config, megatron_config=megatron_config) self.mlp = ParallelLlamaMLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelLlamaRMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelLlamaRMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Note: sequence parallel is hidden inside ColumnParallelLinear # reduce scatter is hidden inside RowParallelLinear # Self Attention hidden_states = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) # TODO: add sequence parallel operator all_gather here hidden_states = self.mlp(hidden_states) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states outputs = hidden_states return outputs class ParallelLlamaDecoderLayerRmPad(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig, layer_idx: int): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.self_attn = ParallelLlamaAttentionRmPad(config=config, megatron_config=megatron_config) self.mlp = ParallelLlamaMLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelLlamaRMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelLlamaRMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None, ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states # (total_nnz // sp, 1, hidden_size) hidden_states = self.input_layernorm(hidden_states) # Self Attention # (total_nnz // sp, 1, hidden_size) -> all-gather (total_nnz, 1, hidden_size) # -> col + row -> reduce-scatter -> (total_nnz // sp, 1, hidden_size) hidden_states = self.self_attn( hidden_states=hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = residual + hidden_states # Fully Connected # shape changes same as attn residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = hidden_states return outputs ================================================ FILE: verl/models/llama/megatron/layers/parallel_linear.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/linear.py import torch from megatron.core import tensor_parallel class QKVParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__( self, input_size, num_heads, num_key_value_heads, head_dim, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs, ): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.q_output_size = num_heads * head_dim self.kv_output_size = num_key_value_heads * head_dim self.head_dim = head_dim self.gather_output = gather_output self.skip_bias_add = skip_bias_add input_size = self.input_size output_size = (num_heads + 2 * num_key_value_heads) * self.head_dim super().__init__( input_size=input_size, output_size=output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs, ) class MergedColumnParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__( self, input_size, gate_ouput_size, up_output_size, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs, ): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.output_size = gate_ouput_size + up_output_size self.gather_output = gather_output self.skip_bias_add = skip_bias_add super().__init__( input_size=self.input_size, output_size=self.output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs, ) class LinearForLastLayer(torch.nn.Linear): def __init__( self, input_size, output_size, *, config, bias=True, ): super().__init__(in_features=input_size, out_features=output_size, bias=bias) self.sequence_parallel = config.sequence_parallel if self.sequence_parallel: self.weight.sequence_parallel = True def forward( self, input_, weight=None, runtime_gather_output=None, ): logits = super().forward(input_) logits = logits.float() if self.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits, None ================================================ FILE: verl/models/llama/megatron/layers/parallel_mlp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from megatron.core import ModelParallelConfig, tensor_parallel from megatron.core import parallel_state as mpu from torch import nn from transformers.activations import ACT2FN from verl.models.llama.megatron.layers.parallel_linear import MergedColumnParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class ParallelLlamaMLP(nn.Module): def __init__(self, config, megatron_config: ModelParallelConfig = None) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size # The weight is only [hidden_size, intermediate_size // model_parallel_world_size] column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" assert row_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_size = mpu.get_tensor_model_parallel_world_size() self.gate_up_proj = MergedColumnParallelLinear( input_size=self.hidden_size, gate_ouput_size=self.intermediate_size, up_output_size=self.intermediate_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) self.gate_size = self.intermediate_size // tp_size self.down_proj = tensor_parallel.RowParallelLinear( input_size=self.intermediate_size, output_size=self.hidden_size, bias=False, input_is_parallel=True, skip_bias_add=False, **row_kwargs, ) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): gate_up = self.gate_up_proj(x)[0] gate, up = gate_up.split(self.gate_size, dim=-1) return self.down_proj(self.act_fn(gate) * up)[0] ================================================ FILE: verl/models/llama/megatron/layers/parallel_rmsnorm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numbers import torch from megatron.core import ModelParallelConfig from torch import nn from transformers import LlamaConfig from verl.utils.megatron import sequence_parallel as sp_utils class ParallelLlamaRMSNorm(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): """ LlamaRMSNorm is equivalent to T5LayerNorm """ super().__init__() if isinstance(config.hidden_size, numbers.Integral): normalized_shape = (config.hidden_size,) self.normalized_shape = torch.Size(normalized_shape) self.weight = nn.Parameter(torch.ones(self.normalized_shape)) self.variance_epsilon = config.rms_norm_eps if megatron_config.sequence_parallel: sp_utils.mark_parameter_as_sequence_parallel(self.weight) def forward(self, hidden_states): from apex.normalization.fused_layer_norm import fused_rms_norm_affine return fused_rms_norm_affine( input=hidden_states, weight=self.weight, normalized_shape=self.normalized_shape, eps=self.variance_epsilon, memory_efficient=True, ) ================================================ FILE: verl/models/llama/megatron/modeling_llama_megatron.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch LLaMA model with Megatron-style acceleration.""" from typing import Optional import torch import torch.utils.checkpoint from megatron.core import ModelParallelConfig, mpu, tensor_parallel from torch import nn from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.models.llama.configuration_llama import LlamaConfig from transformers.models.llama.modeling_llama import CausalLMOutputWithPast from verl.utils.megatron import sequence_parallel as sp_utils from verl.utils.megatron import tensor_parallel as tp_utils from verl.utils.megatron_utils import TransformerConfig, convert_config from .layers import ParallelLlamaDecoderLayer, ParallelLlamaDecoderLayerRmPad, ParallelLlamaRMSNorm """ TODO: 1. Add weight initialization. Here we need to be careful on TP weight init. 2. Add sequence parallel 3. Load checkpoint from meta LLama pretrained checkpoint """ # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class ParallelLlamaModel(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig """ def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs ) self.layers = nn.ModuleList( [ParallelLlamaDecoderLayer(config, megatron_config) for _ in range(config.num_hidden_layers)] ) self.norm = ParallelLlamaRMSNorm(config, megatron_config) # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | BaseModelOutputWithPast: """ Args: input_ids: input ids. shape (batch_size, seq_length) attention_mask: attention_mask. shape (batch_size, seq_length) position_ids: position ids. shape (batch_size, seq_length) Returns: """ batch_size, seq_length = input_ids.shape inputs_embeds = self.embed_tokens(input_ids) # embed positions attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelLlamaForCausalLM(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.model = ParallelLlamaModel(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear( input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = outputs logits = self.lm_head(hidden_states)[0] logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) logits = logits.float() return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa: F401, E402 class ParallelLlamaModelRmPad(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig """ def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() self.megatron_config = megatron_config if megatron_config is not None: assert embedding_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs ) self.layers = nn.ModuleList( [ParallelLlamaDecoderLayerRmPad(config, megatron_config) for _ in range(config.num_hidden_layers)] ) self.norm = ParallelLlamaRMSNorm(config, megatron_config) def forward( self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None, ) -> tuple | BaseModelOutputWithPast: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelLlamaForCausalLMRmPad(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.megatron_config = megatron_config self.model = ParallelLlamaModelRmPad(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size self._init_head(config) def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear( input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head logits = self.lm_head(hidden_states)[0] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) # (total_nnz_padded, 1, vocab_size) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" batch_size, sequence_length = input_ids.shape # remove padding here input_ids, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input( input_ids.unsqueeze(dim=-1), attention_mask ) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids = sp_utils.pad_to_sequence_parallel(input_ids) input_ids = input_ids.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model( input_ids=input_ids, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = outputs logits = self._forward_head(hidden_states) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # add removed padding back logits = pad_input( logits, indices, batch_size, seqlen=sequence_length ) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) class ParallelLlamaForValueRmPad(ParallelLlamaForCausalLMRmPad): def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: output = super().forward(input_ids, attention_mask, position_ids) output.logits = torch.squeeze(output.logits, dim=-1) return output """ Support pipeline parallelism """ class ParallelLlamaModelRmPadPP(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] This model definition supports pipeline parallelism. To support pp and vpp, - This model only contains layer in this pp stage and vpp chunk - When calling get_model in Megatron, this rank will instantiate all the vpp chunks in this pp. Args: config: LlamaConfig """ def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig, pre_process, post_process): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process self.megatron_config = megatron_config embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) if pre_process: self.embed_tokens = tensor_parallel.VocabParallelEmbedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs ) else: self.embed_tokens = None pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = megatron_config.pipeline_model_parallel_size self.num_layer_per_pp = config.num_hidden_layers // pp_size vpp_size = megatron_config.virtual_pipeline_model_parallel_size vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() if vpp_size is not None: self.layers = nn.ModuleList() self.num_layer_vpp_chunk = self.num_layer_per_pp // vpp_size self.num_layer_this_model = self.num_layer_vpp_chunk offset = vpp_rank * (config.num_hidden_layers // vpp_size) + (pp_rank * self.num_layer_vpp_chunk) else: self.num_layer_this_model = self.num_layer_per_pp offset = pp_rank * self.num_layer_per_pp self.layers = nn.ModuleList() for i in range(self.num_layer_this_model): layer = ParallelLlamaDecoderLayerRmPad(config, megatron_config, layer_idx=offset + i) self.layers.add_module(f"{i}", layer) if post_process: self.norm = ParallelLlamaRMSNorm(config, megatron_config) else: self.norm = None def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" self.input_tensor = input_tensor def forward( self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None, ) -> tuple | BaseModelOutputWithPast: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ if self.pre_process: inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # vocab parallel embedding will not do sequence parallel reduce-scatter in open source megatron # so need to deal with it by handle here: # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds else: # self.hidden_states should be passed by Megatron hidden_states = self.input_tensor for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = layer_outputs if self.post_process: hidden_states = self.norm(hidden_states) return hidden_states class ParallelLlamaForCausalLMRmPadPP(nn.Module): def __init__( self, config: LlamaConfig, megatron_config: ModelParallelConfig, pre_process, post_process, share_embeddings_and_output_weights=False, ): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.megatron_config = megatron_config self.model = ParallelLlamaModelRmPadPP( config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process ) assert share_embeddings_and_output_weights is False, ( "Llama Model not supports sharing embedding and output weights" ) self.share_embeddings_and_output_weights = share_embeddings_and_output_weights self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process if post_process: self._init_head(config) def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" assert len(input_tensor) == 1 self.model.set_input_tensor(input_tensor[0]) def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear( input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head # logits shape before forward_head hidden_states.shape: [4, 32, 4096] logits = self.lm_head(hidden_states)[0] # logits shape after forward_head logits.shape: [8, 32, 8] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) return logits def forward( self, # original input *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # Note that input_ids, attention_mask and position_ids should be passed to every pp layer. # In the first pp, input_ids will be used, in other pp layers hidden_states will be used inside self.model batch_size, sequence_length = input_ids.shape # remove padding here input_ids_rmpad, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input( input_ids.unsqueeze(dim=-1), attention_mask ) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids_rmpad = sp_utils.pad_to_sequence_parallel(input_ids_rmpad) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model( input_ids=input_ids_rmpad, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) if self.post_process: hidden_states = outputs # print(f'hidden_states.shape = {hidden_states.shape}') # torch.Size([4, 32, 4096]) logits = self._forward_head(hidden_states) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # torch.Size([8, 32, 16]) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) # add removed padding back. If input is already rmpad, we let the caller pad_input logits = pad_input( logits, indices, batch_size, seqlen=sequence_length ) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) else: return outputs class ParallelLlamaForValueRmPadPP(ParallelLlamaForCausalLMRmPadPP): def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: output = super().forward(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) if self.post_process: output.logits = torch.squeeze(output.logits, dim=-1) return output else: return output ================================================ FILE: verl/models/mcore/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.models.mcore.patch import apply_patch_megatron_v012_with_torch_v28 from .registry import ( get_mcore_forward_fn, get_mcore_forward_fused_fn, get_mcore_forward_fused_no_padding_fn, get_mcore_forward_no_padding_fn, get_mcore_weight_converter, hf_to_mcore_config, init_mcore_model, ) __all__ = [ "hf_to_mcore_config", "init_mcore_model", "get_mcore_forward_fn", "get_mcore_weight_converter", "get_mcore_forward_fused_fn", "get_mcore_forward_fused_no_padding_fn", "get_mcore_forward_no_padding_fn", ] apply_patch_megatron_v012_with_torch_v28() ================================================ FILE: verl/models/mcore/bridge.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. try: from megatron.bridge import AutoBridge from megatron.bridge.models.conversion.param_mapping import AutoMapping from megatron.bridge.peft.canonical_lora import CanonicalLoRA from megatron.bridge.peft.dora import DoRA from megatron.bridge.peft.lora import LoRA, VLMLoRA except ImportError: # `pip install verl[mcore]` or print("Megatron-Bridge package not found. Please install Megatron-Bridge with `pip install megatron-bridge`") raise import torch from megatron.core import tensor_parallel def _ensure_model_list(model): return model if isinstance(model, list) else [model] class LinearForLastLayer(torch.nn.Linear): """ A custom linear layer implementation for the last layer of a model. This layer extends PyTorch's Linear module with functionality specifically designed for handling the final layer in transformer models with sequence parallelism. Attributes: sequence_parallel: Boolean indicating whether sequence parallelism is enabled """ def __init__( self, input_size, output_size, *, sequence_parallel: bool, ): """ Initializes the LinearForLastLayer. Args: input_size: The size of the input features output_size: The size of the output features sequence_parallel (bool): Whether sequence parallelism is enabled """ super().__init__(in_features=input_size, out_features=output_size, bias=False) self.sequence_parallel = sequence_parallel if self.sequence_parallel: self.weight.sequence_parallel = True def forward( self, input_, weight=None, runtime_gather_output=None, ): """ Forward pass for the linear layer. This method computes the linear transformation and handles sequence parallelism if enabled, gathering outputs from different sequence parallel regions. Args: input_: Input tensor weight: Placeholder for compatibility runtime_gather_output: Placeholder for compatibility Returns: tuple: (logits, None) where logits is the output of the linear transformation """ logits = super().forward(input_) logits = logits.float() if self.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits, None # Make Megatron-Bridge AutoMapping treats the custom last layer as replicated. AutoMapping.register_module_type("LinearForLastLayer", "replicated") def make_value_model(hidden_size, sequence_parallel): """Creates a pre-wrap hook that replace the output layer with a value head. Args: hidden_size (int): The hidden size of the model's transformer layers. sequence_parallel (bool): Whether sequence parallelism is enabled. Returns: A hook function that can be used as a `pre_wrap_hook` in Megatron-Bridge. The hook itself takes the model as input and prepares it for value head activation. """ from megatron.core import parallel_state def hook(model): model_post_process = [] if ( parallel_state.get_pipeline_model_parallel_world_size() > 1 and parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None ): for i in range(parallel_state.get_virtual_pipeline_model_parallel_world_size()): model_post_process.append(parallel_state.is_pipeline_last_stage(ignore_virtual=False, vp_stage=i)) else: model_post_process.append(parallel_state.is_pipeline_last_stage()) model_list = _ensure_model_list(model) assert len(model_post_process) == len(model_list), "Model list length and post process list length must match." for index, model_chunk in enumerate(model_list): if not model_post_process[index]: continue model_chunk.output_layer = LinearForLastLayer( input_size=hidden_size, output_size=1, sequence_parallel=sequence_parallel, ) return hook def freeze_moe_router(model): """Pre-wrap hook to freeze MoE router parameters. Args: model: List of MegatronModule instances or single module Returns: The model with frozen router parameters """ for model_chunk in _ensure_model_list(model): if hasattr(model_chunk, "decoder") and hasattr(model_chunk.decoder, "layers"): for layer in model_chunk.decoder.layers: if hasattr(layer.mlp, "router"): if hasattr(layer.mlp.router, "weight"): layer.mlp.router.weight.requires_grad = False if hasattr(layer.mlp.router, "bias"): layer.mlp.router.bias.requires_grad = False if hasattr(layer.mlp, "shared_experts"): if ( hasattr(layer.mlp.shared_experts, "gate_weight") and layer.mlp.shared_experts.gate_weight is not None ): layer.mlp.shared_experts.gate_weight.requires_grad = False if ( hasattr(layer.mlp.shared_experts, "gate_bias") and layer.mlp.shared_experts.gate_bias is not None ): layer.mlp.shared_experts.gate_bias.requires_grad = False return model __all__ = [ "AutoBridge", "make_value_model", "freeze_moe_router", "LoRA", "VLMLoRA", "DoRA", "CanonicalLoRA", ] ================================================ FILE: verl/models/mcore/config_converter.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # convert huggingface config to mcore transformer config import warnings from typing import TypeVar import torch import torch.nn.functional as F from megatron.core import parallel_state as mpu from megatron.core.transformer import MLATransformerConfig, TransformerConfig from transformers import PretrainedConfig T = TypeVar("T", bound=TransformerConfig) def _get_base_transformer_config( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> dict: """ Create a base TransformerConfig with common parameters across different model architectures. TODO: (ycl) use dataclass or converter config? Args: hf_config: HuggingFace model configuration dtype: Data type for the model override_transformer_config_kwargs: Additional parameters to override defaults Returns: TransformerConfig with common parameters """ # Common parallel state parameters overlap_p2p_comm = ( mpu.get_virtual_pipeline_model_parallel_world_size() is not None and mpu.get_virtual_pipeline_model_parallel_world_size() > 1 ) batch_p2p_comm = False # Base configuration with common parameters base_config = { # Model architecture parameters "num_layers": hf_config.num_hidden_layers, "hidden_size": hf_config.hidden_size, "num_attention_heads": hf_config.num_attention_heads, "num_query_groups": hf_config.num_key_value_heads, "ffn_hidden_size": hf_config.intermediate_size, "attention_dropout": hf_config.attention_dropout, "hidden_dropout": getattr(hf_config, "hidden_dropout", 0.0), "kv_channels": getattr(hf_config, "head_dim", None), "layernorm_epsilon": hf_config.rms_norm_eps, "add_bias_linear": True, # Activation and normalization "activation_func": F.silu, "normalization": "RMSNorm", "gated_linear_unit": True, # Data types "pipeline_dtype": dtype, "params_dtype": dtype, "bf16": dtype is torch.bfloat16, # Parallel configuration "tensor_model_parallel_size": mpu.get_tensor_model_parallel_world_size(), "pipeline_model_parallel_size": mpu.get_pipeline_model_parallel_world_size(), "expert_model_parallel_size": mpu.get_expert_model_parallel_world_size(), "expert_tensor_parallel_size": mpu.get_expert_tensor_parallel_world_size(), "virtual_pipeline_model_parallel_size": mpu.get_virtual_pipeline_model_parallel_world_size(), "context_parallel_size": mpu.get_context_parallel_world_size(), "overlap_p2p_comm": overlap_p2p_comm, "batch_p2p_comm": batch_p2p_comm, "sequence_parallel": mpu.get_tensor_model_parallel_world_size() > 1, # Common settings "variable_seq_lengths": True, "masked_softmax_fusion": True, "moe_token_dispatcher_type": "alltoall", } # Update with any provided overrides # override_transformer_config_kwargs as kwargs shall never be none base_config.update(override_transformer_config_kwargs) return base_config def _get_mla_transformer_config( hf_config: PretrainedConfig, mla_rope_config: dict, dtype: torch.dtype, **override_transformer_config_kwargs ) -> dict: """ Create a MLATransformerConfig with common parameters across different model architectures. This is specifically for MLA models like DeepseekV3. Args: hf_config: HuggingFace model configuration mla_rope_config: MLA specific RoPE configuration dtype: Data type for the model override_transformer_config_kwargs: Additional parameters to override defaults Returns: MLATransformerConfig with common parameters """ base_config = _get_base_transformer_config(hf_config=hf_config, dtype=dtype, **override_transformer_config_kwargs) mla_config = { # MLA specific parameters "q_lora_rank": hf_config.q_lora_rank, "kv_lora_rank": hf_config.kv_lora_rank, "qk_head_dim": hf_config.qk_nope_head_dim, "qk_pos_emb_head_dim": hf_config.qk_rope_head_dim, "v_head_dim": hf_config.v_head_dim, "rotary_base": hf_config.rope_theta, "rotary_scaling_factor": mla_rope_config["factor"], "rope_type": mla_rope_config["type"], "max_position_embeddings": mla_rope_config["original_max_position_embeddings"], "beta_fast": mla_rope_config["beta_fast"], "beta_slow": mla_rope_config["beta_slow"], "mscale": mla_rope_config["mscale"], "mscale_all_dim": mla_rope_config["mscale_all_dim"], } base_config.update(mla_config) return base_config def check_and_construct_configs(original_config: dict, cls: type[T]) -> T: """ Check and disable incompatible configurations for older Megatron version. Args: original_config (dict): The original model configuration. Returns: dict: The updated model configuration with incompatible settings disabled. """ removed_keys = [] for key in original_config.keys(): if not hasattr(cls, key): removed_keys.append(key) if removed_keys: warnings.warn( f"The following keys are not supported in the current Megatron version and will be removed: {removed_keys}", stacklevel=2, ) for key in removed_keys: original_config.pop(key) original_config = mapping_string_to_attn_backend(original_config) if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: print(f"Overridden {cls.__name__} init config: {original_config}") return cls(**original_config) def hf_to_mcore_config_dense( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: # for LlamaForCausalLM or Qwen2ForCausalLM qkv_bias = True if "Qwen2" in hf_config.architectures[0] else getattr(hf_config, "attention_bias", False) qk_layernorm = True if "Qwen3" in hf_config.architectures[0] else False args: dict = _get_base_transformer_config( hf_config=hf_config, dtype=dtype, use_cpu_initialization=False, add_bias_linear=False, add_qkv_bias=qkv_bias, qk_layernorm=qk_layernorm, ) # override_transformer_config_kwargs as kwargs shall never be none args.update(override_transformer_config_kwargs) return check_and_construct_configs(args, TransformerConfig) def hf_to_mcore_config_qwen2moe( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: args: dict = _get_base_transformer_config( hf_config=hf_config, dtype=dtype, use_cpu_initialization=False, add_bias_linear=False, layernorm_epsilon=hf_config.rms_norm_eps, # MoE specific moe_ffn_hidden_size=hf_config.moe_intermediate_size, moe_router_bias_update_rate=0.001, moe_router_topk=hf_config.num_experts_per_tok, num_moe_experts=hf_config.num_experts, moe_shared_expert_intermediate_size=hf_config.shared_expert_intermediate_size, moe_aux_loss_coeff=hf_config.router_aux_loss_coef, # moe_aux_loss_coeff=0.0, moe_router_load_balancing_type="none", # turn off aux_loss as it hurts perf in RL moe_shared_expert_overlap=True, moe_grouped_gemm=True, moe_router_score_function="softmax", # Other optimizations persist_layer_norm=True, bias_activation_fusion=True, bias_dropout_fusion=True, # Qwen specific moe_router_pre_softmax=True, add_qkv_bias=True, ) # override_transformer_config_kwargs as kwargs shall never be none args.update(override_transformer_config_kwargs) return check_and_construct_configs(args, TransformerConfig) def hf_to_mcore_config_mixtral( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: args: dict = _get_base_transformer_config( hf_config=hf_config, dtype=dtype, use_cpu_initialization=False, add_bias_linear=False, layernorm_epsilon=hf_config.rms_norm_eps, # MoE specific num_moe_experts=hf_config.num_local_experts, moe_aux_loss_coeff=hf_config.router_aux_loss_coef, moe_router_topk=hf_config.num_experts_per_tok, moe_router_pre_softmax=True, moe_router_load_balancing_type="none", # turn off aux_loss as it hurts perf in RL moe_router_score_function="softmax", moe_shared_expert_intermediate_size=None, # mixtral has no shared expert moe_shared_expert_overlap=False, # mixtral has no shared expert moe_ffn_hidden_size=hf_config.intermediate_size, moe_router_bias_update_rate=0.001, # moe_permute_fusion=True, # need TE 2.1+ moe_grouped_gemm=True, # Other optimizations persist_layer_norm=True, apply_rope_fusion=True, bias_activation_fusion=True, bias_dropout_fusion=True, ) # override_transformer_config_kwargs as kwargs shall never be none args.update(override_transformer_config_kwargs) return check_and_construct_configs(args, TransformerConfig) def hf_to_mcore_config_qwen3moe( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: args: dict = _get_base_transformer_config( hf_config=hf_config, dtype=dtype, use_cpu_initialization=False, add_bias_linear=False, layernorm_epsilon=hf_config.rms_norm_eps, # MoE specific moe_ffn_hidden_size=hf_config.moe_intermediate_size, moe_router_bias_update_rate=0.001, moe_router_topk=hf_config.num_experts_per_tok, num_moe_experts=hf_config.num_experts, moe_aux_loss_coeff=hf_config.router_aux_loss_coef, # moe_aux_loss_coeff=0.0, moe_router_load_balancing_type="none", # turn off aux_loss as it hurts perf in RL moe_grouped_gemm=True, moe_router_score_function="softmax", # Other optimizations persist_layer_norm=True, bias_activation_fusion=True, bias_dropout_fusion=True, # Qwen specific moe_router_pre_softmax=False, qk_layernorm=True, ) # override_transformer_config_kwargs as kwargs shall never be none args.update(override_transformer_config_kwargs) return check_and_construct_configs(args, TransformerConfig) def hf_to_mcore_config_dpskv3( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> MLATransformerConfig: # DeepseekV3ForCausalLM from megatron.core.config import set_experimental_flag from megatron.core.transformer.enums import AttnBackend set_experimental_flag(True) from .patch import apply_patch apply_patch() mla_rope_config = { "beta_fast": 32, "beta_slow": 1, "factor": 1, "mscale": 1.0, "mscale_all_dim": 1.0, "original_max_position_embeddings": 4096, "type": "rope", } if "rope_scaling" in hf_config and hf_config.rope_scaling is not None: mla_rope_config.update(hf_config.rope_scaling) moe_layer_freq = [1] * hf_config.num_hidden_layers for i in range(min(hf_config.first_k_dense_replace, hf_config.num_hidden_layers)): moe_layer_freq[i] = 0 # disable MTP and quantization for now if "num_nextn_predict_layers" in hf_config: assert hf_config.num_nextn_predict_layers == 0, ( "MTP is not supported for now, please modify the config.json to set num_nextn_predict_layers to 0" ) assert "quantization_config" not in hf_config or not hf_config.quantization_config, ( "quantization is not supported for now, please modify the config.json to remove quantization_config" ) args: dict = _get_mla_transformer_config( hf_config=hf_config, mla_rope_config=mla_rope_config, dtype=dtype, # Additional parameters use_cpu_initialization=False, add_bias_linear=False, attention_backend=AttnBackend.fused, qk_layernorm=True, # Standard MoE parameters moe_ffn_hidden_size=hf_config.moe_intermediate_size, moe_token_dispatcher_type="alltoall", moe_router_bias_update_rate=0.001, moe_router_enable_expert_bias=True, moe_router_topk=hf_config.num_experts_per_tok, num_moe_experts=hf_config.n_routed_experts, moe_shared_expert_intermediate_size=hf_config.moe_intermediate_size * hf_config.n_shared_experts, moe_aux_loss_coeff=getattr(hf_config, "aux_loss_alpha", 0.001), moe_router_load_balancing_type="seq_aux_loss", moe_shared_expert_overlap=True, # moe_permute_fusion=True, # need TE 2.1+ moe_grouped_gemm=True, moe_router_score_function="sigmoid", moe_router_pre_softmax=True, moe_router_topk_scaling_factor=hf_config.routed_scaling_factor, moe_layer_freq=moe_layer_freq, # mcore 0.12 moe moe_router_dtype="fp64", disable_bf16_reduced_precision_matmul=True, # Other optimizations # deallocate_pipeline_outputs=True, # gradient_accumulation_fusion=True, persist_layer_norm=True, bias_activation_fusion=True, bias_dropout_fusion=True, ) # override_transformer_config_kwargs as kwargs shall never be none args.update(override_transformer_config_kwargs) transformer_config = check_and_construct_configs(args, MLATransformerConfig) # MTP if "num_nextn_predict_layers" in hf_config: transformer_config.mtp_num_layers = hf_config.num_nextn_predict_layers transformer_config.mtp_loss_scaling_factor = 0.1 return transformer_config def hf_to_mcore_config_qwen2_5_vl( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: # Qwen2_5_VLForConditionalGeneration args = _get_base_transformer_config( hf_config=hf_config, dtype=dtype, add_bias_linear=False, # qwen specific add_qkv_bias=True, mrope_section=hf_config.rope_scaling["mrope_section"], ) # override_transformer_config_kwargs as kwargs shall never be none args.update(override_transformer_config_kwargs) args = mapping_string_to_attn_backend(args) return TransformerConfig(**args) def hf_to_mcore_config_llama4( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: # Llama4ForConditionalGeneration raise NotImplementedError("Llama4ForConditionalGeneration is not supported yet") def mapping_string_to_attn_backend(args: dict) -> dict: if "attention_backend" in args and isinstance(args["attention_backend"], str): from megatron.core.transformer.enums import AttnBackend args["attention_backend"] = AttnBackend[args["attention_backend"]] return args ================================================ FILE: verl/models/mcore/loader.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from verl.utils.device import get_device_id, get_torch_device from .saver import _megatron_calc_global_rank def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_gptmodel(state_dict, wrapped_models, config, params_dtype, is_value_model=False): """Load merged state_dict to sharded Megatron module in training.""" from megatron.core import DistributedDataParallel as LocalDDP from megatron.core import mpu from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model start_time = time.time() def _get_gpt_model(model): return model def broadcast_params(module): for param in module.parameters(): torch.distributed.broadcast( param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group() ) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() cp_rank = mpu.get_context_parallel_rank() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=0, cp_rank=cp_rank) pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == src_rank: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.decoder.layers) == num_layers_per_model def _broadcast_tensor(tensor, name) -> torch.Tensor: """broadcast tensor from rank0 across mp_group""" nonlocal state_dict nonlocal mp_group if torch.distributed.get_rank() == src_rank: if name in state_dict: weight = state_dict[name] tensor_shape = weight.shape else: tensor_shape = None else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not in state_dict, skip load") return if tensor is None: tensor = torch.empty( tensor_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) if torch.distributed.get_rank() == src_rank: tensor.data.copy_(weight) dist.broadcast(tensor, src=src_rank, group=mp_group) def _broadcast_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == src_rank: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == src_rank: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=src_rank, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == src_rank: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == src_rank: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=src_rank, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == src_rank: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty( config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0) ) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank() == src_rank:} tensor {gate_name, up_name} shape " f"{tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == src_rank: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=src_rank, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, bias=False) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == src_rank: assert q_name in state_dict and k_name in state_dict and v_name in state_dict full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) if config.num_key_value_heads >= tp_size: q_size_tp = hidden_size_per_head * config.num_attention_heads // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp sizes = [total_size * tp_size] if not bias: sizes.append(config.hidden_size) new_weight_qkv = torch.empty(*sizes, dtype=params_dtype, device=get_device_id()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp : (i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp : (i + 1) * kv_size_tp] num_query_groups_per_partition = models[0].config.num_query_groups // tp_size new_weight_qkv_this_tp = new_weight_qkv[i * total_size : (i + 1) * total_size] q_part_per_head = torch.chunk(q_part, num_query_groups_per_partition, dim=0) k_part_per_head = torch.chunk(k_part, num_query_groups_per_partition, dim=0) v_part_per_head = torch.chunk(v_part, num_query_groups_per_partition, dim=0) total_size_per_head = total_size // num_query_groups_per_partition for j in range(num_query_groups_per_partition): new_weight_qkv_this_tp[j * total_size_per_head : (j + 1) * total_size_per_head].copy_( torch.cat([q_part_per_head[j], k_part_per_head[j], v_part_per_head[j]], dim=0) ) else: q_size_tp = hidden_size_per_head * config.num_attention_heads // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp sizes = [total_size * tp_size] if not bias: sizes.append(config.hidden_size) new_weight_qkv = torch.empty(*sizes, dtype=params_dtype, device=get_device_id()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv_this_tp = new_weight_qkv[i * total_size : (i + 1) * total_size] q_part_per_head = torch.chunk(q_part, config.num_attention_heads, dim=0) k_part_per_head = torch.chunk(k_part, config.num_attention_heads, dim=0) v_part_per_head = torch.chunk(v_part, config.num_attention_heads, dim=0) total_size_per_head = total_size // config.num_attention_heads for j in range(config.num_attention_heads): new_weight_qkv_this_tp[j * total_size_per_head : (j + 1) * total_size_per_head].copy_( torch.cat([q_part_per_head[j], k_part_per_head[j], v_part_per_head[j]], dim=0) ) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name, k_name, v_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {q_name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == src_rank: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=src_rank, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) if dp_rank == 0: # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) embed_tokens_weight = None if pp_rank == 0: embed_tokens_weight = gpt_model_module.embedding.word_embeddings.weight _broadcast_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): layer_name = f"model.layers.{layer}" print_rank_0(f"loading layer #{layer}, with layer_name model.layers.{layer}...") dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.decoder.layers[dst_layer_idx] _broadcast_tensor( sync_layer.self_attention.linear_qkv.layer_norm_weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) if f"{layer_name}.self_attn.q_norm.weight" in state_dict: _broadcast_tensor( sync_layer.self_attention.q_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_norm.weight", ) _broadcast_tensor( sync_layer.self_attention.k_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.k_norm.weight", ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attention.linear_qkv.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) if f"{layer_name}.self_attn.q_proj.bias" in state_dict: _broadcast_tp_shard_tensor_qkv( sync_layer.self_attention.linear_qkv.bias if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.bias", f"{layer_name}.self_attn.k_proj.bias", f"{layer_name}.self_attn.v_proj.bias", bias=True, ) _broadcast_tp_shard_tensor( sync_layer.self_attention.linear_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _broadcast_tensor( sync_layer.mlp.linear_fc1.layer_norm_weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _broadcast_tp_shard_tensor_gate_up( sync_layer.mlp.linear_fc1.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", ) _broadcast_tp_shard_tensor( sync_layer.mlp.linear_fc2.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.decoder.final_layernorm, "weight", None), "model.norm.weight", ) print_rank_0("loading lm_head...") lm_head_weight = None if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.output_layer.weight if is_value_model: # if torch.distributed.get_rank() == src_rank: if "lm_head.weight" in state_dict and state_dict["lm_head.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "lm_head.weight") elif "reward_head.weight" in state_dict and state_dict["reward_head.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "reward_head.weight") print_rank_0("load lm_head from value_head weight") elif "score.weight" in state_dict and state_dict["score.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "score.weight") print_rank_0("load lm_head from score weight") else: _broadcast_tensor(None, "lm_head.weight") print_rank_0("fail to match lm_head in value_model") # else: # _broadcast_tensor(lm_head_weight, "lm_head.weight") else: _broadcast_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() # Broadcast weights inside data parallel groups for wrapped_model in wrapped_models: broadcast_params(wrapped_model) pass get_torch_device().empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/mcore/mbridge.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # VANILLA_MBRIDGE try: from verl.models.mcore.patch import apply_patch_mbridge apply_patch_mbridge() from mbridge import AutoBridge from mbridge.utils.post_creation_callbacks import freeze_moe_router, make_value_model except ImportError: print("mbridge package not found. Please install mbridge with `pip install verl[mcore]` or `pip install mbridge`") raise __all__ = ["AutoBridge", "make_value_model", "freeze_moe_router"] ================================================ FILE: verl/models/mcore/model_forward.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from torch.nested._internal.nested_tensor import NestedTensor from verl.utils.megatron_utils import unwrap_model from verl.workers.config import MtpConfig from .util import ( postprocess_bshd, postprocess_bshd_no_padding, postprocess_packed_seqs, postprocess_thd_no_padding, preprocess_bshd, preprocess_bshd_no_padding, preprocess_packed_seqs, preprocess_thd_no_padding, ) def model_forward_gen(vision_model: bool = False): def model_forward( model, input_ids, attention_mask, position_ids, multi_modal_inputs: dict, logits_processor=None, logits_processor_args: dict = None, value_model=False, data_format: str = "thd", mtp_config: MtpConfig = None, ): """Forward pass for models with sequence packing.""" assert data_format in ["thd", "bshd"], "data_format must be 'thd' or 'bshd'" pre_process = ( unwrap_model(model).pre_process if not vision_model else False ) # vision model does not need pre_process, because we pack the input_ids to thd in the forward function post_process = unwrap_model(model).post_process sp = unwrap_model(model).config.sequence_parallel fp8 = unwrap_model(model).config.fp8 use_fp8_padding = fp8 in ["e4m3", "hybrid"] model_kwargs = {} if "pixel_values" in multi_modal_inputs: model_kwargs["pixel_values"] = multi_modal_inputs["pixel_values"].to(input_ids.device) if "image_grid_thw" in multi_modal_inputs: model_kwargs["image_grid_thw"] = multi_modal_inputs["image_grid_thw"].to(input_ids.device) if "pixel_values_videos" in multi_modal_inputs: model_kwargs["pixel_values_videos"] = multi_modal_inputs["pixel_values_videos"].to(input_ids.device) if "video_grid_thw" in multi_modal_inputs: model_kwargs["video_grid_thw"] = multi_modal_inputs["video_grid_thw"].to(input_ids.device) batch_size, seq_len = attention_mask.shape[:2] mtp_enable_train = mtp_config and mtp_config.enable_train if data_format == "thd": input_ids_rmpad, packed_seq_params = preprocess_packed_seqs( input_ids, attention_mask, pre_process=pre_process or (post_process and mtp_enable_train), use_fp8_padding=use_fp8_padding, ) input_ids_rmpad = input_ids_rmpad.contiguous() # when pp > 1 and processor is not None, we need to pass the labels and loss_mask to the model if mtp_enable_train and post_process: args = { k: preprocess_packed_seqs(v, attention_mask, pre_process=True, use_fp8_padding=use_fp8_padding)[0] for k, v in logits_processor_args.items() } model_kwargs["labels"] = args["label"].contiguous() model_kwargs["loss_mask"] = args["label_mask"].contiguous() input_args = dict( input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids if not vision_model else None, # vision models will calculate position_ids packed_seq_params=packed_seq_params, **model_kwargs, ) if vision_model: # workaround for supporting sequence packing with context parallelism # cp split with sequence packing will make model lose vision token information, so we need to keep # the original input_ids and pack them after vision embedding is calculated, # cooporate with mbridge input_args["input_ids"] = input_ids input_args["attention_mask"] = attention_mask output_orig = model(**input_args) if post_process and logits_processor is not None: args = { k: preprocess_packed_seqs(v, attention_mask, pre_process=True, use_fp8_padding=use_fp8_padding)[0] for k, v in logits_processor_args.items() } output_dict = logits_processor(output_orig, **args) output = { k: postprocess_packed_seqs( v, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process ) for k, v in output_dict.items() } else: output = postprocess_packed_seqs( output_orig, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process ) elif data_format == "bshd": """ data_format: "thd" or "bshd", default is "thd", why we need this? for some new models, GPT-OSS, the thd format is not supported, so we need to use the bshd format. When using the bshd format, we have to add paddings to the input_ids to meet the longest sequence length, so it is recommended to disable dynamic batch size and set batch size to 1 """ assert fp8 is None, "fp8 is not supported for bshd format yet" batch_size, sequence_length = attention_mask.shape[:2] position_ids_for_preprocess = ( torch.arange(sequence_length, device=input_ids.device).unsqueeze(0).expand(batch_size, -1) if vision_model else position_ids ) pre_process_for_bshd = True if vision_model else pre_process new_input_ids, new_attention_mask, new_position_ids = preprocess_bshd( input_ids, attention_mask, position_ids_for_preprocess, sequence_parallel=sp, pre_process=pre_process_for_bshd, ) output_orig = model( input_ids=new_input_ids, position_ids=None if vision_model else new_position_ids, attention_mask=new_attention_mask, **model_kwargs, ) if post_process and logits_processor is not None: args = { k: preprocess_bshd( v, attention_mask, position_ids_for_preprocess, sequence_parallel=sp, pre_process=True )[0] for k, v in logits_processor_args.items() } output_dict = logits_processor(output_orig, **args) output = { k: postprocess_bshd( v, new_attention_mask, attention_mask, sequence_length, post_process=post_process ) for k, v in output_dict.items() } else: output = postprocess_bshd( output_orig, new_attention_mask, attention_mask, sequence_length, post_process=post_process ) if value_model and post_process: output = output[..., 0] return output return model_forward def _convert_to_nested_tensor(v, input_ids_lengths): """Convert regular tensor to NestedTensor, slicing according to input_ids_lengths. Args: v: Tensor to convert, shape [batch, seq_len] input_ids_lengths: List of valid lengths for each sample Returns: Converted NestedTensor """ if isinstance(v, NestedTensor): return v batch_size = v.shape[0] assert len(input_ids_lengths) == batch_size, ( f"len(input_ids_lengths)={len(input_ids_lengths)} != batch_size={batch_size}" ) v_split_list = [] for i in range(batch_size): vi = v[i] target_len = input_ids_lengths[i] if vi.shape[0] > target_len: vi = vi[:target_len] elif vi.shape[0] < target_len: vi = torch.cat([vi, torch.ones(target_len - vi.shape[0], dtype=vi.dtype, device=vi.device)]) v_split_list.append(vi) v = torch.nested.nested_tensor(v_split_list, layout=torch.jagged) return v def gptmodel_forward_no_padding( model, input_ids, multi_modal_inputs: dict, logits_processor=None, logits_processor_args: dict = None, value_model=False, vision_model=False, pad_token_id=None, data_format: str = "thd", mtp_enable_train: bool = False, ): """Default forward pass for GPT models with optional sequence packing.""" assert data_format in ["thd", "bshd"], "data_format must be 'thd' or 'bshd'" pre_process = unwrap_model(model).pre_process post_process = unwrap_model(model).post_process fp8 = unwrap_model(model).config.fp8 use_fp8_padding = fp8 in ["e4m3", "hybrid"] model_kwargs = {} if "pixel_values" in multi_modal_inputs: model_kwargs["pixel_values"] = multi_modal_inputs["pixel_values"].to(input_ids.device) if "image_grid_thw" in multi_modal_inputs: model_kwargs["image_grid_thw"] = multi_modal_inputs["image_grid_thw"].to(input_ids.device) if "pixel_values_videos" in multi_modal_inputs: model_kwargs["pixel_values_videos"] = multi_modal_inputs["pixel_values_videos"].to(input_ids.device) if "video_grid_thw" in multi_modal_inputs: model_kwargs["video_grid_thw"] = multi_modal_inputs["video_grid_thw"].to(input_ids.device) batch_size = input_ids.shape[0] if data_format == "thd": input_ids_rmpad, packed_seq_params, position_ids_rmpad = preprocess_thd_no_padding( input_ids, pre_process=pre_process or (post_process and mtp_enable_train), use_fp8_padding=use_fp8_padding ) input_ids_rmpad = input_ids_rmpad.contiguous() args = {} if mtp_enable_train and post_process: # Use input_ids sequence length to ensure label and loss_mask alignment input_ids_offsets = input_ids.offsets() input_ids_lengths = input_ids_offsets.diff().tolist() for k in ["label", "loss_mask"]: v = logits_processor_args[k] v = _convert_to_nested_tensor(v, input_ids_lengths) logits_processor_args[k] = v args[k] = preprocess_thd_no_padding( v, pre_process=True, need_roll=True, use_fp8_padding=use_fp8_padding )[0] model_kwargs["labels"] = args["label"].contiguous() model_kwargs["loss_mask"] = args["loss_mask"].contiguous() if logits_processor_args and "loss_mask" in logits_processor_args: logits_processor_args.pop("loss_mask") # For VLM model, need to pass bshd format `input_ids` and `attention_mask`. attention_mask = None if vision_model: input_ids_rmpad = input_ids.to_padded_tensor(pad_token_id) seqlens_in_batch = input_ids.offsets().diff() attention_mask = torch.zeros_like(input_ids_rmpad, dtype=torch.bool) for i, seqlen in enumerate(seqlens_in_batch): attention_mask[i, :seqlen] = True output_orig = model( input_ids=input_ids_rmpad, attention_mask=attention_mask, position_ids=position_ids_rmpad if not vision_model else None, # vision models will calculate position_ids packed_seq_params=packed_seq_params, **model_kwargs, ) if post_process and logits_processor is not None: args = { k: preprocess_thd_no_padding( v, pre_process=True, need_roll=(k == "label"), use_fp8_padding=use_fp8_padding )[0] for k, v in logits_processor_args.items() } output_dict = logits_processor(output_orig, **args) output = { k: postprocess_thd_no_padding(v, packed_seq_params, input_ids, batch_size, post_process=post_process) for k, v in output_dict.items() } else: output = postprocess_thd_no_padding( output_orig, packed_seq_params, input_ids, batch_size, post_process=post_process ) else: """ data_format: "thd" or "bshd", default is "thd", why we need this? for some new models, GPT-OSS, the thd format is not supported, so we need to use the bshd format. When using the bshd format, we have to add paddings to the input_ids to meet the longest sequence length, so it is recommended to disable dynamic batch size and set batch size to 1 """ input_ids_bshd, attention_mask_bshd, position_ids_bshd = preprocess_bshd_no_padding( input_ids, pre_process=pre_process or (post_process and mtp_enable_train), use_fp8_padding=use_fp8_padding ) if mtp_enable_train and post_process: args = {} # Use input_ids sequence length to ensure label and loss_mask alignment input_ids_offsets = input_ids.offsets() input_ids_lengths = input_ids_offsets.diff().tolist() for k in ["label", "loss_mask"]: v = logits_processor_args[k] v = _convert_to_nested_tensor(v, input_ids_lengths) logits_processor_args[k] = v args[k] = preprocess_bshd_no_padding( v, pre_process=True, need_roll=True, use_fp8_padding=use_fp8_padding )[0] model_kwargs["labels"] = args["label"].contiguous() model_kwargs["loss_mask"] = args["loss_mask"].contiguous() if logits_processor_args and "loss_mask" in logits_processor_args: logits_processor_args.pop("loss_mask") output_orig = model( input_ids=input_ids_bshd, attention_mask=attention_mask_bshd, position_ids=None if vision_model else position_ids_bshd, **model_kwargs, ) if post_process and logits_processor is not None: args = { k: preprocess_bshd_no_padding( v, pre_process=True, need_roll=(k == "label"), use_fp8_padding=use_fp8_padding )[0] for k, v in logits_processor_args.items() } output_dict = logits_processor(output_orig, **args) output = { k: postprocess_bshd_no_padding(v, attention_mask_bshd, post_process=post_process) for k, v in output_dict.items() } else: output = postprocess_bshd_no_padding(output_orig, attention_mask_bshd, post_process=post_process) if value_model and post_process: # output = output[..., 0] # while using nested tensor, the advanced indexing operation above will result in an error at backward, i.e. # ValueError: NestedTensor _nested_select_backward_default(grad_output: t, self: jt_all, dim: any, index: any) # so we use `squeeze` to remove the last dimension output = output.squeeze(-1) return output ================================================ FILE: verl/models/mcore/model_forward_1f1b_overlap.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional import torch from megatron.core.models.common.model_chunk_schedule_plan import TransformerModelChunkSchedulePlan from megatron.core.models.gpt.gpt_model import GPTModel from megatron.core.utils import make_viewless_tensor from torch import Tensor from verl.models.mcore.util import preprocess_packed_seqs from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy from verl.utils.megatron_utils import unwrap_model from verl.utils.model import CausalLMOutputForPPO from .util import postprocess_packed_seqs, postprocess_packed_seqs_for_dict_output def gptmodel_forward_1f1b_overlap( model: GPTModel, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, labels: Tensor = None, labels_mask: Tensor = None, multi_modal_inputs: Optional[dict] = None, logits_processor: Optional[Callable] = None, logits_processor_args: Optional[dict] = None, temperature: float = 1.0, ) -> TransformerModelChunkSchedulePlan: pre_process: bool = unwrap_model(model).pre_process post_process: bool = unwrap_model(model).post_process assert logits_processor is None, "only support fused kernel" batch_size, seq_len = attention_mask.shape[:2] input_ids_rmpad, packed_seq_params = preprocess_packed_seqs(input_ids, attention_mask, pre_process=pre_process) input_ids_rmpad = input_ids_rmpad.contiguous() schedule_plan = model.build_schedule_plan( input_ids=input_ids_rmpad, attention_mask=attention_mask, labels=labels, position_ids=position_ids, packed_seq_params=packed_seq_params, ) if post_process: attention_mask_out = attention_mask def _postprocess( self, hidden_states, input_ids, position_ids, labels, rotary_pos_emb, rotary_pos_cos, rotary_pos_sin, mtp_in_postprocess=None, loss_mask=None, decoder_input=None, attention_mask=None, inference_params=None, packed_seq_params=None, sequence_len_offset=None, runtime_gather_output=None, extra_block_kwargs=None, inference_context=None, ): """patched from https://github.com/NVIDIA/Megatron-LM/blob/core_r0.14.0/megatron/core/models/gpt/gpt_model.py#L412""" """Postprocesses decoder hidden states to generate logits or compute loss. Applies Multi-Token Prediction if enabled, generates output logits through the output layer, and computes language model loss when labels are provided. """ from megatron.core import parallel_state from megatron.core.tensor_parallel import gather_from_sequence_parallel_region in_inference_mode = inference_context is not None and not self.training if in_inference_mode: assert runtime_gather_output, "Inference must always gather TP logits" # logits and loss output_weight = None if self.share_embeddings_and_output_weights: output_weight = self.shared_embedding_or_output_weight() if mtp_in_postprocess: hidden_states = self.mtp( input_ids=input_ids, position_ids=position_ids, hidden_states=hidden_states, attention_mask=attention_mask, inference_params=inference_params, rotary_pos_emb=rotary_pos_emb, rotary_pos_cos=rotary_pos_cos, rotary_pos_sin=rotary_pos_sin, packed_seq_params=packed_seq_params, sequence_len_offset=sequence_len_offset, embedding=self.embedding, **(extra_block_kwargs or {}), ) if not self.post_process: return hidden_states if self.mtp_process: from megatron.core.transformer.multi_token_prediction import ( MTPLossAutoScaler, MTPLossLoggingHelper, roll_tensor, ) mtp_labels = labels.clone() hidden_states_list = torch.chunk(hidden_states, 1 + self.config.mtp_num_layers, dim=0) hidden_states = hidden_states_list[0] if loss_mask is None: # if loss_mask is not provided, use all ones as loss_mask loss_mask = torch.ones_like(mtp_labels) for mtp_layer_number in range(self.config.mtp_num_layers): # output mtp_logits, _ = self.output_layer( hidden_states_list[mtp_layer_number + 1], weight=output_weight, runtime_gather_output=runtime_gather_output, ) # Calc loss for the current Multi-Token Prediction (MTP) layers. mtp_labels, _ = roll_tensor(mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group) loss_mask, num_tokens = roll_tensor(loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group) mtp_loss = self.compute_language_model_loss(mtp_labels, mtp_logits) mtp_loss = loss_mask * mtp_loss if self.training: # TODO(shifangx): remove the use of parallel_state here # after moving loss logging to loss_func in pretrain_gpt.py MTPLossLoggingHelper.save_loss_to_tracker( torch.sum(mtp_loss) / num_tokens, mtp_layer_number, self.config.mtp_num_layers, avg_group=parallel_state.get_data_parallel_group(with_context_parallel=True), ) mtp_loss_scale = self.config.mtp_loss_scaling_factor / self.config.mtp_num_layers if self.config.calculate_per_token_loss: hidden_states = MTPLossAutoScaler.apply(hidden_states, mtp_loss_scale * mtp_loss) else: hidden_states = MTPLossAutoScaler.apply(hidden_states, mtp_loss_scale * mtp_loss / num_tokens) if logits_processor is not None: logits, _ = self.output_layer( hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output ) output_orig = logits.transpose(0, 1).contiguous() args = { k: preprocess_packed_seqs(v, attention_mask_out, pre_process=True)[0] for k, v in logits_processor_args.items() } output_dict = logits_processor(output_orig, **args) output = { k: postprocess_packed_seqs( v, packed_seq_params, attention_mask_out, batch_size, seq_len, post_process=post_process ) for k, v in output_dict.items() } else: # fused kernel labels_rmpad, _ = preprocess_packed_seqs(labels, attention_mask, pre_process=True) labels_mask_rmpad, _ = preprocess_packed_seqs(labels_mask, attention_mask, pre_process=True) labels_rmpad = labels_rmpad.contiguous() labels_mask_rmpad = labels_mask_rmpad.contiguous() output = CausalLMOutputForPPO( loss=None, logits=None, past_key_values=None, hidden_states=hidden_states, attentions=None, ) if self.config.sequence_parallel: hidden_states = gather_from_sequence_parallel_region(hidden_states) logprobs, entropy = linear_cross_entropy( hidden_states, self.output_layer.weight, labels_rmpad, temperature, "none", parallel_state.get_tensor_model_parallel_group(), ) output.entropy = entropy output.log_probs = logprobs output = postprocess_packed_seqs_for_dict_output( labels_mask_rmpad, output, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process, ) output_ = [output["log_probs"]] # TODO NOW 1f1b overlap only support one tensor output # if "entropy" in output: # output_.append(output["entropy"]) output_ = tuple(output_) return output_ def _custom_post_process_node_forward_impl(self, hidden_states): if self.gpt_model.decoder.final_layernorm and not self.gpt_model.mtp_process: hidden_states = self.gpt_model.decoder.final_layernorm(hidden_states) # TENorm produces a "viewed" tensor. This will result in schedule.py's # deallocate_output_tensor() throwing an error, so a viewless tensor is # created to prevent this. hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) # Run GPTModel._postprocess output = self.gpt_model._postprocess( hidden_states=hidden_states, input_ids=self.chunk_state.input_ids, position_ids=self.chunk_state.position_ids, labels=self.chunk_state.labels, decoder_input=self.chunk_state.decoder_input, rotary_pos_emb=self.chunk_state.rotary_pos_emb, rotary_pos_cos=self.chunk_state.rotary_pos_cos, rotary_pos_sin=self.chunk_state.rotary_pos_sin, mtp_in_postprocess=False, loss_mask=self.chunk_state.loss_mask, attention_mask=self.chunk_state.attention_mask, packed_seq_params=self.chunk_state.packed_seq_params, sequence_len_offset=self.chunk_state.sequence_len_offset, runtime_gather_output=self.chunk_state.runtime_gather_output, extra_block_kwargs=self.chunk_state.extra_block_kwargs, ) return output schedule_plan.post_process.forward_impl = _custom_post_process_node_forward_impl.__get__( schedule_plan.post_process, schedule_plan.post_process.__class__ ) unwrap_model(model)._postprocess = _postprocess.__get__(unwrap_model(model), unwrap_model(model).__class__) return schedule_plan ================================================ FILE: verl/models/mcore/model_forward_fused.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import OrderedDict from typing import Optional import megatron.core as mcore import torch from megatron.core import parallel_state from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk from megatron.core.inference.contexts import BaseInferenceContext from megatron.core.models.gpt.gpt_model import GPTModel from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.tensor_parallel.mappings import gather_from_sequence_parallel_region from megatron.core.utils import deprecate_inference_params from packaging import version from torch import Tensor from verl.models.mcore.util import preprocess_packed_seqs, preprocess_thd_no_padding from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy from verl.utils.megatron_utils import unwrap_model from verl.utils.model import CausalLMOutputForPPO from .util import postprocess_packed_seqs_for_dict_output, postprocess_thd_no_padding def _get_patching_model(model: torch.nn.Module): model = unwrap_model(model) if isinstance(model, GPTModel): return model if not (hasattr(model, "language_model") and isinstance(model.language_model, GPTModel)): print(f"Model {model.__class__.__name__} is not a supported for fused forward") return None return model.language_model def patch_fused_forward(model: torch.nn.Module): assert version.parse(mcore.__version__) >= version.parse("0.13.0"), ( "Fused forward patching requires mecore >= 0.13.0" ) model = _get_patching_model(model) if model is not None: model.forward_backup = model.forward model.forward = _fused_GPTModel_forward.__get__(model, model.__class__) def unpatch_fused_forward(model: torch.nn.Module): model = _get_patching_model(model) if model is not None: model.forward = model.forward_backup def fused_forward_model_gen(vision_model: bool = False): def fused_forward_model( model, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, labels: Tensor, labels_mask: Tensor, temperature: float, multi_modal_inputs: dict, ): pre_process: bool = ( unwrap_model(model).pre_process if not vision_model else False ) # vision model does not need pre_process, because we pack the input_ids to thd in the forward function post_process: bool = unwrap_model(model).post_process model_kwargs = {} if "pixel_values" in multi_modal_inputs: model_kwargs["pixel_values"] = multi_modal_inputs["pixel_values"].to(input_ids.device) if "image_grid_thw" in multi_modal_inputs: model_kwargs["image_grid_thw"] = multi_modal_inputs["image_grid_thw"].to(input_ids.device) if "pixel_values_videos" in multi_modal_inputs: model_kwargs["pixel_values_videos"] = multi_modal_inputs["pixel_values_videos"].to(input_ids.device) if "video_grid_thw" in multi_modal_inputs: model_kwargs["video_grid_thw"] = multi_modal_inputs["video_grid_thw"].to(input_ids.device) batch_size, seq_len = attention_mask.shape[:2] input_ids_rmpad, packed_seq_params = preprocess_packed_seqs(input_ids, attention_mask, pre_process=pre_process) input_ids_rmpad = input_ids_rmpad.contiguous() labels_rmpad, _ = preprocess_packed_seqs(labels, attention_mask, pre_process=True) labels_mask_rmpad, _ = preprocess_packed_seqs(labels_mask, attention_mask, pre_process=True) labels_rmpad = labels_rmpad.contiguous() labels_mask_rmpad = labels_mask_rmpad.contiguous() input_args = dict( input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids if not vision_model else None, # vision models will calculate position_ids packed_seq_params=packed_seq_params, labels=labels_rmpad, temperature=temperature, **model_kwargs, ) if vision_model: # workaround for supporting sequence packing with context parallelism # cp split with sequence packing will make model lose vision token information, so we need to keep # the original input_ids and pack them after vision embedding is calculated, # cooporate with mbridge input_args["input_ids"] = input_ids input_args["attention_mask"] = attention_mask output_orig: CausalLMOutputForPPO = model(**input_args) if post_process: # output_orig is in type of CausalLMOutputForPPO output = postprocess_packed_seqs_for_dict_output( labels_mask_rmpad, output_orig, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process, ) else: output = output_orig return output return fused_forward_model def fused_forward_no_padding_gen(vision_model: bool = False): def fused_forward_no_padding( model, input_ids: Tensor, labels: Tensor, multi_modal_inputs: dict, temperature: float, calculate_entropy: bool, pad_token_id: int, ): pre_process = unwrap_model(model).pre_process post_process = unwrap_model(model).post_process fp8 = unwrap_model(model).config.fp8 use_fp8_padding = fp8 in ["e4m3", "hybrid"] input_ids_rmpad, packed_seq_params, _ = preprocess_thd_no_padding( input_ids, pre_process=pre_process, use_fp8_padding=use_fp8_padding ) input_ids_rmpad = input_ids_rmpad.contiguous() model_kwargs = {} if "pixel_values" in multi_modal_inputs: model_kwargs["pixel_values"] = multi_modal_inputs["pixel_values"].to(input_ids.device) if "image_grid_thw" in multi_modal_inputs: model_kwargs["image_grid_thw"] = multi_modal_inputs["image_grid_thw"].to(input_ids.device) if "pixel_values_videos" in multi_modal_inputs: model_kwargs["pixel_values_videos"] = multi_modal_inputs["pixel_values_videos"].to(input_ids.device) if "video_grid_thw" in multi_modal_inputs: model_kwargs["video_grid_thw"] = multi_modal_inputs["video_grid_thw"].to(input_ids.device) attention_mask = None if vision_model: input_ids_rmpad = input_ids.to_padded_tensor(pad_token_id) seqlens_in_batch = input_ids.offsets().diff().to(input_ids.device) max_seq_len = input_ids_rmpad.shape[1] attention_mask = torch.arange(max_seq_len, device=input_ids.device).unsqueeze( 0 ) < seqlens_in_batch.unsqueeze(1) labels_rmpad, _, _ = preprocess_thd_no_padding( labels, pre_process=True, need_roll=True, use_fp8_padding=use_fp8_padding ) labels_rmpad = labels_rmpad.contiguous() output_orig: CausalLMOutputForPPO = model( input_ids=input_ids_rmpad, attention_mask=attention_mask, position_ids=None, packed_seq_params=packed_seq_params, labels=labels_rmpad, temperature=temperature, **model_kwargs, ) if not post_process: return output_orig log_probs = output_orig.log_probs if log_probs.dim() == 1: log_probs = log_probs.unsqueeze(0) log_probs = postprocess_thd_no_padding( log_probs, packed_seq_params, input_ids, input_ids.shape[0], post_process=post_process ) output = {"log_probs": log_probs} if calculate_entropy: entropy = output_orig.entropy if entropy.dim() == 1: entropy = entropy.unsqueeze(0) entropy = postprocess_thd_no_padding( entropy, packed_seq_params, input_ids, input_ids.shape[0], post_process=post_process ) output["entropy"] = entropy return output return fused_forward_no_padding def _fused_GPTModel_forward( model, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, decoder_input: Tensor = None, labels: Tensor = None, inference_context: BaseInferenceContext = None, packed_seq_params: PackedSeqParams = None, extra_block_kwargs: dict = None, runtime_gather_output: Optional[bool] = None, *, inference_params: Optional[BaseInferenceContext] = None, loss_mask: Optional[Tensor] = None, temperature: float = 1.0, **kwargs, ) -> CausalLMOutputForPPO: """ Patch self._postprocess in forward for GPT models to enable fused kernel support. https://github.com/NVIDIA/Megatron-LM/blob/core_v0.13.0/megatron/core/models/gpt/gpt_model.py TODO: Currently we still need to patch `forward` because we need to pass `temperature` explicitly to `self._postprocess` when calling, maybe there can be a better way to handle this? """ inference_context = deprecate_inference_params(inference_context, inference_params) preproc_output = model._preprocess( input_ids=input_ids, position_ids=position_ids, decoder_input=decoder_input, inference_context=inference_context, packed_seq_params=packed_seq_params, ) (decoder_input, rotary_pos_emb, rotary_pos_cos, rotary_pos_sin, sequence_len_offset) = preproc_output[:5] # Run decoder. hidden_states = model.decoder( hidden_states=decoder_input, attention_mask=attention_mask, inference_context=inference_context, rotary_pos_emb=rotary_pos_emb, rotary_pos_cos=rotary_pos_cos, rotary_pos_sin=rotary_pos_sin, packed_seq_params=packed_seq_params, sequence_len_offset=sequence_len_offset, **(extra_block_kwargs or {}), **kwargs, ) if not model.post_process: return hidden_states output = CausalLMOutputForPPO( loss=None, logits=None, past_key_values=None, hidden_states=hidden_states, attentions=None, ) if model.config.sequence_parallel: hidden_states = gather_from_sequence_parallel_region(hidden_states) # Get the output weight - use embedding weight if output_layer is None or weight is shared if hasattr(model, "output_layer") and model.output_layer is not None and model.output_layer.weight is not None: output_weight = model.output_layer.weight else: # When embeddings are tied, use the embedding weight output_weight = model.embedding.word_embeddings.weight logprobs, entropy = linear_cross_entropy( hidden_states, output_weight, labels, temperature, "none", parallel_state.get_tensor_model_parallel_group(), ) if has_config_logger_enabled(model.config): payload = OrderedDict( { "input_ids": input_ids, "position_ids": position_ids, "attention_mask": attention_mask, "decoder_input": decoder_input, "logprobs": logprobs, "entropy": entropy, } ) log_config_to_disk(model.config, payload, prefix="input_and_logits") output.entropy = entropy output.log_probs = logprobs return output ================================================ FILE: verl/models/mcore/model_initializer.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # use mcore transformer config to initialize the model import inspect from abc import ABC, abstractmethod from megatron.core.models.gpt.gpt_layer_specs import get_gpt_decoder_block_spec, get_gpt_mtp_block_spec from megatron.core.models.gpt.gpt_model import GPTModel from .config_converter import PretrainedConfig, TransformerConfig class BaseModelInitializer(ABC): """Base class for model initializers.""" def __init__(self, tfconfig: TransformerConfig, hf_config: PretrainedConfig): self.tfconfig = tfconfig self.hf_config = hf_config self.has_vp_stage = inspect.signature(get_gpt_decoder_block_spec).parameters.get("vp_stage", None) is not None @abstractmethod def get_transformer_layer_spec(self, vp_stage=None): """Get the transformer layer specification. https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/models/gpt/gpt_layer_specs.py""" pass def get_rope_scaling_args(self) -> dict: """Get rope scaling args.""" rope_scaling_args = {} if "rope_scaling" in self.hf_config: if self.hf_config.rope_scaling is not None: # assert self.hf_config.rope_scaling["type"] == "linear", "only linear scaling is supported for now" rope_scaling_args["seq_len_interpolation_factor"] = self.hf_config.rope_scaling["factor"] return rope_scaling_args def initialize( self, pre_process: bool = True, post_process: bool = True, share_embeddings_and_output_weights: bool = False, value: bool = False, **extra_kwargs, ) -> GPTModel: """Initialize a GPT model with the given configuration. https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/models/gpt/gpt_model.py Args: pre_process (bool): include embedding layer. post_process (bool): including an output layer. share_embeddings_and_output_weights (bool): input embeddings and output logit weights are shared. value (bool): add an extra linear layer for classification or regression. Returns: GPTModel: An initialized GPT model instance """ vp_stage = extra_kwargs.get("vp_stage", None) transformer_layer_spec = self.get_transformer_layer_spec(vp_stage=vp_stage) rope_scaling_args = self.get_rope_scaling_args() mtp_block_spec = extra_kwargs.get("mtp_block_spec", None) model = GPTModel( config=self.tfconfig, transformer_layer_spec=transformer_layer_spec, vocab_size=self.hf_config.vocab_size, max_sequence_length=self.hf_config.max_position_embeddings, pre_process=pre_process, post_process=post_process, share_embeddings_and_output_weights=share_embeddings_and_output_weights, position_embedding_type="rope", rotary_base=self.hf_config.rope_theta, **rope_scaling_args, mtp_block_spec=mtp_block_spec, **({} if not self.has_vp_stage else {"vp_stage": vp_stage}), ) if post_process and value: from verl.models.llama.megatron.layers.parallel_linear import LinearForLastLayer model.output_layer = LinearForLastLayer( input_size=self.tfconfig.hidden_size, output_size=1, config=self.tfconfig ) return model class DenseModel(BaseModelInitializer): """Initializer for dense models like Llama and Qwen2.""" def get_transformer_layer_spec(self, vp_stage=None): assert self.tfconfig.normalization == "RMSNorm", "only RMSNorm is supported for now" extra_kwargs = {} if not self.has_vp_stage else {"vp_stage": vp_stage} return get_gpt_decoder_block_spec(self.tfconfig, use_transformer_engine=True, **extra_kwargs) class Qwen2MoEModel(BaseModelInitializer): """Initializer for Qwen2 MoE models.""" def get_transformer_layer_spec(self, vp_stage=None): assert self.tfconfig.normalization == "RMSNorm", "only RMSNorm is supported for now" extra_kwargs = {} if not self.has_vp_stage else {"vp_stage": vp_stage} transformer_layer_spec = get_gpt_decoder_block_spec(self.tfconfig, use_transformer_engine=True, **extra_kwargs) # Patch layer spec for shared experts for i in range(len(transformer_layer_spec.layer_specs)): transformer_layer_spec.layer_specs[i].submodules.mlp.submodules.shared_experts.params["gate"] = True return transformer_layer_spec def initialize(self, **kwargs): # Qwen default freeze_moe_router: true model = super().initialize(**kwargs) freeze_moe_router = kwargs.get("freeze_moe_router", True) if freeze_moe_router: for layer in model.decoder.layers: layer.mlp.router.weight.requires_grad = False return model class MixtralModel(BaseModelInitializer): """Initializer for Mixtral models.""" def get_transformer_layer_spec(self, vp_stage=None): assert self.tfconfig.normalization == "RMSNorm", "only RMSNorm is supported for now" extra_kwargs = {} if not self.has_vp_stage else {"vp_stage": vp_stage} transformer_layer_spec = get_gpt_decoder_block_spec(self.tfconfig, use_transformer_engine=True, **extra_kwargs) return transformer_layer_spec def initialize(self, **kwargs): model = super().initialize(**kwargs) freeze_moe_router = kwargs.get("freeze_moe_router", False) if freeze_moe_router: for layer in model.decoder.layers: layer.mlp.router.weight.requires_grad = False return model class Qwen3MoEModel(BaseModelInitializer): """Initializer for Qwen3 MoE models.""" def get_transformer_layer_spec(self, vp_stage=None): assert self.tfconfig.normalization == "RMSNorm", "only RMSNorm is supported for now" extra_kwargs = {} if not self.has_vp_stage else {"vp_stage": vp_stage} transformer_layer_spec = get_gpt_decoder_block_spec(self.tfconfig, use_transformer_engine=True, **extra_kwargs) return transformer_layer_spec def initialize(self, **kwargs): # Qwen default freeze_moe_router: true model = super().initialize(**kwargs) freeze_moe_router = kwargs.get("freeze_moe_router", True) if freeze_moe_router: for layer in model.decoder.layers: layer.mlp.router.weight.requires_grad = False return model class DeepseekV3Model(BaseModelInitializer): """Initializer for DeepseekV3 models.""" def get_transformer_layer_spec(self, vp_stage=None): extra_kwargs = {} if not self.has_vp_stage else {"vp_stage": vp_stage} transformer_layer_spec = get_gpt_decoder_block_spec(self.tfconfig, use_transformer_engine=True, **extra_kwargs) return transformer_layer_spec def get_rope_scaling_args(self) -> dict: """Get rope scaling args.""" rope_scaling_args = {} return rope_scaling_args def initialize( self, **kwargs, ): vp_stage = kwargs.get("vp_stage", None) freeze_moe_router = kwargs.get("freeze_moe_router", True) if freeze_moe_router: self.tfconfig.moe_router_load_balancing_type = "none" # MTP if self.tfconfig.mtp_num_layers is not None and self.tfconfig.mtp_num_layers > 0: transformer_layer_spec = self.get_transformer_layer_spec(vp_stage=vp_stage) mtp_block_spec = get_gpt_mtp_block_spec( self.tfconfig, transformer_layer_spec, use_transformer_engine=True, vp_stage=vp_stage ) kwargs["mtp_block_spec"] = mtp_block_spec model = super().initialize(**kwargs) if freeze_moe_router: for layer in model.decoder.layers: if hasattr(layer.mlp, "router"): layer.mlp.router.weight.requires_grad = False return model class Qwen25VLModel(BaseModelInitializer): """Initializer for Qwen2.5 VL models.""" def get_transformer_layer_spec(self, vp_stage=None): extra_kwargs = {} if not self.has_vp_stage else {"vp_stage": vp_stage} transformer_layer_spec = get_gpt_decoder_block_spec(self.tfconfig, use_transformer_engine=True, **extra_kwargs) return transformer_layer_spec def initialize( self, pre_process=None, post_process=None, share_embeddings_and_output_weights=False, value=False, **extra_kwargs, ): tfconfig = self.tfconfig hf_config = self.hf_config # Qwen2_5_VLForConditionalGeneration from copy import deepcopy transformer_layer_spec = self.get_transformer_layer_spec() from megatron.core.extensions.transformer_engine import TEColumnParallelLinear, TERowParallelLinear from megatron.core.models.gpt.moe_module_specs import MLPSubmodules from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec from .qwen2_5_vl import Qwen2_5VLModel, get_vision_model_config, get_vision_projection_config vision_transformer_config = get_vision_model_config(deepcopy(tfconfig)) vision_transformer_config.pipeline_model_parallel_size = 1 vision_transformer_config.first_pipeline_num_layers = None vision_projection_config = get_vision_projection_config( deepcopy(tfconfig), vision_transformer_config.hidden_size, spatial_merge_size=hf_config.vision_config.spatial_merge_size, ) vision_projection_layer_spec = MLPSubmodules( linear_fc1=TEColumnParallelLinear, linear_fc2=TERowParallelLinear, ) vision_transformer_layer_spec = get_vit_layer_with_transformer_engine_spec() qwen25_vl_model = Qwen2_5VLModel( language_transformer_config=tfconfig, language_transformer_layer_spec=transformer_layer_spec, language_vocab_size=hf_config.vocab_size, language_max_sequence_length=hf_config.max_position_embeddings, vision_transformer_config=vision_transformer_config, vision_transformer_layer_spec=vision_transformer_layer_spec, vision_projection_config=vision_projection_config, vision_projection_layer_spec=vision_projection_layer_spec, vision_projection_type="mlp", language_rotary_base=hf_config.rope_theta, pre_process=pre_process, post_process=post_process, add_decoder=True, add_encoder=True, parallel_output=True, language_share_embeddings_and_output_weights=share_embeddings_and_output_weights, ) if post_process and value: from verl.models.llama.megatron.layers.parallel_linear import LinearForLastLayer qwen25_vl_model.language_model.output_layer = LinearForLastLayer( input_size=tfconfig.hidden_size, output_size=1, config=tfconfig ) return qwen25_vl_model ================================================ FILE: verl/models/mcore/mtp_patch.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # Copyright 2025 Meituan Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable import torch from megatron.core import parallel_state from megatron.core.models.gpt.gpt_model import GPTModel from megatron.core.transformer.multi_token_prediction import ( MTPLossAutoScaler, MTPLossLoggingHelper, roll_tensor, ) try: from megatron.core.utils import unwrap_model except ImportError: from verl.utils.megatron_utils import unwrap_model def _get_patching_model(model: torch.nn.Module): model = unwrap_model(model) if isinstance(model, GPTModel): return model if not (hasattr(model, "language_model") and isinstance(model.language_model, GPTModel)): print(f"Model {model.__class__.__name__} is not a supported for fused forward") return None return model.language_model def patch_postprocess(model: torch.nn.Module): model = _get_patching_model(model) if model is not None: model._postprocess_backup = model._postprocess model._postprocess = _megatron_gptmodel_postprocess.__get__(model, model.__class__) def unpatch_postprocess(model: torch.nn.Module): model = _get_patching_model(model) if model is not None: model._postprocess = model._postprocess_backup # copy from https://github.com/NVIDIA/Megatron-LM/blob/23e092f41ec8bc659020e401ddac9576c1cfed7e/megatron/core/models/gpt/gpt_model.py # patch the postprocess method of GPTModel to support advanced features like MTP, 1f1b overlap, etc. def _megatron_gptmodel_postprocess( self, hidden_states, input_ids, position_ids, labels, rotary_pos_emb, rotary_pos_cos, rotary_pos_sin, mtp_in_postprocess=None, loss_mask=None, decoder_input=None, attention_mask=None, inference_params=None, packed_seq_params=None, sequence_len_offset=None, runtime_gather_output=None, extra_block_kwargs=None, inference_context=None, ): """Postprocesses decoder hidden states to generate logits or compute loss. Applies Multi-Token Prediction if enabled, generates output logits through the output layer, and computes language model loss when labels are provided. """ # logits and loss output_weight = None if self.share_embeddings_and_output_weights: output_weight = self.shared_embedding_or_output_weight() if mtp_in_postprocess and labels is not None: hidden_states = self.mtp( input_ids=input_ids, position_ids=position_ids, hidden_states=hidden_states, attention_mask=attention_mask, inference_params=inference_params, rotary_pos_emb=rotary_pos_emb, rotary_pos_cos=rotary_pos_cos, rotary_pos_sin=rotary_pos_sin, packed_seq_params=packed_seq_params, sequence_len_offset=sequence_len_offset, embedding=self.embedding, **(extra_block_kwargs or {}), ) if not self.post_process: return hidden_states # Skip when mtp_num_layers is None or 0 if self.config.mtp_num_layers and labels is not None: mtp_labels = labels.clone() hidden_states_list = torch.chunk(hidden_states, 1 + self.config.mtp_num_layers, dim=0) hidden_states = hidden_states_list[0] if loss_mask is None: # if loss_mask is not provided, use all ones as loss_mask loss_mask = torch.ones_like(mtp_labels) for mtp_layer_number in range(self.config.mtp_num_layers): # Calc loss for the current Multi-Token Prediction (MTP) layers. mtp_labels, _ = roll_tensor( mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params, ) loss_mask, num_tokens = roll_tensor( loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params, ) # Compute mtp loss without storing logits to save memory. mtp_loss = self.compute_output_layer_and_language_model_loss( hidden_states_list[mtp_layer_number + 1], labels=mtp_labels, weight=self.shared_embedding_or_output_weight(), sequence_parallel_enabled=self.output_layer.sequence_parallel, column_parallel_linear=self.output_layer, col_linear_kwargs={ "weight": output_weight, "runtime_gather_output": runtime_gather_output, }, ) mtp_loss = loss_mask * mtp_loss if self.training: # TODO(shifangx): remove the use of parallel_state here # after moving loss logging to loss_func in pretrain_gpt.py MTPLossLoggingHelper.save_loss_to_tracker( torch.sum(mtp_loss) / num_tokens, mtp_layer_number, self.config.mtp_num_layers, avg_group=parallel_state.get_data_parallel_group(with_context_parallel=True), ) mtp_loss_scale = self.config.mtp_loss_scaling_factor / self.config.mtp_num_layers if self.config.calculate_per_token_loss: hidden_states = MTPLossAutoScaler.apply(hidden_states, mtp_loss_scale * mtp_loss) else: hidden_states = MTPLossAutoScaler.apply(hidden_states, mtp_loss_scale * mtp_loss / num_tokens) logits, _ = self.output_layer(hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output) # [s b h] => [b s h] return logits.transpose(0, 1).contiguous() def patch_mtp_layer_get_embeddings(model: torch.nn.Module): """Patch the _get_embeddings method of MultiTokenPredictionLayer""" from megatron.core.models.gpt.gpt_model import GPTModel from megatron.core.transformer.multi_token_prediction import MultiTokenPredictionLayer # Unwrap each model in the actor_module to get the actual GPTModel model = _get_patching_model(model) # Collect all MultiTokenPredictionLayer instances target_layers = [] if isinstance(model, GPTModel): # Check if GPTModel has MTP and find the layers if hasattr(model, "mtp") and hasattr(model.mtp, "layers"): for layer in model.mtp.layers: if isinstance(layer, MultiTokenPredictionLayer): target_layers.append(layer) elif hasattr(model, "layers"): # Check if any layer in the model is MultiTokenPredictionLayer for layer in model.layers: if isinstance(layer, MultiTokenPredictionLayer): target_layers.append(layer) if target_layers: for layer in target_layers: layer._get_embeddings_backup = layer._get_embeddings layer._get_embeddings = _patched_get_embeddings_for_detach.__get__(layer, layer.__class__) print(f"Found and patched {len(target_layers)} MTP layer(s) in any of the actor modules") return True else: print("No MTP layers found to patch in any of the actor modules") return False def unpatch_mtp_layer_get_embeddings(model: torch.nn.Module): """Unpatch the _get_embeddings method of MultiTokenPredictionLayer""" from megatron.core.models.gpt.gpt_model import GPTModel from megatron.core.transformer.multi_token_prediction import MultiTokenPredictionLayer # Unwrap each model in the actor_module to get the actual GPTModel model = _get_patching_model(model) # Collect all MultiTokenPredictionLayer instances target_layers = [] if isinstance(model, GPTModel): # Check if GPTModel has MTP and find the layers if hasattr(model, "mtp") and hasattr(model.mtp, "layers"): for layer in model.mtp.layers: if isinstance(layer, MultiTokenPredictionLayer): target_layers.append(layer) elif hasattr(model, "layers"): # Check if any layer in the model is MultiTokenPredictionLayer for layer in model.layers: if isinstance(layer, MultiTokenPredictionLayer): target_layers.append(layer) unpatched_count = 0 for layer in target_layers: if hasattr(layer, "_get_embeddings_backup"): layer._get_embeddings = layer._get_embeddings_backup delattr(layer, "_get_embeddings_backup") unpatched_count += 1 if unpatched_count > 0: print(f"Unpatched {unpatched_count} MTP layer(s)") return True return False def _patched_get_embeddings_for_detach( self, input_ids: torch.Tensor, position_ids: torch.Tensor, embedding: Callable, hidden_states: torch.Tensor, packed_seq_params=None, ): """ Patched version of _get_embeddings method for MultiTokenPredictionLayer. This is a modified version that you can customize according to your needs. The original implementation is preserved below with modifications. """ # You can modify the logic here as needed # For example, you could: # - Change the shift amount in roll_tensor # - Apply custom transformations to input_ids or position_ids # - Add debugging information # - Modify the embedding computation # Original logic with custom modifications from megatron.core.transformer.multi_token_prediction import roll_tensor from megatron.core.utils import make_viewless_tensor # Calc logits for the current Multi-Token Prediction (MTP) layers. input_ids, _ = roll_tensor( input_ids, shifts=-1, # You can modify this shift value dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params, ) position_ids, _ = roll_tensor( position_ids, shifts=-1, # You can modify this shift value dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params, ) # embedding computation - you can modify this part decoder_input = embedding(input_ids=input_ids, position_ids=position_ids) # Apply custom transformations if needed # For example: decoder_input = some_custom_function(decoder_input) hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) # detach decoder_input and hidden_states decoder_input = decoder_input.detach() hidden_states = hidden_states.detach() return input_ids, position_ids, decoder_input, hidden_states ================================================ FILE: verl/models/mcore/patch.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # there is some bug in mcore 0.12, so we need to patch it # 1. `get_query_key_value_tensors` in `multi_latent_attention.py` works wrong when packed_seq_params is not None def apply_patch(): import megatron.core import torch import torch.nn.functional as F from megatron.core import parallel_state, tensor_parallel from megatron.core.transformer.multi_latent_attention import ( MLASelfAttention, MultiLatentAttention, apply_rotary_pos_emb, deprecate_inference_params, gather_from_sequence_parallel_region, gather_from_tensor_model_parallel_region, scatter_to_sequence_parallel_region, ) from packaging import version mcore_ge_013 = version.parse(megatron.core.__version__) >= version.parse("0.13.0") def patch_get_query_key_value_tensors( self, hidden_states, key_value_states=None, position_ids=None, packed_seq_params=None, inference_context=None, *, inference_params=None, ): """ Derives `query`, `key` and `value` tensors from `hidden_states`. """ # s = sequence length, b = batch size, h = hidden size, n = num attention heads # Attention heads [s, b, n*h] assert hidden_states.ndim == 3, f"hidden_states should be 3D, [s, b, n*h], got {hidden_states.ndim}D" inference_context = deprecate_inference_params(inference_context, inference_params) # ========================================= # Prepare RoPE and seqlen related params # ========================================= rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( inference_context, None, hidden_states, self.config, packed_seq_params ) # rotary_pos_emb:[s, b, 1, 64] mscale = 1.0 if self.config.rope_type == "rope": packed_seq = packed_seq_params is not None and packed_seq_params.qkv_format == "thd" try: # In case of TypeError: RotaryEmbedding.forward() got an unexpected keyword argument 'packed_seq' rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len, packed_seq=packed_seq) except TypeError: rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len) else: rotary_pos_emb, mscale = self.rotary_pos_emb(rotary_seq_len) # ========================================= # QKV down projection and layernorm # ========================================= if self.config.q_lora_rank is not None: # if linear_q_down_proj is ColumnParallelLinear: # q_compressed: [s, b, q_lora_rank / TP] # elif linear_q_down_proj is Linear: # q_compressed: [s / TP, b, q_lora_rank] q_compressed, _ = self.linear_q_down_proj(hidden_states) # When output is sharded (ColumnParallelLinear), two things are needed to be # identical to a normal Linear. # 1. Manually gather output to restore output dim q_lora_rank; # 2. Scatter sequence back to s / TP if sequence-parallel since it was # gathered by ColumnParallelLinear. if q_compressed.size(-1) != self.config.q_lora_rank: q_compressed = gather_from_tensor_model_parallel_region(q_compressed) if self.config.sequence_parallel: q_compressed = scatter_to_sequence_parallel_region(q_compressed) q_compressed = self.q_layernorm(q_compressed) else: q_compressed = hidden_states # if linear_kv_down_proj is ColumnParallelLinear: # kv_combined: [s, b, (kv_lora_rank + qk_pos_emb_head_dim) / TP] # elif linear_kv_down_proj is Linear: # kv_combined: [s / TP, b, (kv_lora_rank + qk_pos_emb_head_dim)] kv_combined, _ = self.linear_kv_down_proj(hidden_states) if kv_combined.size(-1) != self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim: # kv_combined: [s, b, (kv_lora_rank + qk_pos_emb_head_dim)] kv_combined = gather_from_tensor_model_parallel_region(kv_combined) # kv_compressed:[s, b, kv_lora_rank], k_pos_emb: [s, b, qk_pos_emb_head_dim] kv_compressed, k_pos_emb = torch.split( kv_combined, [self.config.kv_lora_rank, self.config.qk_pos_emb_head_dim], dim=-1 ) if self.config.sequence_parallel: # kv_compressed:[s / TP, b, kv_lora_rank] kv_compressed = scatter_to_sequence_parallel_region(kv_compressed) else: # kv_compressed:[s / TP, b, kv_lora_rank], k_pos_emb: [s / TP, b, qk_pos_emb_head_dim] kv_compressed, k_pos_emb = torch.split( kv_combined, [self.config.kv_lora_rank, self.config.qk_pos_emb_head_dim], dim=-1 ) if parallel_state.get_tensor_model_parallel_world_size() > 1: # k_pos_emb: [s, b, qk_pos_emb_head_dim] k_pos_emb = gather_from_sequence_parallel_region(k_pos_emb) kv_compressed = self.kv_layernorm(kv_compressed) # ========================================= # QKV up projection and RoPE apply # ========================================= def qkv_up_proj_and_rope_apply(q_compressed, kv_compressed, k_pos_emb, rotary_pos_emb): if self.config.q_lora_rank is not None: q, _ = self.linear_q_up_proj(q_compressed) else: # hidden_states:[s, b, 2048], q: [s, b, n * 192] q, _ = self.linear_q_proj(q_compressed) q_len, bsz, _ = q.size() # q: [s, b, n, 192] q = q.view(q_len, bsz, self.num_attention_heads_per_partition, self.q_head_dim) # kv: [s, b, 2048] kv, _ = self.linear_kv_up_proj(kv_compressed) # kv: [s, b, n, 256] kv = kv.view( q_len, bsz, self.num_attention_heads_per_partition, self.config.qk_head_dim + self.config.v_head_dim, ) cp_size = parallel_state.get_context_parallel_world_size() if inference_context is not None: # add offset to the sequence start for inference sequence_start = inference_context.sequence_len_offset sequence_end = sequence_start + q_len rotary_pos_emb = rotary_pos_emb[sequence_start:sequence_end] elif packed_seq_params is None or cp_size == 1: # Shorten rotary_pos_emb to the sequence length when inference_params # is not provided. This makes sure we can run forward directly with # any sequence length. During training, the sequence length is always # the full rotary_pos_emb length, except for sequence packing + CP. # When sequence packing and context parallel are both enabled, the # position embedding will not split rotary_pos_emb, so it may exceed # the sequence length on this CP rank, but we need the full rotary_pos_emb # to cover the full sequence, so we do not shorten it here. rotary_pos_emb = rotary_pos_emb[0:q_len] # [s, b, 64] -> [s, b, 1, 64] k_pos_emb = torch.unsqueeze(k_pos_emb, 2) # q: [s, b, n, 128], q_pos_emb: [s, b, n, 64] q_no_pe, q_pos_emb = torch.split(q, [self.config.qk_head_dim, self.config.qk_pos_emb_head_dim], dim=-1) # k_no_pe: [s, b, n, 128], value: [s, b, n, 128] k_no_pe, value = torch.split(kv, [self.config.qk_head_dim, self.config.v_head_dim], dim=-1) if packed_seq_params is not None: cu_seqlens_q = packed_seq_params.cu_seqlens_q cu_seqlens_kv = packed_seq_params.cu_seqlens_kv q_pos_emb = q_pos_emb.squeeze(1) k_pos_emb = k_pos_emb.squeeze(1) q_no_pe = q_no_pe.squeeze(1) k_no_pe = k_no_pe.squeeze(1) value = value.squeeze(1) else: cu_seqlens_q = cu_seqlens_kv = None # q_pos_emb: [s, b, n, 64], k_pos_emb:[s, b, 1, 64] q_pos_emb = apply_rotary_pos_emb( q_pos_emb, rotary_pos_emb, config=self.config, cu_seqlens=cu_seqlens_q, mscale=mscale, ) k_pos_emb = apply_rotary_pos_emb( k_pos_emb, rotary_pos_emb, config=self.config, cu_seqlens=cu_seqlens_kv, mscale=mscale, ) # query: [s, b, n, 192] query = torch.cat([q_no_pe, q_pos_emb], dim=-1) if packed_seq_params is not None: k_pos_emb = k_pos_emb.expand(-1, self.num_attention_heads_per_partition, -1) key = torch.cat([k_no_pe, k_pos_emb], dim=-1) else: # key: [s, b, n, 192] k_pos_emb = k_pos_emb.expand(-1, -1, self.num_attention_heads_per_partition, -1) key = torch.cat([k_no_pe, k_pos_emb], dim=-1) query = query.contiguous() key = key.contiguous() value = value.contiguous() return query, key, value if self.recompute_up_proj: self.qkv_up_checkpoint = tensor_parallel.CheckpointWithoutOutput() query, key, value = self.qkv_up_checkpoint.checkpoint( qkv_up_proj_and_rope_apply, q_compressed, kv_compressed, k_pos_emb, rotary_pos_emb ) else: query, key, value = qkv_up_proj_and_rope_apply(q_compressed, kv_compressed, k_pos_emb, rotary_pos_emb) return query, key, value def patch_forward( self, hidden_states, attention_mask, key_value_states=None, inference_context=None, rotary_pos_emb=None, rotary_pos_cos=None, rotary_pos_sin=None, attention_bias=None, packed_seq_params=None, position_ids=None, sequence_len_offset=None, *, inference_params=None, **kwargs, ): """Forward pass for multi-latent attention""" assert attention_bias is None, "Attention bias should not be passed into MLA." assert rotary_pos_cos is None and rotary_pos_sin is None, "MLA does not support Flash Decoding" # hidden_states: [sq, b, h] inference_context = deprecate_inference_params(inference_context, inference_params) # ===================== # Query, Key, and Value # ===================== # Get the query, key and value tensors based on the type of attention - # self or cross attn. # query: [96, 1, 16, 128], key:[96, 1, 16, 128], value:[96, 1, 16, 128] qkv = self.get_query_key_value_tensors( hidden_states, key_value_states, position_ids, packed_seq_params, inference_context=inference_context, ) query, key, value = qkv[:3] q_compressed = None # kv_compressed = None if len(qkv) > 4: q_compressed = qkv[3] # kv_compressed = qkv[4] # =================================================== # Adjust key, value for inference # =================================================== # rotary_pos_emb = None if mcore_ge_013: query, key, value, _, attn_mask_type, _ = self._adjust_key_value_for_inference( inference_context, query, key, value, rotary_pos_emb=None ) else: query, key, value, _, attn_mask_type = self._adjust_key_value_for_inference( inference_context, query, key, value, rotary_pos_emb=None ) # TODO: Currently, TE can only accept contiguous tensors for MLA query = query.contiguous() key = key.contiguous() value = value.contiguous() # ================================== # core attention computation # ================================== # Need corresponding TE change non_dsa_thd_qkv_format = ( packed_seq_params and packed_seq_params.qkv_format == "thd" and getattr(self.config, "experimental_attention_variant", None) is None ) v_dim = value.shape[-1] if non_dsa_thd_qkv_format and query.shape[-1] != v_dim: value = F.pad(value, [0, query.shape[-1] - v_dim]) self.core_attention.hidden_size_per_attention_head_v = value.shape[-1] if self.checkpoint_core_attention and self.training: core_attn_out = self._checkpointed_attention_forward( query, key, value, attention_mask, packed_seq_params=packed_seq_params ) else: extra_kwargs = {} if getattr(self.config, "experimental_attention_variant", None) == "dsa": # For dsa we need to pass in the original hidden states and the compressed # query representation. extra_kwargs["x"] = hidden_states extra_kwargs["qr"] = q_compressed core_attn_out = self.core_attention( query, key, value, attention_mask, packed_seq_params=packed_seq_params, attn_mask_type=attn_mask_type, **extra_kwargs, ) if non_dsa_thd_qkv_format: if core_attn_out.ndim == 2: core_attn_out = core_attn_out.reshape(*core_attn_out.shape[:-1], -1, value.shape[-1]) if query.shape[-1] != v_dim: core_attn_out = core_attn_out[..., :v_dim] # reshape to same output shape as unpacked case # (t, np, hn) -> (t, b=1, h=np*hn) # t is the pack size = sum (sq_i) # note that batch is a dummy dimension in the packed case core_attn_out = core_attn_out.reshape(core_attn_out.size(0), 1, -1) if self.recompute_up_proj: assert self.qkv_up_checkpoint is not None self.qkv_up_checkpoint.discard_output_and_register_recompute(core_attn_out) self.qkv_up_checkpoint = None # ================= # Output. [sq, b, h] # ================= output, bias = self.linear_proj(core_attn_out) return output, bias # This patch targets mcore 0.12 MLA behavior only. # For newer mcore, upstream MLA already has packed-seq + CP handling and # overriding it with the legacy implementation can break RoPE shapes. if not mcore_ge_013: MLASelfAttention.get_query_key_value_tensors = patch_get_query_key_value_tensors MultiLatentAttention.forward = patch_forward def apply_patch_mbridge(): try: from megatron.core.utils import get_tensor_model_parallel_group_if_none except ImportError: import warnings import megatron.core.utils import torch from megatron.core import parallel_state def get_tensor_model_parallel_group_if_none(tp_group, is_expert=False, check_initialized=True): """Issue a deprecation warning if tp_group is None and return the default tp group.""" if not torch.distributed.is_initialized(): return None if tp_group is None: if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0: warnings.warn( "Warning: tp_group is None, using default tp group. Passing tp_group will be mandatory soon", DeprecationWarning, stacklevel=2, ) if is_expert: tp_group = parallel_state.get_expert_tensor_parallel_group(check_initialized=check_initialized) else: tp_group = parallel_state.get_tensor_model_parallel_group(check_initialized=check_initialized) return tp_group megatron.core.utils.get_tensor_model_parallel_group_if_none = get_tensor_model_parallel_group_if_none def apply_patch_megatron_v012_with_torch_v28(): # Error due to missing serialization_format in _write_item of megatron v012; # resolved by using megatron v013's implementation. import inspect import logging import os from pathlib import Path import megatron.core import torch from megatron.core.dist_checkpointing.strategies.async_utils import _disable_gc from megatron.core.dist_checkpointing.strategies.filesystem_async import _process_memory from packaging import version from torch import multiprocessing as mp from torch.distributed.checkpoint.filesystem import _write_item if ( version.parse(torch.__version__).base_version != "2.8.0" or version.parse(megatron.core.__version__).base_version != "0.12.1" ): return WriteBucket = tuple[Path, str, tuple[list, list]] @staticmethod @_disable_gc() def write_preloaded_data_patch( transform_list, local_proc_idx: int, write_bucket: WriteBucket, results_queue: mp.SimpleQueue, count_queue: mp.JoinableQueue, use_fsync: bool, **kwargs, ) -> None: """ Performs actual data saving to storage. Args: local_proc_idx (int): index of a local process that performs writing write_bucket (WriteBucket): data to write to storage results_queue (mp.Queue): queue to return the write results to the proxy checkpoint process. count_queue (mp.JoinableQueue): queue to marks worker task as completed use_fsync (bool): if True, calls os.fsync at the end of saving Returns: None, the write result are put into the `queue` """ logger = logging.getLogger(__name__) logger.debug(f"{local_proc_idx} started") mem_before = _process_memory() use_msc = kwargs.get("use_msc", False) local_results = [] try: file_name, storage_key, (bytes_data, tensor_data) = write_bucket extra_kwargs = {} if "serialization_format" in inspect.signature(_write_item).parameters: from torch.distributed.checkpoint.filesystem import SerializationFormat extra_kwargs["serialization_format"] = SerializationFormat.TORCH_SAVE if use_msc: import multistorageclient as msc open_file = msc.open else: open_file = open with open_file(file_name, "wb") as stream: for write_item, data in bytes_data: local_results.append( _write_item(*transform_list, stream, data, write_item, storage_key, **extra_kwargs) ) for write_item, tensor in tensor_data: assert tensor.is_cpu local_results.append( _write_item(*transform_list, stream, tensor, write_item, storage_key, **extra_kwargs) ) if use_fsync: if use_msc: stream.fsync() else: os.fsync(stream.fileno()) local_output = (local_proc_idx, local_results) except Exception as e: logger.debug(f"{local_proc_idx} failed") local_output = (local_proc_idx, e) # type: ignore[assignment] results_queue.put(local_output) # Signal this process is done. count_queue.get() count_queue.task_done() mem_after = _process_memory() logger.debug(f"{local_proc_idx} consumed: {mem_after - mem_before}, before: {mem_before}, after: {mem_after}") from megatron.core.dist_checkpointing.strategies.filesystem_async import FileSystemWriterAsync FileSystemWriterAsync.write_preloaded_data = write_preloaded_data_patch ================================================ FILE: verl/models/mcore/qwen2_5_vl/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .model import Qwen2_5VLModel from .vision_config import get_vision_model_config, get_vision_projection_config __all__ = ["Qwen2_5VLModel", "get_vision_model_config", "get_vision_projection_config"] ================================================ FILE: verl/models/mcore/qwen2_5_vl/attention.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from megatron.core.transformer.attention import * from .rope_utils import apply_rotary_pos_emb_absolute class Qwen2_5VLSelfAttention(SelfAttention): """ Overrides the SelfAttention class, the difference is that qwen2_5_vl uses apply_rotary_pos_emb_absolute instead of apply_rotary_pos_emb """ def forward( self, hidden_states: Tensor, attention_mask: Tensor, key_value_states: Optional[Tensor] = None, inference_context: Optional[BaseInferenceContext] = None, rotary_pos_emb: Optional[Union[Tensor, Tuple[Tensor, Tensor]]] = None, rotary_pos_cos: Optional[Tensor] = None, rotary_pos_sin: Optional[Tensor] = None, attention_bias: Optional[Tensor] = None, packed_seq_params: Optional[PackedSeqParams] = None, sequence_len_offset: Optional[int] = None, *, inference_params: Optional[BaseInferenceContext] = None, rotary_pos_cos_sin: Optional[Tensor] = None, ) -> Tuple[Tensor, Tensor]: """ Perform a forward pass through the attention module. Args: hidden_states (Tensor): Hidden states. attention_mask (Tensor): Attention mask. key_value_states (Optional[Tensor]): Key/value states (for cross attention). inference_context (Optional[BaseInferenceContext]): Inference context that manages KV cache. rotary_pos_emb (Optional[Union[Tensor, Tuple[Tensor, Tensor]]]): Rotary embedding tensor(s). rotary_pos_cos (Optional[Tensor]): Rotary embedding cosine. rotary_pos_sin (Optional[Tensor]): Rotary embedding sine. attention_bias (Optional[Tensor]): Attention bias. packed_seq_params (Optional[PackedSeqparams]): Parameters used for THD format. sequence_len_offset (Optional[int]): Sequence length offset used for inference CUDA graphs. Return: (Tuple[Tensor, Tensor]) Attention output and bias. """ inference_context = deprecate_inference_params(inference_context, inference_params) if inference_context and inference_context.is_dynamic_batching(): assert flash_decode_and_prefill_kernel is not None, ( "Internal use only: install package `nvidia_chunked_flash_attn`." ) # hidden_states: [sq, b, h] if self.config.flash_decode and not self.training and inference_context is not None: rotary_pos_emb = None else: assert rotary_pos_cos is None and rotary_pos_sin is None # For self attention we just duplicate the rotary_pos_emb if it isn't already if rotary_pos_emb is not None and not isinstance(rotary_pos_emb, tuple): rotary_pos_emb = (rotary_pos_emb,) * 2 # ===================== # Query, Key, and Value # ===================== # Get the query, key and value tensors based on the type of attention - # self or cross attn. query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states) # =================================================== # Adjust key, value, and rotary_pos_emb for inference # =================================================== # This branch only runs in the decode phase of flash decoding and returns after the linear # projection. This conditional is not used in the prefill phase or non-flash-decoding cases. if ( self.config.flash_decode and inference_context is not None and inference_context.is_decode_only() and not self.training and rotary_pos_cos is not None ): assert self.layer_number in inference_context.key_value_memory_dict assert inference_context.sequence_len_offset is not None inference_key_memory, inference_value_memory = inference_context.key_value_memory_dict[self.layer_number] output = self.flash_decode( sequence_len_offset=sequence_len_offset, query_layer=query, key_layer=key, value_layer=value, inference_key_memory=inference_key_memory, inference_value_memory=inference_value_memory, rotary_cos=rotary_pos_cos, rotary_sin=rotary_pos_sin, ) out = output.transpose(0, 1).contiguous() context_layer = out.view(out.size(0), out.size(1), -1) output, bias = self.linear_proj(context_layer) return output, bias # Use latest mcore 0.13 API and forward-compatible with previous versions. outputs = self._adjust_key_value_for_inference( inference_context, query, key, value, rotary_pos_emb, rotary_pos_cos, rotary_pos_sin, sequence_len_offset, ) query, key, value, rotary_pos_emb, attn_mask_type = outputs[:5] if packed_seq_params is not None: query = query.squeeze(1) key = key.squeeze(1) value = value.squeeze(1) # ================================================ # relative positional embedding (rotary embedding) # ================================================ if rotary_pos_emb is not None and not self.config.flash_decode: q_pos_emb, k_pos_emb = rotary_pos_emb if packed_seq_params is not None: if packed_seq_params.cu_seqlens_q_padded is not None: cu_seqlens_q = packed_seq_params.cu_seqlens_q_padded else: cu_seqlens_q = packed_seq_params.cu_seqlens_q if packed_seq_params.cu_seqlens_kv_padded is not None: cu_seqlens_kv = packed_seq_params.cu_seqlens_kv_padded else: cu_seqlens_kv = packed_seq_params.cu_seqlens_kv else: cu_seqlens_q = cu_seqlens_kv = None if q_pos_emb is not None: # TODO VIJAY: simplify if inference_context is None or inference_context.is_static_batching(): query = apply_rotary_pos_emb_absolute(query, q_pos_emb, config=self.config, cu_seqlens=cu_seqlens_q) else: query = inference_context.apply_rotary_emb_query(query, q_pos_emb, self.config, cu_seqlens_q) if k_pos_emb is not None: key = apply_rotary_pos_emb_absolute(key, k_pos_emb, config=self.config, cu_seqlens=cu_seqlens_kv) # TODO, can apply positional embedding to value_layer so it has # absolute positional embedding. # otherwise, only relative positional embedding takes effect # value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb) # ================================== # core attention computation # ================================== if self.checkpoint_core_attention and self.training: core_attn_out = self._checkpointed_attention_forward( query, key, value, attention_mask, attn_mask_type=attn_mask_type, attention_bias=attention_bias, packed_seq_params=packed_seq_params, ) else: if inference_context is None or inference_context.is_static_batching(): # Static batching attention kernel. core_attn_out = self.core_attention( query, key, value, attention_mask, attn_mask_type=attn_mask_type, attention_bias=attention_bias, packed_seq_params=packed_seq_params, ) else: # Dynamic batching attention kernel. q, k, v = (query, key, value) cu_query_lengths, max_seqlen_q = inference_context.cu_query_lengths() cu_kv_lengths, max_seqlen_k = inference_context.cu_kv_lengths() core_attn_out = self.flash_decode_and_prefill( q, k, v, max_seqlen_q, max_seqlen_k, cu_query_lengths, cu_kv_lengths ) core_attn_out = core_attn_out.squeeze(0).unsqueeze(1) core_attn_out = rearrange(core_attn_out, "s b h d -> s b (h d)") if packed_seq_params is not None and packed_seq_params.qkv_format == "thd": # reshape to same output shape as unpacked case # (t, np, hn) -> (t, b=1, h=np*hn) # t is the pack size = sum (sq_i) # note that batch is a dummy dimension in the packed case core_attn_out = core_attn_out.reshape(core_attn_out.size(0), 1, -1) # ================= # Output. [sq, b, h] # ================= output, bias = self.linear_proj(core_attn_out) return output, bias ================================================ FILE: verl/models/mcore/qwen2_5_vl/model.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import torch from megatron.core import InferenceParams, mpu, tensor_parallel from megatron.core.models.gpt.gpt_model import GPTModel # from .transformer_config import Qwen2VLTransformerConfig from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.transformer import MegatronModule from megatron.core.transformer.spec_utils import ModuleSpec from megatron.core.transformer.transformer_config import TransformerConfig from verl.models.mcore.util import preprocess_packed_seqs from .attention import Qwen2_5VLSelfAttention from .vision_model import Qwen2_5VisionModel # Note: This is under development and may be missing features. class Qwen2_5VLModel(MegatronModule): """Qwen2.5VL multi-modal model. Args: language_transformer_config (TransformerConfig): Transformer config for the language model. language_transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers of the language model. language_vocab_size (int): Language model vocabulary size. language_max_sequence_length (int): Language model maximum sequence length. This is used for positional embedding. vision_transformer_config (TransformerConfig): Transformer config for the vision model. vision_transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers of the vision model. vision_projection_config (TransformerConfig): Config for the projection from vision model outputs to language model inputs. vision_projection_layer_spec (ModuleSpec): Specifies the module to use for the vision projection. vision_projection_type (str): Type of the vision projection to use. Default is a 2-layer MLP. parallel_output (bool): Do not gather the outputs, keep them split across tensor parallel ranks. This is typically True for training and False for inference. language_rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings in the language model. Defaults to 1.0. pre_process (bool): Include the embedding layer in the gpt decoder (used with pipeline parallelism). Defaults to True. post_process (bool): Include an output layer and a layernorm in the gpt decoder (used with pipeline parallelism). Defaults to True. add_encoder (bool): Construct the encoder module (used with pipeline parallelism). Defaults to True. When we use pipelining, the encoder will live on only a subset of the pipeline stages (specifically, only the first stage). add_decoder (bool): Construct the decoder module (used with pipeline parallelism). Defaults to True. When we use pipelining, the decoder will live on only a subset of the pipeline stages (specifically, every stage after the first one). img_h (int): The height of each image that the ViT will see. img_w (int): The width of each image that the ViT will see. patch_dim (int): The size of each patch side. img_embedding_idx (int): Index in the language_embeddings tensor where image_embeddings should be inserted. Defaults to 0. """ def __init__( self, language_transformer_config: TransformerConfig, language_transformer_layer_spec: ModuleSpec, language_vocab_size: int, language_max_sequence_length: int, vision_transformer_config: TransformerConfig, vision_transformer_layer_spec: ModuleSpec, vision_projection_config: TransformerConfig, vision_projection_layer_spec: ModuleSpec, vision_projection_type: str = "mlp", parallel_output: bool = True, language_rotary_percent: float = 1.0, pre_process: bool = True, post_process: bool = True, add_encoder: bool = True, add_decoder: bool = True, language_rotary_base: int = 10000, fp16_lm_cross_entropy: bool = False, language_share_embeddings_and_output_weights: bool = False, image_token_id: int = 151655, video_token_id: int = 151656, ) -> None: super().__init__(config=language_transformer_config) # patch self_attention to use qwen2_5_vl attention vision_transformer_layer_spec.submodules.self_attention.module = Qwen2_5VLSelfAttention for layer_spec in language_transformer_layer_spec.layer_specs: layer_spec.submodules.self_attention.module = Qwen2_5VLSelfAttention logging.getLogger(__name__).warning("Qwen2VL model is under development and may be missing features.") self.pre_process = pre_process self.post_process = post_process self.add_encoder = add_encoder self.add_decoder = add_decoder self.encoder_hidden_state = None self.vision_model = None self.vision_projection = None self.language_model = None self.image_token_id = image_token_id self.video_token_id = video_token_id self.square_merge_size = vision_projection_config.ffn_hidden_size // vision_transformer_config.hidden_size # This attribute is needed to check if an all-reduce is required # on the word embeddings inside `finalize_model_grads._allreduce_word_embedding_grads`. self.share_embeddings_and_output_weights = False if self.pre_process: self.vision_model = Qwen2_5VisionModel( vision_transformer_config, vision_transformer_layer_spec, vision_projection_config, vision_projection_layer_spec, projection_type=vision_projection_type, pre_process=True, post_process=True, ) self.language_model = GPTModel( config=language_transformer_config, transformer_layer_spec=language_transformer_layer_spec, vocab_size=language_vocab_size, max_sequence_length=language_max_sequence_length, parallel_output=parallel_output, position_embedding_type="mrope", rotary_percent=language_rotary_percent, pre_process=self.pre_process, post_process=self.post_process, rotary_base=language_rotary_base, fp16_lm_cross_entropy=fp16_lm_cross_entropy, share_embeddings_and_output_weights=language_share_embeddings_and_output_weights, scatter_embedding_sequence_parallel=False, ) assert mpu.get_context_parallel_world_size() <= 1, "please use mbridge for qwen2_5_vl with context parallelism" self.share_embeddings_and_output_weights = self.language_model.share_embeddings_and_output_weights def shared_embedding_or_output_weight(self): """This is a convenience method to surface the language model's word embeddings, which is necessary for `finalize_model_grads._allreduce_word_embedding_grads`.""" if self.add_decoder: return self.language_model.shared_embedding_or_output_weight() return None def set_input_tensor(self, input_tensor) -> None: # This is usually handled in schedules.py but some inference code still # gives us non-lists or None if not isinstance(input_tensor, list): input_tensor = [input_tensor] assert len(input_tensor) == 1, "input_tensor should only be length 1 for Qwen2VL" if self.pre_process: self.encoder_hidden_state = input_tensor[0] else: self.language_model.set_input_tensor(input_tensor[0]) def freeze(self, freeze_language_model: bool, freeze_vision_model: bool, freeze_vision_projection: bool): """Freeze model modules. Make specific modules non-trainable by setting requires_grad to False for the module's parameters. Args: freeze_language_model (bool): Freeze the language model module. freeze_vision_model (bool): Freeze the vision model module. freeze_vision_projection (bool): Freeze the vision projection module. """ modules = [] if freeze_language_model and self.language_model is not None: modules.append(self.language_model) if freeze_vision_model and self.vision_model is not None: modules.append(self.vision_model) if freeze_vision_projection and self.vision_projection is not None: modules.append(self.vision_projection) for module in modules: for param in module.parameters(): param.requires_grad = False def forward( self, input_ids: torch.Tensor, position_ids: torch.Tensor, attention_mask: torch.Tensor = None, labels: torch.Tensor = None, inference_params: InferenceParams = None, packed_seq_params: PackedSeqParams = None, extra_block_kwargs: dict = None, pixel_values: torch.Tensor = None, pixel_values_videos: torch.Tensor = None, image_grid_thw: torch.Tensor = None, video_grid_thw: torch.Tensor = None, **kwargs, ) -> torch.Tensor: """Forward function of the Qwen2VL model. ### there is a workaround for supporting sequence packing with context parallelism # cp split with sequence packing will make model lose vision token information, so we need to keep # the original input_ids and pack them after vision embedding is calculated, # cooporate with verl's models/mcore/model_forward.py # pack the combined_embeddings to thd here, we check if packed_seq_params is None to determine if # we need to pack the combined_embeddings to thd # this function needs the position_ids and attention_mask in BSHD format, no matter use packed_seq or not Args: image_data (torch.Tensor): input image of shape [total_thw_size, n_features]. input_ids (torch.Tensor): input text ids [batch, text_seq_len]. position_ids (torch.Tensor): input text position ids [batch, text_seq_len]. attention_mask (torch.Tensor): attention mask for the language model [batch, 1, combined_seq_len, combined_seq_len]. labels (torch.Tensor): Optional target text labels [batch, combined_seq_len]. inference_params (InferenceParams): Inference-time parameters including KV cache. video_start_index: 0 -- all video len(video_seq) -- all image others -- mixture *_input_mask: should not be None in the first PP stage Returns: output (torch.Tensor): Loss of shape [b, s] if labels are provided, otherwise logits of shape [b, s, vocab_size]. """ video_start_index = 0 vision_grid_thw = None vision_data = None if image_grid_thw is not None: image_mask = input_ids == self.image_token_id vision_grid_thw = image_grid_thw vision_data = pixel_values video_start_index = image_mask.sum().item() if video_grid_thw is not None: video_mask = input_ids == self.video_token_id if vision_grid_thw is not None: vision_grid_thw = torch.cat([vision_grid_thw, video_grid_thw], dim=0) vision_data = torch.cat([vision_data, pixel_values_videos], dim=0) else: vision_grid_thw = video_grid_thw vision_data = pixel_values_videos use_inference_kv_cache = ( inference_params is not None and "image_tokens_count" in inference_params.key_value_memory_dict ) if use_inference_kv_cache: raise NotImplementedError() if self.pre_process: vision_embeds = None if vision_grid_thw is not None and vision_grid_thw.shape[0] > 0: vision_embeds = self.vision_model( vision_data=vision_data, # If None, vision model should use intermediate outputs (EPP > 1) grid_thw=vision_grid_thw, # should provided in each EPP stage ) # If running inference, the language model KV cache will be updated for image token positions. # Here we store the image tokens sequence length, which can be used as an offset to the KV cache later. if inference_params is not None: raise NotImplementedError() # inference_params.key_value_memory_dict["image_tokens_count"] = ( # vision_embeddings.shape[0] # ) # If running inference, we can skip image token computation if they were computed already earlier # for this sample. if use_inference_kv_cache: language_embeddings: torch.Tensor = self.language_model.embedding( input_ids=input_ids, position_ids=None, # NOTE: disable ) # [text_seq_len, b, h_language] # NOTE: why not cat here? is it the combined embeddings useless? combined_embeddings = language_embeddings elif vision_embeds is not None: if video_start_index == 0: image_embeds = None video_embeds = vision_embeds elif video_start_index == vision_embeds.shape[0]: image_embeds = vision_embeds video_embeds = None elif 0 < video_start_index < vision_embeds.shape[0]: image_embeds = vision_embeds[:video_start_index] video_embeds = vision_embeds[video_start_index:] else: raise ValueError( f"Expect video token start index in range [0, {vision_embeds.shape[0]}], but got " f"{video_start_index}" ) combined_embeddings = self.language_model.embedding( input_ids=input_ids, position_ids=None, # NOTE: disable ) # [text_seq_len, b, h_language] if image_embeds is not None or video_embeds is not None: combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() if image_embeds is not None: image_mask = (input_ids == self.image_token_id).contiguous() if image_mask.sum() > 0: combined_embeddings = combined_embeddings.clone() combined_embeddings[image_mask] = image_embeds.to( dtype=combined_embeddings.dtype, device=combined_embeddings.device ) if video_embeds is not None: video_mask = (input_ids == self.video_token_id).contiguous() if video_mask.sum() > 0: combined_embeddings = combined_embeddings.clone() combined_embeddings[video_mask] = video_embeds.to( dtype=combined_embeddings.dtype, device=combined_embeddings.device ) combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() else: combined_embeddings = self.language_model.embedding( input_ids=input_ids, position_ids=None, # NOTE: disable ) # [text_seq_len, b, h_language] if packed_seq_params is not None: combined_embeddings = ( preprocess_packed_seqs( combined_embeddings.transpose(0, 1).contiguous(), attention_mask, pre_process=True )[0] .transpose(0, 1) .contiguous() ) if self.config.sequence_parallel: combined_embeddings = tensor_parallel.scatter_to_sequence_parallel_region(combined_embeddings) combined_embeddings = combined_embeddings.contiguous() else: combined_embeddings = None from .rope_utils import get_rope_index # BSHD position_ids, _ = get_rope_index( input_ids, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, attention_mask=attention_mask, ) # THD if packed_seq_params is not None: position_ids = ( preprocess_packed_seqs(position_ids.permute(1, 2, 0), attention_mask, pre_process=True)[0] .permute(2, 0, 1) .contiguous() ) attention_mask = None output = self.language_model( input_ids=None, position_ids=position_ids, # None in encoder attention_mask=attention_mask, # None in encoder decoder_input=combined_embeddings, # only not None in the first decoder PP stage labels=labels, # only not None in the last decoder PP stage # inference_params=inference_params, # currently always None packed_seq_params=packed_seq_params, # currently always None **(extra_block_kwargs or {}), **kwargs, ) return output ================================================ FILE: verl/models/mcore/qwen2_5_vl/rope_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import logging from typing import Optional import torch from megatron.core.models.common.embeddings.rope_utils import * from megatron.core.models.common.embeddings.rope_utils import _apply_rotary_pos_emb_bshd from torch import Tensor logger = logging.getLogger(__name__) # Slightly modified from Qwen2VLForConditionalGeneration.get_rope_index def get_rope_index( input_ids: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, second_per_grid_ts: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, ): """ Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: Each embedding sequence contains vision embedding and text embedding or just contains text embedding. For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs. Examples: input_ids: [T T T T T], here T is for text. temporal position_ids: [0, 1, 2, 3, 4] height position_ids: [0, 1, 2, 3, 4] width position_ids: [0, 1, 2, 3, 4] For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part and 1D rotary position embedding for text part. Examples: Temporal (Time): 3 patches, representing different segments of the video in time. Height: 2 patches, dividing each frame vertically. Width: 2 patches, dividing each frame horizontally. We also have some important parameters: fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second. tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity. temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames. interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs. input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100] vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] text temporal position_ids: [101, 102, 103, 104, 105] text height position_ids: [101, 102, 103, 104, 105] text width position_ids: [101, 102, 103, 104, 105] Here we calculate the text start position_ids as the max vision position_ids plus 1. Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. Returns: position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) """ spatial_merge_size = 2 tokens_per_second = 2 image_token_id = 151655 video_token_id = 151656 vision_start_token_id = 151652 mrope_position_deltas = [] if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): total_input_ids = input_ids if attention_mask is None: attention_mask = torch.ones_like(total_input_ids) position_ids = torch.ones( 3, input_ids.shape[0], input_ids.shape[1], dtype=input_ids.dtype, device=input_ids.device, ) image_index, video_index = 0, 0 attention_mask = attention_mask.to(total_input_ids.device) for i, input_ids in enumerate(total_input_ids): input_ids = input_ids[attention_mask[i] == 1] image_nums, video_nums = 0, 0 vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1) vision_tokens = input_ids[vision_start_indices + 1] image_nums = (vision_tokens == image_token_id).sum() video_nums = (vision_tokens == video_token_id).sum() input_tokens = input_ids.tolist() llm_pos_ids_list: list = [] st = 0 remain_images, remain_videos = image_nums, video_nums for _ in range(image_nums + video_nums): if image_token_id in input_tokens and remain_images > 0: ed_image = input_tokens.index(image_token_id, st) else: ed_image = len(input_tokens) + 1 if video_token_id in input_tokens and remain_videos > 0: ed_video = input_tokens.index(video_token_id, st) else: ed_video = len(input_tokens) + 1 if ed_image < ed_video: t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) second_per_grid_t = 0 image_index += 1 remain_images -= 1 ed = ed_image else: t, h, w = ( video_grid_thw[video_index][0], video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) if second_per_grid_ts is not None: second_per_grid_t = second_per_grid_ts[video_index] else: second_per_grid_t = 1.0 video_index += 1 remain_videos -= 1 ed = ed_video llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) text_len = ed - st st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) range_tensor = torch.arange(llm_grid_t).view(-1, 1) expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w) time_tensor = expanded_range * second_per_grid_t * tokens_per_second time_tensor_long = time_tensor.long() t_index = time_tensor_long.flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx) st = ed + llm_grid_t * llm_grid_h * llm_grid_w if st < len(input_tokens): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 text_len = len(input_tokens) - st llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device) mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i])) mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) return position_ids, mrope_position_deltas else: if attention_mask is not None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1] else: position_ids = ( torch.arange(input_ids.shape[1], device=input_ids.device) .view(1, 1, -1) .expand(3, input_ids.shape[0], -1) ) mrope_position_deltas = torch.zeros( [input_ids.shape[0], 1], device=input_ids.device, dtype=input_ids.dtype, ) return position_ids, mrope_position_deltas def apply_rotary_pos_emb_thd_absolute( t: Tensor, cu_seqlens: Tensor, freqs: Tensor, rotary_interleaved: bool = False ) -> Tensor: """A baseline implementation of applying RoPE for `thd` format. Args: t (Tensor): Input tensor T is of shape [t, h, d] cu_seqlens(Tensor): Cumulative sum of sequence lengths in a batch for `t`, with shape [b + 1] and dtype torch.int32. freqs (Tensor): Rotary Positional embedding tensor freq is of shape [max_s, 1, 1, d] Returns: Tensor: Shape [t, h, d]. The input tensor after applying RoPE. """ return _apply_rotary_pos_emb_bshd(t[:, None], freqs, rotary_interleaved=rotary_interleaved).squeeze(1) def apply_rotary_pos_emb_absolute( t: Tensor, freqs: Tensor, config: TransformerConfig, cu_seqlens: Optional[Tensor] = None, ): """ Reroute to the appropriate apply_rotary_pos_emb function depending on bshd (conventional) / thd (packed seq) format In Qwen2-VL, the shape of freqs is (seq_length, bs, 1, 2 * dim) instead of [max_seqlen, 1, 1, 2 * dim] """ if config.apply_rope_fusion: if cu_seqlens is None: # NOTE: TE backends do not support mRoPE in bshd format when bs > 1 if freqs.shape[1] > 1: return _apply_rotary_pos_emb_bshd(t, freqs, rotary_interleaved=config.rotary_interleaved) else: return fused_apply_rotary_pos_emb(t, freqs) else: # NOTE: as expected, thd format can use bshd return fused_apply_rotary_pos_emb(t[:, None], freqs).squeeze(1) else: if cu_seqlens is None: return _apply_rotary_pos_emb_bshd(t, freqs, rotary_interleaved=config.rotary_interleaved) else: return apply_rotary_pos_emb_thd_absolute(t, cu_seqlens, freqs, rotary_interleaved=config.rotary_interleaved) ================================================ FILE: verl/models/mcore/qwen2_5_vl/vision_config.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from megatron.core import parallel_state from megatron.core.transformer import TransformerConfig def get_vision_model_config(config: TransformerConfig) -> TransformerConfig: # Given a Transformer Config from decoder, build vision encoder config # diff: out_hidden_size & intermediate_size # mlp: hidden_size -> intermediate_size -> embed_dim, silu # NOTE: here we provide a workaround to solve the wrong layer amount when VPP of decoder is on if config.num_layers in [28, 36]: config.ffn_hidden_size = 3420 else: config.ffn_hidden_size = 3456 if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None: config.num_layers = 32 * parallel_state.get_virtual_pipeline_model_parallel_world_size() # depth else: config.num_layers = 32 # depth config.num_attention_heads = 16 # num_heads config.add_bias_linear = True # all nn.Linear has bias (MLP, attn) config.add_qkv_bias = True # qkv_proj in attn has bias config.hidden_size = 1280 # hidden_size config.hidden_dropout = 0.0 config.attention_dropout = 0.0 # config.gated_linear_unit = False # no gated # config.activation_func = quick_gelu # hidden_act config.kv_channels = config.hidden_size // config.num_attention_heads config.num_query_groups = config.num_attention_heads # no GQA config.layernorm_zero_centered_gamma = False # False config.apply_query_key_layer_scaling = False # factor=math.sqrt(head_dim) config.bias_activation_fusion = False # no swiglu, set false config.bias_dropout_fusion = False # no dropout, set false config.attention_softmax_in_fp32 = True # use True # config.normalization = 'LayerNorm' # use RMSNorm config.seq_length = 1 config.tp_comm_overlap = False config.sequence_parallel = False config.temporal_patch_size = 2 config.patch_size = 14 config.in_channels = 3 config.spatial_merge_size = 2 config.fullatt_block_indexes = [7, 15, 23, 31] config._qwen2_5_vl_window_size = 112 return config def get_vision_projection_config( config: TransformerConfig, embed_dim: int, spatial_merge_size: int ) -> TransformerConfig: # merger: # context_dim = hidden_size * merge_size**2 # out_hidden_size = hidden_size # context_dim -> context_dim -> out_hidden_size # MLP: # input_size -> ffn_hidden_size -> hidden_size # spec: LN -> Linear(bias=True) -> GELU -> Linear(bias=True) config.gated_linear_unit = False config.bias_activation_fusion = False config.add_bias_linear = True config.ffn_hidden_size = embed_dim * (spatial_merge_size**2) config.activation_func = torch.nn.functional.gelu config.tp_comm_overlap = False config.sequence_parallel = False return config ================================================ FILE: verl/models/mcore/qwen2_5_vl/vision_model.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional import torch from megatron.core import InferenceParams from megatron.core.models.common.vision_module.vision_module import VisionModule from megatron.core.models.vision.multimodal_projector import MultimodalProjector from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.transformer.enums import ModelType from megatron.core.transformer.spec_utils import ModuleSpec from megatron.core.transformer.transformer_config import TransformerConfig from torch import nn from torch.nn import functional as F from .vision_transformer_block import Qwen2_5VisionTransformerBlock as TransformerBlock # copied from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py class PatchEmbed(nn.Module): def __init__( self, patch_size: int = 14, temporal_patch_size: int = 2, in_channels: int = 3, embed_dim: int = 1152, ) -> None: super().__init__() self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.in_channels = in_channels self.embed_dim = embed_dim kernel_size = [temporal_patch_size, patch_size, patch_size] self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: target_dtype = self.proj.weight.dtype hidden_states = hidden_states.view( -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size ) hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim) return hidden_states # copied from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py class VisionRotaryEmbedding(nn.Module): def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) def forward(self, seqlen: int) -> torch.Tensor: seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype) freqs = torch.outer(seq, self.inv_freq) return freqs.float() class Qwen2_5VisionModel(VisionModule): """Qwen2.5 ViT vision model. Args: transformer_config (TransformerConfig): Transformer config. transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers. ln_pre_impl (ModuleSpec or type): Specifies the layer norm type to use for ln_pre. add_class_token (bool, optional): Include a class token. Defaults to True. class_token_len (int): Class token length. Defaults to 1 but 8 may be faster. patch_dim (int): Image patch size. img_h (int): Input image height. img_w (int): Input image width. """ def __init__( self, transformer_config: TransformerConfig, transformer_layer_spec: ModuleSpec, projection_config: TransformerConfig, projection_layer_spec: ModuleSpec, projection_type: str = "mlp", pre_process: bool = True, post_process: bool = False, ) -> None: super().__init__(config=transformer_config) self.spatial_merge_size = transformer_config.spatial_merge_size embed_dim = transformer_config.hidden_size num_heads = transformer_config.num_attention_heads temporal_patch_size = transformer_config.temporal_patch_size patch_size = transformer_config.patch_size in_channels = transformer_config.in_channels self.patch_size = transformer_config.patch_size self.fullatt_block_indexes = transformer_config.fullatt_block_indexes self.window_size = transformer_config._qwen2_5_vl_window_size self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size self.max_sequence_length = transformer_config.seq_length self.patch_embed = PatchEmbed( patch_size=patch_size, temporal_patch_size=temporal_patch_size, in_channels=in_channels, embed_dim=embed_dim, ) head_dim = embed_dim // num_heads self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2) self.model_type = ModelType.encoder_or_decoder self.pre_process = pre_process self.post_process = post_process # Transformer layers. # TODO: Follow-up changes will make pre and post_process configurable. They are needed for supporting # pipeline parallelism. # NOTE: a final layer norm and/or linear layer present in some implementations are omitted here. self.decoder = TransformerBlock( config=transformer_config, spec=transformer_layer_spec, pre_process=self.pre_process, post_process=self.post_process, post_layer_norm=True, ) self.merge_hidden_size = projection_config.ffn_hidden_size self.square_merge_size = self.merge_hidden_size // embed_dim if self.post_process: self.projection = MultimodalProjector( projection_config, projection_layer_spec, projection_type, projection_config.ffn_hidden_size ) else: self.projection = None self.input_tensor = None def set_input_tensor(self, input_tensor: torch.Tensor) -> None: """Sets input tensor to the model. Args: input_tensor (Tensor): Sets the input tensor for the model. """ if self.pre_process: # always True self.input_tensor = input_tensor else: raise NotImplementedError() def rot_pos_emb(self, grid_thw): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) hpos_ids = hpos_ids.permute(0, 2, 1, 3) hpos_ids = hpos_ids.flatten() wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) wpos_ids = wpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) wpos_ids = wpos_ids.permute(0, 2, 1, 3) wpos_ids = wpos_ids.flatten() pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) pos_ids = torch.cat(pos_ids, dim=0).to(grid_thw.device) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size).to(grid_thw.device) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb def get_window_index(self, grid_thw): window_index: list = [] cu_window_seqlens: list = [0] window_index_id = 0 vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size for grid_t, grid_h, grid_w in grid_thw: llm_grid_h, llm_grid_w = ( grid_h // self.spatial_merge_size, grid_w // self.spatial_merge_size, ) index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def forward( self, vision_data: Optional[torch.Tensor], grid_thw: torch.Tensor, inference_params: Optional[InferenceParams] = None, extra_block_kwargs: dict = None, ) -> torch.Tensor: """Forward function of the Qwen2 Vision Model. This function passes the input tensors through the embedding layer and then the transformer. Args: x (torch.Tensor): input image/video data of shape [n_tokens, n_dims] grid_thw (torch.Tensor): the size tensor indicates grid size of each image/frame packed_seq_params (PackedSeqParams): parameters to build attention mask in the backend Returns: x (torch.Tensor): output after final transformer block of shape [b, s, h]. """ assert grid_thw is not None assert self.input_tensor is None assert inference_params is None # Rotary positional embeddings (embedding is None for PP intermediate devices) vision_data = self.patch_embed(vision_data) window_index, cu_window_seqlens = self.get_window_index(grid_thw) cu_window_seqlens = torch.tensor( cu_window_seqlens, device=vision_data.device, dtype=torch.int32, ) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) seq_len, _ = vision_data.size() vision_data = vision_data.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) vision_data = vision_data[window_index, :, :] vision_data = vision_data.reshape(seq_len, 1, -1) rotary_pos_emb = self.rot_pos_emb(grid_thw) rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) rotary_pos_emb = rotary_pos_emb[window_index, :, :] rotary_pos_emb = rotary_pos_emb.reshape(seq_len, 1, 1, -1).repeat(1, 1, 1, 2) hidden_states = self.decoder( hidden_states=vision_data, attention_mask=None, inference_params=inference_params, rotary_pos_emb=rotary_pos_emb, packed_seq_params=self.build_packed_seq_params(None, cu_window_seqlens), packed_seq_params_full=self.build_packed_seq_params(grid_thw), fullatt_block_indexes=self.fullatt_block_indexes, **(extra_block_kwargs or {}), ) hidden_states = self.projection(hidden_states.view(-1, self.merge_hidden_size)) reverse_indices = torch.argsort(window_index) return hidden_states[reverse_indices, :] def build_packed_seq_params( self, grid_thw: Optional[torch.Tensor], cu_seqlens: Optional[torch.Tensor] = None, ) -> PackedSeqParams: # NOTE: each frame is a sequence (rather than each grid) if grid_thw is not None: seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]) cu_seqlens = seqlens.cumsum(dim=0) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0).int() else: seqlens = cu_seqlens[1:] - cu_seqlens[:-1] max_seqlen_q = seqlens.max() return PackedSeqParams( cu_seqlens_q=cu_seqlens, cu_seqlens_kv=cu_seqlens, qkv_format="thd", max_seqlen_q=max_seqlen_q, max_seqlen_kv=max_seqlen_q, ) ================================================ FILE: verl/models/mcore/qwen2_5_vl/vision_transformer_block.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2024 Alibaba PAI Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from megatron.core.transformer.transformer_block import * class Qwen2_5VisionTransformerBlock(TransformerBlock): def _checkpointed_forward( self, hidden_states: Tensor, attention_mask: Tensor, context: Tensor, context_mask: Tensor, rotary_pos_emb: Tensor, attention_bias: Tensor, packed_seq_params: PackedSeqParams, packed_seq_params_full: PackedSeqParams, fullatt_block_indexes, ): """Forward method with activation checkpointing.""" def custom(start: int, end: int): def custom_forward(hidden_states, attention_mask, context, context_mask, rotary_pos_emb): for index in range(start, end): if index in fullatt_block_indexes: packed_seq_params_now = packed_seq_params_full else: packed_seq_params_now = packed_seq_params layer = self._get_layer(index) hidden_states, context = layer( hidden_states=hidden_states, attention_mask=attention_mask, context=context, context_mask=context_mask, rotary_pos_emb=rotary_pos_emb, attention_bias=attention_bias, inference_context=None, packed_seq_params=packed_seq_params_now, ) return hidden_states, context return custom_forward def checkpoint_handler(forward_func): """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`""" if self.config.fp8: return te_checkpoint( forward_func, self.config.distribute_saved_activations, tensor_parallel.random.get_cuda_rng_tracker, parallel_state.get_tensor_model_parallel_group(), hidden_states, attention_mask, context, context_mask, rotary_pos_emb, ) else: return tensor_parallel.checkpoint( forward_func, self.config.distribute_saved_activations, hidden_states, attention_mask, context, context_mask, rotary_pos_emb, ) if self.config.recompute_method == "uniform": # Uniformly divide the total number of Transformer layers and checkpoint # the input activation of each divided chunk. # A method to further reduce memory usage reducing checkpoints. layer_idx = 0 while layer_idx < self.num_layers_per_pipeline_rank: hidden_states, context = checkpoint_handler( custom(layer_idx, layer_idx + self.config.recompute_num_layers) ) layer_idx += self.config.recompute_num_layers elif self.config.recompute_method == "block": # Checkpoint the input activation of only a set number of individual # Transformer layers and skip the rest. # A method fully use the device memory removing redundant re-computation. recompute_skip_num_layers = 0 for layer_idx in range(self.num_layers_per_pipeline_rank): # Skip recomputation when input grad computation is not needed. # Need to have at least one input tensor with gradient computation # for re-enterant autograd engine. if self.config.fp8 and not hidden_states.requires_grad: recompute_skip_num_layers += 1 if ( layer_idx >= recompute_skip_num_layers and layer_idx < self.config.recompute_num_layers + recompute_skip_num_layers ): hidden_states, context = checkpoint_handler(custom(layer_idx, layer_idx + 1)) else: hidden_states, context = custom(layer_idx, layer_idx + 1)( hidden_states, attention_mask, context, context_mask, rotary_pos_emb ) else: raise ValueError("Invalid activation recompute method.") return hidden_states def forward( self, hidden_states: Union[Tensor, WrappedTensor], attention_mask: Optional[Tensor], context: Optional[Tensor] = None, context_mask: Optional[Tensor] = None, rotary_pos_emb: Optional[Tensor] = None, rotary_pos_cos: Optional[Tensor] = None, rotary_pos_sin: Optional[Tensor] = None, attention_bias: Optional[Tensor] = None, inference_context: Optional[BaseInferenceContext] = None, packed_seq_params: Optional[PackedSeqParams] = None, sequence_len_offset: Optional[Tensor] = None, packed_seq_params_full: PackedSeqParams = None, fullatt_block_indexes=None, *, inference_params: Optional[BaseInferenceContext] = None, ): """ Perform the forward pass through the transformer block. This method handles the core computation of the transformer, including self-attention, optional cross-attention, and feed-forward operations. Args: hidden_states (Union[Tensor, WrappedTensor]): Input tensor of shape [s, b, h] where s is the sequence length, b is the batch size, and h is the hidden size. Can be passed as a WrappedTensor during inference to avoid an obsolete reference in the calling function. attention_mask (Tensor): Boolean tensor of shape [1, 1, s, s] for masking self-attention. context (Tensor, optional): Context tensor for cross-attention. context_mask (Tensor, optional): Mask for cross-attention context rotary_pos_emb (Tensor, optional): Rotary positional embeddings. attention_bias (Tensor): Bias tensor for Q * K.T of shape in shape broadcastable to [b, num_head, sq, skv], e.g. [1, 1, sq, skv]. Used as an alternative to apply attention mask for TE cuDNN attention. inference_context (BaseInferenceContext, optional): Parameters for inference-time optimizations. packed_seq_params (PackedSeqParams, optional): Parameters for packed sequence processing. Returns: Union[Tensor, Tuple[Tensor, Tensor]]: The output hidden states tensor of shape [s, b, h], and optionally the updated context tensor if cross-attention is used. """ inference_context = deprecate_inference_params(inference_context, inference_params) # Delete the obsolete reference to the initial input tensor if necessary if isinstance(hidden_states, WrappedTensor): hidden_states = hidden_states.unwrap() if not self.pre_process: # See set_input_tensor() hidden_states = self.input_tensor # Update the inference parameters with the current batch size in case it is variable if inference_context and not self.training: inference_context.current_batch_size = hidden_states.size(1) # Viewless tensor. # - We only need to create a viewless tensor in the case of micro batch # size (mbs) == 1, since in this case, 'hidden_states.transpose()' # above creates a view tensor, and '.contiguous()' is a pass-through. # For mbs >= 2, '.contiguous()' creates a new tensor, eliminating # the need to make it viewless. # # However, we don't explicitly check mbs == 1 here because # make_viewless_tensor() has negligible overhead when its input # is already viewless. # # - For the 'else' case above, calling make_viewless_tensor() here is # likely redundant, since p2p_communication.py (likely originator) # already creates viewless tensors. That said, make_viewless_tensor() # is called here to be future-proof and corner-case-proof. hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) if self.config.sequence_parallel: rng_context = tensor_parallel.get_cuda_rng_tracker().fork() else: rng_context = nullcontext() # If fp8_recipe is delayed, wrap the entire pass with get_fp8_context(), # otherwise do nothing extra at the outer level # if we are using other fp8 recipes, then the context manager enter&exit are free # we can wrap fp8_context within the for loop over layers, so that we can fine-grained # control which layer will be fp8 or bf16 use_outer_fp8_context = self.config.fp8 and self.config.fp8_recipe == Fp8Recipe.delayed use_inner_fp8_context = self.config.fp8 and self.config.fp8_recipe != Fp8Recipe.delayed outer_fp8_context = get_fp8_context(self.config) if use_outer_fp8_context else nullcontext() with rng_context, outer_fp8_context: # Forward pass. if self.config.recompute_granularity == "full" and self.training: hidden_states = self._checkpointed_forward( hidden_states=hidden_states, attention_mask=attention_mask, context=context, context_mask=context_mask, rotary_pos_emb=rotary_pos_emb, attention_bias=attention_bias, packed_seq_params=packed_seq_params, packed_seq_params_full=packed_seq_params_full, fullatt_block_indexes=fullatt_block_indexes, ) else: for l_no, layer in enumerate(self.layers): inner_fp8_context = ( get_fp8_context(self.config, layer.layer_number - 1) if use_inner_fp8_context else nullcontext() ) if l_no in fullatt_block_indexes: packed_seq_params_now = packed_seq_params_full else: packed_seq_params_now = packed_seq_params with self.offload_context, inner_fp8_context: hidden_states, context = layer( hidden_states=hidden_states, attention_mask=attention_mask, context=context, context_mask=context_mask, rotary_pos_emb=rotary_pos_emb, rotary_pos_cos=rotary_pos_cos, rotary_pos_sin=rotary_pos_sin, attention_bias=attention_bias, inference_context=inference_context, packed_seq_params=packed_seq_params_now, sequence_len_offset=sequence_len_offset, ) if ( torch.is_grad_enabled() and self.config.cpu_offloading and self.group_prefetch_offload_commit_async is not None ): hidden_states = self.group_prefetch_offload_commit_async(hidden_states) # Final layer norm. if self.final_layernorm is not None: hidden_states = self.final_layernorm(hidden_states) # TENorm produces a "viewed" tensor. This will result in schedule.py's # deallocate_output_tensor() throwing an error, so a viewless tensor is # created to prevent this. hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) return hidden_states ================================================ FILE: verl/models/mcore/readme.md ================================================ updated 20251222 # The ways verl integrates megatron-core There has been 3 ways that verl integrates megatron-core as it training backend: 1. the codes inside this directory, which defines the conversion for new models one by one. (deprecated now) 2. through [mbridge](https://github.com/ISEEKYAN/mbridge) (will be deprecated at about v0.8) 3. through [megatron-bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) (the official way for further development) There is a configure option of `megatron.use_mbridge` to choose way#1 (false) or way#2 (true), and after the megatron-bridge is integrated we have a new option `megatron.vanilla_mbridge` to choose way#2 (true) or way#3 (false) Now since we deprecated the way#1, the option `use_mbridge` will be asserted to be true and will be removed after v0.7. The default `vanilla_mbridge` is true for now and will be false one the megatron-bridge backend turns default. With the bridge way(#2 or #3), we can directly load and save the megatron model weight through HuggingFace format, and we can use any megatron version >= 0.13 to adopt new megatron optimization feature as handy as possible by directly add overrided megatron configs such as `+actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform`. # How to support new models 1. Make sure the model is supported by your inference engine (vLLM or SGLang or TensorRT-LLM) with correct version. 2. Make sure the model is supported by the bridge - If it is a model of new architecture, open an issue to `megatron-bridge` or contribute your implementation to `megatron-bridge`. Be cautious to have a matched version of `Megatron` and `TransformerEngine` - If it is a private model, implement your private model with `mbridge` or `megatron-bridge`(prefered). 3. Now the model is supported, just change the model path of your scripts and run the scritps. # #Below are deprecated since 2025.12# # verl Megatron-Core Models Now we use [mbridge](https://github.com/iseekyan/mbridge) to support megatron models. And we will migrate to [megatron-bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) in the future. With the mbridge, we can use allmost all the Megatron-Core features to support new models with little effort. And no offline weights conversion is needed, all the weights conversion is done online. We can directly save the mcore model to huggingface format during training. Also, we can easily upgrade the mcore version to the latest version. In most cases, the upgrade is seamless. (except when the mcore API changes and we need to update the verl code accordingly) ## How to support new models 1. make sure the model is supported by vLLM 2. Support the model in [mbridge](https://github.com/iseekyan/mbridge), see its currently supported models for example. - we will migrate to [megatron-bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) in the future. 3. Register the model forward function in verl, see the example in `verl/verl/models/mcore/registry.py`. # #Below are deprecated since 2025.10# The earlier versions of verl use `Megatron-LM` 0.4 and workaround huggingface model classes. To better use the latest features and speedup of modern Megatron, we are migrating to `Megatron-Core`(mcore), and use the recommended `GPTModel` class for all language models. With mcore `GPTModel`, we can use the latest features like `context parallel`, `expert parallel`, `dist_checkpointing`, etc. and we can update mcore with little effort in the future for new features. The migration has been successful with the help of the mcore team and the community. What we have done is: 1. update `Megatron` version to `0.14.0` 2. migrate `LlamaForCausalLM` and `Qwen2ForCausalLM` to mcore `GPTModel` 3. support sequence packing/thd format. 4. support `tensor parallel`, `pipeline parallel`, `sequence parallel`, `virtual pipeline parallel`, `context parallel`. 5. support the mcore `dist_checkpointing` feature and a basic offline weighs conversion script from huggingface to mcore `dist_checkpointing` format. We are working on the following features: - support `Qwen2MoeForCausalLM` - support `MixtralForCausalLM` - support `DeepseekV3ForCausalLM` - support `expert parallel` Features we invite the community to contribute: - better scripts for offline weights conversion from huggingface to mcore `dist_checkpointing` format. - conversion of large models with multiple GPUs - conversion of large models with single GPU - refactor the `megatron_checkpoint_manager.py` by `dist_checkpointing` format. - support llama4 - support qwen2.5-vl To track the progress of verl mcore integration, please refer to the [mcore integration issue](https://github.com/volcengine/verl/issues/1033). ## How things work now To engage the community in contributing, here are the key steps in our mcore integration process and features under development. The huggingface `transformers` is the de facto standard of model zoo while mcore is good at computation efficiency. The main challenge is conversion between the two. main steps: 1. modelling the huggingface model with mcore `GPTModel` - a. convert the huggingface config to mcore `TransformerConfig` - b. init the mcore `GPTModel` with the converted config - c. load the huggingface model weights to the `GPTModel` 2. online weight conversion from mcore to huggingface (due to the rollout engine `vLLM` is using huggingface format) - a. bridge the gap between mcore and huggingface weights format and name mapping - b. online resharding the mcore weights to rollout engine - this part is very complicated with multiple parallel strategies composition between mcore and rollout engine 3. support the mcore features in verl - a. support `tensor parallel`, `pipeline parallel`, `sequence parallel`, `virtual pipeline parallel`, `context parallel` - b. support recompute and other mcore speed up features 4. checkpointing - a. support recovering the verl training. - b. support exporting the mcore checkpoint to huggingface format, for downstream inference. ### Modelling the huggingface model with mcore `GPTModel` The first step is to convert huggingface config to mcore `TransformerConfig` and init the mcore `GPTModel` with the converted config. See code in `verl/models/mcore/config_converter.py` and `verl/verl/models/mcore/models/model_initializer.py`. The corresponding model forward code is in `verl/verl/models/mcore/models/model_forward.py`. There are two ways of loading the huggingface model weights to the `GPTModel` 1. Runtime loading - every rank loads the entire huggingface model weights and then shard and convert to mcore weights. - speed is slow and memory consumption is high. - this way is deprecated and will not support new models. 2. Offline loading - use offline script to convert the huggingface model weights to mcore weights and save with mcore `dist_checkpointing` format. - online loading and sharding is automatically done by mcore `dist_checkpointing` format. The speed is fast and memory consumption is low. - the offline script is in `verl/scripts/converter_hf_to_mcore.py`. ### online weight conversion from mcore to huggingface See function `convert_megatron_model_to_transformers_model` in `verl/utils/megatron_utils.py` for the details. It should be refatored for extensibility and better performance. ### support the mcore features in verl Most of the features of `GPTModel` is out-of-the-box supported in verl through changing the `TransformerConfig`, except those about parallel strategies, such as `expert parallel`. Features about parallel strategies should be supported with changes about the online weights conversion(especially the resharding part) and verl work dispatching. ### checkpointing The existing checkpointing code is in `verl/utils/checkpoint/megatron_checkpoint_manager.py`. And the script to convert checkpoint to huggingface format is in `verl/scripts/model_merger`. The existing checkpoint format simply saves every rank's weights and optimizer states. It should be refactored by `dist_checkpointing` format. ## How to support new models 1. make sure the model is supported by vLLM 2. modelling the huggingface model with mcore `GPTModel` (The [Pai-Megatron-Path](https://github.com/alibaba/Pai-Megatron-Patch/tree/main) is a good reference) - a. convert the huggingface config to mcore `TransformerConfig` - b. init the mcore `GPTModel` with the converted config - c. load the huggingface model weights to the `GPTModel` - d. for VLM the interface might be different, it is ok to add a new model class with GPTModel as its module. 3. offline weights conversion from huggingface to mcore `dist_checkpointing` format 4. support online weights conversion from mcore to huggingface - it is recommended to initialize a vLLM model with the converted mcore weights, and then test if the generating sequence is correct. ## How to scale up to larger models like deepseek-v3 or other 100B+ models The greatest challenge for scaling up to larger models is the memory consumption. The necessary features under development for scaling up are 1. Training engine part - expert parallel 2. Rollout engine part - pipeline parallel - expert parallel - more efficient and general weight resharding and loading 3. Offline weights conversion - support weights larger than single GPU memory ================================================ FILE: verl/models/mcore/registry.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Registry module for model architecture components. """ from enum import Enum from typing import Callable import torch import torch.nn as nn from .model_forward import gptmodel_forward_no_padding, model_forward_gen from .model_forward_fused import fused_forward_model_gen, fused_forward_no_padding_gen class SupportedVLM(Enum): QWEN2_5_VL = "Qwen2_5_VLForConditionalGeneration" QWEN3_MOE_VL = "Qwen3VLMoeForConditionalGeneration" QWEN3_VL = "Qwen3VLForConditionalGeneration" QWEN3_5_MOE_VL = "Qwen3_5MoeForConditionalGeneration" QWEN3_5_VL = "Qwen3_5ForConditionalGeneration" supported_vlm = [member.value for member in SupportedVLM] def get_mcore_forward_fn(hf_config) -> Callable: """ Get the forward function for given model architecture. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" if hf_config.architectures[0] in supported_vlm: return model_forward_gen(True) else: # default to language model return model_forward_gen(False) def get_mcore_forward_no_padding_fn(hf_config) -> Callable: """ Get the forward function for given model architecture. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" return gptmodel_forward_no_padding def get_mcore_forward_fused_fn(hf_config) -> Callable: """ Get the forward function for given model architecture. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" if hf_config.architectures[0] in supported_vlm: return fused_forward_model_gen(True) else: # default to language model return fused_forward_model_gen(False) def get_mcore_forward_fused_no_padding_fn(hf_config) -> Callable: """ Get the fused forward function for no-padding inputs. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" if hf_config.architectures[0] in supported_vlm: return fused_forward_no_padding_gen(True) else: # default to language model return fused_forward_no_padding_gen(False) # ruff: noqa ######################################################## # below is the deprecated code ######################################################## from .config_converter import ( PretrainedConfig, TransformerConfig, hf_to_mcore_config_dense, hf_to_mcore_config_dpskv3, hf_to_mcore_config_llama4, hf_to_mcore_config_mixtral, hf_to_mcore_config_qwen2_5_vl, hf_to_mcore_config_qwen2moe, hf_to_mcore_config_qwen3moe, ) from .model_initializer import ( BaseModelInitializer, DeepseekV3Model, DenseModel, MixtralModel, Qwen2MoEModel, Qwen3MoEModel, Qwen25VLModel, ) from .weight_converter import ( McoreToHFWeightConverterDense, McoreToHFWeightConverterDpskv3, McoreToHFWeightConverterMixtral, McoreToHFWeightConverterQwen2_5_VL, McoreToHFWeightConverterQwen2Moe, McoreToHFWeightConverterQwen3Moe, ) class SupportedModel(Enum): LLAMA = "LlamaForCausalLM" # tested QWEN2 = "Qwen2ForCausalLM" # tested QWEN2_MOE = "Qwen2MoeForCausalLM" # pending DEEPSEEK_V3 = "DeepseekV3ForCausalLM" # not tested MIXTRAL = "MixtralForCausalLM" # tested QWEN2_5_VL = "Qwen2_5_VLForConditionalGeneration" # not supported LLAMA4 = "Llama4ForConditionalGeneration" # not tested QWEN3 = "Qwen3ForCausalLM" # tested QWEN3_MOE = "Qwen3MoeForCausalLM" # tested GLM4_MOE = "Glm4MoeForCausalLM" QWEN3_TOKEN_CLASSIFICATION = "Qwen3ForTokenClassification" LLAMA_TOKEN_CLASSIFICATION = "LlamaForTokenClassification" QWEN3_MOE_VL = "Qwen3VLMoeForConditionalGeneration" QWEN3_VL = "Qwen3VLForConditionalGeneration" GPT_OSS = "GptOssForCausalLM" MIMO = "MiMoForCausalLM" # Registry for model configuration converters MODEL_CONFIG_CONVERTER_REGISTRY: dict[SupportedModel, Callable[[PretrainedConfig, torch.dtype], TransformerConfig]] = { SupportedModel.LLAMA: hf_to_mcore_config_dense, SupportedModel.QWEN2: hf_to_mcore_config_dense, SupportedModel.QWEN2_MOE: hf_to_mcore_config_qwen2moe, SupportedModel.DEEPSEEK_V3: hf_to_mcore_config_dpskv3, SupportedModel.MIXTRAL: hf_to_mcore_config_mixtral, SupportedModel.QWEN2_5_VL: hf_to_mcore_config_qwen2_5_vl, SupportedModel.LLAMA4: hf_to_mcore_config_llama4, SupportedModel.QWEN3: hf_to_mcore_config_dense, SupportedModel.QWEN3_MOE: hf_to_mcore_config_qwen3moe, SupportedModel.QWEN3_TOKEN_CLASSIFICATION: hf_to_mcore_config_dense, SupportedModel.LLAMA_TOKEN_CLASSIFICATION: hf_to_mcore_config_dense, } # Registry for model initializers MODEL_INITIALIZER_REGISTRY: dict[SupportedModel, type[BaseModelInitializer]] = { SupportedModel.LLAMA: DenseModel, SupportedModel.QWEN2: DenseModel, SupportedModel.QWEN2_MOE: Qwen2MoEModel, SupportedModel.MIXTRAL: MixtralModel, SupportedModel.DEEPSEEK_V3: DeepseekV3Model, SupportedModel.QWEN2_5_VL: Qwen25VLModel, SupportedModel.LLAMA4: DenseModel, SupportedModel.QWEN3: DenseModel, SupportedModel.QWEN3_MOE: Qwen3MoEModel, SupportedModel.QWEN3_TOKEN_CLASSIFICATION: DenseModel, SupportedModel.LLAMA_TOKEN_CLASSIFICATION: DenseModel, } # Registry for model forward functions MODEL_FORWARD_REGISTRY: dict[SupportedModel, Callable] = { SupportedModel.LLAMA: model_forward_gen(), SupportedModel.QWEN2: model_forward_gen(), SupportedModel.QWEN2_MOE: model_forward_gen(), SupportedModel.MIXTRAL: model_forward_gen(), SupportedModel.DEEPSEEK_V3: model_forward_gen(), SupportedModel.LLAMA4: model_forward_gen(), SupportedModel.QWEN3: model_forward_gen(), SupportedModel.QWEN3_MOE: model_forward_gen(), SupportedModel.QWEN2_5_VL: model_forward_gen(True), SupportedModel.QWEN3_MOE_VL: model_forward_gen(True), SupportedModel.QWEN3_VL: model_forward_gen(True), SupportedModel.GLM4_MOE: model_forward_gen(), SupportedModel.QWEN3_TOKEN_CLASSIFICATION: model_forward_gen(), SupportedModel.LLAMA_TOKEN_CLASSIFICATION: model_forward_gen(), SupportedModel.GPT_OSS: model_forward_gen(), SupportedModel.MIMO: model_forward_gen(), } # Registry for model forward functions MODEL_FORWARD_NOPAD_REGISTRY: dict[SupportedModel, Callable] = { SupportedModel.LLAMA: gptmodel_forward_no_padding, SupportedModel.QWEN2: gptmodel_forward_no_padding, SupportedModel.QWEN2_MOE: gptmodel_forward_no_padding, SupportedModel.MIXTRAL: gptmodel_forward_no_padding, SupportedModel.DEEPSEEK_V3: gptmodel_forward_no_padding, SupportedModel.QWEN2_5_VL: gptmodel_forward_no_padding, SupportedModel.QWEN3_MOE_VL: gptmodel_forward_no_padding, SupportedModel.QWEN3_VL: gptmodel_forward_no_padding, SupportedModel.LLAMA4: gptmodel_forward_no_padding, SupportedModel.QWEN3: gptmodel_forward_no_padding, SupportedModel.QWEN3_MOE: gptmodel_forward_no_padding, SupportedModel.GLM4_MOE: gptmodel_forward_no_padding, SupportedModel.QWEN3_TOKEN_CLASSIFICATION: gptmodel_forward_no_padding, SupportedModel.LLAMA_TOKEN_CLASSIFICATION: gptmodel_forward_no_padding, SupportedModel.GPT_OSS: gptmodel_forward_no_padding, SupportedModel.MIMO: gptmodel_forward_no_padding, } # Registry for model forward functions MODEL_FORWARD_FUSED_REGISTRY: dict[SupportedModel, Callable] = { SupportedModel.LLAMA: fused_forward_model_gen(), SupportedModel.QWEN2: fused_forward_model_gen(), SupportedModel.QWEN2_MOE: fused_forward_model_gen(), SupportedModel.MIXTRAL: fused_forward_model_gen(), SupportedModel.QWEN2_5_VL: fused_forward_model_gen(True), SupportedModel.QWEN3_MOE_VL: fused_forward_model_gen(True), SupportedModel.QWEN3_VL: fused_forward_model_gen(True), SupportedModel.LLAMA4: fused_forward_model_gen(), SupportedModel.QWEN3: fused_forward_model_gen(), SupportedModel.QWEN3_MOE: fused_forward_model_gen(), SupportedModel.DEEPSEEK_V3: fused_forward_model_gen(), SupportedModel.GLM4_MOE: fused_forward_model_gen(), SupportedModel.GPT_OSS: fused_forward_model_gen(), SupportedModel.MIMO: fused_forward_model_gen(), } # Registry for model weight converters MODEL_WEIGHT_CONVERTER_REGISTRY: dict[SupportedModel, type] = { SupportedModel.LLAMA: McoreToHFWeightConverterDense, SupportedModel.QWEN2: McoreToHFWeightConverterDense, SupportedModel.QWEN2_MOE: McoreToHFWeightConverterQwen2Moe, SupportedModel.MIXTRAL: McoreToHFWeightConverterMixtral, SupportedModel.DEEPSEEK_V3: McoreToHFWeightConverterDpskv3, SupportedModel.QWEN3: McoreToHFWeightConverterDense, SupportedModel.QWEN3_MOE: McoreToHFWeightConverterQwen3Moe, SupportedModel.QWEN2_5_VL: McoreToHFWeightConverterQwen2_5_VL, SupportedModel.QWEN3_TOKEN_CLASSIFICATION: McoreToHFWeightConverterDense, SupportedModel.LLAMA_TOKEN_CLASSIFICATION: McoreToHFWeightConverterDense, } def get_supported_model(model_type: str) -> SupportedModel: try: return SupportedModel(model_type) except ValueError as err: supported_models = [e.value for e in SupportedModel] raise NotImplementedError( f"Model Type: {model_type} not supported. Supported models: {supported_models}" ) from err def hf_to_mcore_config( hf_config: PretrainedConfig, dtype: torch.dtype, **override_transformer_config_kwargs ) -> TransformerConfig: """Convert huggingface PretrainedConfig to mcore TransformerConfig. Args: hf_config: The huggingface PretrainedConfig. dtype: The dtype of the model. **override_transformer_config_kwargs: The kwargs to override the transformer config. Returns: The mcore TransformerConfig. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" model = get_supported_model(hf_config.architectures[0]) return MODEL_CONFIG_CONVERTER_REGISTRY[model](hf_config, dtype, **override_transformer_config_kwargs) def init_mcore_model( tfconfig: TransformerConfig, hf_config: PretrainedConfig, pre_process: bool = True, post_process: bool = None, *, share_embeddings_and_output_weights: bool = False, value: bool = False, **extra_kwargs, # may be used for vlm and moe ) -> nn.Module: """ Initialize a Mcore model. Args: tfconfig: The transformer config. hf_config: The HuggingFace config. pre_process: Optional pre-processing function. post_process: Optional post-processing function. share_embeddings_and_output_weights: Whether to share embeddings and output weights. value: Whether to use value. **extra_kwargs: Additional keyword arguments. Returns: The initialized model. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" model = get_supported_model(hf_config.architectures[0]) initializer_cls = MODEL_INITIALIZER_REGISTRY[model] initializer = initializer_cls(tfconfig, hf_config) return initializer.initialize( pre_process=pre_process, post_process=post_process, share_embeddings_and_output_weights=share_embeddings_and_output_weights, value=value, **extra_kwargs, ) def get_mcore_weight_converter(hf_config: PretrainedConfig, dtype: torch.dtype) -> Callable: """ Get the weight converter for given model architecture. """ assert len(hf_config.architectures) == 1, "Only one architecture is supported for now" model = get_supported_model(hf_config.architectures[0]) tfconfig = hf_to_mcore_config(hf_config, dtype) return MODEL_WEIGHT_CONVERTER_REGISTRY[model](hf_config, tfconfig) ================================================ FILE: verl/models/mcore/saver.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from megatron.core import mpu from megatron.core.distributed import DistributedDataParallel as LocalDDP from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.device import get_device_id, get_torch_device from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model def _megatron_calc_global_rank( tp_rank: int = 0, dp_rank: int = 0, pp_rank: int = 0, cp_rank: int = 0, ep_rank: int = 0 ): """Calculate global rank with support for CP/EP parallelism""" # Get parallel sizes for each dimension tp_size = mpu.get_tensor_model_parallel_world_size() dp_size = mpu.get_data_parallel_world_size() pp_size = mpu.get_pipeline_model_parallel_world_size() cp_size = mpu.get_context_parallel_world_size() # ep_size = mpu.get_expert_model_parallel_world_size() # Verify total GPU count matches (must be consistent with parallel_state.py) total_size = tp_size * dp_size * pp_size * cp_size assert total_size == torch.distributed.get_world_size(), ( f"{tp_size}x{dp_size}x{pp_size}x{cp_size} != {torch.distributed.get_world_size()}" ) # Core calculation logic (corresponds to RankGenerator order parameter) # Assumes default order is "tp-cp-ep-dp-pp" return ((pp_rank * dp_size + dp_rank) * cp_size + cp_rank) * tp_size + tp_rank def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def merge_megatron_ckpt_gptmodel(wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False): """Merge sharded parameters of a Megatron module into a merged checkpoint. Args: wrapped_models (list of megatron.core.distributed.DistributedDataParallel): The local DDP wrapped megatron modules. config (str or None): HF config for model dtype: model params type is_value_model: if model is value model tie_word_embeddings: tie_word_embeddings Returns: state_dict (dict): The merged state_dict in rank 0, and an empty dictionary in other ranks. """ start_time = time.time() def _get_gpt_model(model): return model dp_rank = mpu.get_data_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() pp_rank = mpu.get_pipeline_model_parallel_rank() cp_rank = mpu.get_context_parallel_rank() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if dist.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) assert len(models[i].decoder.layers) == num_layers_per_model, ( "len model layers {} not equal to num_layers_per_model {}".format( len(models[i].decoder.layers), num_layers_per_model ) ) state_dict = dict() def _get_cpu_tensor(tensor: torch.Tensor): if tensor is None: return None if tensor.device == torch.device("cpu"): return tensor.detach().clone() return tensor.detach().cpu() def _broadcast_tensor(tensor, name, src_pp_rank) -> torch.Tensor: """broadcast tensor across mp_group""" nonlocal state_dict nonlocal mp_group src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) if torch.distributed.get_rank() == src_rank: if tensor is None: weight = None tensor_shape = None else: weight = tensor tensor_shape = weight.shape else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not exist, skip collect") return if weight is None: weight = torch.empty( tensor_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) dist.broadcast(weight, src=src_rank, group=mp_group) if torch.distributed.get_rank() == 0: state_dict[name] = _get_cpu_tensor(weight) def _broadcast_tp_shard_tensor(tensor, name, src_pp_rank, concat_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group # tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=concat_dim) if mutate_func is not None: full_tensor = mutate_func(full_tensor) state_dict[name] = full_tensor def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name, src_pp_rank) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group # tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) intermediate_size_tp = config.intermediate_size // tp_size gate_weight_list = [] up_weight_list = [] for i in range(tp_size): gate_up_weight_tp = full_tensor[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)] gate_weight_tp = gate_up_weight_tp[:intermediate_size_tp] up_weight_tp = gate_up_weight_tp[intermediate_size_tp:] gate_weight_list.append(gate_weight_tp) up_weight_list.append(up_weight_tp) state_dict[gate_name] = torch.cat(gate_weight_list, dim=0) state_dict[up_name] = torch.cat(up_weight_list, dim=0) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, src_pp_rank): """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group # tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank, cp_rank=cp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) q_weight_list = [] k_weight_list = [] v_weight_list = [] hidden_size_per_head = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) if config.num_key_value_heads >= tp_size: q_size_tp = hidden_size_per_head * config.num_attention_heads // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): num_query_groups_per_partition = wrapped_models[0].config.num_query_groups // tp_size qkv_part = full_tensor[i * total_size : (i + 1) * total_size] q_size_chunk = q_size_tp // num_query_groups_per_partition kv_size_chunk = kv_size_tp // num_query_groups_per_partition for qkv_part_chunk in qkv_part.chunk(num_query_groups_per_partition): q_part = qkv_part_chunk[:q_size_chunk] k_part = qkv_part_chunk[q_size_chunk : q_size_chunk + kv_size_chunk] v_part = qkv_part_chunk[q_size_chunk + kv_size_chunk :] q_weight_list.append(q_part) k_weight_list.append(k_part) v_weight_list.append(v_part) else: q_size_tp = hidden_size_per_head * config.num_attention_heads // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): num_query_groups_per_partition = wrapped_models[0].config.num_query_groups // tp_size qkv_part = full_tensor[i * total_size : (i + 1) * total_size] q_size_chunk = q_size_tp // num_query_groups_per_partition kv_size_chunk = kv_size_tp // num_query_groups_per_partition for qkv_part_chunk in qkv_part.chunk(num_query_groups_per_partition): q_part = qkv_part_chunk[:q_size_chunk] k_part = qkv_part_chunk[q_size_chunk : q_size_chunk + kv_size_chunk] v_part = qkv_part_chunk[q_size_chunk + kv_size_chunk :] q_weight_list.append(q_part) if i * config.num_key_value_heads % tp_size == 0: k_weight_list.append(k_part) v_weight_list.append(v_part) state_dict[q_name] = torch.cat(q_weight_list, dim=0) state_dict[k_name] = torch.cat(k_weight_list, dim=0) state_dict[v_name] = torch.cat(v_weight_list, dim=0) # empty cache before collecting weights get_torch_device().empty_cache() # Embeddings # ------------------- if dp_rank == 0 and cp_rank == 0: # models are identical across cp ranks # Embeddings # ------------------- print_rank_0("collecting embeddings...") gpt_model_module = _get_gpt_model(models[0]) _broadcast_tp_shard_tensor( gpt_model_module.embedding.word_embeddings.weight if pp_rank == 0 else None, "model.embed_tokens.weight", src_pp_rank=0, ) # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"collecting layer #{layer}...") layer_name = f"model.layers.{layer}" src_pp_rank, src_virtual_pp_rank, src_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[src_virtual_pp_rank]) sync_layer = gpt_model_module.decoder.layers[src_layer_idx] _broadcast_tensor( sync_layer.self_attention.linear_qkv.layer_norm_weight, f"{layer_name}.input_layernorm.weight", src_pp_rank=src_pp_rank, ) if gpt_model_module.config.qk_layernorm: _broadcast_tensor( sync_layer.self_attention.q_layernorm.weight, f"{layer_name}.self_attn.q_norm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tensor( sync_layer.self_attention.k_layernorm.weight, f"{layer_name}.self_attn.k_norm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attention.linear_qkv.weight, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", src_pp_rank=src_pp_rank, ) if gpt_model_module.config.add_qkv_bias: _broadcast_tp_shard_tensor_qkv( sync_layer.self_attention.linear_qkv.bias, f"{layer_name}.self_attn.q_proj.bias", f"{layer_name}.self_attn.k_proj.bias", f"{layer_name}.self_attn.v_proj.bias", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.self_attention.linear_proj.weight, f"{layer_name}.self_attn.o_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) _broadcast_tensor( sync_layer.mlp.linear_fc1.layer_norm_weight, f"{layer_name}.post_attention_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_gate_up( sync_layer.mlp.linear_fc1.weight, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.mlp.linear_fc2.weight, f"{layer_name}.mlp.down_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) # Final Layernorm # ------------------- print_rank_0("collecting final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.decoder.final_layernorm, "weight", None), "model.norm.weight", src_pp_rank=pp_size - 1, ) if tie_word_embeddings: print_rank_0("tie word embedding skip load lm_head...") else: print_rank_0("collecting lm_head...") if is_value_model: lm_head_weight = None if pp_rank == pp_size - 1: lm_head_weight = getattr(gpt_model_module.output_layer, "weight", None) _broadcast_tensor(lm_head_weight, "lm_head.weight", src_pp_rank=pp_size - 1) else: _broadcast_tp_shard_tensor( getattr(gpt_model_module.output_layer, "weight", None) if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) dist.barrier() get_torch_device().empty_cache() if torch.distributed.get_rank() == 0: for k, v in state_dict.items(): if dtype != v.dtype: state_dict[k] = v.to(dtype) print_rank_0(f"merge megatron ckpt done, time elapsed {time.time() - start_time}s") return state_dict def merge_megatron_ckpt_gptmodel_qwen_moe( wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False ): raise NotImplementedError("merge_megatron_ckpt_gptmodel_qwen_moe is not implemented") def merge_megatron_ckpt_gptmodel_qwen2_5_vl( wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False ): raise NotImplementedError("merge_megatron_ckpt_gptmodel_qwen2_5_vl is not implemented") def merge_megatron_ckpt_gptmodel_dpskv3(wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False): raise NotImplementedError("merge_megatron_ckpt_gptmodel_dpskv3 is not implemented") def merge_megatron_ckpt_gptmodel_mixtral( wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False ): raise NotImplementedError("merge_megatron_ckpt_gptmodel_mixtral is not implemented") ================================================ FILE: verl/models/mcore/util.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import math import os from typing import Optional import torch from megatron.core import parallel_state as mpu from megatron.core.packed_seq_params import PackedSeqParams from verl.utils.model import CausalLMOutputForPPO logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def _compute_fp8_thd_align_size(align_size: int) -> tuple[int, int]: """Compute FP8 alignment sizes for thd-format sequences. For FP8 block quantization, each sequence must be padded to a multiple of lcm(16, align_size), and the total padded length must be divisible by (align_size * 128) for TransformerEngine compatibility. Returns (per_seq_align_size, total_align_size). """ return math.lcm(16, align_size), align_size * 128 def preprocess_packed_seqs( input_ids: torch.Tensor, attention_mask: torch.Tensor, pre_process: bool = True, use_fp8_padding: bool = False ) -> tuple[torch.Tensor, PackedSeqParams]: """ Preprocess packed sequences CP splits sequence into CP*2 chunks, and each GPU gets 2 chunks (GPU0 gets first and last chunks, GPU1 gets second and second last chunks, and so on), this is for load balancing with causal masking. See https://github.com/NVIDIA/TransformerEngine/issues/1368 """ batch_size = input_ids.shape[0] seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) tp_size = mpu.get_tensor_model_parallel_world_size() cp_size = mpu.get_context_parallel_world_size() cp_rank = mpu.get_context_parallel_rank() align_size = tp_size * cp_size * 2 if cp_size > 1 else tp_size if use_fp8_padding: per_seq_align, total_align = _compute_fp8_thd_align_size(align_size) align_size = per_seq_align pad_size = (align_size - seqlens_in_batch % align_size) % align_size seqlens_in_batch_padded = seqlens_in_batch + pad_size cu_seqlens = torch.zeros(batch_size + 1, dtype=torch.int32, device=input_ids.device) cu_seqlens[1:] = torch.cumsum(seqlens_in_batch, dim=0) cu_seqlens_padded = torch.zeros(batch_size + 1, dtype=torch.int32, device=input_ids.device) cu_seqlens_padded[1:] = torch.cumsum(seqlens_in_batch_padded, dim=0) if use_fp8_padding: pad_size_last = (total_align - cu_seqlens_padded[-1] % total_align) % total_align cu_seqlens_padded[-1] += pad_size_last seqlens_in_batch_padded[-1] += pad_size_last # ---------------------------------------------------------------------------- # Move the index information needed in the subsequent loop to the CPU at once, # to avoid frequent .item() calls in the loop that cause D2H synchronization # ---------------------------------------------------------------------------- seqlens_in_batch_cpu: list[int] = seqlens_in_batch.tolist() # original valid lengths seqlens_in_batch_padded_cpu: list[int] = seqlens_in_batch_padded.tolist() # lengths after padding cu_seqlens_padded_cpu: list[int] = cu_seqlens_padded.tolist() # start positions (after padding) # Pure Python int calculation to avoid further synchronization max_seqlen_in_batch = max(seqlens_in_batch_padded_cpu) shape = list(input_ids.shape[1:]) shape[0] = sum(seqlens_in_batch_padded_cpu) // cp_size if pre_process: input_ids_rmpad = torch.zeros(shape, dtype=input_ids.dtype, device=input_ids.device) for i in range(batch_size): # Use Python int, so no GPU→CPU sync in the loop if cp_size <= 1: seqlen = seqlens_in_batch_cpu[i] start_idx = cu_seqlens_padded_cpu[i] input_ids_rmpad[start_idx : start_idx + seqlen] = input_ids[i, attention_mask[i]] continue seqlen_padded_i = seqlens_in_batch_padded_cpu[i] seqlen = seqlen_padded_i // cp_size half_seqlen = seqlen // 2 start_idx = cu_seqlens_padded_cpu[i] // cp_size # split to 2 chunks d = input_ids[i, attention_mask[i]] input_ids_rmpad[start_idx : start_idx + half_seqlen] = d[ half_seqlen * cp_rank : half_seqlen * (cp_rank + 1) ] remain_start = seqlen_padded_i - half_seqlen * (cp_rank + 1) remain_end = seqlen_padded_i - half_seqlen * cp_rank remain_end = min(remain_end, d.shape[0]) remain_len = remain_end - remain_start if remain_len > 0: input_ids_rmpad[start_idx + half_seqlen : start_idx + half_seqlen + remain_len] = d[ remain_start:remain_end ] packed_seq_params = PackedSeqParams( qkv_format="thd", cu_seqlens_q=cu_seqlens_padded, max_seqlen_q=max_seqlen_in_batch, cu_seqlens_kv=cu_seqlens_padded, max_seqlen_kv=max_seqlen_in_batch, cu_seqlens_q_padded=cu_seqlens_padded, cu_seqlens_kv_padded=cu_seqlens_padded, ) if pre_process: return input_ids_rmpad.unsqueeze(0), packed_seq_params else: return input_ids, packed_seq_params def postprocess_packed_seqs( output: torch.Tensor, packed_seq_params: PackedSeqParams, attention_mask: torch.Tensor, batch_size: int, seq_len: int, post_process: bool = True, ) -> torch.Tensor: """ Postprocess packed sequences """ if not post_process: return output # ------------------------------------------------------------------------- # Move the lengths and offsets needed for subsequent Python-level indexing to the CPU in advance, # to avoid a large number of .item() calls in the loop # ------------------------------------------------------------------------- cu_padded_cpu: list[int] = packed_seq_params.cu_seqlens_q_padded.tolist() seq_lens_cpu: list[int] = attention_mask.sum(dim=1, dtype=torch.int32).cpu().tolist() shape = [batch_size, seq_len] + list(output.shape[2:]) # 1,packed, dim -> batch_size, seq_len, dim output_new = torch.zeros(shape, dtype=output.dtype, device=output.device) cp_size = mpu.get_context_parallel_world_size() # all gather output across context parallel group if cp_size > 1: # output shape: [1, packed_len, hidden_dim] # need to gather across cp group and concatenate in sequence dimension output_list = [torch.empty_like(output, dtype=output.dtype) for _ in range(cp_size)] torch.distributed.all_gather(output_list, output.detach(), group=mpu.get_context_parallel_group()) output_list[mpu.get_context_parallel_rank()] = output else: output_list = [output] for i in range(batch_size): if cp_size <= 1: s = seq_lens_cpu[i] start_idx = cu_padded_cpu[i] output_new[i, attention_mask[i]] = output[0][start_idx : start_idx + s] continue s_len_padded_chunk = (cu_padded_cpu[i + 1] - cu_padded_cpu[i]) // cp_size half_seqlen = s_len_padded_chunk // 2 s_len = seq_lens_cpu[i] s_len_padded = s_len_padded_chunk * cp_size tmp = torch.empty(s_len_padded, *output.shape[2:], device=output.device, dtype=output.dtype) for j in range(cp_size): o = output_list[j][0] # split to 2 chunks packed_start_idx = cu_padded_cpu[i] // cp_size o0, o1 = ( o[packed_start_idx : packed_start_idx + half_seqlen], o[packed_start_idx + half_seqlen : packed_start_idx + s_len_padded_chunk], ) tmp[j * half_seqlen : (j + 1) * half_seqlen] = o0 tmp[s_len_padded - (j + 1) * half_seqlen : s_len_padded - j * half_seqlen] = o1 output_new[i, attention_mask[i]] = tmp[:s_len] return output_new def preprocess_bshd( input_ids: torch.Tensor, attention_mask: torch.Tensor, position_ids: torch.Tensor, sequence_parallel: bool = False, pre_process: bool = True, ): """ Remove left padding from input_ids, attention_mask and position_ids return new_input_ids, new_attention_mask, new_position_ids """ assert attention_mask.ndim == 2 assert position_ids.ndim == 2 cp_size = mpu.get_context_parallel_world_size() assert cp_size == 1, "Context parallel size without seq_pack is not supported" batch_size = input_ids.shape[0] shape = list(input_ids.shape) # batch_size, seq_len,... seq_lens = attention_mask.sum(dim=1) seq_len = seq_lens.max().item() if sequence_parallel: sp_world_size = mpu.get_tensor_model_parallel_world_size() pad_size = (sp_world_size - seq_len % sp_world_size) % sp_world_size seq_len = seq_len + pad_size shape[1] = seq_len if pre_process: new_input_ids = torch.zeros(dtype=input_ids.dtype, device=input_ids.device, size=shape) new_attention_mask = torch.zeros( dtype=attention_mask.dtype, device=attention_mask.device, size=(batch_size, seq_len) ) new_position_ids = torch.zeros(dtype=position_ids.dtype, device=position_ids.device, size=(batch_size, seq_len)) for i in range(batch_size): if pre_process: new_input_ids[i, : seq_lens[i]] = input_ids[i, attention_mask[i]] new_attention_mask[i, : seq_lens[i]] = attention_mask[i, attention_mask[i]] new_position_ids[i, : seq_lens[i]] = position_ids[i, attention_mask[i]] if pre_process: return new_input_ids, new_attention_mask, new_position_ids else: return input_ids, new_attention_mask, new_position_ids def postprocess_bshd( result, attention_mask: torch.Tensor, original_attention_mask: torch.Tensor, origin_seqlen: int, post_process: bool = True, ): """ Recover left padding from result return result """ if not post_process: return result shape = list(result.shape) batch_size = shape[0] shape[1] = origin_seqlen new_result = torch.zeros(dtype=result.dtype, device=result.device, size=shape) for i in range(batch_size): new_result[i, original_attention_mask[i]] = result[i, attention_mask[i]] return new_result def postprocess_packed_seqs_for_dict_output( labels_mask: torch.Tensor, output: CausalLMOutputForPPO, packed_seq_params: PackedSeqParams, attention_mask: torch.Tensor, batch_size: int, seq_len: int, post_process: bool = True, ) -> dict[str, torch.Tensor]: """_summary_ For fused kernels, the output is a dictionary with keys like 'log_probs', 'entropy', etc. This function post-processes each tensor in the output dictionary. Args: output (CausalLMOutputForPPO): _description_ packed_seq_params (PackedSeqParams): _description_ attention_mask (torch.Tensor): _description_ batch_size (int): _description_ seq_len (int): _description_ post_process (bool, optional): _description_. Defaults to True. Returns: CausalLMOutputForPPO: _description_ """ ret = {} output.entropy = output.entropy.view(1, -1) output.log_probs = output.log_probs.view(1, -1) output.log_probs = output.log_probs.masked_fill(~labels_mask, 0.0) ret["entropy"] = postprocess_packed_seqs( output.entropy, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process ) ret["log_probs"] = postprocess_packed_seqs( output.log_probs, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process ) return ret ### No padding versions for model engine ### inputs are nested tensors def preprocess_thd_no_padding( input_ids: torch.Tensor, pre_process: bool = True, need_roll: bool = False, use_fp8_padding: bool = False ) -> tuple[torch.Tensor, PackedSeqParams, Optional[torch.Tensor]]: """ Preprocess packed sequences CP splits sequence into CP*2 chunks, and each GPU gets 2 chunks (GPU0 gets first and last chunks, GPU1 gets second and second last chunks, and so on), this is for load balancing with causal masking. See https://github.com/NVIDIA/TransformerEngine/issues/1368 """ batch_size = input_ids.shape[0] tp_size = mpu.get_tensor_model_parallel_world_size() cp_size = mpu.get_context_parallel_world_size() cp_rank = mpu.get_context_parallel_rank() align_size = tp_size * cp_size * 2 if cp_size > 1 else tp_size seqlens_in_batch = input_ids.offsets().diff() if use_fp8_padding: per_seq_align, total_align = _compute_fp8_thd_align_size(align_size) align_size = per_seq_align pad_size = (align_size - seqlens_in_batch % align_size) % align_size seqlens_in_batch_padded = seqlens_in_batch + pad_size cu_seqlens = torch.zeros(batch_size + 1, dtype=torch.int32, device=input_ids.device) cu_seqlens[1:] = torch.cumsum(seqlens_in_batch, dim=0) cu_seqlens_padded = torch.zeros(batch_size + 1, dtype=torch.int32, device=input_ids.device) cu_seqlens_padded[1:] = torch.cumsum(seqlens_in_batch_padded, dim=0) if use_fp8_padding: # Pad the last sequence so total length is divisible by total_align for TE pad_size_last = (total_align - cu_seqlens_padded[-1] % total_align) % total_align cu_seqlens_padded[-1] += pad_size_last seqlens_in_batch_padded[-1] += pad_size_last # ---------------------------------------------------------------------------- # Move the index information needed in the subsequent loop to the CPU at once, # to avoid frequent .item() calls in the loop that cause D2H synchronization # ---------------------------------------------------------------------------- seqlens_in_batch_cpu: list[int] = seqlens_in_batch.tolist() # original valid lengths seqlens_in_batch_padded_cpu: list[int] = seqlens_in_batch_padded.tolist() # lengths after padding cu_seqlens_padded_cpu: list[int] = cu_seqlens_padded.tolist() # start positions (after padding) # Pure Python int calculation to avoid further synchronization max_seqlen_in_batch = max(seqlens_in_batch_padded_cpu) shape = list(input_ids.shape[1:]) shape[0] = sum(seqlens_in_batch_padded_cpu) // cp_size if pre_process: input_ids_rmpad = torch.zeros(shape, dtype=input_ids.dtype, device=input_ids.device) position_ids_rmpad = torch.zeros(shape, dtype=torch.long, device=input_ids.device) if need_roll: saved_roll_dict = {} saved_position_roll_dict = {} for i in range(batch_size): # Use Python int, so no GPU→CPU sync in the loop if cp_size <= 1: seqlen = seqlens_in_batch_cpu[i] start_idx = cu_seqlens_padded_cpu[i] input_ids_rmpad[start_idx : start_idx + seqlen] = input_ids[i] # Build position_ids: 0, 1, 2, ..., seqlen-1 for this sequence position_ids_rmpad[start_idx : start_idx + seqlen] = torch.arange( seqlen, dtype=torch.long, device=input_ids.device ) continue seqlen_padded_i = seqlens_in_batch_padded_cpu[i] seqlen = seqlen_padded_i // cp_size half_seqlen = seqlen // 2 start_idx = cu_seqlens_padded_cpu[i] // cp_size # split to 2 chunks d = input_ids[i] # If the number of elements in `d` is smaller than the required # alignment size, pad the tensor with zeros so that its total # length matches `align_size`. This ensures size alignment for # downstream operations (e.g., communication or memory alignment). if d.numel() < align_size: original_size = d.numel() pad = torch.zeros(align_size - d.numel(), dtype=d.dtype, device=d.device) d = torch.cat([d, pad], dim=0) logger.warning_once( f"Padding tensor for context parallel alignment, original_size={original_size}, " f"align_size={align_size}" ) input_ids_rmpad[start_idx : start_idx + half_seqlen] = d[ half_seqlen * cp_rank : half_seqlen * (cp_rank + 1) ] # Build position_ids for the first chunk position_ids_rmpad[start_idx : start_idx + half_seqlen] = torch.arange( half_seqlen * cp_rank, half_seqlen * (cp_rank + 1), dtype=torch.long, device=input_ids.device ) remain_start = seqlen_padded_i - half_seqlen * (cp_rank + 1) remain_end = seqlen_padded_i - half_seqlen * cp_rank remain_end = min(remain_end, d.shape[0]) remain_len = remain_end - remain_start if remain_len > 0: input_ids_rmpad[start_idx + half_seqlen : start_idx + half_seqlen + remain_len] = d[ remain_start:remain_end ] # Build position_ids for the remaining chunk position_ids_rmpad[start_idx + half_seqlen : start_idx + half_seqlen + remain_len] = torch.arange( seqlen_padded_i - remain_len, seqlen_padded_i, dtype=torch.long, device=input_ids.device ) if need_roll: # Handle roll for cp_size > 1 case saved_roll_dict[start_idx + half_seqlen - 1] = d[(cp_rank + 1) * half_seqlen] saved_position_roll_dict[start_idx + half_seqlen - 1] = position_ids_rmpad[start_idx + half_seqlen - 1] if remain_len > 0: if remain_end == d.shape[0]: saved_roll_dict[start_idx + half_seqlen + remain_len - 1] = d[0] saved_position_roll_dict[start_idx + half_seqlen + remain_len - 1] = 0 else: saved_roll_dict[start_idx + half_seqlen + remain_len - 1] = d[remain_end] saved_position_roll_dict[start_idx + half_seqlen + remain_len - 1] = position_ids_rmpad[ start_idx + half_seqlen + remain_len - 1 ] if need_roll: input_ids_rmpad = torch.roll(input_ids_rmpad, shifts=-1, dims=0) position_ids_rmpad = torch.roll(position_ids_rmpad, shifts=-1, dims=0) if len(saved_roll_dict) > 0: for k, v in saved_roll_dict.items(): input_ids_rmpad[k] = v for k, v in saved_position_roll_dict.items(): position_ids_rmpad[k] = v packed_seq_params = PackedSeqParams( qkv_format="thd", cu_seqlens_q=cu_seqlens_padded, max_seqlen_q=max_seqlen_in_batch, cu_seqlens_kv=cu_seqlens_padded, max_seqlen_kv=max_seqlen_in_batch, cu_seqlens_q_padded=cu_seqlens_padded, cu_seqlens_kv_padded=cu_seqlens_padded, ) if pre_process: return input_ids_rmpad.unsqueeze(0), packed_seq_params, position_ids_rmpad.unsqueeze(0) else: return input_ids, packed_seq_params, None def postprocess_thd_no_padding( output: torch.Tensor, packed_seq_params: PackedSeqParams, input_ids: torch.Tensor, batch_size: int, post_process: bool = True, ) -> torch.Tensor: """ Postprocess packed sequences """ if not post_process: return output # ------------------------------------------------------------------------- # Move the lengths and offsets needed for subsequent Python-level indexing to the CPU in advance, # to avoid a large number of .item() calls in the loop # ------------------------------------------------------------------------- cu_padded_cpu: list[int] = packed_seq_params.cu_seqlens_q_padded.tolist() # The reason why we use input_ids.offsets() instead of packed_seq_params.cu_seqlens_q.diff() # is that the latter one is the padded length, while the former one is the original length. cu_seqlens = input_ids.offsets() seq_lens_cpu: list[int] = cu_seqlens.diff().tolist() output_new = [] cp_size = mpu.get_context_parallel_world_size() # all gather output across context parallel group if cp_size > 1: # output shape: [1, packed_len, hidden_dim] # need to gather across cp group and concatenate in sequence dimension output_list = [torch.empty_like(output) for _ in range(cp_size)] torch.distributed.all_gather(output_list, output.detach(), group=mpu.get_context_parallel_group()) output_list[mpu.get_context_parallel_rank()] = output else: output_list = [output] for i in range(batch_size): if cp_size <= 1: s = seq_lens_cpu[i] start_idx = cu_padded_cpu[i] output_new.append(output[0][start_idx : start_idx + s]) continue s_len_padded_chunk = (cu_padded_cpu[i + 1] - cu_padded_cpu[i]) // cp_size half_seqlen = s_len_padded_chunk // 2 s_len = seq_lens_cpu[i] s_len_padded = s_len_padded_chunk * cp_size tmp = torch.empty(s_len_padded, *output.shape[2:], device=output.device) for j in range(cp_size): o = output_list[j][0] # split to 2 chunks packed_start_idx = cu_padded_cpu[i] // cp_size o0, o1 = ( o[packed_start_idx : packed_start_idx + half_seqlen], o[packed_start_idx + half_seqlen : packed_start_idx + s_len_padded_chunk], ) tmp[j * half_seqlen : (j + 1) * half_seqlen] = o0 tmp[s_len_padded - (j + 1) * half_seqlen : s_len_padded - j * half_seqlen] = o1 output_new.append(tmp[:s_len]) output_new_tensor = torch.nested.as_nested_tensor(output_new, layout=torch.jagged) return output_new_tensor def preprocess_bshd_no_padding( input_ids: torch.Tensor, pre_process: bool = True, need_roll: bool = False, use_fp8_padding: bool = False ): """ Preprocess bshd sequences return "input_ids, attention_mask, position_ids" """ cp_size = mpu.get_context_parallel_world_size() # TODO: support context parallel size > 1 assert cp_size == 1, "Context parallel size without bshd is not supported yet" batch_size = input_ids.shape[0] seqlens_in_batch = input_ids.offsets().diff() max_seqlen = seqlens_in_batch.max().item() tp_size = mpu.get_tensor_model_parallel_world_size() if tp_size > 1: sp_world_size = tp_size pad_size = (sp_world_size - max_seqlen % sp_world_size) % sp_world_size max_seqlen = max_seqlen + pad_size if use_fp8_padding: # For FP8 block quantization, batch_size * max_seqlen / tp_size must be divisible by 128. # We need: max_seqlen % tp_size == 0 (for SP) AND batch_size * max_seqlen % (128 * tp_size) == 0. # Compute the required alignment for max_seqlen: fp8_total_align = 128 * tp_size fp8_seq_align = fp8_total_align // math.gcd(batch_size, fp8_total_align) # Also ensure tp alignment for SP fp8_seq_align = math.lcm(fp8_seq_align, tp_size) max_seqlen = ((max_seqlen + fp8_seq_align - 1) // fp8_seq_align) * fp8_seq_align attention_mask = torch.zeros(batch_size, max_seqlen, dtype=torch.bool, device=input_ids.device) input_ids_bshd = torch.zeros(batch_size, max_seqlen, dtype=input_ids.dtype, device=input_ids.device) for i in range(batch_size): attention_mask[i, : seqlens_in_batch[i]] = True input_ids_bshd[i, : seqlens_in_batch[i]] = input_ids[i] position_ids = torch.arange(max_seqlen, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids_bshd) if need_roll: input_ids_bshd = torch.roll(input_ids_bshd, shifts=-1, dims=1) return input_ids_bshd, attention_mask, position_ids def postprocess_bshd_no_padding( output: torch.Tensor, attention_mask: torch.Tensor, post_process: bool = True, ) -> torch.Tensor: """ Postprocess bshd sequences """ if not post_process: return output batch_size = output.shape[0] output_new = [] for i in range(batch_size): mask = attention_mask[i].bool() output_new.append(output[i][mask]) output_new_tensor = torch.nested.as_nested_tensor(output_new, layout=torch.jagged) return output_new_tensor ================================================ FILE: verl/models/mcore/weight_converter.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # online convert mcore weight to pure huggingface weight, no any fusion # including format conversion and name mapping # not including resharding import torch from megatron.core.transformer import TransformerConfig from transformers import PretrainedConfig class McoreToHFWeightConverterBase: def __init__(self, hf_config: PretrainedConfig, mcore_config: TransformerConfig): self.hf_config = hf_config self.mcore_config = mcore_config def convert_param(self, name: str, params_one_group: list[torch.Tensor]) -> torch.Tensor: raise NotImplementedError class McoreToHFWeightConverterDense(McoreToHFWeightConverterBase): def _convert_attention_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # 'decoder.layers.0.self_attention.linear_proj.weight' # 'decoder.layers.0.self_attention.linear_qkv.layer_norm_weight' # 'decoder.layers.0.self_attention.linear_qkv.weight' # 'decoder.layers.0.self_attention.linear_qkv.bias' layer_number = name.split(".")[2] convert_names = [] if "self_attention.linear_qkv.bias" in name or "self_attention.linear_qkv.weight" in name: param_type = name.split(".")[-1] assert param_type == "bias" or param_type == "weight" convert_names.append(f"model.layers.{layer_number}.self_attn.q_proj.{param_type}") convert_names.append(f"model.layers.{layer_number}.self_attn.k_proj.{param_type}") convert_names.append(f"model.layers.{layer_number}.self_attn.v_proj.{param_type}") assert len(params) == 3 elif "self_attention.linear_proj.weight" in name: convert_names.append(f"model.layers.{layer_number}.self_attn.o_proj.weight") assert len(params) == 1 elif "self_attention.linear_qkv.layer_norm_weight" in name: convert_names.append(f"model.layers.{layer_number}.input_layernorm.weight") assert len(params) == 1 elif "self_attention.q_layernorm.weight" in name: convert_names.append(f"model.layers.{layer_number}.self_attn.q_norm.weight") assert len(params) == 1 elif "self_attention.k_layernorm.weight" in name: convert_names.append(f"model.layers.{layer_number}.self_attn.k_norm.weight") assert len(params) == 1 else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params def _convert_mlp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # 'decoder.layers.0.mlp.linear_fc1.layer_norm_weight' # 'decoder.layers.0.mlp.linear_fc1.weight' # 'decoder.layers.0.mlp.linear_fc2.weight' layer_number = name.split(".")[2] convert_names = [] if "mlp.linear_fc1.weight" in name: # split gate_proj and up_proj convert_names.append(f"model.layers.{layer_number}.mlp.gate_proj.weight") convert_names.append(f"model.layers.{layer_number}.mlp.up_proj.weight") assert len(params) == 2 elif "mlp.linear_fc1.layer_norm_weight" in name: convert_names.append(f"model.layers.{layer_number}.post_attention_layernorm.weight") assert len(params) == 1 elif "mlp.linear_fc2.weight" in name: convert_names.append(f"model.layers.{layer_number}.mlp.down_proj.weight") assert len(params) == 1 else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params def convert_param(self, name: str, params_one_group: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: direct_name_mapping = { "embedding.word_embeddings.weight": "model.embed_tokens.weight", "decoder.final_layernorm.weight": "model.norm.weight", "output_layer.weight": "lm_head.weight", } if name in direct_name_mapping: return [direct_name_mapping[name]], [params_one_group[0]] if "self_attention" in name: return self._convert_attention_param(name, params_one_group) elif "mlp" in name: return self._convert_mlp_param(name, params_one_group) else: raise NotImplementedError(f"Unsupported parameter name: {name}") class McoreToHFWeightConverterQwen2Moe(McoreToHFWeightConverterDense): def _convert_mlp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # 'decoder.layers.0.pre_mlp_layernorm.weight', # 'decoder.layers.0.mlp.router.weight', # 'decoder.layers.0.mlp.shared_experts.gate_weight', # 'decoder.layers.0.mlp.shared_experts.linear_fc1.weight', # 'decoder.layers.0.mlp.shared_experts.linear_fc2.weight' # moe1 # 'decoder.layers.0.mlp.experts.linear_fc1.weight0', # 'decoder.layers.0.mlp.experts.linear_fc1.weight1', # 'decoder.layers.0.mlp.experts.linear_fc1.weight2', # 'decoder.layers.0.mlp.experts.linear_fc1.weight3', # moe2 # 'decoder.layers.0.mlp.experts.linear_fc2.weight0', # 'decoder.layers.0.mlp.experts.linear_fc2.weight1', layer_number = name.split(".")[2] convert_names = [] if "pre_mlp_layernorm" in name: convert_names.append(f"model.layers.{layer_number}.post_attention_layernorm.weight") assert len(params) == 1 elif "mlp.router.weight" in name: convert_names.append(f"model.layers.{layer_number}.mlp.gate.weight") assert len(params) == 1 elif "shared_experts.gate_weight" in name: convert_names.append(f"model.layers.{layer_number}.mlp.shared_expert_gate.weight") assert len(params) == 1 elif "shared_experts.linear_fc1.weight" in name: # split gate_proj and up_proj convert_names.append(f"model.layers.{layer_number}.mlp.shared_expert.gate_proj.weight") convert_names.append(f"model.layers.{layer_number}.mlp.shared_expert.up_proj.weight") assert len(params) == 2 elif "shared_experts.linear_fc2.weight" in name: convert_names.append(f"model.layers.{layer_number}.mlp.shared_expert.down_proj.weight") assert len(params) == 1 elif "mlp.experts.linear_fc1" in name: # split gate_proj and up_proj expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.gate_proj.weight") convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.up_proj.weight") assert len(params) == 2 elif "mlp.experts.linear_fc2" in name: expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.down_proj.weight") assert len(params) == 1 else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params class McoreToHFWeightConverterQwen2_5_VL(McoreToHFWeightConverterDense): def convert_param(self, name: str, params_one_group: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: direct_name_mapping = { "language_model.embedding.word_embeddings.weight": "model.embed_tokens.weight", "language_model.decoder.final_layernorm.weight": "model.norm.weight", "language_model.output_layer.weight": "lm_head.weight", "vision_model.patch_embed.proj.weight": "visual.patch_embed.proj.weight", "vision_model.decoder.final_layernorm.weight": "visual.merger.ln_q.weight", "vision_model.projection.encoder.linear_fc1.weight": "visual.merger.mlp.0.weight", "vision_model.projection.encoder.linear_fc1.bias": "visual.merger.mlp.0.bias", "vision_model.projection.encoder.linear_fc2.weight": "visual.merger.mlp.2.weight", "vision_model.projection.encoder.linear_fc2.bias": "visual.merger.mlp.2.bias", } if name in direct_name_mapping: return [direct_name_mapping[name]], [params_one_group[0]] if "self_attention" in name: return self._convert_attention_param(name, params_one_group) elif "mlp" in name: return self._convert_mlp_param(name, params_one_group) else: raise NotImplementedError(f"Unsupported parameter name: {name}") def _convert_attention_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: model_type, _, _, layer_number = name.split(".")[:4] convert_names = [] if model_type == "language_model": name_map_after_layer = { "self_attention.linear_qkv.bias": [ "self_attn.q_proj.bias", "self_attn.k_proj.bias", "self_attn.v_proj.bias", ], "self_attention.linear_qkv.weight": [ "self_attn.q_proj.weight", "self_attn.k_proj.weight", "self_attn.v_proj.weight", ], "self_attention.linear_proj.weight": "self_attn.o_proj.weight", "self_attention.linear_qkv.layer_norm_weight": "input_layernorm.weight", } name_after_layer = ".".join(name.split(".")[-3:]) mapped_name = name_map_after_layer.get(name_after_layer) if isinstance(mapped_name, list): assert len(params) == len(mapped_name) for one in mapped_name: convert_names.append(f"model.layers.{layer_number}.{one}") else: assert len(params) == 1 convert_names.append(f"model.layers.{layer_number}.{mapped_name}") elif model_type == "vision_model": name_map_after_layer = { "self_attention.linear_proj.weight": "attn.proj.weight", "self_attention.linear_proj.bias": "attn.proj.bias", "self_attention.linear_qkv.layer_norm_weight": "norm1.weight", } name_after_layer = ".".join(name.split(".")[-3:]) mapped_name = name_map_after_layer.get(name_after_layer, None) if mapped_name is None: assert "linear_qkv" in name_after_layer assert len(params) == 3 new_param = torch.cat(params, dim=0) params = [new_param] if "bias" in name_after_layer: convert_names.append(f"visual.blocks.{layer_number}.attn.qkv.bias") else: convert_names.append(f"visual.blocks.{layer_number}.attn.qkv.weight") else: assert len(params) == 1 convert_names.append(f"visual.blocks.{layer_number}.{mapped_name}") else: raise NotImplementedError(f"Unsupported model type: {model_type}") return convert_names, params def _convert_mlp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: model_type, _, _, layer_number = name.split(".")[:4] convert_names = [] if model_type == "language_model": name_map_after_layer = { "mlp.linear_fc1.weight": ["mlp.gate_proj.weight", "mlp.up_proj.weight"], "mlp.linear_fc1.bias": ["mlp.gate_proj.bias", "mlp.up_proj.bias"], "mlp.linear_fc2.weight": "mlp.down_proj.weight", "mlp.linear_fc2.bias": "mlp.down_proj.bias", "mlp.linear_fc1.layer_norm_weight": "post_attention_layernorm.weight", } name_after_layer = ".".join(name.split(".")[-3:]) mapped_name = name_map_after_layer.get(name_after_layer) if isinstance(mapped_name, list): assert len(params) == len(mapped_name) for one in mapped_name: convert_names.append(f"model.layers.{layer_number}.{one}") else: assert len(params) == 1 convert_names.append(f"model.layers.{layer_number}.{mapped_name}") elif model_type == "vision_model": name_map_after_layer = { "mlp.linear_fc1.weight": ["mlp.gate_proj.weight", "mlp.up_proj.weight"], "mlp.linear_fc1.bias": ["mlp.gate_proj.bias", "mlp.up_proj.bias"], "mlp.linear_fc2.weight": "mlp.down_proj.weight", "mlp.linear_fc2.bias": "mlp.down_proj.bias", "mlp.linear_fc1.layer_norm_weight": "norm2.weight", } name_after_layer = ".".join(name.split(".")[-3:]) mapped_name = name_map_after_layer.get(name_after_layer) if isinstance(mapped_name, list): assert len(params) == len(mapped_name) for one in mapped_name: convert_names.append(f"visual.blocks.{layer_number}.{one}") else: assert len(params) == 1 convert_names.append(f"visual.blocks.{layer_number}.{mapped_name}") else: raise NotImplementedError(f"Unsupported model type: {model_type}") return convert_names, params class McoreToHFWeightConverterDpskv3(McoreToHFWeightConverterBase): def _convert_attention_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # mcore # 'decoder.layers.0.input_layernorm.weight' # 'decoder.layers.0.self_attention.linear_proj.weight' # 'decoder.layers.0.self_attention.linear_q_proj.weight' # 'decoder.layers.0.self_attention.linear_kv_down_proj.weight' # 'decoder.layers.0.self_attention.linear_kv_up_proj.layer_norm_weight' # 'decoder.layers.0.self_attention.linear_kv_up_proj.weight' # 'decoder.layers.0.self_attention.linear_q_down_proj.weight' # 'decoder.layers.0.self_attention.linear_q_up_proj.weight' # 'decoder.layers.0.self_attention.linear_q_up_proj.layer_norm_weight' # hf # 'model.layers.0.input_layernorm.weight' # 'model.layers.0.self_attn.o_proj.weight' # 'model.layers.0.self_attn.q_proj.weight' # 'model.layers.0.self_attn.kv_a_proj_with_mqa.weight' # 'model.layers.0.self_attn.kv_a_layernorm.weight' # 'model.layers.0.self_attn.kv_b_proj.weight' # 'model.layers.0.self_attn.q_a_proj.weight' # 'model.layers.0.self_attn.q_b_proj.weight' # 'model.layers.0.self_attn.q_a_layernorm.weight' name_map_after_layer = { "input_layernorm.weight": "input_layernorm.weight", "self_attention.linear_proj.weight": "self_attn.o_proj.weight", "self_attention.linear_q_proj.weight": "self_attn.q_proj.weight", "self_attention.linear_kv_down_proj.weight": "self_attn.kv_a_proj_with_mqa.weight", "self_attention.linear_kv_up_proj.layer_norm_weight": "self_attn.kv_a_layernorm.weight", "self_attention.linear_kv_up_proj.weight": "self_attn.kv_b_proj.weight", "self_attention.linear_q_down_proj.weight": "self_attn.q_a_proj.weight", "self_attention.linear_q_up_proj.weight": "self_attn.q_b_proj.weight", "self_attention.linear_q_up_proj.layer_norm_weight": "self_attn.q_a_layernorm.weight", } assert len(params) == 1 convert_names = [] layer_number = name.split(".")[2] name_after_layer = name.split(f".{layer_number}.")[1] convert_names.append(f"model.layers.{layer_number}.{name_map_after_layer[name_after_layer]}") return convert_names, params def _convert_mlp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # mcore dense # 'decoder.layers.0.mlp.linear_fc1.layer_norm_weight' # 'decoder.layers.0.mlp.linear_fc2.weight' # 'decoder.layers.0.mlp.linear_fc1.weight' # --- # 'decoder.layers.1.mlp.shared_experts.linear_fc1.weight' # --- # 'decoder.layers.1.mlp.shared_experts.linear_fc2.weight' # hf dense # 'model.layers.0.post_attention_layernorm.weight' # 'model.layers.0.mlp.down_proj.weight' # 'model.layers.0.mlp.gate_proj.weight' # 'model.layers.0.mlp.up_proj.weight' # 'model.layers.1.mlp.shared_experts.gate_proj.weight' # 'model.layers.1.mlp.shared_experts.up_proj.weight' # 'model.layers.1.mlp.shared_experts.down_proj.weight' # mcore moe # 'decoder.layers.1.pre_mlp_layernorm.weight' # 'decoder.layers.1.mlp.router.weight' # 'decoder.layers.1.mlp.router.expert_bias' # 'decoder.layers.1.mlp.experts.linear_fc1.weight0' # --- # 'decoder.layers.1.mlp.experts.linear_fc2.weight0' # hf moe # 'model.layers.1.post_attention_layernorm.weight' # 'model.layers.1.mlp.gate.weight' # 'model.layers.1.mlp.gate.e_score_correction_bias' # 'model.layers.1.mlp.experts.0.gate_proj.weight' # 'model.layers.1.mlp.experts.0.up_proj.weight' # 'model.layers.1.mlp.experts.0.down_proj.weight' name_map_after_layer = { "mlp.linear_fc1.layer_norm_weight": "post_attention_layernorm.weight", "mlp.linear_fc2.weight": "mlp.down_proj.weight", "mlp.shared_experts.linear_fc2.weight": "mlp.shared_experts.down_proj.weight", "mlp.linear_fc1.weight": ["mlp.gate_proj.weight", "mlp.up_proj.weight"], "mlp.shared_experts.linear_fc1.weight": [ "mlp.shared_experts.gate_proj.weight", "mlp.shared_experts.up_proj.weight", ], "pre_mlp_layernorm.weight": "post_attention_layernorm.weight", "mlp.router.weight": "mlp.gate.weight", "mlp.router.expert_bias": "mlp.gate.e_score_correction_bias", } convert_names = [] layer_number = name.split(".")[2] name_after_layer = name.split(f".{layer_number}.")[1] if name_after_layer in name_map_after_layer: mapped_name = name_map_after_layer[name_after_layer] if isinstance(mapped_name, list): assert len(params) == len(mapped_name) for one in mapped_name: convert_names.append(f"model.layers.{layer_number}.{one}") else: assert len(params) == 1 convert_names.append(f"model.layers.{layer_number}.{mapped_name}") else: if "mlp.experts.linear_fc1.weight" in name: expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.gate_proj.weight") convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.up_proj.weight") assert len(params) == 2 elif "mlp.experts.linear_fc2.weight" in name: expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.down_proj.weight") assert len(params) == 1 else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params def _convert_mtp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: assert self.mcore_config.mtp_num_layers == 1, "only support one mtp layer for now" assert self.mcore_config.num_layers == 61, "only support 61 layers for now" direct_name_mapping = { "mtp.layers.0.enorm.weight": "model.layers.61.enorm.weight", "mtp.layers.0.hnorm.weight": "model.layers.61.hnorm.weight", "mtp.layers.0.eh_proj.weight": "model.layers.61.eh_proj.weight", "mtp.layers.0.final_layernorm.weight": "model.layers.61.shared_head.norm.weight", } if name in direct_name_mapping: return [direct_name_mapping[name]], [params[0]] assert "mtp.layers.0.transformer_layer" in name, "only support transformer layer for now" # use proxy name to convert proxy_name = name.replace("mtp.layers.0.transformer_layer", "decoder.layers.61") if "self_attention" in proxy_name or "input_layernorm.weight" in proxy_name: convert_names, params = self._convert_attention_param(proxy_name, params) elif "mlp" in proxy_name: convert_names, params = self._convert_mlp_param(proxy_name, params) else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params def convert_param(self, name: str, params_one_group: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: direct_name_mapping = { "embedding.word_embeddings.weight": "model.embed_tokens.weight", "decoder.final_layernorm.weight": "model.norm.weight", "output_layer.weight": "lm_head.weight", } if name in direct_name_mapping: return [direct_name_mapping[name]], [params_one_group[0]] if "mtp" in name: return self._convert_mtp_param(name, params_one_group) elif "self_attention" in name or "input_layernorm.weight" in name: return self._convert_attention_param(name, params_one_group) elif "mlp" in name: return self._convert_mlp_param(name, params_one_group) else: raise NotImplementedError(f"Unsupported parameter name: {name}") class McoreToHFWeightConverterMixtral(McoreToHFWeightConverterDense): def _convert_mlp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # decoder.layers.0.mlp.router.weight # decoder.layers.0.mlp.experts.linear_fc1.weight0 - weight7 # decoder.layers.0.mlp.experts.linear_fc2.weight0 - weight7 layer_number = name.split(".")[2] convert_names = [] if "pre_mlp_layernorm" in name: convert_names.append(f"model.layers.{layer_number}.post_attention_layernorm.weight") elif "mlp.router.weight" in name: convert_names.append(f"model.layers.{layer_number}.block_sparse_moe.gate.weight") elif "mlp.experts.linear_fc1.weight" in name: expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.block_sparse_moe.experts.{expert_id}.w1.weight") convert_names.append(f"model.layers.{layer_number}.block_sparse_moe.experts.{expert_id}.w3.weight") elif "mlp.experts.linear_fc2.weight" in name: expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.block_sparse_moe.experts.{expert_id}.w2.weight") else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params class McoreToHFWeightConverterQwen3Moe(McoreToHFWeightConverterDense): def _convert_mlp_param(self, name: str, params: list[torch.Tensor]) -> tuple[list[str], list[torch.Tensor]]: # qwen3 moe no share expert # 'decoder.layers.0.pre_mlp_layernorm.weight', # 'decoder.layers.0.mlp.router.weight', # moe1 # 'decoder.layers.0.mlp.experts.linear_fc1.weight0', # 'decoder.layers.0.mlp.experts.linear_fc1.weight1', # 'decoder.layers.0.mlp.experts.linear_fc1.weight2', # 'decoder.layers.0.mlp.experts.linear_fc1.weight3', # moe2 # 'decoder.layers.0.mlp.experts.linear_fc2.weight0', # 'decoder.layers.0.mlp.experts.linear_fc2.weight1', layer_number = name.split(".")[2] convert_names = [] if "pre_mlp_layernorm" in name: convert_names.append(f"model.layers.{layer_number}.post_attention_layernorm.weight") assert len(params) == 1 elif "mlp.router.weight" in name: convert_names.append(f"model.layers.{layer_number}.mlp.gate.weight") assert len(params) == 1 elif "mlp.experts.linear_fc1" in name: # split gate_proj and up_proj expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.gate_proj.weight") convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.up_proj.weight") assert len(params) == 2 elif "mlp.experts.linear_fc2" in name: expert_id = name.split("weight")[-1] convert_names.append(f"model.layers.{layer_number}.mlp.experts.{expert_id}.down_proj.weight") assert len(params) == 1 else: raise NotImplementedError(f"Unsupported parameter name: {name}") return convert_names, params ================================================ FILE: verl/models/qwen2/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/qwen2/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .modeling_qwen2_megatron import ( ParallelQwen2ForCausalLM, # rmpad with megatron ParallelQwen2ForCausalLMRmPad, # rmpad with megatron and pipeline parallelism ParallelQwen2ForCausalLMRmPadPP, ParallelQwen2ForValueRmPad, ParallelQwen2ForValueRmPadPP, # original model with megatron ParallelQwen2Model, ) __all__ = [ "ParallelQwen2ForCausalLM", "ParallelQwen2ForCausalLMRmPad", "ParallelQwen2ForCausalLMRmPadPP", "ParallelQwen2ForValueRmPad", "ParallelQwen2ForValueRmPadPP", "ParallelQwen2Model", ] ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/qwen2_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from verl.utils.device import get_device_id, get_torch_device def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_qwen2( state_dict, wrapped_models, config, params_dtype, is_value_model=False, tie_word_embeddings=False ): """Load merged state_dict to sharded Megatron module in training.""" from megatron.core import DistributedDataParallel as LocalDDP from megatron.core import mpu from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model start_time = time.time() def _get_gpt_model(model): return model def fetch_params(module): for param in module.parameters(): torch.distributed.fetch( param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group() ) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers, ( f"num_layers_per_model: {num_layers_per_model} * pp_size: {pp_size} * virtual_pp_size: " f"{virtual_pp_size} != config.num_hidden_layers: {config.num_hidden_layers}" ) models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.model.layers) == num_layers_per_model def _fetch_tensor(tensor, name) -> torch.Tensor: """fetch tensor""" nonlocal state_dict if tensor is not None: tensor = tensor.data.copy_(state_dict[name], non_blocking=True) def _fetch_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """fetch tensor in tp shards""" nonlocal state_dict tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) if tensor is not None: tensor = tensor.data.copy_(tensor_chunk[tp_rank], non_blocking=True) else: print(f"tp_shard tensor:[{name}] not in state_dict, skip loading") def _fetch_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """fetch tensor in tp shards""" nonlocal state_dict tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) if tensor is not None: tensor = tensor.data.copy_(tensor_chunk[tp_rank], non_blocking=True) else: print(f"tp_shard tensor:[{name}] not in state_dict, skip loading") def _fetch_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """fetch gate_up tensor in tp shards""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if gate_name in state_dict and up_name in state_dict: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty( config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0) ) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) if tensor is not None: tensor = tensor.data.copy_(tensor_chunk[tp_rank], non_blocking=True) else: print(f"tp_shard tensor:[{gate_name}, {up_name}] not in state_dict, skip loading") def _fetch_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, bias=False) -> torch.Tensor: """fetch tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() assert q_name in state_dict and k_name in state_dict and v_name in state_dict full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp if not bias: new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) else: new_weight_qkv = torch.empty(total_size * tp_size, dtype=params_dtype, device=get_device_id()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp : (i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp : (i + 1) * kv_size_tp] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp if not bias: new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) else: new_weight_qkv = torch.empty(total_size * tp_size, dtype=params_dtype, device=get_device_id()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) if tensor is not None: tensor = tensor.data.copy_(tensor_chunk[tp_rank], non_blocking=True) # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) if pp_rank == 0: embed_tokens_weight = gpt_model_module.model.embed_tokens.weight _fetch_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() num_layer_per_pp = config.num_hidden_layers // pp_size vpp_size = mpu.get_virtual_pipeline_model_parallel_world_size() layer_list = [] if vpp_size is not None: for vpp_rank in range(vpp_size): num_layer_vpp_chunk = num_layer_per_pp // vpp_size num_layer_this_model = num_layer_vpp_chunk offset = vpp_rank * (config.num_hidden_layers // mpu.get_virtual_pipeline_model_parallel_world_size()) + ( mpu.get_pipeline_model_parallel_rank() * num_layer_vpp_chunk ) layer_list.extend(list(range(offset, offset + num_layer_this_model))) else: num_layer_this_model = num_layer_per_pp offset = pp_rank * num_layer_per_pp layer_list.extend(list(range(offset, offset + num_layer_this_model))) for layer in layer_list: print(f"{torch.distributed.get_rank()} loading layer #{layer}...") layer_name = f"model.layers.{layer}" dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] print( f"{torch.distributed.get_rank()} offset: {offset}, num_layer_this_model: {num_layer_this_model}, " f"layer_name: {layer_name}, layer_map[layer]: {layer_map[layer]}" ) gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[dst_layer_idx] _fetch_tensor( sync_layer.input_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) _fetch_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) _fetch_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.bias if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.bias", f"{layer_name}.self_attn.k_proj.bias", f"{layer_name}.self_attn.v_proj.bias", bias=True, ) _fetch_tp_shard_tensor( sync_layer.self_attn.o_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _fetch_tensor( sync_layer.post_attention_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _fetch_tp_shard_tensor_gate_up( sync_layer.mlp.gate_up_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", ) _fetch_tp_shard_tensor( sync_layer.mlp.down_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _fetch_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", ) if tie_word_embeddings: print_rank_0("tie_word_embeddings skip load lm_head") else: print_rank_0("loading lm_head...") if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.lm_head.weight if is_value_model: if "lm_head.weight" in state_dict and state_dict["lm_head.weight"].shape[0] == 1: _fetch_tensor(lm_head_weight, "lm_head.weight") print_rank_0("load lm_head from value_head weight") elif "reward_head.weight" in state_dict and state_dict["reward_head.weight"].shape[0] == 1: _fetch_tensor(lm_head_weight, "reward_head.weight") print_rank_0("load lm_head from value_head weight") else: _fetch_tensor(None, "lm_head.weight") print_rank_0("fail to match lm_head in value_model") else: _fetch_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() get_torch_device().empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/qwen2_loader_depracated.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from verl.utils.device import get_device_id, get_torch_device def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_qwen2( state_dict, wrapped_models, config, params_dtype, is_value_model=False, tie_word_embeddings=False ): """Load merged state_dict to sharded Megatron module in training.""" from megatron.core import DistributedDataParallel as LocalDDP from megatron.core import mpu from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model start_time = time.time() def _get_gpt_model(model): return model def broadcast_params(module): for param in module.parameters(): torch.distributed.broadcast( param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group() ) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers, ( f"num_layers_per_model: {num_layers_per_model} * pp_size: {pp_size} * virtual_pp_size: " f"{virtual_pp_size} != config.num_hidden_layers: {config.num_hidden_layers}" ) models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.model.layers) == num_layers_per_model def _broadcast_tensor(tensor, name) -> torch.Tensor: """broadcast tensor from rank0 across mp_group""" nonlocal state_dict nonlocal mp_group if torch.distributed.get_rank() == 0: if name in state_dict: weight = state_dict[name] tensor_shape = weight.shape else: tensor_shape = None else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not in state_dict, skip load") return if tensor is None: tensor = torch.empty( tensor_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) if torch.distributed.get_rank() == 0: tensor.data.copy_(weight) dist.broadcast(tensor, src=0, group=mp_group) def _broadcast_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty( config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=get_device_id() ) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp : (i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0) ) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank() == 0:} tensor {gate_name, up_name} shape " f"{tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, bias=False) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: assert q_name in state_dict and k_name in state_dict and v_name in state_dict full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp if not bias: new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) else: new_weight_qkv = torch.empty(total_size * tp_size, dtype=params_dtype, device=get_device_id()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp : (i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp : (i + 1) * kv_size_tp] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_( torch.cat([q_part, k_part, v_part], dim=0) ) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp if not bias: new_weight_qkv = torch.empty( total_size * tp_size, config.hidden_size, dtype=params_dtype, device=get_device_id() ) else: new_weight_qkv = torch.empty(total_size * tp_size, dtype=params_dtype, device=get_device_id()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp : (i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv[i * total_size : (i + 1) * total_size].copy_( torch.cat([q_part, k_part, v_part], dim=0) ) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name, k_name, v_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=get_device_id(), requires_grad=False, ) else: assert tensor.shape == chunk_shape, ( f"rank #{torch.distributed.get_rank()} tensor {q_name} shape {tensor.shape} != {chunk_shape}" ) sync_tensor = torch.empty_like(tensor, device=get_device_id(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) if dp_rank == 0: # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) embed_tokens_weight = None if pp_rank == 0: embed_tokens_weight = gpt_model_module.model.embed_tokens.weight _broadcast_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"loading layer #{layer}...") layer_name = f"model.layers.{layer}" dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[dst_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.bias if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.bias", f"{layer_name}.self_attn.k_proj.bias", f"{layer_name}.self_attn.v_proj.bias", bias=True, ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _broadcast_tp_shard_tensor_gate_up( sync_layer.mlp.gate_up_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", ) _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", ) if tie_word_embeddings: print_rank_0("tie_word_embeddings skip load lm_head") else: print_rank_0("loading lm_head...") lm_head_weight = None if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.lm_head.weight if is_value_model: if "lm_head.weight" in state_dict and state_dict["lm_head.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "lm_head.weight") print_rank_0("load lm_head from value_head weight") elif "reward_head.weight" in state_dict and state_dict["reward_head.weight"].shape[0] == 1: _broadcast_tensor(lm_head_weight, "reward_head.weight") print_rank_0("load lm_head from value_head weight") else: _broadcast_tensor(None, "lm_head.weight") print_rank_0("fail to match lm_head in value_model") else: _broadcast_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() # Broadcast weights inside data parallel groups for wrapped_model in wrapped_models: broadcast_params(wrapped_model) get_torch_device().empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/qwen2_saver.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import torch import torch.distributed as dist from megatron.core import mpu from megatron.core.distributed import DistributedDataParallel as LocalDDP from megatron.core.transformer.module import Float16Module from torch.nn.parallel import DistributedDataParallel as torchDDP from verl.utils.device import get_device_id, get_torch_device from verl.utils.logger import print_rank_0 from verl.utils.megatron_utils import unwrap_model def _megatron_calc_global_rank(tp_rank: int = 0, dp_rank: int = 0, pp_rank: int = 0): """given TP,DP,PP rank to get the global rank.""" tp_size = mpu.get_tensor_model_parallel_world_size() dp_size = mpu.get_data_parallel_world_size() pp_size = mpu.get_pipeline_model_parallel_world_size() assert tp_size * dp_size * pp_size == torch.distributed.get_world_size(), ( f"{tp_size} x {dp_size} x {pp_size} != {torch.distributed.get_world_size()}" ) # We only support TP-DP-PP grouping, for correctness when resharding return (pp_rank * dp_size + dp_rank) * tp_size + tp_rank def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = ( virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model ) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def merge_megatron_ckpt_qwen2(wrapped_models, config, dtype, is_value_model=False, tie_word_embeddings=False): """Merge sharded parameters of a Megatron module into a merged checkpoint. Args: wrapped_models (list of megatron.core.distributed.DistributedDataParallel): The local DDP wrapped megatron modules. config (str or None): HF config for model dtype: model params type is_value_model: if model is value model tie_word_embeddings: tie_word_embeddings Returns: state_dict (dict): The merged state_dict in rank 0, and an empty dictionary in other ranks. """ start_time = time.time() def _get_gpt_model(model): return model dp_rank = mpu.get_data_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() pp_rank = mpu.get_pipeline_model_parallel_rank() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if dist.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, list | tuple): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) assert len(models[i].model.layers) == num_layers_per_model, ( "len model layers {} not equal to num_layers_per_model {}".format( len(models[i].model.layers), num_layers_per_model ) ) state_dict = dict() def _get_cpu_tensor(tensor: torch.Tensor): if tensor is None: return None if tensor.device == torch.device("cpu"): return tensor.detach().clone() return tensor.detach().cpu() def _broadcast_tensor(tensor, name, src_pp_rank) -> torch.Tensor: """broadcast tensor across mp_group""" nonlocal state_dict nonlocal mp_group src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: if tensor is None: weight = None tensor_shape = None else: weight = tensor tensor_shape = weight.shape else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not exist, skip collect") return if weight is None: weight = torch.empty( tensor_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) dist.broadcast(weight, src=src_rank, group=mp_group) if torch.distributed.get_rank() == 0: state_dict[name] = _get_cpu_tensor(weight) def _broadcast_tp_shard_tensor(tensor, name, src_pp_rank, concat_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=concat_dim) if mutate_func is not None: full_tensor = mutate_func(full_tensor) state_dict[name] = full_tensor def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name, src_pp_rank) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) intermediate_size_tp = config.intermediate_size // tp_size gate_weight_list = [] up_weight_list = [] for i in range(tp_size): gate_up_weight_tp = full_tensor[intermediate_size_tp * 2 * i : intermediate_size_tp * 2 * (i + 1)] gate_weight_tp = gate_up_weight_tp[:intermediate_size_tp] up_weight_tp = gate_up_weight_tp[intermediate_size_tp:] gate_weight_list.append(gate_weight_tp) up_weight_list.append(up_weight_tp) state_dict[gate_name] = torch.cat(gate_weight_list, dim=0) state_dict[up_name] = torch.cat(up_weight_list, dim=0) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, src_pp_rank): """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) chunk_shape = tensor.shape if torch.distributed.get_rank() == src_rank else None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=dtype, device=get_device_id(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) q_weight_list = [] k_weight_list = [] v_weight_list = [] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size : (i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp : q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp : total_size] q_weight_list.append(q_part) k_weight_list.append(k_part) v_weight_list.append(v_part) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size : (i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp : q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp : total_size] q_weight_list.append(q_part) if i * config.num_key_value_heads % tp_size == 0: k_weight_list.append(k_part) v_weight_list.append(v_part) state_dict[q_name] = torch.cat(q_weight_list, dim=0) state_dict[k_name] = torch.cat(k_weight_list, dim=0) state_dict[v_name] = torch.cat(v_weight_list, dim=0) # empty cache before collecting weights get_torch_device().empty_cache() # Embeddings # ------------------- if dp_rank == 0: # Embeddings # ------------------- print_rank_0("collecting embeddings...") gpt_model_module = _get_gpt_model(models[0]) _broadcast_tp_shard_tensor( gpt_model_module.model.embed_tokens.weight if pp_rank == 0 else None, "model.embed_tokens.weight", src_pp_rank=0, ) # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"collecting layer #{layer}...") layer_name = f"model.layers.{layer}" src_pp_rank, src_virtual_pp_rank, src_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[src_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[src_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight, f"{layer_name}.input_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.bias, f"{layer_name}.self_attn.q_proj.bias", f"{layer_name}.self_attn.k_proj.bias", f"{layer_name}.self_attn.v_proj.bias", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight, f"{layer_name}.self_attn.o_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight, f"{layer_name}.post_attention_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_gate_up( sync_layer.mlp.gate_up_proj.weight, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight, f"{layer_name}.mlp.down_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) # Final Layernorm # ------------------- print_rank_0("collecting final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", src_pp_rank=pp_size - 1, ) if tie_word_embeddings: print_rank_0("tie word embedding skip load lm_head...") else: print_rank_0("collecting lm_head...") if is_value_model: _broadcast_tensor( gpt_model_module.lm_head.weight if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) _broadcast_tensor( gpt_model_module.reward_head.weight if pp_rank == pp_size - 1 and getattr(gpt_model_module, "reward_weight", None) is not None else None, "reward_head.weight", src_pp_rank=pp_size - 1, ) else: _broadcast_tp_shard_tensor( getattr(gpt_model_module.lm_head, "weight", None) if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) dist.barrier() get_torch_device().empty_cache() if torch.distributed.get_rank() == 0: for k, v in state_dict.items(): if dtype != v.dtype: state_dict[k] = v.to(dtype) print_rank_0(f"merge megatron ckpt done, time elapsed {time.time() - start_time}s") return state_dict ================================================ FILE: verl/models/qwen2/megatron/layers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .parallel_attention import ParallelQwen2Attention from .parallel_decoder import ParallelQwen2DecoderLayer, ParallelQwen2DecoderLayerRmPad from .parallel_mlp import ParallelQwen2MLP from .parallel_rmsnorm import ParallelQwen2RMSNorm __all__ = [ "ParallelQwen2Attention", "ParallelQwen2DecoderLayer", "ParallelQwen2DecoderLayerRmPad", "ParallelQwen2MLP", "ParallelQwen2RMSNorm", ] ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_attention.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Optional import torch.nn.functional as F from einops import rearrange from transformers.utils import is_flash_attn_2_available if is_flash_attn_2_available(): from flash_attn import flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa: F401 import torch from flash_attn.layers.rotary import apply_rotary_emb from megatron.core import ModelParallelConfig, tensor_parallel from megatron.core import parallel_state as mpu from torch import nn from transformers import Qwen2Config from verl.models.qwen2.megatron.layers.parallel_linear import QKVParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class Qwen2RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) class Qwen2LinearScalingRotaryEmbedding(Qwen2RotaryEmbedding): """Qwen2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) class Qwen2DynamicNTKScalingRotaryEmbedding(Qwen2RotaryEmbedding): """Qwen2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class ParallelQwen2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta # assign values after tp tp_size = mpu.get_tensor_model_parallel_world_size() assert self.num_heads % tp_size == 0, ( f"num_head must be divisible by tp_size. Got num_head={self.num_heads}, tp_size={tp_size}" ) assert self.num_key_value_heads % tp_size == 0, ( f"num_key_value_heads must be divisible by tp_size. Got num_key_value_heads=" f"{self.num_key_value_heads}, tp_size={tp_size}" ) self.num_heads_per_tp = self.num_heads // tp_size self.num_key_value_heads_per_tp = self.num_key_value_heads // tp_size self.hidden_size_per_tp = self.hidden_size // tp_size if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and " f"`num_heads`: {self.num_heads})." ) column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" assert row_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) # [self.q_size, self.k_size, self.v_size] self.qkv_proj = QKVParallelLinear( input_size=self.hidden_size, num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads, head_dim=self.head_dim, # bias=config.attention_bias, bias=True, gather_output=False, skip_bias_add=False, **column_kwargs, ) self.q_size = self.num_heads_per_tp * self.head_dim self.k_size = self.num_key_value_heads_per_tp * self.head_dim self.v_size = self.num_key_value_heads_per_tp * self.head_dim self.o_proj = tensor_parallel.RowParallelLinear( input_size=self.num_heads * self.head_dim, output_size=self.hidden_size, # bias=config.attention_bias, bias=False, input_is_parallel=True, skip_bias_add=False, **row_kwargs, ) self._init_rope() def _init_rope(self): self.rotary_emb = Qwen2RotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1) query_states = query_states.view(bsz, q_len, self.num_heads_per_tp, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads_per_tp, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads_per_tp, q_len, kv_seq_len)}, " f"but is {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads_per_tp, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads_per_tp, q_len, self.head_dim)}, " f"but is {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size_per_tp) attn_output = self.o_proj(attn_output)[0] return attn_output """ Remove padding Attention - Using Flash-attn 2 - Compatible with sequence parallel """ def apply_rotary_pos_emb_rmpad(q, k, cos, sin, position_ids, indices, sequence_length): batch_size = position_ids.shape[0] q = pad_input(q, indices, batch_size, sequence_length) # (batch_size, seqlen, num_head, head_dim) k = pad_input(k, indices, batch_size, sequence_length) cos = cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] sin = sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) q_embed = index_first_axis(rearrange(q_embed, "b s ... -> (b s) ..."), indices) k_embed = index_first_axis(rearrange(k_embed, "b s ... -> (b s) ..."), indices) return q_embed, k_embed # use flash-attn rotary embeddings with rmpad # cos/sin shoudl be: (seq_length, rotary_dim / 2) def apply_rotary_pos_emb_rmpad_flash(q, k, cos, sin, cu_seqlens, max_seqlen): q_embed = apply_rotary_emb( q, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen ) k_embed = apply_rotary_emb( k, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen ) return q_embed, k_embed class ParallelQwen2AttentionRmPad(ParallelQwen2Attention): def forward( self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: torch.Tensor = None, max_seqlen_in_batch: int = None, ): total_nnz, _, _ = hidden_states.size() # This is the total_nnz padded after sequence parallel if self.megatron_config.sequence_parallel: total_nnz = total_nnz * mpu.get_tensor_model_parallel_world_size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split( [self.q_size, self.k_size, self.v_size], dim=-1 ) # (total_nnz, 1, hidden_size) if self.megatron_config.sequence_parallel: sequence_parallel_pad = total_nnz - cu_seqlens[-1] total_nnz = cu_seqlens[-1] # total_nnz before sp padding query_states = query_states[:total_nnz] key_states = key_states[:total_nnz] value_states = value_states[:total_nnz] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dime x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(total_nnz, self.num_heads_per_tp, self.head_dim) key_states = key_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) value_states = value_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) cos, sin = self.rotary_emb(value_states, seq_len=sequence_length) cos, sin = cos[:, : cos.shape[1] // 2], sin[:, : sin.shape[1] // 2] # flash attn only needs half query_states, key_states = apply_rotary_pos_emb_rmpad_flash( query_states, key_states, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen_in_batch ) # query_states, key_states = apply_rotary_pos_emb_rmpad(query_states, key_states, cos, sin, # position_ids, indices, # It is recommended to use dropout with FA according to the docs # when training. dropout_rate = 0.0 # if not self.training else self.attn_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (Qwen2RMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: query_states = query_states.to(torch.float16) key_states = key_states.to(torch.float16) value_states = value_states.to(torch.float16) attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens, cu_seqlens_k=cu_seqlens, max_seqlen_q=max_seqlen_in_batch, max_seqlen_k=max_seqlen_in_batch, dropout_p=dropout_rate, softmax_scale=None, causal=True, ) attn_output_unpad = attn_output_unpad.to(input_dtype) attn_output_unpad = attn_output_unpad.reshape(total_nnz, 1, self.hidden_size_per_tp).contiguous() # sequence parallel reduce_scatter is performed inside RowColumnParallel if enabled # Here we need to repad if self.megatron_config.sequence_parallel: attn_output_unpad = F.pad(attn_output_unpad, pad=(0, 0, 0, 0, 0, sequence_parallel_pad)) attn_output_unpad = self.o_proj(attn_output_unpad)[0] return attn_output_unpad ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_decoder.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional import torch from megatron.core import ModelParallelConfig from torch import nn from transformers import Qwen2Config from verl.utils.megatron_utils import TransformerConfig, convert_config from .parallel_attention import ParallelQwen2Attention, ParallelQwen2AttentionRmPad from .parallel_mlp import ParallelQwen2MLP from .parallel_rmsnorm import ParallelQwen2RMSNorm class ParallelQwen2DecoderLayer(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig, layer_idx: int): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.self_attn = ParallelQwen2Attention(config=config, megatron_config=megatron_config) self.mlp = ParallelQwen2MLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelQwen2RMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelQwen2RMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Note: sequence parallel is hidden inside ColumnParallelLinear # reduce scatter is hidden inside RowParallelLinear # Self Attention hidden_states = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) # TODO: add sequence parallel operator all_gather here hidden_states = self.mlp(hidden_states) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states outputs = hidden_states return outputs class ParallelQwen2DecoderLayerRmPad(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig, layer_idx: int): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.hidden_size = config.hidden_size self.layer_idx = layer_idx self.self_attn = ParallelQwen2AttentionRmPad(config=config, megatron_config=megatron_config) self.mlp = ParallelQwen2MLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelQwen2RMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelQwen2RMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None, ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states # (total_nnz // sp, 1, hidden_size) hidden_states = self.input_layernorm(hidden_states) # Self Attention # (total_nnz // sp, 1, hidden_size) -> all-gather (total_nnz, 1, hidden_size) # -> col + row -> reduce-scatter -> (total_nnz // sp, 1, hidden_size) hidden_states = self.self_attn( hidden_states=hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = residual + hidden_states # Fully Connected # shape changes same as attn residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = hidden_states return outputs ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_linear.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/linear.py from megatron.core import tensor_parallel class QKVParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__( self, input_size, num_heads, num_key_value_heads, head_dim, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs, ): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.q_output_size = num_heads * head_dim self.kv_output_size = num_key_value_heads * head_dim self.head_dim = head_dim self.gather_output = gather_output self.skip_bias_add = skip_bias_add input_size = self.input_size output_size = (num_heads + 2 * num_key_value_heads) * self.head_dim super().__init__( input_size=input_size, output_size=output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs, ) class MergedColumnParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__( self, input_size, gate_ouput_size, up_output_size, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs, ): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.output_size = gate_ouput_size + up_output_size self.gather_output = gather_output self.skip_bias_add = skip_bias_add super().__init__( input_size=self.input_size, output_size=self.output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs, ) ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_mlp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from megatron.core import ModelParallelConfig, tensor_parallel from megatron.core import parallel_state as mpu from torch import nn from transformers.activations import ACT2FN from verl.models.qwen2.megatron.layers.parallel_linear import MergedColumnParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class ParallelQwen2MLP(nn.Module): def __init__(self, config, megatron_config: ModelParallelConfig = None) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size # The weight is only [hidden_size, intermediate_size // model_parallel_world_size] column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" assert row_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_size = mpu.get_tensor_model_parallel_world_size() self.gate_up_proj = MergedColumnParallelLinear( input_size=self.hidden_size, gate_ouput_size=self.intermediate_size, up_output_size=self.intermediate_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) self.gate_size = self.intermediate_size // tp_size self.down_proj = tensor_parallel.RowParallelLinear( input_size=self.intermediate_size, output_size=self.hidden_size, bias=False, input_is_parallel=True, skip_bias_add=False, **row_kwargs, ) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): gate_up = self.gate_up_proj(x)[0] gate, up = gate_up.split(self.gate_size, dim=-1) return self.down_proj(self.act_fn(gate) * up)[0] ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_rmsnorm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numbers import torch from apex.normalization.fused_layer_norm import fused_rms_norm_affine from megatron.core import ModelParallelConfig from torch import nn from transformers import Qwen2Config from verl.utils.megatron import sequence_parallel as sp_utils class ParallelQwen2RMSNorm(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): """ Qwen2RMSNorm is equivalent to T5LayerNorm """ super().__init__() if isinstance(config.hidden_size, numbers.Integral): normalized_shape = (config.hidden_size,) self.normalized_shape = torch.Size(normalized_shape) self.weight = nn.Parameter(torch.ones(self.normalized_shape)) self.variance_epsilon = config.rms_norm_eps if megatron_config.sequence_parallel: sp_utils.mark_parameter_as_sequence_parallel(self.weight) def forward(self, hidden_states): return fused_rms_norm_affine( input=hidden_states, weight=self.weight, normalized_shape=self.normalized_shape, eps=self.variance_epsilon, memory_efficient=True, ) ================================================ FILE: verl/models/qwen2/megatron/modeling_qwen2_megatron.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Qwen2 model.""" from typing import Optional import torch import torch.utils.checkpoint from megatron.core import ModelParallelConfig, mpu, parallel_state, tensor_parallel from torch import nn from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.models.qwen2.configuration_qwen2 import Qwen2Config from transformers.models.qwen2.modeling_qwen2 import CausalLMOutputWithPast from verl.utils.device import get_device_name from verl.utils.megatron import sequence_parallel as sp_utils from verl.utils.megatron import tensor_parallel as tp_utils from verl.utils.megatron_utils import TransformerConfig, convert_config from .layers import ParallelQwen2DecoderLayer, ParallelQwen2DecoderLayerRmPad, ParallelQwen2RMSNorm """ TODO: 1. Add weight initialization. Here we need to be careful on TP weight init. 2. Add sequence parallel 3. Load checkpoint from Qwen2 pretrained checkpoint """ # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class ParallelQwen2Model(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config """ def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(embedding_kwargs, megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs ) self.layers = nn.ModuleList( [ParallelQwen2DecoderLayer(config, megatron_config) for _ in range(config.num_hidden_layers)] ) self.norm = ParallelQwen2RMSNorm(config, megatron_config) # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | BaseModelOutputWithPast: """ Args: input_ids: input ids. shape (batch_size, seq_length) attention_mask: attention_mask. shape (batch_size, seq_length) position_ids: position ids. shape (batch_size, seq_length) Returns: """ batch_size, seq_length = input_ids.shape inputs_embeds = self.embed_tokens(input_ids) # embed positions attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelQwen2ForCausalLM(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.model = ParallelQwen2Model(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear( input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = outputs logits = self.lm_head(hidden_states)[0] logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) logits = logits.float() return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa: F401, E402 class ParallelQwen2ModelRmPad(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config """ def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() self.megatron_config = megatron_config if megatron_config is not None: assert embedding_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs ) self.layers = nn.ModuleList( [ParallelQwen2DecoderLayerRmPad(config, megatron_config) for _ in range(config.num_hidden_layers)] ) self.norm = ParallelQwen2RMSNorm(config, megatron_config) def forward( self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None, ) -> tuple | BaseModelOutputWithPast: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelQwen2ForCausalLMRmPad(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.megatron_config = megatron_config self.model = ParallelQwen2ModelRmPad(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size self._init_head(config) def _init_head(self, config: Qwen2Config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear( input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head logits = self.lm_head(hidden_states)[0] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) # (total_nnz_padded, 1, vocab_size) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" batch_size, sequence_length = input_ids.shape # remove padding here input_ids, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input( input_ids.unsqueeze(dim=-1), attention_mask ) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids = sp_utils.pad_to_sequence_parallel(input_ids) input_ids = input_ids.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model( input_ids=input_ids, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = outputs logits = self._forward_head(hidden_states) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # add removed padding back logits = pad_input( logits, indices, batch_size, seqlen=sequence_length ) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) class ParallelQwen2ForValueRmPad(ParallelQwen2ForCausalLMRmPad): def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: output = super().forward(input_ids, attention_mask, position_ids) output.logits = torch.squeeze(output.logits, dim=-1) return output """ Support pipeline parallelism """ class ParallelQwen2ModelRmPadPP(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] This model definition supports pipeline parallelism. To support pp and vpp, - This model only contains layer in this pp stage and vpp chunk - When calling get_model in Megatron, this rank will instantiate all the vpp chunks in this pp. Args: config: Qwen2Config """ def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig, pre_process, post_process): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process self.megatron_config = megatron_config embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) if pre_process: self.embed_tokens = tensor_parallel.VocabParallelEmbedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs ) else: self.embed_tokens = None pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = megatron_config.pipeline_model_parallel_size self.num_layer_per_pp = config.num_hidden_layers // pp_size vpp_size = megatron_config.virtual_pipeline_model_parallel_size vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() if vpp_size is not None: self.num_layer_vpp_chunk = self.num_layer_per_pp // vpp_size self.num_layer_this_model = self.num_layer_vpp_chunk offset = vpp_rank * (config.num_hidden_layers // vpp_size) + (pp_rank * self.num_layer_vpp_chunk) else: self.num_layer_this_model = self.num_layer_per_pp offset = pp_rank * self.num_layer_per_pp self.layers = nn.ModuleList() for i in range(self.num_layer_this_model): layer = ParallelQwen2DecoderLayerRmPad(config, megatron_config, layer_idx=i + offset) self.layers.add_module(f"{i}", layer) if post_process: self.norm = ParallelQwen2RMSNorm(config, megatron_config) else: self.norm = None def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" self.input_tensor = input_tensor def forward( self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None, ) -> tuple | BaseModelOutputWithPast: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ if self.pre_process: inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # vocab parallel embedding will not do sequence parallel reduce-scatter in open source megatron # so need to deal with it by handle here: # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds else: # self.hidden_states should be passed by Megatron hidden_states = self.input_tensor for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) hidden_states = layer_outputs if self.post_process: hidden_states = self.norm(hidden_states) return hidden_states class ParallelQwen2ForCausalLMRmPadPP(nn.Module): def __init__( self, config: Qwen2Config, megatron_config: ModelParallelConfig, pre_process, post_process, share_embeddings_and_output_weights, ): super().__init__() self.config: TransformerConfig = convert_config(config, megatron_config) self.megatron_config = megatron_config self.model = ParallelQwen2ModelRmPadPP( config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process ) self.share_embeddings_and_output_weights = share_embeddings_and_output_weights self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process if post_process: self._init_head(config) if pre_process or post_process: self.setup_embeddings_and_output_layer() def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" assert len(input_tensor) == 1 self.model.set_input_tensor(input_tensor[0]) def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear( input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, skip_weight_param_allocation=self.pre_process and self.share_embeddings_and_output_weights, **column_kwargs, ) def setup_embeddings_and_output_layer(self) -> None: """Sets up embedding layer in first stage and output layer in last stage. This function initializes word embeddings in the final stage when we are using pipeline parallelism and sharing word embeddings, and sets up param attributes on the embedding and output layers. """ # Set `is_embedding_or_output_parameter` attribute. if self.pre_process: self.model.embed_tokens.weight.is_embedding_or_output_parameter = True if self.post_process and self.lm_head.weight is not None: self.lm_head.weight.is_embedding_or_output_parameter = True if not self.share_embeddings_and_output_weights: return if parallel_state.get_pipeline_model_parallel_world_size() == 1: # Zero out wgrad if sharing embeddings between two layers on same # pipeline stage to make sure grad accumulation into main_grad is # correct and does not include garbage values (e.g., from torch.empty). self.shared_embedding_or_output_weight().zero_out_wgrad = True return if parallel_state.is_pipeline_first_stage() and self.pre_process and not self.post_process: self.shared_embedding_or_output_weight().shared_embedding = True if self.post_process and not self.pre_process: assert not parallel_state.is_pipeline_first_stage() # set word_embeddings weights to 0 here, then copy first # stage's weights using all_reduce below. self.lm_head.weight.data.fill_(0) self.lm_head.weight.shared = True self.lm_head.weight.shared_embedding = True if torch.distributed.is_initialized() and parallel_state.is_rank_in_embedding_group(): weight = self.shared_embedding_or_output_weight() weight.data = weight.data.to(get_device_name()) torch.distributed.all_reduce(weight.data, group=parallel_state.get_embedding_group()) def shared_embedding_or_output_weight(self) -> torch.Tensor: if self.pre_process: return self.model.embed_tokens.weight elif self.post_process: return self.lm_head.weight return None def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head # print(f'logits shape before forward_head: {hidden_states.shape}, vocab_size = ' # f'{self.config.vocab_size}') # [4, 32, 4096] output_weight = None if self.share_embeddings_and_output_weights: output_weight = self.shared_embedding_or_output_weight() logits = self.lm_head(hidden_states, weight=output_weight)[0] # print(f'logits shape after forward_head: {logits.shape}') # [8, 32, 8] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) return logits def forward( self, # original input *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # Note that input_ids, attention_mask and position_ids should be passed to every pp layer. # In the first pp, input_ids will be used, in other pp layers hidden_states will be used inside self.model batch_size, sequence_length = input_ids.shape # remove padding here input_ids_rmpad, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input( input_ids.unsqueeze(dim=-1), attention_mask ) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids_rmpad = sp_utils.pad_to_sequence_parallel(input_ids_rmpad) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model( input_ids=input_ids_rmpad, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch, ) if self.post_process: hidden_states = outputs logits = self._forward_head(hidden_states) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # torch.Size([8, 32, 16]) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) # add removed padding back. If input is already rmpad, we let the caller pad_input logits = pad_input( logits, indices, batch_size, seqlen=sequence_length ) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) else: return outputs class ParallelQwen2ForValueRmPadPP(ParallelQwen2ForCausalLMRmPadPP): def _init_head(self, config): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get("config", False), "must have ModelParallelConfig" tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> tuple | CausalLMOutputWithPast: output = super().forward(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) if self.post_process: output.logits = torch.squeeze(output.logits, dim=-1) return output else: return output ================================================ FILE: verl/models/registry.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib from typing import Optional import torch.nn as nn # Supported models in Megatron-LM # Architecture -> (module, class). _MODELS = { "LlamaForCausalLM": ( "llama", ("ParallelLlamaForCausalLMRmPadPP", "ParallelLlamaForValueRmPadPP", "ParallelLlamaForCausalLMRmPad"), ), "Qwen2ForCausalLM": ( "qwen2", ("ParallelQwen2ForCausalLMRmPadPP", "ParallelQwen2ForValueRmPadPP", "ParallelQwen2ForCausalLMRmPad"), ), "MistralForCausalLM": ( "mistral", ("ParallelMistralForCausalLMRmPadPP", "ParallelMistralForValueRmPadPP", "ParallelMistralForCausalLMRmPad"), ), "ApertusForCausalLM": ( "apertus", ("ParallelApertusForCausalLMRmPadPP", "ParallelApertusForValueRmPadPP", "ParallelApertusForCausalLMRmPad"), ), } # return model class class ModelRegistry: @staticmethod def load_model_cls(model_arch: str, value=False) -> Optional[type[nn.Module]]: if model_arch not in _MODELS: return None megatron = "megatron" module_name, model_cls_name = _MODELS[model_arch] if not value: # actor/ref model_cls_name = model_cls_name[0] elif value: # critic/rm model_cls_name = model_cls_name[1] module = importlib.import_module(f"verl.models.{module_name}.{megatron}.modeling_{module_name}_megatron") return getattr(module, model_cls_name, None) @staticmethod def get_supported_archs() -> list[str]: return list(_MODELS.keys()) ================================================ FILE: verl/models/transformers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.models.transformers.monkey_patch import apply_monkey_patch from verl.models.transformers.tiled_mlp import apply_tiled_mlp_monkey_patch __all__ = [ "apply_monkey_patch", "apply_tiled_mlp_monkey_patch", ] ================================================ FILE: verl/models/transformers/apertus.py ================================================ # Copyright 2025 The SwissAI Initiative # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys from typing import Callable, Optional import torch if sys.version_info >= (3, 11): pass else: pass from transformers.cache_utils import Cache from transformers.models.apertus.modeling_apertus import apply_rotary_pos_emb from transformers.utils import logging # Import compatibility wrapper for flash_attn_supports_top_left_mask from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size, validate_ulysses_config, ) logger = logging.get_logger(__name__) def apertus_attn_forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: """ Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0. Key differences from Llama attention: - QK normalization applied after Q/K projections NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.50.0. """ from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS from transformers.models.apertus.modeling_apertus import eager_attention_forward bsz, q_len, _ = hidden_states.shape query_states = self.q_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = self.k_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) query_states = self.q_norm(query_states) key_states = self.k_norm(key_states) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: validate_ulysses_config(self.config.num_attention_heads, ulysses_sp_size) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. " "Falling back to eager attention. This warning can be removed using the argument " '`attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights ================================================ FILE: verl/models/transformers/dense_common.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import Optional, Union import torch from transformers.cache_utils import Cache from transformers.modeling_outputs import CausalLMOutputWithPast @dataclass class CausalLMOutputForPPO(CausalLMOutputWithPast): log_probs: Optional[torch.FloatTensor] = None entropy: Optional[torch.FloatTensor] = None def forward_base_model( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> CausalLMOutputWithPast: r""" Copy paste LLaMa's forward https://github.com/linkedin/Liger-Kernel/blob/main/src/liger_kernel/transformers/model/llama.py This function should be generic enough for all pure text models. ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) return outputs def forward_with_torch_backend( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union["Cache", list[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: int | torch.Tensor = 0, temperature: float = 1.0, **loss_kwargs, ) -> tuple | CausalLMOutputForPPO: from verl.utils.experimental.torch_functional import FusedLinearForPPO outputs = forward_base_model( self, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, cache_position=cache_position, ) hidden_states = outputs[0] if not return_dict: raise NotImplementedError("forward_with_torch_backend has to return_dict") # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_torch_backend, either labels or input_ids must be provided.") fused_linear_for_ppo = FusedLinearForPPO() log_probs, entropy = fused_linear_for_ppo.forward( hidden_states=hidden_states, vocab_weights=self.lm_head.weight, input_ids=rolled_labels, temperature=temperature, ) return CausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def forward_with_triton_backend( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union["Cache", list[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: int | torch.Tensor = 0, temperature: float = 1.0, **loss_kwargs, ) -> tuple | CausalLMOutputForPPO: from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy outputs = forward_base_model( self, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) hidden_states = outputs[0] if not return_dict: raise NotImplementedError("forward_with_triton_backend has to return_dict") # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_triton_backend, either labels or input_ids must be provided.") log_probs, entropy = linear_cross_entropy( hidden_states, self.lm_head.weight, rolled_labels, temperature, "none", ) return CausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) ================================================ FILE: verl/models/transformers/glm4v.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import itertools import logging import os from dataclasses import dataclass from typing import Optional import torch import torch.distributed as dist from transformers.modeling_flash_attention_utils import _flash_attention_forward, fa_peft_integration_check from transformers.models.glm4v.modeling_glm4v import ( Glm4vCausalLMOutputWithPast, Glm4vForConditionalGeneration, Glm4vTextAttention, ) from transformers.utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10 from verl.utils.device import is_npu_available from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_group, get_ulysses_sequence_parallel_world_size, validate_ulysses_config, ) logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func _flash_supports_window_size = "window_size" in inspect.signature(flash_attn_func).parameters _flash_supports_deterministic = "deterministic" in inspect.signature(flash_attn_func).parameters _flash_use_top_left_mask = not is_flash_attn_greater_or_equal_2_10() if is_npu_available: from transformers.integrations.npu_flash_attention import npu_flash_attn_func as flash_attn_func from transformers.integrations.npu_flash_attention import npu_flash_attn_varlen_func as flash_attn_varlen_func from transformers.modeling_flash_attention_utils import flash_attn_supports_top_left_mask _flash_supports_window_size = "window_size" in inspect.signature(flash_attn_func).parameters _flash_supports_deterministic = "deterministic" in inspect.signature(flash_attn_func).parameters _flash_use_top_left_mask = flash_attn_supports_top_left_mask() _flash_deterministic_enabled = os.getenv("FLASH_ATTENTION_DETERMINISTIC", "0") == "1" def get_rope_index( processor, input_ids: torch.Tensor, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Gets the position ids for GLM4V in padding-free format. The batch dim has been removed and the input_ids should be a 1D tensor representing a single example. """ spatial_merge_size = processor.image_processor.merge_size image_token_id = processor.tokenizer.convert_tokens_to_ids("<|image|>") video_start_token_id = processor.tokenizer.convert_tokens_to_ids("<|begin_of_video|>") video_end_token_id = processor.tokenizer.convert_tokens_to_ids("<|end_of_video|>") if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) position_ids = torch.ones(3, input_ids.size(0), dtype=input_ids.dtype, device=input_ids.device) # (3, seqlen) image_index, video_index = 0, 0 video_group_index = 0 input_ids_filtered = input_ids[attention_mask == 1] input_tokens = input_ids_filtered.tolist() input_token_type = [] video_check_flg = False for token in input_tokens: if token == video_start_token_id: video_check_flg = True elif token == video_end_token_id: video_check_flg = False if token == image_token_id and not video_check_flg: input_token_type.append("image") elif token == image_token_id and video_check_flg: input_token_type.append("video") else: input_token_type.append("text") input_type_group = [] for key, group in itertools.groupby(enumerate(input_token_type), lambda x: x[1]): group = list(group) start_index = group[0][0] end_index = group[-1][0] + 1 input_type_group.append((key, start_index, end_index)) llm_pos_ids_list = [] video_frame_num = 1 for modality_type, start_idx, end_idx in input_type_group: st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 if modality_type == "image": t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + st_idx) image_index += 1 video_frame_num = 1 elif modality_type == "video": t, h, w = ( video_frame_num, video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) llm_grid_t, llm_grid_h, llm_grid_w = ( t, h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) for t_idx in range(llm_grid_t): t_index = torch.tensor(t_idx).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(1, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(1, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + st_idx) video_group_index += 1 if video_group_index >= video_grid_thw[video_index][0]: video_index += 1 video_group_index = 0 video_frame_num += 1 else: text_len = end_idx - start_idx llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) video_frame_num = 1 llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., attention_mask == 1] = llm_positions.to(position_ids.device) else: if attention_mask is not None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1).to(input_ids.device) else: position_ids = torch.arange(input_ids.shape[0], device=input_ids.device).view(1, -1).expand(3, -1) return position_ids def prepare_fa2_from_position_ids( query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, position_ids: torch.Tensor ): assert position_ids.ndim == 2 # (batch_size, seq_length) query = query.contiguous().view(-1, query.size(-2), query.size(-1)) key = key.contiguous().view(-1, key.size(-2), key.size(-1)) value = value.contiguous().view(-1, value.size(-2), value.size(-1)) position_ids = position_ids.view(-1) cu_seqlens = torch.cat( ( (position_ids == 0).nonzero().view(-1).to(torch.int32), torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32), ) ) max_length = cu_seqlens.diff().max() # use cu_seqlens to infer max_length for qwen2vl mrope return (query, key, value, (cu_seqlens, cu_seqlens), (max_length, max_length)) def _custom_flash_attention_forward( query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: Optional[torch.Tensor], query_length: int, is_causal: bool = True, position_ids: Optional[torch.Tensor] = None, use_top_left_mask: bool = False, deterministic: Optional[bool] = None, **kwargs, ): """ Patches flash attention forward to handle 3D position ids in mrope. (3, batch_size, seq_length) """ # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length). flash_kwargs = {} if _flash_supports_deterministic: flash_kwargs["deterministic"] = deterministic if deterministic is not None else _flash_deterministic_enabled if kwargs.get("softcap") is not None: flash_kwargs["softcap"] = kwargs.pop("softcap") query_states, key_states, value_states = fa_peft_integration_check( query_states, key_states, value_states, target_dtype=torch.bfloat16 ) if position_ids is not None: assert position_ids.ndim == 2 # (batch_size, seq_length / sp_size) sp_size = get_ulysses_sequence_parallel_world_size() if sp_size > 1: # qkv: (batch_size, seq_length / sp_size, num_head, head_size) validate_ulysses_config(query_states.size(2), sp_size) query_states = gather_seq_scatter_heads(query_states, seq_dim=1, head_dim=2) key_states = gather_seq_scatter_heads(key_states, seq_dim=1, head_dim=2) value_states = gather_seq_scatter_heads(value_states, seq_dim=1, head_dim=2) position_ids_lst = [torch.empty_like(position_ids) for _ in range(sp_size)] position_ids = dist.all_gather(position_ids_lst, position_ids, group=get_ulysses_sequence_parallel_group()) position_ids = torch.cat(position_ids_lst, dim=-1) # (batch_size, seq_length) if position_ids is not None and query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all(): batch_size = query_states.size(0) q, k, v, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k) = prepare_fa2_from_position_ids( query_states, key_states, value_states, position_ids ) attn_output = flash_attn_varlen_func( q=q, k=k, v=v, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, dropout_p=kwargs.pop("dropout", 0.0), softmax_scale=kwargs.pop("softmax_scale", None), causal=is_causal, **flash_kwargs, ) attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1)) else: attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, query_length, is_causal=is_causal, use_top_left_mask=use_top_left_mask, deterministic=deterministic, **kwargs, ) # do not pass position_ids to old flash_attention_forward if sp_size > 1: # (batch_size, seq_length, num_head, head_size) attn_output = gather_heads_scatter_seq(attn_output, head_dim=2, seq_dim=1) return attn_output def glm4v_attn_forward( self: "Glm4vTextAttention", hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 **kwargs, ) -> tuple[torch.Tensor, None, None]: from transformers.models.glm4v.modeling_glm4v import apply_multimodal_rotary_pos_emb, repeat_kv bsz, q_len, _ = hidden_states.size() # q_len = seq_length / sp_size query_states = self.q_proj(hidden_states) # (batch_size, seq_length / sp_size, num_heads * head_size) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) # Because the input can be padded, the absolute sequence length depends on the max position id. cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # This is before the transpose q_len = query_states.shape[2] # FA2 uses non-transposed inputs query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_output = _custom_flash_attention_forward( query_states, key_states, value_states, attention_mask, query_length=q_len, is_causal=getattr(self, "is_causal", True), dropout=dropout_rate, use_top_left_mask=_flash_use_top_left_mask, position_ids=position_ids, # important: pass position ids ) # (batch_size, seq_length / sp_size, num_head, head_size) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.o_proj(attn_output) return attn_output, None def _get_input_embeds( model: "Glm4vForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, ): inputs_embeds = model.get_input_embeddings()(input_ids) if pixel_values is not None: pixel_values = pixel_values.type(model.visual.dtype) image_embeds = model.visual(pixel_values, grid_thw=image_grid_thw) n_image_tokens = (input_ids == model.config.image_token_id).sum().item() n_image_features = image_embeds.shape[0] if n_image_tokens != n_image_features: raise ValueError( f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" ) mask = input_ids == model.config.image_token_id mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) image_mask = mask_expanded.to(inputs_embeds.device) image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) if pixel_values_videos is not None: pixel_values_videos = pixel_values_videos.type(model.visual.dtype) video_embeds = model.visual(pixel_values_videos, grid_thw=video_grid_thw) n_video_tokens = (input_ids == model.config.video_token_id).sum().item() n_video_features = video_embeds.shape[0] if n_video_tokens != n_video_features: raise ValueError( f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" ) mask = input_ids == model.config.video_token_id mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) video_mask = mask_expanded.to(inputs_embeds.device) video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) if pixel_values is None and pixel_values_videos is None: # handle mixed text-image data pixel_values = torch.zeros((16, 1176), dtype=inputs_embeds.dtype, device=inputs_embeds.device) image_grid_thw = torch.tensor([[1, 4, 4]], dtype=torch.long, device=inputs_embeds.device) image_embeds = model.visual(pixel_values, grid_thw=image_grid_thw) inputs_embeds += 0.0 * image_embeds.mean() if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) return inputs_embeds, attention_mask def process_position_ids(position_ids: torch.Tensor) -> torch.Tensor: if position_ids.ndim != 3 or position_ids.size(0) != 4: # we concat the text position ids with the 3D vision position ids by default # see https://github.com/huggingface/transformers/pull/39447 raise ValueError("position_ids should be a 3D tensor of shape (4, batch_size, seq_length).") return position_ids @dataclass class Glm4vCausalLMOutputForPPO(Glm4vCausalLMOutputWithPast): log_probs: Optional[torch.FloatTensor] = None entropy: Optional[torch.FloatTensor] = None def glm4v_base_forward( self: "Glm4vForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, **kwargs, ): kwargs["inputs_embeds"], kwargs["attention_mask"] = _get_input_embeds( self, input_ids, attention_mask, pixel_values, pixel_values_videos, image_grid_thw, video_grid_thw ) # avoid lora module having multiple keyword arguments return self.language_model( input_ids=None, **kwargs, ) def glm4v_forward( self: "Glm4vForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, **kwargs, ): return self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=process_position_ids(position_ids), pixel_values=pixel_values, pixel_values_videos=pixel_values_videos, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, **kwargs, ) def forward_with_normal_backend( self: Glm4vForConditionalGeneration, input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> "Glm4vCausalLMOutputWithPast": outputs = glm4v_forward(self, input_ids, **kwargs) hidden_states = outputs[0] logits = self.lm_head(hidden_states) return Glm4vCausalLMOutputWithPast( logits=logits, hidden_states=outputs.hidden_states, ) def forward_with_torch_backend( self: Glm4vForConditionalGeneration, input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> tuple | Glm4vCausalLMOutputForPPO: from verl.utils.experimental.torch_functional import FusedLinearForPPO outputs = glm4v_forward(self, input_ids, **kwargs) hidden_states = outputs[0] # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_torch_backend, either labels or input_ids must be provided.") fused_linear_for_ppo = FusedLinearForPPO() log_probs, entropy = fused_linear_for_ppo.forward( hidden_states=hidden_states, vocab_weights=self.lm_head.weight, input_ids=rolled_labels, temperature=temperature, ) return Glm4vCausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, hidden_states=outputs.hidden_states, ) def forward_with_triton_backend( self: Glm4vForConditionalGeneration, input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> tuple | Glm4vCausalLMOutputForPPO: from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy outputs = glm4v_forward(self, input_ids, **kwargs) hidden_states = outputs[0] # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_triton_backend, either labels or input_ids must be provided.") log_probs, entropy = linear_cross_entropy( hidden_states, self.lm_head.weight, rolled_labels, temperature, "none", ) return Glm4vCausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, hidden_states=outputs.hidden_states, ) ================================================ FILE: verl/models/transformers/kimi_vl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional import torch import torch.nn.functional as F from transformers.cache_utils import Cache from transformers.modeling_flash_attention_utils import _flash_attention_forward from verl.models.transformers.monkey_patch import is_transformers_version_in_range # Import compatibility wrapper for flash_attn_supports_top_left_mask from verl.utils.transformers_compat import flash_attn_supports_top_left_mask from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size, validate_ulysses_config, ) # Copied from transformers.models.llama.modeling_llama.rotate_half def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) b, h, s, d = q.shape q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) b, h, s, d = k.shape k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def _ulysses_flash_attn_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() if self.q_lora_rank is None: q = self.q_proj(hidden_states) else: q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape compressed_kv = self.kv_a_proj_with_mqa(hidden_states) compressed_kv, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) kv = ( self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) .transpose(1, 2) ) k_nope, value_states = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1) # patch ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: validate_ulysses_config(self.num_heads, ulysses_sp_size) num_key_value_groups = self.config.num_attention_heads // self.config.num_key_value_heads k_pe = repeat_kv(k_pe, ulysses_sp_size) # to keep heads=1 after a2a k_nope = repeat_kv(k_nope, num_key_value_groups) value_states = repeat_kv(value_states, num_key_value_groups) q = gather_seq_scatter_heads(q, seq_dim=2, head_dim=1) k_pe = gather_seq_scatter_heads(k_pe, seq_dim=2, head_dim=1) k_nope = gather_seq_scatter_heads(k_nope, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) # (batch_size, num_head / sp_size, seq_length, head_size) full_q_len = q.size(2) # full_q_len = seq_length else: full_q_len = q_len q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) cos, sin = self.rotary_emb(value_states, seq_len=full_q_len) q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) query_states = k_pe.new_empty(bsz, self.num_heads // ulysses_sp_size, full_q_len, self.q_head_dim) query_states[:, :, :, : self.qk_nope_head_dim] = q_nope query_states[:, :, :, self.qk_nope_head_dim :] = q_pe key_states = k_pe.new_empty(bsz, self.num_heads // ulysses_sp_size, full_q_len, self.q_head_dim) key_states[:, :, :, : self.qk_nope_head_dim] = k_nope key_states[:, :, :, self.qk_nope_head_dim :] = k_pe if self.q_head_dim != self.v_head_dim: value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim]) # TODO: These transpose are quite inefficient but Flash Attention requires the layout # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, full_q_len, dropout=dropout_rate, sliding_window=None, is_causal=self.is_causal, use_top_left_mask=flash_attn_supports_top_left_mask(), position_ids=position_ids, # important: pass position ids softmax_scale=self.softmax_scale, ) if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, head_dim=2, seq_dim=1) if self.q_head_dim != self.v_head_dim: attn_output = attn_output[:, :, :, : self.v_head_dim] attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim).contiguous() attn_output = self.o_proj(attn_output) if is_transformers_version_in_range(min_version="4.53.0"): return attn_output, None else: return attn_output, None, None ================================================ FILE: verl/models/transformers/llama.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys from typing import Callable, Optional import torch if sys.version_info >= (3, 11): pass else: pass from transformers.cache_utils import Cache from transformers.modeling_flash_attention_utils import _flash_attention_forward from transformers.models.llama.modeling_llama import apply_rotary_pos_emb from transformers.utils import logging # Import compatibility wrapper for flash_attn_supports_top_left_mask from verl.utils.transformers_compat import flash_attn_supports_top_left_mask from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size, validate_ulysses_config, ) logger = logging.get_logger(__name__) def llama_flash_attn_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: """ Adapted from transformers 4.47.1 to support Ulysses sequence parallelism. NOTE: This function is used for transformers versions in the range [4.45.0, 4.47.1]. """ output_attentions = False bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) # trade off: repeat first and then all to all # key_states = repeat_kv(key_states, self.num_key_value_groups) # value_states = repeat_kv(value_states, self.num_key_value_groups) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: validate_ulysses_config(self.num_heads, ulysses_sp_size) # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) # full seq length if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " "removed and `position_embeddings` will be mandatory." ) cos, sin = self.rotary_emb(value_states, position_ids) else: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to " f"the fact you have upcasted embedding or layer norm layers in float32. We will cast back the " f"input in {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, full_q_len, position_ids=position_ids, dropout=dropout_rate, sliding_window=getattr(self, "sliding_window", None), use_top_left_mask=flash_attn_supports_top_left_mask(), is_causal=self.is_causal, **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def llama_attn_forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: """ Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0. NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.50.0. """ from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS from transformers.models.llama.modeling_llama import eager_attention_forward bsz, q_len, _ = hidden_states.shape query_states = self.q_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = self.k_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: validate_ulysses_config(self.config.num_attention_heads, ulysses_sp_size) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. " "Falling back to eager attention. This warning can be removed using the argument " '`attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights ================================================ FILE: verl/models/transformers/monkey_patch.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Apply monkey-patch function to models """ import sys from types import SimpleNamespace from typing import Optional import torch from transformers.modeling_flash_attention_utils import _flash_attention_forward from transformers.modeling_utils import PreTrainedModel from verl.utils.import_utils import is_trl_available from verl.utils.transformers_compat import is_transformers_version_in_range from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_group, get_ulysses_sequence_parallel_world_size, slice_input_tensor, ) _PREFIX_GROUPER_PATCHED = False _PREFIX_GROUPER_SUPPORTED_ATTENTIONS = {"flash_attention_2", "flash_attention_3", "sdpa", "flex_attention", "eager"} def _create_prefix_grouper_wrapper(original_fn): """Wrap attention function to support prefix_grouper in kwargs.""" def wrapped(module, query, key, value, attention_mask, *args, **kwargs): prefix_grouper = kwargs.pop("prefix_grouper", None) if prefix_grouper is None: return original_fn(module, query, key, value, attention_mask, *args, **kwargs) def attn_func(q, k, v, attn_mask, *inner_args, **inner_kwargs): out, _ = original_fn(module, q, k, v, attn_mask, *inner_args, **inner_kwargs) return out return prefix_grouper.forward(attn_func, query, key, value, *args, **kwargs), None return wrapped def apply_prefix_grouper_patch(): """Patch ALL_ATTENTION_FUNCTIONS to support prefix_grouper parameter.""" global _PREFIX_GROUPER_PATCHED if _PREFIX_GROUPER_PATCHED: return from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS patched = [] for name in list(ALL_ATTENTION_FUNCTIONS.keys()): if name in _PREFIX_GROUPER_SUPPORTED_ATTENTIONS: ALL_ATTENTION_FUNCTIONS[name] = _create_prefix_grouper_wrapper(ALL_ATTENTION_FUNCTIONS[name]) patched.append(name) _PREFIX_GROUPER_PATCHED = True print(f"[PrefixGrouper] Patched: {patched}") def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=2, repeats=n_rep). The hidden states go from (batch, seqlen, num_key_value_heads, head_dim) to (batch, seqlen, num_attention_heads, head_dim) """ batch, slen, num_key_value_heads, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, :, None, :].expand(batch, slen, num_key_value_heads, n_rep, head_dim) return hidden_states.reshape(batch, slen, num_key_value_heads * n_rep, head_dim) def _ulysses_flash_attention_forward( query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: Optional[torch.Tensor], query_length: int, *args, position_ids: Optional[torch.Tensor] = None, **kwargs, ): """Insert all-to-all before and after flash attention. DeepSpeed-Ulysses: https://arxiv.org/pdf/2309.14509 For transformers>=4.55, the flash attention api has changed, we need to pass the query_length after doing ulysses all2all. See https://github.com/huggingface/transformers/issues/40399 Args: query_states (torch.Tensor): (batch_size, seqlen/sp_size, nheads, head_dim) key_states (torch.Tensor): (batch_size, seqlen/sp_size, nheads_k, head_dim) value_states (torch.Tensor): (batch_size, seqlen/sp_size, nheads_k, head_dim) position_ids (torch.Tensor, optional): (batch_size, seqlen/sp_size) Returns: torch.Tensor: (batch_size, seqlen/sp_size, nheads, head_dim) """ ulysses_sp_size = get_ulysses_sequence_parallel_world_size() ########## AlltoAll for Ulysses ########## # TODO: Disable sp for ViT, there's no elegent way to determine whether it's ViT or not. # Use `position_ids` as condition since ViT doesn't pass it to flash attention. if ulysses_sp_size > 1 and position_ids is not None: # NOTE: repeat kv heads to be divided by sequence parallel. Instead of repeating nheads_q//nheads_k, # we choose to repeat sp_size//nheads_k, since flash_attention supports MQA/GQA. # For example: # - nheads_k=4, sp=8, repeats=2 # - nheads_k=8, sp=8, repeats=1 # - nheads_k=16, sp=8, repeats=1 repeats = max(ulysses_sp_size // key_states.size(2), 1) key_states = repeat_kv(key_states, repeats) value_states = repeat_kv(value_states, repeats) # (bsz, seq_len/n, n_head, head_dim) -> (bsz, seq_len, n_head/n, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=1, head_dim=2) key_states = gather_seq_scatter_heads(key_states, seq_dim=1, head_dim=2) value_states = gather_seq_scatter_heads(value_states, seq_dim=1, head_dim=2) # TODO: all_gather position_ids because `prepare_fa2_from_position_ids` needs it, we can eliminate # this all_gather by passing cu_seq_lens_q, cu_seq_lens_k, max_length_k, max_length_q explicitly. # https://github.com/huggingface/transformers/pull/33932 # (bsz, seq_len/n) -> (bsz, seq_len) position_ids_list = [torch.empty_like(position_ids) for _ in range(ulysses_sp_size)] torch.distributed.all_gather(position_ids_list, position_ids, group=get_ulysses_sequence_parallel_group()) position_ids = torch.concat(position_ids_list, dim=-1) # (bsz, seq_len, n_head/n, head_dim) query_length = query_states.size(1) attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, query_length, *args, position_ids=position_ids, **kwargs ) ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1 and position_ids is not None: # (bsz, seq_len, n_head/n, head_dim) -> (bsz, seq_len/n, n_head, head_dim) attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) return attn_output def patch_vlm_for_ulysses_input_slicing(model_class: type): """ Applies a monkey patch to the forward method of a given model class to enable Ulysses sequence parallelism input slicing. """ def _create_ulysses_wrapped_decoder_forward(original_forward): def ulysses_wrapped_decoder_forward(self, *args, **kwargs): inputs_embeds = kwargs.get("inputs_embeds") position_ids = kwargs.get("position_ids") visual_pos_masks = kwargs.get("visual_pos_masks") deepstack_visual_embeds = kwargs.get("deepstack_visual_embeds") call_kwargs = kwargs.copy() current_ulysses_sp_size = get_ulysses_sequence_parallel_world_size() slice_now = ( inputs_embeds is not None and current_ulysses_sp_size > 1 and getattr(self, "_needs_initial_slice", True) ) if slice_now: call_kwargs["inputs_embeds"] = slice_input_tensor(inputs_embeds, dim=1, padding=False) call_kwargs["position_ids"] = slice_input_tensor(position_ids, dim=-1, padding=False) # Also slice visual_pos_masks and deepstack_visual_embeds for Qwen3 VL models if visual_pos_masks is not None: original_visual_mask = visual_pos_masks sliced_visual_mask = slice_input_tensor(visual_pos_masks, dim=1, padding=False) call_kwargs["visual_pos_masks"] = sliced_visual_mask if deepstack_visual_embeds is not None: sliced_embeds = [] num_visual_before = original_visual_mask.sum().item() num_visual_in_shard = sliced_visual_mask.sum().item() if num_visual_in_shard > 0 and num_visual_before > 0: # Calculate which visual embeddings belong to this shard # We need to find the offset of visual tokens in this shard from verl.utils.ulysses import get_ulysses_sequence_parallel_rank rank = get_ulysses_sequence_parallel_rank() seq_len = original_visual_mask.shape[1] local_seq_len = seq_len // current_ulysses_sp_size start_idx = rank * local_seq_len end_idx = start_idx + local_seq_len # Get total visual tokens before and up to the end of the shard's sequence slice # This correctly handles batches by summing across all samples visual_start = original_visual_mask[:, :start_idx].sum().item() if start_idx > 0 else 0 visual_end = original_visual_mask[:, :end_idx].sum().item() # Slice each tensor in deepstack_visual_embeds for embed in deepstack_visual_embeds: sliced_embeds.append(embed[visual_start:visual_end]) else: # No visual tokens in this shard, create empty tensors to maintain gradient flow for embed in deepstack_visual_embeds: sliced_embeds.append(embed[:0]) call_kwargs["deepstack_visual_embeds"] = sliced_embeds self._needs_initial_slice = False try: return original_forward(self, *args, **call_kwargs) finally: if slice_now: self._needs_initial_slice = True return ulysses_wrapped_decoder_forward original_forward = model_class.forward wrapped_forward = _create_ulysses_wrapped_decoder_forward(original_forward) model_class.forward = wrapped_forward print(f"Monkey patch {model_class.__name__}.forward for Ulysses SP input slicing.") def patch_forward_with_backends( model: PreTrainedModel, use_fused_kernels: bool = False, fused_kernels_backend: str = None, ): """ Choose the forward function based on the model and backend. Args: model (PreTrainedModel): The model to apply the monkey patch. use_fused_kernels (bool): Whether to use fused kernels. fused_kernels_backend (str): The backend to use for fused kernels. """ if not use_fused_kernels or fused_kernels_backend not in ["triton", "torch"]: print( f"Skipping monkey patch for {model.__class__.__name__} as use_fused_kernels is " f"{use_fused_kernels} or fused_kernels_backend is {fused_kernels_backend}" ) return forward_with_torch_backend_function = model.__class__.forward forward_with_triton_backend_function = model.__class__.forward if model.config.model_type in ["qwen2_5_vl", "qwen2_vl"]: from verl.models.transformers.qwen2_vl import forward_with_torch_backend, forward_with_triton_backend forward_with_torch_backend_function = forward_with_torch_backend forward_with_triton_backend_function = forward_with_triton_backend elif model.config.model_type in ["qwen3_vl", "qwen3_vl_moe"]: from verl.models.transformers.qwen3_vl import forward_with_torch_backend, forward_with_triton_backend forward_with_torch_backend_function = forward_with_torch_backend forward_with_triton_backend_function = forward_with_triton_backend elif model.config.model_type == "glm4v": from verl.models.transformers.glm4v import forward_with_torch_backend, forward_with_triton_backend forward_with_torch_backend_function = forward_with_torch_backend forward_with_triton_backend_function = forward_with_triton_backend else: from verl.models.transformers.dense_common import forward_with_torch_backend, forward_with_triton_backend forward_with_torch_backend_function = forward_with_torch_backend forward_with_triton_backend_function = forward_with_triton_backend if fused_kernels_backend == "triton": model.__class__.forward = forward_with_triton_backend_function print(f"Using Triton backend for fused kernels in {model.__class__.__name__}") elif fused_kernels_backend == "torch": model.__class__.forward = forward_with_torch_backend_function print(f"Using Torch backend for fused kernels in {model.__class__.__name__}") else: raise ValueError(f"Unsupported fused_kernels_backend: {fused_kernels_backend}. Choose 'triton' or 'torch'.") def apply_monkey_patch( model: PreTrainedModel, ulysses_sp_size: int = 1, use_remove_padding: bool = True, use_fused_kernels: bool = False, fused_kernels_backend: str = None, use_prefix_grouper: bool = False, use_tiled_mlp: bool = False, tiled_mlp_shards: int = 4, ): """ Apply monkey patch to the models for ulysses sequence parallel, fused kernel, tiled MLP and prefix grouper. In the end of this function forward function of the model is patched for fused kernel. If the model is not supported with fused kernel, please return after patch. Args: model: The model to apply the monkey patch. ulysses_sp_size: The size of ulysses sequence parallel. use_remove_padding: Whether to use remove padding. use_fused_kernels: Whether to use fused kernels. fused_kernels_backend: The backend to use for fused kernels. use_tiled_mlp: Whether to use TiledMLP for memory-efficient MLP computation. tiled_mlp_shards: Number of shards for TiledMLP (higher = lower memory, slightly slower). """ # Apply TiledMLP monkey patch for memory-efficient MLP computation if use_tiled_mlp: from verl.models.transformers.tiled_mlp import apply_tiled_mlp_monkey_patch model_type = getattr(model.config, "model_type", None) apply_tiled_mlp_monkey_patch(num_shards=tiled_mlp_shards, model_type=model_type) # Apply PrefixGrouper patch if enabled if use_prefix_grouper: apply_prefix_grouper_patch() """Replace _flash_attention_forward to _ulysses_flash_attention_forward""" module = sys.modules[model.__module__] try: num_attention_heads, num_key_value_heads = model.config.num_attention_heads, model.config.num_key_value_heads except AttributeError: num_attention_heads, num_key_value_heads = ( model.config.text_config.num_attention_heads, model.config.text_config.num_key_value_heads, ) assert num_attention_heads % ulysses_sp_size == 0, ( f"num_attention_heads {num_attention_heads} must be divisible by ulysses_sp_size {ulysses_sp_size}" ) assert num_key_value_heads % ulysses_sp_size == 0 or ulysses_sp_size % num_key_value_heads == 0, ( f"num_key_value_heads {num_key_value_heads} must be divisible by ulysses_sp_size " f"{ulysses_sp_size}or vise versa. Upon ulysses_sp_size % num_key_value_heads == 0," f"kv heads are repeated to ensure correctness." ) if is_trl_available(): from trl import AutoModelForCausalLMWithValueHead # type: ignore def state_dict(self, *args, **kwargs): return torch.nn.Module.state_dict(self, *args, **kwargs) AutoModelForCausalLMWithValueHead.state_dict = state_dict print("Monkey patch state_dict in AutoModelForCausalLMWithValueHead. ") # TODO: VLM models only, unify monkey patch to LLM models. if model.config.model_type in ["qwen2_5_vl", "qwen2_vl"]: # Step 1: patch model to support image-text mixed data if is_transformers_version_in_range(min_version="4.52.0"): from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import ( Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLModel, Qwen2_5_VLTextModel, ) from transformers.models.qwen2_vl.modeling_qwen2_vl import ( Qwen2VLForConditionalGeneration, Qwen2VLModel, Qwen2VLTextModel, ) else: from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLModel as Qwen2_5_VLTextModel from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLForConditionalGeneration from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLModel as Qwen2VLTextModel Qwen2_5_VLModel = SimpleNamespace(forward=None) Qwen2VLModel = SimpleNamespace(forward=None) from verl.models.transformers.qwen2_vl import forward_with_normal_backend, qwen2_vl_base_forward Qwen2_5_VLModel.forward = qwen2_vl_base_forward Qwen2VLModel.forward = qwen2_vl_base_forward Qwen2_5_VLForConditionalGeneration.forward = forward_with_normal_backend Qwen2VLForConditionalGeneration.forward = forward_with_normal_backend print(f"Monkey patch {model.__class__.__name__} model forward") # Step 2: patch attention to support ulysses parallelism if is_transformers_version_in_range(min_version="4.54.0"): from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLAttention from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLAttention elif is_transformers_version_in_range(min_version="4.53.0"): raise RuntimeError("Transformers 4.53.* is bugged. Use transformers 4.54.0 or later.") else: from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import ( Qwen2_5_VLFlashAttention2 as Qwen2_5_VLAttention, ) from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLFlashAttention2 as Qwen2VLAttention if use_remove_padding or ulysses_sp_size > 1: from verl.models.transformers.qwen2_vl import qwen2_vl_attn_forward Qwen2_5_VLAttention.forward = qwen2_vl_attn_forward Qwen2VLAttention.forward = qwen2_vl_attn_forward print(f"Monkey patch {model.__class__.__name__} attention layer") # Step 3: patch input for multimodal sequence parallelism if ulysses_sp_size > 1: patch_vlm_for_ulysses_input_slicing(Qwen2_5_VLTextModel) patch_vlm_for_ulysses_input_slicing(Qwen2VLTextModel) elif model.config.model_type in ["qwen3_vl", "qwen3_vl_moe"]: # Step 1: patch model to support image-text mixed data from transformers.models.qwen3_vl.modeling_qwen3_vl import ( Qwen3VLForConditionalGeneration, Qwen3VLModel, Qwen3VLTextModel, ) from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import ( Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeModel, Qwen3VLMoeTextModel, ) from verl.models.transformers.qwen3_vl import ( forward_with_normal_backend, patch_qwen3_vl_moe_sparse_moe_block_forward, qwen3_vl_base_forward, ) Qwen3VLModel.forward = qwen3_vl_base_forward Qwen3VLMoeModel.forward = qwen3_vl_base_forward Qwen3VLForConditionalGeneration.forward = forward_with_normal_backend Qwen3VLMoeForConditionalGeneration.forward = forward_with_normal_backend print(f"Monkey patch {model.__class__.__name__} model forward") # Step 1.5: patch Qwen3VLMoeTextSparseMoeBlock to fix transformers 4.57.3 bug if model.config.model_type == "qwen3_vl_moe" and is_transformers_version_in_range(max_version="4.57.3"): patch_qwen3_vl_moe_sparse_moe_block_forward() # Step 2: patch input for multimodal sequence parallelism if ulysses_sp_size > 1: patch_vlm_for_ulysses_input_slicing(Qwen3VLTextModel) patch_vlm_for_ulysses_input_slicing(Qwen3VLMoeTextModel) elif model.config.model_type == "glm4v": # Step 1: patch model to support image-text mixed data from transformers.models.glm4v.modeling_glm4v import ( Glm4vForConditionalGeneration, Glm4vModel, Glm4vTextAttention, Glm4vTextModel, ) from verl.models.transformers.glm4v import forward_with_normal_backend, glm4v_base_forward Glm4vModel.forward = glm4v_base_forward Glm4vForConditionalGeneration.forward = forward_with_normal_backend print(f"Monkey patch {model.__class__.__name__} model forward") # Step 2: patch attention to support ulysses parallelism if use_remove_padding or ulysses_sp_size > 1: from verl.models.transformers.glm4v import glm4v_attn_forward Glm4vTextAttention.forward = glm4v_attn_forward print(f"Monkey patch {model.__class__.__name__} attention layer") # Step 3: patch input for multimodal sequence parallelism if ulysses_sp_size > 1: patch_vlm_for_ulysses_input_slicing(Glm4vTextModel) elif model.config.model_type == "kimi_vl": if use_remove_padding or ulysses_sp_size > 1: # TODO: Changes need to be made when transformers are adapted. from verl.models.transformers.kimi_vl import _ulysses_flash_attn_forward module.DeepseekV3FlashAttention2.forward = _ulysses_flash_attn_forward print("Monkey patch FlashAttention2.forward in KimiVL") if ulysses_sp_size > 1: patch_vlm_for_ulysses_input_slicing(module.DeepseekV3ForCausalLM) if use_fused_kernels: print("Not support fused kernels for KimiVL") return if use_remove_padding or ulysses_sp_size > 1: if hasattr(module, "_flash_attention_forward"): # transformers <= 4.47.1 or legacy models module._flash_attention_forward = _ulysses_flash_attention_forward print(f"Monkey patch _flash_attention_forward in {model.__module__}") else: from transformers.integrations import flash_attention flash_attention._flash_attention_forward = _ulysses_flash_attention_forward print(f"Monkey patch _flash_attention_forward in {flash_attention.__name__}") patch_forward_with_backends(model, use_fused_kernels=use_fused_kernels, fused_kernels_backend=fused_kernels_backend) ================================================ FILE: verl/models/transformers/npu_patch.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Copyright 2025 The Qwen Team and The HuggingFace Inc. team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn.functional as F import torch_npu from torch import nn from transformers.activations import ACT2FN from transformers.models.qwen2 import modeling_qwen2 from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl from transformers.models.qwen3 import modeling_qwen3 from transformers.models.qwen3_moe import modeling_qwen3_moe from transformers.models.qwen3_next import modeling_qwen3_next from transformers.models.qwen3_vl import modeling_qwen3_vl from transformers.models.qwen3_vl_moe import modeling_qwen3_vl_moe from transformers.utils import logging logger = logging.get_logger(__name__) def rms_norm_forward_npu(self, x): """NPU optimized implementation for RMSNorm.""" if x.dtype != self.weight.dtype: x = x.to(self.weight.dtype) return torch_npu.npu_rms_norm(x, self.weight, epsilon=self.variance_epsilon)[0] def silu_forward_npu(self, hidden_state): """NPU optimized implementation for SiLU in `forward` func in MLP layer.""" gate_up = torch.cat((self.gate_proj(hidden_state), self.up_proj(hidden_state)), dim=-1) return self.down_proj(torch_npu.npu_swiglu(gate_up, dim=-1)) def apply_rotary_pos_emb_npu(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """NPU optimized implementation for RoPE.""" cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = torch_npu.npu_rotary_mul(q, cos, sin) k_embed = torch_npu.npu_rotary_mul(k, cos, sin) return q_embed.to(q.dtype), k_embed.to(k.dtype) def qwen3_next_rms_norm_forward_npu(self, x): return torch_npu.npu_rms_norm(x.float(), 1.0 + self.weight.float(), epsilon=self.eps)[0].type_as(x) def qwen3_next_rms_norm_forward_gated_npu(self, hidden_states, gate=None): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) hidden_states = torch_npu.npu_rms_norm(hidden_states, self.weight.float(), epsilon=self.variance_epsilon)[0] hidden_states = hidden_states * F.silu(gate.to(torch.float32)) return hidden_states.to(input_dtype) def qwen3_next_apply_rotary_pos_emb_npu(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) # Keep half or full tensor for later concatenation rotary_dim = cos.shape[-1] q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] q_embed = torch_npu.npu_rotary_mul(q_rot, cos, sin).to(q.dtype) k_embed = torch_npu.npu_rotary_mul(k_rot, cos, sin).to(k.dtype) q_embed = torch.cat([q_embed, q_pass], dim=-1) k_embed = torch.cat([k_embed, k_pass], dim=-1) return q_embed, k_embed class NPUGmmFunction(torch.autograd.Function): @staticmethod def forward(ctx, x, weight, group_list, group_list_type=1): """ Grouped Matmul(GMM) for Ascend NPU. Args: x (torch.Tensor): Input tensor, shape (tokens_num * top_k, hidden_size) weight (torch.Tensor): Expert weights, shape (n_experts, hidden_size, intermediate_size) group_list (torch.Tensor): Expert token counts, shape (n_experts,) - type 0: cumsum of tokens per expert - type 1: direct tokens per expert (default) """ ctx.save_for_backward(x, weight) ctx.group_list = group_list ctx.group_list_type = group_list_type output = torch_npu.npu_grouped_matmul( [x], [weight], bias=None, group_list=group_list, split_item=2, group_type=0, group_list_type=group_list_type )[0] return output @staticmethod def backward(ctx, grad_output): x, weight = ctx.saved_tensors group_list = ctx.group_list group_list_type = ctx.group_list_type dx = torch_npu.npu_grouped_matmul( [grad_output], [weight.transpose(1, 2)], bias=None, group_list=group_list, split_item=2, group_type=0, group_list_type=group_list_type, )[0] dw = torch_npu.npu_grouped_matmul( [x.transpose(0, 1)], [grad_output], bias=None, group_list=group_list, split_item=3, group_type=2, group_list_type=group_list_type, )[0] return dx, dw, None, None def _qwen3_sparse_moe_routed_forward_npu(self, hidden_states: torch.Tensor): """ Shared NPU routed-expert path for Qwen3Moe/Qwen3Next sparse MoE blocks. Returns: tuple: (flattened_input, routed_hidden_states, router_logits) """ hidden_dim = hidden_states.shape[-1] hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (batch * sequence_length, n_experts) router_logits = self.gate(hidden_states) routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) if self.norm_topk_prob: # only diff with mixtral sparse moe block! routing_weights /= routing_weights.sum(dim=-1, keepdim=True) # we cast back to the input dtype routing_weights = routing_weights.to(hidden_states.dtype) # Loop over all available experts in the model and perform the computation on each expert # Concat all weights input_dtype = hidden_states.dtype up_weight_list = [e.up_proj.weight for e in self.experts] gate_weight_list = [e.gate_proj.weight for e in self.experts] down_weight_list = [e.down_proj.weight for e in self.experts] w1 = torch.stack(up_weight_list).transpose(1, 2).to(input_dtype) w2 = torch.stack(gate_weight_list).transpose(1, 2).to(input_dtype) w3 = torch.stack(down_weight_list).transpose(1, 2).to(input_dtype) permuted_tokens, row_ids_map = torch_npu.npu_moe_token_permute(hidden_states, selected_experts.to(torch.int32)) tokens_per_expert = torch.histc(selected_experts, bins=self.num_experts, min=0, max=self.num_experts) up_res = NPUGmmFunction.apply(permuted_tokens, w1, tokens_per_expert) gate_res = NPUGmmFunction.apply(permuted_tokens, w2, tokens_per_expert) act_res = torch_npu.npu_swiglu(torch.cat([gate_res, up_res], dim=-1)) down_res = NPUGmmFunction.apply(act_res, w3, tokens_per_expert) routed_hidden_states = torch_npu.npu_moe_token_unpermute(down_res, row_ids_map, probs=routing_weights) return hidden_states, routed_hidden_states, router_logits def qwen3_moe_sparse_moe_block_forward_npu(self, hidden_states: torch.Tensor) -> torch.Tensor: """NPU optimized implementation for `forward` in Qwen3MoeSparseMoeBlock.""" output_shape = hidden_states.shape _, routed_hidden_states, router_logits = _qwen3_sparse_moe_routed_forward_npu(self, hidden_states) final_hidden_states = routed_hidden_states.reshape(output_shape) return final_hidden_states, router_logits def qwen3_next_sparse_moe_block_forward_npu(self, hidden_states: torch.Tensor) -> torch.Tensor: """NPU optimized implementation for `forward` in Qwen3NextSparseMoeBlock.""" output_shape = hidden_states.shape hidden_states, routed_hidden_states, router_logits = _qwen3_sparse_moe_routed_forward_npu(self, hidden_states) shared_expert_output = self.shared_expert(hidden_states) shared_expert_output = torch.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output final_hidden_states = (routed_hidden_states + shared_expert_output).reshape(output_shape) return final_hidden_states, router_logits class NPUQwen3VLMoeTextExperts(nn.Module): """NPU optimized implementation for Qwen3VLMoeTextExperts.""" def __init__(self, config): super().__init__() self.num_experts = config.num_experts self.intermediate_size = config.moe_intermediate_size self.hidden_size = config.hidden_size self.expert_dim = self.intermediate_size self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, 2 * self.expert_dim)) self.down_proj = nn.Parameter(torch.empty((self.num_experts, self.expert_dim, self.hidden_size))) self.act_fn = ACT2FN[config.hidden_act] def forward( self, hidden_states: torch.Tensor, routing_weights: torch.Tensor, router_indices: torch.Tensor ) -> torch.Tensor: """ When training it is more efficient to just loop over the experts and compute the output for each expert as otherwise the memory would explode. For inference we can sacrifice some memory and compute the output for all experts at once. By repeating the inputs. Args: hidden_states (torch.Tensor): (batch_size * token_num, hidden_size) routing_weights (torch.Tensor): (batch_size * token_num, num_experts) router_indices (torch.Tensor): (batch_size * token_num, top_k) Returns: torch.Tensor """ batch_size = hidden_states.shape[0] hidden_states = hidden_states.reshape(-1, self.hidden_size) # (num_tokens, hidden_size) if self.training: permuted_hidden_states, row_ids_map = torch_npu.npu_moe_token_permute( hidden_states, router_indices.to(torch.int32) ) tokens_per_expert = torch.histc(router_indices, bins=self.num_experts, min=0, max=self.num_experts) intermediate_hidden_states = NPUGmmFunction.apply( permuted_hidden_states, self.gate_up_proj, tokens_per_expert ) intermediate_activations = torch_npu.npu_swiglu(intermediate_hidden_states, dim=-1) output = NPUGmmFunction.apply(intermediate_activations, self.down_proj, tokens_per_expert) num_tokens = hidden_states.shape[0] top_k = router_indices.shape[1] batch_idx = torch.arange(num_tokens, device=routing_weights.device) batch_idx = batch_idx.unsqueeze(1).expand(-1, top_k) selected_probs = routing_weights[batch_idx, router_indices] next_states = torch_npu.npu_moe_token_unpermute(output, row_ids_map, probs=selected_probs) next_states = next_states.view(batch_size, -1, self.hidden_size) else: hidden_states = hidden_states.repeat(self.num_experts, 1) hidden_states = hidden_states.view(self.num_experts, -1, self.hidden_size) gate_up = torch.bmm(hidden_states, self.gate_up_proj) gate, up = gate_up.chunk(2, dim=-1) # not supported for DTensors next_states = torch.bmm((up * self.act_fn(gate)), self.down_proj) next_states = next_states.reshape(self.num_experts, batch_size, -1, self.hidden_size) next_states = ( next_states * routing_weights.transpose(0, 1).view(self.num_experts, batch_size, -1)[..., None] ) next_states = next_states.sum(dim=0) return next_states class NPUQwen3VLMoeTextSparseMoeBlock(nn.Module): """NPU optimized implementation for Qwen3VLMoeTextSparseMoeBlock.""" def __init__(self, config): super().__init__() self.hidden_size = config.hidden_size self.num_experts = config.num_experts self.top_k = config.num_experts_per_tok self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False) self.experts = NPUQwen3VLMoeTextExperts(config) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: batch_size = hidden_states.shape[0] hidden_states = hidden_states.reshape(-1, self.hidden_size) router_logits = self.gate(hidden_states) routing_weights = torch.nn.functional.softmax(router_logits, dim=-1, dtype=torch.float) routing_weights, router_indices = torch.topk(routing_weights, self.top_k, dim=-1) routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True) routing_weights = routing_weights.to(router_logits.dtype) hidden_states = hidden_states.reshape(batch_size, -1, self.hidden_size) if not self.training: routing_weights = torch.zeros_like(router_logits).scatter_(1, router_indices, routing_weights) routed_out = self.experts(hidden_states, routing_weights, router_indices) return routed_out # Patches for Qwen2 Model modeling_qwen2.Qwen2RMSNorm.forward = rms_norm_forward_npu modeling_qwen2.Qwen2MLP.forward = silu_forward_npu modeling_qwen2.apply_rotary_pos_emb = apply_rotary_pos_emb_npu # Patches for Qwen2.5-VL Model modeling_qwen2_5_vl.Qwen2RMSNorm.forward = rms_norm_forward_npu modeling_qwen2_5_vl.Qwen2_5_VLMLP.forward = silu_forward_npu # Patches for Qwen3 Model modeling_qwen3.Qwen3RMSNorm.forward = rms_norm_forward_npu modeling_qwen3.Qwen3MLP.forward = silu_forward_npu modeling_qwen3.apply_rotary_pos_emb = apply_rotary_pos_emb_npu # Patches for Qwen3 MoE Model modeling_qwen3_moe.Qwen3MoeRMSNorm.forward = rms_norm_forward_npu modeling_qwen3_moe.Qwen3MoeSparseMoeBlock.forward = qwen3_moe_sparse_moe_block_forward_npu modeling_qwen3_moe.apply_rotary_pos_emb = apply_rotary_pos_emb_npu # Patches for Qwen3 VL Model modeling_qwen3_vl.Qwen3VLTextRMSNorm.forward = rms_norm_forward_npu modeling_qwen3_vl.Qwen3VLTextMLP.forward = silu_forward_npu # Patches for Qwen3-VL MoE Model modeling_qwen3_vl_moe.Qwen3VLMoeTextSparseMoeBlock = NPUQwen3VLMoeTextSparseMoeBlock modeling_qwen3_vl_moe.Qwen3VLMoeTextRMSNorm.forward = rms_norm_forward_npu modeling_qwen3_vl_moe.apply_rotary_pos_emb = apply_rotary_pos_emb_npu # Patches for Qwen3 Next Model modeling_qwen3_next.Qwen3NextSparseMoeBlock.forward = qwen3_next_sparse_moe_block_forward_npu modeling_qwen3_next.Qwen3NextRMSNormGated.forward = qwen3_next_rms_norm_forward_gated_npu modeling_qwen3_next.Qwen3NextRMSNorm.forward = qwen3_next_rms_norm_forward_npu modeling_qwen3_next.apply_rotary_pos_emb = qwen3_next_apply_rotary_pos_emb_npu ================================================ FILE: verl/models/transformers/qwen2.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional import torch from transformers.cache_utils import Cache from transformers.modeling_flash_attention_utils import _flash_attention_forward from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv from transformers.utils import logging # Import compatibility wrapper for flash_attn_supports_top_left_mask from verl.utils.transformers_compat import flash_attn_supports_top_left_mask from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size, validate_ulysses_config, ) logger = logging.get_logger(__name__) def qwen2_flash_attn_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): """ Adapted from transformers 4.47.1 to support Ulysses sequence parallelism. NOTE: This function is only tested on transformers versions between 4.45.0 and 4.47.1. """ bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: validate_ulysses_config(self.num_heads, ulysses_sp_size) # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) # full seq length if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " "removed and `position_embeddings` will be mandatory." ) cos, sin = self.rotary_emb(value_states, position_ids) else: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to " f"the fact you have upcasted embedding or layer norm layers in float32. We will cast back the " f"input in {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): sliding_window = self.config.sliding_window else: sliding_window = None attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, full_q_len, position_ids=position_ids, dropout=dropout_rate, sliding_window=sliding_window, is_causal=self.is_causal, use_top_left_mask=flash_attn_supports_top_left_mask(), ) # use full_q_len to reshape attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def qwen2_attn_forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: """ Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0. NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.50.0. """ from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS bsz, q_len, _ = hidden_states.shape hidden_shape = (bsz, q_len, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: validate_ulysses_config(self.config.num_attention_heads, ulysses_sp_size) # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) sliding_window = None if ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): sliding_window = self.config.sliding_window from transformers.models.qwen2.modeling_qwen2 import eager_attention_forward attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. " "Falling back to eager attention. This warning can be removed using the argument " '`attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=sliding_window, # main diff with Llama **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: # (bsz, seq_len, n_head/n, head_dim) -> (bsz, seq_len/n, n_head, head_dim) attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights ================================================ FILE: verl/models/transformers/qwen2_vl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import logging import os from dataclasses import dataclass from typing import Optional import torch import torch.distributed as dist from transformers.modeling_flash_attention_utils import _flash_attention_forward, fa_peft_integration_check from transformers.models.qwen2_vl.modeling_qwen2_vl import ( Qwen2VLAttention, Qwen2VLCausalLMOutputWithPast, Qwen2VLForConditionalGeneration, ) from transformers.utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10 from verl.utils.device import is_npu_available from verl.utils.transformers_compat import is_transformers_version_in_range from verl.utils.ulysses import ( gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_group, get_ulysses_sequence_parallel_world_size, validate_ulysses_config, ) logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func _flash_supports_window_size = "window_size" in inspect.signature(flash_attn_func).parameters _flash_supports_deterministic = "deterministic" in inspect.signature(flash_attn_func).parameters _flash_use_top_left_mask = not is_flash_attn_greater_or_equal_2_10() if is_npu_available: from transformers.integrations.npu_flash_attention import npu_flash_attn_func as flash_attn_func from transformers.integrations.npu_flash_attention import npu_flash_attn_varlen_func as flash_attn_varlen_func from transformers.modeling_flash_attention_utils import flash_attn_supports_top_left_mask _flash_supports_window_size = "window_size" in inspect.signature(flash_attn_func).parameters _flash_supports_deterministic = "deterministic" in inspect.signature(flash_attn_func).parameters _flash_use_top_left_mask = flash_attn_supports_top_left_mask() _flash_deterministic_enabled = os.getenv("FLASH_ATTENTION_DETERMINISTIC", "0") == "1" def get_rope_index( processor, input_ids: torch.Tensor, image_grid_thw: Optional[torch.Tensor] = None, video_grid_thw: Optional[torch.Tensor] = None, second_per_grid_ts: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Gets the position ids for Qwen2-VL, it should be generated before sharding the sequence. The batch dim has been removed and the input_ids should be a 1D tensor representing a single example. https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py#L1405 """ spatial_merge_size = processor.image_processor.merge_size tokens_per_second = 2 image_token_id = processor.tokenizer.convert_tokens_to_ids("<|image_pad|>") video_token_id = processor.tokenizer.convert_tokens_to_ids("<|video_pad|>") vision_start_token_id = processor.tokenizer.convert_tokens_to_ids("<|vision_start|>") if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) position_ids = torch.ones(3, input_ids.size(0), dtype=input_ids.dtype, device=input_ids.device) # (3, seqlen) image_index, video_index = 0, 0 input_ids = input_ids[attention_mask == 1] image_nums, video_nums = 0, 0 vision_start_indices = torch.argwhere(input_ids == vision_start_token_id) vision_tokens = input_ids[vision_start_indices + 1] image_nums = (vision_tokens == image_token_id).sum() video_nums = (vision_tokens == video_token_id).sum() input_tokens = input_ids.tolist() llm_pos_ids_list: list = [] st = 0 remain_images, remain_videos = image_nums, video_nums for _ in range(image_nums + video_nums): if image_token_id in input_tokens and remain_images > 0: ed_image = input_tokens.index(image_token_id, st) else: ed_image = len(input_tokens) + 1 if video_token_id in input_tokens and remain_videos > 0: ed_video = input_tokens.index(video_token_id, st) else: ed_video = len(input_tokens) + 1 if ed_image < ed_video: t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) second_per_grid_t = 0 image_index += 1 remain_images -= 1 ed = ed_image else: t, h, w = ( video_grid_thw[video_index][0], video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) second_per_grid_t = second_per_grid_ts[video_index] if second_per_grid_ts is not None else 1.0 video_index += 1 remain_videos -= 1 ed = ed_video llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) text_len = ed - st st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w) t_index = (t_index * second_per_grid_t * tokens_per_second).long().flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx) st = ed + llm_grid_t * llm_grid_h * llm_grid_w if st < len(input_tokens): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 text_len = len(input_tokens) - st llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., attention_mask == 1] = llm_positions.to(position_ids.device) else: if attention_mask is not None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1).to(input_ids.device) else: position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).view(1, -1).expand(3, -1) return position_ids def prepare_fa2_from_position_ids( query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, position_ids: torch.Tensor ): assert position_ids.ndim == 2 # (batch_size, seq_length) query = query.contiguous().view(-1, query.size(-2), query.size(-1)) key = key.contiguous().view(-1, key.size(-2), key.size(-1)) value = value.contiguous().view(-1, value.size(-2), value.size(-1)) position_ids = position_ids.view(-1) cu_seqlens = torch.cat( ( (position_ids == 0).nonzero().view(-1).to(torch.int32), torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32), ) ) max_length = cu_seqlens.diff().max() # use cu_seqlens to infer max_length for qwen2vl mrope return (query, key, value, (cu_seqlens, cu_seqlens), (max_length, max_length)) def _custom_flash_attention_forward( query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: Optional[torch.Tensor], query_length: int, is_causal: bool = True, position_ids: Optional[torch.Tensor] = None, sliding_window: Optional[int] = None, use_top_left_mask: bool = False, deterministic: Optional[bool] = None, **kwargs, ): """ Patches flash attention forward to handle 3D position ids in mrope. (3, batch_size, seq_length) """ # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length). use_sliding_windows = ( _flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window ) flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {} if _flash_supports_deterministic: flash_kwargs["deterministic"] = deterministic if deterministic is not None else _flash_deterministic_enabled if kwargs.get("softcap") is not None: flash_kwargs["softcap"] = kwargs.pop("softcap") query_states, key_states, value_states = fa_peft_integration_check( query_states, key_states, value_states, target_dtype=torch.bfloat16 ) if position_ids is not None: assert position_ids.ndim == 2 # (batch_size, seq_length / sp_size) sp_size = get_ulysses_sequence_parallel_world_size() if sp_size > 1: # qkv: (batch_size, seq_length / sp_size, num_head, head_size) validate_ulysses_config(query_states.size(2), sp_size) query_states = gather_seq_scatter_heads(query_states, seq_dim=1, head_dim=2) key_states = gather_seq_scatter_heads(key_states, seq_dim=1, head_dim=2) value_states = gather_seq_scatter_heads(value_states, seq_dim=1, head_dim=2) position_ids_lst = [torch.empty_like(position_ids) for _ in range(sp_size)] position_ids = dist.all_gather(position_ids_lst, position_ids, group=get_ulysses_sequence_parallel_group()) position_ids = torch.cat(position_ids_lst, dim=-1) # (batch_size, seq_length) if position_ids is not None and query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all(): batch_size = query_states.size(0) q, k, v, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k) = prepare_fa2_from_position_ids( query_states, key_states, value_states, position_ids ) attn_output = flash_attn_varlen_func( q=q, k=k, v=v, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, dropout_p=kwargs.pop("dropout", 0.0), softmax_scale=kwargs.pop("softmax_scale", None), causal=is_causal, **flash_kwargs, ) attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1)) else: attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, query_length, is_causal=is_causal, sliding_window=sliding_window, use_top_left_mask=use_top_left_mask, deterministic=deterministic, **kwargs, ) # do not pass position_ids to old flash_attention_forward if sp_size > 1: # (batch_size, seq_length, num_head, head_size) attn_output = gather_heads_scatter_seq(attn_output, head_dim=2, seq_dim=1) return attn_output def qwen2_vl_attn_forward( self: "Qwen2VLAttention", hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 **kwargs, ) -> tuple[torch.Tensor, None, None]: from transformers.models.qwen2_vl.modeling_qwen2_vl import apply_multimodal_rotary_pos_emb, repeat_kv bsz, q_len, _ = hidden_states.size() # q_len = seq_length / sp_size query_states = self.q_proj(hidden_states) # (batch_size, seq_length / sp_size, num_heads * head_size) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) # Because the input can be padded, the absolute sequence length depends on the max position id. cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb( query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] ) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout sliding_window = None if ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): sliding_window = self.config.sliding_window # This is before the transpose q_len = query_states.shape[2] # FA2 uses non-transposed inputs query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if position_ids.ndim == 3: position_ids = position_ids[0] attn_output = _custom_flash_attention_forward( query_states, key_states, value_states, attention_mask, query_length=q_len, is_causal=getattr(self, "is_causal", True), dropout=dropout_rate, sliding_window=sliding_window, use_top_left_mask=_flash_use_top_left_mask, position_ids=position_ids, # important: pass position ids ) # (batch_size, seq_length / sp_size, num_head, head_size) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.o_proj(attn_output) if is_transformers_version_in_range(min_version="4.54.0"): return attn_output, None else: return attn_output, None, None def _get_input_embeds( model: "Qwen2VLForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, ): inputs_embeds = model.get_input_embeddings()(input_ids) if pixel_values is not None: pixel_values = pixel_values.type(model.visual.dtype) image_embeds = model.visual(pixel_values, grid_thw=image_grid_thw) n_image_tokens = (input_ids == model.config.image_token_id).sum().item() n_image_features = image_embeds.shape[0] if n_image_tokens != n_image_features: raise ValueError( f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" ) mask = input_ids == model.config.image_token_id mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) image_mask = mask_expanded.to(inputs_embeds.device) image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) if pixel_values_videos is not None: pixel_values_videos = pixel_values_videos.type(model.visual.dtype) video_embeds = model.visual(pixel_values_videos, grid_thw=video_grid_thw) n_video_tokens = (input_ids == model.config.video_token_id).sum().item() n_video_features = video_embeds.shape[0] if n_video_tokens != n_video_features: raise ValueError( f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" ) mask = input_ids == model.config.video_token_id mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) video_mask = mask_expanded.to(inputs_embeds.device) video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) if pixel_values is None and pixel_values_videos is None: # handle mixed text-image data config = model.config.vision_config patch_dim = config.in_channels * config.temporal_patch_size * config.patch_size**2 pixel_values = torch.zeros((16, patch_dim), dtype=inputs_embeds.dtype, device=inputs_embeds.device) image_grid_thw = torch.tensor([[1, 4, 4]], dtype=torch.long, device=inputs_embeds.device) image_embeds = model.visual(pixel_values, grid_thw=image_grid_thw) inputs_embeds += 0.0 * image_embeds.mean() if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) return inputs_embeds, attention_mask def process_position_ids(position_ids: torch.Tensor) -> torch.Tensor: if position_ids.ndim != 3 or position_ids.size(0) != 4: # we concat the text position ids with the 3D vision position ids by default # see https://github.com/huggingface/transformers/pull/39447 raise ValueError("position_ids should be a 3D tensor of shape (4, batch_size, seq_length).") if is_transformers_version_in_range(max_version="4.53.3"): # transformers < 4.54.0 only accepts vision position ids, so we discard the text position ids here position_ids = position_ids[1:] return position_ids @dataclass class Qwen2VLCausalLMOutputForPPO(Qwen2VLCausalLMOutputWithPast): log_probs: Optional[torch.FloatTensor] = None entropy: Optional[torch.FloatTensor] = None def qwen2_vl_base_forward( self: "Qwen2VLForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, **kwargs, ): kwargs["inputs_embeds"], kwargs["attention_mask"] = _get_input_embeds( self, input_ids, attention_mask, pixel_values, pixel_values_videos, image_grid_thw, video_grid_thw ) # avoid lora module having multiple keyword arguments return self.language_model(input_ids=None, **kwargs) def qwen2_vl_forward( self: "Qwen2VLForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, **kwargs, ): if is_transformers_version_in_range(min_version="4.52.0"): return self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=process_position_ids(position_ids), pixel_values=pixel_values, pixel_values_videos=pixel_values_videos, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, **kwargs, ) else: inputs_embeds, attention_mask = _get_input_embeds( self, input_ids, attention_mask, pixel_values, pixel_values_videos, image_grid_thw, video_grid_thw ) return self.model( input_ids=None, attention_mask=attention_mask, position_ids=process_position_ids(position_ids), inputs_embeds=inputs_embeds, **kwargs, ) def forward_with_normal_backend( self: Qwen2VLForConditionalGeneration, input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> "Qwen2VLCausalLMOutputWithPast": outputs = qwen2_vl_forward(self, input_ids, **kwargs) hidden_states = outputs[0] logits = self.lm_head(hidden_states) return Qwen2VLCausalLMOutputWithPast( logits=logits, hidden_states=outputs.hidden_states, ) def forward_with_torch_backend( self: Qwen2VLForConditionalGeneration, input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> tuple | Qwen2VLCausalLMOutputForPPO: from verl.utils.experimental.torch_functional import FusedLinearForPPO outputs = qwen2_vl_forward(self, input_ids, **kwargs) hidden_states = outputs[0] # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_torch_backend, either labels or input_ids must be provided.") fused_linear_for_ppo = FusedLinearForPPO() log_probs, entropy = fused_linear_for_ppo.forward( hidden_states=hidden_states, vocab_weights=self.lm_head.weight, input_ids=rolled_labels, temperature=temperature, ) return Qwen2VLCausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, hidden_states=outputs.hidden_states, ) def forward_with_triton_backend( self: Qwen2VLForConditionalGeneration, input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> tuple | Qwen2VLCausalLMOutputForPPO: from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy outputs = qwen2_vl_forward(self, input_ids, **kwargs) hidden_states = outputs[0] # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_triton_backend, either labels or input_ids must be provided.") log_probs, entropy = linear_cross_entropy( hidden_states, self.lm_head.weight, rolled_labels, temperature, "none", ) return Qwen2VLCausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, hidden_states=outputs.hidden_states, ) ================================================ FILE: verl/models/transformers/qwen3_vl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools import logging import os from dataclasses import dataclass from typing import Optional import torch from transformers.models.qwen3_vl.modeling_qwen3_vl import ( Qwen3VLCausalLMOutputWithPast, Qwen3VLForConditionalGeneration, ) logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def get_rope_index( processor, input_ids: torch.Tensor, image_grid_thw: Optional[torch.Tensor] = None, video_grid_thw: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, **kwargs, ) -> torch.Tensor: """ Gets the position ids for Qwen3-VL, it should be generated before sharding the sequence. The batch dim has been removed and the input_ids should be a 1D tensor representing a single example. https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/qwen3_vl/modeling_qwen3_vl.py#L916 """ spatial_merge_size = processor.image_processor.merge_size image_token_id = processor.image_token_id video_token_id = processor.video_token_id vision_start_token_id = processor.vision_start_token_id # Since we use timestamps to separate videos, # like , # the video_grid_thw should also be split if video_grid_thw is not None: video_grid_thw = torch.repeat_interleave(video_grid_thw, video_grid_thw[:, 0], dim=0) video_grid_thw[:, 0] = 1 if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) position_ids = torch.ones(3, input_ids.shape[0], dtype=input_ids.dtype, device=input_ids.device) image_index, video_index = 0, 0 attention_mask = attention_mask.to(input_ids.device) input_ids = input_ids[attention_mask == 1] image_nums, video_nums = 0, 0 vision_start_indices = torch.argwhere(input_ids == vision_start_token_id) vision_tokens = input_ids[vision_start_indices + 1] image_nums = (vision_tokens == image_token_id).sum() video_nums = (vision_tokens == video_token_id).sum() input_tokens = input_ids.tolist() llm_pos_ids_list: list = [] st = 0 remain_images, remain_videos = image_nums, video_nums for _ in range(image_nums + video_nums): if image_token_id in input_tokens and remain_images > 0: ed_image = input_tokens.index(image_token_id, st) else: ed_image = len(input_tokens) + 1 if video_token_id in input_tokens and remain_videos > 0: ed_video = input_tokens.index(video_token_id, st) else: ed_video = len(input_tokens) + 1 if ed_image < ed_video: t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) image_index += 1 remain_images -= 1 ed = ed_image else: t, h, w = ( video_grid_thw[video_index][0], video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) video_index += 1 remain_videos -= 1 ed = ed_video llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) text_len = ed - st st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) # t_index is always 0 because llm_grid_t is always 1 # (we use timestamps to encode the temporal information for videos) t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx) st = ed + llm_grid_t * llm_grid_h * llm_grid_w if st < len(input_tokens): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 text_len = len(input_tokens) - st llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., attention_mask == 1] = llm_positions.to(position_ids.device) else: if attention_mask is not None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1).to(attention_mask.device) else: position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).view(1, -1).expand(3, -1) return position_ids def _get_input_embeds( model: "Qwen3VLForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, ): inputs_embeds = model.get_input_embeddings()(input_ids) image_mask, video_mask = None, None if pixel_values is not None: pixel_values = pixel_values.type(model.visual.dtype) image_embeds, deepstack_image_embeds = model.visual(pixel_values, grid_thw=image_grid_thw) n_image_tokens = (input_ids == model.config.image_token_id).sum().item() n_image_features = image_embeds.shape[0] if n_image_tokens != n_image_features: raise ValueError( f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" ) mask = input_ids == model.config.image_token_id mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) image_mask = mask_expanded.to(inputs_embeds.device) image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) if pixel_values_videos is not None: pixel_values_videos = pixel_values_videos.type(model.visual.dtype) video_embeds, deepstack_video_embeds = model.visual(pixel_values_videos, grid_thw=video_grid_thw) n_video_tokens = (input_ids == model.config.video_token_id).sum().item() n_video_features = video_embeds.shape[0] if n_video_tokens != n_video_features: raise ValueError( f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" ) mask = input_ids == model.config.video_token_id mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) video_mask = mask_expanded.to(inputs_embeds.device) video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) visual_pos_masks = None deepstack_visual_embeds = None if image_mask is not None and video_mask is not None: # aggregate visual_pos_masks and deepstack_visual_embeds image_mask = image_mask[..., 0] video_mask = video_mask[..., 0] visual_pos_masks = image_mask | video_mask deepstack_visual_embeds = [] image_mask_joint = image_mask[visual_pos_masks] video_mask_joint = video_mask[visual_pos_masks] for img_embed, vid_embed in zip(deepstack_image_embeds, deepstack_video_embeds, strict=False): embed_joint = img_embed.new_zeros(visual_pos_masks.sum(), img_embed.shape[-1]).to(img_embed.device) embed_joint[image_mask_joint, :] = img_embed embed_joint[video_mask_joint, :] = vid_embed deepstack_visual_embeds.append(embed_joint) elif image_mask is not None: image_mask = image_mask[..., 0] visual_pos_masks = image_mask deepstack_visual_embeds = deepstack_image_embeds elif video_mask is not None: video_mask = video_mask[..., 0] visual_pos_masks = video_mask deepstack_visual_embeds = deepstack_video_embeds if pixel_values is None and pixel_values_videos is None: config = model.config.vision_config patch_dim = config.in_channels * config.temporal_patch_size * config.patch_size**2 pixel_values = torch.zeros((16, patch_dim), dtype=inputs_embeds.dtype, device=inputs_embeds.device) image_grid_thw = torch.tensor([[1, 4, 4]], dtype=torch.long, device=inputs_embeds.device) image_embeds, dummy_deepstack_image_embeds = model.visual(pixel_values, grid_thw=image_grid_thw) inputs_embeds += 0.0 * image_embeds.mean() for emb in dummy_deepstack_image_embeds or []: inputs_embeds += 0.0 * emb.mean() if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) return { "inputs_embeds": inputs_embeds, "attention_mask": attention_mask, "visual_pos_masks": visual_pos_masks, "deepstack_visual_embeds": deepstack_visual_embeds, } @dataclass class Qwen3VLCausalLMOutputForPPO(Qwen3VLCausalLMOutputWithPast): log_probs: Optional[torch.FloatTensor] = None entropy: Optional[torch.FloatTensor] = None def qwen3_vl_base_forward( self: "Qwen3VLForConditionalGeneration", input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, pixel_values: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, **kwargs, ): input_kwargs = _get_input_embeds( self, input_ids, attention_mask, pixel_values, pixel_values_videos, image_grid_thw, video_grid_thw ) # avoid lora module having multiple keyword arguments kwargs.update(input_kwargs) return self.language_model( input_ids=None, **kwargs, ) def forward_with_normal_backend( self: "Qwen3VLForConditionalGeneration", input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> "Qwen3VLCausalLMOutputForPPO": outputs = self.model(input_ids, **kwargs) hidden_states = outputs[0] logits = self.lm_head(hidden_states) return Qwen3VLCausalLMOutputForPPO( logits=logits, hidden_states=outputs.hidden_states, ) def forward_with_torch_backend( self: "Qwen3VLForConditionalGeneration", input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> "Qwen3VLCausalLMOutputForPPO": from verl.utils.experimental.torch_functional import FusedLinearForPPO outputs = self.model(input_ids, **kwargs) hidden_states = outputs[0] # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_torch_backend, either labels or input_ids must be provided.") fused_linear_for_ppo = FusedLinearForPPO() log_probs, entropy = fused_linear_for_ppo.forward( hidden_states=hidden_states, vocab_weights=self.lm_head.weight, input_ids=rolled_labels, temperature=temperature, ) return Qwen3VLCausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, hidden_states=outputs.hidden_states, ) def forward_with_triton_backend( self: "Qwen3VLForConditionalGeneration", input_ids: torch.LongTensor = None, labels: Optional[torch.LongTensor] = None, temperature: float = 1.0, **kwargs, ) -> "Qwen3VLCausalLMOutputForPPO": from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy outputs = self.model(input_ids, **kwargs) hidden_states = outputs[0] # Loss calculations if labels is not None: rolled_labels = torch.roll(labels, shifts=-1, dims=-1) elif input_ids is not None: rolled_labels = torch.roll(input_ids, shifts=-1, dims=-1) else: raise RuntimeError("To use forward_with_triton_backend, either labels or input_ids must be provided.") log_probs, entropy = linear_cross_entropy( hidden_states, self.lm_head.weight, rolled_labels, temperature, "none", ) return Qwen3VLCausalLMOutputForPPO( log_probs=log_probs, entropy=entropy, hidden_states=outputs.hidden_states, ) def patch_qwen3_vl_moe_sparse_moe_block_forward(): """ Monkey patch to fix a bug in transformers 4.57.3 where Qwen3VLMoeTextSparseMoeBlock.forward incorrectly uses torch.zeros_like(hidden_states) instead of torch.zeros_like(router_logits) when creating router_weights (line 148 in modeling_qwen3_vl_moe.py). This is a minimal fix that only changes the problematic line while keeping the rest of the original implementation intact. """ try: from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import Qwen3VLMoeTextSparseMoeBlock except ImportError: # Model not available, skip patching return # Store the original forward method for reference original_forward = Qwen3VLMoeTextSparseMoeBlock.forward @functools.wraps(original_forward) def patched_forward(self, hidden_states: torch.Tensor) -> torch.Tensor: batch_size = hidden_states.shape[0] hidden_states = hidden_states.reshape(-1, self.hidden_size) router_logits = self.gate(hidden_states) routing_weights = torch.nn.functional.softmax(router_logits, dim=-1, dtype=torch.float) routing_weights, router_indices = torch.topk(routing_weights, self.top_k, dim=-1) routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True) # BUG FIX: Original code incorrectly uses hidden_states here, should use router_logits routing_weights = routing_weights.to(router_logits.dtype) router_weights = torch.zeros_like(router_logits).scatter_(1, router_indices, routing_weights) hidden_states = hidden_states.reshape(batch_size, -1, self.hidden_size) routed_out = self.experts(hidden_states, router_weights, router_indices) return routed_out # Apply the patch Qwen3VLMoeTextSparseMoeBlock.forward = patched_forward logger.info("Monkey patched Qwen3VLMoeTextSparseMoeBlock.forward to fix router_weights bug") ================================================ FILE: verl/models/transformers/tiled_mlp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ FSDP2-compatible TiledMLP implementation for memory-efficient MLP computation. This module provides a tiled MLP implementation that reduces peak memory usage by processing the MLP forward/backward pass in chunks (tiles). This is particularly useful for large models with FSDP2 training. """ import threading from typing import Optional import torch import torch.nn as nn class GradientAccumulator: """Gradient accumulator for TiledMLP (FSDP compatible). This class manages gradient accumulation across multiple shards during the backward pass of TiledMLP. It ensures correct gradient computation when processing input in chunks. """ def __init__(self, params: list[torch.nn.Parameter], total_shards: int, dtype: torch.dtype = None): self.params = params self.total_shards = total_shards self.grad_accumulation_dtype = dtype or torch.float32 self.accumulated_grads = {} self.hooks = [] self.lock = threading.Lock() for param in self.params: if param.grad is not None: self.accumulated_grads[param] = param.grad.to(self.grad_accumulation_dtype) param.grad = None else: self.accumulated_grads[param] = torch.zeros_like(param, dtype=self.grad_accumulation_dtype) def install_hooks(self, is_last_shard: bool): """Install gradient hooks for the current shard.""" self._remove_hooks() def create_hook(param): def hook(grad): with self.lock: grad_to_accum_dtype = grad.to(self.grad_accumulation_dtype) self.accumulated_grads[param] += grad_to_accum_dtype if is_last_shard: param.grad = None # Critical: prevent double accumulation final_grad = self.accumulated_grads[param].to(param.dtype) return final_grad return None return hook for param in self.params: if param.requires_grad: hook = param.register_hook(create_hook(param)) self.hooks.append(hook) def _remove_hooks(self): """Remove all registered hooks.""" for hook in self.hooks: hook.remove() self.hooks.clear() def cleanup(self): """Cleanup hooks and resources.""" self._remove_hooks() class TiledMLP(torch.autograd.Function): """TiledMLP implementation for memory-efficient MLP computation. This autograd function processes MLP forward/backward in tiles (chunks) to reduce peak memory usage. Compatible with FSDP2. """ @staticmethod def forward(ctx, fn, module, x, shards, compute_params): ctx.fn = fn ctx.module = module ctx.shards = shards ctx.compute_params = [p for p in compute_params if p.requires_grad] ctx.save_for_backward(x) # Split on dim=-2 (seqlen dimension) following Liger Kernel style x_shards = list(torch.chunk(x, chunks=shards, dim=-2)) with torch.no_grad(): output_shards = [fn(module, x_shard) for x_shard in x_shards] output_unsharded = torch.cat(output_shards, dim=-2) return output_unsharded @staticmethod def backward(ctx, *grads): fn = ctx.fn (x,) = ctx.saved_tensors module = ctx.module shards = ctx.shards compute_params = ctx.compute_params x_requires_grad = x.requires_grad x = x.detach() x.requires_grad_(x_requires_grad) # Flatten to [bs*seqlen, hidden_size] hidden_size = x.shape[-1] x_shape_orig = x.shape x = x.view(-1, hidden_size) incoming_grad = grads[0].view(-1, hidden_size) # Pre-allocate input gradient x_grad = torch.zeros_like(x) # Split on dim=0 x_shards = list(torch.chunk(x, chunks=shards, dim=0)) grad_accumulator = GradientAccumulator(compute_params, shards, dtype=x.dtype) for i, x_shard in enumerate(x_shards): x_shard.requires_grad_(x_requires_grad) shard_step = x_shards[i].shape[0] shard_offset = i * x_shards[0].shape[0] # narrow(0, ...) creates a contiguous view that can receive gradients x_shard.grad = x_grad.narrow(0, shard_offset, shard_step) incoming_grad_shard = incoming_grad.narrow(0, shard_offset, shard_step) is_last_shard = i + 1 == shards grad_accumulator.install_hooks(is_last_shard) with torch.enable_grad(): output = fn(module, x_shard) torch.autograd.backward(output, incoming_grad_shard) grad_accumulator.cleanup() del grad_accumulator # Restore original shape x_grad = x_grad.view(x_shape_orig) if x_requires_grad else None return (None, None, x_grad, None, None) def _mlp_forward_fn(module, x): """Forward function for LlamaMLP / Qwen2MLP / Qwen3MLP style.""" return module.down_proj(module.act_fn(module.gate_proj(x)) * module.up_proj(x)) # ============================================================================ # Monkey Patch Functions # ============================================================================ # Model type to MLP class mapping _MODEL_TYPE_TO_MLP_CLASS = { "llama": ("transformers.models.llama.modeling_llama", "LlamaMLP"), "qwen2": ("transformers.models.qwen2.modeling_qwen2", "Qwen2MLP"), "qwen2_5": ("transformers.models.qwen2.modeling_qwen2", "Qwen2MLP"), # Qwen2.5 uses Qwen2 MLP "qwen3": ("transformers.models.qwen3.modeling_qwen3", "Qwen3MLP"), } def apply_tiled_mlp_monkey_patch( num_shards: int = 4, model_type: Optional[str] = None, ): """Apply TiledMLP monkey patch based on model_type. This function MUST be called BEFORE model instantiation to take effect. It patches the MLP classes in transformers library to use TiledMLP for memory-efficient computation during training. Args: num_shards: Number of shards to split the input into. Higher values reduce peak memory but may slightly impact performance. model_type: The model type string (e.g., "llama", "qwen2", "qwen3"). If None, patches all supported model types. Returns: List of patched class names. """ if model_type is None: types_to_patch = list(_MODEL_TYPE_TO_MLP_CLASS.keys()) elif model_type in _MODEL_TYPE_TO_MLP_CLASS: types_to_patch = [model_type] else: raise ValueError( f"TiledMLP does not support model_type='{model_type}'. " f"Supported types: {list(_MODEL_TYPE_TO_MLP_CLASS.keys())}. " f"For SwiGLU-style MLPs, you can add support by extending _MODEL_TYPE_TO_MLP_CLASS " f"in verl/models/transformers/tiled_mlp.py" ) patched_classes = [] for mtype in types_to_patch: module_path, class_name = _MODEL_TYPE_TO_MLP_CLASS[mtype] try: import importlib module = importlib.import_module(module_path) mlp_class = getattr(module, class_name) _patch_mlp_class(mlp_class, _mlp_forward_fn, num_shards) if class_name not in patched_classes: patched_classes.append(class_name) except (ImportError, AttributeError) as e: print(f"Warning: Could not patch {mtype} MLP: {e}") if patched_classes: print(f"TiledMLP monkey patch applied to: {', '.join(patched_classes)} (shards={num_shards})") return patched_classes def _patch_mlp_class(mlp_class: type[nn.Module], forward_fn, num_shards: int): """Patch a single MLP class to use TiledMLP.""" def tiled_forward(self, x): compute_params = [p for p in self.parameters() if p.requires_grad] return TiledMLP.apply(forward_fn, self, x, num_shards, compute_params) mlp_class.forward = tiled_forward ================================================ FILE: verl/models/weight_loader_registry.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. def get_weight_loader(arch: str): from verl.models.mcore.loader import load_state_dict_to_megatron_gptmodel _MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY = { "LlamaForCausalLM": load_state_dict_to_megatron_gptmodel, "Qwen2ForCausalLM": load_state_dict_to_megatron_gptmodel, } if arch in _MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY: return _MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY[arch] raise ValueError( f"Model architectures {arch} loader are not supported for now. Supported architectures: " f"{_MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY.keys()}" ) def get_weight_saver(arch: str): from verl.models.mcore.saver import ( merge_megatron_ckpt_gptmodel, merge_megatron_ckpt_gptmodel_dpskv3, merge_megatron_ckpt_gptmodel_mixtral, merge_megatron_ckpt_gptmodel_qwen2_5_vl, merge_megatron_ckpt_gptmodel_qwen_moe, ) _MODEL_WEIGHT_MEGATRON_SAVER_REGISTRY = { "LlamaForCausalLM": merge_megatron_ckpt_gptmodel, "Qwen2ForCausalLM": merge_megatron_ckpt_gptmodel, "MixtralForCausalLM": merge_megatron_ckpt_gptmodel_mixtral, "Qwen2MoeForCausalLM": merge_megatron_ckpt_gptmodel_qwen_moe, "Qwen2_5_VLForConditionalGeneration": merge_megatron_ckpt_gptmodel_qwen2_5_vl, "DeepseekV3ForCausalLM": merge_megatron_ckpt_gptmodel_dpskv3, "Qwen3ForCausalLM": merge_megatron_ckpt_gptmodel, "Qwen3ForTokenClassification": merge_megatron_ckpt_gptmodel, "Qwen3MoeForCausalLM": merge_megatron_ckpt_gptmodel_qwen_moe, "LlamaForTokenClassification": merge_megatron_ckpt_gptmodel, } if arch in _MODEL_WEIGHT_MEGATRON_SAVER_REGISTRY: return _MODEL_WEIGHT_MEGATRON_SAVER_REGISTRY[arch] raise ValueError( f"Model architectures {arch} saver are not supported for now. Supported architectures: " f"{_MODEL_WEIGHT_MEGATRON_SAVER_REGISTRY.keys()}" ) ================================================ FILE: verl/protocol.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement base data transfer protocol between any two functions, modules. We can subclass Protocol to define more detailed batch info with specific keys """ import contextlib import copy import logging import math import os import pickle from dataclasses import dataclass, field from typing import Any, Callable, Optional import numpy as np import ray import tensordict import torch import torch.distributed from packaging import version from packaging.version import parse as parse_version from tensordict import TensorDict from torch.utils.data import DataLoader from verl.utils.device import get_device_id, get_torch_device from verl.utils.py_functional import list_of_dict_to_dict_of_list, union_two_dict from verl.utils.torch_functional import allgather_dict_tensors __all__ = ["DataProto", "union_tensor_dict"] with contextlib.suppress(Exception): tensordict.set_lazy_legacy(False).set() if parse_version(tensordict.__version__) < parse_version("0.10.0"): tensordict.set_list_to_stack(True).set() class _DataProtoConfigMeta(type): _config = {} auto_padding_key = "_verl_auto_padding" @property def auto_padding(cls): enabled_by_env = os.getenv("VERL_AUTO_PADDING", "FALSE").upper() in ["TRUE", "1"] return enabled_by_env or cls._config.get(cls.auto_padding_key, False) @auto_padding.setter def auto_padding(cls, enabled: bool): assert isinstance(enabled, bool), f"enabled must be a boolean, got {enabled} as {type(enabled)}" cls._config[cls.auto_padding_key] = enabled class DataProtoConfig(metaclass=_DataProtoConfigMeta): pass _padding_size_key = "_padding_size_key_x123d" def pad_dataproto_to_divisor(data: "DataProto", size_divisor: int): """Pad a DataProto to size divisible by size_divisor Args: size_divisor (int): size divisor Returns: data: (DataProto): the padded DataProto pad_size (int) """ assert isinstance(data, DataProto), "data must be a DataProto" if len(data) % size_divisor != 0: pad_size = size_divisor - len(data) % size_divisor padding_protos = [] remaining_pad = pad_size while remaining_pad > 0: take_size = min(remaining_pad, len(data)) padding_protos.append(data[:take_size]) remaining_pad -= take_size data_padded = DataProto.concat([data] + padding_protos) else: if len(data) == 0: logging.warning("padding a DataProto with no item, no changed made") pad_size = 0 data_padded = data return data_padded, pad_size def unpad_dataproto(data: "DataProto", pad_size): """Unpad the data proto with pad_size. i.e. `data[:-pad_size]`""" if pad_size != 0: data = data[:-pad_size] return data def union_tensor_dict(tensor_dict1: TensorDict, tensor_dict2: TensorDict) -> TensorDict: """Union two tensordicts.""" assert tensor_dict1.batch_size == tensor_dict2.batch_size, ( f"Two tensor dict must have identical batch size. Got {tensor_dict1.batch_size} and {tensor_dict2.batch_size}" ) for key in tensor_dict2.keys(): if key not in tensor_dict1.keys(): tensor_dict1[key] = tensor_dict2[key] else: assert tensor_dict1[key].equal(tensor_dict2[key]), ( f"{key} in tensor_dict1 and tensor_dict2 are not the same object" ) return tensor_dict1 def _array_equal(array1: np.ndarray, array2: np.ndarray, visited: set[int]) -> bool: """ Recursively compares two NumPy arrays for strict equality, with special handling for object-dtype arrays, NaN values, and circular references. This function assumes that the two arguments provided are NumPy arrays. Args: array1: The first NumPy array. array2: The second NumPy array. Returns: True if the arrays' dtypes, shapes, and all elements are equal. """ # Check dtype and shape first, as this is the fastest failure path. if array1.dtype != array2.dtype or array1.shape != array2.shape: return False # For non-object dtypes, use NumPy's implementation with equal_nan=True. if array1.dtype != "object": return np.array_equal(array1, array2, equal_nan=True) # For object-dtype arrays, we must recursively compare each element. # We delegate to _deep_equal to handle elements, as they could be any # type, including other nested arrays or NaNs. return all(_deep_equal(x, y, visited) for x, y in zip(array1.flat, array2.flat, strict=False)) def _deep_equal(a: Any, b: Any, visited: set[int]) -> bool: """ Recursively performs a deep comparison between two Python objects. - Handles NaN values correctly (NaN == NaN evaluates to True). - Handling circular references. - Dispatches to _array_equal if both objects are NumPy arrays. - Otherwise, uses standard '==' comparison. """ if type(a) is not type(b): return False # If we have seen this object ID before on this path, it's a cycle. # Since we already know the types match, we can safely assume this part # of the structure is equal. obj_id = id(a) if obj_id in visited: return True visited.add(obj_id) # Perform the specific comparison based on type result = False if isinstance(a, float) and math.isnan(a) and math.isnan(b): result = True elif isinstance(a, np.ndarray): # We know b is also an ndarray due to the initial type check result = _array_equal(a, b, visited) else: # Standard equality for all other types result = a == b # Clean up the visited set on the way out of the recursion visited.remove(obj_id) return result def union_numpy_dict(tensor_dict1: dict[str, np.ndarray], tensor_dict2: dict[str, np.ndarray]) -> dict[str, np.ndarray]: for key, val in tensor_dict2.items(): if key in tensor_dict1: assert isinstance(tensor_dict2[key], np.ndarray) assert isinstance(tensor_dict1[key], np.ndarray) # to properly deal with nan and object type assert _deep_equal(tensor_dict1[key], tensor_dict2[key], visited=set()), ( f"`{key}` in tensor_dict1 and tensor_dict2 are not the same object." ) tensor_dict1[key] = val return tensor_dict1 def fold_batch_dim(data: "DataProto", new_batch_size): """ Fold a batch dim from [bsz, xxx] into [new_bsz, bsz // new_bsz, xxx] """ batch_size = data.batch.batch_size[0] assert batch_size % new_batch_size == 0 tensor: TensorDict = data.batch non_tensor = data.non_tensor_batch tensor = tensor.view(new_batch_size, -1) tensor.auto_batch_size_(batch_dims=1) for key, val in non_tensor.items(): non_tensor[key] = np.reshape(val, newshape=(new_batch_size, -1, *val.shape[1:])) return type(data)(batch=tensor, non_tensor_batch=non_tensor, meta_info=data.meta_info) def unfold_batch_dim(data: "DataProto", batch_dims=2): """ Unfold the first n dims as new batch dim """ tensor: TensorDict = data.batch non_tensor = data.non_tensor_batch tensor.auto_batch_size_(batch_dims=batch_dims) tensor = tensor.view(-1) batch_size = tensor.batch_size[0] non_tensor_new = {} for key, val in non_tensor.items(): non_tensor_new[key] = np.reshape(val, newshape=(batch_size, *val.shape[batch_dims:])) return type(data)(batch=tensor, non_tensor_batch=non_tensor_new, meta_info=data.meta_info) def serialize_single_tensor(obj: torch.Tensor) -> tuple[str, tuple[int, ...], int | memoryview]: data = obj.flatten().contiguous().view(torch.uint8).numpy() dtype = str(obj.dtype).removeprefix("torch.") return dtype, obj.shape, data def serialize_tensordict(batch: TensorDict) -> tuple[tuple[int, ...], Optional[str], dict[str, tuple[str, Any]]]: encoded_items: dict[str, tuple[Any]] = {} for k, v in batch.items(): if not v.is_nested: encoded_items[k] = serialize_single_tensor(v) else: layout = str(v.layout).removeprefix("torch.") data = [serialize_single_tensor(tensor) for tensor in v.unbind()] encoded_items[k] = (layout, data) batch_size = tuple(batch.batch_size) device = str(batch.device) if batch.device is not None else None return batch_size, device, encoded_items def deserialize_single_tensor(arr: Any) -> torch.Tensor: dtype, shape, data = arr torch_dtype = getattr(torch, dtype) assert isinstance(torch_dtype, torch.dtype) buffer = bytearray(data) # Create uint8 array arr = torch.frombuffer(buffer, dtype=torch.uint8) # Convert back to proper shape & type return arr.view(torch_dtype).view(shape) def deserialize_tensordict(arr: Any) -> TensorDict: batch_size, device, encoded_items = arr decoded_items: dict[str, Any] = {} for k, v in encoded_items.items(): if len(v) == 3: # decode single tensor decoded_items[k] = deserialize_single_tensor(v) elif len(v) == 2: # decode nested tensor layout, data = v torch_layout = getattr(torch, layout) decoded_items[k] = torch.nested.as_nested_tensor( [deserialize_single_tensor(tensor) for tensor in data], layout=torch_layout ) else: raise ValueError(f"Invalid tensor encoding format, expected length 2 or 3, got {len(v)}") return TensorDict(source=decoded_items, batch_size=batch_size, device=device) def collate_fn(x: list["DataProtoItem"]): batch = [] non_tensor_batch = [] for data in x: batch.append(data.batch) non_tensor_batch.append(data.non_tensor_batch) batch = torch.stack(batch).contiguous() non_tensor_batch = list_of_dict_to_dict_of_list(non_tensor_batch) for key, val in non_tensor_batch.items(): non_tensor_batch[key] = np.array(val, dtype=object) return DataProto(batch=batch, non_tensor_batch=non_tensor_batch) @dataclass class DataProtoItem: # TODO(zhangchi.usc1992) add consistency check batch: TensorDict = None non_tensor_batch: dict = field(default_factory=dict) meta_info: dict = field(default_factory=dict) @dataclass class DataProto: """ A DataProto is a data structure that aims to provide a standard protocol for data exchange between functions. It contains a batch (TensorDict) and a meta_info (Dict). The batch is a TensorDict https://pytorch.org/tensordict/. TensorDict allows you to manipulate a dictionary of Tensors like a single Tensor. Ideally, the tensors with the same batch size should be put inside batch. """ batch: TensorDict = None non_tensor_batch: dict = field(default_factory=dict) meta_info: dict = field(default_factory=dict) def __post_init__(self): # perform necessary checking self.check_consistency() def __len__(self): if self.batch is not None: return self.batch.batch_size[0] elif self.non_tensor_batch is not None and len(self.non_tensor_batch) > 0: random_key = list(self.non_tensor_batch.keys())[0] return self.non_tensor_batch[random_key].shape[0] else: return 0 def __getitem__(self, item): """ Enhanced indexing for DataProto objects. Args: item: Can be one of: - int: A single index - slice: A slice object (start:stop:step) - list: A list of indices - numpy.ndarray: An array of indices - torch.Tensor: A tensor of indices Returns: DataProto: For all indexing types except single integers DataProtoItem: Only for single integer indices """ # Case 1: Slice object - use the slice method if isinstance(item, slice): return self.slice(item.start, item.stop, item.step) # Case 2: List, numpy array, or torch tensor - use sel_idxs elif isinstance(item, list | np.ndarray | torch.Tensor): return self.select_idxs(item) # Case 3: Single integer - return DataProtoItem for backward compatibility elif isinstance(item, int | np.integer): tensor_data = self.batch[item] if self.batch is not None else None non_tensor_data = {key: val[item] for key, val in self.non_tensor_batch.items()} return DataProtoItem(batch=tensor_data, non_tensor_batch=non_tensor_data, meta_info=self.meta_info) # # Case 4: Unsupported type else: raise TypeError(f"Indexing with {type(item)} is not supported") def __getstate__(self): if version.parse(tensordict.__version__) >= version.parse("0.5.0") and self.batch is not None: # Check if batch is empty to avoid torch.cat error in consolidate if len(self.batch.keys()) > 0: batch = self.batch.contiguous().consolidate() else: batch = self.batch else: batch = self.batch if os.getenv("VERL_DATAPROTO_SERIALIZATION_METHOD") == "numpy": if batch is not None: batch = serialize_tensordict(self.batch) return ( batch, self.non_tensor_batch, self.meta_info, ) else: import io buffer = io.BytesIO() torch.save(batch, buffer) buffer_bytes = buffer.getvalue() return buffer_bytes, self.non_tensor_batch, self.meta_info def __setstate__(self, data): batch_deserialized_bytes, non_tensor_batch, meta_info = data if os.getenv("VERL_DATAPROTO_SERIALIZATION_METHOD") == "numpy": if batch_deserialized_bytes is not None: self.batch = deserialize_tensordict(batch_deserialized_bytes) else: self.batch = None else: import io batch_deserialized = io.BytesIO(initial_bytes=batch_deserialized_bytes) batch = torch.load( batch_deserialized, weights_only=False, map_location="cpu" if not get_torch_device().is_available() else None, ) self.batch = batch self.non_tensor_batch = non_tensor_batch self.meta_info = meta_info def save_to_disk(self, filepath): with open(filepath, "wb") as f: pickle.dump(self, f) @staticmethod def load_from_disk(filepath) -> "DataProto": with open(filepath, "rb") as f: data = pickle.load(f) return data def print_size(self, prefix=""): size_of_tensordict = 0 if self.batch is not None: for _, tensor in self.batch.items(): size_of_tensordict += tensor.element_size() * tensor.numel() size_of_numpy_array = 0 for _, numpy_array in self.non_tensor_batch.items(): size_of_numpy_array += numpy_array.nbytes size_of_numpy_array /= 1024**3 size_of_tensordict /= 1024**3 message = f"Size of tensordict: {size_of_tensordict} GB, size of non_tensor_batch: {size_of_numpy_array} GB" if prefix: message = f"{prefix}, " + message print(message) def check_consistency(self): """Check the consistency of the DataProto. Mainly for batch and non_tensor_batch We expose this function as a public one so that user can call themselves directly """ if self.batch is not None: assert len(self.batch.batch_size) == 1, "only support num_batch_dims=1" if self.non_tensor_batch is not None: for key, val in self.non_tensor_batch.items(): assert isinstance(val, np.ndarray) if self.batch is not None and self.non_tensor_batch is not None and len(self.non_tensor_batch) != 0: # TODO: we can actually lift this restriction if needed assert len(self.batch.batch_size) == 1, "only support num_batch_dims=1 when non_tensor_batch is not empty." batch_size = self.batch.batch_size[0] for key, val in self.non_tensor_batch.items(): assert isinstance(val, np.ndarray), ( f"data in the non_tensor_batch must be a numpy.array with dtype=object, but for " f"{key=}, got {type(val)=}" ) assert val.shape[0] == batch_size, ( f"key {key} length {len(val)} is not equal to batch size {batch_size}" ) @classmethod def from_single_dict(cls, data: dict[str, torch.Tensor | np.ndarray], meta_info=None, auto_padding=False): """Create a DataProto from a dict of tensors and non_tensors""" tensors = {} non_tensors = {} for key, val in data.items(): if isinstance(val, torch.Tensor): tensors[key] = val elif isinstance(val, np.ndarray): non_tensors[key] = val else: raise ValueError(f"Unsupported type in data {type(val)}") return cls.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info, auto_padding=auto_padding) @classmethod def from_dict( cls, tensors: Optional[dict[str, torch.Tensor]] = None, non_tensors=None, meta_info=None, num_batch_dims=1, auto_padding=False, ): """Create a DataProto from a dict of tensors. This assumes that 1. All the tensor in tensors have the same dim0 2. Only dim0 is the batch dim """ assert num_batch_dims > 0, "num_batch_dims must be greater than zero" if non_tensors is not None: assert num_batch_dims == 1, "only support num_batch_dims=1 when non_tensors is not None." if tensors is None: tensors = {} if meta_info is None: meta_info = {} if non_tensors is None: non_tensors = {} assert isinstance(non_tensors, dict) # get and check batch size batch_size = None pivot_key = None for key, tensor in tensors.items(): if batch_size is None: batch_size = tensor.shape[:num_batch_dims] pivot_key = key else: current_batch = tensor.shape[:num_batch_dims] assert batch_size == current_batch, ( f"Not all the tensor in tensors have the same batch size with batch_dims={num_batch_dims}. " f"Got {pivot_key} has {batch_size}, {key} has {current_batch}" ) for key, val in non_tensors.items(): if not isinstance(val, np.ndarray): non_tensors[key] = np.array(val, dtype=object) tensor_dict = TensorDict(source=tensors, batch_size=batch_size) if tensors else None if auto_padding: meta_info[DataProtoConfig.auto_padding_key] = True return cls(batch=tensor_dict, non_tensor_batch=non_tensors, meta_info=meta_info) @classmethod def from_tensordict( cls, tensor_dict: TensorDict = None, meta_info=None, num_batch_dims=1, ): """Create a DataProto from a TensorDict. This assumes that 1. All the tensor in tensor_dict have the same dim0 2. Only dim0 is the batch dim """ assert version.parse(tensordict.__version__) >= version.parse("0.10.0"), ( "Build DataProto from TensorDict at least requires tensordict version 0.10.0" ) from tensordict import NonTensorData, NonTensorStack assert num_batch_dims > 0, "num_batch_dims must be greater than zero" if not all(isinstance(val, torch.Tensor) for val in tensor_dict.values()): assert num_batch_dims == 1, "only support num_batch_dims=1 when tensor_dict contains non tensor data." if meta_info is None: meta_info = {} batch = {} non_tensor_batch = {} batch_size = None for key, val in tensor_dict.items(): if isinstance(val, torch.Tensor): batch[key] = val if batch_size is None: batch_size = val.shape[:num_batch_dims] elif isinstance(val, NonTensorStack): non_tensor_batch[key] = np.array([elem.data for elem in val], dtype=object) elif isinstance(val, NonTensorData): meta_info[key] = val.data return cls( batch=TensorDict(batch, batch_size=batch_size), non_tensor_batch=non_tensor_batch, meta_info=meta_info, ) def to(self, device) -> "DataProto": """move the batch to device Args: device (torch.device, str): torch device Returns: DataProto: the current DataProto """ if self.batch is not None: self.batch = self.batch.to(device) return self def select(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_keys=None, deepcopy=False) -> "DataProto": """Select a subset of the DataProto via batch_keys and meta_info_keys Args: batch_keys (list, optional): a list of strings indicating the keys in batch to select meta_info_keys (list, optional): a list of keys indicating the meta info to select Returns: DataProto: the DataProto with the selected batch_keys and meta_info_keys """ # TODO (zhangchi.usc1992) whether to copy if batch_keys is not None: batch_keys = tuple(batch_keys) sub_batch = self.batch.select(*batch_keys) else: sub_batch = self.batch if non_tensor_batch_keys is not None: non_tensor_batch = {key: val for key, val in self.non_tensor_batch.items() if key in non_tensor_batch_keys} else: non_tensor_batch = self.non_tensor_batch if deepcopy: non_tensor_batch = copy.deepcopy(non_tensor_batch) if meta_info_keys is not None: sub_meta_info = {key: val for key, val in self.meta_info.items() if key in meta_info_keys} else: sub_meta_info = self.meta_info if deepcopy: sub_meta_info = copy.deepcopy(sub_meta_info) return type(self)(batch=sub_batch, non_tensor_batch=non_tensor_batch, meta_info=sub_meta_info) def select_idxs(self, idxs): """ Select specific indices from the DataProto. Args: idxs (torch.Tensor or numpy.ndarray or list): Indices to select Returns: DataProto: A new DataProto containing only the selected indices """ if isinstance(idxs, list): idxs = torch.tensor(idxs) if idxs.dtype != torch.bool: idxs = idxs.type(torch.int32) if isinstance(idxs, np.ndarray): idxs_np = idxs idxs_torch = torch.from_numpy(idxs) else: # torch.Tensor idxs_torch = idxs idxs_np = idxs.detach().cpu().numpy() batch_size = int(idxs_np.sum()) if idxs_np.dtype == bool else idxs_np.shape[0] if self.batch is not None: # Use TensorDict's built-in indexing capabilities selected_batch = TensorDict( source={key: tensor[idxs_torch] for key, tensor in self.batch.items()}, batch_size=(batch_size,), device=self.batch.device, ) else: selected_batch = None selected_non_tensor = {} for key, val in self.non_tensor_batch.items(): selected_non_tensor[key] = val[idxs_np] return type(self)(batch=selected_batch, non_tensor_batch=selected_non_tensor, meta_info=self.meta_info) def slice(self, start=None, end=None, step=None): """ Slice the DataProto and return a new DataProto object. This is an improved version of direct slicing which returns a DataProtoItem. Args: start (int, optional): Start index. Defaults to None (start from beginning). end (int, optional): End index (exclusive). Defaults to None (go to end). step (int, optional): Step size. Defaults to None (step=1). Returns: DataProto: A new DataProto containing the sliced data Examples: # Using the slice method directly sliced_data = data_proto.slice(10, 20) # Using enhanced indexing (returns DataProto) sliced_data = data_proto[10:20] sliced_data = data_proto[::2] # Every other element # Using list indexing (returns DataProto) indices = [1, 5, 10] selected_data = data_proto[indices] # Single index still returns DataProtoItem single_item = data_proto[5] """ # Create a slice object slice_obj = slice(start, end, step) # Handle the batch data if self.batch is not None: # Use TensorDict's built-in slicing capabilities sliced_batch = self.batch[slice_obj] else: sliced_batch = None # Handle the non-tensor batch data sliced_non_tensor = {} for key, val in self.non_tensor_batch.items(): sliced_non_tensor[key] = val[slice_obj] # Return a new DataProto object return type(self)(batch=sliced_batch, non_tensor_batch=sliced_non_tensor, meta_info=self.meta_info) def pop(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_keys=None) -> "DataProto": """Pop a subset of the DataProto via `batch_keys` and `meta_info_keys` Args: batch_keys (list, optional): a list of strings indicating the keys in batch to pop meta_info_keys (list, optional): a list of keys indicating the meta info to pop Returns: DataProto: the DataProto with the poped batch_keys and meta_info_keys """ if batch_keys is None: batch_keys = [] if meta_info_keys is None: meta_info_keys = [] if non_tensor_batch_keys is None: non_tensor_batch_keys = [] tensors = {} # tensor batch for key in batch_keys: assert key in self.batch.keys() tensors[key] = self.batch.pop(key) non_tensors = {} # non tensor batch for key in non_tensor_batch_keys: assert key in self.non_tensor_batch.keys() non_tensors[key] = self.non_tensor_batch.pop(key) meta_info = {} for key in meta_info_keys: assert key in self.meta_info.keys() meta_info[key] = self.meta_info.pop(key) return DataProto.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info) def rename(self, old_keys=None, new_keys=None) -> "DataProto": """ Note that this function only rename the key in the batch """ def validate_input(keys): if keys is not None: if isinstance(keys, str): keys = [keys] elif isinstance(keys, list): pass else: raise TypeError(f"keys must be a list or a string, but got {type(keys)}") return keys old_keys = validate_input(old_keys) new_keys = validate_input(new_keys) if len(new_keys) != len(old_keys): raise ValueError( f"new_keys and old_keys must have the same length, but got {len(new_keys)} and {len(old_keys)}" ) self.batch.rename_key_(tuple(old_keys), tuple(new_keys)) return self def union(self, other: "DataProto") -> "DataProto": """Union with another DataProto. Union batch and meta_info separately. Throw an error if - there are conflict keys in batch and they are not equal - the batch size of two data batch is not the same - there are conflict keys in meta_info and they are not the same. Args: other (DataProto): another DataProto to union Returns: DataProto: the DataProto after union """ self.batch = union_tensor_dict(self.batch, other.batch) self.non_tensor_batch = union_numpy_dict(self.non_tensor_batch, other.non_tensor_batch) self.meta_info = union_two_dict(self.meta_info, other.meta_info) return self def make_iterator(self, mini_batch_size, epochs, seed=None, dataloader_kwargs=None): r"""Make an iterator from the DataProto. This is built upon that TensorDict can be used as a normal Pytorch dataset. See https://pytorch.org/tensordict/stable/tutorials/data_fashion for more details. Args: mini_batch_size (int): mini-batch size when iterating the dataset. We require that ``batch.batch_size[0] % mini_batch_size == 0``. epochs (int): number of epochs when iterating the dataset. dataloader_kwargs (Any): internally, it returns a DataLoader over the batch. The dataloader_kwargs is the kwargs passed to the DataLoader. Returns: Iterator: an iterator that yields a mini-batch data at a time. The total number of iteration steps is ``self.batch.batch_size * epochs // mini_batch_size`` """ assert self.batch.batch_size[0] % mini_batch_size == 0, f"{self.batch.batch_size[0]} % {mini_batch_size} != 0" # we can directly create a dataloader from TensorDict if dataloader_kwargs is None: dataloader_kwargs = {} if seed is not None: generator = torch.Generator() generator.manual_seed(seed) else: generator = None assert isinstance(dataloader_kwargs, dict) train_dataloader = DataLoader( dataset=self, batch_size=mini_batch_size, collate_fn=collate_fn, generator=generator, **dataloader_kwargs ) def get_data(): for _ in range(epochs): for d in train_dataloader: d.meta_info = self.meta_info yield d return iter(get_data()) def is_padding_enabled(self): """ Check if padding is enabled for the DataProto. Returns: bool: True if padding is enabled, False otherwise. """ dataproto_specific_padding = self.meta_info.get(DataProtoConfig.auto_padding_key, False) return dataproto_specific_padding or DataProtoConfig.auto_padding def padding(self, padding_size, padding_candidate=""): """Pad the DataProto by concating with padding_candidate.repeat(padding_size) Args: padding_size (int): the number of repeated padding_candidate padding_candidate: the item to be repeated and appended to the DataProto, only supporting ["first", "last"] """ if padding_size == 0: return padding_candidate = self.select_idxs([0 if padding_candidate == "first" else len(self) - 1]) padding_part = padding_candidate.repeat(padding_size) padded_dp = DataProto.concat([self, padding_part]) self.batch = padded_dp.batch self.non_tensor_batch = padded_dp.non_tensor_batch def chunk(self, chunks: int) -> list["DataProto"]: """Split the batch among dim=0 into chunks. The meta_info is passed to each DataProto after split. Args: chunks (int): the number of chunks to split on dim=0 Returns: List[DataProto]: a list of DataProto after splitting """ if not self.is_padding_enabled(): assert len(self) % chunks == 0, ( f"only support equal chunk. Got size of DataProto {len(self)} and chunk {chunks}." ) bsz_in_batch = None if self.batch is not None: batch_lst = self.batch.chunk(chunks=chunks, dim=0) bsz_in_batch = np.array([batch.batch_size[0] for batch in batch_lst]) chunk_indices = np.cumsum(bsz_in_batch)[:-1] else: batch_lst = [None for _ in range(chunks)] non_tensor_batch_lst = [{} for _ in range(chunks)] for key, val in self.non_tensor_batch.items(): assert isinstance(val, np.ndarray) if bsz_in_batch is not None: non_tensor_lst = np.array_split(val, chunk_indices.tolist()) else: non_tensor_lst = np.array_split(val, chunks) assert len(non_tensor_lst) == chunks for i in range(chunks): non_tensor_batch_lst[i][key] = non_tensor_lst[i] output = [] for i in range(chunks): output.append( type(self)(batch=batch_lst[i], non_tensor_batch=non_tensor_batch_lst[i], meta_info=self.meta_info) ) return output def split(self, split_size: int) -> list["DataProto"]: """Split the batch among dim=0 into chunks. The meta_info is passed to each DataProto after split. Args: split_size (int): the size of each split Returns: List[DataProto]: a list of DataProto after splitting """ return [self[i : i + split_size] for i in range(0, len(self), split_size)] @staticmethod def concat(data: list["DataProto"]) -> "DataProto": """Concat a list of DataProto. The batch is concatenated among dim=0. The meta_info is merged, with special handling for metrics from different workers. Args: data (List[DataProto]): list of DataProto Returns: DataProto: concatenated DataProto """ batch_lst = [] for batch in data: batch_lst.append(batch.batch) new_batch = torch.cat(batch_lst, dim=0) if batch_lst[0] is not None else None non_tensor_batch = list_of_dict_to_dict_of_list(list_of_dict=[d.non_tensor_batch for d in data]) for key, val in non_tensor_batch.items(): non_tensor_batch[key] = np.concatenate(val, axis=0) # Merge meta_info with special handling for metrics merged_meta_info = {} if data: # Merge non-metric meta_info and aggregate metrics from all workers. all_metrics = [] for d in data: for k, v in d.meta_info.items(): if k == "metrics": if v is not None: if isinstance(v, list): all_metrics.extend(v) else: all_metrics.append(v) else: if k in merged_meta_info: # Ensure consistency for overlapping non-metric keys assert merged_meta_info[k] == v, f"Conflicting values for meta_info key '{k}'" else: merged_meta_info[k] = v # Flatten list of dicts to dict of lists for consistent metrics structure if all_metrics: merged_meta_info["metrics"] = list_of_dict_to_dict_of_list(all_metrics) cls = type(data[0]) if len(data) > 0 else DataProto return cls(batch=new_batch, non_tensor_batch=non_tensor_batch, meta_info=merged_meta_info) def reorder(self, indices): """ Note that this operation is in-place """ indices_np = indices.detach().numpy() self.batch = self.batch[indices] self.non_tensor_batch = {key: val[indices_np] for key, val in self.non_tensor_batch.items()} def repeat(self, repeat_times=2, interleave=True): """ Repeat the batch data a specified number of times. Args: repeat_times (int): Number of times to repeat the data. interleave (bool): Whether to interleave the repeated data. Returns: DataProto: A new DataProto with repeated data. """ if self.batch is not None: if interleave: # Interleave the data repeated_tensors = { key: tensor.repeat_interleave(repeat_times, dim=0) for key, tensor in self.batch.items() } else: # Stack the data repeated_tensors = { key: tensor.unsqueeze(0).expand(repeat_times, *tensor.shape).reshape(-1, *tensor.shape[1:]) for key, tensor in self.batch.items() } repeated_batch = TensorDict( source=repeated_tensors, batch_size=(self.batch.batch_size[0] * repeat_times,), ) else: repeated_batch = None repeated_non_tensor_batch = {} for key, val in self.non_tensor_batch.items(): if interleave: repeated_non_tensor_batch[key] = np.repeat(val, repeat_times, axis=0) else: repeated_non_tensor_batch[key] = np.tile(val, (repeat_times,) + (1,) * (val.ndim - 1)) return type(self)( batch=repeated_batch, non_tensor_batch=repeated_non_tensor_batch, meta_info=self.meta_info, ) def unfold_column_chunks(self, n_split: int, split_keys: Optional[list[str]] = None): """Split along the second dim into `n_split`, unfold it to the first dim (batch dim) Useful in passing grouped tensors that doesn't want to be shuffled in dataset. keys not in split_keys are repeated to match the shape Note that if the `split_keys` is not provided, it will repeat all the keys in the second dim. """ if self.batch is not None: unfolded_batch = {} for key in self.batch.keys(): if key in split_keys if split_keys is not None else False: shape = list(self.batch[key].shape) shape[0] = self.batch[key].shape[0] * n_split shape[1] = self.batch[key].shape[1] // n_split unfolded_batch[key] = self.batch[key].reshape(*shape) else: unfolded_batch[key] = torch.repeat_interleave(self.batch[key], n_split, dim=0) # locate the `unfolded_batch` as a TensorDict on the same device as the original batch unfolded_batch = TensorDict( source=unfolded_batch, batch_size=(self.batch.batch_size[0] * n_split,), device=self.batch.device ) else: unfolded_batch = None repeated_non_tensor_batch = {} for key, val in self.non_tensor_batch.items(): if key in split_keys: shape = list(val.shape) shape[0] = val.shape[0] * n_split shape[1] = val.shape[1] // n_split repeated_non_tensor_batch[key] = val.reshape(*shape) else: repeated_non_tensor_batch[key] = np.repeat(val, n_split, axis=0) return type(self)( batch=unfolded_batch, non_tensor_batch=repeated_non_tensor_batch, meta_info=self.meta_info, ) def sample_level_repeat(self, repeat_times): """ Repeat each row of the batch data a specified number of times. Args: repeat_times (torch.tensor, list, tuple, ndarray): Number of times to repeat the data. Returns: DataProto: A new DataProto with repeated data. """ if isinstance(repeat_times, tuple): repeat_times = list(repeat_times) elif isinstance(repeat_times, torch.Tensor): assert len(repeat_times.shape) == 1 repeat_times = repeat_times.tolist() elif isinstance(repeat_times, np.ndarray): assert len(repeat_times.shape) == 1 repeat_times = repeat_times.tolist() else: assert isinstance(repeat_times, list), ( f"repeat_times type must be in [list, torch.Tensor, np.ndarray, tuple], got {type(repeat_times)}" ) repeat_times = torch.tensor(repeat_times) if self.batch is not None: # Interleave the data repeated_tensors = { key: tensor.repeat_interleave(repeat_times, dim=0) for key, tensor in self.batch.items() } repeated_batch = TensorDict( source=repeated_tensors, batch_size=(repeat_times.sum().item(),), device=self.batch.device, ) else: repeated_batch = None repeated_non_tensor_batch = {} for key, val in self.non_tensor_batch.items(): repeated_non_tensor_batch[key] = np.repeat(val, repeat_times, axis=0) return type(self)( batch=repeated_batch, non_tensor_batch=repeated_non_tensor_batch, meta_info=self.meta_info, ) def to_tensordict(self) -> TensorDict: """Convert this DataProto to TensorDict. Note that this requires tensordict version at least 0.10 Returns: """ assert parse_version(tensordict.__version__) >= parse_version("0.10"), ( "Convert DataProto to TensorDict at least requires tensordict version 0.10" ) tensor_batch = self.batch.to_dict() non_tensor_batch = self.non_tensor_batch from tensordict.tensorclass import NonTensorData, NonTensorStack from verl.utils import tensordict_utils as tu common_keys = set(tensor_batch.keys()) & set(non_tensor_batch.keys()) assert len(common_keys) == 0, f"tensor_batch and non_tensor_batch have common keys {common_keys}" for key, val in non_tensor_batch.items(): assert isinstance(val, np.ndarray) # Convert to NonTensorStack instead of plain list to handle nested structures tensor_batch[key] = NonTensorStack.from_list([NonTensorData(item) for item in val]) output = tu.get_tensordict(tensor_dict=tensor_batch, non_tensor_dict=self.meta_info) return output def get_data_info(self) -> str: """Return formatted information about stored data with nested type details. Returns: str: Formatted string showing tensor details and recursive metadata types """ info = ["batch"] for key, tensor in self.batch.items(): if hasattr(tensor, "shape") and hasattr(tensor, "dtype") and hasattr(tensor, "device"): info.append(f" {key}: {tuple(tensor.shape)} ({tensor.dtype}) {tensor.device}") elif hasattr(tensor, "shape") and hasattr(tensor, "dtype"): info.append(f" {key}: {tuple(tensor.shape)} ({tensor.dtype})") else: info.append(f" {key}: {type(tensor).__name__}") info.append("non_tensor_batch") for key, array in self.non_tensor_batch.items(): info.append(f" {key}: ndarray{array.shape} ({array.dtype})") info.append("meta_info") for k, v in self.meta_info.items(): type_info = self._get_type_info(v) info.append(f" {k}: {type_info}") return "\n".join(info) def _get_type_info(self, value): """Recursively get type information for nested structures""" if isinstance(value, list): elem_types = {self._get_type_info(v) for v in value[:3]} return f"list[{'|'.join(elem_types) if elem_types else '...'}]" if isinstance(value, tuple): elem_types = [self._get_type_info(v) for v in value] return f"tuple({', '.join(elem_types)})" if isinstance(value, dict): if not value: return "dict" k, v = next(iter(value.items())) return f"dict[{self._get_type_info(k)}: {self._get_type_info(v)}]" if isinstance(value, np.ndarray): return f"ndarray{value.shape} ({value.dtype})" return type(value).__name__ @dataclass class DataProtoFuture: """ DataProtoFuture aims to eliminate actual data fetching on driver. By doing so, the driver doesn't have to wait for data so that asynchronous execution becomes possible. DataProtoFuture contains a list of futures from another WorkerGroup of size world_size. - collect_fn is a Callable that reduces the list of futures to a DataProto - dispatch_fn is a Callable that partitions the DataProto into a list of DataProto of size world_size and then select Potential issue: we can optimize dispatch_fn(collect_fn) such that only needed data is fetched on destination - DataProtoFuture only supports directly passing from the output of a method to another input. You can't perform any operation on the DataProtoFuture in driver. """ collect_fn: Callable futures: list[ray.ObjectRef] dispatch_fn: Callable = None @staticmethod def concat(data: list[ray.ObjectRef]) -> "DataProtoFuture": output = DataProtoFuture(collect_fn=DataProto.concat, futures=data) return output def chunk(self, chunks: int) -> list["DataProtoFuture"]: from functools import partial arg_future_lst = [] for i in range(chunks): # note that we can't directly pass i and chunks def dispatch_fn(x, i, chunks): return x.chunk(chunks=chunks)[i] arg_future = DataProtoFuture( collect_fn=self.collect_fn, dispatch_fn=partial(dispatch_fn, i=i, chunks=chunks), futures=self.futures ) arg_future_lst.append(arg_future) return arg_future_lst def get(self): output = ray.get(self.futures) # dp_size. for o in output: assert isinstance(o, DataProto | TensorDict) if isinstance(output[0], DataProto): output = DataProto.concat(output) # select dp, concat elif isinstance(output[0], TensorDict): from verl.utils.tensordict_utils import concat_tensordict output = concat_tensordict(output) else: raise TypeError(f"Unknown type {type(o[0])} in DataProtoFuture") if self.dispatch_fn is not None: output = self.dispatch_fn(output) # split in batch dim, select using dp return output class BatchData: """Uniform dispatch wrapper for batch data operations. All type-specific logic (isinstance checks) is centralized here so that callers (e.g. decorator.py) never need to branch on the concrete data type. Usage:: # chunk a single data item into N pieces chunks = BatchData(arg).chunk(chunks=N) # concat a list of data items into one merged = BatchData(output_list).concat() # validate before dispatching assert BatchData(arg).is_chunkable() assert BatchData(output_list).is_concatable() """ _CHUNKABLE_TYPES = (TensorDict,) # lazily extended with DataProto etc. _CONCATABLE_TYPES = (TensorDict,) def __init__(self, data): self._data = data # ---- validation ---------------------------------------------------------- def is_chunkable(self) -> bool: """Return True if the wrapped data supports chunk dispatch.""" return isinstance(self._data, self._chunkable_types()) def is_concatable(self) -> bool: """Return True if the wrapped list of data supports concat collect.""" data = self._data if not isinstance(data, list | tuple) or len(data) == 0: return False return isinstance(data[0], self._concatable_types()) # ---- operations ---------------------------------------------------------- def chunk(self, chunks: int): """Split the wrapped data into *chunks* pieces along the batch dim. Returns a tuple/list of the **original data types** (not BatchData). """ data = self._data if isinstance(data, TensorDict): from verl.utils.tensordict_utils import chunk_tensordict, contiguous raw_chunks = chunk_tensordict(data, chunks) return tuple(contiguous(val).consolidate() for val in raw_chunks) # DataProto, DataProtoFuture, BatchMeta all expose .chunk() return data.chunk(chunks=chunks) def concat(self): """Concat the wrapped list of data items into a single result. Returns the **original data type** (not BatchData). """ data = self._data if not data: raise ValueError("Cannot concatenate an empty list of data items.") sample = data[0] if isinstance(sample, ray.ObjectRef): return DataProtoFuture.concat(data) if isinstance(sample, TensorDict): from verl.utils.tensordict_utils import concat_tensordict return concat_tensordict(data) # DataProto, BatchMeta expose .concat() as classmethod / staticmethod return type(sample).concat(data) # ---- helpers (lazy type tuples to avoid import-order issues) ------------- @classmethod def _chunkable_types(cls): return (DataProto, DataProtoFuture, TensorDict) @classmethod def _concatable_types(cls): return (DataProto, ray.ObjectRef, TensorDict) def all_gather_data_proto(data: DataProto, process_group): # Note that this is an inplace operator just like torch.distributed.all_gather group_size = torch.distributed.get_world_size(group=process_group) assert isinstance(data, DataProto) prev_device = data.batch.device data = data.to(get_device_id()) data.batch = allgather_dict_tensors(data.batch.contiguous(), size=group_size, group=process_group, dim=0) data = data.to(prev_device) # all gather non_tensor_batch all_non_tensor_batch = [None for _ in range(group_size)] torch.distributed.all_gather_object(all_non_tensor_batch, data.non_tensor_batch, group=process_group) data.non_tensor_batch = {k: np.concatenate([d[k] for d in all_non_tensor_batch]) for k in data.non_tensor_batch} ================================================ FILE: verl/py.typed ================================================ ================================================ FILE: verl/single_controller/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from . import base from .base import * version_folder = os.path.dirname(os.path.join(os.path.abspath(__file__))) # Note(haibin.lin): single_controller.__version__ is deprecated with open(os.path.join(os.path.join(version_folder, os.pardir), "version/version")) as f: __version__ = f.read().strip() __all__ = base.__all__ ================================================ FILE: verl/single_controller/base/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .worker import Worker from .worker_group import ClassWithInitArgs, ResourcePool, WorkerGroup __all__ = ["Worker", "WorkerGroup", "ClassWithInitArgs", "ResourcePool"] ================================================ FILE: verl/single_controller/base/decorator.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from functools import partial, wraps from types import FunctionType from verl.protocol import DataProtoFuture, _padding_size_key from verl.utils.py_functional import DynamicEnum # here we add a magic number of avoid user-defined function already have this attribute MAGIC_ATTR = "attrs_3141562937" class Dispatch(DynamicEnum): """Enum class defining different dispatch modes for distributed computation. Each mode represents a specific strategy for distributing data across different ranks in a distributed system. The modes are used to control how data is partitioned and processed across different worker groups. """ _registry = {} _next_value = 0 def init_predefined_dispatch_mode(): Dispatch.register("RANK_ZERO") Dispatch.register("ONE_TO_ALL") Dispatch.register("ALL_TO_ALL") Dispatch.register("DP_COMPUTE") Dispatch.register("DP_COMPUTE_PROTO") Dispatch.register("DP_COMPUTE_PROTO_WITH_FUNC") Dispatch.register("DP_COMPUTE_METRIC") # This is a special dispatch mode for vllm ExternalRayDistributedExecutor Dispatch.register("DIRECT_ROLLOUT_METHOD") class Execute(DynamicEnum): """Enum class defining different execution modes for distributed computation. These modes control how a function should be executed across different ranks in a distributed system. """ _registry = {} _next_value = 0 def init_predefined_execute_mode(): Execute.register("ALL") Execute.register("RANK_ZERO") # Initialize the two Dynamic Enum Classes init_predefined_dispatch_mode() init_predefined_execute_mode() def _split_args_kwargs_data_proto(chunks, *args, **kwargs): from verl.protocol import BatchData splitted_args = [] for arg in args: assert BatchData(arg).is_chunkable(), f"arg of type {type(arg)} is not chunkable" chunked_arg = BatchData(arg).chunk(chunks=chunks) assert len(chunked_arg) == chunks splitted_args.append(chunked_arg) splitted_kwargs = {} for key, val in kwargs.items(): assert BatchData(val).is_chunkable(), f"kwarg '{key}' of type {type(val)} is not chunkable" chunked_kwarg = BatchData(val).chunk(chunks=chunks) assert len(chunked_kwarg) == chunks splitted_kwargs[key] = chunked_kwarg return splitted_args, splitted_kwargs def _split_args_kwargs_data_proto_with_auto_padding(chunks, *args, **kwargs): from verl.protocol import DataProto, DataProtoFuture data_proto_len = None padding_size = None def _padding_and_split_data(obj, chunks): nonlocal data_proto_len, padding_size assert isinstance(obj, DataProto | DataProtoFuture) if isinstance(obj, DataProto) and obj.is_padding_enabled(): # for padding, we only support DataProto with same length if data_proto_len is None: data_proto_len = len(obj) padding_size = (chunks - (data_proto_len % chunks)) if (data_proto_len % chunks > 0) else 0 else: assert data_proto_len == len(obj), ( f"expecting all arg share same length of {data_proto_len}, but got {len(obj)}" ) obj.padding(padding_size=padding_size) return obj.chunk(chunks=chunks) splitted_args = [_padding_and_split_data(arg, chunks) for arg in args] splitted_kwargs = {key: _padding_and_split_data(val, chunks) for key, val in kwargs.items()} if padding_size is not None: splitted_kwargs[_padding_size_key] = padding_size return splitted_args, splitted_kwargs def dispatch_one_to_all(worker_group, *args, **kwargs): args = tuple([arg] * worker_group.world_size for arg in args) kwargs = {k: [v] * worker_group.world_size for k, v in kwargs.items()} return args, kwargs def dummy_direct_rollout_call(worker_group, *args, **kwargs): raise NotImplementedError("Direct rollout call is forbidden.") def dispatch_all_to_all(worker_group, *args, **kwargs): return args, kwargs def collect_all_to_all(worker_group, output): return output def _concat_data_proto_or_future(output: list): from verl.protocol import BatchData # make sure all the elements in output has the same type for o in output: assert type(o) is type(output[0]) return BatchData(output).concat() def dispatch_dp_compute(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) for arg in args: assert isinstance(arg, tuple | list) and len(arg) == worker_group.world_size for k, v in kwargs.items(): assert isinstance(v, tuple | list) and len(v) == worker_group.world_size return args, kwargs def collect_dp_compute(worker_group, output): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) assert len(output) == worker_group.world_size return output def dispatch_dp_compute_data_proto(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) # Note: enable auto padding for dp compute DatapProto splitted_args, splitted_kwargs = _split_args_kwargs_data_proto_with_auto_padding( worker_group.world_size, *args, **kwargs, ) return splitted_args, splitted_kwargs def dispatch_dp_compute_data_proto_with_func(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) assert isinstance(args[0], FunctionType) # NOTE: The first one args is a function! splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(worker_group.world_size, *args[1:], **kwargs) splitted_args_with_func = [[args[0]] * worker_group.world_size] + splitted_args return splitted_args_with_func, splitted_kwargs def collect_dp_compute_data_proto(worker_group, output): from verl.protocol import BatchData assert BatchData(output).is_concatable(), ( f"expecting concatable output, but got element type {type(output[0]) if output else 'empty'}" ) output = collect_dp_compute(worker_group, output) return _concat_data_proto_or_future(output) def dispatch_nd_compute(dp_rank_mapping: list[int], dp_size, worker_group, *args, **kwargs): import os from verl.single_controller.base.worker_group import WorkerGroup from verl.utils.ray_utils import parallel_put assert isinstance(worker_group, WorkerGroup) max_workers = max(1, min(len(args[0]), os.cpu_count())) args = [parallel_put(arg, max_workers=max_workers) for arg in args] kwargs = {k: parallel_put(v, max_workers=max_workers) for k, v in kwargs.items()} all_args = [] for arg in args: assert isinstance(arg, tuple | list) and len(arg) == dp_size transformed_args = [] for i in range(worker_group.world_size): local_dp_rank = dp_rank_mapping[i] transformed_args.append(arg[local_dp_rank]) all_args.append(transformed_args) all_args = tuple(all_args) all_kwargs = {} for k, v in kwargs.items(): assert isinstance(v, tuple | list) and len(v) == dp_size transformed_v = [] for i in range(worker_group.world_size): local_dp_rank = dp_rank_mapping[i] transformed_v.append(v[local_dp_rank]) all_kwargs[k] = transformed_v return all_args, all_kwargs def collect_nd_compute(collect_mask: list[bool], worker_group, output): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) assert len(output) == worker_group.world_size output_in_dp = [] for global_rank in range(worker_group.world_size): collect_dp_rank = collect_mask[global_rank] if collect_dp_rank: output_in_dp.append(output[global_rank]) return output_in_dp def dispatch_nd_compute_dataproto(dp_rank_mapping: list[int], dp_size, worker_group, *args, **kwargs): splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(dp_size, *args, **kwargs) return dispatch_nd_compute(dp_rank_mapping, dp_size, worker_group, *splitted_args, **splitted_kwargs) def collect_nd_compute_dataproto(collect_mask: list[bool], worker_group, output): output = collect_nd_compute(collect_mask, worker_group, output) from verl.protocol import BatchData assert BatchData(output).is_concatable(), ( f"expecting concatable output, but got element type {type(output[0]) if output else 'empty'}" ) return _concat_data_proto_or_future(output) def dispatch_lazy_compute_data_proto(mesh_name, worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) # query dispatch info of the worker group if mesh_name not in worker_group._dispatch_info: worker_group._dispatch_info[mesh_name] = worker_group._query_dispatch_info(mesh_name) assert len(worker_group._dispatch_info[mesh_name]) == worker_group.world_size dp_rank_mapping = worker_group._dispatch_info[mesh_name] # perform dispatch dp_size = max(dp_rank_mapping) + 1 return dispatch_nd_compute_dataproto(dp_rank_mapping, dp_size, worker_group, *args, **kwargs) def collect_lazy_compute_data_proto(mesh_name, worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) # the dispatch info is stored in the worker group assert mesh_name in worker_group._dispatch_info if mesh_name not in worker_group._collect_info: worker_group._collect_info[mesh_name] = worker_group._query_collect_info(mesh_name) assert len(worker_group._collect_info[mesh_name]) == worker_group.world_size # a boolean of whether the dp_rank is used for collect collect_mask = worker_group._collect_info[mesh_name] # perform dispatch return collect_nd_compute_dataproto(collect_mask, worker_group, *args, **kwargs) def make_nd_compute_dataproto_dispatch_fn(mesh_name): return { "dispatch_fn": partial(dispatch_lazy_compute_data_proto, mesh_name), "collect_fn": partial(collect_lazy_compute_data_proto, mesh_name), } # Global registry for dispatch mode. DISPATCH_MODE_FN_REGISTRY = { Dispatch.ONE_TO_ALL: { "dispatch_fn": dispatch_one_to_all, "collect_fn": collect_all_to_all, }, Dispatch.ALL_TO_ALL: { "dispatch_fn": dispatch_all_to_all, "collect_fn": collect_all_to_all, }, Dispatch.DP_COMPUTE: {"dispatch_fn": dispatch_dp_compute, "collect_fn": collect_dp_compute}, Dispatch.DP_COMPUTE_PROTO: { "dispatch_fn": dispatch_dp_compute_data_proto, "collect_fn": collect_dp_compute_data_proto, }, Dispatch.DP_COMPUTE_PROTO_WITH_FUNC: { "dispatch_fn": dispatch_dp_compute_data_proto_with_func, "collect_fn": collect_dp_compute_data_proto, }, Dispatch.DP_COMPUTE_METRIC: {"dispatch_fn": dispatch_dp_compute_data_proto, "collect_fn": collect_dp_compute}, Dispatch.DIRECT_ROLLOUT_METHOD: { "dispatch_fn": dummy_direct_rollout_call, "collect_fn": dummy_direct_rollout_call, }, } def get_predefined_dispatch_fn(dispatch_mode): return DISPATCH_MODE_FN_REGISTRY[dispatch_mode] def register_dispatch_mode(dispatch_mode_name, dispatch_fn, collect_fn): """ Register a new dispatch mode. """ dispatch_mode = Dispatch.register(dispatch_mode_name) _check_dispatch_mode(dispatch_mode) assert dispatch_mode not in DISPATCH_MODE_FN_REGISTRY, f"dispatch_mode_name {dispatch_mode_name} already exists" DISPATCH_MODE_FN_REGISTRY[dispatch_mode] = {"dispatch_fn": dispatch_fn, "collect_fn": collect_fn} def update_dispatch_mode(dispatch_mode, dispatch_fn, collect_fn): """ Update the dispatch mode. """ _check_dispatch_mode(dispatch_mode) assert dispatch_mode in DISPATCH_MODE_FN_REGISTRY, f"dispatch_mode {dispatch_mode} not found" DISPATCH_MODE_FN_REGISTRY[dispatch_mode] = {"dispatch_fn": dispatch_fn, "collect_fn": collect_fn} def get_predefined_execute_fn(execute_mode): """ Note that here we only asks execute_all and execute_rank_zero to be implemented Leave the choice of how these two functions handle argument 'blocking' to users """ predefined_execute_mode_fn = { Execute.ALL: {"execute_fn_name": "execute_all"}, Execute.RANK_ZERO: {"execute_fn_name": "execute_rank_zero"}, } return predefined_execute_mode_fn[execute_mode] def _check_dispatch_mode(dispatch_mode): assert isinstance(dispatch_mode, Dispatch | dict), ( f"dispatch_mode must be a Dispatch or a Dict. Got {dispatch_mode}" ) if isinstance(dispatch_mode, dict): necessary_keys = ["dispatch_fn", "collect_fn"] for key in necessary_keys: assert key in dispatch_mode, f"key {key} should be in dispatch_mode if it is a dictionary" def _check_execute_mode(execute_mode): assert isinstance(execute_mode, Execute), f"execute_mode must be a Execute. Got {execute_mode}" def _materialize_futures(*args, **kwargs): new_args = [] for arg in args: if isinstance(arg, DataProtoFuture): arg = arg.get() # add more type to materialize new_args.append(arg) for k, v in kwargs.items(): if isinstance(v, DataProtoFuture): kwargs[k] = v.get() new_args = tuple(new_args) return new_args, kwargs def register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.ALL, blocking=True, materialize_futures=True): """Register a function with distributed execution configuration. This decorator registers a function with specific dispatch and execution modes for distributed computation. It handles both synchronous and asynchronous functions, and optionally materializes futures before execution. Args: dispatch_mode: Dispatch mode for computation distribution. Default: Dispatch.ALL_TO_ALL. execute_mode: Execute mode for computation distribution. Default: Execute.ALL. blocking: Whether the execution should be blocking. Defaults to True. materialize_futures: Whether to materialize the data before dispatching. Defaults to True. Returns: A decorator that wraps the original function with distributed execution configuration. """ _check_dispatch_mode(dispatch_mode=dispatch_mode) _check_execute_mode(execute_mode=execute_mode) def decorator(func): @wraps(func) def inner(*args, **kwargs): if materialize_futures: args, kwargs = _materialize_futures(*args, **kwargs) return func(*args, **kwargs) @wraps(func) async def async_inner(*args, **kwargs): if materialize_futures: args, kwargs = _materialize_futures(*args, **kwargs) return await func(*args, **kwargs) wrapper = async_inner if inspect.iscoroutinefunction(func) else inner attrs = {"dispatch_mode": dispatch_mode, "execute_mode": execute_mode, "blocking": blocking} setattr(wrapper, MAGIC_ATTR, attrs) return wrapper return decorator ================================================ FILE: verl/single_controller/base/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ the class for Worker """ import os import socket import warnings from dataclasses import dataclass import ray from verl.utils.device import ( get_torch_device, get_visible_devices_keyword, is_npu_available, ) from .decorator import Dispatch, Execute, register @dataclass class DistRankInfo: tp_rank: int dp_rank: int pp_rank: int cp_rank: int @dataclass class DistGlobalInfo: tp_size: int dp_size: int pp_size: int cp_size: int class WorkerHelper: @staticmethod def _get_node_ip(): if os.getenv("WG_BACKEND", None) == "ray": return ray.util.get_node_ip_address() else: raise NotImplementedError("WG_BACKEND now just support ray mode.") @staticmethod def _get_free_port(): with socket.socket() as sock: sock.bind(("", 0)) return sock.getsockname()[1] def get_availale_master_addr_port(self): warnings.warn( "This function is deprecated due to typo in name; Please use `get_available_master_addr_port` instead", stacklevel=2, ) return self.get_available_master_addr_port() def get_available_master_addr_port(self): return self._get_node_ip().strip("[]"), str(self._get_free_port()) # we assume that in each WorkerGroup, there is a Master Worker class Worker(WorkerHelper): """A distributed worker that handles initialization and configuration for distributed training. This class manages worker initialization, configuration, and provides methods for executing distributed operations. It handles communication settings, device configuration, and worker metadata management. """ fused_worker_attr_name = "fused_worker_dict" def _register_dispatch_collect_info(self, mesh_name: str, dp_rank: int, is_collect: bool): """Register the dp_rank for a given mesh name. This function is meant to be called by the worker Args: mesh_name (str): Name of the mesh to register dp_rank for. dp_rank (int): dp_rank to register for the given mesh name. is_collect (bool): Whether the dp_rank is used for collect. """ if mesh_name in self.__dispatch_dp_rank or mesh_name in self.__collect_dp_rank: raise ValueError(f"mesh_name {mesh_name} has been registered") self.__dispatch_dp_rank[mesh_name] = dp_rank self.__collect_dp_rank[mesh_name] = is_collect @register(dispatch_mode=Dispatch.ONE_TO_ALL) def _query_dispatch_info(self, mesh_name: str): """Query the dispatch info for a given mesh name. Args: mesh_name (str): Name of the mesh to query dispatch info for. Returns: int: The dp_rank for the given mesh name. """ assert mesh_name in self.__dispatch_dp_rank, f"{mesh_name} is not registered in {self.__class__.__name__}" # note that each rank store its own dp_rank return self.__dispatch_dp_rank[mesh_name] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def _query_collect_info(self, mesh_name: str): return self.query_collect_info(mesh_name) def query_collect_info(self, mesh_name: str): """Query the collect info for a given mesh name. Args: mesh_name (str): Name of the mesh to query collect info for. Returns: bool: Whether the dp_rank is used for collect. """ assert mesh_name in self.__collect_dp_rank, f"{mesh_name} is not registered in {self.__class__.__name__}" return self.__collect_dp_rank[mesh_name] def get_dispatch_collect(self): """Get all registered dispatch and collect dp_ranks. Returns: dict[str, int]: A dictionary mapping mesh names to their dispatch dp_ranks. dict[str, bool]: A dictionary mapping mesh names to whether they are used for collect. """ return {"dispatch_dp_rank": self.__dispatch_dp_rank, "collect_dp_rank": self.__collect_dp_rank} def set_dispatch_collect(self, mesh_name: str, dispatch_dp_rank: dict[str, int], collect_dp_rank: dict[str, bool]): """Set the dispatch and collect dp_ranks for all registered meshes. Args: mesh_name (str): Mesh name to set dispatch and collect dp_ranks for. dispatch_dp_rank (dict[str, int]): A dictionary mapping mesh names to their dispatch dp_ranks. collect_dp_rank (dict[str, bool]): A dictionary mapping mesh names to whether they are used for collect. """ assert mesh_name not in self.__dispatch_dp_rank, ( f"{mesh_name} is already registered, {self.__dispatch_dp_rank.keys()}" ) assert mesh_name not in self.__collect_dp_rank, ( f"{mesh_name} is already registered, {self.__collect_dp_rank.keys()}" ) for dp_rank in dispatch_dp_rank.values(): self.__dispatch_dp_rank[mesh_name] = dp_rank for is_collect in collect_dp_rank.values(): self.__collect_dp_rank[mesh_name] = is_collect @classmethod def env_keys(cls): """The keys of the environment variables that are used to configure the Worker.""" return [ "WORLD_SIZE", "RANK", "LOCAL_WORLD_SIZE", "LOCAL_RANK", "MASTER_ADDR", "MASTER_PORT", get_visible_devices_keyword().upper(), ] def __init__(self, cuda_visible_devices=None) -> None: """Initialize the worker with environment settings and device configuration. Args: cuda_visible_devices (str, optional): CUDA visible devices configuration. Defaults to None. """ # construct a meta from environment variable. Note that the import must be inside the class because # it is executed remotely import os self._setup_env_cuda_visible_devices() world_size = int(os.environ["WORLD_SIZE"]) rank = int(os.environ["RANK"]) self._rank = rank self._world_size = world_size master_addr = os.environ["MASTER_ADDR"] master_port = os.environ["MASTER_PORT"] local_world_size = int(os.getenv("LOCAL_WORLD_SIZE", "1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) store = { "_world_size": world_size, "_rank": rank, "_local_world_size": local_world_size, "_local_rank": local_rank, "_master_addr": master_addr, "_master_port": master_port, } if cuda_visible_devices is not None: store[f"_{get_visible_devices_keyword()}".lower()] = cuda_visible_devices self._configure_with_store(store=store) self.fused_worker_dict = {} self.__dispatch_dp_rank = {} self.__collect_dp_rank = {} def get_fused_worker_by_name(self, worker_name: str): """Get a fused worker by its name. Args: worker_name (str): Name of the worker to retrieve """ return self.fused_worker_dict.get(worker_name, None) def _setup_env_cuda_visible_devices(self): from verl.utils.ray_utils import ray_noset_visible_devices is_ray_noset_visible_devices = ray_noset_visible_devices() # Prevent use of clashing `{CUDA/HIP/ROCR}_VISIBLE_DEVICES`` rocr_val = os.environ.get("ROCR_VISIBLE_DEVICES", None) hip_val = os.environ.get("HIP_VISIBLE_DEVICES", None) cuda_val = os.environ.get("CUDA_VISIBLE_DEVICES", None) if hip_val: # Switch the use of HIP_VISIBLE_DEVICES to CUDA_VISIBLE_DEVICES for consistency. # Make sure that the HIP_VISIBLE_DEVICES is set to the same value as CUDA_VISIBLE_DEVICES # at this point. val = os.environ.pop("HIP_VISIBLE_DEVICES") hip_val = None if cuda_val: assert val == cuda_val, ( f"Please use the same HIP_VISIBLE_DEVICES or CUDA_VISIBLE_DEVICES, inconsistant values " f"found: {val} and {cuda_val}." ) else: cuda_val = val os.environ["CUDA_VISIBLE_DEVICES"] = val # os.environ["HIP_VISIBLE_DEVICES"] = val if rocr_val: # You must take care if both HIP/CUDA and ROCR env vars are set as they have # different meanings. Both env vars accept either a list of ints or a # list of UUIDs. The ROCR env var is processed first which then reduces # the number of GPUs that HIP can select from. # https://github.com/pytorch/pytorch/pull/144026 # To avoid the complexity of this, we simply gives out error if both are set # (Also to keep consistency with ray's practice with 2.45.0). # Otherwise, we will set ROCR_VISIBLE_DEVICES to CUDA_VISIBLE_DEVICES # and remove ROCR_VISIBLE_DEVICES. if cuda_val: raise ValueError("Please don't set ROCR_VISIBLE_DEVICES when HIP/CUDA_VISIBLE_DEVICES is set.") cuda_val = os.environ.pop("ROCR_VISIBLE_DEVICES") os.environ["CUDA_VISIBLE_DEVICES"] = cuda_val rocr_val = None if is_ray_noset_visible_devices: # NOTE: Ray will automatically set the *_VISIBLE_DEVICES # environment variable for each actor, unless # RAY_EXPERIMENTAL_NOSET_*_VISIBLE_DEVICES is set, # so we need to set local rank when the flag is set. device_name = "NPU" if is_npu_available else "GPU" local_rank = ray.get_runtime_context().get_accelerator_ids()[device_name][0] os.environ["LOCAL_RANK"] = local_rank get_torch_device().set_device(int(local_rank)) def _configure_with_store(self, store: dict): """ This function should only be called inside by WorkerGroup """ store_env_dict = {f"_{key.lower()}": store.get(f"_{key.lower()}", None) for key in type(self).env_keys()} self.__dict__.update(store_env_dict) # this is hacky # print(f"__dict__: {self.__dict__}") for key in type(self).env_keys(): val = self.__dict__.get(f"_{key.lower()}", None) if val is not None: # print(f"set {key} to {val}") os.environ[key] = str(val) os.environ["REDIS_STORE_SERVER_HOST"] = ( str(self._master_addr).replace("[", "").replace("]", "") if self._master_addr else "" ) def get_master_addr_port(self): """Get the master address and port for distributed communication.""" return self._master_addr, self._master_port def get_cuda_visible_devices(self): """Get the CUDA visible devices configuration.""" import os visible_devices = os.environ.get(get_visible_devices_keyword().upper(), "not set") return visible_devices @property def world_size(self): """Get the total number of workers in the distributed setup.""" return self._world_size @property def rank(self): """Get the rank of this worker in the distributed setup.""" return self._rank @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO_WITH_FUNC) def execute_with_func_generator(self, func, *args, **kwargs): """Execute a function with function generator dispatch mode. Args: func: Function to execute *args: Positional arguments for the function **kwargs: Keyword arguments for the function """ ret_proto = func(self, *args, **kwargs) return ret_proto @register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.RANK_ZERO) def execute_func_rank_zero(self, func, *args, **kwargs): """Execute a function in rank zero execution mode. Args: func: Function to execute *args: Positional arguments for the function **kwargs: Keyword arguments for the function """ result = func(*args, **kwargs) return result ================================================ FILE: verl/single_controller/base/worker_group.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ the class of WorkerGroup """ import logging import signal import threading import time from typing import Any, Callable from .decorator import MAGIC_ATTR, Dispatch, get_predefined_dispatch_fn, get_predefined_execute_fn class ResourcePool: """ Manages a pool of resources across multiple nodes, tracking process counts and GPU allocations. The class provides methods to calculate world size, local world sizes, and local ranks across all nodes in the pool. """ def __init__(self, process_on_nodes=None, max_colocate_count: int = 10, n_gpus_per_node=8) -> None: """Initialize the ResourcePool with node processes and GPU configuration. Args: process_on_nodes (List[int], optional): List of process counts per node. Defaults to empty list. max_colocate_count (int, optional): Maximum number of processes that can be colocated. Defaults to 10. n_gpus_per_node (int, optional): Number of GPUs available per node. Defaults to 8. """ if process_on_nodes is None: process_on_nodes = [] self._store = process_on_nodes self.max_colocate_count = max_colocate_count self.n_gpus_per_node = n_gpus_per_node # this is left for future huawei GPU that contains 16 GPUs per node def add_node(self, process_count): self._store.append(process_count) @property def world_size(self): """Total number of processes across all nodes in the pool.""" return sum(self._store) def __call__(self) -> Any: return self._store @property def store(self): return self._store def local_world_size_list(self) -> list[int]: """Returns a flat list where each process has its local world size.""" nested_local_world_size_list = [ [local_world_size for _ in range(local_world_size)] for local_world_size in self._store ] return [item for row in nested_local_world_size_list for item in row] def local_rank_list(self) -> list[int]: """Returns a flat list of local ranks for all processes across all nodes.""" nested_local_rank_list = [[i for i in range(local_world_size)] for local_world_size in self._store] return [item for row in nested_local_rank_list for item in row] class ClassWithInitArgs: """ Wrapper class that stores constructor arguments for deferred instantiation. This class is particularly useful for remote class instantiation where the actual construction needs to happen at a different time or location. """ def __init__(self, cls, *args, **kwargs) -> None: """Initialize the ClassWithInitArgs instance. Args: cls: The class to be instantiated later *args: Positional arguments for the class constructor **kwargs: Keyword arguments for the class constructor """ self.cls = cls self.args = args self.kwargs = kwargs self.fused_worker_used = False def __call__(self) -> Any: """Instantiate the stored class with the stored arguments.""" return self.cls(*self.args, **self.kwargs) def check_workers_alive(workers: list, is_alive: Callable, gap_time: float = 1) -> None: """Continuously monitors worker processes and raises SIGABRT if any worker dies. Args: workers (List): List of worker objects to monitor is_alive (Callable): Function to check if a worker is alive gap_time (float): Time interval between checks """ import time while True: for worker in workers: if not is_alive(worker): logging.warning(f"worker {worker} is not alive sending signal to main thread") signal.raise_signal(signal.SIGABRT) time.sleep(gap_time) class WorkerGroup: """ Base class for managing a group of workers in a distributed system. The class provides methods for worker management, aliveness checking, and method binding. """ fused_worker_execute_fn_name = "_fuw_execute" def __init__(self, resource_pool: ResourcePool, **kwargs) -> None: self._is_init_with_detached_workers = resource_pool is None self.fused_worker_used = False if resource_pool is not None: # handle the case when WorkGroup is attached to an existing one self._process_dispatch_config = resource_pool() else: self._process_dispatch_config = None self._workers = [] self._worker_names = [] self._dispatch_info = {} self._collect_info = {} self._master_addr = None self._master_port = None self._checker_thread: threading.Thread = None def _is_worker_alive(self, worker): """Check if a worker is alive. Must be implemented by derived classes.""" raise NotImplementedError("WorkerGroup._is_worker_alive called, should be implemented in derived class.") def _block_until_all_workers_alive(self) -> None: """Blocks until all workers in the group are alive.""" while True: all_state = [self._is_worker_alive(worker) for worker in self._workers] if False in all_state: time.sleep(1) else: break def start_worker_aliveness_check(self, every_n_seconds=1) -> None: """Starts a background thread to monitor worker aliveness. Args: every_n_seconds (int): Interval between aliveness checks """ # before starting checking worker aliveness, make sure all workers are already alive self._block_until_all_workers_alive() self._checker_thread = threading.Thread( target=check_workers_alive, args=(self._workers, self._is_worker_alive, every_n_seconds) ) self._checker_thread.start() @property def world_size(self): """Number of workers in the group.""" return len(self._workers) def _bind_worker_method(self, user_defined_cls, func_generator): """Binds worker methods to the WorkerGroup based on registered attributes. Args: user_defined_cls (type): The class containing methods to bind func_generator (Callable): Function that generates the bound method Returns: List[str]: List of method names that were successfully bound """ method_names = [] for method_name in dir(user_defined_cls): try: method = getattr(user_defined_cls, method_name) assert callable(method), f"{method_name} in {user_defined_cls} is not callable" except Exception: # if it is a property, it will fail because Class doesn't have instance property continue if hasattr(method, MAGIC_ATTR): # this method is decorated by register attribute = getattr(method, MAGIC_ATTR) assert isinstance(attribute, dict), f"attribute must be a dictionary. Got {type(attribute)}" assert "dispatch_mode" in attribute, "attribute must contain dispatch_mode in its key" dispatch_mode = attribute["dispatch_mode"] execute_mode = attribute["execute_mode"] blocking = attribute["blocking"] # get dispatch fn if isinstance(dispatch_mode, Dispatch): # get default dispatch fn fn = get_predefined_dispatch_fn(dispatch_mode=dispatch_mode) dispatch_fn = fn["dispatch_fn"] collect_fn = fn["collect_fn"] else: assert isinstance(dispatch_mode, dict) assert "dispatch_fn" in dispatch_mode assert "collect_fn" in dispatch_mode dispatch_fn = dispatch_mode["dispatch_fn"] collect_fn = dispatch_mode["collect_fn"] # get execute_fn_name execute_mode = get_predefined_execute_fn(execute_mode=execute_mode) wg_execute_fn_name = execute_mode["execute_fn_name"] # get execute_fn from string try: execute_fn = getattr(self, wg_execute_fn_name) assert callable(execute_fn), "execute_fn must be callable" except Exception: print(f"execute_fn {wg_execute_fn_name} is invalid") raise # bind a new method to the RayWorkerGroup func = func_generator( self, method_name, dispatch_fn=dispatch_fn, collect_fn=collect_fn, execute_fn=execute_fn, blocking=blocking, ) try: setattr(self, method_name, func) method_names.append(method_name) except Exception as e: raise ValueError(f"Fail to set method_name {method_name}") from e return method_names ================================================ FILE: verl/single_controller/ray/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import ( RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, ResourcePoolManager, SubRayResourcePool, create_colocated_worker_cls, create_colocated_worker_cls_fused, ) __all__ = [ "RayClassWithInitArgs", "RayResourcePool", "SubRayResourcePool", "RayWorkerGroup", "ResourcePoolManager", "create_colocated_worker_cls", "create_colocated_worker_cls_fused", ] ================================================ FILE: verl/single_controller/ray/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import logging import os import socket from copy import deepcopy from dataclasses import dataclass, field from typing import Any, Optional import numpy as np import ray from ray.experimental.state.api import get_actor from ray.util.placement_group import PlacementGroup, placement_group from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy, PlacementGroupSchedulingStrategy from verl.protocol import DataProto, _padding_size_key from verl.single_controller.base import ClassWithInitArgs, ResourcePool, Worker, WorkerGroup from verl.single_controller.base.decorator import MAGIC_ATTR, Dispatch from verl.utils.device import get_device_name from verl.utils.py_functional import temp_env_var __all__ = ["Worker"] logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def get_random_string(length: int) -> str: import random import string letters_digits = string.ascii_letters + string.digits return "".join(random.choice(letters_digits) for _ in range(length)) def func_generator(self, method_name, dispatch_fn, collect_fn, execute_fn, blocking): class Functor: def __call__(this, *args, **kwargs): args, kwargs = dispatch_fn(self, *args, **kwargs) padding_count = kwargs.pop(_padding_size_key, 0) output = execute_fn(method_name, *args, **kwargs) if blocking: output = ray.get(output) output = collect_fn(self, output) if padding_count > 0: if isinstance(output, DataProto): indices = [i for i in range(len(output))][:-padding_count] output = output.select_idxs(indices) elif isinstance(output, list): output = output[:-padding_count] return output # use class type to pass the method_name to get a better observability return type(method_name, (Functor,), {})() def sort_placement_group_by_node_ip(pgs: list[PlacementGroup]) -> list[PlacementGroup]: """ Sort the placement groups by node ip, all bundles in a single placement group should be on the same node. FSDPCheckpointManager saves sharded model states and optimizer states in local storage, which requires RANK to be consistent across nodes when resume from checkpoint. With this function, if there's only one resource pool and there's no node change, RANK should be consistent across nodes in multiple ray jobs, even if the whole ray cluster is restarted. """ node_ip = {node["NodeID"]: node["NodeManagerAddress"] for node in ray.nodes()} pg_ip = {} for pg in pgs: specs = ray._private.state.state.placement_group_table(pg.id) # all bunles should be on the same node node_id = specs["bundles_to_node_id"][0] pg_ip[pg.id] = node_ip[node_id] return sorted(pgs, key=lambda pg: pg_ip[pg.id]) @ray.remote def get_master_addr_port(master_port_range: Optional[list[int]] = None) -> tuple[str, str]: addr = ray.util.get_node_ip_address().strip("[]") if master_port_range is None: with socket.socket() as s: s.bind(("", 0)) port = s.getsockname()[1] else: port = master_port_range[0] while port < master_port_range[1]: try: with socket.socket() as s: s.bind(("", port)) break except OSError: port += 1 # Increment port number if already in use logger.info("Port %d is already in use, trying port %d", port - 1, port) else: raise RuntimeError(f"Could not find a free port in range {master_port_range}") return addr, str(port) class RayResourcePool(ResourcePool): def __init__( self, process_on_nodes: Optional[list[int]] = None, use_gpu: bool = True, name_prefix: str = None, max_colocate_count: int = 10, detached=False, accelerator_type: Optional[str] = None, ) -> None: super().__init__(process_on_nodes, max_colocate_count) self.use_gpu = use_gpu # print(f"in RayProcessDispatchConfiguration: name_prefix = {name_prefix}") self.name_prefix = get_random_string(length=6) if name_prefix is None else name_prefix self.pgs = None self.detached = detached self.accelerator_type = accelerator_type def get_placement_groups(self, strategy="STRICT_PACK", name=None, device_name="cuda"): if self.pgs is not None: return self.pgs pg_name_prefix = ( name if name else f"{self.name_prefix}verl_group_{'_'.join([str(count) for count in self._store])}:" ) # print(f"pg_name_prefix = {pg_name_prefix}") if device_name == "npu": device_name = "NPU" elif device_name == "cuda": device_name = "GPU" bundle = {"CPU": self.max_colocate_count} if self.use_gpu: bundle[device_name] = 1 if self.accelerator_type is not None: bundle[self.accelerator_type] = 1e-4 pg_scheme = [[bundle.copy() for _ in range(process_count)] for process_count in self._store] lifetime = "detached" if self.detached else None pgs = [ placement_group(bundles=bundles, strategy=strategy, name=pg_name_prefix + str(idx), lifetime=lifetime) for idx, bundles in enumerate(pg_scheme) ] ray.get([pg.ready() for pg in pgs]) self.pgs = sort_placement_group_by_node_ip(pgs) return pgs class SubRayResourcePool(RayResourcePool): def __init__( self, placement_groups: list[PlacementGroup], start_bundle_index: int, subgroup_world_size: int, **kwargs, ) -> None: super().__init__(**kwargs) self.pgs = placement_groups self.start_bundle_index = start_bundle_index self.subgroup_world_size = subgroup_world_size @property def world_size(self): return self.subgroup_world_size @dataclass class ResourcePoolManager: """ Define a resource pool specification. Resource pool will be initialized first. """ resource_pool_spec: dict[str, list[int]] mapping: dict[int, str] resource_pool_dict: dict[str, RayResourcePool] = field(default_factory=dict) def create_resource_pool(self): """Create Ray resource pools for distributed training. Initializes resource pools based on the resource pool specification, with each pool managing GPU resources across multiple nodes. For FSDP backend, uses max_colocate_count=1 to merge WorkerGroups. For Megatron backend, uses max_colocate_count>1 for different models. """ for resource_pool_name, process_on_nodes in self.resource_pool_spec.items(): # max_colocate_count means the number of WorkerGroups (i.e. processes) in each RayResourcePool # For FSDP backend, using max_colocate_count=3: actor_critic_ref, rollout, reward model (optional) # For Megatron backend, we recommend using max_colocate_count>1 # that can utilize different WorkerGroup for differnt models resource_pool = RayResourcePool( process_on_nodes=process_on_nodes, use_gpu=True, max_colocate_count=3, name_prefix=resource_pool_name ) self.resource_pool_dict[resource_pool_name] = resource_pool self._check_resource_available() def get_resource_pool(self, role) -> RayResourcePool: """Get the resource pool of the worker_cls""" return self.resource_pool_dict[self.mapping[role]] def get_n_gpus(self) -> int: """Get the number of gpus in this cluster.""" return sum([n_gpus for process_on_nodes in self.resource_pool_spec.values() for n_gpus in process_on_nodes]) def _check_resource_available(self): """Check if the resource pool can be satisfied in this ray cluster.""" node_available_resources = ray._private.state.available_resources_per_node() node_available_gpus = { node: node_info.get("GPU", 0) if "GPU" in node_info else node_info.get("NPU", 0) for node, node_info in node_available_resources.items() } # check total required gpus can be satisfied total_available_gpus = sum(node_available_gpus.values()) total_required_gpus = sum( [n_gpus for process_on_nodes in self.resource_pool_spec.values() for n_gpus in process_on_nodes] ) if total_available_gpus < total_required_gpus: raise ValueError( f"Total available GPUs {total_available_gpus} is less than total desired GPUs {total_required_gpus}" ) def extract_pg_from_exist( resource_pools: dict[str, RayResourcePool], src_role_names: list[str], resource_pool: RayResourcePool ) -> list: src_pgs = [ pg for role_name, resource_pool in resource_pools.items() for pg in resource_pool.get_placement_groups() if role_name in src_role_names ] sorted_src_pgs = sorted(src_pgs, key=lambda pg: pg.bundle_count, reverse=True) sorted_process_on_nodes = sorted([(val, idx) for idx, val in enumerate(resource_pool.store)], reverse=True) unsorted_pgs: list[tuple[int, PlacementGroup]] = [] searching_idx = 0 for request_process, original_idx in sorted_process_on_nodes: assert searching_idx < len(sorted_src_pgs), f"no enough nodes for request: searching {searching_idx} th node" assert request_process <= sorted_src_pgs[searching_idx].bundle_count, ( f"requesting {request_process} processes, bundle count cannot satisfy" ) unsorted_pgs.append((original_idx, sorted_src_pgs[searching_idx])) searching_idx += 1 return [pg for _, pg in sorted(unsorted_pgs)] # split a RayResourcePool or SubRayResourcePool into multiple SubRayResourcePool def split_resource_pool( resource_pool: RayResourcePool | SubRayResourcePool, split_size: int | list[int] ) -> list[SubRayResourcePool]: """ Split a RayResourcePool into multiple SubRayResourcePool. resouce_pool can also be a SubRayResourcePool (have been splited) for multiple-time spliting. Args: resource_pool (RayResourcePool | SubRayResourcePool): The resource pool to split. split_size (int | list[int]): The size of each split. If int, all splits will have the same size. If list[int], each element in the list represents the size of a split. Returns: list[SubRayResourcePool]: A list of SubRayResourcePool after splitting. """ # convert split_size to list[int] if isinstance(split_size, int): assert resource_pool.world_size % split_size == 0, "split_size must be a divisor of world_size" num_replica = resource_pool.world_size // split_size split_size_list = [split_size] * num_replica else: split_size_list = split_size assert sum(split_size_list) == resource_pool.world_size, "split_size must sum up to world_size" # judge if this resource pool has been splited if isinstance(resource_pool, SubRayResourcePool): start_bundle_idx_list = np.cumsum([resource_pool.start_bundle_index] + split_size_list[:-1]) else: start_bundle_idx_list = np.cumsum([0] + split_size_list[:-1]) # ensure resource_pool.pgs has been initialized placement_groups = resource_pool.get_placement_groups() split_resource_pools = [ SubRayResourcePool( process_on_nodes=resource_pool.store, use_gpu=resource_pool.use_gpu, name_prefix=f"{resource_pool.name_prefix}_split_{split_idx}", max_colocate_count=resource_pool.max_colocate_count, placement_groups=placement_groups, start_bundle_index=start_bundle_idx_list[split_idx], subgroup_world_size=split_size_list[split_idx], ) for split_idx in range(len(split_size_list)) ] return split_resource_pools def merge_resource_pool(rp1: RayResourcePool, rp2: RayResourcePool) -> RayResourcePool: assert rp1.use_gpu == rp2.use_gpu, "Both RayResourcePool must either use_gpu or not" assert rp1.max_colocate_count == rp2.max_colocate_count, "Both RayResourcePool must has the same max_colocate_count" assert rp1.n_gpus_per_node == rp2.n_gpus_per_node, "Both RayResourcePool must has the same n_gpus_per_node" assert rp1.detached == rp2.detached, "Detached ResourcePool cannot be merged with non-detached ResourcePool" new_store = rp1.store + rp2.store merged = type(rp1)( new_store, rp1.use_gpu, f"{rp1.name_prefix}_{rp2.name_prefix}", rp1.max_colocate_count, rp1.detached ) merged.pgs = rp1.get_placement_groups(device_name=get_device_name()) + rp2.get_placement_groups( device_name=get_device_name() ) return merged class RayClassWithInitArgs(ClassWithInitArgs): """A wrapper class for Ray actors with initialization arguments. This class extends ClassWithInitArgs to provide additional functionality for configuring and creating Ray actors with specific resource requirements and scheduling strategies. """ def __init__(self, cls, *args, **kwargs) -> None: # self._options = kwargs.pop('options', dict()) super().__init__(cls, *args, **kwargs) self._options = {} self._additional_resource = {} def set_additional_resource(self, additional_resource): """Set additional resource requirements for the actor. Args: additional_resource: Dictionary specifying additional resource requirements """ self._additional_resource = additional_resource def update_options(self, options: dict): """Update the Ray actor creation options. Args: options: Dictionary of options to update """ self._options.update(options) def __call__( self, placement_group, placement_group_bundle_idx, use_gpu: bool = True, num_gpus=1, sharing_with=None, device_name="cuda", ) -> Any: """Create and return a Ray actor with the configured options. Args: placement_group: Ray placement group for scheduling placement_group_bundle_idx: Index of the bundle in the placement group use_gpu: Whether to use GPU resources num_gpus: Number of GPUs to allocate sharing_with: Actor to share resources with device_name: Device for training Returns: A Ray actor handle with the configured options """ if sharing_with is not None: target_node_id = ray.get(sharing_with.get_node_id.remote()) visible_devices = ray.get(sharing_with.get_cuda_visible_devices.remote()) options = {"scheduling_strategy": NodeAffinitySchedulingStrategy(node_id=target_node_id, soft=False)} return self.cls.options(**options).remote(*self.args, cuda_visible_devices=visible_devices, **self.kwargs) options = { "scheduling_strategy": PlacementGroupSchedulingStrategy( placement_group=placement_group, placement_group_bundle_index=placement_group_bundle_idx ) } options.update(self._options) if use_gpu and device_name == "cuda": options["num_gpus"] = num_gpus if use_gpu and device_name == "npu": options["resources"] = {"NPU": num_gpus} if len(self._additional_resource) > 1: for k, v in self._additional_resource.items(): options[k] = v # print("cls:", self.cls) # print("args: ", self.args) # print("kwargs: ", self.kwargs) return self.cls.options(**options).remote(*self.args, **self.kwargs) class RayWorkerGroup(WorkerGroup): """A group of Ray workers that can be managed collectively. This class extends WorkerGroup to provide Ray-specific functionality for creating and managing groups of Ray actors with specific resource requirements and scheduling strategies. """ def __init__( self, resource_pool: RayResourcePool = None, ray_cls_with_init: RayClassWithInitArgs = None, bin_pack: bool = True, name_prefix: str = None, detached=False, worker_names=None, worker_handles: list[ray.actor.ActorHandle] = None, ray_wait_register_center_timeout: int = 300, **kwargs, ) -> None: """Initialize a RayWorkerGroup. Args: resource_pool: Resource pool for worker allocation ray_cls_with_init: Class with initialization arguments for workers bin_pack: Whether to use strict bin packing for resource allocation name_prefix: Prefix for worker names detached: Whether workers should be detached worker_names: Names of existing workers to attach to ray_wait_register_center_timeout: Timeout for waiting on register center **kwargs: Additional keyword arguments """ self._master_addr = kwargs.pop("master_addr", None) self._master_port = kwargs.pop("master_port", None) self.use_gpu = kwargs.pop("use_gpu", resource_pool.use_gpu if resource_pool is not None else True) self._ray_master_port_range = kwargs.pop("master_port_range", None) super().__init__(resource_pool=resource_pool, **kwargs) self.ray_cls_with_init = ray_cls_with_init self.name_prefix = get_random_string(length=6) if name_prefix is None else name_prefix self._ray_wait_register_center_timeout = ray_wait_register_center_timeout # Whether the WorkerGroup is a Colocate WorkerGroup created by FusedWorker. self.fused_worker_used = False if ray_cls_with_init is None else ray_cls_with_init.fused_worker_used # if a WorkerGroup is spawned from Colocate WorkerGroup, this indicates which sub-class is binded to # this WorkerGroup. self.sub_cls_name = "" self.device_name = kwargs.get("device_name", "cuda") self.profile_steps = kwargs.get("profile_steps", None) self.worker_nsight_options = kwargs.get("worker_nsight_options", None) self.customized_worker_env = kwargs.get("worker_env", {}) if self.worker_nsight_options is not None and self.worker_nsight_options["capture-range-end"] is None: self.worker_nsight_options["capture-range-end"] = f"repeat-shutdown:{6 * len(self.profile_steps)}" if worker_names is not None and (not self.fused_worker_used): assert self._is_init_with_detached_workers self._worker_names = worker_names if self._is_init_with_detached_workers: self._init_with_detached_workers(worker_names=worker_names, worker_handles=worker_handles) elif isinstance(resource_pool, SubRayResourcePool): self._init_with_subresource_pool( resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, bin_pack=bin_pack, detached=detached, worker_env=self.customized_worker_env, ) else: self._init_with_resource_pool( resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, bin_pack=bin_pack, detached=detached, worker_env=self.customized_worker_env, ) if ray_cls_with_init is not None: self._bind_worker_method(self.ray_cls_with_init.cls, func_generator) self.wg_dict = None self.method_names = [] def _is_worker_alive(self, worker: ray.actor.ActorHandle): """Check if a worker actor is still alive. Args: worker: Ray actor handle to check Returns: bool: True if the worker is alive, False otherwise """ worker_state_dict = get_actor(worker._actor_id.hex()) return worker_state_dict.get("state", "undefined") == "ALIVE" if worker_state_dict is not None else False def _init_with_detached_workers(self, worker_names, worker_handles): # ray.get_actor holds a weak reference to the actor, which causes actors garbage collected unexpectedly # if we only hold spawn RayWorkerGroup. By passing actor handle explicitly, spawn RayWorkerGroup have # strong reference to these actors. # https://github.com/ray-project/ray/pull/45699 workers = worker_handles if worker_handles else [ray.get_actor(name=name) for name in worker_names] self._workers = workers self._world_size = len(workers) def _get_master_addr_port(self, pg, bundle_index=0, master_port_range=None): """Get master addr and port for this worker group""" if self._master_addr is None and self._master_port is None: self._master_addr, self._master_port = ray.get( get_master_addr_port.options( scheduling_strategy=PlacementGroupSchedulingStrategy( placement_group=pg, placement_group_bundle_index=bundle_index ), ).remote(master_port_range=master_port_range) ) elif self._master_addr is not None and self._master_port is not None: logger.debug(f"{self._master_addr=} {self._master_port=}") else: raise ValueError( "Both 'master_addr' and 'master_port' must be provided if you intend to manually specify them, " "or neither should be provided to use Ray's default assignment." ) def _init_with_resource_pool( self, resource_pool, ray_cls_with_init, bin_pack, detached, worker_env=None, ): """Initialize the worker group by creating new workers from a resource pool. Args: resource_pool: Resource pool for worker allocation ray_cls_with_init: Class with initialization arguments for workers bin_pack: Whether to use strict bin packing for resource allocation detached: Whether workers should be detached """ self.resource_pool = resource_pool strategy = "PACK" if bin_pack: strategy = "STRICT_PACK" pgs = resource_pool.get_placement_groups(strategy=strategy, device_name=self.device_name) world_size = resource_pool.world_size self._world_size = world_size # cia.add_kwarg("_world_size", world_size) rank = -1 local_world_size = resource_pool.store[0] for pg_idx, pg in enumerate(sort_placement_group_by_node_ip(pgs)): assert local_world_size <= pg.bundle_count, f"when generating for {self.name_prefix}, for the " if pg_idx == 0: self._get_master_addr_port(pg, bundle_index=0, master_port_range=self._ray_master_port_range) for local_rank in range(local_world_size): rank += 1 self._create_worker( rank=rank, pg_idx=pg_idx, pg=pg, local_rank=local_rank, resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, worker_env=worker_env, detached=detached, ) def _init_with_subresource_pool(self, resource_pool, ray_cls_with_init, bin_pack, detached, worker_env=None): """Initialize the worker group by creating new workers from a resource pool or sub resource pool. Args: resource_pool: Resource pool for worker allocation ray_cls_with_init: Class with initialization arguments for workers bin_pack: Whether to use strict bin packing for resource allocation detached: Whether workers should be detached """ strategy = "PACK" if bin_pack: strategy = "STRICT_PACK" pgs = resource_pool.get_placement_groups(strategy=strategy, device_name=self.device_name) world_size = resource_pool.world_size self._world_size = world_size rank = -1 local_world_size = resource_pool.store[0] self._get_master_addr_port( pgs[resource_pool.start_bundle_index // local_world_size], bundle_index=resource_pool.start_bundle_index % local_world_size, master_port_range=self._ray_master_port_range, ) for curr_rank in range(resource_pool.start_bundle_index, resource_pool.start_bundle_index + world_size): pg_idx = curr_rank // local_world_size pg = pgs[pg_idx] local_rank = curr_rank % local_world_size assert local_world_size <= pg.bundle_count, f"when generating for {self.name_prefix}, for the " rank += 1 self._create_worker( rank=rank, pg_idx=pg_idx, pg=pg, local_rank=local_rank, resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, worker_env=worker_env, detached=detached, ) def _create_worker(self, rank, pg_idx, pg, local_rank, resource_pool, ray_cls_with_init, worker_env, detached): world_size = resource_pool.world_size use_gpu = resource_pool.use_gpu if self.use_gpu and not use_gpu: raise ValueError("use_gpu is True but resource_pool.use_gpu is False") local_world_size = resource_pool.store[0] num_gpus = 1 / resource_pool.max_colocate_count # we pass in environment variable at option so that Worker can use environment variable to set env_vars = { "WORLD_SIZE": str(world_size), "RANK": str(rank), "WG_PREFIX": self.name_prefix, "WG_BACKEND": "ray", "RAY_LOCAL_WORLD_SIZE": str(local_world_size), "MASTER_ADDR": self._master_addr, "MASTER_PORT": self._master_port, } if worker_env is not None: logging.debug(f"Appending ray class env, origin: {env_vars}, customized env: {worker_env}") conflict_env_vars = set(env_vars.keys()) & set(worker_env.keys()) if len(conflict_env_vars) > 0: logging.error( f"User customized env vars conflict with system env: {conflict_env_vars} " f"Overriding may cause unexpected behavior." ) raise ValueError(f"Cannot override protected system env: {conflict_env_vars}") env_vars.update(worker_env) import re cia_name = type(ray_cls_with_init.cls).__name__ match = re.search(r"ActorClass\(([^)]+)\)", cia_name) # ray.remote(Obj) -> "ActorClass(Obj)" cia_name = match.group(1) if match else cia_name # "ActorClass(Obj)" -> "Obj" name = f"{self.name_prefix}{cia_name}_{pg_idx}:{local_rank}" # e.g. Worker_2:5 if self.profile_steps and self.device_name == "cuda": ray_cls_with_init.update_options( { "runtime_env": { "env_vars": env_vars, "nsight": self.worker_nsight_options, }, "name": name, } ) else: ray_cls_with_init.update_options({"runtime_env": {"env_vars": env_vars}, "name": name}) if detached: ray_cls_with_init.update_options({"lifetime": "detached"}) # create a worker worker = ray_cls_with_init( placement_group=pg, placement_group_bundle_idx=local_rank, use_gpu=self.use_gpu, num_gpus=num_gpus, device_name=self.device_name, ) self._workers.append(worker) self._worker_names.append(name) @property def worker_names(self): return self._worker_names @classmethod def from_detached( cls, name_prefix=None, worker_names=None, worker_handles=None, ray_cls_with_init=None, **kwargs, ): """Create a worker group from existing detached workers. Args: name_prefix: Prefix for worker names worker_names: Names of existing workers to attach to ray_cls_with_init: Class with initialization arguments for workers Returns: A new RayWorkerGroup instance """ worker_group = cls( resource_pool=None, ray_cls_with_init=ray_cls_with_init, name_prefix=name_prefix, worker_names=worker_names, worker_handles=worker_handles, **kwargs, ) return worker_group def spawn(self, prefix_set): """Spawn to a dictionary of worker groups, each with a subset of method with prefix. Args: prefix_set: Set of prefixes to create worker groups for Returns: Dictionary of worker groups keyed by prefix """ if self.fused_worker_used: return self.spawn_fused(prefix_set) def _rebind_actor_methods(worker_group, actor_name): prefix: str = actor_name + "_" for method_name in dir(worker_group): if method_name.startswith(prefix): original_method_name = method_name.removeprefix(prefix) method = getattr(worker_group, method_name) setattr(worker_group, original_method_name, method) new_worker_group_dict = {} for prefix in prefix_set: new_worker_group = self.from_detached( name_prefix=self.name_prefix, worker_names=self._worker_names, worker_handles=self._workers, ray_cls_with_init=self.ray_cls_with_init, profile_steps=self.profile_steps, worker_nsight_options=self.worker_nsight_options, ) _rebind_actor_methods(new_worker_group, prefix) new_worker_group_dict[prefix] = new_worker_group return new_worker_group_dict def spawn_fused(self, prefix_set): """Create a dictionary of worker groups for fused workers. Args: prefix_set: Set of prefixes to create worker groups for Returns: Dictionary of worker groups keyed by prefix """ wg_dict = dict() for key in prefix_set: new_wg = deepcopy(self) new_wg._bind_worker_method(self.ray_cls_with_init.cls.raw_cls_dict[key], func_generator) new_wg.sub_cls_name = key wg_dict[key] = new_wg return wg_dict def fuse(self, prefix_set): """Fuse multiple worker groups into the current worker group. Args: prefix_set: Set of prefixes to fuse into the worker group """ if self.wg_dict is None: self.wg_dict = self.spawn(prefix_set) for role_name, role_wg in self.wg_dict.items(): setattr(self, role_name, role_wg) self.method_names = self._bind_worker_method(self.ray_cls_with_init.cls, func_generator) def _execute_remote_single_worker(self, worker, method_name: str, *args, **kwargs): """Execute a method on a single worker remotely. Args: worker: The worker actor handle method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: Remote object reference to the method execution """ if self.fused_worker_used and method_name not in self.method_names: remote_call = getattr(worker, self.fused_worker_execute_fn_name) return remote_call.remote(f"{self.sub_cls_name}_fwmn_{method_name}", *args, **kwargs) # fused worker not used remote_call = getattr(worker, method_name) return remote_call.remote(*args, **kwargs) def execute_rank_zero_sync(self, method_name: str, *args, **kwargs): """Execute a method on rank zero worker synchronously. Args: method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: Result of the method execution """ return ray.get(self.execute_rank_zero_async(method_name, *args, **kwargs)) def execute_rank_zero_async(self, method_name: str, *args, **kwargs): """Execute a method on rank zero worker asynchronously. Args: method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: Remote object reference to the method execution """ return self._execute_remote_single_worker(self._workers[0], method_name, *args, **kwargs) def execute_rank_zero(self, method_name: str, *args, **kwargs): """Alias for execute_rank_zero_async. Args: method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: Remote object reference to the method execution """ return self.execute_rank_zero_async(method_name, *args, **kwargs) def execute_all(self, method_name: str, *args, **kwargs): """Alias for execute_all_async. Args: method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: List of remote object references to the method executions """ return self.execute_all_async(method_name, *args, **kwargs) def execute_all_sync(self, method_name: str, *args, **kwargs): """Execute a method on all workers synchronously. Args: method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: List of results from all workers """ return ray.get(self.execute_all_async(method_name, *args, **kwargs)) def execute_all_async(self, method_name: str, *args, **kwargs): """Execute a method on all workers asynchronously. Args: method_name: Name of the method to execute *args: Positional arguments for the method **kwargs: Keyword arguments for the method Returns: List of remote object references to the method executions """ # Here, we assume that if all arguments in args and kwargs are lists, # and their lengths match len(self._workers), we'll distribute each # element in these lists to the corresponding worker # print(f"execute_all_async: method {method_name}({args}, {kwargs})") length = len(self._workers) if all(isinstance(arg, list) for arg in args) and all(isinstance(kwarg, list) for kwarg in kwargs.values()): if all(len(arg) == length for arg in args) and all(len(kwarg) == length for kwarg in kwargs.values()): # print(f"splitting args and kwargs into {length} shards") result = [] for i in range(length): sliced_args = tuple(arg[i] for arg in args) sliced_kwargs = {k: v[i] for k, v in kwargs.items()} result.append( self._execute_remote_single_worker(self._workers[i], method_name, *sliced_args, **sliced_kwargs) ) return result return [self._execute_remote_single_worker(worker, method_name, *args, **kwargs) for worker in self._workers] @property def master_address(self): return self._master_addr @property def master_port(self): return self._master_port @property def workers(self): return self._workers @property def world_size(self): return self._world_size """ Utilities that enables creating workers inside the same ray.Actor, with code written in separate ray.Actors. """ # deprecated, switching to FusedWorker def _bind_workers_method_to_parent(cls, key, user_defined_cls): """ Binds the methods of each worker to the WorkerDict. Note that we only bind public methods that are decorated by register """ for method_name in dir(user_defined_cls): try: method = getattr(user_defined_cls, method_name) assert callable(method), f"{method_name} in {user_defined_cls} is not callable" except Exception: # if it is a property, it will fail because Class doesn't have instance property continue if hasattr(method, MAGIC_ATTR): def generate_function(name, key=key): def func(self, *args, **kwargs): # dispatch to the actual worker return getattr(self.worker_dict[key], name)(*args, **kwargs) async def async_func(self, *args, **kwargs): # dispatch to the actual worker return await getattr(self.worker_dict[key], name)(*args, **kwargs) wrapper = async_func if inspect.iscoroutinefunction(method) else func # noqa: B023 return wrapper func = generate_function(method_name) # pass MAGIC_ATTR for outer worker group attrs = getattr(method, MAGIC_ATTR) setattr(func, MAGIC_ATTR, attrs) try: # bind direct rollout method to class without prefix if attrs["dispatch_mode"] == Dispatch.DIRECT_ROLLOUT_METHOD and "rollout" in key: assert not hasattr(cls, method_name), ( f"conflict direct rollout method {method_name} with role {key}" ) setattr(cls, method_name, func) print(f"bind role {key} method {method_name} to class {cls}") else: method_name_with_prefix = key + "_" + method_name setattr(cls, method_name_with_prefix, func) except Exception as e: raise ValueError(f"Fail to set method_name {method_name}") from e def _unwrap_ray_remote(cls): if hasattr(cls, "__ray_actor_class__"): cls = cls.__ray_actor_class__ return cls def _determine_fsdp_megatron_base_class(mros: list): """ - megatron: base class should be MegatronWorker - fsdp: base class should be Worker """ for cls in mros[0]: if cls.__name__ == "MegatronWorker": return cls if cls.__name__ == "Worker": return cls raise ValueError(f"Cannot determine base class for {mros}") # deprecated, switching to FusedWorker def create_colocated_worker_cls(class_dict: dict[str, RayClassWithInitArgs]): """ This function should return a class instance that delegates the calls to every cls in cls_dict """ cls_dict = {} init_args_dict = {} worker_cls = _determine_fsdp_megatron_base_class( [cls.cls.__ray_actor_class__.__mro__ for cls in class_dict.values()] ) assert issubclass(worker_cls, Worker), f"worker_cls {worker_cls} should be a subclass of Worker" print(f"colocated worker base class {worker_cls}") for key, cls in class_dict.items(): cls_dict[key] = cls.cls init_args_dict[key] = {"args": cls.args, "kwargs": cls.kwargs} assert cls_dict.keys() == init_args_dict.keys() # TODO: create a class with customizable name class WorkerDict(worker_cls): def __init__(self): super().__init__() self.worker_dict = {} for key, user_defined_cls in cls_dict.items(): user_defined_cls = _unwrap_ray_remote(user_defined_cls) # directly instantiate the class without remote # in worker class, e.g. # when DISABLE_WORKER_INIT == 1 it will return immediately with temp_env_var("DISABLE_WORKER_INIT", "1"): self.worker_dict[key] = user_defined_cls( *init_args_dict[key].get("args", ()), **init_args_dict[key].get("kwargs", {}) ) # now monkey-patch the methods from inner class to WorkerDict for key, user_defined_cls in cls_dict.items(): user_defined_cls = _unwrap_ray_remote(user_defined_cls) _bind_workers_method_to_parent(WorkerDict, key, user_defined_cls) remote_cls = ray.remote(WorkerDict) remote_cls = RayClassWithInitArgs(cls=remote_cls) return remote_cls FusedWorkerCLSName = "FusedWorker" def create_colocated_worker_raw_cls(class_dict: dict[str, RayClassWithInitArgs]): """ This function returns a FusedWorker class. `FusedWorker.{class_name}` -> FusedClass Use `class_name` as a param to directly access the underlying class. `FusedWorker._fuw_execute("{class_name}_fwmn_{method_name}", *args, **kwargs)` First param must be "{class_name}_fwmn_{method_name}" in order to access `method_name` of underlying class `{class_name}`. `FusedWorker.fused_worker_dict` -> {"class_name": FusedClass} Stores all underlying classes. `FusedClass.fused_worker_dict` -> {"class_name": FusedClass} The same as `FusedWorker.fused_worker_dict`, enables underlying class to access other underlying classes. """ raw_cls_dict = {cls_name: _unwrap_ray_remote(cia.cls) for cls_name, cia in class_dict.items()} init_args_dict = {cls_name: cia.args for cls_name, cia in class_dict.items()} init_kwargs_dict = {cls_name: cia.kwargs for cls_name, cia in class_dict.items()} cls_names = list(class_dict.keys()) # FusedWorker_Actor_Critic class_name_renamed = "_".join([FusedWorkerCLSName] + cls_names) class FusedWorker(Worker): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.cls_names = cls_names self.raw_cls_dict = raw_cls_dict self.init_args_dict = init_args_dict self.init_kwargs_dict = init_kwargs_dict for cls_name, udc, ud_args, ud_kwargs in zip( self.cls_names, self.raw_cls_dict.values(), self.init_args_dict.values(), self.init_kwargs_dict.values(), strict=True, ): with temp_env_var("DISABLE_WORKER_INIT", "1"): udc._get_ray_actor_cls_name = lambda x, name_renamed=class_name_renamed: name_renamed udc._get_ray_method_prefix = lambda x, name_prefixed=cls_name: f"{name_prefixed}_" # cls_name = "actor", "critic", udc = ActorWorker, CriticWorker self.fused_worker_dict[cls_name] = udc(*ud_args, **ud_kwargs) setattr(self, cls_name, self.fused_worker_dict[cls_name]) # injecting fused_worker to each sub worker so they can be aware of existence of each other for _, worker in self.fused_worker_dict.items(): setattr(worker, Worker.fused_worker_attr_name, self.fused_worker_dict) def _fuw_execute(self, method_name: str, *args, **kwargs): # for fused_worker, method_name is in a form of "{cls_name}_fwmn_{method_name}" # where fwmn stands "fused worker method name" names = method_name.split("_fwmn_") cls_name = names[0] method_name = names[1] assert cls_name in self.fused_worker_dict, ( f"calling {cls_name}'s {method_name}, but {cls_name} not in fused_worker_dict" ) udc_method = getattr(self.fused_worker_dict[cls_name], method_name) return udc_method(*args, **kwargs) renamed_fused_worker_cls = type(class_name_renamed, (FusedWorker,), {}) renamed_fused_worker_cls.is_fused_worker = True renamed_fused_worker_cls.raw_cls_dict = raw_cls_dict return renamed_fused_worker_cls def create_colocated_worker_cls_fused(class_dict: dict[str, RayClassWithInitArgs]): """ This function returns a RayClassWithInitArgs instance of FusedWorker, which is an replacement of `create_colocated_worker_cls`. WorkerGroup constructed using this class will be a colocated WorkerGroup, which will be referenced as `ColocateWorkerGroup` below. `ColocateWorkerGroup.spawn(prefix_set)` returns a dict of WorkerGroup {"class_name": WorkerGroup}, WorkerGroup in this dict will have methods of underlying class `class_name` attached. `ColocateWorkerGroup.fuse(prefix_set)` After executing this function, `ColocateWorkerGroup.{class_name}` will return WorkerGroup with methods of underlying class `class_name` attached. """ raw_colocated_worker_cls = create_colocated_worker_raw_cls(class_dict) remote_cls = ray.remote(raw_colocated_worker_cls) cia = RayClassWithInitArgs(cls=remote_cls) cia.fused_worker_used = True return cia ================================================ FILE: verl/third_party/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/torch/__init__.py ================================================ # official torch 2.6.0 set_model_state_dict API leads to OOM # this is a copy of torch/distributed/checkpoint from torch 2.7.0 # From PyTorch: # Copyright (c) 2016- Facebook, Inc (Adam Paszke) # Copyright (c) 2014- Facebook, Inc (Soumith Chintala) # Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) # Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) # Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) # Copyright (c) 2011-2013 NYU (Clement Farabet) # Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) # Copyright (c) 2006 Idiap Research Institute (Samy Bengio) # Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) # From Caffe2: # Copyright (c) 2016-present, Facebook Inc. All rights reserved. # All contributions by Facebook: # Copyright (c) 2016 Facebook Inc. # All contributions by Google: # Copyright (c) 2015 Google Inc. # All rights reserved. # All contributions by Yangqing Jia: # Copyright (c) 2015 Yangqing Jia # All rights reserved. # All contributions by Kakao Brain: # Copyright 2019-2020 Kakao Brain # All contributions by Cruise LLC: # Copyright (c) 2022 Cruise LLC. # All rights reserved. # All contributions by Tri Dao: # Copyright (c) 2024 Tri Dao. # All rights reserved. # All contributions by Arm: # Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates # All contributions from Caffe: # Copyright(c) 2013, 2014, 2015, the respective contributors # All rights reserved. # All other contributions: # Copyright(c) 2015, 2016 the respective contributors # All rights reserved. # Caffe2 uses a copyright model similar to Caffe: each contributor holds # copyright over their contributions to Caffe2. The project versioning records # all such contribution and copyright details. If a contributor wants to further # mark their specific copyright on a particular contribution, they should # indicate their copyright solely in the commit message of the change when it is # committed. # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America # and IDIAP Research Institute nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: verl/third_party/torch/distributed/__init__.py ================================================ # official torch 2.6.0 set_model_state_dict API leads to OOM # this is a copy of torch/distributed/checkpoint from torch 2.7.0 # From PyTorch: # Copyright (c) 2016- Facebook, Inc (Adam Paszke) # Copyright (c) 2014- Facebook, Inc (Soumith Chintala) # Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) # Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) # Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) # Copyright (c) 2011-2013 NYU (Clement Farabet) # Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) # Copyright (c) 2006 Idiap Research Institute (Samy Bengio) # Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) # From Caffe2: # Copyright (c) 2016-present, Facebook Inc. All rights reserved. # All contributions by Facebook: # Copyright (c) 2016 Facebook Inc. # All contributions by Google: # Copyright (c) 2015 Google Inc. # All rights reserved. # All contributions by Yangqing Jia: # Copyright (c) 2015 Yangqing Jia # All rights reserved. # All contributions by Kakao Brain: # Copyright 2019-2020 Kakao Brain # All contributions by Cruise LLC: # Copyright (c) 2022 Cruise LLC. # All rights reserved. # All contributions by Tri Dao: # Copyright (c) 2024 Tri Dao. # All rights reserved. # All contributions by Arm: # Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates # All contributions from Caffe: # Copyright(c) 2013, 2014, 2015, the respective contributors # All rights reserved. # All other contributions: # Copyright(c) 2015, 2016 the respective contributors # All rights reserved. # Caffe2 uses a copyright model similar to Caffe: each contributor holds # copyright over their contributions to Caffe2. The project versioning records # all such contribution and copyright details. If a contributor wants to further # mark their specific copyright on a particular contribution, they should # indicate their copyright solely in the commit message of the change when it is # committed. # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America # and IDIAP Research Institute nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: verl/third_party/torch/distributed/_state_dict_utils.py ================================================ # official torch 2.6.0 set_model_state_dict API leads to OOM # this is a copy of torch/distributed/checkpoint from torch 2.7.0 # From PyTorch: # Copyright (c) 2016- Facebook, Inc (Adam Paszke) # Copyright (c) 2014- Facebook, Inc (Soumith Chintala) # Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) # Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) # Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) # Copyright (c) 2011-2013 NYU (Clement Farabet) # Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) # Copyright (c) 2006 Idiap Research Institute (Samy Bengio) # Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) # From Caffe2: # Copyright (c) 2016-present, Facebook Inc. All rights reserved. # All contributions by Facebook: # Copyright (c) 2016 Facebook Inc. # All contributions by Google: # Copyright (c) 2015 Google Inc. # All rights reserved. # All contributions by Yangqing Jia: # Copyright (c) 2015 Yangqing Jia # All rights reserved. # All contributions by Kakao Brain: # Copyright 2019-2020 Kakao Brain # All contributions by Cruise LLC: # Copyright (c) 2022 Cruise LLC. # All rights reserved. # All contributions by Tri Dao: # Copyright (c) 2024 Tri Dao. # All rights reserved. # All contributions by Arm: # Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates # All contributions from Caffe: # Copyright(c) 2013, 2014, 2015, the respective contributors # All rights reserved. # All other contributions: # Copyright(c) 2015, 2016 the respective contributors # All rights reserved. # Caffe2 uses a copyright model similar to Caffe: each contributor holds # copyright over their contributions to Caffe2. The project versioning records # all such contribution and copyright details. If a contributor wants to further # mark their specific copyright on a particular contribution, they should # indicate their copyright solely in the commit message of the change when it is # committed. # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America # and IDIAP Research Institute nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # ruff: noqa: B028, UP038, UP007, E721, E501 # mypy: allow-untyped-defs import copy import io import math import weakref from collections.abc import Mapping, MutableMapping from typing import TYPE_CHECKING, Any, Callable, NamedTuple, Optional, Union, cast import torch import torch.distributed as dist import torch.nn.functional as F from torch.distributed._functional_collectives import AsyncCollectiveTensor if dist.is_available() or TYPE_CHECKING: from torch.distributed import distributed_c10d from torch.distributed._shard.sharded_tensor import ShardedTensor from torch.distributed.tensor import DTensor, Replicate, distribute_tensor from torch.distributed.tensor._utils import compute_local_shape_and_global_offset def _identity_func( obj: torch.Tensor, pg: Optional[dist.ProcessGroup], device: Optional[torch.device], companion_obj: Any, ) -> torch.Tensor: return obj def _all_gather_sharded_tensor( sharded_tensor: "ShardedTensor", pg: Optional[dist.ProcessGroup] = None, device: Optional[torch.device] = None, ) -> torch.Tensor: if pg is None: pg = distributed_c10d._get_default_group() world_size = dist.get_world_size(pg) shards = sharded_tensor.local_shards() dim_0_size = sharded_tensor.size()[0] # type: ignore[index] tensor_numel = sharded_tensor.size().numel() # type: ignore[union-attr] chunk_size = math.ceil(dim_0_size / world_size) * tensor_numel // dim_0_size pg_device = distributed_c10d._get_pg_default_device(pg) if device is None else device if shards: local_tensor = shards[0].tensor.flatten() if local_tensor.device.type != pg_device.type: local_tensor = local_tensor.to(pg_device) num_padding = chunk_size - local_tensor.numel() if num_padding > 0: local_tensor = F.pad(local_tensor, [0, num_padding]) else: local_tensor = torch.zeros(chunk_size, dtype=sharded_tensor.dtype, device=pg_device) tensor = torch.empty( chunk_size * world_size, dtype=local_tensor.dtype, device=pg_device, ) dist.all_gather_into_tensor(tensor, local_tensor, group=pg) tensor = tensor.narrow(0, 0, tensor_numel).reshape(sharded_tensor.size()) return tensor class CompanionMismatch(Exception): pass def _iterate_state_dict( iter_object: Any, sharded_tensor_func: Callable, dtensor_func: Callable, tensor_func: Callable, *, pg: Optional[dist.ProcessGroup] = None, device: Optional[torch.device] = None, cpu_offload: bool = False, companion_obj: Any = None, ranks_only: tuple[int, ...] = (), type_check: bool = True, non_blocking: bool = True, ) -> dict[str, Any]: """Iterate through the state dict, applying the given functions to each tensor type. Args: iter_object (Any): the target state_dict. sharded_tensor_func (Callable): the function to apply to ShardedTensor dtensor_func (Callable): the function to apply to DTensor tensor_func (Callable): the function to apply to Tensor pg (Optional[dist.ProcessGroup]): process group passed to tensor functions device (Optional[torch.device]): device passed to tensor functions cpu_offload (bool): whether to offload the tensors to CPU memory. This option is ignored if a companion_obj is supplied. companion_obj (Any): A companion object to the state dict. If this object is supplied, we attempt to copy the tensor to the companion object. ranks_only (Tuple[int, ...]): if this tuple is empty, all ranks will have the same state_dicts. Otherwise only ranks that in ``ranks_only`` have the same state_dicts. Other ranks will get empty state_dicts. type_check (bool): check if the instance data type is a supported type that can be saved by DCP. The current supported data types are torch.Tensor, DTensor, int, float, str, list, dict, None. non_blocking (bool): whether to use non-blocking copy when copying to the companion object. """ # TODO: should we use pytree? cpu_device = torch.device("cpu") if isinstance(iter_object, ShardedTensor): ret = sharded_tensor_func(iter_object, pg, device, companion_obj) elif isinstance(iter_object, DTensor): ret = dtensor_func(iter_object, pg, device, companion_obj) elif isinstance(iter_object, torch.Tensor): ret = tensor_func(iter_object, pg, device, companion_obj) elif isinstance(iter_object, (int, float, str, bytes, io.BytesIO)) or iter_object is None: ret = iter_object elif isinstance(iter_object, dict): if companion_obj is not None and ( not isinstance(companion_obj, dict) or set(companion_obj.keys()) != set(iter_object.keys()) ): msg = "" if isinstance(companion_obj, dict) else f"{set(companion_obj.keys())=} {set(iter_object.keys())=}" raise CompanionMismatch(msg) ret = { key: _iterate_state_dict( value, sharded_tensor_func, dtensor_func, tensor_func, pg=pg, device=device, cpu_offload=cpu_offload, companion_obj=companion_obj[key] if companion_obj is not None else None, ranks_only=ranks_only, type_check=type_check, non_blocking=non_blocking, ) for key, value in iter_object.items() } elif isinstance(iter_object, (list, tuple)): if companion_obj is not None and ( not isinstance(companion_obj, (list, tuple)) or len(companion_obj) != len(iter_object) ): raise CompanionMismatch ret = [ _iterate_state_dict( v, sharded_tensor_func, dtensor_func, tensor_func, pg=pg, device=device, cpu_offload=cpu_offload, companion_obj=companion_obj[idx] if companion_obj is not None else None, ranks_only=ranks_only, type_check=type_check, non_blocking=non_blocking, ) for idx, v in enumerate(iter_object) ] if isinstance(iter_object, tuple): ret = tuple(ret) elif not type_check: ret = copy.deepcopy(iter_object) else: raise ValueError(f"Unexpected value type {type(iter_object)}") if not ranks_only or dist.get_rank(pg) in ranks_only: if isinstance(ret, torch.Tensor): if cpu_offload and companion_obj is None: ret = ret.to(cpu_device) if companion_obj is not None: if isinstance(companion_obj, DTensor): assert isinstance(ret, DTensor) companion_obj._local_tensor.copy_(ret._local_tensor, non_blocking=non_blocking) else: companion_obj.copy_(ret, non_blocking=non_blocking) ret = companion_obj else: ret = {} if isinstance(ret, dict) else None return ret def _gather_state_dict( state_dict: dict[str, Any], *, pg: Optional[dist.ProcessGroup] = None, device: Optional[torch.device] = None, cpu_offload: bool = False, ranks_only: tuple[int, ...] = (), type_check: bool = True, ) -> dict[str, Any]: """ Given a state_dict, this API gathers all the ShardedTensors or DTensors in the state_dict. Args: state_dict (Dict[str, Any]): the target sharded state_dict. pg (Optional[dist.ProcessGroup]): the process group that is used to gather ShardedTensor. Note that gathering a DTensor will use the DeviceMesh. So this argument will be ignored when gathering a DTensor. device: (Optional[torch.device]): the device that is used to perform allgather for ShardedTensor. Note that gathering a DTensor will use the DeviceMesh. So this argument will be ignored when gathering a DTensor. cpu_offload (bool): whether to offload the tensors to CPU memory. The default value is False. ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will have the same state_dicts. Otherwise only ranks that in ``ranks_only`` have the same state_dicts. Other ranks will get empty state_dicts. type_check: (bool): check if the instance data type is a supported type that can be saved by DCP. The current supported data types are torch.Tensor, DTensor, int, float, str, list, dict, None. Returns: The gathered state dictionary. """ def sharded_tensor_func(value, pg, device, companion_obj): # ShardedTensor does not seem to record the original device type. # So if the tensor is moved to CPU, we won't know the original type. # As a result, we have to rely on the user to tell us the correct one. cpu_device = torch.device("cpu") output_tensor = _all_gather_sharded_tensor(value, pg, device) local_shard_device = value.local_shards()[0].tensor.device if value.local_shards() else cpu_device if output_tensor.device != local_shard_device: value = output_tensor.to(local_shard_device) else: value = output_tensor return value def dtensor_func(value, pg, device, companion_obj): if value.device != value.device_mesh.device_type: value = value.to(value.device_mesh.device_type) # FSDP all_gather: [Shard(0)] -> [Replicate()] # HSDP all_gather: [Replicate(), Shard(0)] -> [Replicate(), Replicate()] # 2D FSDP + TP all_gather: # - [Shard(0), Shard(n)] -> [Replicate(), Replicate()] # - [Shard(0), Replicate()] -> [Replicate(), Replicate()] placements = [Replicate() for _ in value.placements] value = value.redistribute( device_mesh=value.device_mesh, placements=placements, ) # Call `wait()` to force the tensor to be synchronous with respect # to the main stream. # See the discussion in https://github.com/pytorch/pytorch/pull/117799. value = value.to_local() if isinstance(value, AsyncCollectiveTensor): value = value.wait() return value return _iterate_state_dict( state_dict, sharded_tensor_func, dtensor_func, _identity_func, pg=pg, device=device, cpu_offload=cpu_offload, ranks_only=ranks_only, type_check=type_check, ) def _offload_state_dict_to_cpu( state_dict: dict[str, Any], *, ranks_only: tuple[int, ...] = (), type_check: bool = True, ) -> dict[str, Any]: """ Given a state_dict, this API offload all the tensors to CPU memory. Args: state_dict (Dict[str, Any]): the target state_dict. pg (Optional[dist.ProcessGroup]): the process group that is used to gather ShardedTensor. Note that gathering a DTensor will use the DeviceMesh. So this argument will be ignored when gathering a DTensor. ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will have the same state_dicts. Otherwise only ranks that in ``ranks_only`` have the same state_dicts. Other ranks will get empty state_dicts. type_check: (bool): check if the instance data type is a supported type that can be saved by DCP. The current supported data types are torch.Tensor, DTensor, int, float, str, list, dict, None. Returns: The gathered state dictionary. """ ret = _iterate_state_dict( state_dict, _identity_func, _identity_func, _identity_func, pg=None, device=None, cpu_offload=True, ranks_only=ranks_only, type_check=type_check, ) return ret @torch.no_grad() def _copy_state_dict( state_dict: dict[str, Any], copy_state_dict: dict[str, Any], non_blocking: bool = False, type_check: bool = True, ) -> dict[str, Any]: """ Copies all tensors in a given state dict into a different state_dict with the same structure. Additionally, a copied state dict with the same value references is returned. Editing the keys on this state dict will not affect the passed in copy_state_dict (but the value references are the same). .. warning:: It is expected by this function that state_dict and copy_state_dict share the same structure and data types. .. warning:: The current supported data types are torch.Tensor, DTensor, int, float, str, list, dict, None. Args: state_dict (Dict[str, Any]): the target state_dict. copy_state_dict (Dict[str, Any]): The state dict we are copying into. This state_dict must have exactly the same structure as the source `state_dict`. non_blocking: (bool): Whether copy ops should be performed asynchronously type_check (bool): check if the instance data type is a supported type that can be saved by DCP. The current supported data types are torch.Tensor, DTensor, int, float, str, list, dict, None. Returns: State Dict copy """ return _iterate_state_dict( state_dict, _identity_func, _identity_func, _identity_func, pg=None, device=None, cpu_offload=False, ranks_only=(), companion_obj=copy_state_dict, type_check=type_check, non_blocking=non_blocking, ) @torch.no_grad() def _create_cpu_state_dict( state_dict: dict[str, Any], pin_memory: bool = False, share_memory: bool = False ) -> dict[str, Any]: """ Given a state_dict, create another state_dict with the same structure and elements. However, all tensors in the returned state_dict are new tensors on CPU. These tensors can be placed on pin_memory or share_memory based on the provided arguments. .. warning:: Setting both `pin_memory` and `share_memory` to True significantly increases the latency of this method because of the nuances which require us to register memory as pinned directly as opposed to relying on the pin_memory cache allocator. This option should only be used for long lived tensors which are required to be shared. This is not the case as long as at least one of `pin_memory` or `share_memory` is set to False. """ def tensor_func( obj: torch.Tensor, pg: Optional[dist.ProcessGroup], device: Optional[torch.device], _: Any, ) -> torch.Tensor: if len(obj.size()) == 0: return torch.tensor(0, dtype=obj.dtype) if share_memory: t = torch.empty(*tuple(obj.size()), dtype=obj.dtype) t = t.share_memory_() if pin_memory: def unpin_memory(t): succ = int(torch.cuda.cudart().cudaHostUnregister(t.data_ptr())) assert succ == 0, f"Unpinning shared memory failed with error-code: {succ}" weakref.finalize(t, unpin_memory, t) succ = int( torch.cuda.cudart().cudaHostRegister( t.data_ptr(), t.numel() * t.element_size(), 1, # lines up with 'cudaHostRegisterPortable' ) ) assert succ == 0, f"Pinning shared memory failed with error-code: {succ}" return t elif pin_memory: return torch.empty(*tuple(obj.size()), dtype=obj.dtype).pin_memory() else: return torch.empty(*tuple(obj.size()), dtype=obj.dtype) def dtensor_func( obj: DTensor, pg: Optional[dist.ProcessGroup], device: Optional[torch.device], _: Any, ) -> DTensor: if len(obj.size()) == 0: return obj if obj.device != torch.device("cpu"): ret = cast(DTensor, obj.to(device="cpu")) else: ret = copy.deepcopy(obj) ret._local_tensor = tensor_func(ret._local_tensor, pg, device, None) return ret ret = _iterate_state_dict( state_dict, _identity_func, dtensor_func, tensor_func, pg=None, device=None, cpu_offload=False, ranks_only=(), type_check=False, ) return ret def _check_state_dict_similarity( state_dict: dict[str, Any], compared_state_dict: dict[str, Any], ) -> bool: """ Given two state_dicts, check if the structures are the same. And if a [key, tensor] pair exist in one state_dict there must be the a corresponding pait, [key, other_tensor], in the other state_dict, where tensor and other_tensor have the same size and dtype. Return the check result. """ def tensor_func( obj: torch.Tensor, pg: Optional[dist.ProcessGroup], device: Optional[torch.device], companion_obj: Any, ) -> torch.Tensor: if companion_obj.dtype != obj.dtype or companion_obj.size() != obj.size(): raise CompanionMismatch return obj try: _iterate_state_dict( state_dict, _identity_func, _identity_func, tensor_func, pg=None, device=None, cpu_offload=False, ranks_only=(), companion_obj=compared_state_dict, type_check=False, ) except CompanionMismatch: return False return True class _TensorInfo(NamedTuple): size: torch.Size dtype: torch.dtype def _broadcast_tensors( full_state_dict: dict[str, Any], local_state_dict: dict[str, Any], keys: list[str], device: torch.device, pg: Optional[dist.ProcessGroup] = None, ) -> None: tensors = [] for key in keys: if dist.get_rank() == 0: full_state = full_state_dict[key] assert isinstance(full_state, torch.Tensor) full_tensor = full_state.detach().to(device) else: tensor_info = full_state_dict[key] full_tensor = torch.empty( size=tensor_info.size, device=device, dtype=tensor_info.dtype, ) tensors.append(full_tensor) local_state = local_state_dict.get(key, None) if local_state is None: continue elif isinstance(local_state, DTensor): local_state_dict[key] = (local_state, full_tensor) else: local_state_dict[key] = full_tensor if pg is None: pg = dist.distributed_c10d._get_default_group() if len(tensors) > 1: dist._broadcast_coalesced(pg, tensors, 500, 0) else: dist.broadcast(tensors[0], src=0, group=pg) _distribute_tensors(local_state_dict, keys, device, pg) def _distribute_tensors( local_state_dict: dict[str, Any], keys: list[str], device: torch.device, pg: Optional[dist.ProcessGroup] = None, ) -> None: if pg is None: pg = dist.distributed_c10d._get_default_group() for key in keys: _local_state = local_state_dict.get(key, None) if _local_state is None or torch.is_tensor(_local_state): continue local_state = _local_state[0] full_tensor = _local_state[1] shape, offset = compute_local_shape_and_global_offset( full_tensor.shape, local_state.device_mesh, local_state.placements ) slices = [ slice(cur_offset, cur_offset + cur_shape) for cur_shape, cur_offset in zip(shape, offset, strict=False) ] if local_state.is_meta: # Use .clone() here rather than view to clone and return only the sliced portion, minimizing memory access and cost. local_tensor = full_tensor[slices].detach().clone() # TODO: currently, we cannot handle strided sharding if the dp dimension is not even. For example, # one of the case that is not yet supported is when placements = (Shard(0), _StridedShard(0, sf=2)). ret = DTensor.from_local( local_tensor, local_state.device_mesh, local_state.placements, shape=local_state.shape, stride=local_state.stride(), ) else: ret = local_state # Copy full_tensor[slices] into local_state.to_local() to reduce memory footprint. ret.to_local().copy_(full_tensor[slices]) local_state_dict[key] = ret def _broadcast_state_dict( full_state_dict: dict[str, Any], local_state_dict: dict[str, Any], device: torch.device, pg: Optional[dist.ProcessGroup] = None, strict: bool = False, cpu_offload: bool = False, ) -> None: # Broadcast from rank0's `full_state_dict` to all ranks' `local_state_dict`. # If strict is True, any keys in `local_state_dict` but not in `full_state_dict` # will be removed from `local_state_dict`. ret = {} if dist.get_rank() == 0: for key, value in full_state_dict.items(): if not torch.is_tensor(value): ret[key] = value elif value.dim() == 0: ret[key] = value.cpu() else: ret[key] = _TensorInfo(value.size(), value.dtype) broadcast_list = [ret] dist.broadcast_object_list(broadcast_list, src=0, group=pg) ret = broadcast_list[0] # Gather values keys = [] local_state_dict_keys = set(local_state_dict.keys()) global_keys = set() for key, value in ret.items(): global_keys.add(key) if not isinstance(value, _TensorInfo): if key in local_state_dict: local_state_dict[key] = value continue if dist.get_rank() == 0: ret[key] = full_state_dict[key] keys.append(key) # Broadcast every tensor to avoid OOM for now. if len(keys) >= 1: _broadcast_tensors(ret, local_state_dict, keys, device, pg) if cpu_offload: for key in keys: local_state_dict[key] = local_state_dict[key].cpu() keys.clear() if strict: if missing_keys := (local_state_dict_keys - global_keys): for key in missing_keys: local_state_dict.pop(key) if keys: _broadcast_tensors(ret, local_state_dict, keys, device, pg) if cpu_offload: for key in keys: local_state_dict[key] = local_state_dict[key].cpu() def _distribute_state_dict( full_state_dict: dict[str, Any], local_state_dict: dict[str, Any], device: torch.device, pg: Optional[dist.ProcessGroup] = None, ) -> None: # Full_state_dict = True, broadcast_from_rank0 = False here. Each rank has # full_state_dict. Skip the broadcast in ``_broadcast_state_dict`` and # distribute tensors in each rank for key, value in full_state_dict.items(): if key not in full_state_dict: continue if not torch.is_tensor(value): local_state_dict[key] = value elif value.dim() == 0: local_state_dict[key] = value.cpu() else: assert isinstance(value, torch.Tensor) local_state = local_state_dict.get(key, None) if local_state is None: continue elif isinstance(local_state, DTensor): local_state_dict[key] = distribute_tensor( value.detach().to(device), local_state.device_mesh, local_state.placements, ) else: local_state_dict[key] = value.detach().to(device) # These APIs are from torch.distributed.checkpoint. # TODO: We should consolidate the code here as some not all modules can depend on # DCP. PATH_ITEM = Union[str, int] OBJ_PATH = tuple[PATH_ITEM, ...] FLATTEN_MAPPING = dict[str, OBJ_PATH] STATE_DICT_TYPE = dict[str, Any] CONTAINER_TYPE = MutableMapping[PATH_ITEM, Any] def _traverse_state_dict( state_dict: STATE_DICT_TYPE, visitor: Callable[[OBJ_PATH, Any], None], ) -> None: """ Invoke ``visitor`` for each value recursively in ``state_dict``. Mapping, list, and tuple will be flattened and other value types are treated as the terminal values and will invoke ``visitor``. """ def _traverse_obj(path: OBJ_PATH, value: Any) -> None: if isinstance(value, Mapping): for k, v in value.items(): _traverse_obj(path + (str(k),), v) elif isinstance(value, (list, tuple)): for i, v in enumerate(value): _traverse_obj(path + (i,), v) else: visitor(path, value) for key, value in state_dict.items(): _traverse_obj((str(key),), value) def _flatten_state_dict( state_dict: STATE_DICT_TYPE, ) -> tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]: """ Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary. Use ``unflatten_state_dict`` to revert this process. Returns: A tuple with the flatten state_dict and a mapping from original to new state_dict. N.B. The new keys are derived from the object paths, joined by dot. For example: ``{ 'a': {'b':...}}`` results in the key `a.b`. """ flattened: STATE_DICT_TYPE = {} mappings: FLATTEN_MAPPING = {} def flat_copy(path: OBJ_PATH, value: Any) -> None: new_fqn = ".".join(map(str, path)) if new_fqn in flattened: raise ValueError(f"duplicated flatten key {new_fqn}") flattened[new_fqn] = value mappings[new_fqn] = path _traverse_state_dict(state_dict, flat_copy) return flattened, mappings def _set_element(root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: Any) -> None: """Set ``value`` in ``root_dict`` along the ``path`` object path.""" cur_container = cast(CONTAINER_TYPE, root_dict) def extend_list(lst: list[Any], idx: int) -> None: while len(lst) <= idx: lst.append(None) for i in range(1, len(path)): prev_key = path[i - 1] key = path[i] def_val: CONTAINER_TYPE | list[Any] = {} if type(key) == str else [] if isinstance(cur_container, Mapping): cur_container = cast(CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)) else: extend_list(cur_container, prev_key) if cur_container[prev_key] is None: cur_container[prev_key] = def_val cur_container = cur_container[prev_key] key = path[-1] if type(key) == int: extend_list(cast(list[Any], cur_container), key) cur_container[key] = value def _unflatten_state_dict(state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING) -> STATE_DICT_TYPE: """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``.""" nested: STATE_DICT_TYPE = {} for key, value in state_dict.items(): _set_element(nested, mapping[key], value) return nested ================================================ FILE: verl/third_party/torch/distributed/checkpoint/__init__.py ================================================ # official torch 2.6.0 set_model_state_dict API leads to OOM # this is a copy of torch/distributed/checkpoint from torch 2.7.0 # From PyTorch: # Copyright (c) 2016- Facebook, Inc (Adam Paszke) # Copyright (c) 2014- Facebook, Inc (Soumith Chintala) # Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) # Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) # Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) # Copyright (c) 2011-2013 NYU (Clement Farabet) # Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) # Copyright (c) 2006 Idiap Research Institute (Samy Bengio) # Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) # From Caffe2: # Copyright (c) 2016-present, Facebook Inc. All rights reserved. # All contributions by Facebook: # Copyright (c) 2016 Facebook Inc. # All contributions by Google: # Copyright (c) 2015 Google Inc. # All rights reserved. # All contributions by Yangqing Jia: # Copyright (c) 2015 Yangqing Jia # All rights reserved. # All contributions by Kakao Brain: # Copyright 2019-2020 Kakao Brain # All contributions by Cruise LLC: # Copyright (c) 2022 Cruise LLC. # All rights reserved. # All contributions by Tri Dao: # Copyright (c) 2024 Tri Dao. # All rights reserved. # All contributions by Arm: # Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates # All contributions from Caffe: # Copyright(c) 2013, 2014, 2015, the respective contributors # All rights reserved. # All other contributions: # Copyright(c) 2015, 2016 the respective contributors # All rights reserved. # Caffe2 uses a copyright model similar to Caffe: each contributor holds # copyright over their contributions to Caffe2. The project versioning records # all such contribution and copyright details. If a contributor wants to further # mark their specific copyright on a particular contribution, they should # indicate their copyright solely in the commit message of the change when it is # committed. # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America # and IDIAP Research Institute nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: verl/third_party/torch/distributed/checkpoint/state_dict.py ================================================ # official torch 2.6.0 set_model_state_dict API leads to OOM # this is a copy of torch/distributed/checkpoint from torch 2.7.0 # From PyTorch: # Copyright (c) 2016- Facebook, Inc (Adam Paszke) # Copyright (c) 2014- Facebook, Inc (Soumith Chintala) # Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) # Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) # Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) # Copyright (c) 2011-2013 NYU (Clement Farabet) # Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) # Copyright (c) 2006 Idiap Research Institute (Samy Bengio) # Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) # From Caffe2: # Copyright (c) 2016-present, Facebook Inc. All rights reserved. # All contributions by Facebook: # Copyright (c) 2016 Facebook Inc. # All contributions by Google: # Copyright (c) 2015 Google Inc. # All rights reserved. # All contributions by Yangqing Jia: # Copyright (c) 2015 Yangqing Jia # All rights reserved. # All contributions by Kakao Brain: # Copyright 2019-2020 Kakao Brain # All contributions by Cruise LLC: # Copyright (c) 2022 Cruise LLC. # All rights reserved. # All contributions by Tri Dao: # Copyright (c) 2024 Tri Dao. # All rights reserved. # All contributions by Arm: # Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates # All contributions from Caffe: # Copyright(c) 2013, 2014, 2015, the respective contributors # All rights reserved. # All other contributions: # Copyright(c) 2015, 2016 the respective contributors # All rights reserved. # Caffe2 uses a copyright model similar to Caffe: each contributor holds # copyright over their contributions to Caffe2. The project versioning records # all such contribution and copyright details. If a contributor wants to further # mark their specific copyright on a particular contribution, they should # indicate their copyright solely in the commit message of the change when it is # committed. # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America # and IDIAP Research Institute nor the names of its contributors may be # used to endorse or promote products derived from this software without # specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # ruff: noqa: B028, UP038, UP007, E721 # mypy: allow-untyped-defs import contextlib import functools import gc import warnings from collections.abc import Generator, Iterable from dataclasses import asdict, dataclass, field from itertools import chain from typing import Any, Callable, Optional, Union, cast, no_type_check import torch import torch.distributed as dist import torch.nn as nn from torch.distributed._shard.sharded_tensor import ShardedTensor from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( _CHECKPOINT_PREFIX, ) from torch.distributed.fsdp import ( FullOptimStateDictConfig, FullStateDictConfig, OptimStateDictConfig, ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictConfig, StateDictType, ) from torch.distributed.fsdp import ( FullyShardedDataParallel as FSDP, ) from torch.distributed.fsdp._common_utils import ( FSDP_WRAPPED_MODULE, _get_module_fsdp_state_if_fully_sharded_module, ) from torch.distributed.tensor import DTensor from torch.nn.modules.module import _IncompatibleKeys from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils._pytree import tree_map_only from verl.third_party.torch.distributed._state_dict_utils import ( _broadcast_state_dict, _distribute_state_dict, _flatten_state_dict, _gather_state_dict, _offload_state_dict_to_cpu, _unflatten_state_dict, ) __all__ = [ "FQNS_T", "PrimitiveType", "ValueType", "DictValueType", "ListDictValueType", "OptimizerStateType", "StateDictOptions", "get_model_state_dict", "get_optimizer_state_dict", "get_state_dict", "set_model_state_dict", "set_optimizer_state_dict", "set_state_dict", ] _FLAT_PARAM = "_flat_param" _PG = "param_groups" _PARAMS = "params" _STATE = "state" FQNS_T = set[str] PrimitiveType = Union[DTensor, ShardedTensor, torch.Tensor, int, float, str] ValueType = Union[PrimitiveType, list[PrimitiveType], tuple[PrimitiveType], dict[str, "ValueType"]] DictValueType = dict[str, ValueType] ListDictValueType = list[DictValueType] OptimizerStateType = dict[str, DictValueType | ListDictValueType] _patched_state_dict: set[Callable] = set() @contextlib.contextmanager def _gc_context(): is_enabled = gc.isenabled() gc.disable() try: yield finally: if is_enabled: gc.enable() @dataclass class StateDictOptions: """ This dataclass specifies how get_state_dict/set_state_dict will work. - ``full_state_dict``: if this is set to True, all the tensors in the returned state_dict will be gathered. No ShardedTensor and DTensor will be in the returned state_dict. - ``cpu_offload``: offload all the tensors to cpu. To prevent CPU OOM, if ``full_state_dict`` is also true, then only the rank0 will get the state_dict and all other ranks will get empty state_dict. - ``ignore_frozen_params``: if the value is True, the returned state_dict won't contain any frozen parameters -- the ``requires_grad`` is False. The default value is False. - ``keep_submodule_prefixes`` (deprecated): when ``submodules`` is not None, this option indicates whether to keep the submodule prefixes from the state_dict keys. or example, if the submodule is ``module.pretrain`` and the full FQN of the parameter is ``pretrain.layer1.weight`` of the param. When this option is True, the parameter's key in the returned state_dict will be ``pretrain.layer1.weight``. If the options is False, the key will be ``layer1.weight``. Note that if ``keep_submodule_prefixes`` is False, there may be conflicted FQNs, hence there should be only one submodule in ``submodules``. - ``strict``: the ``strict`` option when ``set_state_dict`` calls model.load_state_dict(). - ``broadcast_from_rank0``: when the option is True, rank0 should receive a full state_dict and will broadcast the tensors in the state_dict/ optim_state_dict one by one to other ranks. Other ranks will receive the tensors and shard according to the local shards in the model and optimizer. ``full_state_dict`` must be set to True when using this option. This option currently only supports DTensor, not the legacy ShardedTensor. """ full_state_dict: bool = False cpu_offload: bool = False ignore_frozen_params: bool = False keep_submodule_prefixes: bool = True strict: bool = True broadcast_from_rank0: bool = False flatten_optimizer_state_dict: bool = False dsd_fqn_modifiers: str = "_fqn_modifiers" @dataclass class _StateDictInfo(StateDictOptions): fqn_param_mapping: dict[ str | torch.Tensor, FQNS_T | torch.Tensor, ] = field(default_factory=dict) shared_params_mapping: dict[ str | torch.Tensor, FQNS_T | torch.Tensor, ] = field(default_factory=dict) submodule_prefixes: set[str] = field(default_factory=set) handle_model: bool = True handle_optim: bool = True fsdp_context: Callable = contextlib.nullcontext fsdp_modules: list[nn.Module] = field(default_factory=list) @functools.cache def _get_fqns( model: nn.Module, name: str, dsd_fqn_modifiers: str = "_fqn_modifiers", skip_ddp_prefix: bool = True, skip_compiler_prefix: bool = True, ) -> FQNS_T: """ This API is used to convert the name of a parameter to the FQNs. For FSDP without `use_orig_params`, the name of FlatParameter can be mapped to multiple original parameters. As a result, the return type of this function is `set[str]`. Args: module (nn.Module): the root model. name (str): the name skip_ddp_prefix (bool): whether to skip DDP's `module` prefix Returns: The canonical FQNs based on the model traversal. """ # Remove the checkpoint prefix, if it exists. name = name.replace(_CHECKPOINT_PREFIX, "") if "." not in name: return {name} obj_names = name.split(".") fqn_obj_names = [] curr_obj = model for i, curr_obj_name in enumerate(obj_names): if isinstance(curr_obj, DDP): assert curr_obj_name == "module" curr_obj = curr_obj.module if not skip_ddp_prefix: fqn_obj_names.append(curr_obj_name) elif isinstance(curr_obj, FSDP): if i < len(obj_names) - 1 and obj_names[i + 1] == _FLAT_PARAM: prefix = ".".join(fqn_obj_names) flat_param = getattr(curr_obj, _FLAT_PARAM) if prefix: prefix = f"{prefix}." return {f"{prefix}{fqn}" for fqn in flat_param._fqns} curr_obj = getattr(curr_obj, FSDP_WRAPPED_MODULE) if curr_obj_name != FSDP_WRAPPED_MODULE: fqn_obj_names.append(curr_obj_name) curr_obj = getattr(curr_obj, curr_obj_name) elif isinstance(curr_obj, torch._dynamo.eval_frame.OptimizedModule): assert curr_obj_name == "_orig_mod" curr_obj = curr_obj._orig_mod if not skip_compiler_prefix: fqn_obj_names.append(curr_obj_name) else: # In some modeuls, _fqn_modifiers would not shown in the state_dict keys, # skip them in the fqn to ensure load stat dict successfully for them. if hasattr(curr_obj, dsd_fqn_modifiers): if removed_fqn := getattr(curr_obj, dsd_fqn_modifiers)().get(curr_obj_name): if hasattr(curr_obj, removed_fqn): curr_obj = getattr(curr_obj, removed_fqn) fqn_obj_names.append(curr_obj_name) if curr_obj_name == nn.modules.module._EXTRA_STATE_KEY_SUFFIX: if i != len(obj_names) - 1: raise RuntimeError("Expect `_extra_state` to be the last obj name") else: curr_obj = getattr(curr_obj, curr_obj_name) return {".".join(fqn_obj_names).replace(_CHECKPOINT_PREFIX, "")} class _EXTRA_STATE: pass def _iterate_valid_model_state(model, dsd_fqn_modifiers="_fqn_modifiers"): visited_modules: set[nn.Module] = set() def recurse(module: nn.Module, curr_fqn: str) -> Generator: visited_modules.add(module) curr_fqn = f"{curr_fqn}." if curr_fqn else "" for name, submodule in module.named_children(): if submodule in visited_modules: continue # if user have state_dict_hooks in their model, they can add the state_dict key changes # at dsd_fqn_modifiers in input to align with the function of state_dict_hook if hasattr(module, dsd_fqn_modifiers) and name in getattr(module, dsd_fqn_modifiers)().values(): # skip _fqn_modifiers here thus remove the last `.` added new_fqn = curr_fqn[:-1] else: new_fqn = f"{curr_fqn}{name}" yield from recurse(submodule, new_fqn) for name, obj in chain(module.named_buffers(recurse=False), module.named_parameters(recurse=False)): if name in module._non_persistent_buffers_set: continue new_fqn = f"{curr_fqn}{name}" yield new_fqn, obj if getattr(module.__class__, "get_extra_state", nn.Module.get_extra_state) != nn.Module.get_extra_state: new_fqn = f"{curr_fqn}{nn.modules.module._EXTRA_STATE_KEY_SUFFIX}" yield new_fqn, _EXTRA_STATE() yield from recurse(model, "") def _verify_options( model: nn.Module, optims: tuple[torch.optim.Optimizer, ...], optim_only: bool, *, submodules: Optional[set[nn.Module]] = None, options: Optional[StateDictOptions] = None, ) -> _StateDictInfo: """ Verify the model and options passed by the user and generates _StateDictInfo. """ if submodules: warnings.warn( "Getting submodules only model/optim state_dict is deprecated and " "will be removed in 2.5. This feature can be achieved by manually " "filtering out the state_dict returned from get_state_dict.", FutureWarning, ) if optim_only and not optims: raise RuntimeError("Optimizers are not passed in but optim_only is set to True.") options = options or StateDictOptions() fqn_param_mapping: dict[str | torch.Tensor, set[str] | torch.Tensor] = {} shared_params_mapping: dict[str | torch.Tensor, set[str] | torch.Tensor] = {} for name, param in _iterate_valid_model_state(model): if isinstance(param, _EXTRA_STATE): continue fqns = _get_fqns(model, name) fqn = fqn_param_mapping.get(param, None) if fqn is not None: cast(set[str], fqn_param_mapping[param]).update(fqns) shared_params_mapping[param] = fqn_param_mapping[param] else: # We need to do copy as _get_fqns is lru_cached fqn_param_mapping[param] = fqns.copy() for fqn in fqns: if not isinstance(param, _EXTRA_STATE): fqn_param_mapping[fqn] = param for param_, fqns_ in list(shared_params_mapping.items()): for fqn in fqns_: shared_params_mapping[fqn] = cast(torch.Tensor, param_) submodule_prefixes: set[str] = set() if submodules: submodules = set(submodules) for name, module in model.named_modules(): if module not in submodules: continue fqns = _get_fqns(model, name) assert len(fqns) == 1, "Submodule FQN should only have 1 instance" submodule_prefixes.update(f"{fqn}." for fqn in fqns) if options.broadcast_from_rank0 and not options.full_state_dict: raise ValueError("full_state_dict must be True when broadcast_from_rank0 is True.") fsdp_modules = FSDP.fsdp_modules(model) state_dict_config: StateDictConfig optim_state_dict_config: OptimStateDictConfig fsdp_context: Callable if fsdp_modules: # FSDP API only work if at least one FSDP instance exists. if options.full_state_dict: state_dict_config = FullStateDictConfig(offload_to_cpu=options.cpu_offload, rank0_only=options.cpu_offload) optim_state_dict_config = FullOptimStateDictConfig( offload_to_cpu=options.cpu_offload, rank0_only=(options.cpu_offload or options.broadcast_from_rank0), ) state_dict_type = StateDictType.FULL_STATE_DICT else: state_dict_config = ShardedStateDictConfig( offload_to_cpu=options.cpu_offload, ) optim_state_dict_config = ShardedOptimStateDictConfig( offload_to_cpu=options.cpu_offload, ) state_dict_type = StateDictType.SHARDED_STATE_DICT @contextlib.contextmanager def fsdp_state_dict_type_without_warning( module, state_dict_type, state_dict_config, optim_state_dict_config, ): with warnings.catch_warnings(): warnings.filterwarnings("ignore", message="FSDP.state_dict_type", category=FutureWarning) with FSDP.state_dict_type( module=module, state_dict_type=state_dict_type, state_dict_config=state_dict_config, optim_state_dict_config=optim_state_dict_config, ): yield fsdp_context = functools.partial( fsdp_state_dict_type_without_warning, module=model, state_dict_type=state_dict_type, state_dict_config=state_dict_config, optim_state_dict_config=optim_state_dict_config, ) else: fsdp_context = contextlib.nullcontext return _StateDictInfo( **asdict(options), fqn_param_mapping=fqn_param_mapping, shared_params_mapping=shared_params_mapping, submodule_prefixes=submodule_prefixes, fsdp_context=fsdp_context, fsdp_modules=cast(list[nn.Module], fsdp_modules), handle_model=not optim_only, handle_optim=(len(optims) > 0), ) def _verify_state_dict( model_state_dict: dict[str, ValueType], optim_state_dict: OptimizerStateType, info: _StateDictInfo, ) -> None: for module in info.fsdp_modules: fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module) assert fsdp_state is not None, "Expected a fsdp_state with a fsdp module." # Verify if the model_state_dict and optim_state_dict are valid. This API # should give the users an explicit error message to debug or report. if ( info.handle_model and not model_state_dict and not info.submodule_prefixes and not info.ignore_frozen_params and not (info.cpu_offload and info.full_state_dict) and info.strict and not info.broadcast_from_rank0 ): raise RuntimeError( "The option indicates that model state_dict is required to save " "or load, but model state_dict is empty." f"rank = {dist.get_rank()=}." ) if info.handle_optim: if not optim_state_dict and not (info.cpu_offload and info.full_state_dict) and (not info.broadcast_from_rank0): raise RuntimeError( "The option indicates that model state_dict is required to save, " f"or load but optim state_dict is empty. {optim_state_dict}" ) for key in model_state_dict.keys(): if _FLAT_PARAM in key: raise RuntimeError(f"{key} contains {_FLAT_PARAM}. This can happen if the model is not the root module.") def _state_dict_fn(obj: nn.Module | torch.optim.Optimizer, api: str) -> Callable: call = getattr(obj, api) if call in _patched_state_dict: call = functools.partial(getattr(obj.__class__, api), self=obj) return call def _maybe_full_or_cpu_state_dict(state_dict: dict[str, Any], info: _StateDictInfo) -> dict[str, Any]: if info.full_state_dict: ranks_only = () if (not info.cpu_offload or not torch.distributed.is_initialized()) else (0,) return _gather_state_dict(state_dict, cpu_offload=info.cpu_offload, ranks_only=ranks_only) elif info.cpu_offload: return _offload_state_dict_to_cpu(state_dict) else: return state_dict @torch.no_grad() def _get_model_state_dict(model: nn.Module, info: _StateDictInfo) -> dict[str, ValueType]: if not info.handle_model: return {} with info.fsdp_context(): state_dict = _state_dict_fn(model, "state_dict")() for key in list(state_dict.keys()): fqns = _get_fqns(model, key) assert len(fqns) == 1, (key, fqns) fqn = next(iter(fqns)) if fqn != key: # As we only support FSDP, DDP, and TP, the only cases are # wrapper-based DDP and compiler. Verify if the assumption # is correct. def verify(key, fqn) -> bool: if len(fqn) >= len(key): return False fqn_split = fqn.split(".") key_split = key.split(".") fqn_idx = 0 for key_idx, key_name in enumerate(key_split): if key_name == fqn_split[fqn_idx]: fqn_idx += 1 if fqn_idx == len(fqn_split): return key_idx == len(key_split) - 1 elif key_name in ("module", "_orig_mod"): continue else: return False return True if not verify(key, fqn): raise RuntimeError(f"An unexpected key, {key}, exists. FQN is {fqn}") state_dict[fqn] = state_dict.pop(key) if info.submodule_prefixes: new_state_dict: dict[str, ValueType] = {} # TODO: make this faster. for fqn in state_dict.keys(): for prefix in info.submodule_prefixes: if not fqn.startswith(prefix): continue if info.keep_submodule_prefixes: new_state_dict[fqn] = state_dict[fqn] else: new_fqn = fqn[len(prefix) :] new_state_dict[new_fqn] = state_dict[fqn] state_dict = new_state_dict if info.ignore_frozen_params: for key, param in model.named_parameters(): if param.requires_grad: continue fqns = _get_fqns(model, key) for fqn in fqns: state_dict.pop(fqn) for key, p in list(state_dict.items()): if torch.is_tensor(p) and p.is_meta: state_dict.pop(key) return _maybe_full_or_cpu_state_dict(state_dict, info) @torch.no_grad() def _load_model_state_dict( model: nn.Module, state_dict: dict[str, ValueType], info: _StateDictInfo, ) -> _IncompatibleKeys: if not info.handle_model or (not state_dict and not info.broadcast_from_rank0): return _IncompatibleKeys({}, {}) local_state_dict = {} for key, value in _iterate_valid_model_state(model, info.dsd_fqn_modifiers): fqns = _get_fqns(model, key, info.dsd_fqn_modifiers) fqns_with_prefix = _get_fqns( model, key, info.dsd_fqn_modifiers, skip_ddp_prefix=False, skip_compiler_prefix=False, ) for fqn, fqn_with_prefix in zip(fqns, fqns_with_prefix, strict=False): if (not info.broadcast_from_rank0 or dist.get_rank() == 0) and fqn != fqn_with_prefix: load_value = state_dict.pop(fqn, None) if load_value is None: if info.strict: raise RuntimeError(f"Missing key: {fqn}.") else: state_dict[fqn_with_prefix] = load_value local_state_dict[fqn_with_prefix] = value assign = False if info.broadcast_from_rank0 or info.full_state_dict: devices = set() for key, value in local_state_dict.items(): if torch.is_tensor(value) and value.dim() > 0: devices.add(value.device) # In lora state_dict, there could be multiple devices, with meta device inside. # Take the other device in the broadcast/distribtue, and set assign to True if torch.device("meta") in devices: devices.remove(torch.device("meta")) assign = True if len(devices) == 0: devices.add(dist.distributed_c10d._get_pg_default_device()) elif len(devices) > 1: raise ValueError("Multiple devices found") if info.broadcast_from_rank0: _broadcast_state_dict( state_dict, local_state_dict, device=devices.pop(), strict=info.strict, cpu_offload=info.cpu_offload, ) elif info.full_state_dict: _distribute_state_dict(state_dict, local_state_dict, device=devices.pop()) for fqn, local_state in local_state_dict.items(): state_dict[fqn] = local_state with info.fsdp_context(): return cast( _IncompatibleKeys, _state_dict_fn(model, "load_state_dict")(state_dict=state_dict, strict=info.strict, assign=assign), ) def _init_optim_state(optim: torch.optim.Optimizer) -> None: """ Initialize optim states by calling the step() with zero grads. """ if optim.state: # The optimizer state is initialized. return # There are some stateless optimizers like SGD. These optimizer will # not return in the above condition. So if gradients exist, we should also # return. If gradients do not exist, the following initialization should # not disturb SGD because the gradients and lr are both zero. for param_group in optim.param_groups: for param in param_group[_PARAMS]: if param.grad is not None: return for param_group in optim.param_groups: for param in param_group[_PARAMS]: if param.requires_grad: param.grad = torch.zeros_like(param) # Some optimizers will update parameters regardless of grads due to lr, so # make lr to zero when calling `step()`. lrs = [] for param_group in optim.param_groups: if "lr" in param_group: lrs.append(param_group["lr"]) param_group["lr"] = torch.tensor(0.0) if isinstance(param_group["lr"], torch.Tensor) else 0.0 optim.step(closure=None) # Whether to recover the "lr" should not matter too much as we will # restore checkpointing later. for param_group in optim.param_groups: if "lr" in param_group: param_group["lr"] = lrs.pop(0) optim.zero_grad(set_to_none=True) def _flatten_optim_state_dict(state_dict: OptimizerStateType) -> dict[str, ValueType]: """ This API flattens the optimizer state_dict to support optimizer resharding for MPMD, e.g., pipeline parallelism. Without the API, the original optimizer state_dict looks like: { "state": { "layer1.weight": { "step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor }, "layer2.weight": { "step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor }, }, "param_group": [ { "lr": 0.0, "betas": (0.9, 0.95), ..., "params": ["layer1.weight", "layer2.weight"] } ] } With this API, the optimizer state_dict looks like: { "state.layer1.weight.step": 10, "state.layer2.weight.step": 10, "state.layer1.weight.exp_avg": SomeTensor, "state.layer2.weight.exp_avg": SomeTensor, "state.layer1.weight.exp_avg_sq": SomeTensor, "state.layer2.weight.exp_avg_sq": SomeTensor, "param_group.layer1.weight.lr" : 0.1, "param_group.layer2.weight.lr" : 0.1, "param_group.layer1.weight.betas" : (0.9, 0.95), "param_group.layer2.weight.betas" : (0.9, 0.95), } Note that if any of the value is a container, like the betas in the example, this API won't flattent it. """ def _raise_if_type_not_supported(v): if not isinstance(v, (torch.Tensor, int, float)): raise NotImplementedError( f"Flattening optimizer state_dict only supports tensor, int, float states now. Type is {type(v)}." ) ret: dict[str, ValueType] = {} for fqn, state in cast(DictValueType, state_dict[_STATE]).items(): for k, v in cast(DictValueType, state).items(): _raise_if_type_not_supported(v) ret[f"{_STATE}.{fqn}.{k}"] = v for param_group in cast(ListDictValueType, state_dict[_PG]): fqns = param_group.pop(_PARAMS) for fqn in cast(list[str], fqns): for k, v in param_group.items(): ret[f"{_PG}.{fqn}.{k}"] = v return ret def _unflatten_optim_state_dict( optim: torch.optim.Optimizer, state_dict: dict[str, ValueType], info: _StateDictInfo, ) -> OptimizerStateType: """ This API unflattens the state_dict generated by _flatten_optim_state_dict(). See the docstring of _flatten_optim_state_dict() for more detail. """ state: DictValueType = {} pg_state: ListDictValueType = [] return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state} for param_group in optim.param_groups: pg_state.append({_PARAMS: []}) for param in param_group[_PARAMS]: for fqn in info.fqn_param_mapping[param]: # If a parameter is shared, only one of the FQN will be used. # So we need to verify which if this fqn is actually used in # the state_dict. if fqn in info.shared_params_mapping: in_params = False for k in param_group.keys(): if k == _PARAMS: continue flatten_key = f"{_PG}.{fqn}.{k}" if flatten_key in state_dict: in_params = True break else: in_params = True if not in_params: continue params = pg_state[-1][_PARAMS] assert isinstance(params, list) # typing params.append(fqn) if not param.requires_grad: continue state[fqn] = {} for state_name in optim.state[param].keys(): cast(DictValueType, state[fqn])[state_name] = state_dict[f"{_STATE}.{fqn}.{state_name}"] first_param_fqn = cast(list[str], pg_state[-1][_PARAMS])[0] for k in param_group.keys(): if k == _PARAMS: continue value = state_dict[f"{_PG}.{first_param_fqn}.{k}"] if k not in pg_state[-1]: pg_state[-1][k] = value elif pg_state[-1][k] != value: raise RuntimeError( "All the parameters in the same parameter group should have " f"the same saved param_group value. But {first_param_fqn}.{k} " f"is {value} while other(s) is {pg_state[-1][k]}." ) return return_osd @torch.no_grad() def _get_optim_state_dict( model: nn.Module, optimizers: tuple[torch.optim.Optimizer, ...], info: _StateDictInfo, ) -> OptimizerStateType: if not info.handle_optim: return {} optim_state_dict: OptimizerStateType = {_STATE: {}, _PG: []} for optim in optimizers: _init_optim_state(optim) osd = _state_dict_fn(optim, "state_dict")() if info.fsdp_modules: with info.fsdp_context(): osd = FSDP.optim_state_dict(model, optim, osd) # We need to specially handle FlatParameter FSDP as # FlatParameter FSDP converts the FQNs. # There are no easy ways to do this conversion systematically. # We can only use a string replacment without correctness check. if not osd: continue for k in list(osd[_STATE].keys()): if "_orig_mod" in k: osd[_STATE][k.replace("_orig_mod.", "")] = osd[_STATE].pop(k) for g in osd[_PG]: params = [k.replace("_orig_mod.", "") for k in g[_PARAMS]] g[_PARAMS] = params else: params = list(chain.from_iterable(g[_PARAMS] for g in optim.param_groups)) param_pid_mapping = dict(zip(params, range(len(params)), strict=False)) fqn_pid_mapping = {} for key, param in model.named_parameters(): fqns = _get_fqns(model, key) assert len(fqns) == 1 fqn = next(iter(fqns)) if param not in param_pid_mapping: continue pid = param_pid_mapping[param] fqn_pid_mapping[fqn] = pid fqn_pid_mapping[pid] = fqn for key in list(osd[_STATE].keys()): fqn = fqn_pid_mapping[key] osd[_STATE][fqn] = osd[_STATE].pop(key) for group in osd[_PG]: group[_PARAMS] = [fqn_pid_mapping[pid] for pid in group[_PARAMS]] if not osd: continue cast(DictValueType, optim_state_dict[_STATE]).update(osd[_STATE]) cast(ListDictValueType, optim_state_dict[_PG]).extend(osd[_PG]) if info.flatten_optimizer_state_dict: optim_state_dict = cast(OptimizerStateType, _flatten_optim_state_dict(optim_state_dict)) return _maybe_full_or_cpu_state_dict(optim_state_dict, info) def _split_optim_state_dict( model: nn.Module, optim: torch.optim.Optimizer, optim_state_dict: OptimizerStateType, info: _StateDictInfo, ) -> OptimizerStateType: """ Extract the corresponding optim state_dict from ``optim_state_dict`` for ``optim`` and return the result optim state_dict. Args: model (nn.Module): the root model. optim (torch.optim.Optimizer): the optimizer. optim_state_dict (Dict[str, ValueType]): the superset optim state_dict that contains the optim state_dict of ``optim``. info (_StateDictInfo): state dict information. Returns: The optim state_dict of ``optim``. """ state: DictValueType = {} pg_state: ListDictValueType = [] return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state} pg_mapping: dict[int, int] = {} if all(isinstance(k, int) for k in cast(DictValueType, optim_state_dict[_STATE]).keys()): return optim_state_dict for param_group in optim.param_groups: pg_state.append({_PARAMS: []}) for param in param_group[_PARAMS]: for fqn in info.fqn_param_mapping[param]: if fqn in info.shared_params_mapping: in_params = False for loaded_param_group in cast(ListDictValueType, optim_state_dict[_PG]): if fqn in cast(list[str], loaded_param_group[_PARAMS]): in_params = True break else: in_params = True if not in_params: continue params = pg_state[-1][_PARAMS] assert isinstance(params, list) params.append(fqn) if param.requires_grad: state[fqn] = cast(DictValueType, optim_state_dict[_STATE])[fqn] for loaded_param_group in cast(ListDictValueType, optim_state_dict[_PG]): if fqn in cast(list[str], loaded_param_group[_PARAMS]): pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1 if len(param_group[_PARAMS]) == 0: # Param_group with empty params. ret = [] for loaded_param_group in cast(ListDictValueType, optim_state_dict[_PG]): if len(cast(list[str], loaded_param_group[_PARAMS])) == 0: ret.append(loaded_param_group) if len(ret) != 1: raise ValueError( "There are param groups that have zero parameters. " "In such a case, DSD only support exactly one param group " "with zero parameters." "But the loaded state_dict has zero or more than one param groups " "that have zero parameters." ) if len(optim_state_dict[_PG]) != len(optim.param_groups): raise ValueError( "When there is a parameter group that has zero parameters, multiple optimizers are not supported." ) pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1 for param_group in cast(ListDictValueType, optim_state_dict[_PG]): pg_idx = pg_mapping.get(id(param_group), -1) if pg_idx == -1: continue for key, value in param_group.items(): if key == _PARAMS: continue # TODO: check if value is the same if exists. pg_state[pg_idx][key] = value return return_osd @torch.no_grad() def _load_optim_state_dict( model: nn.Module, optimizers: tuple[torch.optim.Optimizer, ...], state_dict: OptimizerStateType, info: _StateDictInfo, ) -> None: if not info.handle_optim: return for optim in optimizers: _init_optim_state(optim) if state_dict: if _STATE in state_dict: optim_state_dict = _split_optim_state_dict(model, optim, state_dict, info) else: optim_state_dict = _unflatten_optim_state_dict(optim, cast(dict[str, ValueType], state_dict), info) else: optim_state_dict = {} if info.fsdp_modules: # We need to specially handle FlatParameter FSDP as # FlatParameter FSDP converts the FQNs. for original_fqn, _ in model.named_parameters(): fqns = _get_fqns(model, original_fqn) fqns_with_compiler = _get_fqns(model, original_fqn, skip_compiler_prefix=False) if fqns == fqns_with_compiler: continue assert len(fqns) == 1 fqn = fqns.pop() fqn_with_compiler = fqns_with_compiler.pop() for g in optim_state_dict[_PG]: val = cast(dict[str, Any], g) params = [key.replace(fqn, fqn_with_compiler) for key in val[_PARAMS]] val[_PARAMS] = params osd_state = cast(DictValueType, optim_state_dict[_STATE]) for k in list(osd_state.keys()): if fqn in k: osd_state[k.replace(fqn, fqn_with_compiler)] = osd_state.pop(k) with info.fsdp_context(): optim_state_dict = FSDP.optim_state_dict_to_load(model, optim, optim_state_dict) elif info.full_state_dict: info.full_state_dict = False local_state_dict = _get_optim_state_dict(model, (optim,), info) info.full_state_dict = True device = None def _device(t): if t.dim() > 0: nonlocal device if device is None: device = t.device elif device != t.device: raise ValueError("Device mismatch") return t _ = tree_map_only(torch.Tensor, _device, local_state_dict) assert device is not None flatten_osd, osd_mapping = _flatten_state_dict(optim_state_dict) flatten_local_osd, local_osd_mapping = _flatten_state_dict(local_state_dict) if info.broadcast_from_rank0: _broadcast_state_dict(flatten_osd, flatten_local_osd, device=device) else: _distribute_state_dict(flatten_osd, flatten_local_osd, device=device) # The modifications listed seek to address the problem where optim might possess # dissimilar parameters in comparison to optim_state_dict. This is achieved by # incorporating differential parameters within local, which may result in optim # having additional parameters ultimately. for optim_key in flatten_osd.keys(): if optim_key not in flatten_local_osd: assert optim_key in osd_mapping flatten_local_osd[optim_key] = flatten_osd[optim_key] local_osd_mapping[optim_key] = osd_mapping[optim_key] optim_state_dict = _unflatten_state_dict(flatten_local_osd, local_osd_mapping) for pg in optim_state_dict[_PG]: if _PARAMS not in pg: cast(dict[str, ValueType], pg)[_PARAMS] = [] # Note that we do not have to convert the FQN back to param id here if # order in optim.param_groups[idx][_PARAMS] is the same as the one in # optim_state_dict[_PG][idx][_PARAMS]. _state_dict_fn(optim, "load_state_dict")(state_dict=optim_state_dict) def get_model_state_dict( model: nn.Module, *, submodules: Optional[set[nn.Module]] = None, options: Optional[StateDictOptions] = None, ) -> dict[str, ValueType]: """ Return the model state_dict of ``model``. See ``get_state_dict`` for the detail usage. Args: model (nn.Module): the nn.Module to the model. submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters that belong to the submodules. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be returned. See `StateDictOptions` for the details. Returns: The state_dict for ``model``. :rtype: typing.Dict[str, ValueType] """ with _gc_context(): info = _verify_options( model, (), optim_only=False, submodules=submodules, options=options, ) model_state_dict = _get_model_state_dict(model, info) _verify_state_dict(model_state_dict, {}, info) return model_state_dict def get_optimizer_state_dict( model: nn.Module, optimizers: torch.optim.Optimizer | Iterable[torch.optim.Optimizer], *, submodules: Optional[set[nn.Module]] = None, options: Optional[StateDictOptions] = None, ) -> OptimizerStateType: """ Return the combined state_dict for optimizers. See ``get_state_dict`` for the detail usage. Args: model (nn.Module): the nn.Module to the model. optimizers (Union[None, Optimizer, Iterable[Optimizer]]): The optimizers that are used to optimize ``model``. submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters that belong to the submodules. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be returned. See `StateDictOptions` for the details. Returns: The state_dict for ``optimizers``. :rtype: OptimizerStateType """ with _gc_context(): optimizers = (optimizers,) if isinstance(optimizers, torch.optim.Optimizer) else tuple(optimizers) info = _verify_options( model, optimizers, optim_only=True, submodules=submodules, options=options, ) optim_state_dict = _get_optim_state_dict(model, optimizers, info) _verify_state_dict({}, optim_state_dict, info) return optim_state_dict def get_state_dict( model: nn.Module, optimizers: torch.optim.Optimizer | Iterable[torch.optim.Optimizer], *, submodules: Optional[set[nn.Module]] = None, options: Optional[StateDictOptions] = None, ) -> tuple[dict[str, ValueType], OptimizerStateType]: """ Return the model state_dict and optimizers state_dict. ``get_state_dict`` can process any module that is parallelized by PyTorch FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any combination of these parallelisms. The main functions of ``get_state_dict`` are: 1.) returning a model and optimizer state_dict that can be resharded with a different number of trainers and/or different parallelisms. 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call these APIs. 3.) sanity checking the result state_dict. The keys of the result state dictionary are the canonical FQNs (Fully Qualified Names). A canonical FQN refers to the FQN based on a parameter's position in an nn.Module hierarchy. More specifically, a canonical FQN to a parameter is the FQN returned by ``module.named_parameters()`` or ``module.named_buffers()`` when the module is not distributed by any parallelisms. Since the optimizer internally uses parameter IDs to represent a parameter, there will be a conversion from the parameter IDs to the canonical FQNs when calling this API. ``get_state_dict`` can also process a module that is not parallelized. In such a case, ``get_state_dict`` only performs one function -- converting the optimizer parameter IDs to the canonical FQNs. Example: >>> # xdoctest: +SKIP >>> import torch >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP >>> from torch.nn.parallel import DistributedDataParallel as DDP >>> from torch.distributed.checkpoint.state_dict import get_state_dict >>> fsdp_model = FSDP(copy.deepcopy(model)) >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) >>> ddp_model = DDP(copy.deepcopy(model)) >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( ... fsdp_model, fsdp_optim ... ) >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), >>> # the asserts will fail. >>> assert ddp_state_dict == fsdp_state_dict >>> assert ddp_optim_state == fsdp_optim_state_dict Args: model (nn.Module): the nn.Module to the model. optimizers (Union[None, Optimizer, Iterable[Optimizer]]): The optimizers that are used to optimize ``model``. submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters that belong to the submodules. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be returned. See `StateDictOptions` for the details. Returns: ``Tuple`` that contain model state_dict and optimizer state_dict. :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] """ with _gc_context(): optimizers = (optimizers,) if isinstance(optimizers, torch.optim.Optimizer) else tuple(optimizers) info = _verify_options( model, optimizers, optim_only=False, submodules=submodules, options=options, ) model_state_dict = _get_model_state_dict(model, info) optim_state_dict = _get_optim_state_dict(model, optimizers, info) _verify_state_dict(model_state_dict, optim_state_dict, info) return model_state_dict, optim_state_dict def _unflatten_model_state_dict( model: nn.Module, state_dict: dict[nn.Module, dict[str, ValueType]] | dict[str, ValueType], ) -> dict[str, ValueType]: if not state_dict: return {} if isinstance(next(iter(state_dict.keys())), nn.Module): warnings.warn( "Passing model_state_dict as a ``Dict[nn.Module, Dict[str, Any]]``" "is deprecated and will be removed in 2.5. If you need this " "feature, please preprocessing the model_state_dict to achieve the " "same functionality.", FutureWarning, ) cast_state_dict = cast(dict[nn.Module, dict[str, ValueType]], state_dict) new_state_dict: dict[str, ValueType] = {} for submodule, sub_state_dict in cast_state_dict.items(): for name, m in model.named_modules(): if m != submodule: continue fqns = _get_fqns(model, name) assert len(fqns) == 1, "FQNs for a submodule should only have 1 element" prefix = f"{next(iter(fqns))}." new_state_dict.update({prefix + subfqn: value for subfqn, value in sub_state_dict.items()}) return new_state_dict else: return cast(dict[str, ValueType], state_dict) def set_model_state_dict( model: nn.Module, model_state_dict: dict[str, ValueType], *, options: Optional[StateDictOptions] = None, ) -> _IncompatibleKeys: """Load the model state_dict. The counterpart of ``get_model_state_dict`` to set the state_dict to the model. See ``set_state_dict`` for the detail usage. Args: model (nn.Module): the nn.Module to the model. model_state_dict: (Dict[str, ValueType]): the model state_dict to load. If the key of the ``model_state_dict`` is nn.Module, the key is a submodule of ``model`` and the value should be the state_dict of the submodule. When loading the state_dict, the prefix of the submodule will be append to the state_dict. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be loaded. See `StateDictOptions` for the details. Returns: ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: * **missing_keys** is a list of str containing the missing keys * **unexpected_keys** is a list of str containing the unexpected keys :type model_state_dict: typing.Dict[str, ValueType] """ model_state_dict: dict[str, ValueType] = _unflatten_model_state_dict(model, model_state_dict) with _gc_context(): info = _verify_options(model, (), optim_only=False, options=options) _verify_state_dict(model_state_dict, {}, info) return _load_model_state_dict(model, model_state_dict, info) def set_optimizer_state_dict( model: nn.Module, optimizers: torch.optim.Optimizer | Iterable[torch.optim.Optimizer], optim_state_dict: OptimizerStateType, *, options: Optional[StateDictOptions] = None, ) -> None: """Load the optimizers state_dict. The counterpart of ``get_optimizer_state_dict`` to set the state_dict to the optimizers. See ``set_state_dict`` for the detail usage. WARN: ``set_optimizer_state_dict`` can only be called before ``backward()`` or after ``step()`` is called on the optimizers. Otherwise, the optimizer states won't be initialized correctly. Args: model (nn.Module): the nn.Module to the model. optimizers (Union[Optimizer, Iterable[Optimizer]]): The optimizers that are used to optimize ``model``. optim_state_dict: OptimizerStateType: the optimizer state_dict to load. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be loaded. See `StateDictOptions` for the details. Returns: None :type optim_state_dict: typing.OptimizerStateType """ with _gc_context(): optimizers = (optimizers,) if isinstance(optimizers, torch.optim.Optimizer) else tuple(optimizers) info = _verify_options(model, optimizers, optim_only=True, options=options) _verify_state_dict({}, optim_state_dict, info) _load_optim_state_dict(model, optimizers, optim_state_dict, info) def set_state_dict( model: nn.Module, optimizers: torch.optim.Optimizer | Iterable[torch.optim.Optimizer], *, model_state_dict: dict[str, ValueType], optim_state_dict: OptimizerStateType, options: Optional[StateDictOptions] = None, ) -> _IncompatibleKeys: """Load the model state_dict and optimizers state_dict. The counterpart of ``get_state_dict`` to set the state_dict to the model and optimizers. The given ``model_state_dict`` and ``optim_state_dict`` do not have to be returned by ``get_state_dict`` but must meet the following requirements: 1) all FQNs are canonical FQNs as defined in ``get_state_dict``, 2) if a tensor is sharded, it must be either a ShardedTensor or DTensor, 3) optimizer state_dict cannot contain the parameter IDs; the keys should be the canonical FQNs. WARN: ``set_state_dict`` can only be called before ``backward()`` or after ``step()`` is called on the optimizers. Otherwise, the optimizer states won't be initialized correctly. Args: model (nn.Module): the nn.Module to the model. optimizers (Union[Optimizer, Iterable[Optimizer]]): The optimizers that are used to optimize ``model``. model_state_dict: (Union[Dict[nn.Module, Dict[str, ValueType]], Dict[str, ValueType]]): the model state_dict to load. If the key of the ``model_state_dict`` is nn.Module, the key is a submodule of ``model`` and the value should be the state_dict of the submodule. When loading the state_dict, the prefix of the submodule will be append to the state_dict. optim_state_dict: OptimizerStateType: the optimizer state_dict to load. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be loaded. See `StateDictOptions` for the details. Returns: ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: * **missing_keys** is a list of str containing the missing keys of the model state_dict. * **unexpected_keys** is a list of str containing the unexpected keys of the model state_dict. :type model_state_dict: typing.Dict[str, ValueType] :type optim_state_dict: typing.OptimizerStateType """ model_state_dict: dict[str, ValueType] = _unflatten_model_state_dict(model, model_state_dict) with _gc_context(): optimizers = (optimizers,) if isinstance(optimizers, torch.optim.Optimizer) else tuple(optimizers) info = _verify_options(model, optimizers, optim_only=not model_state_dict, options=options) _verify_state_dict(model_state_dict, optim_state_dict, info) _load_optim_state_dict(model, optimizers, optim_state_dict, info) return _load_model_state_dict(model, model_state_dict, info) # TODO: correct the state_dict function signature. # TODO: this API is not yet fully tested. Make it private @no_type_check def _patch_model_state_dict( model: nn.Module, *, options: Optional[StateDictOptions] = None, ) -> None: """Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model``. Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model`` to be a partial function to call ``get_state_dict`` and ``set_state_dict``. Example: from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.checkpoint.state_dict import patch_model_state_dict model = fsdp(model) patch_model_state_dict(model) Args: model (nn.Module): the nn.Module to the model. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be loaded. See `StateDictOptions` for the details. Returns: None """ _state_dict_call = functools.partial( get_model_state_dict, model=model, options=options, ) def state_dict_call(): return _state_dict_call() model.state_dict = state_dict_call _load_state_dict_call = functools.partial( set_model_state_dict, model=model, options=options, ) def load_state_dict_call(state_dict: dict[str, Any]): _load_state_dict_call(model_state_dict=state_dict) model.load_state_dict = load_state_dict_call _patched_state_dict.add(state_dict_call) _patched_state_dict.add(load_state_dict_call) # TODO: correct the load_state_dict function signature. # TODO: this API is not yet fully tested. Make it private @no_type_check def _patch_optimizer_state_dict( model: nn.Module, *, optimizers: tuple[torch.optim.Optimizer, ...], options: Optional[StateDictOptions] = None, ) -> None: """Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers``. Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers`` to be a partial function to call ``get_state_dict`` and ``set_state_dict``. Note that if there are multiple optimizers, all of the optimizers will be patched. So users only need to call one of the state_dict() to get the full result. Example: from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.checkpoint.state_dict import patch_model_state_dict model = fsdp(model) patch_model_state_dict(model) Args: model (nn.Module): the nn.Module to the model. options (StateDictOptions): the options to control how model state_dict and optimizer state_dict should be loaded. See `StateDictOptions` for the details. Returns: None """ _state_dict_call = functools.partial( get_optimizer_state_dict, model=model, optimizers=optimizers, options=options, ) def state_dict_call(): return _state_dict_call() _load_state_dict_call = functools.partial( set_optimizer_state_dict, model=model, optimizers=optimizers, options=options, ) def load_state_dict_call(state_dict: dict[str, Any]): _load_state_dict_call(optim_state_dict=state_dict) _patched_state_dict.add(state_dict_call) _patched_state_dict.add(load_state_dict_call) optimizers = (optimizers,) if isinstance(optimizers, torch.optim.Optimizer) else tuple(optimizers) for optim in optimizers: optim.state_dict = state_dict_call optim.load_state_dict = load_state_dict_call ================================================ FILE: verl/third_party/vllm/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from importlib.metadata import PackageNotFoundError, version from packaging import version as vs from verl.utils.device import is_npu_available from verl.utils.import_utils import is_sglang_available def get_version(pkg): try: return version(pkg) except PackageNotFoundError: return None package_name = "vllm" package_version = get_version(package_name) vllm_version = None VLLM_SLEEP_LEVEL = 1 if package_version is None: if not is_sglang_available(): raise ValueError( f"vllm version {package_version} not supported and SGLang also not Found. Currently supported " f"vllm versions are 0.7.0+" ) elif is_npu_available: # sleep_mode=2 is not supported on vllm-ascend for now, will remove this restriction when this ability is ready. VLLM_SLEEP_LEVEL = 1 from vllm import LLM from vllm.distributed import parallel_state elif vs.parse(package_version) >= vs.parse("0.7.0"): vllm_version = package_version if vs.parse(package_version) >= vs.parse("0.8.5"): VLLM_SLEEP_LEVEL = 2 from vllm import LLM from vllm.distributed import parallel_state else: if vs.parse(package_version) in [vs.parse("0.5.4"), vs.parse("0.6.3")]: raise ValueError( f"vLLM version {package_version} support has been removed. vLLM 0.5.4 and 0.6.3 are no longer " f"supported. Please use vLLM 0.7.0 or later." ) if not is_sglang_available(): raise ValueError( f"vllm version {package_version} not supported and SGLang also not Found. Currently supported " f"vllm versions are 0.7.0+" ) __all__ = ["LLM", "parallel_state"] ================================================ FILE: verl/tools/__init__.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/tools/base_tool.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from typing import Any, Optional from uuid import uuid4 from verl.utils.rollout_trace import rollout_trace_op from .schemas import OpenAIFunctionToolSchema, ToolResponse class BaseTool: """Base class for tools. A tool should support the following methods: - `get_openai_tool_schema`: return the tool schema in OpenAI format. - `create`: create a tool instance for a trajectory. - `execute`: execute the tool. - `calc_reward`: calculate the reward respect to tool state. - `release`: release the tool instance. """ def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): self.config = config self.tool_schema = tool_schema or self.get_openai_tool_schema() assert self.tool_schema is not None, "Tool schema is not set!" self.name = self.tool_schema.function.name print(json.dumps(self.tool_schema.model_dump(exclude_unset=True, exclude_none=True), indent=2)) def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: return self.tool_schema async def create(self, instance_id: Optional[str] = None, **kwargs) -> tuple[str, ToolResponse]: """Create a tool instance. Args: instance_id: The instance id of the tool. Returns: The instance id of the tool. tool_creation_response: The response of the tool when creating the instance. """ if instance_id is None: return str(uuid4()), ToolResponse() else: return instance_id, ToolResponse() @rollout_trace_op async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: """Execute the tool. Args: instance_id: The instance id of the tool. parameters: The json string of the parameters of the tool. Returns: tool_response, tool_reward_score, tool_metrics tool_response: The ToolResponse object containing text, image, and/or video content. tool_reward_score: The step reward score of the tool. tool_metrics: The metrics of the tool. """ return ToolResponse(text="Updated the tool state."), 0.0, {} async def calc_reward(self, instance_id: str, **kwargs) -> float: """Calculate the reward of the tool. Args: instance_id: The instance id of the tool. Returns: The reward of the tool. """ return 0.0 async def release(self, instance_id: str, **kwargs) -> None: """Release the tool instance. Args: instance_id: The instance id of the tool. """ pass ================================================ FILE: verl/tools/geo3k_tool.py ================================================ # Copyright 2023-2025 SGLang Team # Copyright Amazon.com, Inc. or its affiliates. # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Any, Optional from uuid import uuid4 from verl.utils.reward_score import geo3k from verl.utils.rollout_trace import rollout_trace_op from .base_tool import BaseTool from .schemas import OpenAIFunctionToolSchema, ToolResponse logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class Geo3kTool(BaseTool): """A demo tool for calculating the reward of geo3k. - `get_openai_tool_schema`: return the tool schema in OpenAI format. - `create`: create a tool instance for a trajectory. - `execute`: execute the tool. - `calc_reward`: calculate the reward respect to tool state. - `release`: release the tool instance. """ def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): """ _tool_schema = OpenAIFunctionToolSchema.model_validate({ "type": "function", "function": { "name": "calc_geo3k_reward", "description": "A tool for calculating the reward of geo3k", "parameters": { "type": "object", "properties": { "answer": { "type": "string", "description": "The answer to the question, enclosed in \\boxed{}", }, }, "required": ["answer"], }, } }) """ super().__init__(config, tool_schema) self._instance_dict = {} def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: return self.tool_schema async def create( self, instance_id: Optional[str] = None, ground_truth: Optional[str] = None, **kwargs ) -> tuple[str, ToolResponse]: if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "ground_truth": ground_truth, "reward": 0.0, } return instance_id, ToolResponse() @rollout_trace_op async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: answer = parameters.get("answer", "") if not isinstance(answer, str): answer = str(answer) self._instance_dict[instance_id]["response"] = answer reward = await self.calc_reward(instance_id) # penalty for non improved answer submission tool_reward = 0.0 if reward > self._instance_dict[instance_id]["reward"] else -0.05 # update the reward self._instance_dict[instance_id]["reward"] = reward return ToolResponse(text=f"Current parsed {answer=} {reward=}"), tool_reward, {} async def calc_reward(self, instance_id: str, **kwargs) -> float: return geo3k.compute_score( self._instance_dict[instance_id]["response"], self._instance_dict[instance_id]["ground_truth"], use_boxed=False, format_score=0.0, ) async def release(self, instance_id: str, **kwargs) -> None: del self._instance_dict[instance_id] ================================================ FILE: verl/tools/gsm8k_tool.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from typing import Any, Optional from uuid import uuid4 from verl.utils.reward_score import gsm8k from verl.utils.rollout_trace import rollout_trace_op from .base_tool import BaseTool from .schemas import OpenAIFunctionToolSchema, ToolResponse logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class Gsm8kTool(BaseTool): """A demo tool for calculating the reward of gsm8k. - `get_openai_tool_schema`: return the tool schema in OpenAI format. - `create`: create a tool instance for a trajectory. - `execute`: execute the tool. - `calc_reward`: calculate the reward respect to tool state. - `release`: release the tool instance. """ def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): """ _tool_schema = OpenAIFunctionToolSchema.model_validate({ "type": "function", "function": { "name": "calc_gsm8k_reward", "description": "A tool for calculating the reward of gsm8k", "parameters": { "type": "object", "properties": { "answer": { "type": "string", "description": "The answer to the question", }, }, "required": ["answer"], }, } }) """ super().__init__(config, tool_schema) self._instance_dict = {} def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: return self.tool_schema async def create( self, instance_id: Optional[str] = None, ground_truth: Optional[str] = None, **kwargs ) -> tuple[str, ToolResponse]: if instance_id is None: instance_id = str(uuid4()) if ground_truth is None: ground_truth = kwargs.get("create_kwargs", {}).get("ground_truth", None) self._instance_dict[instance_id] = { "response": "", "ground_truth": ground_truth, "reward": 0.0, } return instance_id, ToolResponse() @rollout_trace_op async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: answer = parameters.get("answer", "") if not isinstance(answer, str): answer = str(answer) if answer.startswith("#### "): self._instance_dict[instance_id]["response"] = answer else: self._instance_dict[instance_id]["response"] = "#### " + answer reward = await self.calc_reward(instance_id) # penalty for non improved answer submission tool_reward = 0.0 if reward > self._instance_dict[instance_id]["reward"] else -0.05 # update the reward self._instance_dict[instance_id]["reward"] = reward return ToolResponse(text=f"Current parsed {answer=} {reward=}"), tool_reward, {} async def calc_reward(self, instance_id: str, **kwargs) -> float: return gsm8k.compute_score( self._instance_dict[instance_id]["response"], self._instance_dict[instance_id]["ground_truth"], method="flexible", format_score=0.0, score=1.0, ) async def release(self, instance_id: str, **kwargs) -> None: del self._instance_dict[instance_id] ================================================ FILE: verl/tools/image_zoom_in_tool.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import threading from contextlib import ExitStack from enum import Enum from math import ceil, floor from typing import Any, Callable, Optional, TypeVar from uuid import uuid4 import ray import ray.actor from qwen_vl_utils import fetch_image from .base_tool import BaseTool from .schemas import OpenAIFunctionToolSchema, ToolResponse logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) T = TypeVar("T") # Adapted from verl/tools/sandbox_fusion_tools.py class PoolMode(Enum): """Execution pool mode enumeration.""" ThreadMode = 1 ProcessMode = 2 @ray.remote(concurrency_groups={"acquire": 1, "release": 10}) class TokenBucketWorker: """Ray actor for rate limiting using token bucket algorithm.""" def __init__(self, rate_limit: int): self.rate_limit = rate_limit self.current_count = 0 # For observability self._semaphore = threading.Semaphore(rate_limit) @ray.method(concurrency_group="acquire") def acquire(self): """Acquire a token from the bucket.""" self._semaphore.acquire() self.current_count += 1 @ray.method(concurrency_group="release") def release(self): """Release a token back to the bucket.""" self._semaphore.release() self.current_count -= 1 def get_current_count(self): """Get current number of acquired tokens.""" return self.current_count class VisualExecutionWorker: """Worker for executing visual processing operations with optional rate limiting.""" def __init__(self, enable_global_rate_limit=True, rate_limit=10): self.rate_limit_worker = self._init_rate_limit(rate_limit) if enable_global_rate_limit else None def _init_rate_limit(self, rate_limit): """Initialize singleton rate limiter.""" return TokenBucketWorker.options(name="rate-limiter", get_if_exists=True).remote(rate_limit) def ping(self): """Health check method.""" return True def execute(self, fn: Callable[..., T], *fn_args, **fn_kwargs) -> T: """Execute function with optional rate limiting.""" if self.rate_limit_worker: with ExitStack() as stack: stack.callback(self.rate_limit_worker.release.remote) ray.get(self.rate_limit_worker.acquire.remote()) try: return fn(*fn_args, **fn_kwargs) except Exception as e: # TODO we should make this available to the tool caller logger.warning(f"Error when executing visual processing: {e}") else: return fn(*fn_args, **fn_kwargs) def init_visual_execution_pool( num_workers: int, enable_global_rate_limit=True, rate_limit=10, mode: PoolMode = PoolMode.ThreadMode ): """Initialize visual execution pool.""" if mode == PoolMode.ThreadMode: return ( ray.remote(VisualExecutionWorker) .options(max_concurrency=num_workers) .remote(enable_global_rate_limit=enable_global_rate_limit, rate_limit=rate_limit) ) else: raise NotImplementedError("Process mode is not implemented yet") class ImageZoomInTool(BaseTool): """A tool for zooming in on an image by cropping it based on a bounding box. This tool provides a zoom-in functionality by cropping a region from an image, with rate limiting and concurrent execution support through Ray. Methods: get_openai_tool_schema: Return the tool schema in OpenAI format create: Create a tool instance for a trajectory execute: Execute the zoom-in operation calc_reward: Calculate the reward with respect to tool state release: Release the tool instance """ MIN_DIMENSION = 28 def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): """ _tool_schema = OpenAIFunctionToolSchema.model_validate({ "type": "function", "function": { "name": "image_zoom_in_tool", "description": ( "Zoom in on a specific region of an image by cropping it based on a bounding box (bbox) and an " "optional object label." ), "parameters": { "type": "object", "properties": { "bbox_2d": { "type": "array", "items":{"type":"number"}, "minItems":4, "maxItems":4, "description": ( "The bounding box of the region to zoom in, as [x1, y1, x2, y2], where (x1, y1) is " "the top-left corner and (x2, y2) is the bottom-right corner." ), }, "label": { "type": "string", "description": "The name or label of the object in the specified bounding box (optional).", }, }, "required": ["bbox_2d"], }, } }) """ super().__init__(config, tool_schema) self._instance_dict = {} # Worker and rate limiting configuration self.num_workers = config.get("num_workers", 20) self.rate_limit = config.get("rate_limit", 50) self.timeout = config.get("timeout", 30) self.enable_global_rate_limit = config.get("enable_global_rate_limit", True) self.execution_pool = init_visual_execution_pool( num_workers=self.num_workers, enable_global_rate_limit=self.enable_global_rate_limit, rate_limit=self.rate_limit, mode=PoolMode.ThreadMode, ) logger.info(f"Initialized ImageZoomInTool with config: {config}") def _validate_bbox(self, left: float, top: float, right: float, bottom: float) -> bool: """Validate the bounding box dimensions and aspect ratio.""" try: if not (left < right and top < bottom): logger.warning(f"Invalid bbox shape: left={left}, top={top}, right={right}, bottom={bottom}") return False height = bottom - top width = right - left # Prevent division by zero for zero-sized boxes if min(height, width) == 0: logger.warning(f"Bbox has zero width or height: left={left}, top={top}, right={right}, bottom={bottom}") return False if max(height, width) / min(height, width) > 100: logger.warning(f"Bbox aspect ratio > 100: left={left}, top={top}, right={right}, bottom={bottom}") return False return True except Exception as e: logger.warning(f"Bbox validation error: {e}") return False def _maybe_resize_bbox(self, bbox_2d: list[float], image_width: int, image_height: int) -> Optional[list[float]]: """ Clamp, validate, and potentially resize a bounding box. This function ensures the final bounding box is within image bounds and meets the minimum dimension requirements. If the initial box is too small, it attempts to expand it from its center. It performs a final check to guarantee the output dimensions are valid. Returns: A valid bounding box as a list of coordinates, or None if validation fails. """ left, top, right, bottom = bbox_2d # 1. Clamp the initial bounding box to the image dimensions. left = max(0.0, float(left)) top = max(0.0, float(top)) right = min(float(image_width), float(right)) bottom = min(float(image_height), float(bottom)) # 2. If clamped bbox is invalid, return immediately. if not self._validate_bbox(left, top, right, bottom): return None current_bbox = [left, top, right, bottom] height = bottom - top width = right - left # 3. If the box is too small, attempt to resize it. if height < self.MIN_DIMENSION or width < self.MIN_DIMENSION: logger.info(f"Bbox {width}x{height} is smaller than {self.MIN_DIMENSION}, attempting resize.") center_x = (left + right) / 2.0 center_y = (top + bottom) / 2.0 min_dim = min(height, width) if min_dim == 0: # Safeguard for zero-area boxes return None # 1. Calculate the target dimensions to make the smallest side MIN_DIMENSION. ratio = self.MIN_DIMENSION / min_dim target_width = width * ratio target_height = height * ratio # 2. If the target size is larger than the image, scale it down to fit. # This preserves the aspect ratio while respecting image boundaries. if target_width > image_width: scale_down = image_width / target_width target_width = image_width target_height *= scale_down if target_height > image_height: scale_down = image_height / target_height target_height = image_height target_width *= scale_down # 3. Determine the coordinates for the box centered on the original center. new_half_width = target_width / 2.0 new_half_height = target_height / 2.0 new_left = center_x - new_half_width new_top = center_y - new_half_height # 4. Shift the box if it extends beyond the image boundaries to keep its size. if new_left < 0: new_left = 0 if new_top < 0: new_top = 0 if new_left + target_width > image_width: new_left = image_width - target_width if new_top + target_height > image_height: new_top = image_height - target_height new_right = new_left + target_width new_bottom = new_top + target_height # Use floor and ceil for final integer coordinates. current_bbox = [floor(new_left), floor(new_top), ceil(new_right), ceil(new_bottom)] # 4. Final validation on the resulting bounding box (either original or resized). final_left, final_top, final_right, final_bottom = current_bbox if not self._validate_bbox(final_left, final_top, final_right, final_bottom): logger.warning(f"Final bbox is invalid after processing: {current_bbox}") return None final_height = floor(final_bottom) - floor(final_top) final_width = floor(final_right) - floor(final_left) if final_height < self.MIN_DIMENSION or final_width < self.MIN_DIMENSION: logger.warning( f"Final bbox size ({final_width}x{final_height}) are still smaller than minimum ({self.MIN_DIMENSION})." f"Original bbox: {bbox_2d}, original image size: {image_width}x{image_height}" ) return None return current_bbox def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: return self.tool_schema async def create(self, instance_id: Optional[str] = None, **kwargs) -> tuple[str, ToolResponse]: """ Creates a new instance for image zoom-in tool. This method initializes a new session for an image, which can then be used for operations like zooming. It fetches the image from various sources and stores it internally. Args: instance_id: An optional unique identifier for the instance. If not provided, a new UUID will be generated. **kwargs: Should contain 'image' key with image data, or 'create_kwargs' containing {'image': image_data}. Image can be one of the following: - A PIL.Image.Image object. - A string containing an HTTP or HTTPS URL. - A string containing a local file path. - A string containing a file URI (e.g., "file:///path/to/image.jpg"). - A string containing a base64-encoded image in the format of "data:image/jpeg;base64,..." Returns: Tuple of (instance_id, ToolResponse) """ if instance_id is None: instance_id = str(uuid4()) # Handle create_kwargs parameter if passed create_kwargs = kwargs.get("create_kwargs", {}) if create_kwargs: kwargs.update(create_kwargs) # Get image from kwargs image = kwargs.get("image") if image is None: raise ValueError("Missing required 'image' parameter in kwargs") img = fetch_image({"image": image}) self._instance_dict[instance_id] = { "image": img, "response": "", "reward": 0.0, } return instance_id, ToolResponse() async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: bbox_2d = parameters.get("bbox_2d") label = parameters.get("label", "") if not bbox_2d or len(bbox_2d) != 4: return ( ToolResponse(text="Error: bbox_2d parameter is missing or not a list of 4 numbers."), -0.05, {"success": False}, ) instance_data = self._instance_dict[instance_id] image = instance_data["image"] image_width, image_height = image.size try: resized_bbox = self._maybe_resize_bbox(bbox_2d, image_width=image_width, image_height=image_height) if resized_bbox is None: error_msg = ( f"Error: The specified bounding box {bbox_2d} is invalid or results in a crop smaller than " f"the minimum size of {self.MIN_DIMENSION}x{self.MIN_DIMENSION}." ) logger.warning(f"Tool execution failed: {error_msg}") return ToolResponse(text=error_msg), -0.05, {"success": False} cropped_image = image.crop(resized_bbox) logger.info(f"Cropped image size: {cropped_image.size}") except Exception as e: logger.error(f"Error processing image zoom-in: {e}") return ToolResponse(text=f"Error processing image zoom-in: {e}"), -0.05, {"success": False} response_text = f"Zoomed in on the image to the region {bbox_2d}." if label: response_text = f"Zoomed in on the image to the region {bbox_2d} with label {label}." return ( ToolResponse( image=[cropped_image], text=response_text, ), 0.0, {"success": True}, ) async def release(self, instance_id: str, **kwargs) -> None: if instance_id in self._instance_dict: del self._instance_dict[instance_id] ================================================ FILE: verl/tools/mcp_base_tool.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import os from typing import Any, Optional from uuid import uuid4 from fastmcp.exceptions import ClientError from verl.tools.utils.mcp_clients.McpClientManager import ClientManager from verl.utils.rollout_trace import rollout_trace_op from .base_tool import BaseTool from .schemas import OpenAIFunctionToolSchema, ToolResponse logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class MCPBaseTool(BaseTool): def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): super().__init__(config, tool_schema) self._instance_dict = {} self.timeout = config.get("timeout", 30) # TODO(hechanghao): create a global client manager to manage the rate limit, client and pool logger.info(f"Initialized MCPBaseTool with config: {config}") def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: """Return the OpenAI tool schema.""" return self.tool_schema async def create(self, instance_id: Optional[str] = None, **kwargs) -> tuple[str, ToolResponse]: """Create a tool instance. Args: instance_id: The instance id of the tool. Returns: The instance id of the tool. tool_crtool_creation_response: The response of the tool when creating the instance. """ if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "reward": [], } return instance_id, ToolResponse() async def _call_tool(self, instance_id, parameters) -> tuple[str, dict]: err_msg = "" metadata = {} try: call_tool_result = await ClientManager.call_tool(self.name, parameters, self.timeout) logger.debug(f"Tool result for instance {instance_id} with tool {self.name}: {call_tool_result.content}") result, metadata = self._parse_tool_result(call_tool_result.content) except ClientError as e: err_msg = f"\n Tool call failed: {e}" except ConnectionError as e: err_msg = f"\n Connection failed: {e}" except Exception as e: err_msg = f"\n An unexpected error occurred: {e}" finally: if err_msg: result = err_msg metadata["api_request_error"] = err_msg else: metadata["api_request_error"] = None return result, metadata @rollout_trace_op async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: if self.name == "" or self.name is None or parameters is None: error_msg = "Error: 'parameters' is missing or empty." logger.error(f"[MCPTool] {error_msg} Received tool name: {self.name}, parameters: {parameters}") return ToolResponse(text=json.dumps({"result": error_msg})), 0.0, {} try: result_text, metadata = await self._call_tool(instance_id, parameters) # Store results in instance dictionary self._instance_dict[instance_id]["reward"].append(result_text.strip()) # Convert metadata to metrics metrics = { "query_count": metadata.get("query_count", 0), "status": metadata.get("status", "unknown"), "total_results": metadata.get("total_results", 0), "api_request_error": metadata.get("api_request_error"), } return ToolResponse(text=result_text), 0.0, metrics except Exception as e: error_result = json.dumps({"result": f"Tool execution failed: {e}"}) logger.error(f"[MCPBaseTool] Execution failed: {e}") return ToolResponse(text=error_result), 0.0, {"error": str(e)} async def calc_reward(self, instance_id: str, **kwargs) -> str: return self._instance_dict[instance_id]["reward"] async def release(self, instance_id: str, **kwargs) -> None: if instance_id in self._instance_dict: del self._instance_dict[instance_id] def _parse_tool_result(self, content: list) -> tuple[str, dict]: tools_content = [part.text for part in filter(lambda x: x.type == "text", content)] return " ".join(tools_content), {} ================================================ FILE: verl/tools/mcp_search_tool.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import os import re from verl.tools.mcp_base_tool import MCPBaseTool from .schemas import OpenAIFunctionToolSchema logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class MCPSearchTool(MCPBaseTool): def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): super().__init__(config, tool_schema) def _parse_tool_result(self, content: list) -> tuple[str, dict]: res = "" res_cnt = 0 query_list = [] metadata = { "api_request_error": "", "status": "unknown", "total_results": 0, } try: for part in content: if part.type != "text": continue text = part.text.replace("'", '"') query_match = re.search(r'query"\s*:\s*"([^"]+)"', text) query = query_match.group(1) if query_match else "" query_list.append(query) title_matches = re.findall(r'"title"\s*:', text) title_count = len(title_matches) results_match = re.search(r'"results"\s*:\s*(\[.*?\])', text, re.DOTALL) results_content = results_match.group(1) if results_match else "" res += results_content res_cnt += title_count except json.JSONDecodeError: err_msg = "json parse error." logger.error(err_msg) metadata["api_request_error"] = err_msg metadata["status"] = "error" # update metadata metadata["status"] = "success" metadata["queries"] = query_list metadata["query_count"] = len(query_list) metadata["total_results"] = res_cnt return res, metadata ================================================ FILE: verl/tools/sandbox_fusion_tools.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import threading from contextlib import ExitStack from enum import Enum from typing import Any, Callable, Optional, TypeVar from uuid import uuid4 import ray from verl.tools.base_tool import BaseTool from verl.utils.reward_score.sandbox_fusion.utils import _process_single_case from verl.utils.rollout_trace import rollout_trace_op from .schemas import OpenAIFunctionToolSchema, ToolResponse logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) T = TypeVar("T") class PoolMode(Enum): ThreadMode = 1 ProcessMode = 2 @ray.remote(concurrency_groups={"acquire": 1, "release": 10}) class TokenBucketWorker: def __init__(self, rate_limit: int): self.rate_limit = rate_limit # this only used for observalability self.current_count = 0 self._semaphore = threading.Semaphore(rate_limit) @ray.method(concurrency_group="acquire") def acquire(self): self._semaphore.acquire() self.current_count += 1 @ray.method(concurrency_group="release") def release(self): self._semaphore.release() self.current_count -= 1 def get_current_count(self): return self.current_count class ExecutionWorker: def __init__(self, enable_global_rate_limit=True, rate_limit=10): self.rate_limit_worker = self._init_rate_limit(rate_limit) if enable_global_rate_limit else None def _init_rate_limit(self, rate_limit): # TODO validation for rate_limit # A Singleton Rate Limitor return TokenBucketWorker.options(name="rate-limiter", get_if_exists=True).remote(rate_limit) def ping(self): return True def execute(self, fn: Callable[..., T], *fn_args, **fn_kwargs) -> T: with ExitStack() as stack: stack.callback(self.rate_limit_worker.release.remote) ray.get(self.rate_limit_worker.acquire.remote()) try: return fn(*fn_args, **fn_kwargs) except Exception as e: # TODO we should make this available to the tool caller logger.warning(f"Error when executing code: {e}") def init_execution_pool( num_workers: int, enable_global_rate_limit=True, rate_limit=10, mode: PoolMode = PoolMode.ThreadMode ): if mode == PoolMode.ThreadMode: return ( ray.remote(ExecutionWorker) .options(max_concurrency=num_workers) .remote(enable_global_rate_limit=enable_global_rate_limit, rate_limit=rate_limit) ) else: raise NotImplementedError("Process mode is not implemented yet") # return ray.util.multiprocessing.Pool(processes=num_workers) class SandboxFusionTool(BaseTool): """A tool for executing the code using sanbox fusion image. - `get_openai_tool_schema`: return the tool schema in OpenAI format. - `create`: create a tool instance for a trajectory. - `execute`: execute the tool. - `calc_reward`: calculate the reward respect to tool state. - `release`: release the tool instance. """ def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): """ _tool_schema = OpenAIFunctionToolSchema.model_validate({ "type": "function", "function": { "name": "code_interpreter", "description": "A tool for execute code", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "code needs to be execute and grad", }, }, "required": ["code"], }, } }) """ super().__init__(config, tool_schema) self._instance_dict = {} # TODO: better documentation for the config self.num_workers = config.get("num_workers", 10) self.rate_limit = config.get("rate_limit", 10) self.default_timeout = config.get("default_timeout", 30) self.default_language = config.get("default_language", "python") self.enable_global_rate_limit = config.get("enable_global_rate_limit", True) self.execution_pool = init_execution_pool( num_workers=self.num_workers, enable_global_rate_limit=self.enable_global_rate_limit, rate_limit=self.rate_limit, mode=PoolMode.ThreadMode, ) self.sandbox_fusion_url = config.get("sandbox_fusion_url", "") self.memory_limit_mb = config.get("memory_limit_mb", 1024) if self.sandbox_fusion_url == "": raise ValueError("sandbox_fusion_url is not set") log_msg = f"Init SandboxFusionTool with config: {config}" logger.info(log_msg) def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: return self.tool_schema async def create( self, instance_id: Optional[str] = None, ground_truth: Optional[str] = None, **kwargs ) -> tuple[str, ToolResponse]: if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "ground_truth": ground_truth, "reward": [], } return instance_id, ToolResponse() @rollout_trace_op async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: code = parameters.get("code", "") timeout = parameters.get("timeout", self.default_timeout) language = parameters.get("language", self.default_language) if not isinstance(code, str): code = str(code) result = await self.execution_pool.execute.remote(self.execute_code, instance_id, code, timeout, language) # sandbox has no score or metrics, use Nones if isinstance(result, ToolResponse): return result, None, None return ToolResponse(text=None if result is None else str(result)), None, None def execute_code(self, instance_id, code, timeout=30, language="python"): result_status, metadata = _process_single_case( 0, None, None, self.sandbox_fusion_url, code, timeout, self.memory_limit_mb, language ) # we should always expect this since we don't have correct answer if metadata["run_status"] == "Finished": actual_output = metadata["stdout"] + metadata["stderr"] logger.debug(f"actual_output from sandbox fusion: {actual_output},{instance_id}") return ToolResponse(text=actual_output) else: return ToolResponse(text="no stdout here") async def calc_reward(self, instance_id: str, **kwargs) -> str: return self._instance_dict[instance_id]["reward"] async def release(self, instance_id: str, **kwargs) -> None: del self._instance_dict[instance_id] ================================================ FILE: verl/tools/schemas.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from typing import Any, Literal from pydantic import BaseModel, Field, model_validator class OpenAIFunctionPropertySchema(BaseModel): """The schema of a parameter in OpenAI format.""" type: str description: str | None = None enum: list[str] | None = None class OpenAIFunctionParametersSchema(BaseModel): """The schema of parameters in OpenAI format.""" type: str properties: dict[str, OpenAIFunctionPropertySchema] required: list[str] class OpenAIFunctionSchema(BaseModel): """The schema of a function in OpenAI format.""" name: str description: str parameters: OpenAIFunctionParametersSchema = Field( default_factory=lambda: OpenAIFunctionParametersSchema(type="object", properties={}, required=[]) ) strict: bool = False class OpenAIFunctionToolSchema(BaseModel): """The schema of a tool in OpenAI format.""" type: str function: OpenAIFunctionSchema class OpenAIFunctionParsedSchema(BaseModel): """The parsed schema of a tool in OpenAI format.""" name: str arguments: str # JSON string class OpenAIFunctionCallSchema(BaseModel): """The parsed schema of a tool in OpenAI format.""" name: str arguments: dict[str, Any] @staticmethod def from_openai_function_parsed_schema( parsed_schema: OpenAIFunctionParsedSchema, ) -> tuple["OpenAIFunctionCallSchema", bool]: has_decode_error = False try: arguments = json.loads(parsed_schema.arguments) except json.JSONDecodeError: arguments = {} has_decode_error = True # If the arguments is not a dict, it means the arguments is not a valid JSON string if not isinstance(arguments, dict): arguments = {} has_decode_error = True return OpenAIFunctionCallSchema(name=parsed_schema.name, arguments=arguments), has_decode_error class OpenAIFunctionToolCall(BaseModel): """The tool call in OpenAI format.""" id: str type: Literal["function"] = "function" function: OpenAIFunctionCallSchema class ToolResponse(BaseModel): """The response from a tool execution.""" text: str | None = None image: list[Any] | None = None video: list[Any] | None = None @model_validator(mode="before") @classmethod def initialize_request(cls, values): if "image" in values and not isinstance(values["image"], list): raise ValueError( f"Image must be a list, but got {type(values['image'])}. Please check the tool.execute(). " f"For single images, wrap in a list: [image]. " f"Example: {{'image': [img1]}} or {{'image': [img1, img2, ...]}}." ) if "video" in values and not isinstance(values["video"], list): raise ValueError( f"Video must be a list, but got {type(values['video'])}. Please check the tool.execute(). " f"For single videos, wrap in a list: [video]. " f"Example: {{'video': [video1]}} or {{'video': [video1, video2, ...]}}." ) return values def is_empty(self) -> bool: return not self.text and not self.image and not self.video def is_text_only(self) -> bool: return self.text and not self.image and not self.video ================================================ FILE: verl/tools/search_tool.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import os import threading from contextlib import ExitStack from enum import Enum from typing import Any, Callable, Optional, TypeVar from uuid import uuid4 import ray import ray.actor from verl.tools.utils.search_r1_like_utils import perform_single_search_batch from verl.utils.rollout_trace import rollout_trace_op from .base_tool import BaseTool from .schemas import OpenAIFunctionToolSchema, ToolResponse logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) T = TypeVar("T") # Adapted from verl/tools/sandbox_fusion_tools.py class PoolMode(Enum): """Execution pool mode enumeration.""" ThreadMode = 1 ProcessMode = 2 @ray.remote(concurrency_groups={"acquire": 1, "release": 10}) class TokenBucketWorker: """Ray actor for rate limiting using token bucket algorithm.""" def __init__(self, rate_limit: int): self.rate_limit = rate_limit self.current_count = 0 # For observability self._semaphore = threading.Semaphore(rate_limit) @ray.method(concurrency_group="acquire") def acquire(self): """Acquire a token from the bucket.""" self._semaphore.acquire() self.current_count += 1 @ray.method(concurrency_group="release") def release(self): """Release a token back to the bucket.""" self._semaphore.release() self.current_count -= 1 def get_current_count(self): """Get current number of acquired tokens.""" return self.current_count class SearchExecutionWorker: """Worker for executing search operations with optional rate limiting.""" def __init__(self, enable_global_rate_limit=True, rate_limit=10): self.rate_limit_worker = self._init_rate_limit(rate_limit) if enable_global_rate_limit else None def _init_rate_limit(self, rate_limit): """Initialize singleton rate limiter.""" return TokenBucketWorker.options(name="rate-limiter", get_if_exists=True).remote(rate_limit) def ping(self): """Health check method.""" return True def execute(self, fn: Callable[..., T], *fn_args, **fn_kwargs) -> T: """Execute function with optional rate limiting.""" if self.rate_limit_worker: with ExitStack() as stack: stack.callback(self.rate_limit_worker.release.remote) ray.get(self.rate_limit_worker.acquire.remote()) try: return fn(*fn_args, **fn_kwargs) except Exception as e: # TODO we should make this available to the tool caller logger.warning(f"Error when executing search: {e}") else: return fn(*fn_args, **fn_kwargs) def init_search_execution_pool( num_workers: int, enable_global_rate_limit=True, rate_limit=10, mode: PoolMode = PoolMode.ThreadMode ): """Initialize search execution pool.""" if mode == PoolMode.ThreadMode: return ( ray.remote(SearchExecutionWorker) .options(max_concurrency=num_workers) .remote(enable_global_rate_limit=enable_global_rate_limit, rate_limit=rate_limit) ) else: raise NotImplementedError("Process mode is not implemented yet") class SearchTool(BaseTool): """Search tool for retrieving information using external retrieval services. This tool provides search functionality with rate limiting and concurrent execution support through Ray. It integrates with external retrieval services to perform semantic search operations. Methods: get_openai_tool_schema: Return the tool schema in OpenAI format create: Create a tool instance for a trajectory execute: Execute the search tool calc_reward: Calculate the reward with respect to tool state release: Release the tool instance """ def __init__(self, config: dict, tool_schema: OpenAIFunctionToolSchema): """Initialize SearchTool with configuration and schema. Args: config: Configuration dictionary containing tool settings tool_schema: OpenAI function tool schema definition Example tool_schema: { "type": "function", "function": { "name": "search", "description": "Searches for relevant information based on queries.", "parameters": { "type": "object", "properties": { "query_list": { "type": "array", "items": {"type": "string"}, "description": "List of search queries" } }, "required": ["query_list"] } } } """ super().__init__(config, tool_schema) self._instance_dict = {} # Worker and rate limiting configuration self.num_workers = config.get("num_workers", 120) self.rate_limit = config.get("rate_limit", 120) self.timeout = config.get("timeout", 30) self.enable_global_rate_limit = config.get("enable_global_rate_limit", True) self.execution_pool = init_search_execution_pool( num_workers=self.num_workers, enable_global_rate_limit=self.enable_global_rate_limit, rate_limit=self.rate_limit, mode=PoolMode.ThreadMode, ) # Retrieval service configuration self.retrieval_service_url = config.get("retrieval_service_url") assert self.retrieval_service_url, "Configuration must include 'retrieval_service_url'" self.topk = config.get("topk", 3) if self.retrieval_service_url == "": raise ValueError("retrieval_service_url is not set") logger.info(f"Initialized SearchTool with config: {config}") def get_openai_tool_schema(self) -> OpenAIFunctionToolSchema: """Return the OpenAI tool schema.""" return self.tool_schema async def create(self, instance_id: Optional[str] = None, **kwargs) -> tuple[str, ToolResponse]: """Create a tool instance. Args: instance_id: The instance id of the tool. Returns: The instance id of the tool. tool_creation_response: The response of the tool when creating the instance. """ if instance_id is None: instance_id = str(uuid4()) self._instance_dict[instance_id] = { "response": "", "reward": [], } return instance_id, ToolResponse() def execute_search(self, instance_id: str, query_list: list, retrieval_service_url: str, topk: int, timeout: int): """Execute search operation using retrieval service. Args: instance_id: Tool instance ID query_list: List of search queries retrieval_service_url: URL of the retrieval service topk: Number of top results to return timeout: Request timeout in seconds Returns: Tuple of (result_text, metadata) """ result_text, metadata = perform_single_search_batch( retrieval_service_url=retrieval_service_url, query_list=query_list, topk=topk, concurrent_semaphore=None, # Ray handles concurrency control timeout=timeout, ) logger.debug(f"Search result for instance {instance_id}: {result_text}") return result_text, metadata @rollout_trace_op async def execute(self, instance_id: str, parameters: dict[str, Any], **kwargs) -> tuple[ToolResponse, float, dict]: """Execute the search tool. Args: instance_id: The instance ID of the tool parameters: Tool parameters containing query_list and optional timeout Returns: tool_response, tool_reward_score, tool_metrics tool_response: The response str of the tool. tool_reward_score: The step reward score of the tool. tool_metrics: The metrics of the tool. """ timeout = self.timeout query_list_from_params = parameters.get("query_list") if not query_list_from_params or not isinstance(query_list_from_params, list): error_msg = "Error: 'query_list' is missing, empty, or not a list in parameters." logger.error(f"[SearchTool] {error_msg} Received parameters: {parameters}") return ToolResponse(text=json.dumps({"result": error_msg})), 0.0, {} # Execute search using Ray execution pool try: result_text, metadata = await self.execution_pool.execute.remote( self.execute_search, instance_id, query_list_from_params, self.retrieval_service_url, self.topk, timeout ) # Store results in instance dictionary self._instance_dict[instance_id]["reward"].append(result_text.strip()) # Convert metadata to metrics metrics = { "query_count": metadata.get("query_count", 0), "status": metadata.get("status", "unknown"), "total_results": metadata.get("total_results", 0), "api_request_error": metadata.get("api_request_error"), } return ToolResponse(text=result_text), 0.0, metrics except Exception as e: error_result = json.dumps({"result": f"Search execution failed: {e}"}) logger.error(f"[SearchTool] Execution failed: {e}") return ToolResponse(text=error_result), 0.0, {"error": str(e)} async def calc_reward(self, instance_id: str, **kwargs) -> str: return self._instance_dict[instance_id]["reward"] async def release(self, instance_id: str, **kwargs) -> None: if instance_id in self._instance_dict: del self._instance_dict[instance_id] ================================================ FILE: verl/tools/utils/__init__.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/tools/utils/mcp_clients/McpClientManager.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import json import logging from typing import Any from fastmcp import Client from fastmcp.client.transports import SSETransport from verl.tools.utils.mcp_clients.utils import TokenBucket, mcp2openai logger = logging.getLogger(__name__) class MCPClientManager: rootServerName = "mcpServers" initialized = False clients = [] tool_client_mapping = {} rate_limiter = None async def initialize(self, config_path, rate_limit: float = 10.0): if self.initialized: return """Initialize the MCP Client Manager and start all clients""" result = self._load_config(config_path) servers = result[self.rootServerName] exclude_sse_servers = {self.rootServerName: {}} for server_name in servers.keys(): server = servers[server_name] if "auth_token" in server: transport = SSETransport(url=server["url"], headers={"Authorization": f"Bearer {server['auth_token']}"}) client = Client(transport) self.clients.append(client) else: exclude_sse_servers[self.rootServerName][server_name] = server if exclude_sse_servers[self.rootServerName]: self.clients.append(Client(exclude_sse_servers)) # Initialize rate limiter self.rate_limiter = TokenBucket(rate_limit) self.initialized = True async def call_tool(self, tool_name, parameters, timeout): # Apply rate limiting while not self.rate_limiter.acquire(): await asyncio.sleep(0.1) client = self.get_client_with_tool_name(tool_name) async with client: return await client.call_tool_mcp(tool_name, parameters) async def fetch_tool_schemas(self, tool_selected_list: list[str]) -> list[dict]: tool_schemas = [] for client in self.clients: async with client: tools = await client.list_tools_mcp() for tool in tools.tools: if not tool_selected_list: self.tool_client_mapping[tool.name] = client tool_schemas.append(mcp2openai(tool)) elif tool.name in tool_selected_list: self.tool_client_mapping[tool.name] = client tool_schemas.append(mcp2openai(tool)) return tool_schemas def get_client_with_tool_name(self, tool_name: str): return self.tool_client_mapping[tool_name] def _load_config(self, file: str) -> dict[str, Any]: try: with open(file) as f: return json.load(f) except FileNotFoundError: logger.warning(f'the "{file}" file was not found') except Exception: logger.error(f'there was an error reading the "{file}" file') return {} ClientManager = MCPClientManager() ================================================ FILE: verl/tools/utils/mcp_clients/utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import threading import time from mcp import Tool logger = logging.getLogger(__file__) class TokenBucket: def __init__(self, rate_limit: float): self.rate_limit = rate_limit # tokens per second self.tokens = rate_limit self.last_update = time.time() self.lock = threading.Lock() def acquire(self) -> bool: with self.lock: now = time.time() # Add new tokens based on time elapsed new_tokens = (now - self.last_update) * self.rate_limit self.tokens = min(self.rate_limit, self.tokens + new_tokens) self.last_update = now if self.tokens >= 1: self.tokens -= 1 return True return False def mcp2openai(mcp_tool: Tool) -> dict: """Convert a MCP Tool to an OpenAI ChatCompletionTool.""" openai_format = { "type": "function", "function": { "name": mcp_tool.name, "description": mcp_tool.description, "parameters": mcp_tool.inputSchema, "strict": False, }, } if not openai_format["function"]["parameters"].get("required", None): openai_format["function"]["parameters"]["required"] = [] return openai_format ================================================ FILE: verl/tools/utils/search_r1_like_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import threading import time import traceback import uuid from typing import Any, Optional import requests DEFAULT_TIMEOUT = 30 # Default search request timeout MAX_RETRIES = 10 INITIAL_RETRY_DELAY = 1 API_TIMEOUT = 10 logger = logging.getLogger(__name__) def call_search_api( retrieval_service_url: str, query_list: list[str], topk: int = 3, return_scores: bool = True, timeout: int = DEFAULT_TIMEOUT, ) -> tuple[Optional[dict[str, Any]], Optional[str]]: """ Calls the remote search API to perform retrieval with retry logic for various errors, using increasing delay between retries. Logs internal calls with a unique ID. Args: retrieval_service_url: The URL of the retrieval service API. query_list: List of search queries. topk: Number of top results to return. return_scores: Whether to return scores. timeout: Request timeout in seconds. Returns: A tuple (response_json, error_message). If successful, response_json is the API's returned JSON object, error_message is None. If failed after retries, response_json is None, error_message contains the error information. """ request_id = str(uuid.uuid4()) log_prefix = f"[Search Request ID: {request_id}] " payload = {"queries": query_list, "topk": topk, "return_scores": return_scores} headers = {"Content-Type": "application/json", "Accept": "application/json"} last_error = None for attempt in range(MAX_RETRIES): try: logger.info( f"{log_prefix}Attempt {attempt + 1}/{MAX_RETRIES}: Calling search API at {retrieval_service_url}" ) response = requests.post( retrieval_service_url, headers=headers, json=payload, timeout=timeout, ) # Check for Gateway Timeout (504) and other server errors for retrying if response.status_code in [500, 502, 503, 504]: last_error = ( f"{log_prefix}API Request Error: Server Error ({response.status_code}) on attempt " f"{attempt + 1}/{MAX_RETRIES}" ) logger.warning(last_error) if attempt < MAX_RETRIES - 1: delay = INITIAL_RETRY_DELAY * (attempt + 1) logger.info(f"{log_prefix}Retrying after {delay} seconds...") time.sleep(delay) continue # Check for other HTTP errors (e.g., 4xx) response.raise_for_status() # If successful (status code 2xx) logger.info(f"{log_prefix}Search API call successful on attempt {attempt + 1}") return response.json(), None except requests.exceptions.ConnectionError as e: last_error = f"{log_prefix}Connection Error: {e}" logger.warning(last_error) if attempt < MAX_RETRIES - 1: delay = INITIAL_RETRY_DELAY * (attempt + 1) logger.info(f"{log_prefix}Retrying after {delay} seconds...") time.sleep(delay) continue except requests.exceptions.Timeout as e: last_error = f"{log_prefix}Timeout Error: {e}" logger.warning(last_error) if attempt < MAX_RETRIES - 1: delay = INITIAL_RETRY_DELAY * (attempt + 1) logger.info(f"{log_prefix}Retrying after {delay} seconds...") time.sleep(delay) continue except requests.exceptions.RequestException as e: last_error = f"{log_prefix}API Request Error: {e}" break # Exit retry loop on other request errors except json.JSONDecodeError as e: raw_response_text = response.text if "response" in locals() else "N/A" last_error = f"{log_prefix}API Response JSON Decode Error: {e}, Response: {raw_response_text[:200]}" break # Exit retry loop on JSON decode errors except Exception as e: last_error = f"{log_prefix}Unexpected Error: {e}" break # Exit retry loop on other unexpected errors # If loop finishes without returning success, return the last recorded error logger.error(f"{log_prefix}Search API call failed. Last error: {last_error}") return None, last_error.replace(log_prefix, "API Call Failed: ") if last_error else "API Call Failed after retries" def _passages2string(retrieval_result): """Convert retrieval results to formatted string.""" format_reference = "" for idx, doc_item in enumerate(retrieval_result): content = doc_item["document"]["contents"] title = content.split("\n")[0] text = "\n".join(content.split("\n")[1:]) format_reference += f"Doc {idx + 1} (Title: {title})\n{text}\n\n" return format_reference.strip() def perform_single_search_batch( retrieval_service_url: str, query_list: list[str], topk: int = 3, concurrent_semaphore: Optional[threading.Semaphore] = None, timeout: int = DEFAULT_TIMEOUT, ) -> tuple[str, dict[str, Any]]: """ Performs a single batch search for multiple queries (original search tool behavior). Args: retrieval_service_url: The URL of the retrieval service API. query_list: List of search queries. topk: Number of top results to return. concurrent_semaphore: Optional semaphore for concurrency control. timeout: Request timeout in seconds. Returns: A tuple (result_text, metadata). result_text: The search result JSON string. metadata: Metadata dictionary for the batch search. """ logger.info(f"Starting batch search for {len(query_list)} queries.") api_response = None error_msg = None try: if concurrent_semaphore: with concurrent_semaphore: api_response, error_msg = call_search_api( retrieval_service_url=retrieval_service_url, query_list=query_list, topk=topk, return_scores=True, timeout=timeout, ) else: api_response, error_msg = call_search_api( retrieval_service_url=retrieval_service_url, query_list=query_list, topk=topk, return_scores=True, timeout=timeout, ) except Exception as e: error_msg = f"API Request Exception during batch search: {e}" logger.error(f"Batch search: {error_msg}") traceback.print_exc() metadata = { "query_count": len(query_list), "queries": query_list, "api_request_error": error_msg, "api_response": None, "status": "unknown", "total_results": 0, "formatted_result": None, } result_text = json.dumps({"result": "Search request failed or timed out after retries."}, ensure_ascii=False) if error_msg: metadata["status"] = "api_error" result_text = json.dumps({"result": f"Search error: {error_msg}"}, ensure_ascii=False) logger.error(f"Batch search: API error occurred: {error_msg}") elif api_response: logger.debug(f"Batch search: API Response: {api_response}") metadata["api_response"] = api_response try: raw_results = api_response.get("result", []) if raw_results: pretty_results = [] total_results = 0 for retrieval in raw_results: formatted = _passages2string(retrieval) pretty_results.append(formatted) total_results += len(retrieval) if isinstance(retrieval, list) else 1 final_result = "\n---\n".join(pretty_results) result_text = json.dumps({"result": final_result}, ensure_ascii=False) metadata["status"] = "success" metadata["total_results"] = total_results metadata["formatted_result"] = final_result logger.info(f"Batch search: Successful, got {total_results} total results") else: result_text = json.dumps({"result": "No search results found."}, ensure_ascii=False) metadata["status"] = "no_results" metadata["total_results"] = 0 logger.info("Batch search: No results found") except Exception as e: error_msg = f"Error processing search results: {e}" result_text = json.dumps({"result": error_msg}, ensure_ascii=False) metadata["status"] = "processing_error" logger.error(f"Batch search: {error_msg}") else: metadata["status"] = "unknown_api_state" result_text = json.dumps( {"result": "Unknown API state (no response and no error message)."}, ensure_ascii=False ) logger.error("Batch search: Unknown API state.") return result_text, metadata ================================================ FILE: verl/tools/utils/tool_registry.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import importlib import logging import os import sys import threading from enum import Enum from omegaconf import OmegaConf from verl.tools.schemas import OpenAIFunctionToolSchema logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class ToolType(Enum): NATIVE = "native" MCP = "mcp" async def initialize_mcp_tool(tool_cls, tool_config) -> list: from verl.tools.utils.mcp_clients.McpClientManager import ClientManager tool_list = [] mcp_servers_config_path = tool_config.mcp.mcp_servers_config_path tool_selected_list = tool_config.mcp.tool_selected_list if "tool_selected_list" in tool_config.mcp else None await ClientManager.initialize(mcp_servers_config_path, tool_config.config.rate_limit) # Wait for MCP client to be ready max_retries = 10 retry_interval = 2 # seconds for i in range(max_retries): tool_schemas = await ClientManager.fetch_tool_schemas(tool_selected_list) if tool_schemas: break if i < max_retries - 1: logger.debug(f"Waiting for MCP client to be ready, attempt {i + 1}/{max_retries}") await asyncio.sleep(retry_interval) else: raise RuntimeError("Failed to initialize MCP tools after maximum retries") # mcp registry assert len(tool_schemas), "mcp tool is empty" for tool_schema_dict in tool_schemas: logger.debug(f"tool_schema_dict: {tool_schema_dict}") tool_schema = OpenAIFunctionToolSchema.model_validate(tool_schema_dict) tool = tool_cls( config=OmegaConf.to_container(tool_config.config, resolve=True), tool_schema=tool_schema, ) tool_list.append(tool) return tool_list def get_tool_class(cls_name): module_name, class_name = cls_name.rsplit(".", 1) if module_name not in sys.modules: spec = importlib.util.find_spec(module_name) module = importlib.util.module_from_spec(spec) sys.modules[module_name] = module spec.loader.exec_module(module) else: module = sys.modules[module_name] tool_cls = getattr(module, class_name) return tool_cls def initialize_tools_from_config(tools_config_file): """Initialize tools from config file. Supports both NATIVE and MCP tool types. For MCP tools, a temporary event loop is created only when needed and properly closed after use to prevent memory leaks. """ tools_config = OmegaConf.load(tools_config_file) tool_list = [] # Lazy initialization for MCP support - only create event loop when needed tmp_event_loop = None thread = None def get_mcp_event_loop(): """Lazily create event loop and thread for MCP tools.""" nonlocal tmp_event_loop, thread if tmp_event_loop is None: tmp_event_loop = asyncio.new_event_loop() thread = threading.Thread(target=tmp_event_loop.run_forever, name="mcp tool list fetcher", daemon=True) thread.start() return tmp_event_loop def run_coroutine(coroutine): """Run coroutine in the MCP event loop.""" loop = get_mcp_event_loop() future = asyncio.run_coroutine_threadsafe(coroutine, loop) return future.result() try: for tool_config in tools_config.tools: cls_name = tool_config.class_name tool_type = ToolType(tool_config.config.type) tool_cls = get_tool_class(cls_name) match tool_type: case ToolType.NATIVE: if tool_config.get("tool_schema", None) is None: tool_schema = None else: tool_schema_dict = OmegaConf.to_container(tool_config.tool_schema, resolve=True) tool_schema = OpenAIFunctionToolSchema.model_validate(tool_schema_dict) tool = tool_cls( config=OmegaConf.to_container(tool_config.config, resolve=True), tool_schema=tool_schema, ) tool_list.append(tool) case ToolType.MCP: mcp_tools = run_coroutine(initialize_mcp_tool(tool_cls, tool_config)) tool_list.extend(mcp_tools) case _: raise NotImplementedError finally: # Properly cleanup event loop if it was created if tmp_event_loop is not None: # stop first and then close tmp_event_loop.call_soon_threadsafe(tmp_event_loop.stop) if thread is not None and thread.is_alive(): thread.join(timeout=5.0) tmp_event_loop.close() return tool_list ================================================ FILE: verl/trainer/README.md ================================================ # verl Main Entrypoints ## SFT Trainer - sft_trainer.py: SFT trainer based on model engine, support various backends: fsdp, megatron, veomni, torchtitan. Launched by `torchrun` and run in multi-controller mode. - **[EXPERIMENTAL]** sft_trainer_ray.py: SFT trainer based on model engine with single-controller mode. Launched by ray with a driver process coordinating multiple worker processes. ## RL Trainer |trainer|description|sync/async|trainer/rollout|partial rollout| |----|----|----|----|----| |main_ppo.py|rollout until a batch is completed, then train|synchronous|colocated|No| |TBD|[kimi-1.5](https://arxiv.org/pdf/2501.12599) style trainer: streaming rollout with capped length partial rollout|asynchronous|colocated|Yes| |TBD|[Areal](https://arxiv.org/pdf/2505.24298) style trainer: fully decoupled trainer and rollout with staleness control|asynchronous|disaggregated|Yes| ## Inference and Evaluation - main_generation_server.py: Launch standalone servers and generate responses for a specified prompt dataset. - main_eval.py: Evaluate the performance of generated responses with reward function on a specified prompt dataset. ================================================ FILE: verl/trainer/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/trainer/config/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import algorithm, config from .algorithm import * # noqa: F401 from .config import * # noqa: F401 __all__ = config.__all__ + algorithm.__all__ ================================================ FILE: verl/trainer/config/_generated_ppo_megatron_trainer.yaml ================================================ # This reference configration yaml is automatically generated via 'scripts/generate_trainer_config.sh' # in which it invokes 'python3 scripts/print_cfg.py --cfg job --config-name=ppo_megatron_trainer.yaml' to flatten the 'verl/trainer/config/ppo_megatron_trainer.yaml' config fields into a single file. # Do not modify this file directly. # The file is usually only for reference and never used. actor_rollout_ref: actor: optim: _target_: verl.workers.config.McoreOptimizerConfig lr: 1.0e-06 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 optimizer: adam lr_warmup_init: 0.0 lr_decay_steps: null lr_decay_style: constant min_lr: 0.0 weight_decay_incr_style: constant lr_wsd_decay_style: exponential lr_wsd_decay_steps: null use_checkpoint_opt_param_scheduler: false override_optimizer_config: {} megatron: _target_: verl.workers.config.McoreEngineConfig param_offload: false grad_offload: false optimizer_offload: false tensor_model_parallel_size: 1 expert_model_parallel_size: 1 expert_tensor_parallel_size: null pipeline_model_parallel_size: 1 virtual_pipeline_model_parallel_size: null context_parallel_size: 1 sequence_parallel: true use_distributed_optimizer: true use_dist_checkpointing: false dist_checkpointing_path: null dist_checkpointing_prefix: '' dist_ckpt_optim_fully_reshardable: true distrib_optim_fully_reshardable_mem_efficient: false seed: 42 override_ddp_config: {} override_transformer_config: recompute_granularity: null recompute_modules: - core_attn recompute_method: null recompute_num_layers: null attention_backend: flash override_mcore_model_config: {} use_mbridge: true vanilla_mbridge: true use_remove_padding: true forward_only: false dtype: bfloat16 router_replay: _target_: verl.workers.config.EngineRouterReplayConfig mode: disabled record_file: null replay_file: null _target_: verl.workers.config.McoreActorConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: megatron ppo_mini_batch_size: 256 ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: false ppo_max_token_len_per_gpu: 16384 clip_ratio: 0.2 clip_ratio_low: 0.2 clip_ratio_high: 0.2 tau_pos: 1.0 tau_neg: 1.05 freeze_vision_tower: false policy_loss: _target_: verl.workers.config.PolicyLossConfig loss_mode: vanilla clip_cov_ratio: 0.0002 clip_cov_lb: 1.0 clip_cov_ub: 5.0 kl_cov_ratio: 0.0002 ppo_kl_coef: 0.1 clip_ratio_c: 3.0 loss_agg_mode: token-mean loss_scale_factor: null entropy_coeff: 0 calculate_entropy: false use_kl_loss: false use_prefix_grouper: false use_torch_compile: true kl_loss_coef: 0.001 kl_loss_type: low_var_kl ppo_epochs: 1 shuffle: false data_loader_seed: 42 checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null load_weight: true ref: rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: megatron use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true} log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null megatron: _target_: verl.workers.config.McoreEngineConfig param_offload: ${oc.select:actor_rollout_ref.actor.megatron.param_offload,False} grad_offload: false optimizer_offload: false tensor_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.tensor_model_parallel_size,1} expert_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.expert_model_parallel_size,1} expert_tensor_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.expert_tensor_parallel_size,null} pipeline_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.pipeline_model_parallel_size,1} virtual_pipeline_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size,null} context_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.context_parallel_size,1} sequence_parallel: true use_distributed_optimizer: true use_dist_checkpointing: false dist_checkpointing_path: null dist_checkpointing_prefix: '' dist_ckpt_optim_fully_reshardable: true distrib_optim_fully_reshardable_mem_efficient: false seed: ${oc.select:actor_rollout_ref.actor.megatron.seed,42} override_ddp_config: {} override_transformer_config: ${oc.select:actor_rollout_ref.actor.megatron.override_transformer_config,{}} override_mcore_model_config: {} use_mbridge: ${oc.select:actor_rollout_ref.actor.megatron.use_mbridge,False} vanilla_mbridge: ${oc.select:actor_rollout_ref.actor.megatron.vanilla_mbridge,True} use_remove_padding: ${oc.select:actor_rollout_ref.actor.megatron.use_remove_padding,True} forward_only: true dtype: bfloat16 router_replay: _target_: verl.workers.config.EngineRouterReplayConfig mode: disabled record_file: null replay_file: null _target_: verl.workers.config.McoreActorConfig load_weight: true rollout: _target_: verl.workers.config.RolloutConfig name: ??? mode: async nnodes: 0 n_gpus_per_node: ${oc.select:trainer.n_gpus_per_node,8} temperature: 1.0 top_k: -1 top_p: 1 prompt_length: ${oc.select:data.max_prompt_length,512} response_length: ${oc.select:data.max_response_length,512} dtype: bfloat16 gpu_memory_utilization: 0.5 ignore_eos: false enforce_eager: false cudagraph_capture_sizes: null free_cache_engine: true tensor_model_parallel_size: 2 data_parallel_size: 1 expert_parallel_size: 1 pipeline_model_parallel_size: 1 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 enable_chunked_prefill: true enable_prefix_caching: true logprobs_mode: processed_logprobs scheduling_policy: fcfs load_format: dummy log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} disable_log_stats: true do_sample: true 'n': 1 over_sample_rate: 0 multi_stage_wake_up: false engine_kwargs: vllm: {} sglang: {} trtllm: {} val_kwargs: _target_: verl.workers.config.SamplingConfig top_k: -1 top_p: 1.0 temperature: 0 'n': 1 do_sample: false multi_turn: _target_: verl.workers.config.MultiTurnConfig enable: false max_assistant_turns: null tool_config_path: null max_user_turns: null max_parallel_calls: 1 max_tool_response_length: 256 tool_response_truncate_side: middle interaction_config_path: null use_inference_chat_template: false tokenization_sanity_check_mode: strict format: hermes num_repeat_rollouts: null calculate_log_probs: false agent: _target_: verl.workers.config.AgentLoopConfig num_workers: 8 default_agent_loop: single_turn_agent agent_loop_config_path: null custom_async_server: _target_: verl.workers.config.CustomAsyncServerConfig path: null name: null checkpoint_engine: _target_: verl.workers.config.CheckpointEngineConfig backend: naive update_weights_bucket_megabytes: 2048 engine_kwargs: {} trace: _target_: verl.workers.config.TraceConfig project_name: ${oc.select:trainer.project_name,null} experiment_name: ${oc.select:trainer.experiment_name,null} backend: null token2text: false max_samples_per_step_per_worker: null skip_rollout: false skip_dump_dir: /tmp/rollout_dump skip_tokenizer_init: true enable_rollout_routing_replay: false profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false} all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false} ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]} save_path: ${oc.select:global_profiler.save_path,null} tool_config: npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.contents,[]} level: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.level,level0} analysis: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.analysis,false} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.discrete,false} torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.contents,[]} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.discrete,false} prometheus: _target_: verl.workers.config.PrometheusConfig enable: false port: 9090 file: /tmp/ray/session_latest/metrics/prometheus/prometheus.yml served_model_name: ${oc.select:actor_rollout_ref.model.path,null} quantization: null quantization_config_file: null mtp: ${oc.select:actor_rollout_ref.model.mtp, null} qat: ${oc.select:actor_rollout_ref.actor.fsdp_config.qat,null} layer_name_map: qkv_layer_name: qkv gate_proj_layer_name: gate_up model: _target_: verl.workers.config.HFModelConfig path: ~/models/deepseek-llm-7b-chat hf_config_path: null tokenizer_path: null use_shm: false trust_remote_code: false custom_chat_template: null external_lib: null override_config: model_config: {} moe_config: freeze_moe_router: false enable_gradient_checkpointing: true enable_activation_offload: false use_remove_padding: false lora_rank: 0 lora_alpha: 16 target_modules: all-linear exclude_modules: null lora_adapter_path: null use_liger: false use_fused_kernels: false fused_kernel_options: impl_backend: torch tiled_mlp: enabled: false num_shards: 4 mtp: _target_: verl.workers.config.MtpConfig enable: false enable_train: false enable_rollout: false detach_encoder: false mtp_loss_scaling_factor: 0.1 speculative_algorithm: EAGLE speculative_num_steps: 3 speculative_eagle_topk: 1 speculative_num_draft_tokens: 4 method: mtp num_speculative_tokens: 1 lora: type: lora merge: false rank: 0 alpha: 32 dropout: 0.0 target_modules: - linear_qkv - linear_proj - linear_fc1 - linear_fc2 exclude_modules: [] dropout_position: pre lora_A_init_method: xavier lora_B_init_method: zero a2a_experimental: false dtype: null adapter_path: null freeze_vision_model: true freeze_vision_projection: true freeze_language_model: true hybrid_engine: true nccl_timeout: 600 data: tokenizer: null use_shm: false train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 val_max_samples: -1 prompt_key: prompt reward_fn_key: data_source max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null tool_config_path: ${oc.select:actor_rollout_ref.rollout.multi_turn.tool_config_path, null} return_raw_input_ids: false return_raw_chat: true return_full_prompt: false shuffle: true seed: null dataloader_num_workers: 8 image_patch_size: 14 validation_shuffle: false filter_overlong_prompts: false filter_overlong_prompts_workers: 1 truncation: error image_key: images video_key: videos trust_remote_code: false custom_cls: path: null name: null return_multi_modal_inputs: true sampler: class_path: null class_name: null datagen: path: null name: null apply_chat_template_kwargs: {} critic: optim: _target_: verl.workers.config.McoreOptimizerConfig lr: 1.0e-05 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 optimizer: adam lr_warmup_init: 0.0 lr_decay_steps: null lr_decay_style: constant min_lr: 0.0 weight_decay_incr_style: constant lr_wsd_decay_style: exponential lr_wsd_decay_steps: null use_checkpoint_opt_param_scheduler: false override_optimizer_config: {} megatron: _target_: verl.workers.config.McoreEngineConfig param_offload: false grad_offload: false optimizer_offload: false tensor_model_parallel_size: 1 expert_model_parallel_size: 1 expert_tensor_parallel_size: null pipeline_model_parallel_size: 1 virtual_pipeline_model_parallel_size: null context_parallel_size: 1 sequence_parallel: true use_distributed_optimizer: true use_dist_checkpointing: false dist_checkpointing_path: null dist_checkpointing_prefix: '' dist_ckpt_optim_fully_reshardable: true distrib_optim_fully_reshardable_mem_efficient: false seed: 42 override_ddp_config: {} override_transformer_config: recompute_granularity: null recompute_modules: - core_attn recompute_method: null recompute_num_layers: null attention_backend: flash override_mcore_model_config: {} use_mbridge: true vanilla_mbridge: true use_remove_padding: true forward_only: false dtype: bfloat16 router_replay: _target_: verl.workers.config.EngineRouterReplayConfig mode: disabled record_file: null replay_file: null _target_: verl.workers.config.McoreCriticConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: megatron enable: null model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"} override_config: model_config: {} moe_config: freeze_moe_router: false external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null} trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false} _target_: verl.trainer.config.BaseModelConfig lora: type: lora rank: 0 alpha: 32 dropout: 0.0 target_modules: - linear_qkv - linear_proj - linear_fc1 - linear_fc2 exclude_modules: [] dropout_position: pre lora_A_init_method: xavier lora_B_init_method: zero a2a_experimental: false dtype: null adapter_path: null freeze_vision_model: true freeze_vision_projection: true freeze_language_model: true ppo_mini_batch_size: ${oc.select:actor_rollout_ref.actor.ppo_mini_batch_size,256} ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size,null} use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} ppo_max_token_len_per_gpu: 32768 forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu} ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1} shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false} data_loader_seed: ${oc.select:actor_rollout_ref.actor.data_loader_seed,null} cliprange_value: 0.5 loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean} checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} nccl_timeout: 600 load_weight: true custom_reward_function: path: null name: null reward_model: num_workers: null reward_manager: null enable: null enable_resource_pool: null n_gpus_per_node: null nnodes: null reward_loop_source: null reward_loop_module_path: null reward_loop_class_name: null model: path: null external_lib: null trust_remote_code: null rollout: name: null dtype: null gpu_memory_utilization: null enforce_eager: null cudagraph_capture_sizes: null free_cache_engine: null data_parallel_size: null expert_parallel_size: null tensor_model_parallel_size: null max_num_batched_tokens: null max_model_len: null max_num_seqs: null load_format: null engine_kwargs: null limit_images: null enable_chunked_prefill: null enable_prefix_caching: null disable_log_stats: null skip_tokenizer_init: null prompt_length: null response_length: null sandbox_fusion: url: null max_concurrent: null memory_limit_mb: null reward: num_workers: 8 custom_reward_function: path: null name: compute_score reward_manager: _target_: verl.workers.config.reward_model.RewardManagerConfig source: register name: naive module: _target_: verl.trainer.config.config.ModuleConfig path: null name: custom_reward_manager reward_model: enable: false enable_resource_pool: false n_gpus_per_node: 8 nnodes: 0 model_path: null rollout: _target_: verl.workers.config.RolloutConfig name: ??? dtype: bfloat16 gpu_memory_utilization: 0.5 enforce_eager: true cudagraph_capture_sizes: null free_cache_engine: true data_parallel_size: 1 expert_parallel_size: 1 tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 load_format: auto engine_kwargs: {} limit_images: null enable_chunked_prefill: true enable_prefix_caching: true disable_log_stats: true skip_tokenizer_init: false prompt_length: 2048 response_length: 2048 sandbox_fusion: url: null max_concurrent: 64 memory_limit_mb: 1024 algorithm: rollout_correction: rollout_is: null rollout_is_threshold: 2.0 rollout_rs: null rollout_rs_threshold: null bypass_mode: false loss_type: ppo_clip rollout_is_batch_normalize: false _target_: verl.trainer.config.AlgoConfig gamma: 1.0 lam: 1.0 adv_estimator: gae norm_adv_by_std_in_grpo: true use_kl_in_reward: false kl_penalty: kl kl_ctrl: _target_: verl.trainer.config.KLControlConfig type: fixed kl_coef: 0.001 horizon: 10000 target_kl: 0.1 use_pf_ppo: false pf_ppo: reweight_method: pow weight_pow: 2.0 trainer: balance_batch: true total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: - console - wandb log_val_generations: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 esi_redundant_time: 0 resume_mode: auto resume_from_path: null del_local_ckpt_after_load: false val_before_train: true test_freq: -1 critic_warmup: 0 default_hdfs_dir: null default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} max_actor_ckpt_to_keep: null max_critic_ckpt_to_keep: null ray_wait_register_center_timeout: 300 device: cuda rollout_data_dir: null use_legacy_worker_impl: auto global_profiler: _target_: verl.utils.profiler.ProfilerConfig tool: null steps: null profile_continuous_steps: false save_path: outputs/profile global_tool_config: nsys: discrete: false controller_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph worker_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph capture-range: cudaProfilerApi capture-range-end: null kill: none torch_memory: trace_alloc_max_entries: 100000 stack_depth: 32 context: all stacks: all kw_args: {} transfer_queue: enable: false ray_kwargs: ray_init: num_cpus: null timeline_json_file: null ================================================ FILE: verl/trainer/config/_generated_ppo_torchtitan_trainer.yaml ================================================ # This reference configration yaml is automatically generated via 'scripts/generate_trainer_config.sh' # in which it invokes 'python3 scripts/print_cfg.py --cfg job model_engine=torchtitan' to flatten the 'verl/trainer/config/ppo_trainer.yaml' config fields into a single file. # Do not modify this file directly. # The file is usually only for reference and never used. actor_rollout_ref: actor: optim: _target_: verl.workers.config.TorchtitanOptimizerConfig name: AdamW lr: 1.0e-06 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 eps: 1.0e-08 decay_type: linear min_lr_factor: 0.0 torchtitan: _target_: verl.workers.config.TorchtitanEngineConfig param_offload: false optimizer_offload: false wrap_policy: min_num_params: 0 reshard_after_forward: default forward_prefetch: false use_orig_params: false mixed_precision: false use_torch_compile: true entropy_from_logits_with_chunking: false entropy_checkpointing: false data_parallel_size: 1 data_parallel_replicate_size: 1 data_parallel_shard_size: 1 tensor_parallel_size: 1 expert_parallel_size: 1 pipeline_parallel_size: 1 context_parallel_size: 1 attn_type: flex max_seq_len: null strategy: torchtitan seed: 42 full_determinism: false forward_only: false dtype: bfloat16 _target_: verl.workers.config.TorchTitanActorConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: torchtitan ppo_mini_batch_size: 256 ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: false ppo_max_token_len_per_gpu: 16384 clip_ratio: 0.2 clip_ratio_low: 0.2 clip_ratio_high: 0.2 tau_pos: 1.0 tau_neg: 1.05 freeze_vision_tower: false policy_loss: _target_: verl.workers.config.PolicyLossConfig loss_mode: vanilla clip_cov_ratio: 0.0002 clip_cov_lb: 1.0 clip_cov_ub: 5.0 kl_cov_ratio: 0.0002 ppo_kl_coef: 0.1 clip_ratio_c: 3.0 loss_agg_mode: token-mean loss_scale_factor: null entropy_coeff: 0 calculate_entropy: false use_kl_loss: false use_prefix_grouper: false use_torch_compile: true kl_loss_coef: 0.001 kl_loss_type: low_var_kl ppo_epochs: 1 shuffle: false data_loader_seed: 42 checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null ref: optim: _target_: verl.workers.config.TorchtitanOptimizerConfig name: AdamW lr: 0.001 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 eps: 1.0e-08 decay_type: linear min_lr_factor: 0.0 torchtitan: _target_: verl.workers.config.TorchtitanEngineConfig param_offload: false optimizer_offload: false wrap_policy: min_num_params: 0 reshard_after_forward: default forward_prefetch: false use_orig_params: false mixed_precision: false use_torch_compile: true entropy_from_logits_with_chunking: false entropy_checkpointing: false data_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.data_parallel_size,1} data_parallel_replicate_size: ${oc.select:actor_rollout_ref.actor.torchtitan.data_parallel_replicate_size,1} data_parallel_shard_size: ${oc.select:actor_rollout_ref.actor.torchtitan.data_parallel_shard_size,1} tensor_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.tensor_parallel_size,1} expert_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.expert_parallel_size,1} pipeline_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.pipeline_parallel_size,1} context_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.context_parallel_size,1} attn_type: ${oc.select:actor_rollout_ref.actor.torchtitan.attn_type,flex} max_seq_len: null strategy: torchtitan seed: ${oc.select:actor_rollout_ref.actor.torchtitan.seed,42} full_determinism: false forward_only: true dtype: bfloat16 rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: torchtitan use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true} log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null _target_: verl.workers.config.TorchTitanActorConfig rollout: _target_: verl.workers.config.RolloutConfig name: ??? mode: async nnodes: 0 n_gpus_per_node: ${oc.select:trainer.n_gpus_per_node,8} temperature: 1.0 top_k: -1 top_p: 1 prompt_length: ${oc.select:data.max_prompt_length,512} response_length: ${oc.select:data.max_response_length,512} dtype: bfloat16 gpu_memory_utilization: 0.5 ignore_eos: false enforce_eager: false cudagraph_capture_sizes: null free_cache_engine: true tensor_model_parallel_size: 2 data_parallel_size: 1 expert_parallel_size: 1 pipeline_model_parallel_size: 1 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 enable_chunked_prefill: true enable_prefix_caching: true logprobs_mode: processed_logprobs scheduling_policy: fcfs load_format: dummy log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} disable_log_stats: true do_sample: true 'n': 1 over_sample_rate: 0 multi_stage_wake_up: false engine_kwargs: vllm: {} sglang: {} trtllm: {} val_kwargs: _target_: verl.workers.config.SamplingConfig top_k: -1 top_p: 1.0 temperature: 0 'n': 1 do_sample: false multi_turn: _target_: verl.workers.config.MultiTurnConfig enable: false max_assistant_turns: null tool_config_path: null max_user_turns: null max_parallel_calls: 1 max_tool_response_length: 256 tool_response_truncate_side: middle interaction_config_path: null use_inference_chat_template: false tokenization_sanity_check_mode: strict format: hermes num_repeat_rollouts: null calculate_log_probs: false agent: _target_: verl.workers.config.AgentLoopConfig num_workers: 8 default_agent_loop: single_turn_agent agent_loop_config_path: null custom_async_server: _target_: verl.workers.config.CustomAsyncServerConfig path: null name: null checkpoint_engine: _target_: verl.workers.config.CheckpointEngineConfig backend: naive update_weights_bucket_megabytes: 2048 engine_kwargs: {} trace: _target_: verl.workers.config.TraceConfig project_name: ${oc.select:trainer.project_name,null} experiment_name: ${oc.select:trainer.experiment_name,null} backend: null token2text: false max_samples_per_step_per_worker: null skip_rollout: false skip_dump_dir: /tmp/rollout_dump skip_tokenizer_init: true enable_rollout_routing_replay: false profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false} all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false} ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]} save_path: ${oc.select:global_profiler.save_path,null} tool_config: npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.contents,[]} level: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.level,level0} analysis: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.analysis,false} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.discrete,false} torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.contents,[]} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.discrete,false} prometheus: _target_: verl.workers.config.PrometheusConfig enable: false port: 9090 file: /tmp/ray/session_latest/metrics/prometheus/prometheus.yml served_model_name: ${oc.select:actor_rollout_ref.model.path,null} quantization: null quantization_config_file: null mtp: ${oc.select:actor_rollout_ref.model.mtp, null} qat: ${oc.select:actor_rollout_ref.actor.fsdp_config.qat,null} layered_summon: false model: _target_: verl.workers.config.HFModelConfig path: ~/models/deepseek-llm-7b-chat hf_config_path: null tokenizer_path: null use_shm: false trust_remote_code: false custom_chat_template: null external_lib: null override_config: {} enable_gradient_checkpointing: true enable_activation_offload: false use_remove_padding: true lora_rank: 0 lora_alpha: 16 target_modules: all-linear exclude_modules: null lora_adapter_path: null use_liger: false use_fused_kernels: false fused_kernel_options: impl_backend: torch tiled_mlp: enabled: false num_shards: 4 mtp: _target_: verl.workers.config.MtpConfig enable: false enable_train: false enable_rollout: false detach_encoder: false mtp_loss_scaling_factor: 0.1 speculative_algorithm: EAGLE speculative_num_steps: 3 speculative_eagle_topk: 1 speculative_num_draft_tokens: 4 method: mtp num_speculative_tokens: 1 hybrid_engine: true nccl_timeout: 600 data: tokenizer: null use_shm: false train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 val_max_samples: -1 prompt_key: prompt reward_fn_key: data_source max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null tool_config_path: ${oc.select:actor_rollout_ref.rollout.multi_turn.tool_config_path, null} return_raw_input_ids: false return_raw_chat: true return_full_prompt: false shuffle: true seed: null dataloader_num_workers: 8 image_patch_size: 14 validation_shuffle: false filter_overlong_prompts: false filter_overlong_prompts_workers: 1 truncation: error image_key: images video_key: videos trust_remote_code: false custom_cls: path: null name: null return_multi_modal_inputs: true sampler: class_path: null class_name: null datagen: path: null name: null apply_chat_template_kwargs: {} critic: optim: _target_: verl.workers.config.TorchtitanOptimizerConfig name: AdamW lr: 1.0e-05 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 eps: 1.0e-08 decay_type: linear min_lr_factor: 0.0 torchtitan: _target_: verl.workers.config.TorchtitanEngineConfig param_offload: false optimizer_offload: false wrap_policy: min_num_params: 0 reshard_after_forward: default forward_prefetch: false use_orig_params: false mixed_precision: false use_torch_compile: true entropy_from_logits_with_chunking: false entropy_checkpointing: false data_parallel_size: 1 data_parallel_replicate_size: 1 data_parallel_shard_size: 1 tensor_parallel_size: 1 expert_parallel_size: 1 pipeline_parallel_size: 1 context_parallel_size: 1 attn_type: flex max_seq_len: null strategy: torchtitan seed: 42 full_determinism: false forward_only: false dtype: bfloat16 _target_: verl.workers.config.TorchTitanCriticConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: torchtitan enable: null model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"} override_config: {} external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null} trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false} _target_: verl.trainer.config.BaseModelConfig ppo_mini_batch_size: ${oc.select:actor_rollout_ref.actor.ppo_mini_batch_size,256} ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size,null} use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} ppo_max_token_len_per_gpu: 32768 forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu} ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1} shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false} data_loader_seed: ${oc.select:actor_rollout_ref.actor.data_loader_seed,null} cliprange_value: 0.5 loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean} checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} custom_reward_function: path: null name: null reward_model: num_workers: null reward_manager: null enable: null enable_resource_pool: null n_gpus_per_node: null nnodes: null reward_loop_source: null reward_loop_module_path: null reward_loop_class_name: null model: path: null external_lib: null trust_remote_code: null rollout: name: null dtype: null gpu_memory_utilization: null enforce_eager: null cudagraph_capture_sizes: null free_cache_engine: null data_parallel_size: null expert_parallel_size: null tensor_model_parallel_size: null max_num_batched_tokens: null max_model_len: null max_num_seqs: null load_format: null engine_kwargs: null limit_images: null enable_chunked_prefill: null enable_prefix_caching: null disable_log_stats: null skip_tokenizer_init: null prompt_length: null response_length: null sandbox_fusion: url: null max_concurrent: null memory_limit_mb: null reward: num_workers: 8 custom_reward_function: path: null name: compute_score reward_manager: _target_: verl.workers.config.reward_model.RewardManagerConfig source: register name: naive module: _target_: verl.trainer.config.config.ModuleConfig path: null name: custom_reward_manager reward_model: enable: false enable_resource_pool: false n_gpus_per_node: 8 nnodes: 0 model_path: null rollout: _target_: verl.workers.config.RolloutConfig name: ??? dtype: bfloat16 gpu_memory_utilization: 0.5 enforce_eager: true cudagraph_capture_sizes: null free_cache_engine: true data_parallel_size: 1 expert_parallel_size: 1 tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 load_format: auto engine_kwargs: {} limit_images: null enable_chunked_prefill: true enable_prefix_caching: true disable_log_stats: true skip_tokenizer_init: false prompt_length: 2048 response_length: 2048 sandbox_fusion: url: null max_concurrent: 64 memory_limit_mb: 1024 algorithm: rollout_correction: rollout_is: null rollout_is_threshold: 2.0 rollout_rs: null rollout_rs_threshold: null bypass_mode: false loss_type: ppo_clip rollout_is_batch_normalize: false _target_: verl.trainer.config.AlgoConfig gamma: 1.0 lam: 1.0 adv_estimator: gae norm_adv_by_std_in_grpo: true use_kl_in_reward: false kl_penalty: kl kl_ctrl: _target_: verl.trainer.config.KLControlConfig type: fixed kl_coef: 0.001 horizon: 10000 target_kl: 0.1 use_pf_ppo: false pf_ppo: reweight_method: pow weight_pow: 2.0 trainer: balance_batch: true total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: - console - wandb log_val_generations: 0 rollout_data_dir: null validation_data_dir: null nnodes: 1 n_gpus_per_node: 8 save_freq: -1 esi_redundant_time: 0 resume_mode: auto resume_from_path: null val_before_train: true val_only: false test_freq: -1 critic_warmup: 0 default_hdfs_dir: null del_local_ckpt_after_load: false default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} max_actor_ckpt_to_keep: null max_critic_ckpt_to_keep: null ray_wait_register_center_timeout: 300 device: cuda use_legacy_worker_impl: auto global_profiler: _target_: verl.utils.profiler.ProfilerConfig tool: null steps: null profile_continuous_steps: false save_path: outputs/profile global_tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: false controller_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph worker_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph capture-range: cudaProfilerApi capture-range-end: null kill: none torch_memory: trace_alloc_max_entries: 100000 stack_depth: 32 context: all stacks: all kw_args: {} transfer_queue: enable: false ray_kwargs: ray_init: num_cpus: null timeline_json_file: null ================================================ FILE: verl/trainer/config/_generated_ppo_trainer.yaml ================================================ # This reference configration yaml is automatically generated via 'scripts/generate_trainer_config.sh' # in which it invokes 'python3 scripts/print_cfg.py --cfg job ' to flatten the 'verl/trainer/config/ppo_trainer.yaml' config fields into a single file. # Do not modify this file directly. # The file is usually only for reference and never used. actor_rollout_ref: actor: optim: _target_: verl.workers.config.FSDPOptimizerConfig optimizer: AdamW optimizer_impl: torch.optim lr: 1.0e-06 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 min_lr_ratio: 0.0 num_cycles: 0.5 lr_scheduler_type: constant zero_indexed_step: true warmup_style: null override_optimizer_config: null fsdp_config: _target_: verl.workers.config.FSDPEngineConfig wrap_policy: min_num_params: 0 param_offload: false optimizer_offload: false offload_policy: false reshard_after_forward: true fsdp_size: -1 forward_prefetch: false model_dtype: fp32 use_orig_params: false seed: 42 full_determinism: false ulysses_sequence_parallel_size: 1 entropy_from_logits_with_chunking: false use_torch_compile: true entropy_checkpointing: false forward_only: false strategy: fsdp dtype: bfloat16 qat: _target_: verl.workers.config.QATEngineConfig enable: false mode: w4a16 group_size: 16 ignore_patterns: - lm_head - embed_tokens - re:.*mlp.gate$ activation_observer: static_minmax quantization_config_path: null _target_: verl.workers.config.FSDPActorConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: fsdp ppo_mini_batch_size: 256 ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: false ppo_max_token_len_per_gpu: 16384 clip_ratio: 0.2 clip_ratio_low: 0.2 clip_ratio_high: 0.2 tau_pos: 1.0 tau_neg: 1.05 freeze_vision_tower: false policy_loss: _target_: verl.workers.config.PolicyLossConfig loss_mode: vanilla clip_cov_ratio: 0.0002 clip_cov_lb: 1.0 clip_cov_ub: 5.0 kl_cov_ratio: 0.0002 ppo_kl_coef: 0.1 clip_ratio_c: 3.0 loss_agg_mode: token-mean loss_scale_factor: null entropy_coeff: 0 calculate_entropy: false use_kl_loss: false use_prefix_grouper: false use_torch_compile: true kl_loss_coef: 0.001 kl_loss_type: low_var_kl ppo_epochs: 1 shuffle: false data_loader_seed: 42 checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null grad_clip: 1.0 ulysses_sequence_parallel_size: 1 entropy_from_logits_with_chunking: false entropy_checkpointing: false use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false} calculate_sum_pi_squared: false sum_pi_squared_checkpointing: false qat: enable: false mode: w4a16 group_size: 16 ignore_patterns: - lm_head - embed_tokens - re:.*mlp.gate$ activation_observer: static_minmax quantization_config_path: null ref: rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: ${actor_rollout_ref.actor.strategy} use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true} log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null fsdp_config: _target_: verl.workers.config.FSDPEngineConfig wrap_policy: min_num_params: 0 param_offload: false optimizer_offload: false offload_policy: false reshard_after_forward: true fsdp_size: -1 forward_prefetch: false model_dtype: fp32 use_orig_params: false seed: 42 full_determinism: false ulysses_sequence_parallel_size: 1 entropy_from_logits_with_chunking: false use_torch_compile: true entropy_checkpointing: false forward_only: true strategy: fsdp dtype: bfloat16 qat: _target_: verl.workers.config.QATEngineConfig enable: false mode: w4a16 group_size: 16 ignore_patterns: - lm_head - embed_tokens - re:.*mlp.gate$ activation_observer: static_minmax quantization_config_path: null _target_: verl.workers.config.FSDPActorConfig ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1} entropy_from_logits_with_chunking: false entropy_checkpointing: false rollout: _target_: verl.workers.config.RolloutConfig name: ??? mode: async nnodes: 0 n_gpus_per_node: ${oc.select:trainer.n_gpus_per_node,8} temperature: 1.0 top_k: -1 top_p: 1 prompt_length: ${oc.select:data.max_prompt_length,512} response_length: ${oc.select:data.max_response_length,512} dtype: bfloat16 gpu_memory_utilization: 0.5 ignore_eos: false enforce_eager: false cudagraph_capture_sizes: null free_cache_engine: true tensor_model_parallel_size: 2 data_parallel_size: 1 expert_parallel_size: 1 pipeline_model_parallel_size: 1 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 enable_chunked_prefill: true enable_prefix_caching: true logprobs_mode: processed_logprobs scheduling_policy: fcfs load_format: dummy log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} disable_log_stats: true do_sample: true 'n': 1 over_sample_rate: 0 multi_stage_wake_up: false engine_kwargs: vllm: {} sglang: {} trtllm: {} val_kwargs: _target_: verl.workers.config.SamplingConfig top_k: -1 top_p: 1.0 temperature: 0 'n': 1 do_sample: false multi_turn: _target_: verl.workers.config.MultiTurnConfig enable: false max_assistant_turns: null tool_config_path: null max_user_turns: null max_parallel_calls: 1 max_tool_response_length: 256 tool_response_truncate_side: middle interaction_config_path: null use_inference_chat_template: false tokenization_sanity_check_mode: strict format: hermes num_repeat_rollouts: null calculate_log_probs: false agent: _target_: verl.workers.config.AgentLoopConfig num_workers: 8 default_agent_loop: single_turn_agent agent_loop_config_path: null custom_async_server: _target_: verl.workers.config.CustomAsyncServerConfig path: null name: null checkpoint_engine: _target_: verl.workers.config.CheckpointEngineConfig backend: naive update_weights_bucket_megabytes: 2048 engine_kwargs: {} trace: _target_: verl.workers.config.TraceConfig project_name: ${oc.select:trainer.project_name,null} experiment_name: ${oc.select:trainer.experiment_name,null} backend: null token2text: false max_samples_per_step_per_worker: null skip_rollout: false skip_dump_dir: /tmp/rollout_dump skip_tokenizer_init: true enable_rollout_routing_replay: false profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false} all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false} ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]} save_path: ${oc.select:global_profiler.save_path,null} tool_config: npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.contents,[]} level: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.level,level0} analysis: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.analysis,false} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.discrete,false} torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.contents,[]} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.discrete,false} prometheus: _target_: verl.workers.config.PrometheusConfig enable: false port: 9090 file: /tmp/ray/session_latest/metrics/prometheus/prometheus.yml served_model_name: ${oc.select:actor_rollout_ref.model.path,null} quantization: null quantization_config_file: null mtp: ${oc.select:actor_rollout_ref.model.mtp, null} qat: ${oc.select:actor_rollout_ref.actor.fsdp_config.qat,null} layered_summon: false model: _target_: verl.workers.config.HFModelConfig path: ~/models/deepseek-llm-7b-chat hf_config_path: null tokenizer_path: null use_shm: false trust_remote_code: false custom_chat_template: null external_lib: null override_config: {} enable_gradient_checkpointing: true enable_activation_offload: false use_remove_padding: true lora_rank: 0 lora_alpha: 16 target_modules: all-linear exclude_modules: null lora_adapter_path: null use_liger: false use_fused_kernels: false fused_kernel_options: impl_backend: torch tiled_mlp: enabled: false num_shards: 4 mtp: _target_: verl.workers.config.MtpConfig enable: false enable_train: false enable_rollout: false detach_encoder: false mtp_loss_scaling_factor: 0.1 speculative_algorithm: EAGLE speculative_num_steps: 3 speculative_eagle_topk: 1 speculative_num_draft_tokens: 4 method: mtp num_speculative_tokens: 1 hybrid_engine: true nccl_timeout: 600 data: tokenizer: null use_shm: false train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 val_max_samples: -1 prompt_key: prompt reward_fn_key: data_source max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null tool_config_path: ${oc.select:actor_rollout_ref.rollout.multi_turn.tool_config_path, null} return_raw_input_ids: false return_raw_chat: true return_full_prompt: false shuffle: true seed: null dataloader_num_workers: 8 image_patch_size: 14 validation_shuffle: false filter_overlong_prompts: false filter_overlong_prompts_workers: 1 truncation: error image_key: images video_key: videos trust_remote_code: false custom_cls: path: null name: null return_multi_modal_inputs: true sampler: class_path: null class_name: null datagen: path: null name: null apply_chat_template_kwargs: {} critic: optim: _target_: verl.workers.config.FSDPOptimizerConfig optimizer: AdamW optimizer_impl: torch.optim lr: 1.0e-05 lr_warmup_steps_ratio: 0.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 min_lr_ratio: 0.0 num_cycles: 0.5 lr_scheduler_type: constant zero_indexed_step: true warmup_style: null override_optimizer_config: null model: fsdp_config: _target_: verl.workers.config.FSDPEngineConfig wrap_policy: min_num_params: 0 param_offload: false optimizer_offload: false offload_policy: false reshard_after_forward: true fsdp_size: -1 forward_prefetch: false model_dtype: fp32 use_orig_params: false seed: 42 full_determinism: false ulysses_sequence_parallel_size: 1 entropy_from_logits_with_chunking: false use_torch_compile: true entropy_checkpointing: false forward_only: false strategy: fsdp dtype: bfloat16 qat: _target_: verl.workers.config.QATEngineConfig enable: false mode: w4a16 group_size: 16 ignore_patterns: - lm_head - embed_tokens - re:.*mlp.gate$ activation_observer: static_minmax quantization_config_path: null path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"} override_config: {} external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null} trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false} _target_: verl.workers.config.FSDPCriticModelCfg use_shm: false enable_gradient_checkpointing: true enable_activation_offload: false use_remove_padding: false lora_rank: 0 lora_alpha: 16 target_modules: all-linear tiled_mlp: enabled: false num_shards: 4 _target_: verl.workers.config.FSDPCriticConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: fsdp enable: null ppo_mini_batch_size: ${oc.select:actor_rollout_ref.actor.ppo_mini_batch_size,256} ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size,null} use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} ppo_max_token_len_per_gpu: 32768 forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu} ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1} shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false} data_loader_seed: 42 cliprange_value: 0.5 loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean} checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null} forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null} ulysses_sequence_parallel_size: 1 grad_clip: 1.0 custom_reward_function: path: null name: null reward_model: num_workers: null reward_manager: null enable: null enable_resource_pool: null n_gpus_per_node: null nnodes: null reward_loop_source: null reward_loop_module_path: null reward_loop_class_name: null model: path: null external_lib: null trust_remote_code: null rollout: name: null dtype: null gpu_memory_utilization: null enforce_eager: null cudagraph_capture_sizes: null free_cache_engine: null data_parallel_size: null expert_parallel_size: null tensor_model_parallel_size: null max_num_batched_tokens: null max_model_len: null max_num_seqs: null load_format: null engine_kwargs: null limit_images: null enable_chunked_prefill: null enable_prefix_caching: null disable_log_stats: null skip_tokenizer_init: null prompt_length: null response_length: null sandbox_fusion: url: null max_concurrent: null memory_limit_mb: null reward: num_workers: 8 custom_reward_function: path: null name: compute_score reward_manager: _target_: verl.workers.config.reward_model.RewardManagerConfig source: register name: naive module: _target_: verl.trainer.config.config.ModuleConfig path: null name: custom_reward_manager reward_model: enable: false enable_resource_pool: false n_gpus_per_node: 8 nnodes: 0 model_path: null rollout: _target_: verl.workers.config.RolloutConfig name: ??? dtype: bfloat16 gpu_memory_utilization: 0.5 enforce_eager: true cudagraph_capture_sizes: null free_cache_engine: true data_parallel_size: 1 expert_parallel_size: 1 tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 load_format: auto engine_kwargs: {} limit_images: null enable_chunked_prefill: true enable_prefix_caching: true disable_log_stats: true skip_tokenizer_init: false prompt_length: 2048 response_length: 2048 sandbox_fusion: url: null max_concurrent: 64 memory_limit_mb: 1024 algorithm: rollout_correction: rollout_is: null rollout_is_threshold: 2.0 rollout_rs: null rollout_rs_threshold: null bypass_mode: false loss_type: ppo_clip rollout_is_batch_normalize: false _target_: verl.trainer.config.AlgoConfig gamma: 1.0 lam: 1.0 adv_estimator: gae norm_adv_by_std_in_grpo: true use_kl_in_reward: false kl_penalty: kl kl_ctrl: _target_: verl.trainer.config.KLControlConfig type: fixed kl_coef: 0.001 horizon: 10000 target_kl: 0.1 use_pf_ppo: false pf_ppo: reweight_method: pow weight_pow: 2.0 trainer: balance_batch: true total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: - console - wandb log_val_generations: 0 rollout_data_dir: null validation_data_dir: null nnodes: 1 n_gpus_per_node: 8 save_freq: -1 esi_redundant_time: 0 resume_mode: auto resume_from_path: null val_before_train: true val_only: false test_freq: -1 critic_warmup: 0 default_hdfs_dir: null del_local_ckpt_after_load: false default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} max_actor_ckpt_to_keep: null max_critic_ckpt_to_keep: null ray_wait_register_center_timeout: 300 device: cuda use_legacy_worker_impl: auto global_profiler: _target_: verl.utils.profiler.ProfilerConfig tool: null steps: null profile_continuous_steps: false save_path: outputs/profile global_tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: false controller_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph worker_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph capture-range: cudaProfilerApi capture-range-end: null kill: none torch_memory: trace_alloc_max_entries: 100000 stack_depth: 32 context: all stacks: all kw_args: {} transfer_queue: enable: false ray_kwargs: ray_init: num_cpus: null timeline_json_file: null ================================================ FILE: verl/trainer/config/_generated_ppo_veomni_trainer.yaml ================================================ # This reference configration yaml is automatically generated via 'scripts/generate_trainer_config.sh' # in which it invokes 'python3 scripts/print_cfg.py --cfg job model_engine=veomni' to flatten the 'verl/trainer/config/ppo_trainer.yaml' config fields into a single file. # Do not modify this file directly. # The file is usually only for reference and never used. actor_rollout_ref: actor: optim: _target_: verl.workers.config.VeOmniOptimizerConfig optimizer: adamw lr: 1.0e-06 lr_min: 0.0 lr_start: 0.0 lr_warmup_steps_ratio: 0.0 lr_decay_ratio: 1.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 lr_scheduler_type: cosine override_optimizer_config: {} veomni: _target_: verl.workers.config.VeOmniEngineConfig param_offload: false optimizer_offload: false fsdp_size: -1 ulysses_parallel_size: 1 expert_parallel_size: 1 mixed_precision: true seed: 42 full_determinism: false init_device: meta enable_full_shard: true ckpt_manager: dcp forward_prefetch: true strategy: veomni use_torch_compile: false forward_only: false enable_fsdp_offload: false enable_reentrant: false attn_implementation: flash_attention_2 moe_implementation: fused force_use_huggingface: false activation_gpu_limit: 0.0 _target_: verl.workers.config.VeOmniActorConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: veomni ppo_mini_batch_size: 256 ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: false ppo_max_token_len_per_gpu: 16384 clip_ratio: 0.2 clip_ratio_low: 0.2 clip_ratio_high: 0.2 tau_pos: 1.0 tau_neg: 1.05 freeze_vision_tower: false policy_loss: _target_: verl.workers.config.PolicyLossConfig loss_mode: vanilla clip_cov_ratio: 0.0002 clip_cov_lb: 1.0 clip_cov_ub: 5.0 kl_cov_ratio: 0.0002 ppo_kl_coef: 0.1 clip_ratio_c: 3.0 loss_agg_mode: token-mean loss_scale_factor: null entropy_coeff: 0 calculate_entropy: false use_kl_loss: false use_prefix_grouper: false use_torch_compile: true kl_loss_coef: 0.001 kl_loss_type: low_var_kl ppo_epochs: 1 shuffle: false data_loader_seed: 42 checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null ref: rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: veomni use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true} log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} router_replay: _target_: verl.workers.config.RouterReplayConfig mode: disabled record_file: null replay_file: null veomni: _target_: verl.workers.config.VeOmniEngineConfig param_offload: ${oc.select:actor_rollout_ref.actor.veomni.param_offload,False} optimizer_offload: false fsdp_size: ${oc.select:actor_rollout_ref.actor.veomni.fsdp_size,-1} ulysses_parallel_size: ${oc.select:actor_rollout_ref.actor.veomni.ulysses_parallel_size,1} expert_parallel_size: ${oc.select:actor_rollout_ref.actor.veomni.expert_parallel_size,1} mixed_precision: true seed: ${oc.select:actor_rollout_ref.actor.veomni.seed,42} full_determinism: false init_device: meta enable_full_shard: true ckpt_manager: dcp forward_prefetch: true strategy: veomni use_torch_compile: false forward_only: true enable_fsdp_offload: false enable_reentrant: false attn_implementation: ${oc.select:actor_rollout_ref.actor.veomni.attn_implementation,flash_attention_2} moe_implementation: ${oc.select:actor_rollout_ref.actor.veomni.moe_implementation,fused} force_use_huggingface: false activation_gpu_limit: 0.0 _target_: verl.workers.config.VeOmniActorConfig rollout: _target_: verl.workers.config.RolloutConfig name: ??? mode: async nnodes: 0 n_gpus_per_node: ${oc.select:trainer.n_gpus_per_node,8} temperature: 1.0 top_k: -1 top_p: 1 prompt_length: ${oc.select:data.max_prompt_length,512} response_length: ${oc.select:data.max_response_length,512} dtype: bfloat16 gpu_memory_utilization: 0.5 ignore_eos: false enforce_eager: false cudagraph_capture_sizes: null free_cache_engine: true tensor_model_parallel_size: 2 data_parallel_size: 1 expert_parallel_size: 1 pipeline_model_parallel_size: 1 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 enable_chunked_prefill: true enable_prefix_caching: true logprobs_mode: processed_logprobs scheduling_policy: fcfs load_format: dummy log_prob_micro_batch_size: null log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} disable_log_stats: true do_sample: true 'n': 1 over_sample_rate: 0 multi_stage_wake_up: false engine_kwargs: vllm: {} sglang: {} trtllm: {} val_kwargs: _target_: verl.workers.config.SamplingConfig top_k: -1 top_p: 1.0 temperature: 0 'n': 1 do_sample: false multi_turn: _target_: verl.workers.config.MultiTurnConfig enable: false max_assistant_turns: null tool_config_path: null max_user_turns: null max_parallel_calls: 1 max_tool_response_length: 256 tool_response_truncate_side: middle interaction_config_path: null use_inference_chat_template: false tokenization_sanity_check_mode: strict format: hermes num_repeat_rollouts: null calculate_log_probs: false agent: _target_: verl.workers.config.AgentLoopConfig num_workers: 8 default_agent_loop: single_turn_agent agent_loop_config_path: null custom_async_server: _target_: verl.workers.config.CustomAsyncServerConfig path: null name: null checkpoint_engine: _target_: verl.workers.config.CheckpointEngineConfig backend: naive update_weights_bucket_megabytes: 2048 engine_kwargs: {} trace: _target_: verl.workers.config.TraceConfig project_name: ${oc.select:trainer.project_name,null} experiment_name: ${oc.select:trainer.experiment_name,null} backend: null token2text: false max_samples_per_step_per_worker: null skip_rollout: false skip_dump_dir: /tmp/rollout_dump skip_tokenizer_init: true enable_rollout_routing_replay: false profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false} all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false} ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]} save_path: ${oc.select:global_profiler.save_path,null} tool_config: npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.contents,[]} level: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.level,level0} analysis: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.analysis,false} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.discrete,false} torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.contents,[]} discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.discrete,false} prometheus: _target_: verl.workers.config.PrometheusConfig enable: false port: 9090 file: /tmp/ray/session_latest/metrics/prometheus/prometheus.yml served_model_name: ${oc.select:actor_rollout_ref.model.path,null} quantization: null quantization_config_file: null mtp: ${oc.select:actor_rollout_ref.model.mtp, null} qat: ${oc.select:actor_rollout_ref.actor.fsdp_config.qat,null} layered_summon: false model: _target_: verl.workers.config.HFModelConfig path: ~/models/deepseek-llm-7b-chat hf_config_path: null tokenizer_path: null use_shm: false trust_remote_code: false custom_chat_template: null external_lib: null override_config: {} enable_gradient_checkpointing: true enable_activation_offload: false use_remove_padding: true lora_rank: 0 lora_alpha: 16 target_modules: all-linear exclude_modules: null lora_adapter_path: null use_liger: false use_fused_kernels: false fused_kernel_options: impl_backend: torch tiled_mlp: enabled: false num_shards: 4 mtp: _target_: verl.workers.config.MtpConfig enable: false enable_train: false enable_rollout: false detach_encoder: false mtp_loss_scaling_factor: 0.1 speculative_algorithm: EAGLE speculative_num_steps: 3 speculative_eagle_topk: 1 speculative_num_draft_tokens: 4 method: mtp num_speculative_tokens: 1 hybrid_engine: true nccl_timeout: 600 data: tokenizer: null use_shm: false train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet train_max_samples: -1 val_max_samples: -1 prompt_key: prompt reward_fn_key: data_source max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null tool_config_path: ${oc.select:actor_rollout_ref.rollout.multi_turn.tool_config_path, null} return_raw_input_ids: false return_raw_chat: true return_full_prompt: false shuffle: true seed: null dataloader_num_workers: 8 image_patch_size: 14 validation_shuffle: false filter_overlong_prompts: false filter_overlong_prompts_workers: 1 truncation: error image_key: images video_key: videos trust_remote_code: false custom_cls: path: null name: null return_multi_modal_inputs: true sampler: class_path: null class_name: null datagen: path: null name: null apply_chat_template_kwargs: {} critic: optim: _target_: verl.workers.config.VeOmniOptimizerConfig optimizer: adamw lr: 1.0e-05 lr_min: 0.0 lr_start: 0.0 lr_warmup_steps_ratio: 0.0 lr_decay_ratio: 1.0 total_training_steps: -1 weight_decay: 0.01 lr_warmup_steps: -1 betas: - 0.9 - 0.999 clip_grad: 1.0 lr_scheduler_type: cosine override_optimizer_config: {} veomni: _target_: verl.workers.config.VeOmniEngineConfig param_offload: false optimizer_offload: false fsdp_size: -1 ulysses_parallel_size: 1 expert_parallel_size: 1 mixed_precision: true seed: 42 full_determinism: false init_device: meta enable_full_shard: true ckpt_manager: dcp forward_prefetch: true strategy: veomni use_torch_compile: false forward_only: false enable_fsdp_offload: false enable_reentrant: false attn_implementation: flash_attention_2 moe_implementation: fused force_use_huggingface: false activation_gpu_limit: 0.0 _target_: verl.workers.config.VeOmniCriticConfig rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} strategy: veomni enable: null model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"} override_config: {} external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null} trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false} _target_: verl.trainer.config.BaseModelConfig ppo_mini_batch_size: ${oc.select:actor_rollout_ref.actor.ppo_mini_batch_size,256} ppo_micro_batch_size: null ppo_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size,null} use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} ppo_max_token_len_per_gpu: 32768 forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu} ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1} shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false} data_loader_seed: ${oc.select:actor_rollout_ref.actor.data_loader_seed,null} cliprange_value: 0.5 loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean} checkpoint: _target_: verl.trainer.config.CheckpointConfig save_contents: - model - optimizer - extra load_contents: ${.save_contents} async_save: false mbridge_config: {} profiler: _target_: verl.utils.profiler.ProfilerConfig tool: ${oc.select:global_profiler.tool,null} enable: false all_ranks: false ranks: [] save_path: ${oc.select:global_profiler.save_path,null} tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} npu: _target_: verl.utils.profiler.config.NPUToolConfig contents: [] level: level0 analysis: true discrete: false torch: _target_: verl.utils.profiler.config.TorchProfilerToolConfig contents: [] discrete: false torch_memory: _target_: verl.utils.profiler.config.TorchMemoryToolConfig trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} custom_reward_function: path: null name: null reward_model: num_workers: null reward_manager: null enable: null enable_resource_pool: null n_gpus_per_node: null nnodes: null reward_loop_source: null reward_loop_module_path: null reward_loop_class_name: null model: path: null external_lib: null trust_remote_code: null rollout: name: null dtype: null gpu_memory_utilization: null enforce_eager: null cudagraph_capture_sizes: null free_cache_engine: null data_parallel_size: null expert_parallel_size: null tensor_model_parallel_size: null max_num_batched_tokens: null max_model_len: null max_num_seqs: null load_format: null engine_kwargs: null limit_images: null enable_chunked_prefill: null enable_prefix_caching: null disable_log_stats: null skip_tokenizer_init: null prompt_length: null response_length: null sandbox_fusion: url: null max_concurrent: null memory_limit_mb: null reward: num_workers: 8 custom_reward_function: path: null name: compute_score reward_manager: _target_: verl.workers.config.reward_model.RewardManagerConfig source: register name: naive module: _target_: verl.trainer.config.config.ModuleConfig path: null name: custom_reward_manager reward_model: enable: false enable_resource_pool: false n_gpus_per_node: 8 nnodes: 0 model_path: null rollout: _target_: verl.workers.config.RolloutConfig name: ??? dtype: bfloat16 gpu_memory_utilization: 0.5 enforce_eager: true cudagraph_capture_sizes: null free_cache_engine: true data_parallel_size: 1 expert_parallel_size: 1 tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 load_format: auto engine_kwargs: {} limit_images: null enable_chunked_prefill: true enable_prefix_caching: true disable_log_stats: true skip_tokenizer_init: false prompt_length: 2048 response_length: 2048 sandbox_fusion: url: null max_concurrent: 64 memory_limit_mb: 1024 algorithm: rollout_correction: rollout_is: null rollout_is_threshold: 2.0 rollout_rs: null rollout_rs_threshold: null bypass_mode: false loss_type: ppo_clip rollout_is_batch_normalize: false _target_: verl.trainer.config.AlgoConfig gamma: 1.0 lam: 1.0 adv_estimator: gae norm_adv_by_std_in_grpo: true use_kl_in_reward: false kl_penalty: kl kl_ctrl: _target_: verl.trainer.config.KLControlConfig type: fixed kl_coef: 0.001 horizon: 10000 target_kl: 0.1 use_pf_ppo: false pf_ppo: reweight_method: pow weight_pow: 2.0 trainer: balance_batch: true total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: - console - wandb log_val_generations: 0 rollout_data_dir: null validation_data_dir: null nnodes: 1 n_gpus_per_node: 8 save_freq: -1 esi_redundant_time: 0 resume_mode: auto resume_from_path: null val_before_train: true val_only: false test_freq: -1 critic_warmup: 0 default_hdfs_dir: null del_local_ckpt_after_load: false default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} max_actor_ckpt_to_keep: null max_critic_ckpt_to_keep: null ray_wait_register_center_timeout: 300 device: cuda use_legacy_worker_impl: auto global_profiler: _target_: verl.utils.profiler.ProfilerConfig tool: null steps: null profile_continuous_steps: false save_path: outputs/profile global_tool_config: nsys: _target_: verl.utils.profiler.config.NsightToolConfig discrete: false controller_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph worker_nsight_options: trace: cuda,nvtx,cublas,ucx cuda-memory-usage: 'true' cuda-graph-trace: graph capture-range: cudaProfilerApi capture-range-end: null kill: none torch_memory: trace_alloc_max_entries: 100000 stack_depth: 32 context: all stacks: all kw_args: {} transfer_queue: enable: false ray_kwargs: ray_init: num_cpus: null timeline_json_file: null ================================================ FILE: verl/trainer/config/actor/actor.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. # Target class for this configuration _target_: verl.workers.config.ActorConfig # Number of rollouts per update (mirrors actor rollout_n) rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} # the abstract actor configs # fsdp, fsdp2 or megatron. must be set. strategy: ??? # Split each sample into sub-batches of this size for PPO ppo_mini_batch_size: 256 # [Deprecated] Global micro batch size ppo_micro_batch_size: null # Local per-GPU micro batch size ppo_micro_batch_size_per_gpu: null # Whether to automatically adjust batch size at runtime # oc.select: the default val for ref.log_prob_use_dynamic_bsz use_dynamic_bsz: false # Max tokens per GPU in one PPO batch; affects gradient accumulation # Typically it should be: n * ${data.max_prompt_length} + ${data.max_response_length} # oc.select: the default val for ref.log_prob_max_token_len_per_gpu ppo_max_token_len_per_gpu: 16384 # PPO clip ratio clip_ratio: 0.2 # Lower bound for asymmetric clipping (used in dual-clip PPO) clip_ratio_low: 0.2 # Upper bound for asymmetric clipping (used in dual-clip PPO) clip_ratio_high: 0.2 # Positive and negative tau for smoothing function in SAPO (https://arxiv.org/pdf/2511.20347) # default values used in the paper with Qwen3-30B-A3B-Base tau_pos: 1.0 # negative tau for smoothing function in SAPO tau_neg: 1.05 # Whether to freeze vision model, if set true, it will be freeze vision model freeze_vision_tower: false # policy loss config policy_loss: # # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.PolicyLossConfig # Loss function mode: vanilla / clip-cov / kl-cov /gpg from https://arxiv.org/abs/2505.22617 loss_mode: "vanilla" # Ratio of tokens to be clipped for clip-cov loss clip_cov_ratio: 0.0002 # Lower bound for clip-cov loss clip_cov_lb: 1.0 # Upper bound for clip-cov loss clip_cov_ub: 5.0 # Ratio of tokens to be applied kl penalty for kl-cov loss kl_cov_ratio: 0.0002 # KL divergence penalty coefficient ppo_kl_coef: 0.1 # Constant C in Dual-clip PPO; clips when advantage < 0 and ratio > C clip_ratio_c: 3.0 # Loss aggregation mode: "token-mean", "seq-mean-token-sum", "seq-mean-token-mean", or "seq-mean-token-sum-norm" loss_agg_mode: token-mean # Scale factor for "seq-mean-token-sum-norm" loss aggregation mode. # If null, uses response_length. Set to a constant to ensure consistent normalization. loss_scale_factor: null # Entropy regularization coefficient in PPO loss entropy_coeff: 0 # When true, the actor forward will request entropy from the model calculate_entropy: false # Whether to use KL loss instead of KL reward penalty. True for GRPO use_kl_loss: false # Whether to enable PrefixGrouper shared-prefix forward use_prefix_grouper: false # Whether to use torch.compile() # oc.select: the default val for ref.use_torch_compile use_torch_compile: true # KL loss coefficient when use_kl_loss is enabled. For GRPO kl_loss_coef: 0.001 # Type of KL divergence loss. Options: "kl"(k1), "abs", "mse"(k2), "low_var_kl"(k3), "full" kl_loss_type: low_var_kl # Number of PPO epochs per batch ppo_epochs: 1 # Shuffle training data across PPO epochs shuffle: false # The seed used to construct mini-batch data_loader_seed: 42 # checkpoint configs checkpoint: # Target dataclass for this configuration _target_: verl.trainer.config.CheckpointConfig # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] # For more flexibility, you can specify the contents to load from the checkpoint. # .xxx refers to the local variable xxx from the same level of hierarchy similar to python pkg load_contents: ${.save_contents} # Whether to save checkpoints asynchronously. Only effective for Megatron as of now. async_save: False # Mbridge config extension. # when vanilla_mbridge=True, and your filesystem is a distributed filesystem,(which means you write a file in node A # and you can read the file in node B immediately) # set `mbridge_config.distributed_filesystem=True` and `mbridge_config.memory_efficient=True` to # speed up the checkpoint saving by 10x speed. mbridge_config: {} # optimizer configs optim: # Learning rate lr: 1e-6 # Warmup steps ratio (used if lr_warmup_steps is 0 or negative) lr_warmup_steps_ratio: 0.0 # Total training steps (must be overridden at runtime) total_training_steps: -1 # Weight decay weight_decay: 0.01 # Prioritized. None, 0 or Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps: -1 # Whether to use custom fused kernels (e.g., FlashAttention, fused MLP) use_fused_kernels: ${oc.select:actor_rollout_ref.model.use_fused_kernels,false} # profile the actor model in `update_policy` profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # profiler tool, default same as profiler.tool in global config # choices: nsys, npu, torch tool: ${oc.select:global_profiler.tool,null} # whether enable profile on Actor enable: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # profile results saving path save_path: ${oc.select:global_profiler.save_path,null} # specific tool config which only related to the role tool_config: # nsys tool config nsys: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NsightToolConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} # npu config npu: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NPUToolConfig # Contents to profile, can be empty # options: npu, cpu, memory, shapes, module, stack contents: [] # Collection level, optional values: level_none, level0, level1, level2. level: "level0" # Whether to automatically parse the data. analysis: True # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # torch profiler config torch: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchProfilerToolConfig # Contents to profile, can be empty # options: cuda, cpu, memory, shapes, stack contents: [] # True for each task has its own database, False for all tasks in one training step share one database. discrete: false # torch memory profiler config torch_memory: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchMemoryToolConfig # Maximum number of memory allocation entries to track trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} # Stack trace depth for memory allocations stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} # Router replay configuration for MoE models router_replay: # Target dataclass for this configuration _target_: verl.workers.config.RouterReplayConfig # Router replay mode: disabled, R2, R3 # - R2: Use R2 routing strategy (record mode) # - R3: Use R3 routing strategy (record mode) mode: disabled # File path to save recorded routing decisions # Required when mode is 'record', 'R2', or 'R3' record_file: null # File path to load recorded routing decisions for replay # Required when mode is 'replay' replay_file: null ================================================ FILE: verl/trainer/config/actor/dp_actor.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. # defaults specify the default config from each component defaults: # fsdp optimizer config - ../optim@optim: fsdp # fsdp engine config - ../engine@fsdp_config: fsdp # dp actor config, inheriting from trainer/config/actor/actor.yaml - actor # load the reference default config, then apply the fields in the current yaml - _self_ # Target class for this configuration _target_: verl.workers.config.FSDPActorConfig # TODO(haibin.lin): switch to fsdp2 strategy: fsdp # Gradient clipping for actor updates, specific to the strategy. grad_clip: 1.0 # Sequence parallelism size for Ulysses-style model parallelism # oc.select: the default val for ref.ulysses_sequence_parallel_size # [DEPRECATED] use fsdp_config.ulysses_sequence_parallel_size instead ulysses_sequence_parallel_size: 1 # calculate entropy with chunking to reduce memory peak entropy_from_logits_with_chunking: False # recompute entropy entropy_checkpointing: False # Whether to remove padding tokens in inputs during training use_remove_padding: ${oc.select:actor_rollout_ref.model.use_remove_padding,false} # This computes Σπ² needed for the Logit-Gradient Norm proxy W(τ) = Σ_t[1 - 2π_t + Σπ²] # c.f. https://yingru.notion.site/The-Optimal-Token-Baseline-399211a558b782cfa936014c0d42dfb8 calculate_sum_pi_squared: False # Enable gradient checkpointing for sum_pi_squared computation (saves memory) sum_pi_squared_checkpointing: False # QAT (Quantization-Aware Training) configuration # When enabled: # - QAT is automatically applied to actor model during training # - Fused scales (QKV/GateUp) are automatically enabled for training-inference consistency # - Fast quantization is used when syncing weights to vLLM rollout # Supported modes: "w4a16" (NVFP4 weight-only) # Note: "w4a4" mode is included in the code but currently has KL divergence issues and is NOT recommended for use. # For usage examples, see: https://github.com/verl-project/verl-recipe/blob/main/qat/README.md qat: # Whether to enable QAT enable: false # Quantization mode: "w4a16" (weight-only). "w4a4" is experimental and not recommended. mode: "w4a16" # Quantization group size (NVFP4 requires 16) group_size: 16 # Patterns to ignore (e.g., lm_head, embed_tokens) ignore_patterns: - "lm_head" - "embed_tokens" - "re:.*mlp.gate$" # Activation observer for W4A4 mode: "static_minmax", "memoryless_minmax", or "minmax" activation_observer: "static_minmax" # Path to vLLM quantization config JSON file quantization_config_path: null ================================================ FILE: verl/trainer/config/actor/megatron_actor.yaml ================================================ # megatron actor config, inheriting from trainer/config/actor/actor.yaml defaults: # megatron optimizer config - ../optim@optim: megatron # megatron engine config - ../engine@megatron: megatron - actor # load the reference default config, then apply the fields in the current yaml - _self_ _target_: verl.workers.config.McoreActorConfig strategy: megatron load_weight: True ================================================ FILE: verl/trainer/config/actor/torchtitan_actor.yaml ================================================ # torchtitan actor config, inheriting from trainer/config/actor/actor.yaml defaults: # torchtitan optimizer config - ../optim@optim: torchtitan # torchtitan engine config - ../engine@torchtitan: torchtitan - actor # load the reference default config, then apply the fields in the current yaml - _self_ _target_: verl.workers.config.TorchTitanActorConfig strategy: torchtitan ================================================ FILE: verl/trainer/config/actor/veomni_actor.yaml ================================================ # veomni actor config, inheriting from trainer/config/actor/actor.yaml defaults: # veomni optimizer config - ../optim@optim: veomni # veomni engine config - ../engine@veomni: veomni - actor # load the reference default config, then apply the fields in the current yaml - _self_ _target_: verl.workers.config.VeOmniActorConfig strategy: veomni ================================================ FILE: verl/trainer/config/algorithm/rollout_correction.yaml ================================================ # Rollout Correction: corrects off-policy distribution shifts # See documentation: docs/algo/rollout_corr.md # Use presets: RolloutCorrectionConfig.decoupled_seq_is(), .bypass_pg_is(), etc. # IS aggregation level: null (disabled), "token" (per-token), "sequence" (per-sequence) rollout_is: null # Upper threshold for IS weight truncation (typical: 2.0-5.0) rollout_is_threshold: 2.0 # RS aggregation level: null (disabled), e.g. "token_k1", "seq_sum_k1", "seq_mean_k3" rollout_rs: null # Threshold for rejection sampling (string or float; see code docs) rollout_rs_threshold: null # Operating mode: false = Decoupled (3 policies), true = Bypass (2 policies) bypass_mode: false # Loss type in bypass mode (bypass_mode=true): # - "ppo_clip": PPO clipped objective (IS handled by ratio, default) # - "reinforce": REINFORCE with explicit IS weights (no PPO clipping) loss_type: ppo_clip # Batch normalize IS weights: false = raw weights, true = normalize to mean=1.0 rollout_is_batch_normalize: false ================================================ FILE: verl/trainer/config/algorithm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass, field from typing import Any, Optional from verl.base_config import BaseConfig __all__ = ["AlgoConfig", "FilterGroupsConfig", "KLControlConfig", "RolloutCorrectionConfig"] @dataclass class KLControlConfig(BaseConfig): """Configuration for KL control. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: type (str): Type of KL control. Can be "fixed" or "adaptive". kl_coef (float): Initial coefficient for KL penalty. horizon (int): Horizon value for adaptive controller. target_kl (float): Target KL divergence for adaptive controller. """ type: str = "fixed" kl_coef: float = 0.001 horizon: int = 10000 target_kl: float = 0.1 @dataclass class FilterGroupsConfig(BaseConfig): """Configuration for filter groups (used in DAPO and Entropy). The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: enable (bool): Whether to enable filter groups. metric (Optional[str]): Metric to use for filtering: "acc", "score", "seq_reward", "seq_final_reward", etc. max_num_gen_batches (int): Non-positive values mean no upper limit. """ enable: bool = False metric: Optional[str] = None max_num_gen_batches: int = 0 @dataclass class RolloutCorrectionConfig(BaseConfig): """Configuration for Rollout Correction (addresses off-policy issues in RL training). The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Rollout Correction handles off-policiness from multiple sources: 1. Policy mismatch: Rollout policy (e.g., vLLM BF16) vs Training policy (e.g., FSDP FP32) 2. Model update staleness: Rollout data collected from older policy checkpoints 3. General off-policy scenarios: Any distribution shift between data collection and training For more details, see: "When Speed Kills Stability: Demystifying RL Collapse from the Training-Inference Mismatch" https://richardli.xyz/rl-collapse This typed config replaces the old dict-based approach and provides: - Type safety and validation - Clear documentation of all parameters - Named factory methods for common presets (TIS, MIS, etc.) - Sensible defaults Args: rollout_is (Optional[str]): IS weight aggregation level. - None: No IS weights (metrics only) - "token": Per-token IS weights (low variance, biased) - "sequence": Per-sequence IS weights (unbiased, high variance) Default: "sequence" rollout_is_threshold (float): Upper threshold for IS weight truncation/rejection. Typical range: 1.5-5.0 for token level, 2.0-10.0 for sequence level. Default: 2.0 rollout_is_batch_normalize (bool): Apply batch normalization to IS weights. - True: Normalize IS weights to have mean=1.0 within each batch - False: Use raw (truncated) IS weights (standard) - Reduces variance by ensuring average weight is 1.0 per batch - Only affects IS weight values, not rejection sampling Default: False (no batch normalization) rollout_rs (Optional[str]): Rejection sampling aggregation modes. Accepts a comma-delimited list (duplicates removed) of canonical options implemented in ``rollout_corr_helper``: - "token_k1": Token-level rejection with ``-log r`` (ratio thresholds supplied via ``rollout_rs_threshold`` as ``lower_upper``) - "token_k2": Token-level rejection with ``0.5 * (log r)^2`` (upper bound only) - "token_k3": Token-level rejection with ``exp(log r) - 1 - log r`` (upper bound only) - "seq_sum_k1": Sequence sum of ``-log r`` (ratio bounds) - "seq_sum_k2": Sequence sum of rejection with ``0.5 * (log r)^2`` (upper bound only) - "seq_sum_k3": Sequence sum of rejection with ``exp(log r) - 1 - log r`` (upper bound only) - "seq_mean_k1": Sequence mean of ``-log r`` (ratio bounds) - "seq_mean_k2": Sequence mean of rejection with ``0.5 * (log r)^2`` (upper bound only) - "seq_mean_k3": Sequence mean of rejection with ``exp(log r) - 1 - log r`` (upper bound only) - "seq_max_k2": Sequence max of rejection with ``0.5 * (log r)^2`` (upper bound only) - "seq_max_k3": Sequence max of rejection with ``exp(log r) - 1 - log r`` (upper bound only) names automatically. Default: None rollout_rs_threshold (Optional[Union[str, float]]): Threshold specification for rejection sampling. Provide one value per option (single entry is broadcast when multiple options are supplied). Ratio-based modes (``*k1``) expect ``lower_upper`` strings; supplying a single float implies only the upper ratio bound, with the lower bound inferred as its reciprocal. Divergence modes (k2/k3) expect positive upper bounds (float or string). Default: None bypass_mode (bool): Operating mode - bypass or decoupled. - True: Bypass mode - reuse rollout_log_prob as old_log_prob (2 policies) Uses compute_policy_loss_bypass_mode() with loss_type selection - False: Decoupled mode - compute old_log_prob separately (3 policies) Uses standard PPO loss with IS weight correction Default: False (decoupled mode) loss_type (str): Loss function type in bypass mode (bypass_mode=True). - "reinforce": REINFORCE-style policy gradient with explicit IS weights L = -E[w * log π(a|s) * A] where w = π_current / π_rollout - "ppo_clip": PPO clipped objective (IS handled by ratio, no explicit weights) L = -E[min(r*A, clip(r)*A)] where r = π_current / π_rollout Default: "ppo_clip" Example: # Create with defaults config = RolloutCorrectionConfig() # Decoupled PPO mode presets (3 policies: π_rollout, π_old, π_θ) # IS weights correct for gap between π_old and π_rollout config = RolloutCorrectionConfig.decoupled_token_is() # Token-TIS config = RolloutCorrectionConfig.decoupled_seq_is() # Seq-TIS config = RolloutCorrectionConfig.decoupled_seq_is_rs() # Seq-MIS config = RolloutCorrectionConfig.decoupled_geo_rs() # Geo-RS (ratio mode) # Bypass mode presets (2 policies: π_rollout = π_old, π_θ) # loss_type controls the loss function # PPO-clip presets (ratio handles IS, so no separate IS weights needed): config = RolloutCorrectionConfig.bypass_ppo_clip() # PPO-clip only config = RolloutCorrectionConfig.bypass_ppo_clip_geo_rs() # PPO-clip + Geo-RS config = RolloutCorrectionConfig.bypass_ppo_clip_k3_rs() # PPO-clip + K3-RS # REINFORCE presets (explicit IS weights): config = RolloutCorrectionConfig.bypass_pg_is() # REINFORCE + Seq-TIS config = RolloutCorrectionConfig.bypass_pg_geo_rs() # REINFORCE + Geo-RS config = RolloutCorrectionConfig.bypass_pg_geo_rs_seq_tis() # REINFORCE + Geo-RS + Seq-TIS config = RolloutCorrectionConfig.bypass_pg_geo_rs_token_tis() # REINFORCE + Geo-RS + Token-TIS # Decoupled Geometric ratio presets (length-normalized IS ratio) config = RolloutCorrectionConfig.decoupled_geo_rs_seq_tis() # Decoupled Geo-RS + Seq-TIS config = RolloutCorrectionConfig.decoupled_geo_rs_token_tis() # Decoupled Geo-RS + Token-TIS # Decoupled K3 KL Estimator presets (more stable for small KL values) config = RolloutCorrectionConfig.decoupled_k3_rs() # Decoupled K3-RS config = RolloutCorrectionConfig.decoupled_k3_rs_seq_tis() # Decoupled K3-RS + Seq-TIS config = RolloutCorrectionConfig.decoupled_k3_rs_token_tis() # Decoupled K3-RS + Token-TIS Reference: Liu, Li, Fu, Wang, Liu, Shen (2025) "When Speed Kills Stability: Demystifying RL Collapse from the Training-Inference Mismatch" https://richardli.xyz/rl-collapse """ rollout_is: Optional[str] = "sequence" rollout_is_threshold: float = 2.0 rollout_is_batch_normalize: bool = False rollout_rs: Optional[str] = None rollout_rs_threshold: Optional[str | float] = None bypass_mode: bool = False loss_type: str = "ppo_clip" @classmethod def decoupled_token_is(cls, threshold: float = 2.0) -> "RolloutCorrectionConfig": """Decoupled Mode with Token-level Importance Sampling. IS weight correction at token level in decoupled mode (three policies). Args: threshold (float): Upper threshold for IS weights. Default: 2.0 Returns: RolloutCorrectionConfig configured for decoupled mode with token-level IS """ return cls(rollout_is="token", rollout_is_threshold=threshold, rollout_rs=None) @classmethod def decoupled_seq_is(cls, threshold: float = 2.0) -> "RolloutCorrectionConfig": """Decoupled Mode with Sequence-level Importance Sampling. IS weight correction at sequence level in decoupled mode (three policies). Args: threshold (float): Upper threshold for IS weights. Default: 2.0 Returns: RolloutCorrectionConfig configured for decoupled mode with sequence-level IS """ return cls(rollout_is="sequence", rollout_is_threshold=threshold, rollout_rs=None) @classmethod def decoupled_seq_is_rs( cls, is_threshold: float = 2.0, rs_threshold: Optional[str | float] = "0.5_2.0", ) -> "RolloutCorrectionConfig": """Decoupled Mode with Sequence-level IS + Rejection Sampling. Sequence-level IS with sequence-level rejection sampling in decoupled mode. Rejects entire sequences based on sequence-level IS weight. Args: is_threshold (float): Upper threshold for IS weights. Default: 2.0 rs_threshold (Optional[Union[str, float]]): Upper threshold for rejection sampling. Default: 0.5_2.0 Returns: RolloutCorrectionConfig configured for decoupled mode with sequence IS + RS """ return cls( rollout_is="sequence", rollout_is_threshold=is_threshold, rollout_rs="seq_sum_k1", rollout_rs_threshold=rs_threshold, ) @classmethod def decoupled_geo_rs( cls, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Decoupled Mode with Geometric Mean Rejection Sampling (ratio-based). Uses geometric mean IS ratio E[log(r)] for rejection sampling at sequence level. This is a ratio-based mode (ideal = 0.0) with [lower, upper] threshold bounds. Length-normalized but still uses IS ratio semantics. Args: rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for decoupled mode with Geo-RS """ return cls( rollout_is=None, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, ) @classmethod def bypass_ppo_clip(cls) -> "RolloutCorrectionConfig": """Bypass mode with PPO-clip loss. PPO clipped objective in bypass mode. The PPO ratio = π_θ/π_rollout already handles IS correction, so no explicit IS weights are applied. Skips old_log_prob computation for faster execution (2 policies instead of 3). Returns: RolloutCorrectionConfig configured for bypass mode with PPO-clip """ return cls( rollout_is=None, rollout_rs=None, bypass_mode=True, loss_type="ppo_clip", ) @classmethod def bypass_ppo_clip_geo_rs( cls, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Bypass mode with PPO-clip loss and Geometric Mean RS (ratio-based). PPO clipped objective in bypass mode with geometric mean IS ratio RS. Uses E[log(r)] (ideal = 0.0) with [lower, upper] threshold bounds. Args: rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for bypass mode with PPO-clip + Geo-RS """ return cls( rollout_is=None, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, bypass_mode=True, loss_type="ppo_clip", ) @classmethod def bypass_ppo_clip_k3_rs( cls, rs_threshold: float = 0.01, ) -> "RolloutCorrectionConfig": """Bypass mode with PPO-clip loss and K3 Rejection Sampling. PPO clipped objective in bypass mode with K3 KL estimator RS to mask outliers. K3 is more stable than K1 for small KL values. The PPO ratio = π_θ/π_rollout already handles IS correction. Args: rs_threshold (float): Max allowed K3 divergence. Default: 0.01 Returns: RolloutCorrectionConfig configured for bypass mode with PPO-clip + K3-RS """ return cls( rollout_is=None, rollout_rs="seq_mean_k3", rollout_rs_threshold=rs_threshold, bypass_mode=True, loss_type="ppo_clip", ) @classmethod def bypass_pg_is(cls, threshold: float = 2.0) -> "RolloutCorrectionConfig": """Bypass mode with REINFORCE loss and IS Correction. Uses REINFORCE loss with explicit IS correction in bypass mode. No PPO clipping. Args: threshold (float): Upper threshold for IS weights. Default: 2.0 Returns: RolloutCorrectionConfig configured for bypass mode with REINFORCE + IS """ return cls( rollout_is="sequence", rollout_is_threshold=threshold, rollout_rs=None, bypass_mode=True, loss_type="reinforce", ) @classmethod def bypass_pg_geo_rs( cls, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Bypass mode with REINFORCE loss and Geometric Mean RS (ratio-based). REINFORCE with geometric mean IS ratio rejection sampling in bypass mode. Uses E[log(r)] (ideal = 0.0) with [lower, upper] threshold bounds. Args: rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for bypass mode with REINFORCE + Geo-RS """ return cls( rollout_is=None, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, bypass_mode=True, loss_type="reinforce", ) @classmethod def decoupled_geo_rs_seq_tis( cls, is_threshold: float = 2.0, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Decoupled mode with Geometric Mean RS and Sequence-level Truncated IS (ratio-based). Combines the Geometric Mean Filter (ratio-based validity check) with Clipped Sequence Weight (debiasing). Uses E[log(r)] (ideal = 0.0). Args: is_threshold (float): Upper threshold for sequence IS weights. Default: 2.0 rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for Geo-RS-Seq-TIS """ return cls( rollout_is="sequence", rollout_is_threshold=is_threshold, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, ) @classmethod def decoupled_geo_rs_token_tis( cls, is_threshold: float = 2.0, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Decoupled mode with Geometric Mean RS and Token-level Truncated IS (ratio-based). Combines the Geometric Mean Filter (ratio-based validity check) with Token-level IS weights. Uses E[log(r)] (ideal = 0.0). Args: is_threshold (float): Upper threshold for token IS weights. Default: 2.0 rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for Geo-RS-Token-TIS """ return cls( rollout_is="token", rollout_is_threshold=is_threshold, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, ) @classmethod def bypass_pg_geo_rs_seq_tis( cls, is_threshold: float = 2.0, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Bypass mode with REINFORCE loss, Geo-RS, and Sequence-level IS. Combines geometric mean IS ratio rejection with sequence-level IS in bypass mode with REINFORCE loss (no PPO clipping). Uses E[log(r)] (ideal = 0.0) with [lower, upper] threshold bounds. Args: is_threshold (float): Upper threshold for sequence IS weights. Default: 2.0 rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for bypass mode with REINFORCE + Geo-RS + Seq-TIS """ return cls( rollout_is="sequence", rollout_is_threshold=is_threshold, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, bypass_mode=True, loss_type="reinforce", ) @classmethod def bypass_pg_geo_rs_token_tis( cls, is_threshold: float = 2.0, rs_threshold: Optional[str | float] = "0.999_1.001", ) -> "RolloutCorrectionConfig": """Bypass mode with REINFORCE loss, Geo-RS, and Token-level IS. Combines geometric mean IS ratio rejection with token-level IS weights in bypass mode with REINFORCE loss (no PPO clipping). Uses E[log(r)] (ideal = 0.0) with [lower, upper] threshold bounds. Token-level IS has lower variance but introduces bias. Args: is_threshold (float): Upper threshold for token IS weights. Default: 2.0 rs_threshold (Optional[Union[str, float]]): Geometric RS threshold (upper). Default: 0.999_1.001 (±0.1%) Returns: RolloutCorrectionConfig configured for bypass mode with REINFORCE + Geo-RS + Token-TIS """ return cls( rollout_is="token", rollout_is_threshold=is_threshold, rollout_rs="seq_mean_k1", rollout_rs_threshold=rs_threshold, bypass_mode=True, loss_type="reinforce", ) @classmethod def decoupled_k3_rs( cls, rs_threshold: float = 0.01, ) -> "RolloutCorrectionConfig": """Decoupled mode with K3 KL Estimator Rejection Sampling. Uses K3 KL estimator at sequence level for rejection sampling. K3 = E[r - log(r) - 1] where r = π_train/π_rollout. More stable than geometric mean for small KL values. K3 >= 0 always (equals 0 when policies match exactly). Args: rs_threshold (float): Max allowed K3 divergence. Default: 0.01 Typical range: 0.001-0.1 Returns: RolloutCorrectionConfig configured for K3 RS """ return cls( rollout_is=None, rollout_rs="seq_mean_k3", rollout_rs_threshold=rs_threshold, ) @classmethod def decoupled_k3_rs_seq_tis( cls, is_threshold: float = 2.0, rs_threshold: float = 0.01, ) -> "RolloutCorrectionConfig": """Decoupled mode with K3 RS and Sequence-level Truncated IS. Combines K3 KL estimator rejection with sequence-level IS weights. K3 provides more stable outlier detection than geometric mean. Args: is_threshold (float): Upper threshold for sequence IS weights. Default: 2.0 rs_threshold (float): Max allowed K3 divergence. Default: 0.01 Returns: RolloutCorrectionConfig configured for K3-RS-Seq-TIS """ return cls( rollout_is="sequence", rollout_is_threshold=is_threshold, rollout_rs="seq_mean_k3", rollout_rs_threshold=rs_threshold, ) @classmethod def decoupled_k3_rs_token_tis( cls, is_threshold: float = 2.0, rs_threshold: float = 0.01, ) -> "RolloutCorrectionConfig": """Decoupled mode with K3 RS and Token-level Truncated IS. Combines K3 KL estimator rejection with token-level IS weights. K3 provides more stable outlier detection than geometric mean. Token-level IS has lower variance but introduces bias. Args: is_threshold (float): Upper threshold for token IS weights. Default: 2.0 rs_threshold (float): Max allowed K3 divergence. Default: 0.01 Returns: RolloutCorrectionConfig configured for K3-RS-Token-TIS """ return cls( rollout_is="token", rollout_is_threshold=is_threshold, rollout_rs="seq_mean_k3", rollout_rs_threshold=rs_threshold, ) @classmethod def disabled(cls) -> "RolloutCorrectionConfig": """Disabled - Metrics Only Mode. Computes and logs off-policy metrics without applying correction. Returns: RolloutCorrectionConfig with all correction disabled """ return cls(rollout_is=None, rollout_rs=None) @dataclass class AlgoConfig(BaseConfig): """Configuration for the algorithm. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: gamma (float): Discount factor for future rewards. lam (float): Trade-off between bias and variance in the GAE estimator. adv_estimator (str): Advantage estimator type: "gae", "grpo", "reinforce_plus_plus", etc. norm_adv_by_std_in_grpo (bool): Whether to normalize advantages by std (specific to GRPO). use_kl_in_reward (bool): Whether to enable in-reward KL penalty. kl_penalty (str): How to estimate KL divergence: "kl", "abs", "mse", "low_var_kl", or "full". kl_ctrl (KLControlConfig): KL control configuration. use_pf_ppo (bool): Whether to enable preference feedback PPO. pf_ppo (dict[str, Any]): Preference feedback PPO settings. filter_groups (Optional[FilterGroupsConfig]): Filter groups configuration, used in DAPO and Entropy rollout_correction (Optional[RolloutCorrectionConfig]): Rollout Correction configuration. Addresses off-policy issues from policy mismatch, model staleness, and general distribution shifts. Set to None to disable entirely. Use factory methods for common presets: - RolloutCorrectionConfig.decoupled_token_is() - Decoupled mode with token-level IS - RolloutCorrectionConfig.decoupled_seq_is() - Decoupled mode with sequence-level IS - RolloutCorrectionConfig.decoupled_seq_is_rs() - Decoupled mode with sequence IS + RS - RolloutCorrectionConfig.decoupled_k1_rs() - Decoupled mode with K1-RS (divergence) - RolloutCorrectionConfig.decoupled_geo_rs() - Decoupled mode with Geo-RS (ratio) - RolloutCorrectionConfig.bypass_ppo_clip() - Bypass mode with PPO-clip - RolloutCorrectionConfig.bypass_ppo_clip_k1_rs() - Bypass mode with PPO-clip + K1-RS - RolloutCorrectionConfig.bypass_pg_is() - Bypass mode with REINFORCE + IS - RolloutCorrectionConfig.bypass_pg_k1_rs() - Bypass mode with REINFORCE + K1-RS For backward compatibility, you can still pass a dict, which will be converted to RolloutCorrectionConfig automatically. """ gamma: float = 1.0 lam: float = 1.0 adv_estimator: str = "gae" norm_adv_by_std_in_grpo: bool = True use_kl_in_reward: bool = False kl_penalty: str = "kl" kl_ctrl: KLControlConfig = field(default_factory=KLControlConfig) use_pf_ppo: bool = False pf_ppo: dict[str, Any] = field(default_factory=dict) filter_groups: Optional[FilterGroupsConfig] = None # Rollout Correction: corrects off-policy issues (policy mismatch, model staleness, distribution shifts) # Set to None to disable, use RolloutCorrectionConfig presets (e.g., .tis(), .mis()), or pass dict rollout_correction: Optional[RolloutCorrectionConfig] = None # GDPO (Group reward-Decoupled Normalization Policy Optimization) settings. # gdpo_reward_keys: keys in non_tensor_batch (from compute_score's return dict) that # correspond to individual reward dimensions, e.g. ["format_reward", "accuracy_reward"]. # gdpo_reward_weights: per-dimension weights for aggregation (default: equal weights). gdpo_reward_keys: Optional[list[str]] = None gdpo_reward_weights: Optional[list[float]] = None ================================================ FILE: verl/trainer/config/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass, field from typing import Any, Optional from verl.base_config import BaseConfig __all__ = ["CheckpointConfig", "ProfileConfig", "BaseModelConfig"] @dataclass class CheckpointConfig(BaseConfig): """Configuration for model checkpointing. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: save_contents (list[str]): What to include in saved checkpoints. Options: 'model', 'optimizer', 'extra', 'hf_model'. load_contents (list[str]): Contents to load from checkpoint. Defaults to same as save_contents. async_save (bool): Whether to save checkpoints asynchronously. Only implemented for Megatron as of now. """ save_contents: list[str] = field(default_factory=lambda: ["model", "optimizer", "extra"]) load_contents: list[str] = field(default_factory=lambda: ["model", "optimizer", "extra"]) async_save: bool = False mbridge_config: dict[str, Any] = field(default_factory=dict) @dataclass class ProfileConfig(BaseConfig): """Configuration for profiling. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: profile_ranks (Optional[list[int]]): List of ranks to profile. None means all ranks. step_start (int): Starting step for profiling. step_end (int): Ending step for profiling. save_path (Optional[str]): Path to save profiling results. """ profile_ranks: Optional[list[int]] = None step_start: int = -1 step_end: int = -1 save_path: Optional[str] = None @dataclass class BaseModelConfig(BaseConfig): """Base configuration for a model. Contains core settings for loading and initializing a pretrained model checkpoint. Args: path (str): Path to pretrained model weights. tokenizer_path (Optional[str]): Tokenizer path (defaults to actor's model path if not set). override_config (dict): Hugging Face config override. external_lib (Optional[str]): External model implementation (optional). trust_remote_code (bool): Whether to trust remote code from Hugging Face models. lora (dict[str, Any]): LoRA configuration dictionary. """ path: str = "~/models/deepseek-llm-7b-chat" tokenizer_path: Optional[str] = None override_config: dict[str, Any] = field(default_factory=dict) external_lib: Optional[str] = None trust_remote_code: bool = False lora: dict[str, Any] = field(default_factory=dict) @dataclass class ModuleConfig(BaseConfig): """Configuration for external Python module, which can be loaded, executed (and optionally, ``import``ed). Args: path (str, optional): Path to the module file to load and execute. name (str, optional): Name of the module to ``import``. Format: ``"import.path.to.module"``. If ``None``, the module will be loaded with a hased name and will not be added to ``sys.modules``, thus can not be ``import``ed as ``name``. """ path: Optional[str] = None name: Optional[str] = None ================================================ FILE: verl/trainer/config/critic/critic.yaml ================================================ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.CriticConfig # Number of rollouts per update (mirrors actor rollout_n) rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} # fsdp or fsdp2 strategy used for critic model training strategy: ??? # whether to enable the critic worker. # by default it is only enabled if advantage estimator is gae # set it to True manually if you always want to enable critic worker enable: null # optimizer configs optim: # Learning rate lr: 1e-5 # Warmup steps ratio; total steps will be injected at runtime lr_warmup_steps_ratio: 0.0 # Total training steps (must be overridden at runtime) total_training_steps: -1 # Weight decay weight_decay: 0.01 # Prioritized. None, 0 or Negative values mean delegating to lr_warmup_steps_ratio. lr_warmup_steps: -1 # model config for the critic model: # Path to pretrained model weights path: ~/models/deepseek-llm-7b-chat # Tokenizer path (defaults to actor's model path) tokenizer_path: ${oc.select:actor_rollout_ref.model.path,"~/models/deepseek-llm-7b-chat"} # Hugging Face config override override_config: {} # External model implementation (optional) external_lib: ${oc.select:actor_rollout_ref.model.external_lib,null} # Whether to trust remote code from Hugging Face models trust_remote_code: ${oc.select:actor_rollout_ref.model.trust_remote_code,false} # PPO mini-batch size per update ppo_mini_batch_size: ${oc.select:actor_rollout_ref.actor.ppo_mini_batch_size,256} # [Deprecated] Global micro batch size ppo_micro_batch_size: null # Local per-GPU micro batch size ppo_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size,null} # Whether to automatically adjust batch size at runtime use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} # Max tokens per GPU in one PPO batch (doubled for critic) ppo_max_token_len_per_gpu: 32768 # Max token length per GPU in forward pass forward_max_token_len_per_gpu: ${.ppo_max_token_len_per_gpu} # Number of PPO epochs per batch ppo_epochs: ${oc.select:actor_rollout_ref.actor.ppo_epochs,1} # Shuffle training data across PPO epochs shuffle: ${oc.select:actor_rollout_ref.actor.shuffle,false} # The seed used to construct mini-batch data_loader_seed: 42 # PPO value function clipping range cliprange_value: 0.5 # Loss aggregation mode: "token-mean", "seq-mean-token-sum", or "seq-mean-token-mean" loss_agg_mode: ${oc.select:actor_rollout_ref.actor.loss_agg_mode,token-mean} # checkpoint configs checkpoint: # Target dataclass for this configuration _target_: verl.trainer.config.CheckpointConfig # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ['model', 'optimizer', 'extra'] # What to include when loading checkpoints load_contents: ${.save_contents} # Whether to save checkpoints asynchronously. Only effective for Megatron as of now. async_save: False # Mbridge config extension. mbridge_config: {} # profile the critic model in `update_critic` profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # profiler tool, default same as profiler.tool in global config # choices: nsys, npu, torch, torch_memory tool: ${oc.select:global_profiler.tool,null} # whether enable profile on Critic enable: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # profile results saving path save_path: ${oc.select:global_profiler.save_path,null} # specific tool config which only related to the role tool_config: # nsys tool config nsys: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NsightToolConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} # npu config npu: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NPUToolConfig # Contents to profile, can be empty # options: npu, cpu, memory, shapes, module, stack contents: [] # Collection level, optional values: level_none, level0, level1, level2. level: "level0" # Whether to automatically parse the data. analysis: True # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # torch profiler config torch: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchProfilerToolConfig # Contents to profile, can be empty # options: cuda, cpu, memory, shapes, stack contents: [] # True for each task has its own database, False for all tasks in one training step share one database. discrete: false # torch memory profiler config torch_memory: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchMemoryToolConfig # Maximum number of memory allocation entries to track trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} # Stack trace depth for memory allocations stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} ================================================ FILE: verl/trainer/config/critic/dp_critic.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. # defaults specify the default config from each component defaults: # fsdp optimizer config - ../optim@optim: fsdp # fsdp engine config - ../engine@model.fsdp_config: fsdp # dp actor config, inheriting from trainer/config/critic/critic.yaml - critic # load the reference default config, then apply the fields in the current yaml - _self_ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.FSDPCriticConfig # distribution strategy. Options: fsdp (deprecating), fsdp2 strategy: fsdp # model config for the critic model: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.FSDPCriticModelCfg # Whether to use shared memory for loading the model use_shm: False # Enable gradient checkpointing to save memory enable_gradient_checkpointing: True # Offload activations to CPU to reduce GPU memory usage enable_activation_offload: False # Use remove padding optimization (saves compute) use_remove_padding: False # Set to positive value to enable LoRA (e.g., 32) lora_rank: 0 # LoRA scaling factor lora_alpha: 16 # LoRA target modules: "all-linear" or list of linear projection layers target_modules: all-linear # TiledMLP configuration for memory-efficient MLP computation. tiled_mlp: # whether to enable TiledMLP enabled: False # number of shards to split the input num_shards: 4 # Forward-only batch size during inference (global) forward_micro_batch_size: ${oc.select:.ppo_micro_batch_size,null} # Forward-only batch size during inference (per GPU) forward_micro_batch_size_per_gpu: ${oc.select:.ppo_micro_batch_size_per_gpu,null} # Sequence parallelism size for Ulysses-style model parallelism # [DEPRECATED] use fsdp_config.ulysses_sequence_parallel_size instead ulysses_sequence_parallel_size: 1 # Gradient clipping for critic updates grad_clip: 1.0 ================================================ FILE: verl/trainer/config/critic/megatron_critic.yaml ================================================ # defaults specify the default config from each component defaults: # megatron optimizer config - ../optim@optim: megatron # megatron engine config - ../engine@megatron: megatron # dp actor config, inheriting from trainer/config/critic/critic.yaml - critic # load the reference default config, then apply the fields in the current yaml - _self_ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.McoreCriticConfig strategy: megatron # seconds, default is 10 minutes for torch, you can set it to a larger value if you have long-running operations like 32B or 72B model using megatron nccl_timeout: 600 # model config for the critic model: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.BaseModelConfig # override default empty mapping override_config: model_config: {} moe_config: freeze_moe_router: False # LoRA (Low-Rank Adaptation) configuration for parameter-efficient fine-tuning lora: # LoRA type: "lora", "vlm_lora", "canonical_lora", or "dora" type: lora # LoRA rank (Dimension of the low-rank projection space.). Set to 0 to disable LoRA rank: 0 # typical values: 8, 16, 32, 64 # Weighting factor for the low-rank projection. Defaults to 32 alpha: 32 # Dropout rate for the low-rank projection. Defaults to 0.0 dropout: 0.0 # A list of module names to apply LoRA to. # For fused LoRA, Defaults to all linear layers ['linear_qkv', 'linear_proj', 'linear_fc1', 'linear_fc2']. # For canonical LoRA: ["linear_q", "linear_k", "linear_v", "linear_proj", "linear_fc1_up", "linear_fc1_gate", "linear_fc2"] # - 'linear_qkv': Apply LoRA to the fused linear layer used for query, key, and value projections in self-attention # - 'linear_proj': Apply LoRA to the linear layer used for projecting the output of self-attention # - 'linear_fc1': Apply LoRA to the first fully-connected layer in MLP # - 'linear_fc2': Apply LoRA to the second fully-connected layer in MLP # Target modules can also contain wildcards. For example, you can specify # target_modules=['*.layers.0.*.linear_qkv', '*.layers.1.*.linear_qkv'] to add LoRA to only linear_qkv on the first two layers # # Note: # For MLA (e.g., DeepSeek), you should use ["linear_kv_down_proj","linear_kv_up_proj","linear_q_down_proj","linear_q_up_proj","linear_q_proj"] # Instead of "linear_qkv" or ["linear_q","linear_k","linear_v"] # By default, MoE routers are excluded from LoRA adaptation, and you will need to specify "router" in target_modules to include them. target_modules: - linear_qkv - linear_proj - linear_fc1 - linear_fc2 # A list of module names not to apply LoRa to. It will match all nn.Linear & nn.Linear-adjacent modules whose name # does not match any string in exclude_modules. If used, will require target_modules to be empty list or null exclude_modules: [] # Position for applying dropout, can be 'pre' (before the low-rank projection) or 'post' (after). Defaults to 'pre' dropout_position: pre # Initialization method for the low-rank matrix A. Defaults to "xavier". lora_A_init_method: xavier # Initialization method for the low-rank matrix B. Defaults to "zero". lora_B_init_method: zero # Enables the experimental All-to-All (A2A) communication strategy. Defaults to False a2a_experimental: False # Parameter data type for LoRA weights. Default to null, which will use model's dtype. dtype: null # Path to pre-trained LoRA adapter weights (null to train from scratch) adapter_path: null # VLMLoRA additionally allows the user to specify whether the language or vision models should be frozen. # For example, a common finetuning workload for multimodal models is to apply adapters to language model and fully # finetune the vision model. freeze_vision_model: True freeze_vision_projection: True freeze_language_model: True # Whether to load initial weights load_weight: True # seed for data loader data_loader_seed: ${oc.select:actor_rollout_ref.actor.data_loader_seed,null} ================================================ FILE: verl/trainer/config/critic/torchtitan_critic.yaml ================================================ # defaults specify the default config from each component defaults: # torchtitan optimizer config - ../optim@optim: torchtitan # torchtitan engine config - ../engine@torchtitan: torchtitan # critic config, inheriting from trainer/config/critic/critic.yaml - critic # load the reference default config, then apply the fields in the current yaml - _self_ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.TorchTitanCriticConfig strategy: torchtitan # model config for the critic model: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.BaseModelConfig # seed for data loader data_loader_seed: ${oc.select:actor_rollout_ref.actor.data_loader_seed,null} ================================================ FILE: verl/trainer/config/critic/veomni_critic.yaml ================================================ # defaults specify the default config from each component defaults: # veomni optimizer config - ../optim@optim: veomni # veomni engine config - ../engine@veomni: veomni # critic config, inheriting from trainer/config/critic/critic.yaml - critic # load the reference default config, then apply the fields in the current yaml - _self_ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.VeOmniCriticConfig strategy: veomni # model config for the critic model: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.BaseModelConfig # seed for data loader data_loader_seed: ${oc.select:actor_rollout_ref.actor.data_loader_seed,null} ================================================ FILE: verl/trainer/config/data/legacy_data.yaml ================================================ # Tokenizer class or path. If null, it will be inferred from the model. tokenizer: null # Whether to use shared memory for data loading. use_shm: False # Training set parquet. Can be a list or a single file. # The program will read all files into memory, so it can't be too large (< 100GB). # The path can be either a local path or an HDFS path. # For HDFS path, we provide utils to download it to DRAM and convert it to a local path. train_files: ~/data/rlhf/gsm8k/train.parquet # Validation parquet. Can be a list or a single file. val_files: ~/data/rlhf/gsm8k/test.parquet # Maximum sample length to be used. # Set to -1 to use full dataset, otherwise, randomly # select the specified number of samples from train dataset train_max_samples: -1 # Maximum sample length to be used. # Set to -1 to use full dataset, otherwise, randomly # select the specified number of samples from val dataset val_max_samples: -1 # The field in the dataset where the prompt is located. Default is 'prompt'. prompt_key: prompt # The field used to select the reward function (if using different ones per example). reward_fn_key: data_source # Maximum prompt length. All prompts will be left-padded to this length. # An error will be reported if the length is too long. # oc.select: default val for rollout.prompt_length max_prompt_length: 512 # Maximum response length. Rollout in RL algorithms (e.g. PPO) generates up to this length. # oc.select: default val for rollout.response_length max_response_length: 512 # Batch size sampled for one training iteration of different RL algorithms. train_batch_size: 1024 # Batch size used during validation. Can be null. val_batch_size: null # use tool config to calculate true prompt length tool_config_path: ${oc.select:actor_rollout_ref.rollout.multi_turn.tool_config_path, null} # Whether to return the original input_ids without adding chat template. # This is used when the reward model's chat template differs from the policy. # If using a model-based RM with different templates, this should be True. return_raw_input_ids: False # Whether to return the original chat (prompt) without applying chat template. return_raw_chat: True # Whether to return the full prompt with chat template. return_full_prompt: False # Whether to shuffle the data in the dataloader. shuffle: True # Seed to use when shuffling the data seed: null # num dataloader workers dataloader_num_workers: 8 # image patch size image_patch_size: 14 # Whether to shuffle the validation set. validation_shuffle: False # Whether to filter overlong prompts. filter_overlong_prompts: False # Number of workers for filtering overlong prompts. # For large-scale datasets, filtering can be time-consuming. # Use multiprocessing to speed up. Default is 1. filter_overlong_prompts_workers: 1 # Truncate the input_ids or prompt if they exceed max_prompt_length. # Options: 'error', 'left', 'right', 'middle'. Default is 'error'. truncation: error # The field in the multi-modal dataset where the image is located. Default is 'images'. image_key: images # The field in the multi-modal dataset where the video is located. video_key: videos # If the remote tokenizer has a Python file, this flag determines whether to allow using it. trust_remote_code: False # Optional: specify a custom dataset class path and name if overriding default loading behavior. custom_cls: # The path to the file containing your customized dataset class. If not specified, pre-implemented dataset will be used. path: null # The name of the dataset class within the specified file. name: null # Whether to return multi-modal inputs in the dataset. Set to False if rollout generates new multi-modal inputs. return_multi_modal_inputs: True # settings related to data sampler sampler: # the path to the module containing a curriculum class which implements the # AbstractSampler interface class_path: null # the name of the curriculum class like `MySampler` class_name: null # Data generation configuration for augmenting the dataset. datagen: # The path to the file containing your customized data generation class. # E.g. 'pkg://verl.experimental.dynamic_dataset.dynamicgen_dataset' path: null # The class name of the data generation class within the specified file. # E.g. 'MockDataGenerator' name: null # Additional kwargs when calling tokenizer.apply_chat_template apply_chat_template_kwargs: {} ================================================ FILE: verl/trainer/config/engine/automodel.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.AutomodelEngineConfig # Backend strategy identifier strategy: automodel # Distributed training strategy: "fsdp2", "megatron_fsdp", or "ddp" distributed_strategy: fsdp2 # Parallelism sizes tp_size: 1 pp_size: 1 cp_size: 1 ep_size: 1 dp_replicate_size: 1 sequence_parallel: false defer_fsdp_grad_sync: true # Whether to offload model parameters to CPU param_offload: false # Whether to offload optimizer state to CPU optimizer_offload: false # Whether to enable activation checkpointing activation_checkpointing: false # Whether to enable FP8 training enable_fp8: false # Whether to enable torch.compile for the model enable_compile: false # Model data type for loading weights ("fp32", "bf16", "fp16") model_dtype: fp32 # Attention implementation ("sdpa", "flash_attention_2", "eager", "te") attn_implementation: flash_attention_2 # Backend settings backend_config: attn: sdpa # "te", "sdpa" linear: te # "torch", "te" rms_norm: torch_fp32 # "torch", "torch_fp32", "te" rope_fusion: true dispatcher: torch # "torch", "deepep" experts: gmm # "gmm", "torch_mm", "torch", "te" gate_precision: null enable_hf_state_dict_adapter: true enable_fsdp_optimizations: false fake_balanced_gate: false fake_gate_noise: 0.0 # MoE settings (MoEParallelizerConfig) moe_config: ignore_router_for_ac: false reshard_after_forward: false lm_head_precision: null wrap_outer_model: true # Mixed precision policy (FSDP2 MixedPrecisionPolicy) mp_param_dtype: bf16 mp_reduce_dtype: fp32 mp_output_dtype: bf16 # Random seed for reproducibility seed: 42 # Whether to enable full determinism for distributed training, only for debugging full_determinism: false # Whether to use forward only mode forward_only: false # Whether to use torch compile for entropy computation use_torch_compile: false # Whether to use chunked entropy computation entropy_from_logits_with_chunking: false # Whether to use checkpointing for entropy computation entropy_checkpointing: false ================================================ FILE: verl/trainer/config/engine/fsdp.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.FSDPEngineConfig # policy for wrapping the model wrap_policy: # Minimum number of parameters to trigger wrapping a layer with FSDP min_num_params: 0 # Whether to offload model parameters to CPU (trades speed for memory) # Note that this differs from the offload_policy in FSDP param_offload: false # Whether to offload optimizer state to CPU # Note that this differs from the offload_policy in FSDP optimizer_offload: false # Only for FSDP2: offload param/grad/optimizer during train offload_policy: false # Reshard after forward pass to reduce memory footprint # For FSDP1, `false` enables `ShardingStrategy.SHARD_GRAD_OP` reshard_after_forward: true # Number of GPUs in each FSDP shard group; -1 means auto fsdp_size: -1 # Only for FSDP1: FSDP1 configuration, prefetch the next forward-pass all-gather # before the current forward computation. forward_prefetch: False # model dtype of fsdp model_dtype: fp32 # Whether to use original parameters in fsdp. Only avaiable in fsdp1 use_orig_params: false # Random seed for reproducibility. seed: 42 # Whether to enable full determinism for distributed training, only for debugging. full_determinism: false # ulysses sequence parallel size ulysses_sequence_parallel_size: 1 # Whether to use entropy_from_logits_with_chunking in fsdp. entropy_from_logits_with_chunking: false # Whether to use torch compile in fsdp. use_torch_compile: true # Whether to use entropy checkpointing in fsdp. entropy_checkpointing: false # Whether to use forward only in fsdp. forward_only: false # fsdp or fsdp2 strategy: fsdp # Mixed precision training param dtype dtype: bfloat16 # ["bfloat16", "float16"] # QAT (Quantization-Aware Training) configuration qat: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.QATEngineConfig # Whether to enable QAT enable: false # Quantization mode: "w4a16" (weight-only). "w4a4" is experimental and not recommended. mode: "w4a16" # Quantization group size (NVFP4 requires 16) group_size: 16 # Patterns to ignore (e.g., lm_head, embed_tokens) ignore_patterns: - "lm_head" - "embed_tokens" - "re:.*mlp.gate$" # Activation observer for W4A4 mode activation_observer: "static_minmax" # Path to vLLM quantization config JSON file quantization_config_path: null ================================================ FILE: verl/trainer/config/engine/megatron.yaml ================================================ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.McoreEngineConfig # Whether to offload model parameters to CPU param_offload: False # Whether to offload gradients to CPU grad_offload: False # Whether to offload optimizer state to CPU optimizer_offload: False # tensor model parallel size tensor_model_parallel_size: 1 # expert model parallel size expert_model_parallel_size: 1 # expert tensor parallel size (null to be same as TP) expert_tensor_parallel_size: null # pipeline model parallel size pipeline_model_parallel_size: 1 # virtual pipeline model parallel size virtual_pipeline_model_parallel_size: null # context parallel size context_parallel_size: 1 # sequence parallel sequence_parallel: True # Whether to use distributed optimizer use_distributed_optimizer: True # Whether to use distributed checkpointing use_dist_checkpointing: False # distributed checkpointing path dist_checkpointing_path: null # distributed checkpointing prefix, e.g. Nemo2 will append prefix 'module.' to the state dict keys dist_checkpointing_prefix: '' # Make optimizer distributed checkpoint fully reshardable (TP/PP/EP/DP) as opposed to plain DP reshardability dist_ckpt_optim_fully_reshardable: True # Use as little memory as possible during save and load by using Gloo. # Has effect only when `dist_ckpt_optim_fully_reshardable` is enabled distrib_optim_fully_reshardable_mem_efficient: False # oc.select: default val for ref.megatron.seed seed: 42 # Allow to override Distributed Data Parallel (DDP) config override_ddp_config: {} # additional transformer config like: num_layers_in_first(/last)_pipeline_stage # oc.select: default val for ref.megatron.override_transformer_config override_transformer_config: # Recompute configuration, same as in megatron.training.arguments # default use minimal performance-interference recompute methods # Recompute granualarity, choices: ["full", "selective"] recompute_granularity: null # Recompute modules, multiple choices: ["core_attn", "moe_act", "layernorm", "mla_up_proj", "mlp", "moe"] # Please use correct module in matched model recompute_modules: ["core_attn"] # 'uniform', 'block' # 'uniform' divides the total number of transformer layers and checkpoints the input activation of each chunk # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity recompute_method: null # 'full' will checkpoint the entire transformer layer and 'selective' only checkpoints memory intensive part of attention recompute_num_layers: null # Attention backend to use (flash,fused,unfused,local,auto). Defaults to auto in mcore, flash in verl attention_backend: flash override_mcore_model_config: {} # oc.select: default val for ref.megatron.use_mbridge use_mbridge: True # oc.select: default val for ref.megatron.vanilla_mbridge vanilla_mbridge: True # whether to use thd format (sequence packing), if not, use bshd format, padding the input_ids to the longest sequence length use_remove_padding: True # whether to use forward only forward_only: False # Mixed precision training param dtype dtype: bfloat16 # ["bfloat16", "float16"] # Router replay configuration for MoE models router_replay: # Target dataclass for this configuration _target_: verl.workers.config.EngineRouterReplayConfig # Router replay mode: disabled, R2, R3 # - R2: Use R2 routing strategy (record mode) # - R3: Use R3 routing strategy (record mode) mode: disabled # File path to save recorded routing decisions # Required when mode is 'record', 'R2', or 'R3' record_file: null # File path to load recorded routing decisions for replay # Required when mode is 'replay' replay_file: null ================================================ FILE: verl/trainer/config/engine/torchtitan.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.TorchtitanEngineConfig # Whether to offload model parameters to CPU param_offload: False # Whether to offload optimizer state to CPU optimizer_offload: False # policy for wrapping the model wrap_policy: # Minimum number of parameters to trigger wrapping a layer with FSDP min_num_params: 0 # The policy for applying `reshard_after_forward` within an FSDP setup # Options: "default", "always", "never" reshard_after_forward: default # Prefetch the next forward-pass all-gather before the current forward computation. forward_prefetch: false # Whether to use original parameters use_orig_params: false # Mixed precision configuration for FSDP mixed_precision: false # Whether to use torch compile use_torch_compile: true # Whether to use entropy_from_logits_with_chunking entropy_from_logits_with_chunking: false # Whether to use entropy checkpointing entropy_checkpointing: false # Data parallel size (FSDP group size) data_parallel_size: 1 # Data parallel replicate size data_parallel_replicate_size: 1 # Data parallel shard size data_parallel_shard_size: 1 # Tensor parallel size tensor_parallel_size: 1 # Expert parallel size expert_parallel_size: 1 # Pipeline parallel size pipeline_parallel_size: 1 # Context parallel size context_parallel_size: 1 # Attention type for torchtitan's model (e.g., "sdpa", "flex", "varlen") attn_type: flex # Maximum sequence length for RoPE cache. If null, defaults to torchtitan's TrainingConfig.seq_len (2048). max_seq_len: null # Strategy strategy: torchtitan # Random seed for reproducibility seed: 42 # Whether to enable full determinism for distributed training, only for debugging full_determinism: false # Whether to use forward only forward_only: false # Mixed precision training param dtype dtype: bfloat16 ================================================ FILE: verl/trainer/config/engine/veomni.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.VeOmniEngineConfig # Whether to offload model parameters to CPU param_offload: False # Whether to offload optimizer state to CPU optimizer_offload: False # FSDP group size. -1 means use all available GPUs. fsdp_size: -1 ulysses_parallel_size: 1 expert_parallel_size: 1 mixed_precision: true # Random seed for reproducibility. seed: 42 # Whether to enable full determinism for distributed training, only for debugging. full_determinism: false init_device: meta enable_full_shard: true ckpt_manager: dcp # Only for FSDP1: FSDP1 configuration, prefetch the next forward-pass all-gather # before the current forward computation. forward_prefetch: true strategy: veomni # Whether to use torch compile in fsdp. use_torch_compile: false # Whether to use forward only in fsdp. forward_only: false enable_fsdp_offload: false enable_reentrant: false # support eager, sdpa, flash_attention_2, flash_attention_3, veomni_flash_attention_2_with_sp, # veomni_flash_attention_3_with_sp and native-sparse attn_implementation: flash_attention_2 # eager or fused moe_implementation: fused force_use_huggingface: false activation_gpu_limit: 0.0 ================================================ FILE: verl/trainer/config/evaluation.yaml ================================================ data: path: /tmp/math_Qwen2-7B-Instruct.parquet prompt_key: prompt response_key: responses data_source_key: data_source reward_model_key: reward_model custom_reward_function: path: null name: compute_score ray_kwargs: ray_init: num_cpus: null # `None` means using all CPUs, which might cause hang if limited in systems like SLURM. Please set to a number allowed then. timeline_json_file: null ================================================ FILE: verl/trainer/config/legacy_reward_impl.yaml ================================================ custom_reward_function: path: null name: null reward_model: num_workers: null reward_manager: null enable: null enable_resource_pool: null n_gpus_per_node: null nnodes: null reward_loop_source: null reward_loop_module_path: null reward_loop_class_name: null model: path: null external_lib: null trust_remote_code: null rollout: name: null dtype: null gpu_memory_utilization: null enforce_eager: null cudagraph_capture_sizes: null free_cache_engine: null data_parallel_size: null expert_parallel_size: null tensor_model_parallel_size: null max_num_batched_tokens: null max_model_len: null max_num_seqs: null load_format: null engine_kwargs: null limit_images: null enable_chunked_prefill: null enable_prefix_caching: null disable_log_stats: null skip_tokenizer_init: null prompt_length: null response_length: null sandbox_fusion: url: null max_concurrent: null memory_limit_mb: null ================================================ FILE: verl/trainer/config/model/hf_model.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. _target_: verl.workers.config.HFModelConfig # path to the huggingface model path: ~/models/deepseek-llm-7b-chat # config to the huggingface config. In case it is not the same as path hf_config_path: null # path to the huggingface tokenizer. In case it is not the same as path tokenizer_path: null # whether to use shared memory for model loading use_shm: False # whether to trust remote code. trust_remote_code: False # custom chat template for the model custom_chat_template: null # whether to use external libs for the model external_lib: null # override hf config override_config: {} # whether to enable gradient checkpointing. Only valid when we use hf model definition enable_gradient_checkpointing: True # whether to enable activation offload. Only valid when we use hf model definition enable_activation_offload: False # whether to use remove padding. Only valid when we use hf model definition use_remove_padding: True # Set to positive value to enable LoRA (e.g., 32) lora_rank: 0 # LoRA scaling factor lora_alpha: 16 # Target modules for LoRA adaptation target_modules: all-linear # Exclude modules from LoRA adaptation exclude_modules: null # Path to pre-trained LoRA adapter to load for continued training lora_adapter_path: null # whether to use liger. Only valid when we use hf model definition use_liger: False # whether to use fused kernels. use_fused_kernels: False # fused kernel options. fused_kernel_options: # the implementation backend for fused kernels. impl_backend: torch # TiledMLP configuration for memory-efficient MLP computation. # Reduces peak memory by processing MLP forward/backward in tiles. tiled_mlp: # whether to enable TiledMLP enabled: False # number of shards to split the input. Higher values reduce peak memory but may slightly impact performance. num_shards: 4 # MTP mtp: _target_: verl.workers.config.MtpConfig enable: False enable_train: False enable_rollout: False detach_encoder: False mtp_loss_scaling_factor: 0.1 speculative_algorithm: EAGLE speculative_num_steps: 3 speculative_eagle_topk: 1 speculative_num_draft_tokens: 4 method: mtp num_speculative_tokens: 1 ================================================ FILE: verl/trainer/config/model_engine/dp.yaml ================================================ # @package _global_ model_engine: dp ================================================ FILE: verl/trainer/config/model_engine/torchtitan.yaml ================================================ # @package _global_ model_engine: torchtitan ================================================ FILE: verl/trainer/config/model_engine/veomni.yaml ================================================ # @package _global_ model_engine: veomni ================================================ FILE: verl/trainer/config/npu_profile/npu_profile.yaml ================================================ # Options for the npu profiler options: # Storage path of collected data. save_path: ./profiler_data # The roles that will be profiled. Only takes effect in discrete mode. # optional values: all, rollout_generate, actor_compute_log_prob, actor_update and ref_compute_log_prob. # "all" means all roles will be profiled. roles: ["all"] # Collection level, optional values: level_none, level0, level1, level2. level: level0 # Whether to enable memory analysis. with_memory: False # Whether to record tensor shape. record_shapes: False # Whether to record Device-side performance data. with_npu: True # Whether to record Host-side performance data. with_cpu: True # Whether to record Python call stack information. with_module: False # Whether to record operator call stack information. with_stack: False # Whether to automatically parse the data. analysis: True ================================================ FILE: verl/trainer/config/optim/automodel.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.AutomodelOptimizerConfig optimizer: AdamW # Module path to import optimizer from optimizer_impl: torch.optim # Learning rate (maps to max_lr in Automodel's OptimizerParamScheduler) lr: 1e-5 # LR warmup steps ratio (used when lr_warmup_steps <= 0) lr_warmup_steps_ratio: 0.0 # Total training steps (injected at runtime) total_training_steps: -1 # Weight decay weight_decay: 0.01 # LR warmup steps (set > 0 to override lr_warmup_steps_ratio) lr_warmup_steps: -1 # Betas for Adam optimizer betas: [0.9, 0.999] # Clip gradient norm clip_grad: 1.0 # Initial LR ratio for warmup start (init_lr = lr * init_lr_ratio) init_lr_ratio: 0.1 # Minimum LR ratio after decay (min_lr = lr * min_lr_ratio) min_lr_ratio: 0.01 # LR scheduler type (Automodel OptimizerParamScheduler decay style) # Options: "constant", "cosine", "linear", "inverse-square-root" lr_scheduler_type: cosine # Weight decay increment style: "constant", "linear", or "cosine" wd_incr_style: constant # Kept for backward compatibility (unused by Automodel scheduler) num_cycles: 0.5 zero_indexed_step: true # Common optimizer kwargs eps: 1e-8 master_weights: false store_param_remainders: false exp_avg_dtype: null # "fp32", "bf16" exp_avg_sq_dtype: null # "fp32", "bf16" master_weight_dtype: null # "fp32", "bf16" # Additional optimizer kwargs (passed directly to constructor) override_optimizer_config: {} ================================================ FILE: verl/trainer/config/optim/fsdp.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.FSDPOptimizerConfig # Optimizer class name (e.g., "AdamW", "AdamW8bit", "_AdamW", "Adam") optimizer: AdamW # Module path to import optimizer # Examples: "torch.optim", "torchao.optim", "bitsandbytes.optim" optimizer_impl: torch.optim # Learning rate lr: 1e-3 # LR warmup steps ratio lr_warmup_steps_ratio: 0.0 # Total training steps total_training_steps: -1 # Weight decay weight_decay: 0.01 # LR warmup steps lr_warmup_steps: -1 # Betas for Adam optimizer betas: [0.9, 0.999] # Clip gradient clip_grad: 1.0 # Minimum LR ratio for cosine schedule min_lr_ratio: 0.0 # Number of cosine cycles in LR schedule num_cycles: 0.5 # LR scheduler type: "constant" or "cosine" lr_scheduler_type: constant # Whether the LR schedule uses 0-indexed steps zero_indexed_step: true # deprecated warmup_style: null # Additional optimizer-specific keyword arguments # Example for torchao with bf16 stochastic rounding: # optimizer_impl: torchao.optim # optimizer: _AdamW # override_optimizer_config: # bf16_stochastic_round: true override_optimizer_config: null ================================================ FILE: verl/trainer/config/optim/megatron.yaml ================================================ _target_: verl.workers.config.McoreOptimizerConfig # Learning rate lr: 1e-3 # LR warmup steps ratio lr_warmup_steps_ratio: 0.0 # Total training steps total_training_steps: -1 # Weight decay weight_decay: 0.01 # LR warmup steps lr_warmup_steps: -1 # Betas for Adam optimizer betas: [0.9, 0.999] # Clip gradient clip_grad: 1.0 # optimizer type optimizer: adam # initial learning rate for warmup, default to 0.0 lr_warmup_init: 0.0 lr_decay_steps: null # select from constant/linear/cosine/inverse_square_root lr_decay_style: constant # minimum learning rate, default to 0.0 min_lr: 0.0 # select from constant/linear/cosine weight_decay_incr_style: constant # select from constant/exponential/cosine lr_wsd_decay_style: exponential lr_wsd_decay_steps: null # use checkpoint optimizer parameter scheduler use_checkpoint_opt_param_scheduler: False override_optimizer_config: {} ================================================ FILE: verl/trainer/config/optim/torchtitan.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.TorchtitanOptimizerConfig # Optimizer name name: AdamW # Learning rate lr: 1e-3 # LR warmup steps ratio lr_warmup_steps_ratio: 0.0 # Total training steps total_training_steps: -1 # Weight decay weight_decay: 0.01 # LR warmup steps lr_warmup_steps: -1 # Betas for Adam optimizer betas: [0.9, 0.999] # Clip gradient clip_grad: 1.0 # Epsilon for Adam optimizer eps: 1e-8 # Decay type: "linear", "sqrt", or "cosine" decay_type: linear # Minimum LR factor for cosine schedule min_lr_factor: 0.0 ================================================ FILE: verl/trainer/config/optim/veomni.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.VeOmniOptimizerConfig optimizer: adamw # Learning rate lr: 1e-3 # Minimum learning rate lr_min: 0.0 # Starting learning rate for warmup lr_start: 0.0 # LR warmup steps ratio lr_warmup_steps_ratio: 0.0 # LR decay steps ratio lr_decay_ratio: 1.0 # Total training steps total_training_steps: -1 # Weight decay weight_decay: 0.01 # LR warmup steps lr_warmup_steps: -1 # Betas for Adam optimizer betas: [0.9, 0.999] # Clip gradient clip_grad: 1.0 # LR scheduler type: "constant" or "cosine" lr_scheduler_type: cosine override_optimizer_config: {} ================================================ FILE: verl/trainer/config/ppo_megatron_trainer.yaml ================================================ # specify the default per-component configs defaults: # @.: # actor_rollout_ref.actor: trainer/config/actor/megatron_actor.yaml - actor@actor_rollout_ref.actor: megatron_actor # data: trainer/config/data/legacy_data.yaml - data@data: legacy_data # load the reference default config, then apply the fields in the current yaml # Reference model config. # Reference model will be enabled when actor.use_kl_loss or/and algorithm.use_kl_in_reward is/are True. - ref@actor_rollout_ref.ref: megatron_ref # Rollout model config. - rollout@actor_rollout_ref.rollout: rollout # Model config. - model@actor_rollout_ref.model: hf_model # Critic model config. - critic@critic: megatron_critic # legacy reward impl config, for backward compatibility - legacy_reward_impl # Reward model config. - reward@reward: reward # Rollout correction config. - algorithm@algorithm.rollout_correction: rollout_correction - _self_ actor_rollout_ref: hybrid_engine: True nccl_timeout: 600 # seconds, default is 10 minutes for torch, you can set it to a larger value if you have long-running operations like 32B or 72B model using megatron model: override_config: model_config: {} moe_config: freeze_moe_router: False use_fused_kernels: False # Whether to use custom fused kernels (PostProcessing, for memory efficiency) trust_remote_code: False # Whether to remove padding tokens in inputs during training use_remove_padding: false # LoRA (Low-Rank Adaptation) configuration for parameter-efficient fine-tuning lora: # LoRA type: "lora", "vlm_lora", "canonical_lora", or "dora" type: lora # whether to sync weights / refit by either merging LoRA adapters into the base model weights before transferring to vLLM (for better inference speed but more refit time and potential precision loss). If this is False, it will load separate adapters. merge: False # LoRA rank (Dimension of the low-rank projection space.). Set to 0 to disable LoRA rank: 0 # typical values: 8, 16, 32, 64 # Weighting factor for the low-rank projection. Defaults to 32 alpha: 32 # Dropout rate for the low-rank projection. Defaults to 0.0 dropout: 0.0 # A list of module names to apply LoRA to. # For fused LoRA, Defaults to all linear layers ['linear_qkv', 'linear_proj', 'linear_fc1', 'linear_fc2']. # For canonical LoRA: ["linear_q", "linear_k", "linear_v", "linear_proj", "linear_fc1_up", "linear_fc1_gate", "linear_fc2"] # - 'linear_qkv': Apply LoRA to the fused linear layer used for query, key, and value projections in self-attention # - 'linear_proj': Apply LoRA to the linear layer used for projecting the output of self-attention # - 'linear_fc1': Apply LoRA to the first fully-connected layer in MLP # - 'linear_fc2': Apply LoRA to the second fully-connected layer in MLP # Target modules can also contain wildcards. For example, you can specify # target_modules=['*.layers.0.*.linear_qkv', '*.layers.1.*.linear_qkv'] to add LoRA to only linear_qkv on the first two layers # # Note: # For MLA (e.g., DeepSeek), you should use ["linear_kv_down_proj","linear_kv_up_proj","linear_q_down_proj","linear_q_up_proj","linear_q_proj"] # Instead of "linear_qkv" or ["linear_q","linear_k","linear_v"] # By default, MoE routers are excluded from LoRA adaptation, and you will need to specify "router" in target_modules to include them. target_modules: - linear_qkv - linear_proj - linear_fc1 - linear_fc2 # A list of module names not to apply LoRa to. It will match all nn.Linear & nn.Linear-adjacent modules whose name # does not match any string in exclude_modules. If used, will require target_modules to be empty list or None exclude_modules: [] # Position for applying dropout, can be 'pre' (before the low-rank projection) or 'post' (after). Defaults to 'pre' dropout_position: pre # Initialization method for the low-rank matrix A. Defaults to "xavier". lora_A_init_method: xavier # Initialization method for the low-rank matrix B. Defaults to "zero". lora_B_init_method: zero # Enables the experimental All-to-All (A2A) communication strategy. Defaults to False a2a_experimental: False # Parameter data type for LoRA weights. Default to null, which will use model's dtype. dtype: null # Path to pre-trained LoRA adapter weights (null to train from scratch) adapter_path: null # VLMLoRA additionally allows the user to specify whether the language or vision models should be frozen. # For example, a common finetuning workload for multimodal models is to apply adapters to language model and fully # finetune the vision model. freeze_vision_model: True freeze_vision_projection: True freeze_language_model: True rollout: quantization: null layer_name_map: qkv_layer_name: qkv gate_proj_layer_name: gate_up algorithm: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.AlgoConfig gamma: 1.0 lam: 1.0 adv_estimator: gae norm_adv_by_std_in_grpo: True use_kl_in_reward: False kl_penalty: kl # how to estimate kl divergence kl_ctrl: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.KLControlConfig type: fixed kl_coef: 0.001 horizon: 10000 target_kl: 0.1 use_pf_ppo: False pf_ppo: reweight_method: pow # ["pow", "max_min", "max_random"] weight_pow: 2.0 trainer: balance_batch: True total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: ["console", "wandb"] log_val_generations: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 esi_redundant_time: 0 # auto: find the last ckpt to resume. If can't find, start from scratch resume_mode: auto # or disable or resume_path if resume_from_path is set resume_from_path: null del_local_ckpt_after_load: False val_before_train: True test_freq: -1 critic_warmup: 0 default_hdfs_dir: null default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} max_actor_ckpt_to_keep: null max_critic_ckpt_to_keep: null # The timeout for ray worker group to wait for the register center to be ready ray_wait_register_center_timeout: 300 device: cuda # Directory for logging rollout data; no dump if null rollout_data_dir: null # whether to use legacy worker implementation # mode: "auto", "enable", or "disable" use_legacy_worker_impl: auto global_profiler: _target_: verl.utils.profiler.ProfilerConfig tool: null # choose between nsys, npu, torch, torch_memory steps: null # profile steps profile_continuous_steps: False save_path: "outputs/profile" # profiler saving path # Specific tool configs, can use +profiler.tool_config.[tool].xxx to config global_tool_config: # nsys config nsys: # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # controller Nvidia Nsight Systems Options. Must set when profile_steps is not None. ## reference https://docs.nvidia.com/nsight-systems/UserGuide/index.html ## reference https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html controller_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # worker Nvidia Nsight Systems Options. Must set when profile_steps is not None. worker_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # Profiling only in a range of torch.cuda.profiler.start and stop. Do not change this config. capture-range: "cudaProfilerApi" # Specify the desired behavior when a capture range ends. # In verl we need the torch.cuda.profiler.start/stop pair to repeats n times. # valid values are "repeat-shutdown:n" or null. # For normal whole step profiling, n = len(profile_steps); # but for discrete profiling, n = len(profile_steps) * Number(subtasks). # Or you can just leave it null and the program will use n = len(profile_steps) * 6; capture-range-end: null # Send signal to the target application's process group. We let the program to exit by itself. kill: none # enable memory visualization for debugging memory usage torch_memory: # Maximum number of allocation entries to record trace_alloc_max_entries: 100_000 # The depth of the call stack to capture for each allocation stack_depth: 32 # 'alloc': records only allocation events || 'state': records memory state changes || 'all': records both. context: "all" # 'python': records Python stacks || 'cpp': records C++ stacks (available in some versions) || 'all': records both. stacks: "all" # devices, record_context etc. kw_args: {} # configs for TransferQueue transfer_queue: # Whether to enable transfer queue enable: False ray_kwargs: ray_init: num_cpus: null # `None` means using all CPUs, which might cause hang if limited in systems like SLURM. Please set to a number allowed then. timeline_json_file: null ================================================ FILE: verl/trainer/config/ppo_trainer.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. # specify the default per-component configs defaults: - model_engine: dp # @.: # actor_rollout_ref.actor: trainer/config/actor/dp_actor.yaml - actor@actor_rollout_ref.actor: ${model_engine}_actor # data: trainer/config/data/legacy_data.yaml - data@data: legacy_data # Reference model config. # Reference model will be enabled when actor.use_kl_loss or/and algorithm.use_kl_in_reward is/are True. - ref@actor_rollout_ref.ref: ${model_engine}_ref # Rollout model config. - rollout@actor_rollout_ref.rollout: rollout # Model config. - model@actor_rollout_ref.model: hf_model # Critic model config. - critic@critic: ${model_engine}_critic # legacy reward impl config, for backward compatibility - legacy_reward_impl # Reward config. - reward@reward: reward # Rollout correction config. - algorithm@algorithm.rollout_correction: rollout_correction # load the reference default config, then apply the fields in the current yaml # self config override anything above - _self_ # config for actor, rollout and reference model actor_rollout_ref: # Whether it's a hybrid engine, currently only supports hybrid engine hybrid_engine: true # Timeout for operations executed against the process group nccl_timeout: 600 # Rollout model config. rollout: # for huge model, layered summon can save memory (prevent OOM) but make it slower layered_summon: False # config for the algorithm algorithm: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.AlgoConfig # Discount factor for future rewards gamma: 1.0 # Trade-off between bias and variance in the GAE estimator lam: 1.0 # Advantage estimator type: "gae", "grpo", "reinforce_plus_plus", etc. adv_estimator: gae # Whether to normalize advantages by std (specific to GRPO) norm_adv_by_std_in_grpo: True # Whether to enable in-reward KL penalty use_kl_in_reward: False # How to estimate KL divergence: "kl", "abs", "mse", "low_var_kl", or "full" kl_penalty: kl # KL control configuration kl_ctrl: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.trainer.config.KLControlConfig # KL control type: "fixed" or "adaptive" type: fixed # Initial coefficient for KL penalty kl_coef: 0.001 # Horizon value for adaptive controller (if enabled) horizon: 10000 # Target KL divergence (used for adaptive controller) target_kl: 0.1 # Whether to enable preference feedback PPO use_pf_ppo: False # Preference feedback PPO settings pf_ppo: # Method for reweighting samples: "pow", "max_min", or "max_random" reweight_method: pow # Power used for weight scaling in "pow" method weight_pow: 2.0 # config for the trainer trainer: # Whether to balance batch sizes across distributed workers balance_batch: True # Number of epochs in training total_epochs: 30 # Total training steps (can be set explicitly or derived from epochs) total_training_steps: null # Project name for experiment tracking (e.g., wandb) project_name: verl_examples # Experiment name for run identification in tracking tools experiment_name: gsm8k # Logging backends to use: "console", "wandb", etc. logger: ["console", "wandb"] # Number of generations to log during validation log_val_generations: 0 # Directory for logging rollout data; no dump if null rollout_data_dir: null # Directory for logging validation data; no dump if null validation_data_dir: null # Number of nodes used in the training nnodes: 1 # Number of GPUs per node n_gpus_per_node: 8 # Save frequency (by iteration) for model checkpoints save_freq: -1 # ESI refers to the elastic server instance used during training, similar to the training plan. For example, # if you purchase 10 hours of computing power, the ESI will automatically shut down after 10 hours of training. # To ensure a checkpoint is saved before ESI shuts down, the system will start saving a checkpoint in advance. # The advance time is calculated as: Advance Time = Longest historical step duration + Checkpoint save duration + esi_redundant_time. # Here, esi_redundant_time is a user-defined value that further extends the advance time for added safety. esi_redundant_time: 0 # Resume mode: "auto", "disable", or "resume_path" # "auto": resume from last checkpoint if available # "disable": start from scratch # "resume_path": resume from a user-defined path resume_mode: auto # Path to resume training from (only used when resume_mode is "resume_path") resume_from_path: null # Whether to run validation before training begins val_before_train: True # Whether to run validation only val_only: False # Validation frequency (in training iterations) test_freq: -1 # Number of iterations to warm up the critic before updating policy critic_warmup: 0 # Default path to distributed filesystem for saving checkpoints default_hdfs_dir: null # Whether to delete local checkpoints after loading del_local_ckpt_after_load: False # Default local directory for saving checkpoints default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} # Maximum number of actor checkpoints to keep max_actor_ckpt_to_keep: null # Maximum number of critic checkpoints to keep max_critic_ckpt_to_keep: null # Timeout (in seconds) for Ray worker to wait for registration ray_wait_register_center_timeout: 300 # Device to run training on (e.g., "cuda", "cpu") device: cuda # whether to use legacy worker implementation # mode: "auto", "enable", or "disable" use_legacy_worker_impl: auto # profiler configs global_profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # Profiling tool: choose between nsys, npu, torch, torch_memory tool: null # profile steps steps: null # Whether to combine continuous steps into one database. ## If True, worker.profiler.discrete must be False, [1,2] in one, [5] in another. ## If False, [1] in one, [2] in another, [5] in another. profile_continuous_steps: False # Path to save profiling contents save_path: "outputs/profile" # Specific tool configs, can use +profiler.tool_config.[tool].xxx to config global_tool_config: # nsys config nsys: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NsightToolConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # controller Nvidia Nsight Systems Options. Must set when profile_steps is not None. ## reference https://docs.nvidia.com/nsight-systems/UserGuide/index.html ## reference https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html controller_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # worker Nvidia Nsight Systems Options. Must set when profile_steps is not None. worker_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # Profiling only in a range of torch.cuda.profiler.start and stop. Do not change this config. capture-range: "cudaProfilerApi" # Specify the desired behavior when a capture range ends. # In verl we need the torch.cuda.profiler.start/stop pair to repeats n times. # valid values are "repeat-shutdown:n" or null. # For normal whole step profiling, n = len(profile_steps); # but for discrete profiling, n = len(profile_steps) * Number(subtasks). # Or you can just leave it null and the program will use n = len(profile_steps) * 6; capture-range-end: null # Send signal to the target application's process group. We let the program to exit by itself. kill: none # enable memory visualization for debugging memory usage torch_memory: # Maximum number of allocation entries to record trace_alloc_max_entries: 100_000 # The depth of the call stack to capture for each allocation stack_depth: 32 # 'alloc': records only allocation events || 'state': records memory state changes || 'all': records both. context: "all" # 'python': records Python stacks || 'cpp': records C++ stacks (available in some versions) || 'all': records both. stacks: "all" # devices, record_context etc. kw_args: {} # configs for TransferQueue transfer_queue: # Whether to enable transfer queue enable: False # configs related to ray ray_kwargs: # configs related to ray initialization ray_init: # Number of CPUs for Ray. Use a fixed number instead of null when using SLURM. num_cpus: null # Path to save Ray timeline JSON for performance profiling timeline_json_file: null ================================================ FILE: verl/trainer/config/profiler/profiler.yaml ================================================ # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # profiler tool, default same as profiler.tool in global config # choices: nsys, npu, torch tool: torch # whether enable profile on Actor enable: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # profile results saving path save_path: "outputs/profile" tool_config: npu: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NPUToolConfig # Contents to profile, can be empty # options: npu, cpu, memory, shapes, module, stack contents: [ ] # Collection level, optional values: level_none, level0, level1, level2. level: "level0" # Whether to automatically parse the data. analysis: True # True for each task has its own database, False for all tasks in one training step share one database. discrete: False name: npu nsys: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NsightToolConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} name: nsight torch: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchProfilerToolConfig # Contents to profile, can be empty # options: cuda, cpu, memory, shapes, stack contents: [] # True for each task has its own database, False for all tasks in one training step share one database. discrete: false name: torch torch_memory: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchMemoryToolConfig # Maximum number of memory allocation entries to track trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} # Stack trace depth for memory allocations stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} name: torch_memory ================================================ FILE: verl/trainer/config/ref/dp_ref.yaml ================================================ # defaults specify the default config from each component defaults: # dp ref config, inheriting from trainer/config/ref/ref.yaml - ref # fsdp engine config - ../engine@fsdp_config: fsdp # load the reference default config, then apply the fields in the current yaml - _self_ # Target class for this configuration _target_: verl.workers.config.FSDPActorConfig # fsdp config fsdp_config: # ref model is forward only forward_only: True # sequence parallel size # same as actor_rollout_ref.actor.ulysses_sequence_parallel_size if it exists, otherwise 1 ulysses_sequence_parallel_size: ${oc.select:actor_rollout_ref.actor.ulysses_sequence_parallel_size,1} # calculate entropy with chunking to reduce memory peak entropy_from_logits_with_chunking: False # recompute entropy entropy_checkpointing: False ================================================ FILE: verl/trainer/config/ref/megatron_ref.yaml ================================================ # megatron ref config, inheriting from trainer/config/ref/ref.yaml defaults: - ref # megatron engine config - ../engine@megatron: megatron # load the reference default config, then apply the fields in the current yaml - _self_ _target_: verl.workers.config.McoreActorConfig strategy: megatron megatron: seed: ${oc.select:actor_rollout_ref.actor.megatron.seed,42} override_transformer_config: ${oc.select:actor_rollout_ref.actor.megatron.override_transformer_config,{}} use_mbridge: ${oc.select:actor_rollout_ref.actor.megatron.use_mbridge,False} vanilla_mbridge: ${oc.select:actor_rollout_ref.actor.megatron.vanilla_mbridge,True} use_remove_padding: ${oc.select:actor_rollout_ref.actor.megatron.use_remove_padding,True} tensor_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.tensor_model_parallel_size,1} pipeline_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.pipeline_model_parallel_size,1} virtual_pipeline_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.virtual_pipeline_model_parallel_size,null} context_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.context_parallel_size,1} expert_model_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.expert_model_parallel_size,1} expert_tensor_parallel_size: ${oc.select:actor_rollout_ref.actor.megatron.expert_tensor_parallel_size,null} param_offload: ${oc.select:actor_rollout_ref.actor.megatron.param_offload,False} forward_only: True load_weight: True ================================================ FILE: verl/trainer/config/ref/ref.yaml ================================================ # Number of rollouts per update (mirrors actor rollout_n) rollout_n: ${oc.select:actor_rollout_ref.rollout.n,1} # actor_rollout_ref.ref: FSDP config same as actor. For models larger than 7B, it’s recommended to turn on offload for ref by default strategy: ${actor_rollout_ref.actor.strategy} # whether to enable torch.compile # same as actor_rollout_ref.actor.use_torch_compile if it exists, otherwise 1 use_torch_compile: ${oc.select:actor_rollout_ref.actor.use_torch_compile,true} # [Will be deprecated, use log_prob_micro_batch_size_per_gpu] # The batch size for one forward pass in the computation of log_prob. Global batch size. log_prob_micro_batch_size: null # The batch size for one forward pass in the computation of log_prob. Local batch size per GPU. log_prob_micro_batch_size_per_gpu: null # enable dynamic batch size (sequence packing) for log_prob computation # same as actor_rollout_ref.actor.use_dynamic_bsz if it exists, otherwise false log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} # the max token length per GPU # same as actor_rollout_ref.actor.ppo_max_token_len_per_gpu if it exists, otherwise 16384 log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} # profile the ref model in `compute_log_prob` profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # choices: nsys, npu, torch, torch_memory tool: ${oc.select:global_profiler.tool,null} # whether enable profile on Ref enable: False # Whether to profile all ranks. all_ranks: False # The ranks that will be profiled. [] or [0,1,...] ranks: [] # profile results saving path save_path: ${oc.select:global_profiler.save_path,null} # specific tool config which only related to the role tool_config: # nsys tool config nsys: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NsightToolConfig # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:global_profiler.global_tool_config.nsys.discrete} # npu config npu: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NPUToolConfig # Contents to profile, can be empty # options: npu, cpu, memory, shapes, module, stack contents: [] # Collection level, optional values: level_none, level0, level1, level2. level: "level0" # Whether to automatically parse the data. analysis: True # True for each task has its own database, False for all tasks in one training step share one database. discrete: False # torch profiler config torch: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchProfilerToolConfig # Contents to profile, can be empty # options: cuda, cpu, memory, shapes, stack contents: [] # True for each task has its own database, False for all tasks in one training step share one database. discrete: false # torch memory profiler config torch_memory: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchMemoryToolConfig # Maximum number of memory allocation entries to track trace_alloc_max_entries: ${oc.select:global_profiler.global_tool_config.torch_memory.trace_alloc_max_entries,100000} # Stack trace depth for memory allocations stack_depth: ${oc.select:global_profiler.global_tool_config.torch_memory.stack_depth,32} # Router replay configuration for MoE models router_replay: # Target dataclass for this configuration _target_: verl.workers.config.RouterReplayConfig # Router replay mode: disabled, R2, R3 # - R2: Use R2 routing strategy (record mode) # - R3: Use R3 routing strategy (record mode) mode: disabled # File path to save recorded routing decisions # Required when mode is 'record', 'R2', or 'R3' record_file: null # File path to load recorded routing decisions for replay # Required when mode is 'replay' replay_file: null ================================================ FILE: verl/trainer/config/ref/torchtitan_ref.yaml ================================================ # torchtitan ref config, inheriting from trainer/config/ref/ref.yaml defaults: # torchtitan optimizer config - ../optim@optim: torchtitan # torchtitan engine config - ../engine@torchtitan: torchtitan - ref # load the reference default config, then apply the fields in the current yaml - _self_ _target_: verl.workers.config.TorchTitanActorConfig strategy: torchtitan torchtitan: seed: ${oc.select:actor_rollout_ref.actor.torchtitan.seed,42} data_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.data_parallel_size,1} data_parallel_replicate_size: ${oc.select:actor_rollout_ref.actor.torchtitan.data_parallel_replicate_size,1} data_parallel_shard_size: ${oc.select:actor_rollout_ref.actor.torchtitan.data_parallel_shard_size,1} tensor_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.tensor_parallel_size,1} expert_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.expert_parallel_size,1} pipeline_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.pipeline_parallel_size,1} context_parallel_size: ${oc.select:actor_rollout_ref.actor.torchtitan.context_parallel_size,1} attn_type: ${oc.select:actor_rollout_ref.actor.torchtitan.attn_type,flex} forward_only: True ================================================ FILE: verl/trainer/config/ref/veomni_ref.yaml ================================================ # veomni ref config, inheriting from trainer/config/ref/ref.yaml defaults: - ref # veomni engine config - ../engine@veomni: veomni # load the reference default config, then apply the fields in the current yaml - _self_ _target_: verl.workers.config.VeOmniActorConfig strategy: veomni veomni: seed: ${oc.select:actor_rollout_ref.actor.veomni.seed,42} fsdp_size: ${oc.select:actor_rollout_ref.actor.veomni.fsdp_size,-1} ulysses_parallel_size: ${oc.select:actor_rollout_ref.actor.veomni.ulysses_parallel_size,1} expert_parallel_size: ${oc.select:actor_rollout_ref.actor.veomni.expert_parallel_size,1} param_offload: ${oc.select:actor_rollout_ref.actor.veomni.param_offload,False} attn_implementation: ${oc.select:actor_rollout_ref.actor.veomni.attn_implementation,flash_attention_2} moe_implementation: ${oc.select:actor_rollout_ref.actor.veomni.moe_implementation,fused} forward_only: True ================================================ FILE: verl/trainer/config/reward/reward.yaml ================================================ # configs for the reward computation # we launch num_workers reward managers to parallelize the reward computation num_workers: 8 # custom reward function definition custom_reward_function: # The path to the file containing your customized reward function. # If not specified, pre-implemented reward functions will be used. path: null # The name of the reward function within the specified file. Default is 'compute_score'. name: compute_score # reward manager, see `verl/trainer/config/reward_manager.yaml` for details. reward_manager: _target_: verl.workers.config.reward_model.RewardManagerConfig source: register name: naive module: _target_: verl.trainer.config.config.ModuleConfig path: null name: custom_reward_manager # Inference config for reward models, # support both discriminative and generative models reward_model: enable: False # Whether to deploy the model to a separate resource pool. # If true, n_gpus_per_node & nnodes will be used to determine the resource node. enable_resource_pool: False n_gpus_per_node: 8 nnodes: 0 model_path: null rollout: _target_: verl.workers.config.RolloutConfig name: ??? dtype: bfloat16 gpu_memory_utilization: 0.5 enforce_eager: true cudagraph_capture_sizes: null free_cache_engine: true data_parallel_size: 1 expert_parallel_size: 1 tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 load_format: auto engine_kwargs: {} limit_images: null enable_chunked_prefill: true enable_prefix_caching: true disable_log_stats: true skip_tokenizer_init: false prompt_length: 2048 response_length: 2048 # Cloud/local sandbox fusion configuration for custom reward logic sandbox_fusion: # Cloud /local function URL for sandbox execution url: null # Max concurrent requests allowed to sandbox max_concurrent: 64 # Max memory limit for each sandbox process in MB memory_limit_mb: 1024 ================================================ FILE: verl/trainer/config/rollout/rollout.yaml ================================================ # Target class for this configuration _target_: verl.workers.config.RolloutConfig # actor_rollout_ref.rollout.name: hf/vllm/sglang/trtllm. The default value will be removed in the future name: ??? # sync: LLM, async: AsyncLLM mode: async # Number of nodes for standalone rollout server, must be > 0 in one-step-off/fully async training. nnodes: 0 # Number of GPUs per node for rollout server. n_gpus_per_node: ${oc.select:trainer.n_gpus_per_node,8} # Sampling temperature for rollout. temperature: 1.0 # Top-k sampling parameter. -1 for vLLM rollout, 0 for HF rollout. top_k: -1 # Top-p sampling parameter. Default 1.0. top_p: 1 # typically the same as data max prompt length # same as data.max_prompt_length if it exists prompt_length: ${oc.select:data.max_prompt_length,512} # typically the same as data max response length # same as data.max_response_length if it exists response_length: ${oc.select:data.max_response_length,512} # for vllm rollout # Rollout model parameters type. Align with actor model's FSDP/Megatron type. dtype: bfloat16 # Fraction of GPU memory used by vLLM/SGLang/TRTLLM for KV cache. gpu_memory_utilization: 0.5 # Whether to ignore EOS and continue generating after EOS is hit. ignore_eos: False # Whether to disable CUDA graph. Default False to best performance. enforce_eager: False # batch size of cudagraph to capture. Require enforce_eager: False to use this option # Since cudagraph in inference engine can not be offloaded during update policy, # you can use smaller batch size to save memory used in cuda graph, eg: [1 ,2, 4, 8, 16, 32] # supported engines: vllm cudagraph_capture_sizes: null # Whether to free engine KVCache after generation. free_cache_engine: True # TP size for rollout. Not effective for hf tensor_model_parallel_size: 2 # DP size for rollout data_parallel_size: 1 # EP size for rollout # For MoE models in vllm, EP=1 refers to ETP parallel in fused_moe with TP*DP weight splits, # EP>1 (should satisfy EP=TP*DP) refers to EP parallel in fused_moe expert_parallel_size: 1 # PP size for rollout. pipeline_model_parallel_size: 1 # max number of tokens in a batch max_num_batched_tokens: 8192 # max length for rollout max_model_len: null # max length of sequences max_num_seqs: 1024 # may get higher throughput when set to True. When activated, Please increase max_num_batched_tokens or decrease max_model_len. enable_chunked_prefill: True # Prefix caching kv-cache blocks is a popular optimization in LLM inference to avoid redundant prompt computations. enable_prefix_caching: True # logprobs mode for rollout logprobs logprobs_mode: processed_logprobs # scheduling policy for vllm rollout scheduling_policy: fcfs # Which loader to use for rollout model weights: dummy, hf, megatron, etc. # safetensors (for huge model, and set use_shm=True); dummy: randomly init model weight load_format: dummy # [Will be deprecated, use log_prob_micro_batch_size_per_gpu] The batch size for one forward pass in the computation of log_prob. Global batch size. log_prob_micro_batch_size: null # The batch size for one forward pass in the computation of log_prob. Local batch size per GPU. log_prob_micro_batch_size_per_gpu: null # enable dynamic batch size (sequence packing) for log_prob computation # same as actor_rollout_ref.actor.use_dynamic_bsz if it exists, otherwise false log_prob_use_dynamic_bsz: ${oc.select:actor_rollout_ref.actor.use_dynamic_bsz,false} # max token length for log_prob computation # same as actor_rollout_ref.actor.ppo_max_token_len_per_gpu if it exists, otherwise 16384 log_prob_max_token_len_per_gpu: ${oc.select:actor_rollout_ref.actor.ppo_max_token_len_per_gpu,16384} # disable logging statistics disable_log_stats: True # for hf rollout # Whether to sample during training rollout. False uses greedy sampling. do_sample: True # number of responses (i.e. num sample times). > 1 for grpo n: 1 # The over_sample_rate parameter controls the early termination threshold for training rollouts, # where the system will abort remaining requests when (1 - over_sample_rate) * total_requests completions are reached. over_sample_rate: 0 # Whether to wake up inference engine in multi-stage for SGLang # to reduce peak memory during training-rollout transition. # This is only effective for SGLang rollout. multi_stage_wake_up: false # Extra inference engine arguments (vllm, sglang, trtllm), please refer vllm/sglang/trtllm official doc for detail engine_kwargs: # vllm engine config vllm: {} # sglang engine config sglang: {} # trtllm engine config trtllm: {} # Sampling parameters used during validation. val_kwargs: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.SamplingConfig # sampling parameters for validation # Top-k sampling parameter. -1 for vLLM rollout, 0 for HF rollout. top_k: -1 # Top-p sampling parameter. Default 1.0. top_p: 1.0 # Sampling temperature for rollout. temperature: 0 # whether to repeat n times for validation n: 1 # Whether to sample during training rollout. False uses greedy sampling. do_sample: False # Multi-turn interaction config for tools or chat. multi_turn: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.MultiTurnConfig # set to True for multi-turn tool interaction tasks; should set rollout.name to sglang as well enable: False # null for no limit (default max_length // 3) max_assistant_turns: null # null for no tool tool_config_path: null # null for no limit (default max_length // 3) max_user_turns: null # max parallel call for tools in single turn max_parallel_calls: 1 # max length of tool response max_tool_response_length: 256 # truncate side of tool response: left, middle, right tool_response_truncate_side: middle # null for no interaction interaction_config_path: null # - When set to True, the model's default chat template is used for multi-turn rollout, which typically matches production behavior. # - When set to False, the token ids recorded for training are used instead; unlike the default chat template, these always include the model's full output, # which may contain additional content such as reasoning content. This maintains the consistency between training and rollout, but it will lead to longer prompts. use_inference_chat_template: False # Tokenization is performed turn by turn and the resulting token ids are concatenated to form the full conversation. # To ensure this matches the result of tokenizing the entire conversation at once, a sanity check is run at the end of each multi-turn rollout to compare the two sets of token ids. # Some models are known to produce different tokenization results when tokenizing turn by turn vs. all at once. aThis behavior has already been validated for them. # To reduce excessive warnings, you can turn off the sanity check for these models if you are using their default chat template: # Qwen/QwQ-32B, Qwen/Qwen3-xxB # - disable: disable tokenization sanity check # - strict: enable strict tokenization sanity check (default) # - ignore_strippable: ignore strippable tokens when checking tokenization sanity tokenization_sanity_check_mode: strict # Format of the multi-turn interaction. Options: hermes, llama3_json, ... format: hermes # Number of repeat rollouts for each interaction num_repeat_rollouts: null # support logging rollout prob for debugging purpose # "Truncated importance sampling" requires rollout log probs, set to True when turning on Truncated importance sampling calculate_log_probs: False # [Experimental] agent loop based rollout configs agent: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.AgentLoopConfig # Number of agent loop workers num_workers: 8 # default agent loop to use if `agent_name` not set in RL dataset default_agent_loop: single_turn_agent # custom agent loop config path, which should contain list of configs to initialize AgentLoop instances. # https://hydra.cc/docs/advanced/instantiate_objects/overview/ # # - name: react_agent # _target_: recipe.langgraph_agent.react_agent_loop.ReactAgentLoop # tools: ["get_current_temperature"] # - name: math_expression # _target_: recipe.langgraph_agent.example.math_expression.MathExpressionReactAgentLoop # min_terms: 2 # max_terms: 6 agent_loop_config_path: null # custom async server configs custom_async_server: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.CustomAsyncServerConfig # Path to the custom async server implementation path: null # Class name of the custom async server class (e.g. AsyncvLLMServer) name: null # Checkpoint Engine config for update weights from trainer to rollout checkpoint_engine: # Target class for checkpoint engine config _target_: verl.workers.config.CheckpointEngineConfig # Backend for checkpoint engine: naive, nccl, nixl, hccl backend: naive # Specifies the tensor bucket size (in megabytes) for batch weight updates during rollout operations. # This parameter controls the maximum payload size for a single weight update request. # Reference: https://github.com/volcengine/verl/pull/2418 # Currently only supported in SGLang rollout implementations # Larger values may improve throughput but increase memory overhead # Detailed performance comparison: # https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/issues/169#issuecomment-3070686720 # Default value (512MB) is optimized for typical GPU memory configurations # For the best performance of `rebuild_cuda_tensor`, it is recommended to: # 1. Enable `RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES` # 2. Manually set `CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7` # when using Tensor Parallelism (TP) >= 8. update_weights_bucket_megabytes: 2048 # Additional keyword arguments to pass to the checkpoint engine constructor engine_kwargs: {} # trace rollout data trace: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.TraceConfig # Project name for experiment tracking (e.g., wandb) project_name: ${oc.select:trainer.project_name,null} # Experiment name for run identification in tracking tools experiment_name: ${oc.select:trainer.experiment_name,null} # trace backend, support mlflow, weave backend: null # whether translate token id to text in output token2text: False # Maximum number of unique samples to trace per agent worker per training step. # If null, all samples are traced. If set to N, each agent loop worker will randomly # select N unique samples to trace (including all their rollouts for GRPO). # Total traces per step = max_samples_per_step_per_worker * num_workers * n_rollouts_per_sample max_samples_per_step_per_worker: null # When enabled (True), the trainer will attempt to load previously generated rollout data from the specified directory instead of computing new rollouts. # If no cached data is found or loading fails, new rollouts will be generated and automatically saved. # This feature is useful for debugging or when you want to reuse computation results across multiple runs. skip_rollout: False # Specifies the filesystem path where rollout data should be cached when skip_rollout is enabled. # Note: Giving path under /tmp/ray/session* is not recommended as these are temporary Ray cluster directories. skip_dump_dir: /tmp/rollout_dump # Whether to skip tokenizer initialization for rollout engine # When enabled (True), the rollout assume token in token out for generation skip_tokenizer_init: True # Whether to enable rollout routing replay for MoE models # When enabled (True), the rollout will record the routing decisions. enable_rollout_routing_replay: False # profile the rollout model in `generate_sequence` profiler: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.ProfilerConfig # profiler tool, default same as profiler.tool in global config # choices: npu, torch tool: ${oc.select:global_profiler.tool,null} # whether enable profile on rollout enable: ${oc.select:actor_rollout_ref.actor.profiler.enable,false} # Whether to profile all ranks. all_ranks: ${oc.select:actor_rollout_ref.actor.profiler.all_ranks,false} # The ranks that will be profiled. [] or [0,1,...] ranks: ${oc.select:actor_rollout_ref.actor.profiler.ranks,[]} # profile results saving path save_path: ${oc.select:global_profiler.save_path,null} # specific tool config tool_config: # npu config npu: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.NPUToolConfig # Contents to profile, can be empty # options: npu, cpu, memory, shapes, module, stack contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.contents,[]} # Collection level, optional values: level_none, level0, level1, level2. level: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.level,level0} # Whether to automatically parse the data. analysis: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.analysis,false} # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.npu.discrete,false} # torch profiler config torch: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.utils.profiler.config.TorchProfilerToolConfig # Contents to profile, can be empty # options: cuda, cpu, memory, shapes, stack contents: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.contents,[]} # True for each task has its own database, False for all tasks in one training step share one database. discrete: ${oc.select:actor_rollout_ref.actor.profiler.tool_config.torch.discrete,false} # prometheus configuration for vllm/sglang server mode prometheus: # Required when using verl.utils.omega_conf_to_dataclass to instantiate dataclass configs _target_: verl.workers.config.PrometheusConfig # whether enable prometheus on server mode rollout enable: false # Port number that Prometheus listens on, default is 9090 port: 9090 # Path to Prometheus configuration file file: /tmp/ray/session_latest/metrics/prometheus/prometheus.yml # Specify served_model_name to avoid displaying overly long model paths in Grafana served_model_name: ${oc.select:actor_rollout_ref.model.path,null} # type of quantization in vllm, currently support fp8 and torchao quantization: null # extra quantization information serialized in a config file, e.g. torchao_config.json quantization_config_file: null # MTP configuration, reuse model configuration mtp: ${oc.select:actor_rollout_ref.model.mtp, null} # QAT configuration (inherited from actor's engine config) qat: ${oc.select:actor_rollout_ref.actor.fsdp_config.qat,null} ================================================ FILE: verl/trainer/config/sft_trainer_engine.yaml ================================================ # Format checks enforced on CI: # 1. Comments must appear above each field. # 2. There must be a blank line between each field. # 3. Inline comments (after a field on the same line) are not allowed. # 4. Indentation level is respected for nested fields. # @.: defaults: - model@model: hf_model - engine@engine: fsdp - optim@optim: fsdp - profiler@profiler: profiler - _self_ data: train_batch_size: 256 # global batch size micro_batch_size_per_gpu: 4 # this is also val batch size max_token_len_per_gpu: 8192 use_dynamic_bsz: True train_files: ~/data/gsm8k/train.parquet val_files: null train_max_samples: -1 # set to -1 to use full dataset val_max_samples: -1 # set to -1 to use full dataset # Multi-turn settings messages_key: messages # Key for messages list in multi-turn mode tools_key: tools # Key for tools list in multi-turn mode enable_thinking_key: enable_thinking # Whether to enable thinking in multi-turn mode enable_thinking_default: none # The default value when enable_thinking_key is not present in the dataset pad_mode: no_padding # for right padding max_length: 1024 truncation: error balance_dp_token: False # to be implement custom_cls: path: null name: null use_shm: False apply_chat_template_kwargs: {} num_workers: 8 # MultiTurnSFTDataset apply_chat_template to each turn separately and concat `input_ids` # as a whole sequence, which may not equal to apply_chat_template to whole messages at once. # For example, Qwen Thinking series models add tags to last turn, please check # your tokenizer chat template settings. # Set to True to ignore input_ids mismatch and use the concatenated input_ids as the final input_ids. ignore_input_ids_mismatch: False # Checkpoint configuration checkpoint: _target_: verl.trainer.config.CheckpointConfig # What to include in saved checkpoints # with 'hf_model' you can save whole model as hf format, now only use sharded model checkpoint to save space save_contents: ["model", "optimizer", "extra"] # For more flexibility, you can specify the contents to load from the checkpoint. load_contents: ${checkpoint.save_contents} # Mbridge config extension. mbridge_config: {} trainer: default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} default_hdfs_dir: null project_name: gsm8k-sft experiment_name: test total_epochs: 4 total_training_steps: null logger: [ 'console', 'wandb' ] seed: 1 save_freq: -1 test_freq: -1 max_ckpt_to_keep: null # Maximum number of checkpoints to keep, set to null to keep all # Resume mode: "auto", "disable", or "resume_path" # "auto": resume from last checkpoint if available # "disable": start from scratch # "resume_path": resume from a user-defined path resume_mode: auto # Path to resume training from (used when resume_mode is "resume_path" or "auto") resume_from_path: null device: cuda nnodes: 1 n_gpus_per_node: 1 profile_interval: [-1, -1] global_profiler: global_tool_config: # nsys config nsys: # controller Nvidia Nsight Systems Options. Must set when profile_steps is not None. ## reference https://docs.nvidia.com/nsight-systems/UserGuide/index.html ## reference https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html worker_nsight_options: # Select the API(s) to be traced. trace: "cuda,nvtx,cublas,ucx" # Track the GPU memory usage by CUDA kernels. Must be string type "true" or "false". cuda-memory-usage: "true" # CUDA graphs will be traced as a whole cuda-graph-trace: "graph" # Profiling only in a range of torch.cuda.profiler.start and stop. Do not change this config. capture-range: "cudaProfilerApi" # Specify the desired behavior when a capture range ends. # In verl we need the torch.cuda.profiler.start/stop pair to repeats n times. # valid values are "repeat-shutdown:n" or null. # For normal whole step profiling, n = len(profile_steps); # but for discrete profiling, n = len(profile_steps) * Number(subtasks). # Or you can just leave it null and the program will use n = len(profile_steps) * 6; capture-range-end: null # Send signal to the target application's process group. We let the program to exit by itself. kill: none ================================================ FILE: verl/trainer/constants_ppo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from ray._private.runtime_env.constants import RAY_JOB_CONFIG_JSON_ENV_VAR PPO_RAY_RUNTIME_ENV = { "env_vars": { "TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_LOGGING_LEVEL": "WARN", "VLLM_ALLOW_RUNTIME_LORA_UPDATING": "true", "CUDA_DEVICE_MAX_CONNECTIONS": "1", # TODO: disable compile cache due to cache corruption issue # https://github.com/vllm-project/vllm/issues/31199 "VLLM_DISABLE_COMPILE_CACHE": "1", # Needed for multi-processes colocated on same NPU device # https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/maintenref/envvar/envref_07_0143.html "HCCL_HOST_SOCKET_PORT_RANGE": "auto", "HCCL_NPU_SOCKET_PORT_RANGE": "auto", }, } def get_ppo_ray_runtime_env(): """ A filter function to return the PPO Ray runtime environment. To avoid repeat of some environment variables that are already set. """ working_dir = ( json.loads(os.environ.get(RAY_JOB_CONFIG_JSON_ENV_VAR, "{}")).get("runtime_env", {}).get("working_dir", None) ) runtime_env = { "env_vars": PPO_RAY_RUNTIME_ENV["env_vars"].copy(), **({"working_dir": None} if working_dir is None else {}), } for key in list(runtime_env["env_vars"].keys()): if os.environ.get(key) is not None: runtime_env["env_vars"].pop(key, None) return runtime_env ================================================ FILE: verl/trainer/main_eval.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Offline evaluate the performance of a generated file using reward model and ground truth verifier. The input is a parquet file that contains N generated sequences and (optional) the ground truth. """ from collections import defaultdict import hydra import numpy as np import pandas as pd import ray from omegaconf import OmegaConf from tqdm import tqdm from verl.trainer.ppo.reward import get_custom_reward_fn from verl.utils.fs import copy_to_local @ray.remote def process_item(config, data_source, response_lst, reward_data): reward_fn = get_custom_reward_fn(config) ground_truth = reward_data["ground_truth"] score_lst = [reward_fn(data_source, r, ground_truth) for r in response_lst] return data_source, np.mean(score_lst) @hydra.main(config_path="config", config_name="evaluation", version_base=None) def main(config): local_path = copy_to_local(config.data.path, use_shm=config.data.get("use_shm", False)) dataset = pd.read_parquet(local_path) responses = dataset[config.data.response_key] data_sources = dataset[config.data.data_source_key] reward_model_data = dataset[config.data.reward_model_key] total = len(dataset) # Initialize Ray if not ray.is_initialized(): ray.init(**OmegaConf.to_container(config.ray_kwargs.get("ray_init", {}))) # evaluate test_score based on data source data_source_reward = defaultdict(list) # Create remote tasks remote_tasks = [ process_item.remote(config, data_sources[i], responses[i], reward_model_data[i]) for i in range(total) ] # Process results as they come in with tqdm(total=total) as pbar: while len(remote_tasks) > 0: # Use ray.wait to get completed tasks done_ids, remote_tasks = ray.wait(remote_tasks) for result_id in done_ids: data_source, score = ray.get(result_id) data_source_reward[data_source].append(score) pbar.update(1) metric_dict = {} for data_source, rewards in data_source_reward.items(): metric_dict[f"test_score/{data_source}"] = np.mean(rewards) print(metric_dict) if __name__ == "__main__": main() ================================================ FILE: verl/trainer/main_generation_server.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Generate responses given a dataset of prompts """ import os import aiohttp import hydra import numpy as np import ray os.environ["NCCL_DEBUG"] = "WARN" os.environ["TOKENIZERS_PARALLELISM"] = "true" # os.environ['TORCH_COMPILE_DISABLE'] = '1' import asyncio from pprint import pprint import pandas as pd from omegaconf import OmegaConf from openai.types.chat import ChatCompletion from verl.utils.hdfs_io import makedirs from verl.workers.rollout.replica import get_rollout_replica_class async def start_server(config): tp_size = config.actor_rollout_ref.rollout.tensor_model_parallel_size num_replicas = (config.trainer.n_gpus_per_node * config.trainer.nnodes) // tp_size rollout_config = config.actor_rollout_ref.rollout model_config = config.actor_rollout_ref.model # create standalone rollout server rollout_server_class = get_rollout_replica_class(config.actor_rollout_ref.rollout.name) rollout_servers = [ rollout_server_class( replica_rank=replica_rank, config=rollout_config, model_config=model_config, gpus_per_node=config.trainer.n_gpus_per_node, ) for replica_rank in range(num_replicas) ] await asyncio.gather(*[server.init_standalone() for server in rollout_servers]) server_handles = [server._server_handle for server in rollout_servers] server_addresses = [server._server_address for server in rollout_servers] assert len(server_handles) == num_replicas assert len(server_addresses) == num_replicas return server_handles, server_addresses async def submit_request(server_address, **chat_complete_request): try: extra_headers = chat_complete_request.pop("extra_headers", {}) timeout = aiohttp.ClientTimeout(total=None) session = aiohttp.ClientSession(timeout=timeout) async with session.post( url=f"http://{server_address}/v1/chat/completions", headers={"Authorization": "Bearer token-abc123", **extra_headers}, json=chat_complete_request, ) as resp: data = await resp.json() return ChatCompletion(**data) finally: await session.close() async def generate_per_replica(server_address, model_path: str, n_samples: int, sampling_params: dict, chat_lst: list): # here we should sample n_samples for each chat_lst. # we use aiohttp to avoid hang in AsyncOpenAI when the number of requests is large. # client = AsyncOpenAI( # api_key="123-abc", # base_url=f"http://{server_address}/v1", # ) chat_complete_request = [ { "model": model_path, "messages": messages, **sampling_params, } for messages in chat_lst for _ in range(n_samples) ] tasks = [submit_request(server_address, **req) for req in chat_complete_request] results = await asyncio.gather(*tasks) return results async def generate( server_addresses: list, model_path: str, n_samples: int, sampling_params: dict, chat_numpy: np.ndarray ): num_replicas = len(server_addresses) chat_sub_array = np.array_split(chat_numpy, num_replicas) chat_sub_array = [chat.tolist() for chat in chat_sub_array] assert len(server_addresses) == len(chat_sub_array) results = await asyncio.gather( *[ generate_per_replica(server_addresses[i], model_path, n_samples, sampling_params, chat_sub_array[i]) for i in range(num_replicas) ] ) return results @hydra.main(config_path="config", config_name="ppo_trainer", version_base=None) def main(config): ray.init(runtime_env={"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN", "VLLM_USE_V1": "1"}}) pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) n_samples = config.actor_rollout_ref.rollout.n if config.actor_rollout_ref.rollout.temperature == 0.0: assert n_samples == 1, "When temperature=0, n_samples must be 1." assert n_samples >= 1, "n_samples should always >= 1" sampling_params = { "temperature": config.actor_rollout_ref.rollout.temperature, "top_p": config.actor_rollout_ref.rollout.top_p, # "top_k": config.actor_rollout_ref.rollout.top_k, "max_tokens": config.actor_rollout_ref.rollout.response_length, } from omegaconf import ListConfig train_files = config.data.train_files if not isinstance(train_files, list | ListConfig): train_files = [train_files] # read dataset. Note that the dataset should directly contain chat template format (e.g., a list of dictionary) datasets = [] for train_file in train_files: dataset = pd.read_parquet(train_file) datasets.append(dataset) # concat dataset dataset = pd.concat(datasets, axis=0, ignore_index=True) chat_lst = dataset[config.data.prompt_key].tolist() chat_lst = [chat.tolist() for chat in chat_lst] chat_numpy = np.array(chat_lst) # start native server server_handles, server_addresses = asyncio.run(start_server(config)) # run generate gen_results = asyncio.run( generate(server_addresses, config.actor_rollout_ref.model.path, n_samples, sampling_params, chat_numpy) ) # reshape results into a numpy array import itertools results = list(itertools.chain.from_iterable(gen_results)) # extract content from results results = np.array([result.choices[0].message.content for result in results]) results = np.reshape(results, (-1, n_samples)) assert results.shape == (len(chat_lst), n_samples) results = results.tolist() # add to the data frame dataset["responses"] = results # write to a new parquet output_dir = os.path.dirname(config.data.output_path) makedirs(output_dir, exist_ok=True) print(f"Saving results to {config.data.output_path}") dataset.to_parquet(config.data.output_path) if __name__ == "__main__": main() ================================================ FILE: verl/trainer/main_ppo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Note that we don't combine the main with ray_trainer as ray_trainer is used by other mpain. """ import os import socket import hydra import ray from omegaconf import OmegaConf from verl.experimental.dataset.sampler import AbstractSampler from verl.experimental.reward_loop import migrate_legacy_reward_impl from verl.trainer.constants_ppo import get_ppo_ray_runtime_env from verl.trainer.ppo.ray_trainer import RayPPOTrainer from verl.trainer.ppo.utils import need_critic, need_reference_policy from verl.utils.config import validate_config from verl.utils.device import auto_set_device, is_cuda_available from verl.utils.import_utils import load_extern_object @hydra.main(config_path="config", config_name="ppo_trainer", version_base=None) def main(config): """Main entry point for PPO training with Hydra configuration management. Args: config: Hydra configuration dictionary containing training parameters. """ # Automatically set `config.trainer.device = npu` when running on Ascend NPU. auto_set_device(config) config = migrate_legacy_reward_impl(config) run_ppo(config) # Define a function to run the PPO-like training process def run_ppo(config, task_runner_class=None) -> None: """Initialize Ray cluster and run distributed PPO training process. Args: config: Training configuration object containing all necessary parameters for distributed PPO training including Ray initialization settings, model paths, and training hyperparameters. task_runner_class: For recipe to change TaskRunner. """ # Check if Ray is not initialized if not ray.is_initialized(): # Initialize Ray with a local cluster configuration # Set environment variables in the runtime environment to control tokenizer parallelism, # NCCL debug level, VLLM logging level, and allow runtime LoRA updating # `num_cpus` specifies the number of CPU cores Ray can use, obtained from the configuration default_runtime_env = get_ppo_ray_runtime_env() ray_init_kwargs = config.ray_kwargs.get("ray_init", {}) runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {}) if config.transfer_queue.enable: # Add runtime environment variables for transfer queue runtime_env_vars = runtime_env_kwargs.get("env_vars", {}) runtime_env_vars["TRANSFER_QUEUE_ENABLE"] = "1" runtime_env_kwargs["env_vars"] = runtime_env_vars runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs) ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env}) print(f"ray init kwargs: {ray_init_kwargs}") ray.init(**OmegaConf.to_container(ray_init_kwargs)) if task_runner_class is None: task_runner_class = ray.remote(num_cpus=1)(TaskRunner) # please make sure main_task is not scheduled on head # Create a remote instance of the TaskRunner class, and # Execute the `run` method of the TaskRunner instance remotely and wait for it to complete if ( is_cuda_available and config.global_profiler.tool == "nsys" and config.global_profiler.get("steps") is not None and len(config.global_profiler.get("steps", [])) > 0 ): from verl.utils.import_utils import is_nvtx_available assert is_nvtx_available(), "nvtx is not available in CUDA platform. Please 'pip3 install nvtx'" nsight_options = OmegaConf.to_container( config.global_profiler.global_tool_config.nsys.controller_nsight_options ) runner = task_runner_class.options(runtime_env={"nsight": nsight_options}).remote() else: runner = task_runner_class.remote() ray.get(runner.run.remote(config)) # [Optional] get the path of the timeline trace file from the configuration, default to None # This file is used for performance analysis timeline_json_file = config.ray_kwargs.get("timeline_json_file", None) if timeline_json_file: ray.timeline(filename=timeline_json_file) class TaskRunner: """Ray remote class for executing distributed PPO training tasks. This class encapsulates the main training logic and runs as a Ray remote actor to enable distributed execution across multiple nodes and GPUs. Attributes: role_worker_mapping: Dictionary mapping Role enums to Ray remote worker classes mapping: Dictionary mapping Role enums to resource pool IDs for GPU allocation """ def __init__(self): self.role_worker_mapping = {} self.mapping = {} def add_actor_rollout_worker(self, config): """Add actor rollout worker based on the actor strategy.""" from verl.single_controller.ray import RayWorkerGroup from verl.trainer.ppo.ray_trainer import Role use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") # use new model engine implementation if use_legacy_worker_impl == "disable": from verl.workers.engine_workers import ActorRolloutRefWorker actor_rollout_cls = ActorRolloutRefWorker ray_worker_group_cls = RayWorkerGroup lora_rank = config.actor_rollout_ref.model.get("lora", {}).get("rank", 0) if lora_rank <= 0: lora_rank = config.actor_rollout_ref.model.get("lora_rank", 0) ref_in_actor = lora_rank > 0 or config.actor_rollout_ref.model.get("lora_adapter_path") is not None # NOTE: In new model engine, ref policy and actor rollout are in same ActorRolloutRefWorker, # while in legacy model engine, ref policy is in a separate ActorRolloutRefWorker. if need_reference_policy(config) and not ref_in_actor: role = Role.ActorRolloutRef else: role = Role.ActorRollout self.role_worker_mapping[role] = ray.remote(actor_rollout_cls) self.mapping[role] = "global_pool" return actor_rollout_cls, ray_worker_group_cls # Note: sync mode validation is now handled in RolloutConfig.__post_init__ # Always use async worker since sync mode is deprecated and rejected if config.actor_rollout_ref.actor.strategy in {"fsdp", "fsdp2"}: from verl.workers.fsdp_workers import AsyncActorRolloutRefWorker actor_rollout_cls = AsyncActorRolloutRefWorker ray_worker_group_cls = RayWorkerGroup elif config.actor_rollout_ref.actor.strategy == "megatron": from verl.workers.megatron_workers import AsyncActorRolloutRefWorker actor_rollout_cls = AsyncActorRolloutRefWorker ray_worker_group_cls = RayWorkerGroup elif ( config.actor_rollout_ref.actor.strategy == "veomni" or config.actor_rollout_ref.actor.strategy == "torchtitan" ): raise NotImplementedError( f"{config.actor_rollout_ref.actor.strategy} does not support legacy worker implementation" ) else: raise NotImplementedError self.role_worker_mapping[Role.ActorRollout] = ray.remote(actor_rollout_cls) self.mapping[Role.ActorRollout] = "global_pool" return actor_rollout_cls, ray_worker_group_cls def add_critic_worker(self, config): """Add critic worker to role mapping.""" use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") if config.critic.strategy in {"fsdp", "fsdp2"}: if use_legacy_worker_impl in ["auto", "enable"]: from verl.workers.fsdp_workers import CriticWorker elif use_legacy_worker_impl == "disable": # we don't need to specialize critic worker. Just use TrainingWorker from verl.workers.engine_workers import TrainingWorker CriticWorker = TrainingWorker print("Using new worker implementation") else: raise ValueError(f"Invalid use_legacy_worker_impl: {use_legacy_worker_impl}") elif config.critic.strategy == "megatron": # TODO: switch this to TrainingWorker as well from verl.workers.megatron_workers import CriticWorker elif config.critic.strategy == "veomni" or config.critic.strategy == "torchtitan": if use_legacy_worker_impl == "disable": from verl.workers.engine_workers import TrainingWorker CriticWorker = TrainingWorker print(f"Using new worker implementation for {config.critic.strategy}") else: raise ValueError( f"Invalid use_legacy_worker_impl for {config.critic.strategy}: {use_legacy_worker_impl}" ) else: raise NotImplementedError from verl.trainer.ppo.ray_trainer import Role self.role_worker_mapping[Role.Critic] = ray.remote(CriticWorker) self.mapping[Role.Critic] = "global_pool" def init_resource_pool_mgr(self, config): """Initialize resource pool manager.""" global_pool_id = "global_pool" resource_pool_spec = { global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes, } if config.reward.reward_model.enable_resource_pool: if config.reward.reward_model.n_gpus_per_node <= 0: raise ValueError("config.reward.reward_model.n_gpus_per_node must be greater than 0") if config.reward.reward_model.nnodes <= 0: raise ValueError("config.reward.reward_model.nnodes must be greater than 0") reward_pool = [config.reward.reward_model.n_gpus_per_node] * config.reward.reward_model.nnodes resource_pool_spec["reward_pool"] = reward_pool else: config.reward.reward_model.nnodes = config.trainer.nnodes config.reward.reward_model.n_gpus_per_node = config.trainer.n_gpus_per_node from verl.trainer.ppo.ray_trainer import ResourcePoolManager resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=self.mapping) return resource_pool_manager def add_reward_model_resource_pool(self, config): """Add reward model worker if enabled.""" from verl.trainer.ppo.ray_trainer import Role if config.reward.reward_model.enable: # we do not use reward model workers, so we only register reward model in resource pool # without continue to register reward model worker in role mapping if config.reward.reward_model.enable_resource_pool: self.mapping[Role.RewardModel] = "reward_pool" else: self.mapping[Role.RewardModel] = "global_pool" def add_ref_policy_worker(self, config, ref_policy_cls): """Add reference policy worker if KL loss or KL reward is used.""" from verl.trainer.ppo.ray_trainer import Role # Ref policy has been fused into ActorRolloutRefWorker in new model engine, # we don't need to add a separate ref policy worker group. use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") if use_legacy_worker_impl == "disable": return if need_reference_policy(config): self.role_worker_mapping[Role.RefPolicy] = ray.remote(ref_policy_cls) self.mapping[Role.RefPolicy] = "global_pool" def run(self, config): """Execute the main PPO training workflow. This method sets up the distributed training environment, initializes workers, datasets, and reward functions, then starts the training process. Args: config: Training configuration object containing all parameters needed for setting up and running the PPO training process. """ # Print the initial configuration. `resolve=True` will evaluate symbolic values. from pprint import pprint from omegaconf import OmegaConf from verl.utils.fs import copy_to_local print(f"TaskRunner hostname: {socket.gethostname()}, PID: {os.getpid()}") pprint(OmegaConf.to_container(config, resolve=True)) OmegaConf.resolve(config) actor_rollout_cls, ray_worker_group_cls = self.add_actor_rollout_worker(config) self.add_critic_worker(config) self.add_reward_model_resource_pool(config) # Add a reference policy worker if KL loss or KL reward is used. self.add_ref_policy_worker(config, actor_rollout_cls) # validate config validate_config( config=config, use_reference_policy=need_reference_policy(config), use_critic=need_critic(config), ) # Download the checkpoint from HDFS to the local machine. # `use_shm` determines whether to use shared memory, which could lead to faster model loading if turned on local_path = copy_to_local( config.actor_rollout_ref.model.path, use_shm=config.actor_rollout_ref.model.get("use_shm", False) ) # Instantiate the tokenizer and processor. from verl.utils import hf_processor, hf_tokenizer trust_remote_code = config.data.get("trust_remote_code", False) tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) # Used for multimodal LLM, could be None processor = hf_processor(local_path, trust_remote_code=trust_remote_code, use_fast=True) resource_pool_manager = self.init_resource_pool_mgr(config) from verl.utils.dataset.rl_dataset import collate_fn # Create training and validation datasets. train_dataset = create_rl_dataset( config.data.train_files, config.data, tokenizer, processor, is_train=True, max_samples=config.data.get("train_max_samples", -1), ) val_dataset = create_rl_dataset( config.data.val_files, config.data, tokenizer, processor, is_train=False, max_samples=config.data.get("val_max_samples", -1), ) train_sampler = create_rl_sampler(config.data, train_dataset) # Initialize the PPO trainer. trainer = RayPPOTrainer( config=config, tokenizer=tokenizer, processor=processor, role_worker_mapping=self.role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, train_dataset=train_dataset, val_dataset=val_dataset, collate_fn=collate_fn, train_sampler=train_sampler, ) # Initialize the workers of the trainer. trainer.init_workers() # Start the training process. trainer.fit() def create_rl_dataset(data_paths, data_config, tokenizer, processor, is_train=True, max_samples: int = -1): """Create a dataset. Arguments: data_paths: List of paths to data files. data_config: The data config. tokenizer (Tokenizer): The tokenizer. processor (Processor): The processor. Returns: dataset (Dataset): The dataset. """ from verl.utils.dataset.rl_dataset import get_dataset_class # Get the dataset class dataset_cls = get_dataset_class(data_config) # Instantiate the dataset using the determined dataset class dataset = dataset_cls( data_files=data_paths, tokenizer=tokenizer, processor=processor, config=data_config, max_samples=max_samples, ) return dataset def create_rl_sampler(data_config, dataset): """Create a sampler for the dataset. Arguments: data_config: The data config. dataset (Dataset): The dataset. Returns: sampler (Sampler): The sampler. """ import torch from torch.utils.data import SequentialSampler # torch.utils.data.RandomSampler could not recover properly from torchdata.stateful_dataloader.sampler import RandomSampler if data_config.sampler is not None and data_config.sampler.get("class_path", None) is not None: curriculum_class = load_extern_object( data_config.sampler.class_path, data_config.sampler.class_name, ) sampler = curriculum_class( data_source=dataset, data_config=data_config, ) assert isinstance(sampler, AbstractSampler) assert data_config.get("dataloader_num_workers", 8) == 0, ( "If using curriculum, num_workers must be 0 to prevent data caching. " "If the dataloader caches data before the batch is done the " "curriculum sampler won't have the opportunity to reorder it. " ) # Use a sampler to facilitate checkpoint resumption. # If shuffling is enabled in the data configuration, create a random sampler. elif data_config.shuffle: train_dataloader_generator = torch.Generator() seed = data_config.get("seed") if seed is not None: train_dataloader_generator.manual_seed(seed) sampler = RandomSampler(data_source=dataset, generator=train_dataloader_generator) else: # If shuffling is disabled, use a sequential sampler to iterate through the dataset in order. sampler = SequentialSampler(data_source=dataset) return sampler if __name__ == "__main__": main() ================================================ FILE: verl/trainer/ppo/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/trainer/ppo/core_algos.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Core functions to implement PPO algorithms. The function implemented in this file should be used by trainer with different distributed strategies to implement PPO-like algorithms. """ __all__ = ["register_adv_est", "get_adv_estimator_fn", "AdvantageEstimator"] from collections import defaultdict from enum import Enum from typing import Any, Callable, Optional import numpy as np import torch from omegaconf import DictConfig import verl.utils.torch_functional as verl_F from verl.trainer.config import AlgoConfig from verl.utils import as_torch_index, group_mean_std from verl.utils.import_utils import deprecated from verl.workers.config import ActorConfig PolicyLossFn = Callable[ [ torch.Tensor, # old_log_prob torch.Tensor, # log_prob torch.Tensor, # advantages torch.Tensor, # response_mask str, # loss_agg_mode Optional[DictConfig | ActorConfig], # config torch.Tensor | None, # rollout_log_probs ], tuple[torch.Tensor, dict[str, Any]], ] POLICY_LOSS_REGISTRY: dict[str, PolicyLossFn] = {} def register_policy_loss(name: str) -> Callable[[PolicyLossFn], PolicyLossFn]: """Register a policy loss function with the given name. Args: name (str): The name to register the policy loss function under. Returns: function: Decorator function that registers the policy loss function. """ def decorator(func: PolicyLossFn) -> PolicyLossFn: POLICY_LOSS_REGISTRY[name] = func return func return decorator def get_policy_loss_fn(name): """Get the policy loss with a given name. Args: name: `(str)` The name of the policy loss. Returns: `(callable)`: The policy loss function. """ loss_name = name if loss_name not in POLICY_LOSS_REGISTRY: raise ValueError( f"Unsupported loss mode: {loss_name}. Supported modes are: {list(POLICY_LOSS_REGISTRY.keys())}" ) return POLICY_LOSS_REGISTRY[loss_name] class AdvantageEstimator(str, Enum): """Using an enumeration class to avoid spelling errors in adv_estimator. Note(haibin.lin): this enum class is immutable after creation. Extending this enum for new estimators may not be necessary since users can always just call `verl.trainer.ppo.core_algos.register` with string name for a custom advantage estimator instead. """ GAE = "gae" GRPO = "grpo" REINFORCE_PLUS_PLUS = "reinforce_plus_plus" REINFORCE_PLUS_PLUS_BASELINE = "reinforce_plus_plus_baseline" REMAX = "remax" RLOO = "rloo" OPO = "opo" GRPO_PASSK = "grpo_passk" GPG = "gpg" RLOO_VECTORIZED = "rloo_vectorized" GRPO_VECTORIZED = "grpo_vectorized" OPTIMAL_TOKEN_BASELINE = "optimal_token_baseline" TIR_OPTIMAL_TOKEN_BASELINE = "tir_optimal_token_baseline" GDPO = "gdpo" ADV_ESTIMATOR_REGISTRY: dict[str, Any] = {} def register_adv_est(name_or_enum: str | AdvantageEstimator) -> Any: """Decorator to register a advantage estimator function with a given name. Args: name_or_enum: `(str)` or `(AdvantageEstimator)` The name or enum of the advantage estimator. """ def decorator(fn): name = name_or_enum.value if isinstance(name_or_enum, Enum) else name_or_enum if name in ADV_ESTIMATOR_REGISTRY and ADV_ESTIMATOR_REGISTRY[name] != fn: raise ValueError( f"Adv estimator {name} has already been registered: {ADV_ESTIMATOR_REGISTRY[name]} vs {fn}" ) ADV_ESTIMATOR_REGISTRY[name] = fn return fn return decorator def get_adv_estimator_fn(name_or_enum): """Get the advantage estimator function with a given name. Args: name_or_enum: `(str)` or `(AdvantageEstimator)` The name or enum of the advantage estimator. Returns: `(callable)`: The advantage estimator function. """ name = name_or_enum.value if isinstance(name_or_enum, Enum) else name_or_enum if name not in ADV_ESTIMATOR_REGISTRY: raise ValueError(f"Unknown advantage estimator simply: {name}") return ADV_ESTIMATOR_REGISTRY[name] class AdaptiveKLController: """ Adaptive KL controller described in the paper: https://arxiv.org/pdf/1909.08593.pdf """ def __init__(self, init_kl_coef, target_kl, horizon): self.value = init_kl_coef self.target = target_kl self.horizon = horizon def update(self, current_kl, n_steps): """Update the KL coefficient based on current KL divergence. Args: current_kl (float): Current KL divergence value. n_steps (int): Number of steps taken. """ target = self.target proportional_error = np.clip(current_kl / target - 1, -0.2, 0.2) mult = 1 + proportional_error * n_steps / self.horizon self.value *= mult class FixedKLController: """Fixed KL controller.""" def __init__(self, kl_coef): self.value = kl_coef def update(self, current_kl, n_steps): """Update method for fixed KL controller (no-op). Args: current_kl (float): Current KL divergence value (unused). n_steps (int): Number of steps taken (unused). """ pass def get_kl_controller(kl_ctrl): """Factory function to create appropriate KL controller based on configuration. Args: kl_ctrl: Configuration object containing KL controller settings. Returns: KL controller instance (FixedKLController or AdaptiveKLController). Raises: NotImplementedError: If controller type is not supported. AssertionError: If adaptive controller horizon is not positive. """ if kl_ctrl.type == "fixed": return FixedKLController(kl_coef=kl_ctrl.kl_coef) elif kl_ctrl.type == "adaptive": assert kl_ctrl.horizon > 0, f"horizon must be larger than 0. Got {kl_ctrl.horizon}" return AdaptiveKLController(init_kl_coef=kl_ctrl.kl_coef, target_kl=kl_ctrl.target_kl, horizon=kl_ctrl.horizon) else: raise NotImplementedError @register_adv_est(AdvantageEstimator.GAE) # or simply: @register_adv_est("gae") def compute_gae_advantage_return( token_level_rewards: torch.Tensor, values: torch.Tensor, response_mask: torch.Tensor, gamma: torch.Tensor, lam: torch.Tensor, ): """Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py Args: token_level_rewards: `(torch.Tensor)` shape is (bs, response_length) values: `(torch.Tensor)` shape is (bs, response_length) response_mask: `(torch.Tensor)` shape is (bs, response_length). [EOS] mask. The token after [EOS] have mask zero. gamma is `(float)` discounted factor used in RL lam: `(float)` lambda value when computing Generalized Advantage Estimation (https://arxiv.org/abs/1506.02438) Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ with torch.no_grad(): nextvalues = 0 lastgaelam = 0 advantages_reversed = [] gen_len = token_level_rewards.shape[-1] for t in reversed(range(gen_len)): delta = token_level_rewards[:, t] + gamma * nextvalues - values[:, t] lastgaelam_ = delta + gamma * lam * lastgaelam # skip values and TD-error on observation tokens nextvalues = values[:, t] * response_mask[:, t] + (1 - response_mask[:, t]) * nextvalues lastgaelam = lastgaelam_ * response_mask[:, t] + (1 - response_mask[:, t]) * lastgaelam advantages_reversed.append(lastgaelam) advantages = torch.stack(advantages_reversed[::-1], dim=1) returns = advantages + values advantages = verl_F.masked_whiten(advantages, response_mask) return advantages, returns # NOTE(sgm): this implementation only consider outcome supervision, where the reward is a scalar. @register_adv_est(AdvantageEstimator.GRPO) # or simply: @register_adv_est("grpo") def compute_grpo_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, norm_adv_by_std_in_grpo: bool = True, config: Optional[AlgoConfig] = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for GRPO, operating only on Outcome reward (with only one scalar reward for each response). Args: token_level_rewards: `(torch.Tensor)` shape is (bs, response_length) response_mask: `(torch.Tensor)` shape is (bs, response_length) index: `(np.ndarray)` index array for grouping epsilon: `(float)` small value to avoid division by zero norm_adv_by_std_in_grpo: `(bool)` whether to scale the GRPO advantage config: `(Optional[AlgoConfig])` algorithm configuration object Note: If norm_adv_by_std_in_grpo is True, the advantage is scaled by the std, as in the original GRPO. If False, the advantage is not scaled, as in Dr.GRPO (https://arxiv.org/abs/2503.20783). Returns: advantages: `(torch.Tensor)` shape is (bs, response_length) Returns: `(torch.Tensor)` shape is (bs, response_length) """ scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2mean = {} id2std = {} with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): id2score[index[i]].append(scores[i]) for idx in id2score: if len(id2score[idx]) == 1: id2mean[idx] = torch.tensor(0.0) id2std[idx] = torch.tensor(1.0) elif len(id2score[idx]) > 1: scores_tensor = torch.stack(id2score[idx]) id2mean[idx] = torch.mean(scores_tensor) id2std[idx] = torch.std(scores_tensor) else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): if norm_adv_by_std_in_grpo: scores[i] = (scores[i] - id2mean[index[i]]) / (id2std[index[i]] + epsilon) else: scores[i] = scores[i] - id2mean[index[i]] scores = scores.unsqueeze(-1) * response_mask return scores, scores @register_adv_est(AdvantageEstimator.GRPO_VECTORIZED) def compute_grpo_vectorized_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, norm_adv_by_std_in_grpo: bool = True, config: Optional[AlgoConfig] = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Vectorized GRPO（outcome-only）: For each group g: a_i = \\frac{r_i - \\mu_g}{\\sigma_g} (or without dividing by \\sigma_g), then broadcast the scalar across the token dimension (multiplied by response_mask).。 """ with torch.no_grad(): scores = token_level_rewards.sum(dim=-1) g = as_torch_index(index, device=scores.device) mean_g, std_g, _ = group_mean_std(scores, g, eps=epsilon, device=scores.device) if norm_adv_by_std_in_grpo: scalars = (scores - mean_g[g]) / (std_g[g] + epsilon) else: scalars = scores - mean_g[g] advantages = scalars.unsqueeze(-1) * response_mask return advantages, advantages @register_adv_est(AdvantageEstimator.GDPO) # or simply: @register_adv_est("gdpo") def compute_gdpo_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, norm_adv_by_std_in_grpo: bool = True, config: Optional[AlgoConfig] = None, non_tensor_batch: Optional[dict] = None, batch: Optional[dict] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ GDPO: Group reward-Decoupled Normalization Policy Optimization. Instead of summing all reward dimensions first (like GRPO), GDPO normalizes each reward dimension independently within each group before aggregation. This prevents a dominant reward signal from drowning out weaker ones. Mathematical formulation: Step 1 – Group-wise decoupled normalization (via GRPO per dimension): For each reward dimension k, within each group g: A_k = (r_k - μ_group(r_k)) / (σ_group(r_k) + ε) Step 2 – Weighted aggregation: A_sum = Σ_k w_k · A_k Step 3 – Batch-level normalization (via masked_whiten): A_final = whiten(A_sum, response_mask) Args: token_level_rewards: (bs, response_length) – standard token-level rewards. Used as fallback when per-dimension rewards are not provided. response_mask: (bs, response_length) index: (bs,) – group id per sample (from ``uid``). epsilon: Numerical stability constant. norm_adv_by_std_in_grpo: Whether to normalize by std in GRPO. config: Algorithm configuration (optional). non_tensor_batch: Non-tensor batch data containing per-dimension reward scores. batch: Batch data containing prompts, attention_mask, etc. Note: Ref GDPO (https://arxiv.org/abs/2601.05242). Returns: advantages: (bs, response_length) returns: (bs, response_length) – same as advantages (outcome-only). """ score_list = None reward_weights = None if config is not None and non_tensor_batch is not None and batch is not None: gdpo_reward_keys = config.get("gdpo_reward_keys", None) assert gdpo_reward_keys, ( "GDPO requires 'algorithm.gdpo_reward_keys' listing the individual reward " "component keys returned by compute_score (e.g. ['format_reward', 'accuracy_reward'])." ) device = token_level_rewards.device prompt_length = batch["prompts"].size(1) valid_response_length = batch["attention_mask"][:, prompt_length:].sum(dim=1) - 1 score_list = [] for key in gdpo_reward_keys: assert key in non_tensor_batch, ( f"GDPO reward key '{key}' not found in non_tensor_batch. " f"Available keys: {list(non_tensor_batch.keys())}. " f"Make sure your compute_score returns a dict containing '{key}'." ) comp = non_tensor_batch[key] rm_score = torch.tensor(np.asarray(comp, dtype=np.float32), device=device) rm_scores = torch.zeros_like(response_mask, dtype=torch.float32) rm_scores[torch.arange(rm_scores.size(0), device=device), valid_response_length] = rm_score score_list.append(rm_scores) gdpo_weights = config.get("gdpo_reward_weights", None) if gdpo_weights is not None: reward_weights = list(gdpo_weights) if score_list is None: score_list = [token_level_rewards] num_scores = len(score_list) if reward_weights is not None: weights = torch.tensor(reward_weights, dtype=torch.float32, device=token_level_rewards.device) else: weights = torch.ones(num_scores, dtype=torch.float32, device=token_level_rewards.device) new_advantage = None for i in range(num_scores): normalized_score, _ = compute_grpo_outcome_advantage( token_level_rewards=score_list[i], response_mask=response_mask, index=index, epsilon=epsilon, norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo, config=config, ) if new_advantage is None: new_advantage = weights[i] * normalized_score else: new_advantage += weights[i] * normalized_score advantages = verl_F.masked_whiten(new_advantage, response_mask) * response_mask return advantages, advantages @register_adv_est(AdvantageEstimator.GRPO_PASSK) # or simply: @register_adv_est("grpo_passk") def compute_grpo_passk_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, norm_adv_by_std_in_grpo: bool = True, config: Optional[AlgoConfig] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for Pass@k using a GRPO-style outcome reward formulation. Only the best response per group gets a non-zero advantage: r_max - r_second_max. Implemented as described in https://arxiv.org/abs/2503.19595. Args: token_level_rewards: (bs, response_length) response_mask: (bs, response_length) index: (bs,) → group ID per sample epsilon: float for numerical stability config: (AlgoConfig) algorithm settings, which contains "norm_adv_by_std_in_grpo" Returns: advantages: (bs, response_length) returns: (bs, response_length) """ assert config is not None # if True, normalize advantage by std within group norm_adv_by_std_in_grpo = config.get("norm_adv_by_std_in_grpo", True) scores = token_level_rewards.sum(dim=-1) # (bs,) advantages = torch.zeros_like(scores) id2scores = defaultdict(list) id2indices = defaultdict(list) with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): idx = index[i] id2scores[idx].append(scores[i]) id2indices[idx].append(i) for idx in id2scores: rewards = torch.stack(id2scores[idx]) # (k,) if rewards.numel() < 2: raise ValueError( f"Pass@k requires at least 2 samples per group. Got {rewards.numel()} for group {idx}." ) topk, topk_idx = torch.topk(rewards, 2) r_max, r_second_max = topk[0], topk[1] i_max = id2indices[idx][topk_idx[0].item()] advantage = r_max - r_second_max if norm_adv_by_std_in_grpo: std = torch.std(rewards) advantage = advantage / (std + epsilon) advantages[i_max] = advantage advantages = advantages.unsqueeze(-1) * response_mask return advantages, advantages @register_adv_est( AdvantageEstimator.REINFORCE_PLUS_PLUS_BASELINE ) # or simply: @register_adv_est("reinforce_plus_plus_baseline") def compute_reinforce_plus_plus_baseline_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: torch.Tensor, epsilon: float = 1e-6, config: Optional[AlgoConfig] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for RF++-baseline (https://arxiv.org/abs/2501.03262), operating only on Outcome reward (with only one scalar reward for each response). Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) config: (AlgoConfig) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ response_length = token_level_rewards.shape[-1] scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2mean = {} with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): id2score[index[i]].append(scores[i]) for idx in id2score: if len(id2score[idx]) == 1: id2mean[idx] = torch.tensor(0.0) elif len(id2score[idx]) > 1: id2mean[idx] = torch.mean(torch.stack(id2score[idx])) else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): scores[i] = scores[i] - id2mean[index[i]] scores = scores.unsqueeze(-1).tile([1, response_length]) * response_mask scores = verl_F.masked_whiten(scores, response_mask) * response_mask return scores, scores @register_adv_est(AdvantageEstimator.RLOO) # or simply: @register_adv_est("rloo") def compute_rloo_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, config: Optional[AlgoConfig] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for RLOO based on https://arxiv.org/abs/2402.14740 Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) config: (AlgoConfig) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2mean = {} with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): id2score[index[i]].append(scores[i]) for idx in id2score: if len(id2score[idx]) == 1: id2mean[idx] = torch.tensor(0.0) elif len(id2score[idx]) > 1: id2mean[idx] = torch.mean(torch.stack(id2score[idx])) else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): response_num = len(id2score[index[i]]) if response_num > 1: scores[i] = scores[i] * response_num / (response_num - 1) - id2mean[index[i]] * response_num / ( response_num - 1 ) scores = scores.unsqueeze(-1) * response_mask return scores, scores @register_adv_est(AdvantageEstimator.OPO) # or simply: @register_adv_est("opo") def compute_opo_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, config: Optional[AlgoConfig] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for OPO based on https://arxiv.org/pdf/2505.23585 Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) config: (AlgoConfig) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ response_length = response_mask.sum(dim=-1) scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2len = defaultdict(list) id2bsl = {} with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): id2score[index[i]].append(scores[i]) id2len[index[i]].append(response_length[i]) for idx in id2score: if len(id2score[idx]) == 1: id2bsl[idx] = torch.tensor(0.0) elif len(id2score[idx]) > 1: score_tensor = torch.stack(id2score[idx]) len_tensor = torch.stack(id2len[idx]) id2bsl[idx] = (len_tensor * score_tensor).sum() / len_tensor.sum() else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): scores[i] = scores[i] - id2bsl[index[i]] scores = scores.unsqueeze(-1) * response_mask return scores, scores @register_adv_est(AdvantageEstimator.REINFORCE_PLUS_PLUS) # or simply: @register_adv_est("reinforce_plus_plus") def compute_reinforce_plus_plus_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, config: Optional[AlgoConfig] = None, **kwargs ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for REINFORCE++. This implementation is based on the paper: https://arxiv.org/abs/2501.03262 Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) config: (AlgoConfig) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ assert config is not None gamma = config.gamma with torch.no_grad(): returns = torch.zeros_like(token_level_rewards) running_return = 0 for t in reversed(range(token_level_rewards.shape[1])): running_return = token_level_rewards[:, t] + gamma * running_return returns[:, t] = running_return # Reset after EOS running_return = running_return * response_mask[:, t] advantages = verl_F.masked_whiten(returns, response_mask) advantages = advantages * response_mask return advantages, returns @register_adv_est(AdvantageEstimator.REMAX) # or simply: @register_adv_est("remax") def compute_remax_outcome_advantage( token_level_rewards: torch.Tensor, reward_baselines: torch.Tensor, response_mask: torch.Tensor, config: Optional[AlgoConfig] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for ReMax, operating only on Outcome reward This implementation is based on the paper: https://arxiv.org/abs/2310.10505 (with only one scalar reward for each response). Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) reward_baselines: `(torch.Tensor)` shape: (bs,) response_mask: `(torch.Tensor)` shape: (bs, response_length) config: (AlgoConfig) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ with torch.no_grad(): returns = (token_level_rewards * response_mask).flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1]) advantages = returns - reward_baselines.unsqueeze(-1) * response_mask return advantages, returns @register_adv_est(AdvantageEstimator.GPG) # or simply: @register_adv_est("gpg") def compute_gpg_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, f_norm: float = 1.0, alpha: float = 1.0, config=None, **kwargs, ): """ Compute advantage for GPG, operating only on Outcome reward (with only one scalar reward for each response). Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) index: `(np.ndarray)` shape: (bs,) epsilon: (float) f_norm: (float) alpha: (float) config: (dict) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2mean = {} id2std = {} with torch.no_grad(): bsz = scores.shape[0] m = torch.count_nonzero(scores) alpha = bsz / m.clamp(min=1) for i in range(bsz): id2score[index[i]].append(scores[i]) for idx in id2score: if len(id2score[idx]) == 1: id2mean[idx] = torch.tensor(0.0) id2std[idx] = torch.tensor(1.0) elif len(id2score[idx]) > 1: scores_tensor = torch.stack(id2score[idx]) id2mean[idx] = torch.mean(scores_tensor) id2std[idx] = torch.std(scores_tensor) else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): scores[i] = alpha * (scores[i] - id2mean[index[i]]) / (f_norm) scores = scores.unsqueeze(-1) * response_mask return scores, scores @register_adv_est(AdvantageEstimator.RLOO_VECTORIZED) # or simply: @register_adv_est("rloo_vectorized") def compute_rloo_vectorized_outcome_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6, config: Optional[AlgoConfig] = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantage for RLOO based on https://arxiv.org/abs/2402.14740 Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) config: (AlgoConfig) algorithm config Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ scores = token_level_rewards.sum(dim=-1) with torch.no_grad(): inv = torch.from_numpy(np.unique(index, return_inverse=True)[1]).to(scores.device) c = torch.bincount(inv)[inv].to(scores.dtype) adv = ((c * scores - torch.bincount(inv, weights=scores)[inv]) / (c - 1).clamp_min(1)) * (c > 1) adv = adv.unsqueeze(-1) * response_mask return adv, adv @register_adv_est(AdvantageEstimator.OPTIMAL_TOKEN_BASELINE) def compute_optimal_token_baseline_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, old_log_probs: torch.Tensor, sum_pi_squared: torch.Tensor, rollout_is_weights: torch.Tensor = None, handle_zero_tail: bool = True, epsilon: float = 1e-8, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantages using Optimal Token Baseline (OTB). Unlike the group mean based baseline which uses a single baseline per trajectory, this computes a unique baseline for each timestep using cumulative path variance. Theory: For each timestep t in each prompt group: B_t* = E[G_t × W_t] / E[W_t] where W_t = Σ_{j=1}^t ||s_j||² (cumulative path-variance proxy) and ||s_j||² = 1 - 2π_j + Σπ² The cumulative sum W_t captures the "realized energy" of trajectory has been up to timestep t, giving higher weight to predicting rewards on high-variance paths. Args: token_level_rewards: Rewards at each token position [shape: (bs, response_length)] response_mask: Binary mask for valid tokens (1) vs padding (0) [shape: (bs, response_length)] index: Prompt indices for grouping trajectories from same prompt [shape: (bs,)] old_log_probs: Log probabilities from training policy during generation [shape: (bs, response_length)] sum_pi_squared: Sum of squared probabilities over vocabulary Σπ² [shape: (bs, response_length)] rollout_is_weights: Pre-computed IS weights for W correction [shape: (bs, response_length)], None if not using IS handle_zero_tail: If True, zero baselines will be set in the portion of the longest trajectory that extends beyond the second-longest trajectory in the prompt group. Default: True epsilon: Small constant for numerical stability (default: 1e-8) Returns: advantages: OTB advantage estimates [shape: (bs, response_length)] returns: Cumulative rewards (returns) from each position [shape: (bs, response_length)] Note on Rollout Importance Sampling: When rollout_is_weights is provided, W_t is scaled by ρ̄²(t) to minimize MSE under truncated IS: B_t* = Σ[G_t × ρ̄²(t) × W_t] / Σ[ρ̄²(t) × W_t] """ with torch.no_grad(): batch_size, seq_len = token_level_rewards.shape device = token_level_rewards.device # Compute returns (reward-to-go) for each timestep returns = (token_level_rewards * response_mask).flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1]) # Step 1: Compute w_per_timestep = 1 - 2π_t + Σπ²) pi_t = torch.exp(old_log_probs) w_per_timestep = 1 - 2 * pi_t + sum_pi_squared # Step 2: Apply rollout importance sampling correction (if enabled) if rollout_is_weights is not None: # Scale W by ρ̄² to minimize MSE under truncated IS w_per_timestep = w_per_timestep * (rollout_is_weights**2) # Step 3: Compute cumulative path-variance proxy: W_t = Σ_{j=1}^t w_j # This measures accumulated variance from the start of the trajectory up to timestep t w_cumulative = (w_per_timestep * response_mask).cumsum(dim=-1) # Group trajectories by prompt prompt_groups = defaultdict(list) for i in range(batch_size): prompt_groups[index[i]].append(i) # Initialize baselines tensor [batch_size, seq_len] baselines = torch.zeros_like(returns) # Compute per-step baseline for each prompt group for _, trajectory_indices in prompt_groups.items(): N = len(trajectory_indices) if N == 1: # Single trajectory - no baseline (advantage = return) continue traj_idx = torch.tensor(trajectory_indices, device=device) # Extract group data [N, seq_len] returns_group = returns[traj_idx] w_cumulative_group = w_cumulative[traj_idx] mask_group = response_mask[traj_idx] # Compute per-timestep baseline: B_t = Σ[G_t × W_t] / Σ[W_t] # where W_t = Σ_{j=1}^t ||s_j||² (cumulative path variance) # Shape: [seq_len] numerator = (returns_group * w_cumulative_group * mask_group).sum(dim=0) # Sum over trajectories denominator = (w_cumulative_group * mask_group).sum(dim=0) + epsilon baseline_per_step = numerator / denominator # [seq_len] # Assign to all trajectories in this group baselines[traj_idx] = baseline_per_step.unsqueeze(0).expand(N, -1) if handle_zero_tail: # Optionally zero out the portion of the longest trajectory that extends # beyond the second-longest trajectory in the prompt group. response_lengths = mask_group.sum(dim=-1) sorted_lengths, _ = torch.sort(response_lengths) max_length = int(sorted_lengths[-1].item()) second_max_length = int(sorted_lengths[-2].item()) max_length_idx = (response_lengths == max_length).nonzero(as_tuple=True)[0] if max_length_idx.numel() == 1 and max_length > second_max_length: max_length_traj_idx = trajectory_indices[int(max_length_idx[0])] baselines[max_length_traj_idx, second_max_length:] = 0.0 # Compute advantages: A_t = G_t - B_t advantages = (returns - baselines) * response_mask return advantages, returns @register_adv_est(AdvantageEstimator.TIR_OPTIMAL_TOKEN_BASELINE) def compute_multi_turn_optimal_token_baseline_advantage( token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, old_log_probs: torch.Tensor, sum_pi_squared: torch.Tensor, rollout_is_weights: torch.Tensor = None, handle_zero_tail: bool = True, epsilon: float = 1e-8, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute advantages using Optimal Token Baseline (OTB). Unlike the group mean based baseline which uses a single baseline per trajectory, this computes a unique baseline for each timestep using cumulative path variance. Theory: For each timestep t in each prompt group: B_t* = E[G_t × W_t] / E[W_t] where W_t = Σ_{j=1}^t ||s_j||² (cumulative path-variance proxy) and ||s_j||² = 1 - 2π_j + Σπ² The cumulative sum W_t captures the "realized energy" of trajectory has been up to timestep t, giving higher weight to predicting rewards on high-variance paths. Args: token_level_rewards: Rewards at each token position [shape: (bs, response_length)] response_mask: Binary mask for valid tokens (1) vs padding (0) [shape: (bs, response_length)] index: Prompt indices for grouping trajectories from same prompt [shape: (bs,)] old_log_probs: Log probabilities from training policy during generation [shape: (bs, response_length)] sum_pi_squared: Sum of squared probabilities over vocabulary Σπ² [shape: (bs, response_length)] rollout_is_weights: Pre-computed IS weights for W correction [shape: (bs, response_length)], None if not using IS handle_zero_tail: If True, zero baselines will be set in the portion of the longest trajectory that extends beyond the second-longest trajectory in the prompt group. Default: False epsilon: Small constant for numerical stability (default: 1e-8) Returns: advantages: OTB advantage estimates [shape: (bs, response_length)] returns: Cumulative rewards (returns) from each position [shape: (bs, response_length)] Note on Rollout Importance Sampling: When rollout_is_weights is provided, W_t is scaled by ρ̄²(t) to minimize MSE under truncated IS: B_t* = Σ[G_t × ρ̄²(t) × W_t] / Σ[ρ̄²(t) × W_t] """ with torch.no_grad(): # Compute returns (reward-to-go) for each timestep token_returns = (token_level_rewards * response_mask).flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1]) # Step 1: Compute w_per_timestep = 1 - 2π_t + Σπ²) pi_t = torch.exp(old_log_probs) w_per_timestep = 1 - 2 * pi_t + sum_pi_squared # Step 2: Apply rollout importance sampling correction (if enabled) if rollout_is_weights is not None: # Scale W by ρ̄² to minimize MSE under truncated IS w_per_timestep = w_per_timestep * (rollout_is_weights**2) # Step 3: Compute cumulative path-variance proxy: W_t = Σ_{j=1}^t w_j # This measures accumulated variance from the start of the trajectory up to timestep t w_cumulative = (w_per_timestep * response_mask).cumsum(dim=-1) # Step 4: Concatenate returns and w_cumulative for each trajectory # This allows us to compute baseline per timestep for each trajectory response_lengths = response_mask.sum(dim=-1).to(dtype=torch.long) # [shape: (bs * n, )] max_response_length = int(response_lengths.max().item()) if response_lengths.numel() > 0 else 0 all_w_values = w_cumulative.new_zeros( (len(response_lengths), max_response_length) ) # [shape: (bs * n, max_response_length)] all_returns = torch.zeros_like(all_w_values) for i in range(len(response_lengths)): length = int(response_lengths[i].item()) if length == 0: continue mask = response_mask[i].bool() all_w_values[i, :length] = w_cumulative[i, mask] all_returns[i, :length] = token_returns[i, mask] # Group trajectories by prompt prompt_groups = defaultdict(list) for i in range(len(response_lengths)): if response_lengths[i] == 0: continue prompt_groups[index[i]].append(i) # Compute optimal baseline for each prompt group baselines = torch.zeros_like(all_returns) for _, trajectory_indices in prompt_groups.items(): N = len(trajectory_indices) traj_idx = torch.tensor(trajectory_indices, device=all_returns.device) if N == 1: # Single trajectory - no baseline (keep original reward as advantage) baselines[traj_idx[0]] = 0.0 continue # Extract group data w_group = all_w_values[traj_idx] # [shape: (N, max_response_length)] R_group = all_returns[traj_idx] # [shape: (N, max_response_length)] # Direct optimal baseline - single value for all in group b_star = (R_group * w_group).sum(dim=0) / (w_group.sum(dim=0) + epsilon) # Convert to match baselines dtype (epsilon can cause float64 promotion) baselines[traj_idx] = b_star.to(baselines.dtype) if handle_zero_tail: # Optionally zero out the portion of the longest trajectory that extends # beyond the second-longest trajectory in the prompt group. response_lengths_group = response_lengths[traj_idx] sorted_lengths, _ = torch.sort(response_lengths_group) max_length = int(sorted_lengths[-1].item()) second_max_length = int(sorted_lengths[-2].item()) max_length_idx = (response_lengths_group == max_length).nonzero(as_tuple=True)[0] if max_length_idx.numel() == 1 and max_length > second_max_length: max_length_traj_idx = trajectory_indices[int(max_length_idx[0])] baselines[max_length_traj_idx, second_max_length:] = 0.0 # Compute advantages all_advantages = all_returns - baselines # [shape: (bs * n, max_response_length)] advantages = torch.zeros_like(token_returns) # [shape: (bs * n, turn * response_length)] for i in range(len(response_lengths)): if response_lengths[i] == 0: continue advantages[i, response_mask[i].bool()] = all_advantages[i, : response_lengths[i]] advantages = advantages * response_mask # [shape: (bs * n * turn, response_length)] return advantages, token_returns def compute_rewards(token_level_scores, old_log_prob, ref_log_prob, kl_ratio): """Compute token-level rewards with KL penalty. Args: token_level_scores (torch.Tensor): Token-level reward scores. old_log_prob (torch.Tensor): Log probabilities from current policy. ref_log_prob (torch.Tensor): Log probabilities from reference policy. kl_ratio (float): KL penalty coefficient. Returns: torch.Tensor: Token-level rewards with KL penalty applied. """ kl = old_log_prob - ref_log_prob return token_level_scores - kl * kl_ratio def agg_loss( loss_mat: torch.Tensor, loss_mask: torch.Tensor, loss_agg_mode: str, dp_size: int = 1, batch_num_tokens: Optional[int] = None, global_batch_size: Optional[int] = None, loss_scale_factor: Optional[int] = None, ): """ Aggregate the loss across global batch to ensure the loss is invariant to fsdp/megatron parallelism. NOTE: The returned loss has different behaviors for different backend: - FSDP: the loss is directly used for backward. - Megatron: the loss should be scaled by `num_microbatches` and `cp_size` for pp schedule. Args: loss_mat: micro batch loss matrix, (bs, response_length) loss_mask: micro batch loss mask, (bs, response_length) loss_agg_mode: method to aggregate the loss matrix into a scalar dp_size: data parallel size batch_num_tokens: number of valid tokens in global batch global_batch_size: global batch size loss_scale_factor: scale factor for "seq-mean-token-sum-norm" mode. If None, uses loss_mask.shape[-1]. Set this to a constant value to ensure consistent normalization throughout training. Returns: loss: `a scalar torch.Tensor` aggregated loss """ if loss_agg_mode == "token-mean": if batch_num_tokens is None: if dp_size > 1: raise ValueError("(global) batch_num_tokens is required when dp_size > 1") batch_num_tokens = loss_mask.sum() loss = verl_F.masked_sum(loss_mat, loss_mask) / batch_num_tokens * dp_size elif loss_agg_mode in ["seq-mean-token-sum", "seq-mean-token-sum-norm"]: seq_losses = torch.sum(loss_mat * loss_mask, dim=-1) # token-sum seq_mask = (torch.sum(loss_mask, dim=-1) > 0).float() # exclude fully masked sequences if global_batch_size is None: if dp_size > 1: raise ValueError("global_batch_size is required when dp_size > 1") global_batch_size = seq_mask.sum() loss = verl_F.masked_sum(seq_losses, seq_mask) / global_batch_size * dp_size # seq-mean if loss_agg_mode == "seq-mean-token-sum-norm": if loss_scale_factor is None: horizon = loss_mask.shape[-1] loss_scale_factor = horizon loss /= loss_scale_factor elif loss_agg_mode == "seq-mean-token-mean": seq_mask = torch.sum(loss_mask, dim=-1) # per-sequence token count seq_losses = torch.sum(loss_mat * loss_mask, dim=-1) / (seq_mask + 1e-8) # token-mean seq_mask = (seq_mask > 0).float() # exclude fully masked sequences if global_batch_size is None: if dp_size > 1: raise ValueError("global_batch_size is required when dp_size > 1") global_batch_size = seq_mask.sum() loss = verl_F.masked_sum(seq_losses, seq_mask) / global_batch_size * dp_size # seq-mean else: raise ValueError(f"Invalid loss_agg_mode: {loss_agg_mode}") return loss @deprecated("verl.trainer.ppo.core_algos.compute_policy_loss_vanilla") def compute_policy_loss( old_log_prob, log_prob, advantages, response_mask, cliprange=None, cliprange_low=None, cliprange_high=None, clip_ratio_c=3.0, loss_agg_mode: str = "token-mean", ): """ Compute the clipped policy objective and related metrics for PPO. Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1122 Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). cliprange (float, optional): Clipping parameter ε for standard PPO. See https://arxiv.org/abs/1707.06347. Defaults to None (must be provided). cliprange_low (float, optional): Lower clip range for dual-clip PPO. Defaults to same as `cliprange`. cliprange_high (float, optional): Upper clip range for dual-clip PPO. Defaults to same as `cliprange`. clip_ratio_c (float, optional): Lower bound of the ratio for dual-clip PPO. See https://arxiv.org/pdf/1912.09729. Defaults to 3.0. loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". """ assert clip_ratio_c > 1.0, ( "The lower bound of the clip_ratio_c for dual-clip PPO should be greater than 1.0," + f" but get the value: {clip_ratio_c}." ) negative_approx_kl = log_prob - old_log_prob # Clamp negative_approx_kl for stability negative_approx_kl = torch.clamp(negative_approx_kl, min=-20.0, max=20.0) ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) pg_losses1 = -advantages * ratio if cliprange_low is None: cliprange_low = cliprange if cliprange_high is None: cliprange_high = cliprange pg_losses2 = -advantages * torch.clamp( ratio, 1 - cliprange_low, 1 + cliprange_high ) # - clip(ratio, 1-cliprange, 1+cliprange) * A clip_pg_losses1 = torch.maximum( pg_losses1, pg_losses2 ) # max(-ratio * A, -clip(ratio, 1-cliprange, 1+cliprange) * A) pg_clipfrac = verl_F.masked_mean(torch.gt(pg_losses2, pg_losses1).float(), response_mask) pg_losses3 = -advantages * clip_ratio_c clip_pg_losses2 = torch.min(pg_losses3, clip_pg_losses1) pg_clipfrac_lower = verl_F.masked_mean( torch.gt(clip_pg_losses1, pg_losses3) * (advantages < 0).float(), response_mask ) pg_losses = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1) pg_loss = agg_loss(loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode) return pg_loss, pg_clipfrac, ppo_kl, pg_clipfrac_lower @register_policy_loss("vanilla") # type: ignore[arg-type] def compute_policy_loss_vanilla( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for PPO. Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1122 Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". config: `(verl.trainer.config.ActorConfig)`: config for the actor. rollout_log_probs: `(torch.Tensor)`: log probabilities of actions under the rollout policy, shape (batch_size, response_length). """ assert config is not None assert not isinstance(config, AlgoConfig) clip_ratio = config.clip_ratio # Clipping parameter ε for standard PPO. See https://arxiv.org/abs/1707.06347. clip_ratio_low = config.clip_ratio_low if config.clip_ratio_low is not None else clip_ratio clip_ratio_high = config.clip_ratio_high if config.clip_ratio_high is not None else clip_ratio clip_ratio_c = config.get( # Lower bound of the ratio for dual-clip PPO. See https://arxiv.org/pdf/1912.09729. "clip_ratio_c", 3.0 ) cliprange = clip_ratio cliprange_low = clip_ratio_low cliprange_high = clip_ratio_high assert clip_ratio_c > 1.0, ( "The lower bound of the clip_ratio_c for dual-clip PPO should be greater than 1.0," + f" but get the value: {clip_ratio_c}." ) negative_approx_kl = log_prob - old_log_prob # Clamp negative_approx_kl for stability negative_approx_kl = torch.clamp(negative_approx_kl, min=-20.0, max=20.0) ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) pg_losses1 = -advantages * ratio if cliprange_low is None: cliprange_low = cliprange if cliprange_high is None: cliprange_high = cliprange pg_losses2 = -advantages * torch.clamp( ratio, 1 - cliprange_low, 1 + cliprange_high ) # - clip(ratio, 1-cliprange, 1+cliprange) * A clip_pg_losses1 = torch.maximum( pg_losses1, pg_losses2 ) # max(-ratio * A, -clip(ratio, 1-cliprange, 1+cliprange) * A) pg_clipfrac = verl_F.masked_mean(torch.gt(pg_losses2, pg_losses1).float(), response_mask) pg_losses3 = -advantages * clip_ratio_c clip_pg_losses2 = torch.min(pg_losses3, clip_pg_losses1) pg_clipfrac_lower = verl_F.masked_mean( torch.gt(clip_pg_losses1, pg_losses3) * (advantages < 0).float(), response_mask ) pg_losses = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1) # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("dppo_tv") def compute_policy_loss_dppo_tv( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for DPPO-Binary-TV. See https://arxiv.org/pdf/2602.04879 for more details. Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". config: `(verl.trainer.config.ActorConfig)`: config for the actor. rollout_log_probs: `(torch.Tensor)`: log probabilities of actions under the rollout policy, shape (batch_size, response_length). """ assert config is not None assert not isinstance(config, AlgoConfig) # Note: the clip_ratio is different from the standard PPO, it is the TV divergence threshold for DPPO. clip_divergence = config.clip_ratio clip_divergence_low = config.clip_ratio_low if config.clip_ratio_low is not None else clip_divergence clip_divergence_high = config.clip_ratio_high if config.clip_ratio_high is not None else clip_divergence negative_approx_kl = log_prob - old_log_prob # Clamp negative_approx_kl for stability negative_approx_kl = torch.clamp(negative_approx_kl, min=-20.0, max=20.0) ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) # Instead of dual-clip PPO, we use truncated importance sampling (TIS) to clip the policy loss. # However, a large threshold is recommended to avoid performance degradation due to the truncation bias. # See Section 5.4 in https://arxiv.org/pdf/2602.04879 for more details. clip_ratio_c = config.get("clip_ratio_c", 20.0) truncated_ratio = torch.clamp(ratio, max=clip_ratio_c) truncated_ratio = truncated_ratio.detach() # Compute valid mask for DPPO-Binary-TV prob = torch.exp(log_prob) old_prob = torch.exp(old_log_prob) valid_positive_mask = (prob - old_prob) <= clip_divergence_high valid_negative_mask = (prob - old_prob) >= -clip_divergence_low valid_mask = torch.where(advantages > 0, valid_positive_mask, valid_negative_mask) valid_mask = valid_mask.detach().float() pg_losses = -advantages * truncated_ratio * log_prob * valid_mask # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) pg_clipfrac = verl_F.masked_mean((1.0 - valid_mask).float(), response_mask) pg_clipfrac_lower = verl_F.masked_mean((ratio > clip_ratio_c).float() * valid_mask, response_mask) pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("dppo_kl") def compute_policy_loss_dppo_kl( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for DPPO-Binary-KL. See https://arxiv.org/pdf/2602.04879 for more details. Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". config: `(verl.trainer.config.ActorConfig)`: config for the actor. rollout_log_probs: `(torch.Tensor)`: log probabilities of actions under the rollout policy, shape (batch_size, response_length). """ assert config is not None assert not isinstance(config, AlgoConfig) # Note: the clip_ratio is different from the standard PPO, it is the KL divergence threshold for DPPO. clip_divergence = config.clip_ratio clip_divergence_low = config.clip_ratio_low if config.clip_ratio_low is not None else clip_divergence clip_divergence_high = config.clip_ratio_high if config.clip_ratio_high is not None else clip_divergence negative_approx_kl = log_prob - old_log_prob # Clamp negative_approx_kl for stability negative_approx_kl = torch.clamp(negative_approx_kl, min=-20.0, max=20.0) ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) # Instead of dual-clip PPO, we use truncated importance sampling (TIS) to clip the policy loss. # However, a large threshold is recommended to avoid performance degradation due to the truncation bias. # See Section 5.4 in https://arxiv.org/pdf/2602.04879 for more details. clip_ratio_c = config.get("clip_ratio_c", 20.0) truncated_ratio = torch.clamp(ratio, max=clip_ratio_c) truncated_ratio = truncated_ratio.detach() # Compute valid mask for DPPO-Binary-KL prob = torch.exp(log_prob) old_prob = torch.exp(old_log_prob) binary_kl = old_prob * (old_log_prob - log_prob) + (1 - old_prob) * torch.log( (1.0 - old_prob + 1e-8) / (1.0 - prob + 1e-8) ) valid_positive_mask = (binary_kl <= clip_divergence_high) | (prob <= old_prob) valid_negative_mask = (binary_kl <= clip_divergence_low) | (prob >= old_prob) valid_mask = torch.where(advantages > 0, valid_positive_mask, valid_negative_mask) valid_mask = valid_mask.detach().float() pg_losses = -advantages * truncated_ratio * log_prob * valid_mask # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) # For compatibility, return zero for pg_clipfrac_lower (not used in standard DPPO) pg_clipfrac = verl_F.masked_mean((1.0 - valid_mask).float(), response_mask) pg_clipfrac_lower = verl_F.masked_mean((ratio > clip_ratio_c).float() * valid_mask, response_mask) pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("gspo") def compute_policy_loss_gspo( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "seq-mean-token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for GSPO. See https://arxiv.org/pdf/2507.18071 for more details. Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. For GSPO, it is recommended to use "seq-mean-token-mean". """ assert config is not None assert isinstance(config, ActorConfig) clip_ratio_low = config.clip_ratio_low if config.clip_ratio_low is not None else config.clip_ratio clip_ratio_high = config.clip_ratio_high if config.clip_ratio_high is not None else config.clip_ratio negative_approx_kl = log_prob - old_log_prob # compute sequence-level importance ratio: # si(θ) = (π_θ(yi|x)/π_θold(yi|x))^(1/|yi|) = # exp [(1/|y_i|) * Σ_t log(π_θ(y_i,t|x,y_i, tuple[torch.Tensor, dict[str, Any]]: """ Compute the smoothed policy objective and related metrics for SAPO. See https://arxiv.org/pdf/2511.20347 for more details. Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. For SAPO, it is recommended to use "seq-mean-token-mean". """ assert config is not None assert isinstance(config, ActorConfig) # temperature for positive and negative token updates tau_pos = torch.as_tensor(config.tau_pos, dtype=advantages.dtype, device=advantages.device) tau_neg = torch.as_tensor(config.tau_neg, dtype=advantages.dtype, device=advantages.device) def gate_function(x, tau): """The gating function used in SAPO""" return torch.sigmoid(tau * (x - 1.0)) * (4.0 / tau) # compute IS at token level: # r_{i,t}(θ) = π_θ(y_{i,t}|x, y_{i, 0 else tau_neg taus = torch.where( condition=advantages > 0, input=tau_pos, # if A_{i,t} > 0 we set to tau_pos other=tau_neg, # if A_{i,t} <= 0 we set to tau_neg ) # compute the gates f_{i,t}(r_{i,t}(θ)) at token level gates = gate_function(ratio, taus) # compute policy gradient loss pg_losses = -gates * advantages # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights # for SAPO, we need to aggregate the loss at the sequence level (seq-mean-token-mean) pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode="seq-mean-token-mean", **config.global_batch_info ) # For compatibility, return zero for both pg_clipfrac and pg_clipfrac_lower (not used in SAPO) pg_clipfrac = torch.tensor(0.0, device=pg_loss.device) pg_clipfrac_lower = torch.tensor(0.0, device=pg_loss.device) # compute KL for metrics tracking ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) # return metrics dict pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("gpg") def compute_policy_loss_gpg( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """Adapted from https://github.com/AMAP-ML/GPG/blob/main/VisualThinker-R1-Zero/src/open-r1-multimodal/src/open_r1/trainer/grpo_trainer.py#L495 Args: log_prob: `(torch.Tensor)` shape: (bs, response_length) advantages: `(torch.Tensor)` shape: (bs, response_length) response_mask: `(torch.Tensor)` shape: (bs, response_length) return: pg_loss: `a scalar torch.Tensor` policy gradient loss computed via GPG """ assert config is not None pg_losses = -log_prob * advantages # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) return pg_loss, {} @register_policy_loss("clip_cov") def compute_policy_loss_clip_cov( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for Clip-Cov. Adapted from https://github.com/PRIME-RL/Entropy-Mechanism-of-RL/blob/main/verl/trainer/ppo/core_algos.py Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). cliprange (float, optional): Clipping parameter ε for standard PPO. See https://arxiv.org/abs/1707.06347. Defaults to None (must be provided). cliprange_low (float, optional): Lower clip range for dual-clip PPO. Defaults to same as `cliprange`. cliprange_high (float, optional): Upper clip range for dual-clip PPO. Defaults to same as `cliprange`. loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". clip_cvo_ratio (float, optional): Ratio for clipping the covariance. Defaults to 0.0002. clip_cov_lb (float, optional): Lower bound for clipping covariance. Defaults to 1.0. clip_cov_ub (float, optional): Upper bound for clipping covariance. Defaults to 5.0. """ assert config is not None assert not isinstance(config, AlgoConfig), "passing AlgoConfig not supported yet" assert config.policy_loss is not None clip_cov_ratio = config.policy_loss.clip_cov_ratio if config.policy_loss.clip_cov_ratio is not None else 0.0002 cliprange = config.clip_ratio cliprange_low = config.clip_ratio_low if config.clip_ratio_low is not None else cliprange cliprange_high = config.clip_ratio_high if config.clip_ratio_high is not None else cliprange clip_cov_ub = config.policy_loss.clip_cov_ub if config.policy_loss.clip_cov_ub is not None else 5.0 clip_cov_lb = config.policy_loss.clip_cov_lb if config.policy_loss.clip_cov_lb is not None else 1.0 assert clip_cov_ratio > 0, "clip_ratio should be larger than 0." negative_approx_kl = log_prob - old_log_prob ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) pg_losses1 = -advantages * ratio if cliprange_low is None: cliprange_low = cliprange if cliprange_high is None: cliprange_high = cliprange corr = torch.ones_like(advantages) pg_losses2 = -advantages * torch.clamp(ratio, 1 - cliprange_low, 1 + cliprange_high) clip_by_origin = (pg_losses2 > pg_losses1) & (response_mask > 0) cov_all = (advantages - verl_F.masked_mean(advantages, response_mask)) * ( log_prob - verl_F.masked_mean(log_prob.detach(), response_mask) ) cov_all[response_mask == 0] = -torch.inf cov_all[clip_by_origin] = -torch.inf clip_num = max(int(clip_cov_ratio * response_mask.sum().item()), 1) top_k_idx = (cov_all < clip_cov_ub) & (cov_all > clip_cov_lb) & (response_mask > 0) top_k_idx = torch.nonzero(top_k_idx) if len(top_k_idx) > 0: perm = torch.randperm(len(top_k_idx)) top_k_idx = top_k_idx[perm[: min(clip_num, len(top_k_idx))]] else: top_k_idx = torch.empty((0, 2), device=cov_all.device, dtype=torch.long) corr[top_k_idx[:, 0], top_k_idx[:, 1]] = 0 pg_clipfrac = verl_F.masked_mean((corr == 0).float(), response_mask) pg_losses = torch.maximum(pg_losses1, pg_losses2) * corr # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("kl_cov") def compute_policy_loss_kl_cov( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for Clip-Cov. Adapted from https://github.com/PRIME-RL/Entropy-Mechanism-of-RL/blob/main/verl/trainer/ppo/core_algos.py Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". kl_cov_ratio (float, optional): Ratio for selecting the top-k covariance values. Defaults to 0.0002. ppo_kl_coef (float, optional): Coefficient for the KL penalty term in the loss. Defaults to 1. """ assert config is not None assert not isinstance(config, AlgoConfig), "passing AlgoConfig not supported yet" assert config.policy_loss is not None kl_cov_ratio = config.policy_loss.kl_cov_ratio if config.policy_loss.kl_cov_ratio is not None else 0.0002 ppo_kl_coef = config.policy_loss.ppo_kl_coef if config.policy_loss.ppo_kl_coef is not None else 1.0 assert kl_cov_ratio > 0, "kl_cov_ratio should be larger than 0." negative_approx_kl = log_prob - old_log_prob abs_kl = negative_approx_kl.abs() ratio = torch.exp(negative_approx_kl) ppo_kl_abs = verl_F.masked_mean(negative_approx_kl.abs(), response_mask) pg_losses1 = -advantages * ratio pg_losses_kl = -advantages * ratio + ppo_kl_coef * abs_kl pg_losses = pg_losses1 all_valid = response_mask > 0 all_valid_idx = torch.nonzero(all_valid.reshape(-1), as_tuple=True)[0] all_valid_adv = advantages[all_valid].detach().reshape(-1).cpu() all_valid_logp = log_prob[all_valid].detach().reshape(-1).cpu() k = min(kl_cov_ratio, len(all_valid_adv)) if k != 0: cov_lst_all = (all_valid_adv - all_valid_adv.mean()) * (all_valid_logp - all_valid_logp.mean()) k_percent_nums = max(1, int(len(cov_lst_all) * kl_cov_ratio)) large_cov_idxs = torch.topk(cov_lst_all, k_percent_nums, largest=True).indices if len(large_cov_idxs) != 0: large_cov_idxs = all_valid_idx[large_cov_idxs] pg_losses[large_cov_idxs // advantages.shape[1], large_cov_idxs % advantages.shape[1]] = pg_losses_kl[ large_cov_idxs // advantages.shape[1], large_cov_idxs % advantages.shape[1] ] # Apply rollout correction weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) pg_metrics = { "actor/ppo_kl": ppo_kl_abs.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("geo_mean") def compute_policy_loss_geo_mean( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for GMPO. Adapted from paper https://arxiv.org/abs/2507.20673 https://github.com/callsys/GMPO/blob/main/train_zero_math_gmpo.py Args: old_log_prob (torch.Tensor): Log-probabilities of actions under the old policy, shape (batch_size, response_length). log_prob (torch.Tensor): Log-probabilities of actions under the current policy, shape (batch_size, response_length). advantages (torch.Tensor): Advantage estimates for each action, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the loss, shape (batch_size, response_length). loss_agg_mode (str, optional): not used """ assert config is not None assert not isinstance(config, AlgoConfig) clip_ratio = config.clip_ratio # Clipping parameter. See https://arxiv.org/abs/1707.06347. clip_ratio_low = config.clip_ratio_low if config.clip_ratio_low is not None else clip_ratio clip_ratio_high = config.clip_ratio_high if config.clip_ratio_high is not None else clip_ratio cliprange = clip_ratio cliprange_low = clip_ratio_low cliprange_high = clip_ratio_high if cliprange_low is None: cliprange_low = cliprange if cliprange_high is None: cliprange_high = cliprange negative_approx_kl = log_prob - old_log_prob # Clamp negative_approx_kl for stability (uncomment it if you like) # negative_approx_kl = torch.clamp(negative_approx_kl, min=-20.0, max=20.0) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) # Clipping at token-level & Clipping wider sgn_advantage = torch.sign(advantages) negative_approx_kl_clamp = torch.clamp(negative_approx_kl, -cliprange_low, cliprange_high) negative_approx_kl_min = torch.min(sgn_advantage * negative_approx_kl, sgn_advantage * negative_approx_kl_clamp) negative_approx_kl_min = sgn_advantage * negative_approx_kl_min # Geometric-Mean Policy Optimization response_mask_sum = response_mask.sum(dim=-1) ratio = torch.exp((negative_approx_kl_min * response_mask).sum(dim=-1) / (response_mask_sum + 1e-8)) # we only support sequence level advantage for now, # otherwise, below would be not consistent with the paper advantage = (advantages * response_mask).sum(dim=-1) / (response_mask_sum + 1e-8) pg_losses = -advantage * ratio # Apply rollout correction weights if provided # For geo_mean, IS weights are 2D (batch_size, seq_length) and need to be aggregated to sequence level if rollout_is_weights is not None: # Aggregate token-level weights to sequence level using geometric mean for consistency # Note: rollout_is_weights is always 2D regardless of aggregation mode seq_is_weights = torch.exp( (torch.log(rollout_is_weights + 1e-10) * response_mask).sum(dim=-1) / (response_mask_sum + 1e-8) ) pg_losses = pg_losses * seq_is_weights pg_loss = torch.mean(pg_losses) # higher: ratio is too large that need clamp to clip_high (when adv > 0) clipped = torch.ne(negative_approx_kl, negative_approx_kl_clamp) pg_clipfrac = verl_F.masked_mean((clipped * (advantages > 0)).float(), response_mask) pg_clipfrac_lower = verl_F.masked_mean((clipped * (advantages < 0)).float(), response_mask) pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("cispo") def compute_policy_loss_cispo( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[DictConfig | ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """ Compute the clipped policy objective and related metrics for CISPO. See https://arxiv.org/pdf/2506.13585 for more details. """ assert config is not None assert isinstance(config, ActorConfig) clip_ratio_low = config.clip_ratio_low if config.clip_ratio_low is not None else config.clip_ratio clip_ratio_high = config.clip_ratio_high if config.clip_ratio_high is not None else config.clip_ratio # Compute importance sampling ratio: π_θ / π_θ_old negative_approx_kl = log_prob - old_log_prob # Clamp for numerical stability negative_approx_kl = torch.clamp(negative_approx_kl, min=-20.0, max=20.0) ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask) # CISPO: Clip the importance sampling weights # KEY: Apply stop gradient to the clipped ratio # This prevents gradients from flowing through the ratio computation and clipping # Gradients only flow through log_prob in the final loss term clipped_ratio = torch.clamp(ratio, 1 - clip_ratio_low, 1 + clip_ratio_high) clipped_ratio_sg = clipped_ratio.detach() # CISPO objective function (to maximize): J = sg(clip(ratio)) * A * log π_θ # Loss function (to minimize): L = -J = -sg(clip(ratio)) * A * log_prob pg_losses = -clipped_ratio_sg * advantages * log_prob # Track clipping statistics pg_clipfrac = verl_F.masked_mean((ratio != clipped_ratio).float(), response_mask) # Apply rollout importance sampling weights if provided if rollout_is_weights is not None: pg_losses = pg_losses * rollout_is_weights pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) # For compatibility, return zero for pg_clipfrac_lower (not used in CISPO) pg_clipfrac_lower = torch.tensor(0.0, device=pg_loss.device) pg_metrics = { "actor/pg_clipfrac": pg_clipfrac.detach().item(), "actor/ppo_kl": ppo_kl.detach().item(), "actor/pg_clipfrac_lower": pg_clipfrac_lower.detach().item(), } return pg_loss, pg_metrics def compute_entropy_loss(logits, response_mask, loss_agg_mode: str = "token-mean"): """Compute categorical entropy loss (For backward compatibility) Args: logits (torch.Tensor): shape is (bs, response_length, vocab_size) response_mask (torch.Tensor): shape is (bs, response_length) Returns: entropy: a scalar torch.Tensor """ # compute entropy token_entropy = verl_F.entropy_from_logits(logits) # (bs, response_len) entropy_loss = agg_loss(loss_mat=token_entropy, loss_mask=response_mask, loss_agg_mode=loss_agg_mode) return entropy_loss def compute_value_loss( vpreds: torch.Tensor, returns: torch.Tensor, values: torch.Tensor, response_mask: torch.Tensor, cliprange_value: float, loss_agg_mode: str = "token-mean", ): """ Compute the clipped value-function loss for PPO. Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1151 Args: vpreds (torch.FloatTensor): Predicted values from the value head, shape (batch_size, response_length). values (torch.FloatTensor): Old (baseline) values from the value head, shape (batch_size, response_length). returns (torch.FloatTensor): Ground-truth returns, shape (batch_size, response_length). response_mask (torch.Tensor): Mask indicating which tokens to include in the value loss calculation. cliprange_value (float): Clip range for value prediction updates. loss_agg_mode (str, optional): Aggregation mode for `agg_loss`. Defaults to "token-mean". Returns: vf_loss (torch.FloatTensor): A scalar tensor containing the aggregated value-function loss. vf_clipfrac (float): Fraction of elements where the clipped loss was used. """ vpredclipped = verl_F.clip_by_value(vpreds, values - cliprange_value, values + cliprange_value) vf_losses1 = (vpreds - returns) ** 2 vf_losses2 = (vpredclipped - returns) ** 2 clipped_vf_losses = torch.max(vf_losses1, vf_losses2) vf_loss = 0.5 * agg_loss(loss_mat=clipped_vf_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode) vf_clipfrac = verl_F.masked_mean(torch.gt(vf_losses2, vf_losses1).float(), response_mask) return vf_loss, vf_clipfrac def kl_penalty(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_penalty) -> torch.FloatTensor: """Compute KL divergence given logprob and ref_logprob. Optionally using straight through to bind k2 on other kl penalty compute method for unbiased KL gradient estimation. See more description in http://joschu.net/blog/kl-approx.html Args: logprob: ref_logprob: Returns: kl_estimate """ forward_score = kl_penalty_forward(logprob, ref_logprob, kl_penalty) if not kl_penalty.endswith("+") or kl_penalty in ("mse", "k2"): return forward_score """ The expectation of k1 and k3 estimator is the expected value of KL, but the expected gradient of k1 and k3 estimator is not the expected gradient of KL. On the other hand k2 estimator gives right gradient estimator, so we use a straight through trick here if the kl_penalty method ends with '+', e.g., k3+. """ backward_score = 0.5 * (logprob - ref_logprob).square() return backward_score - backward_score.detach() + forward_score.detach() def kl_penalty_forward(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_penalty) -> torch.FloatTensor: """Compute KL divergence given logprob and ref_logprob. Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1104 See more description in http://joschu.net/blog/kl-approx.html Args: logprob: ref_logprob: Returns: kl_estimate """ if kl_penalty in ("kl", "k1"): return logprob - ref_logprob if kl_penalty == "abs": return (logprob - ref_logprob).abs() if kl_penalty in ("mse", "k2"): return 0.5 * (logprob - ref_logprob).square() # J. Schulman. Approximating kl divergence, 2020. # # URL http://joschu.net/blog/kl-approx.html. if kl_penalty in ("low_var_kl", "k3"): kl = ref_logprob - logprob # For numerical stability kl = torch.clamp(kl, min=-20, max=20) ratio = torch.exp(kl) kld = (ratio - kl - 1).contiguous() return torch.clamp(kld, min=-10, max=10) if kl_penalty == "full": # so, here logprob and ref_logprob should contain the logits for every token in vocabulary raise NotImplementedError raise NotImplementedError def compute_pf_ppo_reweight_data( data, reweight_method: str = "pow", weight_pow: float = 2.0, ): """Reweight the data based on the token_level_scores. Args: data: DataProto object, containing batch, non_tensor_batch and meta_info reweight_method: str, choices: "pow", "max_min", "max_random" weight_pow: float, the power of the weight Returns: """ @torch.no_grad() def compute_weights(scores: torch.Tensor, reweight_method: str, weight_pow: float) -> torch.Tensor: """Compute importance weights for resampling based on scores. Args: scores (torch.Tensor): Tensor of scores to compute weights from. reweight_method (str): Method for computing weights ('pow', 'max_min', 'max_random'). weight_pow (float): Power exponent for 'pow' method. Returns: torch.Tensor: Computed importance weights. Raises: ValueError: If reweight_method is not supported. """ if reweight_method == "pow": weights = torch.pow(torch.abs(scores), weight_pow) elif reweight_method == "max_min": max_score = torch.max(scores) min_score = torch.min(scores) weights = torch.where((scores == max_score) | (scores == min_score), 1.0, 0.0) elif reweight_method == "max_random": max_score = torch.max(scores) weights = torch.where(scores == max_score, 0.4, 0.1) else: raise ValueError(f"Unsupported reweight_method: {reweight_method}") return weights scores = data.batch["token_level_scores"].sum(dim=-1) weights = compute_weights(scores, reweight_method, weight_pow) weights = torch.clamp(weights + 1e-8, min=1e-8) batch_size = scores.shape[0] sample_indices = torch.multinomial(weights, batch_size, replacement=True) resampled_batch = {key: tensor[sample_indices] for key, tensor in data.batch.items()} sample_indices_np = sample_indices.numpy() resampled_non_tensor_batch = {} for key, array in data.non_tensor_batch.items(): if isinstance(array, np.ndarray): resampled_non_tensor_batch[key] = array[sample_indices_np] else: resampled_non_tensor_batch[key] = [array[i] for i in sample_indices_np] resampled_meta_info = {} for key, value in data.meta_info.items(): if isinstance(value, list) and len(value) == batch_size: resampled_meta_info[key] = [value[i] for i in sample_indices_np] else: resampled_meta_info[key] = value from copy import deepcopy resampled_data = deepcopy(data) resampled_data.batch = type(data.batch)(resampled_batch) resampled_data.batch.batch_size = data.batch.batch_size resampled_data.non_tensor_batch = resampled_non_tensor_batch resampled_data.meta_info = resampled_meta_info return resampled_data def compute_policy_loss_reinforce( rollout_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "seq-mean-token-sum", config: Optional[ActorConfig] = None, rollout_is_weights: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """Compute REINFORCE-style policy gradient loss with optional IS correction. This function implements policy gradient (REINFORCE) with optional importance sampling correction for rollout-training policy mismatch. Mathematical formulation: Without IS (rollout_is_weights=None): L = -E[log π(a|s) * A(s,a)] Gradient: ∇_θ L = -E[∇log π(a|s) * A] (standard REINFORCE) With IS (rollout_is_weights provided): L = -E_π_rollout[w * log π(a|s) * A(s,a)] where w = π_current / π_rollout (truncated IS weight) Gradient: ∇_θ L = -E[w * ∇log π(a|s) * A] (IS-corrected policy gradient) Args: rollout_log_prob: Log probabilities from rollout policy (e.g., vLLM BF16). Shape: (batch_size, seq_length). Used for KL computation. log_prob: Log probabilities from current training policy. Shape: (batch_size, seq_length) advantages: Advantage estimates for each token. Shape: (batch_size, seq_length) response_mask: Mask indicating valid tokens (1 for valid, 0 for padding). Shape: (batch_size, seq_length). Should already include rejection sampling. loss_agg_mode: Loss aggregation strategy (see agg_loss for details). config: Actor config (required for global_batch_info). rollout_is_weights: Pre-computed IS weights (π_current / π_rollout). Shape: (batch_size, seq_length). None to disable IS correction. Returns: Tuple of (loss, metrics): loss: Scalar policy gradient loss metrics: Dictionary with "actor/ppo_kl" Note: Unlike PPO (compute_policy_loss_vanilla), this function: - Does NOT use PPO clipping - Uses log π(a|s) directly (not ratio) - IS weights are applied as multiplicative factor """ assert config is not None, "ActorConfig must be provided for REINFORCE loss" # Compute pure policy gradient loss with optional IS correction # Standard REINFORCE: L = -E[log π(a|s) * A] # With IS: L = -E[w * log π(a|s) * A] where w = π_current / π_rollout if rollout_is_weights is not None: # IS-corrected policy gradient: L = -E[stopgrad(w) · log π · A] pg_losses = -advantages * log_prob * rollout_is_weights else: # Standard REINFORCE: L = -E[log π · A] pg_losses = -advantages * log_prob # Aggregate loss pg_loss = agg_loss( loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info, ) # Compute KL divergence between current and rollout policy negative_approx_kl = log_prob - rollout_log_prob kl_divergence = verl_F.masked_mean(-negative_approx_kl, response_mask) pg_metrics = { "actor/ppo_kl": kl_divergence.detach().item(), } return pg_loss, pg_metrics @register_policy_loss("bypass_mode") def compute_policy_loss_bypass_mode( old_log_prob: torch.Tensor, log_prob: torch.Tensor, advantages: torch.Tensor, response_mask: torch.Tensor, loss_agg_mode: str = "token-mean", config: Optional[ActorConfig] = None, rollout_is_weights: torch.Tensor | None = None, ) -> tuple[torch.Tensor, dict[str, Any]]: """Bypass mode policy loss supporting both REINFORCE and PPO-clip. This function is the entry point for bypass mode, where old_log_prob = rollout_log_prob. It computes IS weights and rejection masks, then dispatches to either REINFORCE or PPO-clip loss based on the loss_type configuration. IMPORTANT - Bypass mode semantics: In bypass mode, the trainer sets old_log_prob = rollout_log_prob. This means: - For REINFORCE: We use IS weights w = π_current / π_rollout explicitly - For PPO-clip: The PPO ratio π_current / π_old = π_current / π_rollout already incorporates the IS correction through clipping, so we do NOT apply additional IS weights (would be double-counting) Loss types: - "ppo_clip" (default): PPO clipped objective (compute_policy_loss_vanilla) L = -E[min(r*A, clip(r)*A)] where r = π_current / π_rollout Note: IS weights are NOT applied (clipping handles the ratio) - "reinforce": REINFORCE-style policy gradient with IS correction L = -E[w * log π(a|s) * A] where w = π_current / π_rollout Args: old_log_prob: In bypass mode, this is actually rollout_log_prob. Shape: (batch_size, seq_length) log_prob: Current policy log probabilities. Shape: (batch_size, seq_length) advantages: Advantage estimates. Shape: (batch_size, seq_length) response_mask: Valid token mask (1=valid, 0=padding). Shape: (batch_size, seq_length) loss_agg_mode: Loss aggregation mode (passed to underlying loss function). config: Actor config containing rollout_correction settings in policy_loss. rollout_is_weights: Pre-computed IS weights (ignored, computed internally). Config options (in config.policy_loss.rollout_correction): loss_type: "ppo_clip" (default) or "reinforce" rollout_is: IS aggregation level ("token", "sequence", or None) rollout_is_threshold: Upper threshold for truncating IS weights (default: 2.0) rollout_rs: Rejection sampling level (see rollout_corr_helper for supported modes) rollout_rs_threshold: Threshold specification for rejection sampling rollout_is_batch_normalize: Whether to normalize IS weights to mean=1.0 Returns: Tuple of (loss, metrics): loss: Scalar policy loss metrics: Dictionary with rollout correction metrics and actor/ppo_kl """ from verl.trainer.ppo.rollout_corr_helper import compute_rollout_correction_and_rejection_mask assert config is not None, "config is required for bypass_mode loss" # Extract rollout_correction config from policy_loss rollout_corr_config = config.policy_loss.get("rollout_correction", None) if hasattr(config, "policy_loss") else None if rollout_corr_config is None: raise ValueError( "rollout_correction config not found in policy_loss. " "When using loss_mode='bypass_mode', ensure rollout_correction config is passed." ) # Extract parameters loss_type = rollout_corr_config.get("loss_type", "ppo_clip") rollout_is = rollout_corr_config.get("rollout_is", None) rollout_is_threshold = rollout_corr_config.get("rollout_is_threshold", 2.0) rollout_is_batch_normalize = rollout_corr_config.get("rollout_is_batch_normalize", False) rollout_rs = rollout_corr_config.get("rollout_rs", None) rollout_rs_threshold = rollout_corr_config.get("rollout_rs_threshold", None) # In bypass mode: old_log_prob IS rollout_log_prob rollout_log_prob = old_log_prob # Compute IS weights and rejection mask # Note: For PPO-clip, we still compute IS weights for metrics, but don't apply them with torch.no_grad(): rollout_is_weights_proto, modified_response_mask, rollout_metrics = ( compute_rollout_correction_and_rejection_mask( old_log_prob=log_prob, # Current policy (for IS ratio: π_current / π_rollout) rollout_log_prob=rollout_log_prob, # Rollout policy response_mask=response_mask, rollout_is=rollout_is, rollout_is_threshold=rollout_is_threshold, rollout_is_batch_normalize=rollout_is_batch_normalize, rollout_rs=rollout_rs, rollout_rs_threshold=rollout_rs_threshold, ) ) # Extract IS weights tensor (or None if disabled) computed_is_weights = rollout_is_weights_proto.batch["rollout_is_weights"] if rollout_is_weights_proto else None # Apply rejection mask (RS + veto) effective_mask = modified_response_mask # Dispatch to appropriate loss function based on loss_type if loss_type == "reinforce": # REINFORCE: Apply IS weights explicitly pg_loss, pg_metrics = compute_policy_loss_reinforce( rollout_log_prob=rollout_log_prob, log_prob=log_prob, advantages=advantages, response_mask=effective_mask, loss_agg_mode=loss_agg_mode, config=config, rollout_is_weights=computed_is_weights, ) elif loss_type == "ppo_clip": # PPO-clip: The ratio π_current/π_old = π_current/π_rollout already handles IS # DO NOT apply IS weights - would be double-counting! # The clipping mechanism constrains the effective IS ratio pg_loss, pg_metrics = compute_policy_loss_vanilla( # type: ignore[call-arg] old_log_prob=rollout_log_prob, # = old_log_prob in bypass mode log_prob=log_prob, advantages=advantages, response_mask=effective_mask, loss_agg_mode=loss_agg_mode, config=config, rollout_is_weights=None, # Explicitly None - no IS weights for PPO-clip ) else: raise ValueError(f"Invalid loss_type: {loss_type}. Must be 'reinforce' or 'ppo_clip'.") # Merge rollout correction metrics pg_metrics.update(rollout_metrics) return pg_loss, pg_metrics ================================================ FILE: verl/trainer/ppo/metric_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Metrics related to the PPO trainer. """ from collections import defaultdict from functools import partial from typing import Any, Callable import numpy as np import torch import verl.utils.torch_functional as verl_F from verl import DataProto from verl.utils.import_utils import deprecated @deprecated("verl.utils.metric.reduce_metrics") def reduce_metrics(metrics: dict[str, list[Any]]) -> dict[str, Any]: """ Reduces a dictionary of metric lists by computing the mean of each list. Args: metrics: A dictionary mapping metric names to lists of metric values. Returns: A dictionary with the same keys but with each list replaced by its mean value. Example: >>> metrics = {"loss": [1.0, 2.0, 3.0], "accuracy": [0.8, 0.9, 0.7]} >>> reduce_metrics(metrics) {"loss": 2.0, "accuracy": 0.8} """ from verl.utils.metric import reduce_metrics return reduce_metrics(metrics) def _compute_response_info(batch: DataProto) -> dict[str, Any]: """ Computes information about prompts and responses from a batch. This is an internal helper function that extracts masks and lengths for prompts and responses. Args: batch: A DataProto object containing batch data with responses and attention masks. Returns: A dictionary containing: - response_mask: Attention mask for the response tokens - prompt_length: Tensor of prompt lengths for each item in the batch - response_length: Tensor of response lengths for each item in the batch """ response_length = batch.batch["responses"].shape[-1] prompt_mask = batch.batch["attention_mask"][:, :-response_length] response_mask = batch.batch["attention_mask"][:, -response_length:] prompt_length = prompt_mask.sum(-1).float() response_length = response_mask.sum(-1).float() # (batch_size,) return dict( response_mask=response_mask, prompt_length=prompt_length, response_length=response_length, ) def compute_data_metrics(batch: DataProto, use_critic: bool = True) -> dict[str, Any]: """ Computes various metrics from a batch of data for PPO training. This function calculates metrics related to scores, rewards, advantages, returns, values, and sequence lengths from a batch of data. It provides statistical information (mean, max, min) for each metric category. Args: batch: A DataProto object containing batch data with token-level scores, rewards, advantages, etc. use_critic: Whether to include critic-specific metrics. Defaults to True. Returns: A dictionary of metrics including: - critic/score/mean, max, min: Statistics about sequence scores - critic/rewards/mean, max, min: Statistics about sequence rewards - critic/advantages/mean, max, min: Statistics about advantages - critic/returns/mean, max, min: Statistics about returns - critic/values/mean, max, min: Statistics about critic values (if use_critic=True) - critic/vf_explained_var: Explained variance of the value function (if use_critic=True) - response_length/mean, max, min, clip_ratio: Statistics about response lengths - prompt_length/mean, max, min, clip_ratio: Statistics about prompt lengths - num_turns/mean, max, min: Statistics about the number of multi-turn conversations """ sequence_score = batch.batch["token_level_scores"].sum(-1) sequence_reward = batch.batch["token_level_rewards"].sum(-1) advantages = batch.batch["advantages"] returns = batch.batch["returns"] max_response_length = batch.batch["responses"].shape[-1] prompt_mask = batch.batch["attention_mask"][:, :-max_response_length].bool() response_mask = batch.batch["response_mask"].bool() max_prompt_length = prompt_mask.size(-1) response_info = _compute_response_info(batch) prompt_length = response_info["prompt_length"] response_length = response_info["response_length"] aborted_mask = (response_length == 0).bool() non_aborted_mask = ~aborted_mask non_aborted_sequence_score = sequence_score[non_aborted_mask] non_aborted_sequence_reward = sequence_reward[non_aborted_mask] score_mean = torch.mean(non_aborted_sequence_score).detach().item() score_max = torch.max(non_aborted_sequence_score).detach().item() score_min = torch.min(non_aborted_sequence_score).detach().item() reward_mean = torch.mean(non_aborted_sequence_reward).detach().item() reward_max = torch.max(non_aborted_sequence_reward).detach().item() reward_min = torch.min(non_aborted_sequence_reward).detach().item() valid_adv = torch.masked_select(advantages, response_mask) valid_returns = torch.masked_select(returns, response_mask) if use_critic: values = batch.batch["values"] valid_values = torch.masked_select(values, response_mask) return_diff_var = torch.var(valid_returns - valid_values) return_var = torch.var(valid_returns) # Aborted samples and non-aborted response length statistics # response_length_non_aborted/*: statistics computed on non-aborted samples only aborted_ratio = torch.mean(aborted_mask.float()).detach().item() non_aborted_response_length = response_length[non_aborted_mask] if non_aborted_response_length.numel() > 0: non_aborted_response_length_mean = torch.mean(non_aborted_response_length).detach().item() non_aborted_response_length_max = torch.max(non_aborted_response_length).detach().item() non_aborted_response_length_min = torch.min(non_aborted_response_length).detach().item() non_aborted_response_length_clip_ratio = ( torch.mean(torch.eq(non_aborted_response_length, max_response_length).float()).detach().item() ) else: raise ValueError("All samples are aborted, this should not happen.") metrics = { # score "critic/score/mean": score_mean, "critic/score/max": score_max, "critic/score/min": score_min, # reward "critic/rewards/mean": reward_mean, "critic/rewards/max": reward_max, "critic/rewards/min": reward_min, # adv "critic/advantages/mean": torch.mean(valid_adv).detach().item(), "critic/advantages/max": torch.max(valid_adv).detach().item(), "critic/advantages/min": torch.min(valid_adv).detach().item(), # returns "critic/returns/mean": torch.mean(valid_returns).detach().item(), "critic/returns/max": torch.max(valid_returns).detach().item(), "critic/returns/min": torch.min(valid_returns).detach().item(), **( { # values "critic/values/mean": torch.mean(valid_values).detach().item(), "critic/values/max": torch.max(valid_values).detach().item(), "critic/values/min": torch.min(valid_values).detach().item(), # vf explained var "critic/vf_explained_var": (1.0 - return_diff_var / (return_var + 1e-5)).detach().item(), } if use_critic else {} ), # response length "response_length/mean": torch.mean(response_length).detach().item(), "response_length/max": torch.max(response_length).detach().item(), "response_length/min": torch.min(response_length).detach().item(), "response_length/clip_ratio": torch.mean(torch.eq(response_length, max_response_length).float()) .detach() .item(), # response length (non-aborted only) # These statistics exclude aborted samples to avoid skew from zeros "response_length_non_aborted/mean": non_aborted_response_length_mean, "response_length_non_aborted/max": non_aborted_response_length_max, "response_length_non_aborted/min": non_aborted_response_length_min, "response_length_non_aborted/clip_ratio": non_aborted_response_length_clip_ratio, # aborted ratio # Fraction of samples whose response length is zero "response/aborted_ratio": aborted_ratio, # prompt length "prompt_length/mean": torch.mean(prompt_length).detach().item(), "prompt_length/max": torch.max(prompt_length).detach().item(), "prompt_length/min": torch.min(prompt_length).detach().item(), "prompt_length/clip_ratio": torch.mean(torch.eq(prompt_length, max_prompt_length).float()).detach().item(), } # multi-turn conversation if "__num_turns__" in batch.non_tensor_batch: num_turns = batch.non_tensor_batch["__num_turns__"] metrics["num_turns/min"] = num_turns.min() metrics["num_turns/max"] = num_turns.max() metrics["num_turns/mean"] = num_turns.mean() if "tool_call_counts" in batch.non_tensor_batch: tool_call_counts = batch.non_tensor_batch["tool_call_counts"] metrics["tool_call_counts/min"] = tool_call_counts.min() metrics["tool_call_counts/max"] = tool_call_counts.max() metrics["tool_call_counts/mean"] = tool_call_counts.mean() return metrics def compute_timing_metrics(batch: DataProto, timing_raw: dict[str, float]) -> dict[str, Any]: """ Computes timing metrics for different processing stages in PPO training. This function calculates both raw timing metrics (in seconds) and per-token timing metrics (in milliseconds) for various processing stages like generation, reference computation, value computation, advantage computation, and model updates. Args: batch: A DataProto object containing batch data with responses and attention masks. timing_raw: A dictionary mapping stage names to their execution times in seconds. Returns: A dictionary containing: - timing_s/{name}: Raw timing in seconds for each stage - timing_per_token_ms/{name}: Per-token timing in milliseconds for each stage Note: Different stages use different token counts for normalization: - "gen" uses only response tokens - Other stages ("ref", "values", "adv", "update_critic", "update_actor") use all tokens (prompt + response) """ response_info = _compute_response_info(batch) num_prompt_tokens = torch.sum(response_info["prompt_length"]).item() num_response_tokens = torch.sum(response_info["response_length"]).item() num_overall_tokens = num_prompt_tokens + num_response_tokens num_tokens_of_section = { "gen": num_response_tokens, **{name: num_overall_tokens for name in ["ref", "values", "adv", "update_critic", "update_actor"]}, } return { **{f"timing_s/{name}": value for name, value in timing_raw.items()}, **{ f"timing_per_token_ms/{name}": timing_raw[name] * 1000 / num_tokens_of_section[name] for name in set(num_tokens_of_section.keys()) & set(timing_raw.keys()) }, } def compute_throughout_metrics(batch: DataProto, timing_raw: dict[str, float], n_gpus: int) -> dict[str, Any]: """ Computes throughput metrics for PPO training. This function calculates performance metrics related to token processing speed, including the total number of tokens processed, time per step, and throughput (tokens per second per GPU). Args: batch: A DataProto object containing batch data with meta information about token counts. timing_raw: A dictionary mapping stage names to their execution times in seconds. Must contain a "step" key with the total step time. n_gpus: Number of GPUs used for training. Returns: A dictionary containing: - perf/total_num_tokens: Total number of tokens processed in the batch - perf/time_per_step: Time taken for the step in seconds - perf/throughput: Tokens processed per second per GPU Note: The throughput is calculated as total_tokens / (time * n_gpus) to normalize across different GPU counts. """ total_num_tokens = sum(batch.meta_info["global_token_num"]) time = timing_raw["step"] # estimated_flops, promised_flops = flops_function.estimate_flops(num_tokens, time) # f'Actual TFLOPs/s/GPU​': estimated_flops/(n_gpus), # f'Theoretical TFLOPs/s/GPU​': promised_flops, return { "perf/total_num_tokens": total_num_tokens, "perf/time_per_step": time, "perf/throughput": total_num_tokens / (time * n_gpus), } def compute_variance_proxy_metrics(batch: DataProto, gradient_norm: float = None) -> dict[str, float]: """ Compute variance proxy metrics using the simplified expected squared norm approach. This metric provides a computationally efficient way to monitor gradient variance during training. It works for any advantage estimator as long as sum_pi_squared is available from the actor. Theory: - Full variance: Var(g̃) = E[||g̃||²] - ||g_true||² - Simplified proxy (when ||g_true||² ≈ 0): Var(g̃) ≈ E[||g̃||²] - Using W-score approximation: E[||g̃||²] ≈ E[A² × W(τ)] Where W(τ) = Σ_t[1 - 2π_t(y_t) + Σπ²] is the score-norm proxy. """ metrics = {} # Check if we have the necessary data (sum_pi_squared is required for W-score) if "sum_pi_squared" not in batch.batch or "old_log_probs" not in batch.batch or "advantages" not in batch.batch: return metrics # Compute W(τ) = Σ_t[1 - 2π_t(y_t) + Σπ²] pi_t = torch.exp(batch.batch["old_log_probs"]) w_per_timestep = 1 - 2 * pi_t + batch.batch["sum_pi_squared"] # Get response mask to only consider valid tokens response_mask = batch.batch["response_mask"] # Use pre-computed rollout IS weights from batch (for variance proxy consistency with training loss) # IS weights are computed centrally in ray_trainer.py to avoid duplication rollout_is_weights = None if "rollout_is_weights" in batch.batch: # Extract pre-computed IS weights from batch (already computed in trainer) rollout_is_weights = batch.batch["rollout_is_weights"] # Scale W by (rollout IS weight)² for optimal baseline under biased estimation w_per_timestep = w_per_timestep * (rollout_is_weights**2).detach() # Note: IS weight statistics and mismatch metrics are logged in ray_trainer.py # Get scalar advantages (mean over timesteps) advantages = batch.batch["advantages"] # Compute mean advantage per trajectory using masked_mean advantages_scalar = verl_F.masked_mean(advantages, response_mask, axis=-1) # Compute W values (sum over timesteps) w_values = verl_F.masked_sum(w_per_timestep, response_mask, axis=-1) # ====== COMPUTE VARIANCE PROXIES ====== # Variance proxy should match the actual gradient computation: # - If IS weights were computed/applied: use them in variance proxy calculation # - Otherwise: compute on-policy variance proxy # ====== PROXY 1: Signal Strength ||ḡ||² ====== # The squared norm of the mean gradient (provided from training loop) proxy1_signal_strength = gradient_norm**2 if gradient_norm is not None else None # ====== PROXY 2: Total Power E[||ĝ_τ||²] ====== # Measures the average of squared gradient norms (Signal + Noise) if rollout_is_weights is not None: # Off-policy with IS correction applied: use clamped weights consistently with actual gradient computation rollout_is_weights_scalar = verl_F.masked_mean(rollout_is_weights, response_mask, axis=-1) # Recover original W (before IS correction was applied in line 657) # Clamp to avoid division by zero when IS weights are zero w_original = verl_F.masked_sum( w_per_timestep / torch.clamp((rollout_is_weights**2).detach(), min=1e-10), response_mask, axis=-1 ) # Clamp W to avoid negative values (which would cause NaN in sqrt) w_original = torch.clamp(w_original, min=0.0) # Proxy 2 for off-policy: E[ρ̄² × A² × W] proxy2_total_power = ((rollout_is_weights_scalar**2) * (advantages_scalar**2) * w_original).mean() else: # On-policy Proxy 2: E[A² × W] # Clamp W to avoid negative values (which would cause NaN in sqrt) w_values_clamped = torch.clamp(w_values, min=0.0) proxy2_total_power = (advantages_scalar**2 * w_values_clamped).mean() # ====== PROXY 3: Pure Noise - Variance of Mean Vector ====== # Requires ||ḡ||² from actual batch gradient # Formula: (1/(N-1)) × (Proxy2 - Proxy1) proxy3_pure_noise = None if proxy1_signal_strength is not None: batch_size = advantages_scalar.shape[0] if batch_size > 1: proxy3_pure_noise = (1.0 / (batch_size - 1)) * (proxy2_total_power - proxy1_signal_strength) # Ensure non-negative (can be negative due to numerical errors) proxy3_pure_noise = max( 0.0, proxy3_pure_noise.item() if torch.is_tensor(proxy3_pure_noise) else proxy3_pure_noise ) # Decompose into components for analysis expected_a_squared = (advantages_scalar**2).mean() expected_w = w_values.mean() metrics.update( { # Proxy 1: Signal Strength ||ḡ||² "variance_proxy/proxy1_signal_strength": ( proxy1_signal_strength if proxy1_signal_strength is not None else 0.0 ), # Proxy 2: Total Power E[||ĝ_τ||²] "variance_proxy/proxy2_total_power": proxy2_total_power.detach().item(), # Proxy 3: Pure Noise - Variance of Mean Vector "variance_proxy/proxy3_pure_noise": proxy3_pure_noise if proxy3_pure_noise is not None else 0.0, # Component metrics for debugging "variance_proxy/expected_a_squared": expected_a_squared.detach().item(), "variance_proxy/expected_w": expected_w.detach().item(), } ) return metrics def bootstrap_metric( data: list[Any], subset_size: int, reduce_fns: list[Callable[[np.ndarray], float]], n_bootstrap: int = 1000, seed: int = 42, ) -> list[tuple[float, float]]: """ Performs bootstrap resampling to estimate statistics of metrics. This function uses bootstrap resampling to estimate the mean and standard deviation of metrics computed by the provided reduction functions on random subsets of the data. Args: data: List of data points to bootstrap from. subset_size: Size of each bootstrap sample. reduce_fns: List of functions that compute a metric from a subset of data. n_bootstrap: Number of bootstrap iterations. Defaults to 1000. seed: Random seed for reproducibility. Defaults to 42. Returns: A list of tuples, where each tuple contains (mean, std) for a metric corresponding to each reduction function in reduce_fns. Example: >>> data = [1, 2, 3, 4, 5] >>> reduce_fns = [np.mean, np.max] >>> bootstrap_metric(data, 3, reduce_fns) [(3.0, 0.5), (4.5, 0.3)] # Example values """ np.random.seed(seed) data_np = np.array(data, dtype=object) n_data = len(data_np) # generate bootstrap indices, shape: (n_bootstrap, subset_size) bootstrap_idxs = np.random.choice(n_data, size=(n_bootstrap, subset_size), replace=True) # pre-allocate result array, shape: (n_fns, n_bootstrap) n_fns = len(reduce_fns) metric_results = np.empty((n_fns, n_bootstrap), dtype=np.float64) # compute metric results for each bootstrap sample for fn_idx, reduce_fn in enumerate(reduce_fns): # bootstrap sample and compute metric for boot_idx in range(n_bootstrap): sample = data_np[bootstrap_idxs[boot_idx]] metric_results[fn_idx, boot_idx] = reduce_fn(sample) # compute mean and std for each metric function result = [ (float(np.mean(metric_results[fn_idx])), float(np.std(metric_results[fn_idx]))) for fn_idx in range(n_fns) ] return result def calc_maj_val(data: list[dict[str, Any]], vote_key: str, val_key: str) -> float: """ Calculate a value based on majority voting. This function identifies the most common value for a specified vote key in the data, then returns the corresponding value for that majority vote. Args: data: List of dictionaries, where each dictionary contains both vote_key and val_key. vote_key: The key in each dictionary used for voting/counting. val_key: The key in each dictionary whose value will be returned for the majority vote. Returns: The value associated with the most common vote. Example: >>> data = [ ... {"pred": "A", "val": 0.9}, ... {"pred": "B", "val": 0.8}, ... {"pred": "A", "val": 0.7} ... ] >>> calc_maj_val(data, vote_key="pred", val_key="val") 0.9 # Returns the first "val" for the majority vote "A" """ vote2vals = defaultdict(list) for d in data: vote2vals[d[vote_key]].append(d[val_key]) vote2cnt = {k: len(v) for k, v in vote2vals.items()} maj_vote = max(vote2cnt, key=vote2cnt.get) maj_val = vote2vals[maj_vote][0] return maj_val def process_validation_metrics( data_sources: list[str], sample_uids: list[str], infos_dict: dict[str, list[Any]], seed: int = 42 ) -> dict[str, dict[str, dict[str, float]]]: """ Process validation metrics into a structured format with statistical analysis. This function organizes validation metrics by data source and prompt, then computes various statistical measures including means, standard deviations, best/worst values, and majority voting results. It also performs bootstrap sampling to estimate statistics for different sample sizes. Args: data_sources: List of data source identifiers for each sample. sample_uids: List of sample uids corresponding to each sample. infos_dict: Dictionary mapping variable names to lists of values for each sample. seed: Random seed for bootstrap sampling. Defaults to 42. Returns: A nested dictionary with the structure: { data_source: { variable_name: { metric_name: value } } } Where metric_name includes: - "mean@N": Mean value across N samples - "std@N": Standard deviation across N samples - "best@N/mean": Mean of the best values in bootstrap samples of size N - "best@N/std": Standard deviation of the best values in bootstrap samples - "worst@N/mean": Mean of the worst values in bootstrap samples - "worst@N/std": Standard deviation of the worst values in bootstrap samples - "maj@N/mean": Mean of majority voting results in bootstrap samples (if "pred" exists) - "maj@N/std": Standard deviation of majority voting results (if "pred" exists) Example: >>> data_sources = ["source1", "source1", "source2"] >>> sample_uids = ["uid1", "uid1", "uid2"] >>> infos_dict = {"score": [0.8, 0.9, 0.7], "pred": ["A", "A", "B"]} >>> result = process_validation_metrics(data_sources, sample_uids, infos_dict) >>> # result will contain statistics for each data source and variable """ # Group metrics by data source, prompt and variable data_src2uid2var2vals = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) for sample_idx, data_source in enumerate(data_sources): uid = sample_uids[sample_idx] var2vals = data_src2uid2var2vals[data_source][uid] for var_name, var_vals in infos_dict.items(): var2vals[var_name].append(var_vals[sample_idx]) np_mean = np.mean np_std = np.std reduce_fns_best_worst = [np.max, np.min] n_bootstrap = 1000 # 2. cache ns list def gen_ns(n_resps: int) -> list[int]: if n_resps <= 1: return [] ns = [] n = 2 while n < n_resps: ns.append(n) n *= 2 ns.append(n_resps) return ns ns_cache = {} # 3. cache metric results data_src2uid2var2metric = {} # 4. flatten loop for data_source, uid2var2vals in data_src2uid2var2vals.items(): # create uid dict uid_dict = data_src2uid2var2metric.setdefault(data_source, {}) for uid, var2vals in uid2var2vals.items(): pred_vals = var2vals.get("pred") has_pred = pred_vals is not None var_dict = uid_dict.setdefault(uid, {}) for var_name, var_vals in var2vals.items(): # skip empty or string values if not var_vals or isinstance(var_vals[0], str): continue # compute mean and std n_resps = len(var_vals) metric = {f"mean@{n_resps}": float(np_mean(var_vals))} if n_resps > 1: metric[f"std@{n_resps}"] = float(np_std(var_vals)) # cache ns list if n_resps not in ns_cache: ns_cache[n_resps] = gen_ns(n_resps) ns = ns_cache[n_resps] # compute best/worst metrics for n in ns: # compute best/worst metrics (bon_mean, bon_std), (won_mean, won_std) = bootstrap_metric( data=var_vals, subset_size=n, reduce_fns=reduce_fns_best_worst, n_bootstrap=n_bootstrap, seed=seed, ) metric[f"best@{n}/mean"] = bon_mean metric[f"best@{n}/std"] = bon_std metric[f"worst@{n}/mean"] = won_mean metric[f"worst@{n}/std"] = won_std # compute maj metrics if has_pred: # create vote_data vote_data = [ {"val": val, "pred": pred} for val, pred in zip(var_vals, pred_vals, strict=True) ] # compute maj metrics [(maj_n_mean, maj_n_std)] = bootstrap_metric( data=vote_data, subset_size=n, reduce_fns=[partial(calc_maj_val, vote_key="pred", val_key="val")], n_bootstrap=n_bootstrap, seed=seed, ) metric[f"maj@{n}/mean"] = maj_n_mean metric[f"maj@{n}/std"] = maj_n_std var_dict[var_name] = metric # Aggregate metrics across uids data_src2var2metric2uid_vals = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) for data_source, uid2var2metric in data_src2uid2var2metric.items(): for uid, var2metric in uid2var2metric.items(): for var_name, metric in var2metric.items(): for metric_name, metric_val in metric.items(): data_src2var2metric2uid_vals[data_source][var_name][metric_name].append(metric_val) data_src2var2metric2val = defaultdict(lambda: defaultdict(lambda: defaultdict(float))) for data_source, var2metric2uid_vals in data_src2var2metric2uid_vals.items(): for var_name, metric2uid_vals in var2metric2uid_vals.items(): for metric_name, uid_vals in metric2uid_vals.items(): data_src2var2metric2val[data_source][var_name][metric_name] = np.mean(uid_vals) return data_src2var2metric2val ================================================ FILE: verl/trainer/ppo/prefix_grouper_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import torch from prefix_grouper import PrefixGrouper from verl.utils.torch_functional import logprobs_from_logits def build_position_ids_for_prefix_grouper(prefix_grouper: PrefixGrouper) -> torch.Tensor: """Build position_ids for PrefixGrouper where each response restarts from prefix_len.""" num_samples = len(prefix_grouper.group_info) max_len = prefix_grouper.padding_mask.size(1) device = prefix_grouper.padding_mask.device position_ids = torch.zeros(num_samples, max_len, dtype=torch.long, device=device) for i, group in enumerate(prefix_grouper.group_info): prefix_len = group.prefix_len position_ids[i, :prefix_len] = torch.arange(prefix_len, device=device) cur_pos = prefix_len for suffix_len in group.suffix_lens: if suffix_len > 0: position_ids[i, cur_pos : cur_pos + suffix_len] = torch.arange( prefix_len, prefix_len + suffix_len, device=device ) cur_pos += suffix_len return position_ids def build_pg_from_micro_batch( micro_batch: dict, pad_token_id: int, padding_mode: str = "right", ): """Build PrefixGrouper from micro_batch dict containing prompts, responses, response_mask, uid.""" prompts = micro_batch["prompts"] responses = micro_batch["responses"] response_mask = micro_batch["response_mask"] uids = micro_batch["uid"] bs = responses.size(0) group_sizes = [] cur = 1 for i in range(1, bs): if uids[i] == uids[i - 1]: cur += 1 else: group_sizes.append(cur) cur = 1 group_sizes.append(cur) prefix_indices = [] cursor = 0 for gs in group_sizes: prefix_indices.append(cursor) cursor += gs prefix_indices = torch.tensor(prefix_indices, device=prompts.device) prefix_ids = prompts.index_select(0, prefix_indices) prefix_mask = prefix_ids.ne(pad_token_id) prefix_grouper = PrefixGrouper.from_ungrouped_masks( prefix_mask=prefix_mask, suffix_mask=response_mask, group_sizes=group_sizes, padding_mode=padding_mode, device=prompts.device, ) concat_input_ids = prefix_grouper.concat_input(prefix_ids, prefix_mask, responses, response_mask) attention_mask = prefix_grouper.padding_mask position_ids = build_position_ids_for_prefix_grouper(prefix_grouper) return ( prefix_grouper, concat_input_ids, attention_mask, position_ids, responses, response_mask, ) def pg_forward( model, prefix_grouper, concat_input_ids, attention_mask, position_ids, completion_ids, completion_mask, *, temperature=1.0, padding_mode="right", include_prefix_last=1, calculate_entropy=False, entropy_fn=None, ): logits = model( input_ids=concat_input_ids, attention_mask=attention_mask, position_ids=position_ids, use_cache=False, prefix_grouper=prefix_grouper, ).logits prefix_out, prefix_mask, suffix_out_raw, suffix_mask_raw = prefix_grouper.split_output( logits, include_prefix_last=include_prefix_last ) completion_ids_right = prefix_grouper.convert_padding( completion_ids, completion_mask, padding_mode=padding_mode, ) suffix_out = suffix_out_raw[:, :-1].float() suffix_mask = suffix_mask_raw[:, 1:] suffix_out /= temperature log_probs = logprobs_from_logits(suffix_out, completion_ids_right) entropy = None if calculate_entropy and entropy_fn is not None: entropy = entropy_fn(suffix_out) return log_probs, entropy, suffix_mask def forward_micro_batch_with_prefix_grouper( micro_batch: dict, model, temperature: float, calculate_entropy: bool, device_name: str, param_dtype, use_chunking_entropy: bool = False, ): """ Forward pass using PrefixGrouper for shared-prefix optimization. Args: micro_batch: Dict containing prompts, responses, response_mask, uid, etc. model: The actor module. temperature: Temperature for logits scaling. calculate_entropy: Whether to compute entropy. device_name: Device name for autocast. param_dtype: Parameter dtype for autocast. use_chunking_entropy: Whether to use chunking entropy function. Returns: tuple: (entropy, log_probs) where entropy may be None if not calculated. """ import verl.utils.torch_functional as verl_F entropy_fn = None if calculate_entropy: if use_chunking_entropy: entropy_fn = verl_F.entropy_from_logits_with_chunking else: entropy_fn = verl_F.entropy_from_logits pad_token_id = micro_batch.get("pad_token_id", 0) ( prefix_grouper, concat_input_ids, attention_mask, position_ids, responses, response_mask, ) = build_pg_from_micro_batch( micro_batch, pad_token_id=pad_token_id, padding_mode="right", ) with torch.autocast(device_type=device_name, dtype=param_dtype): log_probs, entropy, suffix_mask_from_pg = pg_forward( model=model, prefix_grouper=prefix_grouper, concat_input_ids=concat_input_ids, attention_mask=attention_mask, position_ids=position_ids, completion_ids=responses, completion_mask=response_mask, temperature=temperature, padding_mode="right", include_prefix_last=1, calculate_entropy=calculate_entropy, entropy_fn=entropy_fn, ) # Zero out padding positions padding_mask = suffix_mask_from_pg == 0 log_probs = log_probs.masked_fill(padding_mask, 0.0) if entropy is not None: entropy = entropy.masked_fill(padding_mask, 0.0) # Pad to target response length if needed target_response_length = responses.size(1) if log_probs.size(1) != target_response_length: batch_size = log_probs.size(0) current_len = log_probs.size(1) full_log_probs = log_probs.new_zeros(batch_size, target_response_length) full_log_probs[:, :current_len] = log_probs log_probs = full_log_probs if entropy is not None: full_entropy = entropy.new_zeros(batch_size, target_response_length) full_entropy[:, :current_len] = entropy entropy = full_entropy return entropy, log_probs ================================================ FILE: verl/trainer/ppo/ray_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PPO Trainer with Ray-based single controller. This trainer supports model-agonistic model initialization with huggingface """ import json import os import uuid from collections import defaultdict from copy import deepcopy from pprint import pprint from typing import Any, Optional import numpy as np import torch from omegaconf import OmegaConf, open_dict from torch.utils.data import Dataset, Sampler from torchdata.stateful_dataloader import StatefulDataLoader from tqdm import tqdm from verl import DataProto from verl.checkpoint_engine import CheckpointEngineManager from verl.experimental.dataset.sampler import AbstractCurriculumSampler from verl.protocol import pad_dataproto_to_divisor, unpad_dataproto from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup, ResourcePoolManager from verl.single_controller.ray.base import create_colocated_worker_cls from verl.trainer.config import AlgoConfig from verl.trainer.ppo import core_algos from verl.trainer.ppo.core_algos import AdvantageEstimator, agg_loss from verl.trainer.ppo.metric_utils import ( compute_data_metrics, compute_throughout_metrics, compute_timing_metrics, compute_variance_proxy_metrics, process_validation_metrics, ) from verl.trainer.ppo.reward import extract_reward from verl.trainer.ppo.utils import Role, WorkerType, need_critic, need_reference_policy, need_reward_model from verl.utils import tensordict_utils as tu from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path, should_save_ckpt_esi from verl.utils.config import omega_conf_to_dataclass from verl.utils.debug import marked_timer from verl.utils.import_utils import load_class_from_fqn from verl.utils.metric import reduce_metrics from verl.utils.py_functional import rename_dict from verl.utils.rollout_skip import RolloutSkip from verl.utils.seqlen_balancing import calculate_workload, get_seqlen_balanced_partitions, log_seqlen_unbalance from verl.utils.torch_functional import masked_mean from verl.utils.tracking import ValidationGenerationsLogger from verl.workers.config import FSDPEngineConfig from verl.workers.utils.padding import left_right_2_no_padding, no_padding_2_padding def apply_kl_penalty(data: DataProto, kl_ctrl: core_algos.AdaptiveKLController, kl_penalty="kl"): """Apply KL penalty to the token-level rewards. This function computes the KL divergence between the reference policy and current policy, then applies a penalty to the token-level rewards based on this divergence. Args: data (DataProto): The data containing batched model outputs and inputs. kl_ctrl (core_algos.AdaptiveKLController): Controller for adaptive KL penalty. kl_penalty (str, optional): Type of KL penalty to apply. Defaults to "kl". Returns: tuple: A tuple containing: - The updated data with token-level rewards adjusted by KL penalty - A dictionary of metrics related to the KL penalty """ response_mask = data.batch["response_mask"] token_level_scores = data.batch["token_level_scores"] batch_size = data.batch.batch_size[0] # compute kl between ref_policy and current policy # When apply_kl_penalty, algorithm.use_kl_in_reward=True, so the reference model has been enabled. kld = core_algos.kl_penalty( data.batch["old_log_probs"], data.batch["ref_log_prob"], kl_penalty=kl_penalty ) # (batch_size, response_length) kld = kld * response_mask beta = kl_ctrl.value token_level_rewards = token_level_scores - beta * kld current_kl = masked_mean(kld, mask=response_mask, axis=-1) # average over sequence current_kl = torch.mean(current_kl, dim=0).item() # according to https://github.com/huggingface/trl/blob/951ca1841f29114b969b57b26c7d3e80a39f75a0/trl/trainer/ppo_trainer.py#L837 kl_ctrl.update(current_kl=current_kl, n_steps=batch_size) data.batch["token_level_rewards"] = token_level_rewards metrics = {"actor/reward_kl_penalty": current_kl, "actor/reward_kl_penalty_coeff": beta} return data, metrics def compute_response_mask(data: DataProto): """Compute the attention mask for the response part of the sequence. This function extracts the portion of the attention mask that corresponds to the model's response, which is used for masking computations that should only apply to response tokens. Args: data (DataProto): The data containing batched model outputs and inputs. Returns: torch.Tensor: The attention mask for the response tokens. """ responses = data.batch["responses"] response_length = responses.size(1) attention_mask = data.batch["attention_mask"] return attention_mask[:, -response_length:] def compute_advantage( data: DataProto, adv_estimator: AdvantageEstimator, gamma: float = 1.0, lam: float = 1.0, num_repeat: int = 1, norm_adv_by_std_in_grpo: bool = True, config: Optional[AlgoConfig] = None, ) -> DataProto: """Compute advantage estimates for policy optimization. This function computes advantage estimates using various estimators like GAE, GRPO, REINFORCE++, etc. The advantage estimates are used to guide policy optimization in RL algorithms. Args: data (DataProto): The data containing batched model outputs and inputs. adv_estimator (AdvantageEstimator): The advantage estimator to use (e.g., GAE, GRPO, REINFORCE++). gamma (float, optional): Discount factor for future rewards. Defaults to 1.0. lam (float, optional): Lambda parameter for GAE. Defaults to 1.0. num_repeat (int, optional): Number of times to repeat the computation. Defaults to 1. norm_adv_by_std_in_grpo (bool, optional): Whether to normalize advantages by standard deviation in GRPO. Defaults to True. config (dict, optional): Configuration dictionary for algorithm settings. Defaults to None. Returns: DataProto: The updated data with computed advantages and returns. """ # Back-compatible with trainers that do not compute response mask in fit if "response_mask" not in data.batch.keys(): data.batch["response_mask"] = compute_response_mask(data) # prepare response group if adv_estimator == AdvantageEstimator.GAE: # Compute advantages and returns using Generalized Advantage Estimation (GAE) advantages, returns = core_algos.compute_gae_advantage_return( token_level_rewards=data.batch["token_level_rewards"], values=data.batch["values"], response_mask=data.batch["response_mask"], gamma=gamma, lam=lam, ) data.batch["advantages"] = advantages data.batch["returns"] = returns if config.get("use_pf_ppo", False): data = core_algos.compute_pf_ppo_reweight_data( data, config.pf_ppo.get("reweight_method"), config.pf_ppo.get("weight_pow"), ) elif adv_estimator == AdvantageEstimator.GRPO: # Initialize the mask for GRPO calculation grpo_calculation_mask = data.batch["response_mask"] # Call compute_grpo_outcome_advantage with parameters matching its definition advantages, returns = core_algos.compute_grpo_outcome_advantage( token_level_rewards=data.batch["token_level_rewards"], response_mask=grpo_calculation_mask, index=data.non_tensor_batch["uid"], norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo, ) data.batch["advantages"] = advantages data.batch["returns"] = returns else: # handle all other adv estimator type other than GAE and GRPO adv_estimator_fn = core_algos.get_adv_estimator_fn(adv_estimator) adv_kwargs = { "token_level_rewards": data.batch["token_level_rewards"], "response_mask": data.batch["response_mask"], "config": config, } if "uid" in data.non_tensor_batch: # optional adv_kwargs["index"] = data.non_tensor_batch["uid"] if "reward_baselines" in data.batch: # optional adv_kwargs["reward_baselines"] = data.batch["reward_baselines"] # GDPO: pass raw data for per-dimension reward extraction if adv_estimator in (AdvantageEstimator.GDPO, "gdpo"): adv_kwargs["non_tensor_batch"] = data.non_tensor_batch adv_kwargs["batch"] = data.batch # Add sum_pi_squared for Optimal Token Baseline if adv_estimator in (AdvantageEstimator.OPTIMAL_TOKEN_BASELINE, AdvantageEstimator.TIR_OPTIMAL_TOKEN_BASELINE): # Check if sum_pi_squared is available assert "sum_pi_squared" in data.batch, ( "Step-dependent optimal baseline requires sum_pi_squared from actor. " "Please set actor.calculate_sum_pi_squared=True in config." ) adv_kwargs["sum_pi_squared"] = data.batch["sum_pi_squared"] # old_log_probs needed for path-variance proxy: w_t = 1 - 2*exp(old_log_probs) + sum_pi_squared adv_kwargs["old_log_probs"] = data.batch["old_log_probs"] # Get pre-computed rollout IS weights if available rollout_is_weights = data.batch.get("rollout_is_weights", None) adv_kwargs["rollout_is_weights"] = rollout_is_weights # calculate advantage estimator advantages, returns = adv_estimator_fn(**adv_kwargs) data.batch["advantages"] = advantages data.batch["returns"] = returns return data class RayPPOTrainer: """Distributed PPO trainer using Ray for scalable reinforcement learning. This trainer orchestrates distributed PPO training across multiple nodes and GPUs, managing actor rollouts, critic training, and reward computation with Ray backend. Supports various model architectures including FSDP, Megatron, vLLM, and SGLang integration. """ # TODO: support each role have individual ray_worker_group_cls, # i.e., support different backend of different role def __init__( self, config, tokenizer, role_worker_mapping: dict[Role, WorkerType], resource_pool_manager: ResourcePoolManager, ray_worker_group_cls: type[RayWorkerGroup] = RayWorkerGroup, processor=None, train_dataset: Optional[Dataset] = None, val_dataset: Optional[Dataset] = None, collate_fn=None, train_sampler: Optional[Sampler] = None, device_name=None, ): """ Initialize distributed PPO trainer with Ray backend. Note that this trainer runs on the driver process on a single CPU/GPU node. Args: config: Configuration object containing training parameters. tokenizer: Tokenizer used for encoding and decoding text. role_worker_mapping (dict[Role, WorkerType]): Mapping from roles to worker classes. resource_pool_manager (ResourcePoolManager): Manager for Ray resource pools. ray_worker_group_cls (RayWorkerGroup, optional): Class for Ray worker groups. Defaults to RayWorkerGroup. processor: Optional data processor, used for multimodal data train_dataset (Optional[Dataset], optional): Training dataset. Defaults to None. val_dataset (Optional[Dataset], optional): Validation dataset. Defaults to None. collate_fn: Function to collate data samples into batches. train_sampler (Optional[Sampler], optional): Sampler for the training dataset. Defaults to None. device_name (str, optional): Device name for training (e.g., "cuda", "cpu"). Defaults to None. """ # Store the tokenizer for text processing self.tokenizer = tokenizer self.processor = processor self.config = config self.hybrid_engine = config.actor_rollout_ref.hybrid_engine assert self.hybrid_engine, "Currently, only support hybrid engine" if self.hybrid_engine: assert Role.ActorRollout in role_worker_mapping or Role.ActorRolloutRef in role_worker_mapping, ( f"{role_worker_mapping.keys()=}" ) self.role_worker_mapping = role_worker_mapping self.resource_pool_manager = resource_pool_manager self.use_reference_policy = need_reference_policy(self.config) self.use_rm = need_reward_model(self.config) self.use_critic = need_critic(self.config) self.ray_worker_group_cls = ray_worker_group_cls self.device_name = device_name if device_name else self.config.trainer.device self.validation_generations_logger = ValidationGenerationsLogger( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, ) # if ref_in_actor is True, the reference policy will be actor without lora applied lora_rank = config.actor_rollout_ref.model.get("lora", {}).get("rank", 0) if lora_rank <= 0: lora_rank = config.actor_rollout_ref.model.get("lora_rank", 0) self.ref_in_actor = lora_rank > 0 or config.actor_rollout_ref.model.get("lora_adapter_path") is not None # define in-reward KL control # kl loss control currently not suppoorted if self.config.algorithm.use_kl_in_reward: self.kl_ctrl_in_reward = core_algos.get_kl_controller(self.config.algorithm.kl_ctrl) self.use_prefix_grouper = self.config.actor_rollout_ref.actor.get("use_prefix_grouper", False) self.use_legacy_worker_impl = config.trainer.get("use_legacy_worker_impl", "auto") self._create_dataloader(train_dataset, val_dataset, collate_fn, train_sampler) self.checkpoint_manager = None def _create_dataloader(self, train_dataset, val_dataset, collate_fn, train_sampler: Optional[Sampler]): """ Creates the train and validation dataloaders. """ # TODO: we have to make sure the batch size is divisible by the dp size from verl.trainer.main_ppo import create_rl_dataset, create_rl_sampler if train_dataset is None: train_dataset = create_rl_dataset( self.config.data.train_files, self.config.data, self.tokenizer, self.processor, max_samples=self.config.data.get("train_max_samples", -1), ) if val_dataset is None: val_dataset = create_rl_dataset( self.config.data.val_files, self.config.data, self.tokenizer, self.processor, max_samples=self.config.data.get("val_max_samples", -1), ) self.train_dataset, self.val_dataset = train_dataset, val_dataset if train_sampler is None: train_sampler = create_rl_sampler(self.config.data, self.train_dataset) if collate_fn is None: from verl.utils.dataset.rl_dataset import collate_fn as default_collate_fn collate_fn = default_collate_fn num_workers = self.config.data["dataloader_num_workers"] self.train_dataloader = StatefulDataLoader( dataset=self.train_dataset, batch_size=self.config.data.get("gen_batch_size", self.config.data.train_batch_size), num_workers=num_workers, drop_last=True, collate_fn=collate_fn, sampler=train_sampler, ) val_batch_size = self.config.data.val_batch_size # Prefer config value if set if val_batch_size is None: val_batch_size = len(self.val_dataset) self.val_dataloader = StatefulDataLoader( dataset=self.val_dataset, batch_size=val_batch_size, num_workers=num_workers, shuffle=self.config.data.get("validation_shuffle", True), drop_last=False, collate_fn=collate_fn, ) assert len(self.train_dataloader) >= 1, "Train dataloader is empty!" assert len(self.val_dataloader) >= 1, "Validation dataloader is empty!" print( f"Size of train dataloader: {len(self.train_dataloader)}, Size of val dataloader: " f"{len(self.val_dataloader)}" ) total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs if self.config.trainer.total_training_steps is not None: total_training_steps = self.config.trainer.total_training_steps self.total_training_steps = total_training_steps print(f"Total training steps: {self.total_training_steps}") try: OmegaConf.set_struct(self.config, True) with open_dict(self.config): if OmegaConf.select(self.config, "actor_rollout_ref.actor.optim"): self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps if OmegaConf.select(self.config, "critic.optim"): self.config.critic.optim.total_training_steps = total_training_steps except Exception as e: print(f"Warning: Could not set total_training_steps in config. Structure missing? Error: {e}") def _dump_generations(self, inputs, outputs, gts, scores, reward_extra_infos_dict, dump_path): """Dump rollout/validation samples as JSONL.""" os.makedirs(dump_path, exist_ok=True) filename = os.path.join(dump_path, f"{self.global_steps}.jsonl") n = len(inputs) base_data = { "input": inputs, "output": outputs, "gts": gts, "score": scores, "step": [self.global_steps] * n, } for k, v in reward_extra_infos_dict.items(): if len(v) == n: base_data[k] = v lines = [] for i in range(n): entry = {k: v[i] for k, v in base_data.items()} lines.append(json.dumps(entry, ensure_ascii=False)) with open(filename, "w") as f: f.write("\n".join(lines) + "\n") print(f"Dumped generations to {filename}") def _log_rollout_data( self, batch: DataProto, reward_extra_infos_dict: dict, timing_raw: dict, rollout_data_dir: str ): """Log rollout data to disk. Args: batch (DataProto): The batch containing rollout data reward_extra_infos_dict (dict): Additional reward information to log timing_raw (dict): Timing information for profiling rollout_data_dir (str): Directory path to save the rollout data """ with marked_timer("dump_rollout_generations", timing_raw, color="green"): inputs = self.tokenizer.batch_decode(batch.batch["prompts"], skip_special_tokens=True) outputs = self.tokenizer.batch_decode(batch.batch["responses"], skip_special_tokens=True) scores = batch.batch["token_level_scores"].sum(-1).cpu().tolist() sample_gts = [item.non_tensor_batch.get("reward_model", {}).get("ground_truth", None) for item in batch] reward_extra_infos_to_dump = reward_extra_infos_dict.copy() if "request_id" in batch.non_tensor_batch: reward_extra_infos_dict.setdefault( "request_id", batch.non_tensor_batch["request_id"].tolist(), ) self._dump_generations( inputs=inputs, outputs=outputs, gts=sample_gts, scores=scores, reward_extra_infos_dict=reward_extra_infos_to_dump, dump_path=rollout_data_dir, ) def _maybe_log_val_generations(self, inputs, outputs, scores): """Log a table of validation samples to the configured logger (wandb or swanlab)""" generations_to_log = self.config.trainer.log_val_generations if generations_to_log == 0: return import numpy as np # Create tuples of (input, output, score) and sort by input text samples = list(zip(inputs, outputs, scores, strict=True)) samples.sort(key=lambda x: x[0]) # Sort by input text # Use fixed random seed for deterministic shuffling rng = np.random.RandomState(42) rng.shuffle(samples) # Take first N samples after shuffling samples = samples[:generations_to_log] # Log to each configured logger self.validation_generations_logger.log(self.config.trainer.logger, samples, self.global_steps) def _get_gen_batch(self, batch: DataProto) -> DataProto: reward_keys = set({"data_source", "reward_model", "extra_info", "uid"}) & batch.non_tensor_batch.keys() # pop those keys for generation batch_keys_to_pop = [] non_tensor_batch_keys_to_pop = set(batch.non_tensor_batch.keys()) - reward_keys gen_batch = batch.pop( batch_keys=batch_keys_to_pop, non_tensor_batch_keys=list(non_tensor_batch_keys_to_pop), ) # For agent loop, we need reward model keys to compute score. gen_batch.non_tensor_batch.update(batch.non_tensor_batch) return gen_batch def _compute_reward_colocate(self, batch: DataProto) -> tuple[torch.Tensor, dict[str, Any]] | torch.Tensor: """ compute reward use colocate reward model """ assert self.reward_loop_manager is not None, "RewardLoopManager is None" batch_reward = self.reward_loop_manager.compute_rm_score(batch) return batch_reward def _validate(self, merged: bool = False): data_source_lst = [] reward_extra_infos_dict: dict[str, list] = defaultdict(list) # Lists to collect samples for the table sample_inputs = [] sample_outputs = [] sample_gts = [] sample_scores = [] sample_turns = [] sample_uids = [] for test_data in self.val_dataloader: test_batch = DataProto.from_single_dict(test_data) if "uid" not in test_batch.non_tensor_batch: test_batch.non_tensor_batch["uid"] = np.array( [str(uuid.uuid4()) for _ in range(len(test_batch.batch))], dtype=object ) # repeat test batch test_batch = test_batch.repeat( repeat_times=self.config.actor_rollout_ref.rollout.val_kwargs.n, interleave=True ) ground_truths = [ item.non_tensor_batch.get("reward_model", {}).get("ground_truth", None) for item in test_batch ] sample_gts.extend(ground_truths) test_gen_batch = self._get_gen_batch(test_batch) test_gen_batch.meta_info = { "eos_token_id": self.tokenizer.eos_token_id, "pad_token_id": self.tokenizer.pad_token_id, "recompute_log_prob": False, "do_sample": self.config.actor_rollout_ref.rollout.val_kwargs.do_sample, "validate": True, "global_steps": self.global_steps, } print(f"test_gen_batch meta info: {test_gen_batch.meta_info}") # pad to be divisible by dp_size size_divisor = self.config.actor_rollout_ref.rollout.agent.num_workers test_gen_batch_padded, pad_size = pad_dataproto_to_divisor(test_gen_batch, size_divisor) test_output_gen_batch_padded = self.async_rollout_manager.generate_sequences(test_gen_batch_padded) if self.use_rm and "rm_scores" not in test_output_gen_batch_padded.batch.keys(): # for colocate reward models, we need to sleep rollout model # to spare GPU memory for reward model self.checkpoint_manager.sleep_replicas() batch_reward = self._compute_reward_colocate(test_output_gen_batch_padded) test_output_gen_batch_padded = test_output_gen_batch_padded.union(batch_reward) # wake up rollout model # replace with wake_up method once supported self.checkpoint_manager.update_weights(self.global_steps) # unpad test_output_gen_batch = unpad_dataproto(test_output_gen_batch_padded, pad_size=pad_size) print("validation generation end") # Store generated outputs output_ids = test_output_gen_batch.batch["responses"] output_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in output_ids] sample_outputs.extend(output_texts) test_batch = test_batch.union(test_output_gen_batch) test_batch.meta_info["validate"] = True # Store original inputs input_ids = test_batch.batch["prompts"] # TODO: Can we keep special tokens except for padding tokens? input_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in input_ids] sample_inputs.extend(input_texts) sample_uids.extend(test_batch.non_tensor_batch["uid"]) # evaluate using reward_function reward_tensor, reward_extra_info = extract_reward(test_batch) scores = reward_tensor.sum(-1).cpu().tolist() sample_scores.extend(scores) reward_extra_infos_dict["reward"].extend(scores) for key, values in reward_extra_info.items(): if key not in reward_extra_infos_dict: reward_extra_infos_dict[key] = [] if isinstance(values, np.ndarray): reward_extra_infos_dict[key].extend(values.tolist()) else: reward_extra_infos_dict[key].extend(values if isinstance(values, list) else [values]) # collect num_turns of each prompt if "__num_turns__" in test_batch.non_tensor_batch: sample_turns.append(test_batch.non_tensor_batch["__num_turns__"]) data_source_lst.append(test_batch.non_tensor_batch.get("data_source", ["unknown"] * reward_tensor.shape[0])) self._maybe_log_val_generations(inputs=sample_inputs, outputs=sample_outputs, scores=sample_scores) # dump generations val_data_dir = self.config.trainer.get("validation_data_dir", None) if val_data_dir: self._dump_generations( inputs=sample_inputs, outputs=sample_outputs, gts=sample_gts, scores=sample_scores, reward_extra_infos_dict=reward_extra_infos_dict, dump_path=val_data_dir, ) for key_info, lst in reward_extra_infos_dict.items(): assert len(lst) == 0 or len(lst) == len(sample_scores), f"{key_info}: {len(lst)=}, {len(sample_scores)=}" if merged: print("_merge_validation_results validate result will be merged") return { "data_sources": data_source_lst, "sample_uids": sample_uids, "sample_turns": sample_turns, "reward_extra_infos_dict": reward_extra_infos_dict, } data_sources = np.concatenate(data_source_lst, axis=0) return self._val_metrics_update(data_sources, sample_uids, reward_extra_infos_dict, sample_turns) def _val_metrics_update(self, data_sources, sample_uids, reward_extra_infos_dict, sample_turns): data_src2var2metric2val = process_validation_metrics(data_sources, sample_uids, reward_extra_infos_dict) metric_dict = {} for data_source, var2metric2val in data_src2var2metric2val.items(): core_var = "acc" if "acc" in var2metric2val else "reward" for var_name, metric2val in var2metric2val.items(): n_max = max([int(name.split("@")[-1].split("/")[0]) for name in metric2val.keys()]) for metric_name, metric_val in metric2val.items(): if ( (var_name == core_var) and any(metric_name.startswith(pfx) for pfx in ["mean", "maj", "best"]) and (f"@{n_max}" in metric_name) ): metric_sec = "val-core" else: metric_sec = "val-aux" pfx = f"{metric_sec}/{data_source}/{var_name}/{metric_name}" metric_dict[pfx] = metric_val if len(sample_turns) > 0: sample_turns = np.concatenate(sample_turns) metric_dict["val-aux/num_turns/min"] = sample_turns.min() metric_dict["val-aux/num_turns/max"] = sample_turns.max() metric_dict["val-aux/num_turns/mean"] = sample_turns.mean() return metric_dict def _merge_validation_results(self, result_a, result_b): if result_a is None and result_b is None: return {} if result_a is None: result_a = {"data_sources": [], "sample_uids": [], "sample_turns": [], "reward_extra_infos_dict": {}} if result_b is None: result_b = {"data_sources": [], "sample_uids": [], "sample_turns": [], "reward_extra_infos_dict": {}} if not result_a.get("data_sources") and not result_b.get("data_sources"): return {} data_sources = np.concatenate(result_a["data_sources"] + result_b["data_sources"], axis=0) sample_uids = result_a["sample_uids"] + result_b["sample_uids"] sample_turns = result_a["sample_turns"] + result_b["sample_turns"] reward_extra_infos_dict = {} all_keys = set(result_a["reward_extra_infos_dict"].keys()) | set(result_b["reward_extra_infos_dict"].keys()) for key in all_keys: list_a = result_a["reward_extra_infos_dict"].get(key, []) list_b = result_b["reward_extra_infos_dict"].get(key, []) reward_extra_infos_dict[key] = list_a + list_b return self._val_metrics_update(data_sources, sample_uids, reward_extra_infos_dict, sample_turns) def init_workers(self): """Initialize distributed training workers using Ray backend. Creates: 1. Ray resource pools from configuration 2. Worker groups for each role (actor, critic, etc.) """ self.resource_pool_manager.create_resource_pool() self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()} # create actor and rollout actor_role = Role.ActorRolloutRef if Role.ActorRolloutRef in self.role_worker_mapping else Role.ActorRollout if self.hybrid_engine: actor_rollout_resource_pool = self.resource_pool_manager.get_resource_pool(actor_role) actor_rollout_cls = RayClassWithInitArgs( cls=self.role_worker_mapping[actor_role], config=self.config.actor_rollout_ref, role=str(actor_role), ) self.resource_pool_to_cls[actor_rollout_resource_pool][str(actor_role)] = actor_rollout_cls else: raise NotImplementedError # create critic if self.use_critic: resource_pool = self.resource_pool_manager.get_resource_pool(Role.Critic) from verl.workers.config import CriticConfig critic_cfg: CriticConfig = omega_conf_to_dataclass(self.config.critic) if self.use_legacy_worker_impl == "disable": # convert critic_cfg into TrainingWorkerConfig from verl.workers.engine_workers import TrainingWorkerConfig orig_critic_cfg = critic_cfg if orig_critic_cfg.strategy == "fsdp": engine_config: FSDPEngineConfig = orig_critic_cfg.model.fsdp_config engine_config.infer_max_token_len_per_gpu = critic_cfg.ppo_infer_max_token_len_per_gpu engine_config.max_token_len_per_gpu = critic_cfg.ppo_max_token_len_per_gpu else: raise NotImplementedError(f"Unknown strategy {orig_critic_cfg.strategy=}") critic_cfg = TrainingWorkerConfig( model_type="value_model", model_config=orig_critic_cfg.model_config, engine_config=engine_config, optimizer_config=orig_critic_cfg.optim, checkpoint_config=orig_critic_cfg.checkpoint, ) critic_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.Critic], config=critic_cfg) self.resource_pool_to_cls[resource_pool][str(Role.Critic)] = critic_cls # create reference policy if needed if self.use_reference_policy and Role.RefPolicy in self.role_worker_mapping: resource_pool = self.resource_pool_manager.get_resource_pool(Role.RefPolicy) ref_policy_cls = RayClassWithInitArgs( self.role_worker_mapping[Role.RefPolicy], config=self.config.actor_rollout_ref, role=str(Role.RefPolicy), ) self.resource_pool_to_cls[resource_pool][str(Role.RefPolicy)] = ref_policy_cls # initialize WorkerGroup # NOTE: if you want to use a different resource pool for each role, which can support different parallel size, # you should not use `create_colocated_worker_cls`. # Instead, directly pass different resource pool to different worker groups. # See https://github.com/volcengine/verl/blob/master/examples/ray/tutorial.ipynb for more information. all_wg = {} wg_kwargs = {} # Setting up kwargs for RayWorkerGroup if OmegaConf.select(self.config.trainer, "ray_wait_register_center_timeout") is not None: wg_kwargs["ray_wait_register_center_timeout"] = self.config.trainer.ray_wait_register_center_timeout if OmegaConf.select(self.config.global_profiler, "steps") is not None: wg_kwargs["profile_steps"] = OmegaConf.select(self.config.global_profiler, "steps") # Only require nsight worker options when tool is nsys if OmegaConf.select(self.config.global_profiler, "tool") == "nsys": assert ( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") is not None ), "worker_nsight_options must be set when using nsys with profile_steps" wg_kwargs["worker_nsight_options"] = OmegaConf.to_container( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") ) wg_kwargs["device_name"] = self.device_name for resource_pool, class_dict in self.resource_pool_to_cls.items(): if not class_dict: continue worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = self.ray_worker_group_cls( resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls, **wg_kwargs, ) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) if self.use_critic: self.critic_wg = all_wg[str(Role.Critic)] if self.use_legacy_worker_impl == "disable": self.critic_wg.reset() # assign critic loss from functools import partial from verl.workers.utils.losses import value_loss value_loss_ = partial(value_loss, config=orig_critic_cfg) self.critic_wg.set_loss_fn(value_loss_) else: self.critic_wg.init_model() if self.use_reference_policy and not self.ref_in_actor: if str(Role.RefPolicy) in all_wg: self.ref_policy_wg = all_wg[str(Role.RefPolicy)] self.ref_policy_wg.init_model() else: # Model engine: ActorRolloutRefWorker assert str(Role.ActorRolloutRef) in all_wg, f"{all_wg.keys()=}" self.ref_policy_wg = all_wg[str(Role.ActorRolloutRef)] # we should create rollout at the end so that vllm can have a better estimation of kv cache memory self.actor_rollout_wg = all_wg[str(actor_role)] self.actor_rollout_wg.init_model() if self.ref_in_actor: self.ref_policy_wg = self.actor_rollout_wg # create reward loop manager from verl.experimental.reward_loop import RewardLoopManager # initalize reward loop manager # reward model (colocate or standalone): get resource_pool # no reward model: resource_pool = None resource_pool = self.resource_pool_manager.get_resource_pool(Role.RewardModel) if self.use_rm else None self.reward_loop_manager = RewardLoopManager( config=self.config, rm_resource_pool=resource_pool, ) # create async rollout manager and request scheduler # Note: mode is always "async" since sync mode is deprecated self.async_rollout_mode = True # Support custom AgentLoopManager via config manager_class_fqn = self.config.actor_rollout_ref.rollout.get("agent", {}).get("agent_loop_manager_class") if manager_class_fqn: AgentLoopManager = load_class_from_fqn(manager_class_fqn, "AgentLoopManager") else: from verl.experimental.agent_loop import AgentLoopManager # infrastructure overview: https://verl.readthedocs.io/en/latest/advance/reward_loop.html#architecture-design # agent_reward_loop: streaming reward computation with actor rollout # two conditions satisfied: (1) no reward model, or (2) reward model with extra resource pool enable_agent_reward_loop = not self.use_rm or self.config.reward.reward_model.enable_resource_pool # if enable_agent_reward_loop, we directly pass reward_loop_workers to agent loop manager # to stream reward computation with actor rollout reward_loop_worker_handles = self.reward_loop_manager.reward_loop_workers if enable_agent_reward_loop else None self.async_rollout_manager = AgentLoopManager.create( config=self.config, worker_group=self.actor_rollout_wg, rollout_resource_pool=actor_rollout_resource_pool, reward_loop_worker_handles=reward_loop_worker_handles, ) checkpoint_engine_config = omega_conf_to_dataclass(self.config.actor_rollout_ref.rollout.checkpoint_engine) self.checkpoint_manager = CheckpointEngineManager( config=checkpoint_engine_config, trainer=self.actor_rollout_wg, replicas=self.async_rollout_manager.rollout_replicas, ) # sleep all replicas to load checkpoint self.checkpoint_manager.sleep_replicas() def _save_checkpoint(self): from verl.utils.fs import local_mkdir_safe # path: given_path + `/global_step_{global_steps}` + `/actor` local_global_step_folder = os.path.join( self.config.trainer.default_local_dir, f"global_step_{self.global_steps}" ) print(f"local_global_step_folder: {local_global_step_folder}") actor_local_path = os.path.join(local_global_step_folder, "actor") actor_remote_path = ( None if self.config.trainer.default_hdfs_dir is None else os.path.join(self.config.trainer.default_hdfs_dir, f"global_step_{self.global_steps}", "actor") ) remove_previous_ckpt_in_save = self.config.trainer.get("remove_previous_ckpt_in_save", False) if remove_previous_ckpt_in_save: print( "Warning: remove_previous_ckpt_in_save is deprecated," + " set max_actor_ckpt_to_keep=1 and max_critic_ckpt_to_keep=1 instead" ) max_actor_ckpt_to_keep = ( self.config.trainer.get("max_actor_ckpt_to_keep", None) if not remove_previous_ckpt_in_save else 1 ) max_critic_ckpt_to_keep = ( self.config.trainer.get("max_critic_ckpt_to_keep", None) if not remove_previous_ckpt_in_save else 1 ) self.actor_rollout_wg.save_checkpoint( actor_local_path, actor_remote_path, self.global_steps, max_ckpt_to_keep=max_actor_ckpt_to_keep ) if self.use_critic: critic_local_path = os.path.join(local_global_step_folder, str(Role.Critic)) critic_remote_path = ( None if self.config.trainer.default_hdfs_dir is None else os.path.join( self.config.trainer.default_hdfs_dir, f"global_step_{self.global_steps}", str(Role.Critic) ) ) self.critic_wg.save_checkpoint( critic_local_path, critic_remote_path, self.global_steps, max_ckpt_to_keep=max_critic_ckpt_to_keep ) # save dataloader local_mkdir_safe(local_global_step_folder) dataloader_local_path = os.path.join(local_global_step_folder, "data.pt") dataloader_state_dict = self.train_dataloader.state_dict() torch.save(dataloader_state_dict, dataloader_local_path) # latest checkpointed iteration tracker (for atomic usage) if ( hasattr(self.config.actor_rollout_ref.actor.checkpoint, "async_save") and self.config.actor_rollout_ref.actor.checkpoint.async_save ) or ( "async_save" in self.config.actor_rollout_ref.actor.checkpoint and self.config.actor_rollout_ref.actor.checkpoint["async_save"] ): print("skip write latest_checkpointed_iteration.txt when async_save is True") return local_latest_checkpointed_iteration = os.path.join( self.config.trainer.default_local_dir, "latest_checkpointed_iteration.txt" ) with open(local_latest_checkpointed_iteration, "w") as f: f.write(str(self.global_steps)) def _load_checkpoint(self): if self.config.trainer.resume_mode == "disable": return 0 # load from hdfs if self.config.trainer.default_hdfs_dir is not None: raise NotImplementedError("load from hdfs is not implemented yet") else: checkpoint_folder = self.config.trainer.default_local_dir # TODO: check path if not os.path.isabs(checkpoint_folder): working_dir = os.getcwd() checkpoint_folder = os.path.join(working_dir, checkpoint_folder) global_step_folder = find_latest_ckpt_path(checkpoint_folder) # None if no latest # find global_step_folder if self.config.trainer.resume_mode == "auto": if global_step_folder is None: print("Training from scratch") return 0 else: if self.config.trainer.resume_mode == "resume_path": assert isinstance(self.config.trainer.resume_from_path, str), "resume ckpt must be str type" assert "global_step_" in self.config.trainer.resume_from_path, ( "resume ckpt must specify the global_steps" ) global_step_folder = self.config.trainer.resume_from_path if not os.path.isabs(global_step_folder): working_dir = os.getcwd() global_step_folder = os.path.join(working_dir, global_step_folder) print(f"Load from checkpoint folder: {global_step_folder}") # set global step self.global_steps = int(global_step_folder.split("global_step_")[-1]) print(f"Setting global step to {self.global_steps}") print(f"Resuming from {global_step_folder}") actor_path = os.path.join(global_step_folder, "actor") critic_path = os.path.join(global_step_folder, str(Role.Critic)) # load actor self.actor_rollout_wg.load_checkpoint( actor_path, del_local_after_load=self.config.trainer.del_local_ckpt_after_load ) # load critic if self.use_critic: self.critic_wg.load_checkpoint( critic_path, del_local_after_load=self.config.trainer.del_local_ckpt_after_load ) # load dataloader, # TODO: from remote not implemented yet dataloader_local_path = os.path.join(global_step_folder, "data.pt") if os.path.exists(dataloader_local_path): dataloader_state_dict = torch.load(dataloader_local_path, weights_only=False) self.train_dataloader.load_state_dict(dataloader_state_dict) else: print(f"Warning: No dataloader state found at {dataloader_local_path}, will start from scratch") def _start_profiling(self, do_profile: bool) -> None: """Start profiling for all worker groups if profiling is enabled.""" if do_profile: self.actor_rollout_wg.start_profile(role="e2e", profile_step=self.global_steps) if self.use_reference_policy: self.ref_policy_wg.start_profile(profile_step=self.global_steps) if self.use_critic: self.critic_wg.start_profile(profile_step=self.global_steps) def _stop_profiling(self, do_profile: bool) -> None: """Stop profiling for all worker groups if profiling is enabled.""" if do_profile: self.actor_rollout_wg.stop_profile() if self.use_reference_policy: self.ref_policy_wg.stop_profile() if self.use_critic: self.critic_wg.stop_profile() def _get_dp_size(self, worker_group, role: str) -> int: """Get data parallel size from worker group dispatch info. This method retrieves the data parallel size by querying the dispatch info for the specified role. The dispatch info is cached for subsequent calls. Args: worker_group: The worker group to query dispatch info from. role: The role name (e.g., "actor", "critic") to get DP size for. Returns: The data parallel size (number of DP ranks). """ if role not in worker_group._dispatch_info: dp_rank_mapping = worker_group._query_dispatch_info(role) worker_group._dispatch_info[role] = dp_rank_mapping else: dp_rank_mapping = worker_group._dispatch_info[role] return max(dp_rank_mapping) + 1 def _balance_batch(self, batch: DataProto, metrics, logging_prefix="global_seqlen", keep_minibatch=False): """Reorder the data on single controller such that each dp rank gets similar total tokens. When use_prefix_grouper is enabled, uses group-level balancing to keep samples with the same uid together on the same rank for prefix sharing optimization. """ attention_mask = batch.batch["attention_mask"] batch_size = attention_mask.shape[0] global_seqlen_lst = batch.batch["attention_mask"].view(batch_size, -1).sum(-1) # (train_batch_size,) workload_lst = calculate_workload(global_seqlen_lst) # Get dp_size from dispatch info to correctly balance across data parallel ranks # Note: world_size may include tensor/pipeline parallel dimensions, but we only want DP dp_size = self._get_dp_size(self.actor_rollout_wg, "actor") # Use group-level balancing for PrefixGrouper to keep same-uid samples together if getattr(self, "use_prefix_grouper", False) and "uid" in batch.non_tensor_batch: from verl.utils.seqlen_balancing import get_group_balanced_partitions uid_list = list(batch.non_tensor_batch["uid"]) seqlen_list = global_seqlen_lst.tolist() # Count number of uid groups num_groups = len(set(uid_list)) if num_groups % dp_size != 0: raise ValueError( f"PrefixGrouper with balance_batch requires num_uid_groups ({num_groups}) " f"% dp_size ({dp_size}) == 0. " f"This ensures each rank gets equal number of groups. " f"Current batch_size={batch_size}, adjust batch_size to be a multiple of " f"dp_size * rollout.n." ) global_partition_lst = get_group_balanced_partitions( seqlen_list=seqlen_list, uid_list=uid_list, k_partitions=dp_size, ) elif keep_minibatch: # Decouple the DP balancing and mini-batching. minibatch_size = self.config.actor_rollout_ref.actor.get("ppo_mini_batch_size") minibatch_num = len(workload_lst) // minibatch_size global_partition_lst = [[] for _ in range(dp_size)] for i in range(minibatch_num): rearrange_minibatch_lst = get_seqlen_balanced_partitions( workload_lst[i * minibatch_size : (i + 1) * minibatch_size], k_partitions=dp_size, equal_size=True, ) for j, part in enumerate(rearrange_minibatch_lst): global_partition_lst[j].extend([x + minibatch_size * i for x in part]) else: global_partition_lst = get_seqlen_balanced_partitions(workload_lst, k_partitions=dp_size, equal_size=True) # Place smaller micro-batches at both ends to reduce the bubbles in pipeline parallel. # Skip reordering within partitions for PrefixGrouper to maintain uid grouping if not getattr(self, "use_prefix_grouper", False): for idx, partition in enumerate(global_partition_lst): partition.sort(key=lambda x: (workload_lst[x], x)) ordered_partition = partition[::2] + partition[1::2][::-1] global_partition_lst[idx] = ordered_partition # reorder based on index. The data will be automatically equally partitioned by dispatch function global_idx = torch.tensor([j for partition in global_partition_lst for j in partition]) batch.reorder(global_idx) global_balance_stats = log_seqlen_unbalance( seqlen_list=global_seqlen_lst.tolist(), partitions=global_partition_lst, prefix=logging_prefix ) metrics.update(global_balance_stats) def _compute_values(self, batch: DataProto) -> DataProto: if self.use_legacy_worker_impl == "disable": batch_td = batch.to_tensordict() # step 2: convert from padding to nopadding batch_td = left_right_2_no_padding(batch_td) # step 3: add meta info tu.assign_non_tensor(batch_td, compute_loss=False) output = self.critic_wg.infer_batch(batch_td) output = output.get() values = tu.get(output, "values") values = no_padding_2_padding(values, batch_td) values = tu.get_tensordict({"values": values.float()}) values = DataProto.from_tensordict(values) else: values = self.critic_wg.compute_values(batch) return values def _compute_ref_log_prob(self, batch: DataProto) -> DataProto: if self.use_legacy_worker_impl == "disable": # step 1: convert dataproto to tensordict. batch_td = batch.to_tensordict() # step 2: convert from padding to nopadding batch_td = left_right_2_no_padding(batch_td) # step 3: add meta info metadata = {"calculate_entropy": False, "compute_loss": False} if self.ref_in_actor: metadata["no_lora_adapter"] = True tu.assign_non_tensor(batch_td, **metadata) if self.ref_in_actor: output = self.actor_rollout_wg.compute_log_prob(batch_td) else: output = self.ref_policy_wg.compute_ref_log_prob(batch_td) # gather output log_probs = tu.get(output, "log_probs") # step 4. No padding to padding log_probs = no_padding_2_padding(log_probs, batch_td) # step 5: rebuild a tensordict and convert to dataproto ref_log_prob = tu.get_tensordict({"ref_log_prob": log_probs.float()}) ref_log_prob = DataProto.from_tensordict(ref_log_prob) else: ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch) return ref_log_prob def _compute_old_log_prob(self, batch: DataProto): if self.use_legacy_worker_impl == "disable": # TODO: remove step 1, 2, 4 after we make the whole training tensordict and padding free # step 1: convert dataproto to tensordict. batch_td = batch.to_tensordict() # step 2: convert from padding to nopadding batch_td = left_right_2_no_padding(batch_td) # step 3: add meta info tu.assign_non_tensor(batch_td, calculate_entropy=True, compute_loss=False) output = self.actor_rollout_wg.compute_log_prob(batch_td) # gather output entropy = tu.get(output, "entropy") log_probs = tu.get(output, "log_probs") routed_experts = tu.get(output, "routed_experts") old_log_prob_mfu = tu.get(output, "metrics")["mfu"] # step 4. No padding to padding entropy = no_padding_2_padding(entropy, batch_td) log_probs = no_padding_2_padding(log_probs, batch_td) # step 5: rebuild a tensordict and convert to dataproto if routed_experts is not None: old_log_prob = tu.get_tensordict( {"old_log_probs": log_probs.float(), "entropys": entropy.float(), "routed_experts": routed_experts} ) else: old_log_prob = tu.get_tensordict({"old_log_probs": log_probs.float(), "entropys": entropy.float()}) old_log_prob = DataProto.from_tensordict(old_log_prob) else: old_log_prob = self.actor_rollout_wg.compute_log_prob(batch) old_log_prob_mfu = 0 return old_log_prob, old_log_prob_mfu def _update_actor(self, batch: DataProto) -> DataProto: rollout_config = self.config.actor_rollout_ref.rollout batch.meta_info["multi_turn"] = rollout_config.multi_turn.enable # TODO: Make "temperature" single source of truth from generation. batch.meta_info["temperature"] = rollout_config.temperature # update actor if self.use_legacy_worker_impl == "disable": batch_td = batch.to_tensordict() # step 2: convert from padding to no-padding batch_td = left_right_2_no_padding(batch_td) calculate_entropy = self.config.actor_rollout_ref.actor.entropy_coeff != 0.0 ppo_mini_batch_size = self.config.actor_rollout_ref.actor.ppo_mini_batch_size ppo_mini_batch_size = ppo_mini_batch_size * self.config.actor_rollout_ref.rollout.n ppo_epochs = self.config.actor_rollout_ref.actor.ppo_epochs seed = self.config.actor_rollout_ref.actor.data_loader_seed shuffle = self.config.actor_rollout_ref.actor.shuffle tu.assign_non_tensor( batch_td, calculate_entropy=calculate_entropy, global_batch_size=ppo_mini_batch_size, mini_batch_size=ppo_mini_batch_size, epochs=ppo_epochs, seed=seed, dataloader_kwargs={"shuffle": shuffle}, ) actor_output = self.actor_rollout_wg.update_actor(batch_td) actor_output = tu.get(actor_output, "metrics") actor_output = rename_dict(actor_output, "actor/") # modify key name actor_output["perf/mfu/actor"] = actor_output.pop("actor/mfu") actor_output = DataProto.from_single_dict(data={}, meta_info={"metrics": actor_output}) else: actor_output = self.actor_rollout_wg.update_actor(batch) return actor_output def _update_critic(self, batch: DataProto) -> DataProto: if self.use_legacy_worker_impl == "disable": batch_td = batch.to_tensordict() # step 2: convert from padding to no-padding batch_td = left_right_2_no_padding(batch_td) ppo_mini_batch_size = self.config.critic.ppo_mini_batch_size ppo_mini_batch_size = ppo_mini_batch_size * self.config.actor_rollout_ref.rollout.n ppo_epochs = self.config.critic.ppo_epochs seed = self.config.critic.data_loader_seed shuffle = self.config.critic.shuffle tu.assign_non_tensor( batch_td, global_batch_size=ppo_mini_batch_size, mini_batch_size=ppo_mini_batch_size, epochs=ppo_epochs, seed=seed, dataloader_kwargs={"shuffle": shuffle}, ) output = self.critic_wg.train_mini_batch(batch_td) output = output.get() output = tu.get(output, "metrics") output = rename_dict(output, "critic/") # modify key name output["perf/mfu/critic"] = output.pop("critic/mfu") critic_output = DataProto.from_single_dict(data={}, meta_info={"metrics": output}) else: critic_output = self.critic_wg.update_critic(batch) return critic_output def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ from omegaconf import OmegaConf from verl.utils.tracking import Tracking logger = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) self.global_steps = 0 # load checkpoint and update weights before doing anything self._load_checkpoint() self.checkpoint_manager.update_weights(self.global_steps) current_epoch = self.global_steps // len(self.train_dataloader) # perform validation before training # currently, we only support validation using the reward_function. if self.config.trainer.get("val_before_train", True): val_metrics = self._validate() assert val_metrics, f"{val_metrics=}" pprint(f"Initial validation metrics: {val_metrics}") logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get("val_only", False): return if self.config.actor_rollout_ref.rollout.get("skip_rollout", False): rollout_skip = RolloutSkip(self.config, self.async_rollout_manager) rollout_skip.wrap_generate_sequences() # add tqdm progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress") # we start from step 1 self.global_steps += 1 last_val_metrics = None self.max_steps_duration = 0 prev_step_profile = False curr_step_profile = ( self.global_steps in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) next_step_profile = False for epoch in range(current_epoch, self.config.trainer.total_epochs): for batch_dict in self.train_dataloader: if hasattr(self.actor_rollout_wg, "async_calls_finalize_fn_exec"): self.actor_rollout_wg.async_calls_finalize_fn_exec(blocking=False) metrics = {} timing_raw = {} with marked_timer("start_profile", timing_raw): self._start_profiling( not prev_step_profile and curr_step_profile if self.config.global_profiler.profile_continuous_steps else curr_step_profile ) batch: DataProto = DataProto.from_single_dict(batch_dict) batch.meta_info["temperature"] = self.config.actor_rollout_ref.rollout.temperature # add uid to batch batch.non_tensor_batch["uid"] = np.array( [str(uuid.uuid4()) for _ in range(len(batch.batch))], dtype=object ) gen_batch = self._get_gen_batch(batch) # pass global_steps to trace gen_batch.meta_info["global_steps"] = self.global_steps gen_batch_output = gen_batch.repeat( repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True ) is_last_step = self.global_steps >= self.total_training_steps with marked_timer("step", timing_raw): # generate a batch with marked_timer("gen", timing_raw, color="red"): if curr_step_profile: self.async_rollout_manager.start_profile() gen_batch_output = self.async_rollout_manager.generate_sequences(gen_batch_output) self.checkpoint_manager.sleep_replicas() if curr_step_profile: self.async_rollout_manager.stop_profile() timing_raw.update(gen_batch_output.meta_info["timing"]) gen_batch_output.meta_info.pop("timing", None) if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX: with marked_timer("gen_max", timing_raw, color="purple"): gen_baseline_batch = deepcopy(gen_batch) gen_baseline_batch.meta_info["do_sample"] = False if curr_step_profile: self.async_rollout_manager.start_profile() gen_baseline_output = self.async_rollout_manager.generate_sequences(gen_baseline_batch) self.checkpoint_manager.sleep_replicas() if curr_step_profile: self.async_rollout_manager.stop_profile() batch = batch.union(gen_baseline_output) # compute reward model score on batch rm_scores = None if self.use_rm and "rm_scores" not in batch.batch.keys(): batch_reward = self._compute_reward_colocate(batch) batch = batch.union(batch_reward) # Compute or extract reward for REMAX baseline reward_baseline_tensor = batch.batch["rm_scores"].sum(dim=-1) keys_to_pop = set(gen_baseline_output.batch.keys()) if rm_scores is not None: keys_to_pop.update(rm_scores.batch.keys()) batch.pop(batch_keys=list(keys_to_pop)) batch.batch["reward_baselines"] = reward_baseline_tensor del rm_scores, gen_baseline_batch, gen_baseline_output # repeat to align with repeated responses in rollout batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) batch = batch.union(gen_batch_output) if "response_mask" not in batch.batch.keys(): batch.batch["response_mask"] = compute_response_mask(batch) # Balance the number of valid tokens across DP ranks. # NOTE: This usually changes the order of data in the `batch`, # which won't affect the advantage calculation (since it's based on uid), # but might affect the loss calculation (due to the change of mini-batching). if self.config.trainer.balance_batch: self._balance_batch(batch, metrics=metrics) # compute global_valid tokens batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist() # get images_seqlens images_seqlens_all = [] for multi_modal_input in batch.non_tensor_batch["multi_modal_inputs"]: if "image_grid_thw" not in multi_modal_input.keys(): continue images_seqlens_all.extend(multi_modal_input["images_seqlens"].tolist()) batch.meta_info["images_seqlens"] = images_seqlens_all with marked_timer("reward", timing_raw, color="yellow"): # compute reward model score if self.use_rm and "rm_scores" not in batch.batch.keys(): batch_reward = self._compute_reward_colocate(batch) batch = batch.union(batch_reward) # extract reward_tensor and reward_extra_infos_dict for training reward_tensor, reward_extra_infos_dict = extract_reward(batch) # Operating Mode Selection: # - Bypass mode: Sets old_log_probs = rollout_log_probs (2 policies: π_rollout, π_θ) # - Decoupled mode: Recomputes old_log_probs as proximal anchor (3 policies: π_rollout, π_old, π_θ) # Note: π_old computed once per data batch, serves as stable reference during mini-batch updates rollout_corr_config = self.config.algorithm.get("rollout_correction", None) bypass_recomputing_logprobs = rollout_corr_config and rollout_corr_config.get("bypass_mode", False) if bypass_recomputing_logprobs: # Use `rollout_log_probs` from verl.trainer.ppo.rollout_corr_helper import apply_bypass_mode apply_bypass_mode( batch=batch, rollout_corr_config=rollout_corr_config, policy_loss_config=self.config.actor_rollout_ref.actor.policy_loss, ) else: # Recompute old_log_probs with marked_timer("old_log_prob", timing_raw, color="blue"): old_log_prob, old_log_prob_mfu = self._compute_old_log_prob(batch) entropys = old_log_prob.batch["entropys"] response_masks = batch.batch["response_mask"] actor_config = self.config.actor_rollout_ref.actor entropy_agg = agg_loss( loss_mat=entropys, loss_mask=response_masks, loss_agg_mode=actor_config.loss_agg_mode, loss_scale_factor=actor_config.loss_scale_factor, ) old_log_prob_metrics = { "actor/entropy": entropy_agg.detach().item(), "perf/mfu/actor_infer": old_log_prob_mfu, } metrics.update(old_log_prob_metrics) old_log_prob.batch.pop("entropys") if "routed_experts" in batch.batch and "routed_experts" in old_log_prob.batch: raise ValueError( "Detected conflicting router replay configuration: " "router_replay.mode='R2' and enable_rollout_routing_replay=True " "cannot be enabled simultaneously. " "The enable_rollout_routing_replay option is only used in R3 mode; " "it should not be set when using R2 mode." ) batch = batch.union(old_log_prob) if "rollout_log_probs" in batch.batch.keys(): # TODO: we may want to add diff of probs too. from verl.utils.debug.metrics import calculate_debug_metrics metrics.update(calculate_debug_metrics(batch)) assert "old_log_probs" in batch.batch, f'"old_log_prob" not in {batch.batch.keys()=}' if self.use_reference_policy: # compute reference log_prob with marked_timer(str(Role.RefPolicy), timing_raw, color="olive"): ref_log_prob = self._compute_ref_log_prob(batch) batch = batch.union(ref_log_prob) # compute values if self.use_critic: with marked_timer("values", timing_raw, color="cyan"): values = self._compute_values(batch) batch = batch.union(values) with marked_timer("adv", timing_raw, color="brown"): # we combine with rule-based rm reward_extra_infos_dict: dict[str, list] batch.batch["token_level_scores"] = reward_tensor if reward_extra_infos_dict: batch.non_tensor_batch.update({k: np.array(v) for k, v in reward_extra_infos_dict.items()}) # compute rewards. apply_kl_penalty if available if self.config.algorithm.use_kl_in_reward: batch, kl_metrics = apply_kl_penalty( batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty ) metrics.update(kl_metrics) else: batch.batch["token_level_rewards"] = batch.batch["token_level_scores"] # Compute rollout correction: IS weights, rejection sampling, and metrics # Only runs in decoupled mode (computes once per batch using stable π_old) # In bypass mode, this is skipped - actor computes metrics from evolving π_θ vs π_rollout if ( rollout_corr_config is not None and "rollout_log_probs" in batch.batch and not bypass_recomputing_logprobs # Only in decoupled mode ): from verl.trainer.ppo.rollout_corr_helper import compute_rollout_correction_and_add_to_batch # Compute IS weights, apply rejection sampling, compute metrics batch, is_metrics = compute_rollout_correction_and_add_to_batch(batch, rollout_corr_config) # IS and off-policy metrics already have rollout_corr/ prefix metrics.update(is_metrics) # compute advantages, executed on the driver process norm_adv_by_std_in_grpo = self.config.algorithm.get( "norm_adv_by_std_in_grpo", True ) # GRPO adv normalization factor batch = compute_advantage( batch, adv_estimator=self.config.algorithm.adv_estimator, gamma=self.config.algorithm.gamma, lam=self.config.algorithm.lam, num_repeat=self.config.actor_rollout_ref.rollout.n, norm_adv_by_std_in_grpo=norm_adv_by_std_in_grpo, config=self.config.algorithm, ) # update critic if self.use_critic: with marked_timer("update_critic", timing_raw, color="pink"): critic_output = self._update_critic(batch) critic_output_metrics = reduce_metrics(critic_output.meta_info["metrics"]) metrics.update(critic_output_metrics) # implement critic warmup if self.config.trainer.critic_warmup <= self.global_steps: # update actor with marked_timer("update_actor", timing_raw, color="red"): actor_output = self._update_actor(batch) # Check if the ESI (Elastic Server Instance)/training plan is close to expiration. esi_close_to_expiration = should_save_ckpt_esi( max_steps_duration=self.max_steps_duration, redundant_time=self.config.trainer.esi_redundant_time, ) # Check if the conditions for saving a checkpoint are met. # The conditions include a mandatory condition (1) and # one of the following optional conditions (2/3/4): # 1. The save frequency is set to a positive value. # 2. It's the last training step. # 3. The current step number is a multiple of the save frequency. # 4. The ESI(Elastic Server Instance)/training plan is close to expiration. if self.config.trainer.save_freq > 0 and ( is_last_step or self.global_steps % self.config.trainer.save_freq == 0 or esi_close_to_expiration ): if esi_close_to_expiration: print("Force saving checkpoint: ESI instance expiration approaching.") with marked_timer("save_checkpoint", timing_raw, color="green"): self._save_checkpoint() # update weights from trainer to rollout with marked_timer("update_weights", timing_raw, color="red"): self.checkpoint_manager.update_weights(self.global_steps) actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"]) metrics.update(actor_output_metrics) # Log rollout generations if enabled rollout_data_dir = self.config.trainer.get("rollout_data_dir", None) if rollout_data_dir: self._log_rollout_data(batch, reward_extra_infos_dict, timing_raw, rollout_data_dir) # validate if self.config.trainer.test_freq > 0 and ( is_last_step or self.global_steps % self.config.trainer.test_freq == 0 ): with marked_timer("testing", timing_raw, color="green"): val_metrics: dict = self._validate() if is_last_step: last_val_metrics = val_metrics metrics.update(val_metrics) with marked_timer("stop_profile", timing_raw): next_step_profile = ( self.global_steps + 1 in self.config.global_profiler.steps if self.config.global_profiler.steps is not None else False ) self._stop_profiling( curr_step_profile and not next_step_profile if self.config.global_profiler.profile_continuous_steps else curr_step_profile ) prev_step_profile = curr_step_profile curr_step_profile = next_step_profile steps_duration = timing_raw["step"] self.max_steps_duration = max(self.max_steps_duration, steps_duration) # training metrics metrics.update( { "training/global_step": self.global_steps, "training/epoch": epoch, } ) # collect metrics metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic)) # GDPO per-component reward metrics gdpo_reward_keys = self.config.algorithm.get("gdpo_reward_keys", None) if gdpo_reward_keys and self.config.algorithm.adv_estimator in ("gdpo", AdvantageEstimator.GDPO): for key in gdpo_reward_keys: if key in batch.non_tensor_batch: vals = np.asarray(batch.non_tensor_batch[key], dtype=np.float32) metrics[f"gdpo/{key}/mean"] = float(np.mean(vals)) metrics[f"gdpo/{key}/std"] = float(np.std(vals)) metrics[f"gdpo/{key}/max"] = float(np.max(vals)) metrics[f"gdpo/{key}/min"] = float(np.min(vals)) metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw)) # TODO: implement actual tflpo and theoretical tflpo n_gpus = self.resource_pool_manager.get_n_gpus() metrics.update(compute_throughout_metrics(batch=batch, timing_raw=timing_raw, n_gpus=n_gpus)) # compute variance proxy metrics gradient_norm = metrics.get("actor/grad_norm", None) metrics.update(compute_variance_proxy_metrics(batch=batch, gradient_norm=gradient_norm)) # Note: mismatch metrics (KL, PPL, etc.) are collected at line 1179 after advantage computation # this is experimental and may be changed/removed in the future in favor of a general-purpose one if isinstance(self.train_dataloader.sampler, AbstractCurriculumSampler): self.train_dataloader.sampler.update(batch=batch) # TODO: make a canonical logger that supports various backend logger.log(data=metrics, step=self.global_steps) progress_bar.update(1) self.global_steps += 1 if ( hasattr(self.config.actor_rollout_ref.actor, "profiler") and self.config.actor_rollout_ref.actor.profiler.tool == "torch_memory" ): self.actor_rollout_wg.dump_memory_snapshot( tag=f"post_update_step{self.global_steps}", sub_dir=f"step{self.global_steps}" ) if is_last_step: if hasattr(self.actor_rollout_wg, "async_calls_finalize_fn_exec"): self.actor_rollout_wg.async_calls_finalize_fn_exec(blocking=True) pprint(f"Final validation metrics: {last_val_metrics}") progress_bar.close() return # this is experimental and may be changed/removed in the future # in favor of a general-purpose data buffer pool if hasattr(self.train_dataset, "on_batch_end"): # The dataset may be changed after each training batch self.train_dataset.on_batch_end(batch=batch) ================================================ FILE: verl/trainer/ppo/reward.py ================================================ # Copyright 2025 Individual Contributor: Thibaut Barroyer # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import inspect import multiprocessing from functools import partial from typing import TYPE_CHECKING, Any, Optional, cast from verl import DataProto from verl.utils.reward_score import default_compute_score if TYPE_CHECKING: from omegaconf import DictConfig from verl.experimental.reward_loop.reward_manager.base import RawRewardFn, RewardManagerBase from verl.trainer.config.config import ModuleConfig from verl.workers.config.reward import RewardManagerConfig def _call_with_kwargs(raw_fn, extra_kwargs, *args, **kwargs): """Calls `raw_fn` by merging `extra_kwargs` into call-time `kwargs`, with `extra_kwargs` taking precedence. This function is used to merge additional keyword arguments with the original function's arguments. """ merged_kwargs = {**kwargs, **extra_kwargs} return raw_fn(*args, **merged_kwargs) async def _call_with_kwargs_async(raw_fn, extra_kwargs, *args, **kwargs): """Calls `raw_fn` by merging `extra_kwargs` into call-time `kwargs`, with `extra_kwargs` taking precedence. This function is used to merge additional keyword arguments with the original function's arguments. """ merged_kwargs = {**kwargs, **extra_kwargs} return await raw_fn(*args, **merged_kwargs) def get_custom_reward_fn(config: DictConfig) -> Optional[RawRewardFn]: """Load and return a custom reward function from external file. Dynamically imports a reward function from a specified file path and wraps it with additional keyword arguments from the configuration. Args: config (dict): Configuration dictionary containing custom_reward_function settings with 'path', 'name', and 'reward_kwargs' fields. Returns: callable or None: Wrapped reward function with merged kwargs, or None if no custom reward function is configured. Raises: FileNotFoundError: If the specified reward function file doesn't exist. RuntimeError: If there's an error loading the module from file. AttributeError: If the specified function name isn't found in the module. """ reward_fn_config = config.reward.get("custom_reward_function") or {} module_path = reward_fn_config.get("path") if not module_path: return None fn_name = reward_fn_config.get("name") assert fn_name is not None from verl.utils.import_utils import load_extern_object raw_fn = load_extern_object(module_path=module_path, object_name=fn_name) reward_kwargs = dict(reward_fn_config.get("reward_kwargs", {})) if not inspect.iscoroutinefunction(raw_fn): return partial(_call_with_kwargs, raw_fn, reward_kwargs) else: return partial(_call_with_kwargs_async, raw_fn, reward_kwargs) def load_reward_manager(config: DictConfig, tokenizer: Any, **reward_kwargs: Any) -> RewardManagerBase: """ Load and initialize a reward manager based on the configuration. Args: config: PPO trainer configuration object containing reward_model fields. tokenizer: Tokenizer object used for processing text. **reward_kwargs: Additional keyword arguments for the reward manager. Returns: An instance of the specified reward manager class. """ # Try to get a custom reward function based on the configuration # user defined reward manager can be registered in custom_reward_fn compute_score = get_custom_reward_fn(config) final_compute_score = compute_score reward_manager_cfg: RewardManagerConfig = config.reward.reward_manager reward_manager_cls: type[RewardManagerBase] if reward_manager_cfg.source == "register": from verl.experimental.reward_loop.reward_manager import get_reward_manager_cls reward_manager_cls = get_reward_manager_cls(reward_manager_cfg.name) elif reward_manager_cfg.source == "importlib": from verl.utils.import_utils import load_extern_object module_cfg: ModuleConfig | None = reward_manager_cfg.module assert module_cfg is not None and module_cfg.path is not None, ( f"Module path is required when {reward_manager_cfg.source=}, but got {module_cfg=}" ) reward_manager_cls_name = reward_manager_cfg.name reward_manager_cls = cast( "type[RewardManagerBase]", load_extern_object(module_path=module_cfg.path, object_name=reward_manager_cls_name), ) if compute_score is None: sandbox_config = config.reward.get("sandbox_fusion") sandbox_url = sandbox_config.get("url") if sandbox_config else None memory_limit_mb = sandbox_config.get("memory_limit_mb", 1024) if sandbox_config else 1024 if sandbox_url: sandbox_manager = multiprocessing.Manager() # Create a semaphore to control concurrent access to the sandbox _concurrent_semaphore = sandbox_manager.Semaphore(sandbox_config.get("max_concurrent", 64)) final_compute_score = partial( default_compute_score, sandbox_fusion_url=sandbox_url, concurrent_semaphore=_concurrent_semaphore, memory_limit_mb=memory_limit_mb, ) else: final_compute_score = default_compute_score # Instantiate and return the reward manager with the specified parameters return reward_manager_cls( config=config, tokenizer=tokenizer, compute_score=final_compute_score, **reward_kwargs, ) def extract_reward(batch: DataProto): """ Extract reward tensor and extra info from batch data. """ reward_tensor = batch.batch["rm_scores"] reward_extra_keys = batch.meta_info.get("reward_extra_keys", []) reward_extra_infos_dict = {key: batch.non_tensor_batch[key] for key in reward_extra_keys} return reward_tensor, reward_extra_infos_dict ================================================ FILE: verl/trainer/ppo/rollout_corr_helper.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Rollout Correction Helper Module This module provides a complete pipeline to address **off-policy issues** in RL training, including: 1. Policy mismatch between rollout and training implementations (e.g., vLLM BFloat16 vs FSDP FP32) 2. Model update staleness (training on trajectories from older checkpoints) 3. General distribution shifts between data collection and training Its core capabilities include computing importance sampling (IS) weights, filtering outlier samples via rejection sampling (RS), and tracking metrics to diagnose and correct off-policy issues. ## Core Capabilities 1. **Multi-Granularity Aggregation**: - Importance Sampling (IS): Token-level Sequence-level - Rejection Sampling (RS): Divergence-based filters (token_k*, seq_sum_k*, seq_mean_k*, seq_max_k*) 2. **Memory-Efficient Design**: - Log-space computations to avoid numerical overflow/underflow. - Fixed safety bounds (exp(±20)) for stable exponentiation. - Metrics calculated without large intermediate tensors (prevents CUDA OOM). 3. **Comprehensive Metrics Tracking**: - IS/RS statistics (mean/max/min, effective sample size ESS, rejection rate). - Off-policy diagnostics (KL divergence, perplexity PPL, log PPL difference, χ² divergence). - Sequence-level breakdowns (deviation from ideal weights, outlier fraction). ## Key Interfaces & Usage - compute_rollout_correction_and_rejection_mask(): compute IS weights + rejection mask. - compute_rollout_correction_weights(): only compute truncated IS weights (for variance reduction, no outlier rejection). - compute_rollout_rejection_mask(): only filter outliers (for sample cleaning, no IS weight computation). - compute_offpolicy_metrics(): called by core functions to calculate off-policy diagnostics (KL/PPL/χ²) — no direct external calls needed. ### Integration Notes - Used in `ray_trainer.py` via `compute_rollout_correction_and_add_to_batch()` (batch training pipeline). - Used in `dp_actor.py` for distributed worker computations (distributed training scenarios). - All functions support batch inputs and valid token masking (via `response_mask`). ## References - "When Speed Kills Stability: Demystifying RL Collapse from the Training-Inference Mismatch": https://richardli.xyz/rl-collapse - Off-policy RL (theoretical basis for IS): https://fengyao.notion.site/off-policy-rl """ import math from typing import Any, Optional import torch import verl.utils.torch_functional as verl_F from verl.protocol import DataProto from verl.trainer.config.algorithm import RolloutCorrectionConfig from verl.workers.config.actor import PolicyLossConfig # Safety bound to prevent numerical overflow/underflow when exponentiating # exp(20) ≈ 485 million (upper limit for stable weights), exp(-20) ≈ 2e-9 (lower limit) SAFETY_BOUND = 20.0 SUPPORTED_ROLLOUT_RS_OPTIONS: set[str] = { "token_k1", "token_k2", "token_k3", "seq_sum_k1", "seq_sum_k2", "seq_sum_k3", "seq_mean_k1", "seq_mean_k2", "seq_mean_k3", "seq_max_k2", "seq_max_k3", } TOKEN_LEVEL_ROLLOUT_RS_OPTIONS: set[str] = {"token_k1", "token_k2", "token_k3"} def _parse_rollout_rs_thresholds( options: list[str], threshold_spec: Optional[str | float] ) -> dict[str, dict[str, Optional[float]]]: if threshold_spec is None: raise ValueError("rollout_rs_threshold must be provided for rejection sampling.") if isinstance(threshold_spec, int | float): raw_specs: list[str] = [str(threshold_spec)] elif isinstance(threshold_spec, str): raw_specs = [part.strip() for part in threshold_spec.split(",") if part.strip()] else: raise TypeError("rollout_rs_threshold must be a string or numeric value specifying per-option thresholds.") if not raw_specs: raise ValueError("rollout_rs_threshold must contain at least one threshold value.") if len(raw_specs) not in (1, len(options)): raise ValueError( f"rollout_rs_threshold expects either one threshold shared by all options or exactly " f"{len(options)} thresholds to match the provided rollout_rs options." ) if len(raw_specs) == 1 and len(options) > 1: raw_specs = raw_specs * len(options) thresholds: dict[str, dict[str, Optional[float]]] = {} for option, spec in zip(options, raw_specs, strict=False): if option.endswith("k1"): if "_" in spec: lower_str, upper_str = spec.split("_", 1) else: upper_str = spec lower_str = str(1.0 / float(upper_str)) try: lower = float(lower_str) upper = float(upper_str) except ValueError as exc: raise ValueError(f"Invalid numeric threshold '{spec}' for option '{option}'.") from exc if lower <= 0 or upper <= 0: raise ValueError(f"Thresholds for option '{option}' must be positive, got {spec}.") thresholds[option] = { "lower": lower, "upper": upper, } else: if "_" in spec: raise ValueError( f"rollout_rs_threshold for option '{option}' must provide a single upper bound " f"without '_'. Received '{spec}'." ) try: upper = float(spec) except ValueError as exc: raise ValueError(f"Invalid numeric threshold '{spec}' for option '{option}'.") from exc if upper <= 0: raise ValueError(f"Threshold for option '{option}' must be positive, got {spec}.") thresholds[option] = { "lower": None, "upper": upper, } return thresholds def compute_rollout_rejection_mask( log_ratio: torch.Tensor, response_mask: torch.Tensor, rollout_rs: str = "token_k1", rollout_rs_threshold: Optional[str | float] = None, ) -> tuple[torch.Tensor, dict[str, float]]: """Compute hard trust region mask using divergence estimators. This function enforces a hard trust region constraint by masking tokens/sequences where the estimated divergence (between training and rollout policies) exceeds a threshold. Unlike PPO's soft clipping, this provides a hard boundary. Multiple rejection criteria can be supplied via a comma separated `rollout_rs` string. All requested options must pass for a token/sequence to remain valid. Supported KL divergence-based modes (ideal = 0.0 unless noted): - "token_k{1,2,3}": Token-level divergences. - "seq_sum_k{1,2,3}": Sum of token divergences per sequence. - "seq_mean_k{1,2,3}": Mean of token divergences per sequence. - "seq_max_k{2,3}": Maximum token divergence per sequence. Args: log_ratio: Log ratio of training policy probability to rollout policy probability, shape (batch_size, seq_length). response_mask: Binary mask for valid tokens (1=valid, 0=padding), shape (batch_size, seq_length). rollout_rs: Comma separated rejection sampling options (e.g. "token_k1,seq_sum_k3"). rollout_rs_threshold: Threshold specification string (required). Provide one entry per rollout_rs option separated by commas. Each entry must be a positive number. For K1-style options (``*k1``), specify ``lower_upper`` (e.g. ``"0.1_1.2"``) to denote lower/upper ratio bounds; other options accept a single upper bound. Returns: Tuple containing: modified_response_mask: Response mask with trust region violations masked (0=rejected), shape (batch_size, seq_length). metrics: Dictionary of trust region metrics (all scalars). """ if rollout_rs is None or not isinstance(rollout_rs, str): raise ValueError("rollout_rs must be a non-empty string (comma separated for multiple options).") if rollout_rs_threshold is None: raise ValueError("rollout_rs_threshold must be provided for rejection sampling.") if log_ratio.shape[0] == 0: return response_mask, {} # rollout_rs supports chained criteria via comma separation (e.g. "token_k1,seq_mean_k3"). # Every listed option must pass; combined_mask aggregates them via logical AND. option_modes = [opt.strip() for opt in rollout_rs.split(",") if opt.strip()] if not option_modes: raise ValueError("rollout_rs must contain at least one valid option.") normalized_options: list[str] = [] seen: set[str] = set() for opt in option_modes: if opt not in SUPPORTED_ROLLOUT_RS_OPTIONS: raise ValueError( f"Invalid rollout_rs option: {opt}. Must be one of {sorted(SUPPORTED_ROLLOUT_RS_OPTIONS)}." ) if opt not in seen: normalized_options.append(opt) seen.add(opt) threshold_specs = _parse_rollout_rs_thresholds(normalized_options, rollout_rs_threshold) log_ratio_safe: torch.Tensor = torch.clamp(log_ratio, min=-SAFETY_BOUND, max=SAFETY_BOUND) token_k1: torch.Tensor = -log_ratio_safe token_k2: torch.Tensor = 0.5 * log_ratio_safe**2 token_k3: torch.Tensor = torch.exp(log_ratio_safe) - 1.0 - log_ratio_safe response_mask_bool: torch.Tensor = response_mask.bool() seq_valid_mask: torch.Tensor = response_mask.sum(dim=-1) > 0 # combined_mask accumulates per-option passes; any failure flips tokens to 0. combined_mask: torch.Tensor = torch.ones_like(response_mask, dtype=log_ratio.dtype) metrics: dict[str, float] = {} def _sequence_sum(values: torch.Tensor) -> torch.Tensor: return verl_F.masked_sum(values, response_mask, axis=-1) def _sequence_mean(values: torch.Tensor) -> torch.Tensor: return verl_F.masked_mean(values, response_mask, axis=-1) def _sequence_max(values: torch.Tensor) -> torch.Tensor: mask_bool = response_mask.bool() neg_inf = torch.tensor(float("-inf"), device=values.device, dtype=values.dtype) masked_values = values.masked_fill(~mask_bool, neg_inf) max_values = masked_values.max(dim=-1).values return torch.where(max_values == neg_inf, torch.zeros_like(max_values), max_values) for option_name in normalized_options: thresholds_info = threshold_specs[option_name] is_k1_option = option_name.endswith("k1") upper_value = thresholds_info["upper"] lower_value = thresholds_info["lower"] apply_lower_threshold = is_k1_option lower_log: Optional[float] = None upper_log: Optional[float] = None if is_k1_option: if lower_value is None or upper_value is None: raise ValueError( f"rollout_rs_threshold for option '{option_name}' must specify both lower and upper bounds." ) lower_log = math.log(lower_value) upper_log = math.log(upper_value) else: if upper_value is None: raise ValueError(f"rollout_rs_threshold for option '{option_name}' must specify an upper bound.") level = "sequence" if option_name not in TOKEN_LEVEL_ROLLOUT_RS_OPTIONS else "token" per_token_stat: torch.Tensor per_sequence_stat: Optional[torch.Tensor] = None token_keep_bool: torch.Tensor if option_name == "token_k1": if lower_log is None: raise ValueError("Threshold specification for token_k1 must include lower and upper bounds.") per_token_stat = token_k1 token_keep_bool = (per_token_stat >= lower_log) & (per_token_stat <= upper_log) elif option_name == "token_k2": per_token_stat = token_k2 token_keep_bool = per_token_stat <= upper_value elif option_name == "token_k3": per_token_stat = token_k3 token_keep_bool = per_token_stat <= upper_value elif option_name.startswith("seq_sum"): if option_name.endswith("k1"): if lower_log is None: raise ValueError( f"Threshold specification for option '{option_name}' must include lower and upper bounds." ) seq_stat = _sequence_sum(token_k1) seq_keep_bool_direct = (seq_stat >= lower_log) & (seq_stat <= upper_log) elif option_name.endswith("k2"): seq_stat = _sequence_sum(token_k2) seq_keep_bool_direct = seq_stat <= upper_value elif option_name.endswith("k3"): seq_stat = _sequence_sum(token_k3) seq_keep_bool_direct = seq_stat <= upper_value else: raise ValueError(f"Unsupported rollout_rs option: {option_name}.") per_sequence_stat = seq_stat token_keep_bool = seq_keep_bool_direct.unsqueeze(-1).expand_as(response_mask_bool) per_token_stat = seq_stat.unsqueeze(-1).expand_as(response_mask) elif option_name.startswith("seq_mean"): if option_name.endswith("k1"): if lower_log is None: raise ValueError( f"Threshold specification for option '{option_name}' must include lower and upper bounds." ) seq_stat = _sequence_mean(token_k1) seq_keep_bool_direct = (seq_stat >= lower_log) & (seq_stat <= upper_log) elif option_name.endswith("k2"): seq_stat = _sequence_mean(token_k2) seq_keep_bool_direct = seq_stat <= upper_value elif option_name.endswith("k3"): seq_stat = _sequence_mean(token_k3) seq_keep_bool_direct = seq_stat <= upper_value else: raise ValueError(f"Unsupported rollout_rs option: {option_name}.") per_sequence_stat = seq_stat token_keep_bool = seq_keep_bool_direct.unsqueeze(-1).expand_as(response_mask_bool) per_token_stat = seq_stat.unsqueeze(-1).expand_as(response_mask) elif option_name.startswith("seq_max"): if option_name.endswith("k2"): seq_stat = _sequence_max(token_k2) seq_keep_bool_direct = seq_stat <= upper_value elif option_name.endswith("k3"): seq_stat = _sequence_max(token_k3) seq_keep_bool_direct = seq_stat <= upper_value else: raise ValueError(f"Unsupported rollout_rs option: {option_name}.") per_sequence_stat = seq_stat token_keep_bool = seq_keep_bool_direct.unsqueeze(-1).expand_as(response_mask_bool) per_token_stat = seq_stat.unsqueeze(-1).expand_as(response_mask) else: raise ValueError(f"Unsupported rollout_rs option: {option_name}.") metrics_upper_threshold = upper_log if is_k1_option else upper_value metrics_lower_threshold = lower_log if (is_k1_option and lower_log is not None) else 0.0 token_keep_mask = token_keep_bool.to(dtype=log_ratio.dtype) combined_mask = combined_mask * token_keep_mask seq_keep_bool_tensor = (~((~token_keep_bool) & response_mask_bool)).all(dim=-1) option_metrics = compute_rs_metrics( option_name=option_name, rs_statistic=per_token_stat, response_mask=response_mask, seq_valid_mask=seq_valid_mask, level=level, per_sequence_values=per_sequence_stat, rollout_rs_threshold=metrics_upper_threshold, rollout_rs_threshold_lower=metrics_lower_threshold, apply_lower_threshold=apply_lower_threshold, ) metrics.update(option_metrics) token_masked_fraction = verl_F.masked_mean(1 - token_keep_mask, response_mask).item() seq_valid_float = seq_valid_mask.float() if seq_valid_float.sum() > 0: seq_keep_float = seq_keep_bool_tensor.to(dtype=log_ratio.dtype) seq_masked_fraction = (((1.0 - seq_keep_float) * seq_valid_float).sum() / seq_valid_float.sum()).item() else: seq_masked_fraction = 0.0 metrics[f"rollout_rs_{option_name}_masked_fraction"] = token_masked_fraction metrics[f"rollout_rs_{option_name}_seq_masked_fraction"] = seq_masked_fraction final_mask = combined_mask metrics["rollout_rs_masked_fraction"] = verl_F.masked_mean(1 - final_mask, response_mask).item() final_keep_bool = (final_mask > 0.5) & response_mask_bool seq_has_masked: torch.Tensor = (~final_keep_bool & response_mask_bool).any(dim=-1) metrics["rollout_rs_seq_masked_fraction"] = seq_has_masked.float().mean().item() modified_response_mask: torch.Tensor = (response_mask * final_mask).to(dtype=response_mask.dtype) return modified_response_mask, metrics def compute_rs_metrics( option_name: str, rs_statistic: torch.Tensor, response_mask: torch.Tensor, seq_valid_mask: torch.Tensor, *, level: str, per_sequence_values: Optional[torch.Tensor], rollout_rs_threshold: float, rollout_rs_threshold_lower: float, apply_lower_threshold: bool, ) -> dict[str, float]: """Compute metrics for hard trust region enforcement (per-option). Args: option_name: Original option string supplied by the user. rs_statistic: Trust region statistic (per token) used for thresholding. response_mask: Binary mask for valid tokens (1=valid, 0=padding). seq_valid_mask: Boolean mask indicating sequences with at least one valid token. level: "token" or "sequence" describing aggregation level. per_sequence_values: Optional per-sequence statistic (same semantics as rs_statistic). rollout_rs_threshold: Upper threshold. rollout_rs_threshold_lower: Lower threshold (ignored if ``apply_lower_threshold`` is False). apply_lower_threshold: Whether to mask/log metrics for values below the lower threshold. """ if not response_mask.any(): raise ValueError("response_mask must contain at least one valid token (1).") metrics: dict[str, float] = {} prefix = f"rollout_rs_{option_name}" mask_bool: torch.Tensor = response_mask.bool() # Compute sequence statistics (used by several metrics). if per_sequence_values is not None: seq_values = per_sequence_values else: seq_values = verl_F.masked_mean(rs_statistic, response_mask, axis=-1) if seq_values.dim() > 1: seq_values = seq_values.squeeze(-1) seq_values_valid = seq_values[seq_valid_mask] # Mean of the statistic (always reported). metrics[f"{prefix}_mean"] = verl_F.masked_mean(rs_statistic, response_mask).item() # Max/min values. if level == "sequence" and seq_values_valid.numel() > 0: metrics[f"{prefix}_max"] = seq_values_valid.max().item() metrics[f"{prefix}_min"] = seq_values_valid.min().item() else: metrics[f"{prefix}_max"] = rs_statistic.masked_fill(~mask_bool, float("-inf")).max().item() metrics[f"{prefix}_min"] = rs_statistic.masked_fill(~mask_bool, float("inf")).min().item() # Fractions above/below the thresholds. if level == "sequence" and seq_values_valid.numel() > 0: fraction_high = (seq_values_valid > rollout_rs_threshold).float().mean().item() fraction_low = ( (seq_values_valid < rollout_rs_threshold_lower).float().mean().item() if apply_lower_threshold else 0.0 ) else: fraction_high = verl_F.masked_mean((rs_statistic > rollout_rs_threshold).float(), response_mask).item() fraction_low = ( verl_F.masked_mean((rs_statistic < rollout_rs_threshold_lower).float(), response_mask).item() if apply_lower_threshold else 0.0 ) metrics[f"{prefix}_fraction_high"] = fraction_high metrics[f"{prefix}_fraction_low"] = fraction_low # Standard deviation (clamped for stability). mask_count: torch.Tensor = response_mask.sum() if mask_count > 1: if apply_lower_threshold: clamp_min = rollout_rs_threshold_lower else: clamp_min = 0.0 stat_for_std: torch.Tensor = rs_statistic.clamp(min=clamp_min, max=rollout_rs_threshold) mean_clamped: torch.Tensor = verl_F.masked_mean(stat_for_std, response_mask) stat_var: torch.Tensor = verl_F.masked_mean(stat_for_std.square(), response_mask) - mean_clamped.square() metrics[f"{prefix}_std"] = torch.sqrt(torch.clamp(stat_var, min=0.0)).item() else: metrics[f"{prefix}_std"] = 0.0 # Sequence-level summary metrics. if seq_values_valid.numel() > 0: metrics[f"{prefix}_seq_mean"] = seq_values_valid.mean().item() metrics[f"{prefix}_seq_std"] = seq_values_valid.std().item() if seq_values_valid.numel() > 1 else 0.0 metrics[f"{prefix}_seq_max"] = seq_values_valid.max().item() metrics[f"{prefix}_seq_min"] = seq_values_valid.min().item() metrics[f"{prefix}_seq_max_deviation"] = (seq_values_valid - 0.0).abs().max().item() metrics[f"{prefix}_seq_fraction_high"] = (seq_values_valid > rollout_rs_threshold).float().mean().item() if apply_lower_threshold: metrics[f"{prefix}_seq_fraction_low"] = ( (seq_values_valid < rollout_rs_threshold_lower).float().mean().item() ) else: metrics[f"{prefix}_seq_mean"] = 0.0 metrics[f"{prefix}_seq_std"] = 0.0 metrics[f"{prefix}_seq_max"] = 0.0 metrics[f"{prefix}_seq_min"] = 0.0 metrics[f"{prefix}_seq_max_deviation"] = 0.0 metrics[f"{prefix}_seq_fraction_high"] = 0.0 metrics[f"{prefix}_seq_fraction_low"] = 0.0 return metrics def compute_rollout_correction_weights( log_ratio: torch.Tensor, response_mask: torch.Tensor, rollout_is: str = "token", rollout_is_threshold: float = 2.0, rollout_is_batch_normalize: bool = False, ) -> tuple[torch.Tensor, dict[str, float]]: """Compute importance sampling weights to correct for off-policy distribution shifts. This function calculates IS weights (π_train / π_rollout) using log ratios for numerical stability. It supports multiple aggregation levels and truncates extreme weights to prevent training instability. Key design: - Log-space computations to avoid overflow - Truncation of extreme weights (TIS: Truncated Importance Sampling) - Optional batch normalization (normalize to mean=1.0) - Metrics tracking for weight distribution analysis Args: log_ratio: Log ratio of training policy probability to rollout policy probability, shape (batch_size, seq_length). response_mask: Binary mask for valid tokens (1=valid, 0=padding), shape (batch_size, seq_length). rollout_is: IS weight aggregation level, must be one of: - "token": Per-token weights (biased, low variance) - "sequence": Per-sequence weight (product of tokens; unbiased, high variance) rollout_is_threshold: Upper threshold for truncating extreme weights (e.g., 2.0), default 2.0. rollout_is_batch_normalize: Whether to normalize IS weights to have mean=1.0 per batch, default False. Returns: Tuple containing: rollout_is_weights: Truncated IS weights (masked to zero for padding tokens), shape (batch_size, seq_length). If batch_normalize=True, normalized to mean=1.0. metrics: Dictionary of IS weight metrics (all scalars), including: - rollout_is_mean/max/min: Statistic of weights (before batch normalization) - rollout_is_eff_sample_size: Effective sample size (ESS) - rollout_is_seq_*: Sequence-level weight statistics - rollout_is_batch_norm_factor: Normalization factor (only if batch_normalize=True) """ # Validate input parameters valid_is_levels = {"token", "sequence"} if rollout_is not in valid_is_levels: raise ValueError(f"Invalid rollout_is: {rollout_is}. Must be one of {valid_is_levels}.") if rollout_is_threshold <= 0: raise ValueError(f"rollout_is_threshold must be positive, got {rollout_is_threshold}.") # Compute IS weights from log ratio (handles different aggregation levels) if rollout_is == "token": # Per-token IS weight: exp(log(π_train/π_rollout)) with safety clamp log_ratio_for_metrics: torch.Tensor = log_ratio log_ratio_safe: torch.Tensor = torch.clamp(log_ratio, min=-SAFETY_BOUND, max=SAFETY_BOUND) rollout_is_weights: torch.Tensor = torch.exp(log_ratio_safe) elif rollout_is == "sequence": # Sequence-level IS weight: product of token ratios (exp(sum(log ratios))) log_ratio_sum: torch.Tensor = verl_F.masked_sum(log_ratio, response_mask, axis=-1).unsqueeze( -1 ) # Shape: (batch_size, 1) log_ratio_for_metrics = log_ratio_sum log_ratio_sum_safe: torch.Tensor = torch.clamp(log_ratio_sum, min=-SAFETY_BOUND, max=SAFETY_BOUND) rollout_is_weights = torch.exp(log_ratio_sum_safe).expand_as(log_ratio) # Broadcast to sequence length else: raise ValueError(f"Unsupported rollout_is: {rollout_is}") # Zero out weights for padding tokens using response mask rollout_is_weights = rollout_is_weights * response_mask # Compute IS weight metrics (BEFORE truncation to get accurate fraction_high/low) metrics: dict[str, float] = compute_is_metrics( rollout_is_weights=rollout_is_weights, log_ratio_for_metrics=log_ratio_for_metrics, response_mask=response_mask, rollout_is=rollout_is, rollout_is_threshold=rollout_is_threshold, ) # Truncate extreme weights (TIS: Truncated Importance Sampling) rollout_is_weights = rollout_is_weights.clamp(max=rollout_is_threshold) # Detach weights to prevent gradient flow (mathematically required by IS theory) # IS weights change the measure, not the objective. See §3.2.2 in docs/algo/rollout_corr_math.md rollout_is_weights = rollout_is_weights.detach() # Apply batch normalization if requested if rollout_is_batch_normalize: # Compute mean based on aggregation level mask_float = response_mask.to(dtype=rollout_is_weights.dtype) if rollout_is == "token": # Token-level: normalize over all token weights if torch.distributed.is_available() and torch.distributed.is_initialized(): weights_mean = verl_F.distributed_masked_mean(rollout_is_weights, mask_float) else: weights_mean = verl_F.masked_mean(rollout_is_weights, response_mask) elif rollout_is == "sequence": # Sequence-level: normalize over sequence weights (one weight per sequence) # For each sequence, compute mean over valid tokens (they all have the same weight) # then average across sequences seq_weights = verl_F.masked_mean(rollout_is_weights, response_mask, axis=-1) # (batch_size,) seq_mask = (response_mask.sum(dim=-1) > 0).to(dtype=rollout_is_weights.dtype) if torch.distributed.is_available() and torch.distributed.is_initialized(): weights_mean = verl_F.distributed_masked_mean(seq_weights, seq_mask) else: weights_mean = (seq_weights * seq_mask).sum() / seq_mask.sum().clamp_min(1e-8) else: raise ValueError(f"Unsupported rollout_is: {rollout_is}") # Normalize to mean=1.0 (avoid division by zero) if weights_mean > 1e-8: rollout_is_weights = rollout_is_weights / weights_mean metrics["rollout_is_batch_norm_factor"] = weights_mean.item() else: metrics["rollout_is_batch_norm_factor"] = 1.0 return rollout_is_weights, metrics def compute_is_metrics( rollout_is_weights: torch.Tensor, log_ratio_for_metrics: torch.Tensor, response_mask: torch.Tensor, rollout_is: str, rollout_is_threshold: float, ) -> dict[str, float]: """Compute comprehensive metrics for truncated importance sampling weights. This function calculates statistics for truncated IS weights (TIS), using log-space for accurate threshold checks and clamped weights for stable mean/std calculations. Args: rollout_is_weights: Truncated IS weights (π_train / π_rollout), shape (batch_size, seq_length). log_ratio_for_metrics: Log ratio of training to rollout probabilities (unclamped), shape varies by aggregation level. response_mask: Binary mask for valid tokens (1=valid, 0=padding), shape (batch_size, seq_length). rollout_is: IS weight aggregation level (matches compute_rollout_correction_weights). rollout_is_threshold: Upper threshold for truncated IS weights. Returns: Dictionary of IS weight metrics (all scalars). """ if not response_mask.any(): raise ValueError("response_mask must contain at least one valid token (1).") metrics: dict[str, float] = {} device: torch.device = rollout_is_weights.device # Default lower threshold (reciprocal of upper threshold) rollout_is_threshold_lower: float = 1.0 / rollout_is_threshold # Precompute log thresholds for accurate checks log_threshold_upper: torch.Tensor = torch.log(torch.tensor(rollout_is_threshold, device=device)) log_threshold_lower: torch.Tensor = torch.log(torch.tensor(rollout_is_threshold_lower, device=device)) # Compute metrics based on aggregation level if rollout_is == "sequence": # Sequence-level aggregation: use log-space for unclamped stats log_max: torch.Tensor = log_ratio_for_metrics.max() log_min: torch.Tensor = log_ratio_for_metrics.min() metrics["rollout_is_max"] = torch.exp(torch.clamp(log_max, max=SAFETY_BOUND)).item() metrics["rollout_is_min"] = torch.exp(log_min).item() # Mean uses truncated weights to avoid overflow metrics["rollout_is_mean"] = verl_F.masked_mean(rollout_is_weights, response_mask).item() # Fraction of weights exceeding thresholds (log-space for accuracy) exceeds_upper: torch.Tensor = log_ratio_for_metrics > log_threshold_upper below_lower: torch.Tensor = log_ratio_for_metrics < log_threshold_lower metrics["rollout_is_ratio_fraction_high"] = exceeds_upper.float().mean().item() metrics["rollout_is_ratio_fraction_low"] = below_lower.float().mean().item() else: # token-level # Token-level aggregation: compute directly from truncated weights metrics["rollout_is_mean"] = verl_F.masked_mean(rollout_is_weights, response_mask).item() # Fraction of tokens exceeding thresholds rollout_is_above_threshold: torch.Tensor = rollout_is_weights > rollout_is_threshold rollout_is_below_threshold: torch.Tensor = rollout_is_weights < rollout_is_threshold_lower metrics["rollout_is_ratio_fraction_high"] = verl_F.masked_mean( rollout_is_above_threshold.float(), response_mask ).item() metrics["rollout_is_ratio_fraction_low"] = verl_F.masked_mean( rollout_is_below_threshold.float(), response_mask ).item() # Max/min (mask out padding tokens) mask_bool: torch.Tensor = response_mask.bool() metrics["rollout_is_max"] = rollout_is_weights.masked_fill(~mask_bool, float("-inf")).max().item() metrics["rollout_is_min"] = rollout_is_weights.masked_fill(~mask_bool, float("inf")).min().item() # Compute standard deviation (using clamped weights for stability) mask_count: torch.Tensor = response_mask.sum() if mask_count > 1: weights_for_std: torch.Tensor = rollout_is_weights.clamp( min=rollout_is_threshold_lower, max=rollout_is_threshold ) mean_clamped: torch.Tensor = verl_F.masked_mean(weights_for_std, response_mask) rollout_is_var: torch.Tensor = ( verl_F.masked_mean(weights_for_std.square(), response_mask) - mean_clamped.square() ) metrics["rollout_is_std"] = torch.sqrt(torch.clamp(rollout_is_var, min=0.0)).item() else: metrics["rollout_is_std"] = 0.0 # Compute Effective Sample Size (ESS) for truncated weights weights_for_ess: torch.Tensor = rollout_is_weights.clamp(min=rollout_is_threshold_lower, max=rollout_is_threshold) mean_for_ess: torch.Tensor = verl_F.masked_mean(weights_for_ess, response_mask) is_weights_normalized: torch.Tensor = weights_for_ess / (mean_for_ess + 1e-8) # Avoid division by zero metrics["rollout_is_eff_sample_size"] = ( 1.0 / verl_F.masked_mean(is_weights_normalized.square(), response_mask).item() ) # Add sequence-level metrics if weights have batch dimension if rollout_is_weights.dim() > 1: seq_mean_weights: torch.Tensor = verl_F.masked_mean(rollout_is_weights, response_mask, axis=-1) metrics["rollout_is_seq_mean"] = seq_mean_weights.mean().item() metrics["rollout_is_seq_std"] = seq_mean_weights.std().item() if seq_mean_weights.numel() > 1 else 0.0 metrics["rollout_is_seq_max"] = seq_mean_weights.max().item() metrics["rollout_is_seq_min"] = seq_mean_weights.min().item() # Sequence deviation from ideal weight (1.0) seq_deviation: torch.Tensor = (seq_mean_weights - 1.0).abs() metrics["rollout_is_seq_max_deviation"] = seq_deviation.max().item() # Fraction of sequences with extreme weights metrics["rollout_is_seq_fraction_high"] = (seq_mean_weights > rollout_is_threshold).float().mean().item() metrics["rollout_is_seq_fraction_low"] = (seq_mean_weights < rollout_is_threshold_lower).float().mean().item() return metrics def compute_rollout_correction_and_rejection_mask( old_log_prob: torch.Tensor, rollout_log_prob: torch.Tensor, response_mask: torch.Tensor, rollout_is: Optional[str] = None, rollout_is_threshold: Optional[float] = 2.0, rollout_is_batch_normalize: bool = False, rollout_rs: Optional[str] = None, rollout_rs_threshold: Optional[str | float] = None, ) -> tuple[Optional[DataProto], torch.Tensor, dict[str, float]]: """Unified interface for computing IS weights and rejection masks. This function combines IS weight calculation (truncated) and rejection sampling (masked) into a single pipeline. Key design: - Separation of IS weights (for variance reduction) and rejection masks (for sample filtering) - Comprehensive metrics tracking for mismatch diagnosis Args: old_log_prob: Log probabilities from the training policy (e.g., FSDP FP32), shape (batch_size, seq_length). rollout_log_prob: Log probabilities from the rollout policy (e.g., vLLM BF16), shape (batch_size, seq_length). response_mask: Binary mask for valid tokens (1=valid, 0=padding), shape (batch_size, seq_length). rollout_is: IS weight aggregation level (see compute_rollout_correction_weights for options). Set to None to disable IS weight computation. rollout_is_threshold: Upper threshold for truncated IS weights (used if rollout_is is set), default 2.0. rollout_rs: Rejection sampling aggregation modes as a comma separated string (see compute_rollout_rejection_mask for the full list). Set to None to disable rejection sampling. rollout_rs_threshold: Threshold specification string (see compute_rollout_rejection_mask for details). Provide one threshold per option (comma separated). For K1-style options, specify ``lower_upper`` to denote the lower/upper ratio bounds. rollout_is_batch_normalize: Whether to normalize IS weights to have mean=1.0 per batch. Default: False. Returns: Tuple containing: rollout_is_weights_proto: DataProto with IS weights (None if rollout_is is None), key "rollout_is_weights", shape (batch_size, seq_length). modified_response_mask: Response mask with rejection sampling applied, shape (batch_size, seq_length). metrics: Dictionary of all metrics (prefixed with "rollout_corr/"), including: - IS weight statistics - Rejection sampling rates - Policy mismatch metrics (KL, PPL, etc.) """ # Validate input masks if not response_mask.any(): raise ValueError("response_mask must contain at least one valid token (1).") if old_log_prob.shape != rollout_log_prob.shape: raise ValueError( f"old_log_prob shape {old_log_prob.shape} does not match rollout_log_prob shape {rollout_log_prob.shape}." ) if old_log_prob.shape != response_mask.shape: raise ValueError( f"log_prob shape {old_log_prob.shape} does not match response_mask shape {response_mask.shape}." ) # Step 1: Compute log ratio (log(π_train / π_rollout)) log_ratio: torch.Tensor = old_log_prob - rollout_log_prob metrics: dict[str, float] = {} # Step 2: Compute IS weights (if enabled) rollout_is_weights: Optional[torch.Tensor] = None if rollout_is is not None and rollout_is_threshold is not None: rollout_is_weights, is_metrics = compute_rollout_correction_weights( log_ratio=log_ratio, response_mask=response_mask, rollout_is=rollout_is, rollout_is_threshold=rollout_is_threshold, rollout_is_batch_normalize=rollout_is_batch_normalize, ) metrics.update(is_metrics) # Step 3: Compute rejection mask (if enabled) modified_response_mask: torch.Tensor = response_mask.clone() if rollout_rs is not None: if rollout_rs_threshold is None: raise ValueError( "rollout_rs_threshold must be explicitly provided when rollout_rs is enabled. " "Set rollout_rs_threshold to the desired threshold value." ) modified_response_mask, rs_metrics = compute_rollout_rejection_mask( log_ratio=log_ratio, response_mask=response_mask, rollout_rs=rollout_rs, rollout_rs_threshold=rollout_rs_threshold, ) metrics.update(rs_metrics) # Step 4: Compute off-policy metrics (KL, PPL, χ², etc.) offpolicy_metrics: dict[str, float] = compute_offpolicy_metrics( old_log_prob=old_log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, ) metrics.update(offpolicy_metrics) # Step 6: Add "rollout_corr/" prefix to all metrics for logging consistency metrics_scalar: dict[str, float] = {} for key, value in metrics.items(): if isinstance(value, torch.Tensor): metrics_scalar[f"rollout_corr/{key}"] = value.item() else: metrics_scalar[f"rollout_corr/{key}"] = value # Step 7: Wrap IS weights in DataProto for consistency with API rollout_is_weights_proto: Optional[DataProto] = None if rollout_is_weights is not None: rollout_is_weights_proto = DataProto.from_dict(tensors={"rollout_is_weights": rollout_is_weights}) return rollout_is_weights_proto, modified_response_mask, metrics_scalar def compute_offpolicy_metrics( old_log_prob: torch.Tensor, rollout_log_prob: Optional[torch.Tensor], response_mask: torch.Tensor, ) -> dict[str, Any]: """Compute off-policy diagnostic metrics (helper function). This helper function operates on raw tensors and is used internally by: - compute_rollout_correction_and_rejection_mask() in this module (automatically included) - Tests (test_rollout_corr.py, test_rollout_corr_integration.py) These metrics help diagnose the off-policy gap between rollout and training policies, which can arise from: - Policy mismatch (e.g., vLLM BF16 vs FSDP FP32) - Model staleness (training on trajectories from older checkpoints) - General distribution shifts Key metrics: - kl: Direct KL divergence estimator KL(π_rollout || π_training) - k3_kl: K3 KL estimator for stability (more stable for small KL) - training_ppl: Perplexity of training policy - rollout_ppl: Perplexity of rollout policy - log_ppl_diff: Difference in log perplexities - ppl_ratio: Ratio of training PPL to rollout PPL - chi2_token: Token-level χ² divergence E[ρ²] - 1 - chi2_seq: Sequence-level χ² divergence E[(∏ρ_t)²] - 1 Args: old_log_prob: Log probabilities from training policy, shape (batch_size, seq_length) rollout_log_prob: Log probabilities from rollout policy, shape (batch_size, seq_length) response_mask: Mask for valid tokens, shape (batch_size, seq_length) Returns: Dictionary of off-policy metrics (without prefix) """ # Validate that we have at least one valid token assert response_mask.any(), "Expected at least one valid token in response_mask" metrics = {} # 1. Training policy perplexity (always available) # Formula: exp(-1/|T| * Σ log π_training(y_t|y_ tuple[DataProto, dict]: """Compute rollout correction weights and apply rejection sampling. Computes importance sampling weights to correct for off-policy issues between rollout and training policies. Applies rejection sampling by modifying response_mask. Always updates response_mask; conditionally adds IS weights. Key behavior: - response_mask: ALWAYS updated with rejection (RS exclusions removed from training) - rollout_is_weights: Added to batch ONLY if rollout_is parameter is set This separation ensures: - Rejection works independently of IS weight application - Metrics can be monitored before enabling IS weight correction Args: batch: DataProto with old_log_probs, rollout_log_probs, response_mask Returns: Tuple of (updated_batch, metrics): updated_batch: Batch with modified response_mask (always) and rollout_is_weights (if enabled) metrics: Dict of IS and off-policy metrics, all with "rollout_corr/" prefix Note: The implementation is copied from szrlee . """ # Get new API parameters directly from config rollout_is = rollout_corr_config.get("rollout_is", None) rollout_is_threshold = rollout_corr_config.get("rollout_is_threshold", 2.0) rollout_is_batch_normalize = rollout_corr_config.get("rollout_is_batch_normalize", False) rollout_rs = rollout_corr_config.get("rollout_rs", None) rollout_rs_threshold = rollout_corr_config.get("rollout_rs_threshold", None) # Compute IS weights and get modified response_mask rollout_is_weights, modified_response_mask, rollout_corr_metrics = compute_rollout_correction_and_rejection_mask( old_log_prob=batch.batch["old_log_probs"], rollout_log_prob=batch.batch["rollout_log_probs"], response_mask=batch.batch["response_mask"], rollout_is=rollout_is, rollout_is_threshold=rollout_is_threshold, rollout_is_batch_normalize=rollout_is_batch_normalize, rollout_rs=rollout_rs, rollout_rs_threshold=rollout_rs_threshold, ) # ALWAYS update response_mask with rejection applied batch.batch["response_mask"] = modified_response_mask # Add IS weights to batch if computed if rollout_is_weights is not None: batch = batch.union(rollout_is_weights) return batch, rollout_corr_metrics def compute_rollout_corr_metrics_from_logprobs( log_prob: torch.Tensor, rollout_log_prob: torch.Tensor, response_mask: torch.Tensor, ) -> dict[str, float]: """Compute rollout correction metrics from log probabilities during training. This function is used in the actor to compute metrics using the CURRENT policy log probabilities versus rollout log probabilities, allowing tracking of the off-policy gap as training progresses. It computes off-policy diagnostic metrics (KL, PPL, χ²) from log probabilities. Args: log_prob: Current policy log probabilities, shape (batch_size, seq_length) rollout_log_prob: Rollout policy log probabilities, shape (batch_size, seq_length) response_mask: Valid token mask, shape (batch_size, seq_length) Returns: Dictionary of metrics with "rollout_corr/" prefix """ # Compute off-policy diagnostic metrics offpolicy_metrics = compute_offpolicy_metrics( old_log_prob=log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, ) # Add rollout_corr/ prefix to all metrics metrics_with_prefix = {} for key, value in offpolicy_metrics.items(): if isinstance(value, torch.Tensor): metrics_with_prefix[f"rollout_corr/{key}"] = value.item() else: metrics_with_prefix[f"rollout_corr/{key}"] = value return metrics_with_prefix def apply_bypass_mode( batch: DataProto, rollout_corr_config: Optional[RolloutCorrectionConfig] = None, policy_loss_config: PolicyLossConfig = None, ) -> None: """ Setup bypass mode: Use rollout_log_probs as old_log_probs. Bypass mode skips expensive actor forward pass for old_log_prob computation by setting old_log_probs = rollout_log_probs (2 policies instead of 3). Uses compute_policy_loss_bypass_mode() which supports: - loss_type="ppo_clip" (default): PPO clipped objective (IS handled by ratio) - loss_type="reinforce": REINFORCE with explicit IS weights Both loss types benefit from rejection sampling (RS) which masks out-of-distribution samples. Note: The implementation is copied from szrlee . """ from omegaconf import open_dict if "rollout_log_probs" not in batch.batch: raise ValueError( "bypass_mode=True requires rollout_log_probs in batch. " "Ensure rollout worker is configured to calculate_log_probs=true." ) # Use rollout log probs as old log probs (zero-cost substitution) batch.batch["old_log_probs"] = batch.batch["rollout_log_probs"] with open_dict(policy_loss_config): # Pass rollout_correction config to actor for loss computation and metrics policy_loss_config["rollout_correction"] = rollout_corr_config # Always use bypass_mode loss function which handles both loss_types policy_loss_config["loss_mode"] = "bypass_mode" ================================================ FILE: verl/trainer/ppo/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from enum import Enum from omegaconf import DictConfig from verl.single_controller.base import Worker from verl.trainer.ppo.core_algos import AdvantageEstimator WorkerType = type[Worker] class Role(Enum): """ To create more roles dynamically, you can subclass Role and add new members """ Actor = 0 Rollout = 1 ActorRollout = 2 Critic = 3 RefPolicy = 4 RewardModel = 5 ActorRolloutRef = 6 Env = 7 def __str__(self): return self._get_role_string() def _get_role_string(self): role_mapping = { Role.Actor: "actor", Role.Rollout: "rollout", Role.ActorRollout: "actor_rollout", Role.Critic: "critic", Role.RefPolicy: "ref", Role.RewardModel: "rm", Role.ActorRolloutRef: "actor_rollout_ref", } return role_mapping.get(self, self.name.lower()) @classmethod def from_string(cls, name: str): string_mapping = { "actor": cls.Actor, "rollout": cls.Rollout, "actor_rollout": cls.ActorRollout, "critic": cls.Critic, "ref": cls.RefPolicy, "rm": cls.RewardModel, "actor_rollout_ref": cls.ActorRolloutRef, } role = string_mapping.get(name.lower()) if role is None: raise ValueError(f"No Role found for string: {name}") return role def need_reference_policy( config: DictConfig, ) -> bool: """Given the config, do we need ref policy.""" return config.algorithm.use_kl_in_reward or config.actor_rollout_ref.actor.use_kl_loss def need_reward_model( config: DictConfig, ) -> bool: """Given the config, do we need reward model.""" return config.reward.reward_model.enable def need_critic(config: DictConfig) -> bool: """Given a config, do we need critic.""" if config.critic.enable is not None: return bool(config.critic.enable) elif config.algorithm.adv_estimator == AdvantageEstimator.GAE: return True else: warnings.warn( "Disabled critic as algorithm.adv_estimator != gae. If it is not intended, please set critic.enable=True", stacklevel=2, ) return False ================================================ FILE: verl/trainer/runtime_env.yaml ================================================ working_dir: ./ excludes: ["/.git/"] env_vars: TORCH_NCCL_AVOID_RECORD_STREAMS: "1" CUDA_DEVICE_MAX_CONNECTIONS: "1" HCCL_HOST_SOCKET_PORT_RANGE: "auto" HCCL_NPU_SOCKET_PORT_RANGE: "auto" ================================================ FILE: verl/trainer/sft_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from functools import partial from tensordict.tensorclass import NonTensorData os.environ["NCCL_DEBUG"] = "WARN" os.environ["TOKENIZERS_PARALLELISM"] = "true" import logging import hydra import torch import torch.distributed from omegaconf import OmegaConf from torch.utils.data import DistributedSampler from torchdata.stateful_dataloader import StatefulDataLoader from tqdm import tqdm from verl.utils import tensordict_utils as tu from verl.utils.checkpoint import CheckpointHandler from verl.utils.dataset.dataset_utils import SFTTensorCollator from verl.utils.dataset.multiturn_sft_dataset import MultiTurnSFTDataset from verl.utils.device import auto_set_device, get_device_name from verl.utils.distributed import destroy_global_process_group from verl.utils.logger import log_with_rank from verl.utils.memory_utils import aggressive_empty_cache from verl.utils.profiler import log_gpu_memory_usage from verl.utils.tracking import Tracking from verl.workers.engine_workers import TrainingWorker logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_SFT_LOGGING_LEVEL", "WARN")) class SFTTrainer: def __init__( self, config, ): self.config = config log_gpu_memory_usage(f"rank {torch.distributed.get_rank()}: Before SFTTrainer init", logger=logger) self.rank = torch.distributed.get_rank() self._build_config() self._build_dataset() self._build_engine() self._build_dataloader() self._init_engine() self._build_ckpt_handler() # Initialize resume-related variables self.resume_global_step = self.ckpt_handler.load_checkpoint() self.device_name = self.config.trainer.device if self.rank == 0: print(self.config) log_gpu_memory_usage(f"rank {self.rank}: After SFTTrainer init", logger=logger) def _build_ckpt_handler(self): resume_mode = getattr(self.config.trainer, "resume_mode", "auto") resume_from_path = getattr(self.config.trainer, "resume_from_path", None) max_ckpt_to_keep = getattr(self.config.trainer, "max_ckpt_to_keep", None) default_hdfs_dir = getattr(self.config.trainer, "default_hdfs_dir", None) lora_train_meta = self._get_lora_train_meta() self.ckpt_handler = CheckpointHandler( engine=self.engine, train_dataloader=self.train_dataloader, default_local_dir=self.config.trainer.default_local_dir, max_ckpt_to_keep=max_ckpt_to_keep, default_hdfs_dir=default_hdfs_dir, resume_mode=resume_mode, resume_from_path=resume_from_path, lora_train_meta=lora_train_meta, ) def _get_lora_train_meta(self): lora_adapter_path = self.config.model.get("lora_adapter_path", None) lora_rank = int(getattr(self.config.model, "lora_rank", 0) or 0) if lora_adapter_path is None and lora_rank <= 0: return None raw_lora_alpha = self.config.model.get("lora_alpha", None) if raw_lora_alpha is None: log_with_rank( "LoRA is enabled but `model.lora_alpha` is not set; fallback to 0 in checkpoint metadata.", logger=logger, rank=self.rank, level=logging.WARNING, log_only_rank_0=True, ) lora_alpha = 0 else: lora_alpha = int(raw_lora_alpha) if lora_alpha == 0: log_with_rank( "LoRA is enabled but `model.lora_alpha` is 0; this may lead to ineffective LoRA scaling.", logger=logger, rank=self.rank, level=logging.WARNING, log_only_rank_0=True, ) task_type = self.config.model.get("task_type", None) if task_type is None: task_type = "CAUSAL_LM" return { "r": lora_rank, "lora_alpha": int(lora_alpha or 0), "task_type": str(task_type), } def _build_config(self): from verl.utils.config import omega_conf_to_dataclass self.model_config = omega_conf_to_dataclass(self.config.model) self.engine_config = omega_conf_to_dataclass(self.config.engine) self.optimizer_config = omega_conf_to_dataclass(self.config.optim) self.checkpoint_config = omega_conf_to_dataclass(self.config.checkpoint) self.profiler_config = omega_conf_to_dataclass(self.config.profiler) # check profile interval self.profiler_interval = self.config.trainer.profile_interval self._validate_profiler_interval() def _validate_profiler_interval(self): assert len(self.profiler_interval) == 2 self.start_profile_step = self.profiler_interval[0] self.end_profile_step = self.profiler_interval[1] assert self.end_profile_step >= self.start_profile_step if self.start_profile_step < 0: assert self.end_profile_step < 0 def _build_engine(self): from verl.workers.engine_workers import TrainingWorkerConfig from verl.workers.utils.losses import sft_loss self.loss_fn = partial(sft_loss, config=None) config = TrainingWorkerConfig( model_type="language_model", model_config=self.model_config, engine_config=self.engine_config, optimizer_config=self.optimizer_config, checkpoint_config=self.checkpoint_config, profiler_config=self.profiler_config, ) self.training_client = TrainingWorker(config=config) self.training_client.set_loss_fn(loss_fn=self.loss_fn) # Note that in SPMD world, this abstraction has to break self.engine = self.training_client.engine def _init_engine(self): # patch optimizer config if self.config.trainer.total_training_steps is not None: self.total_training_steps = self.config.trainer.total_training_steps else: self.total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs self.optimizer_config.total_training_steps = self.total_training_steps self.steps_per_epoch = len(self.train_dataloader) # manage save and test frequency self.save_freq = self.config.trainer.save_freq if self.save_freq == "after_each_epoch": self.save_freq = self.steps_per_epoch self.test_freq = self.config.trainer.test_freq if self.test_freq == "after_each_epoch": self.test_freq = self.steps_per_epoch self.training_client.reset() def _build_dataset(self): config = self.config tokenizer = self.model_config.tokenizer processor = self.model_config.processor train_dataset = create_sft_dataset( config.data.train_files, config.data, tokenizer, processor, max_samples=config.data.get("train_max_samples", -1), ) if config.data.val_files: val_dataset = create_sft_dataset( config.data.val_files, config.data, tokenizer, processor, max_samples=config.data.get("val_max_samples", -1), ) else: val_dataset = None self.train_dataset, self.val_dataset = train_dataset, val_dataset def _build_dataloader(self): # build dataset config = self.config # build dataloader # Use data parallel rank and size instead of global rank and world size # Set pin_memory_device when pin_memory is enabled. device_name = get_device_name() dp_rank = self.engine.get_data_parallel_rank() dp_size = self.engine.get_data_parallel_size() self.train_sampler = DistributedSampler( self.train_dataset, shuffle=True, num_replicas=dp_size, rank=dp_rank, drop_last=True ) self.global_batch_size = config.data.train_batch_size self.train_batch_size_per_dp = self.global_batch_size // dp_size self.collate_fn = SFTTensorCollator(config.data.pad_mode) self.train_dataloader = StatefulDataLoader( dataset=self.train_dataset, batch_size=self.train_batch_size_per_dp, sampler=self.train_sampler, collate_fn=self.collate_fn, num_workers=self.config.data.num_workers, pin_memory=False, drop_last=True, pin_memory_device=device_name, ) if self.val_dataset: self.val_sampler = DistributedSampler( self.val_dataset, shuffle=False, num_replicas=dp_size, rank=dp_rank, drop_last=True ) self.val_dataloader = StatefulDataLoader( dataset=self.val_dataset, batch_size=self.train_batch_size_per_dp, sampler=self.val_sampler, collate_fn=self.collate_fn, num_workers=self.config.data.num_workers, pin_memory=False, drop_last=True, pin_memory_device=device_name, ) else: self.val_dataloader = None def _get_batch_seqlens(self, data): # mean over dp group is_nested = data["input_ids"].is_nested if is_nested: batch_seqlens: torch.Tensor = data["input_ids"].offsets().diff() else: batch_seqlens: torch.Tensor = data["attention_mask"].sum(dim=-1) batch_seqlens = batch_seqlens.to(self.device_name) # (global_bsz // dp) dp_group = self.engine.get_data_parallel_group() dp_size = self.engine.get_data_parallel_size() if dp_size == 1 or dp_group is None: return batch_seqlens.tolist() output_tensor = torch.empty( (batch_seqlens.shape[0] * dp_size,), dtype=batch_seqlens.dtype, device=self.device_name, ) # (global_bsz,) torch.distributed.all_gather_into_tensor( output_tensor=output_tensor, input_tensor=batch_seqlens, group=dp_group, ) batch_seqlens = output_tensor.tolist() return batch_seqlens def fit(self): is_logging = self.engine.is_mp_src_rank_with_outputs() and self.engine.get_data_parallel_rank() == 0 # TODO: add a unified tracking if is_logging: tracking = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) global_step = self.resume_global_step # Start from resumed step last_valid_metric = None log_with_rank( f"Total training steps: {self.total_training_steps},", logger=logger, rank=0, log_only_rank_0=True, ) # With StatefulDataLoader, we don't need to manually calculate epochs and steps # The dataloader will automatically resume from where it left off if global_step > 0: log_with_rank( f"StatefulDataLoader will automatically resume from global step: {global_step}", logger=logger, rank=0, log_only_rank_0=True, ) # Calculate which epoch we're starting from for sampler.set_epoch() start_epoch = global_step // self.steps_per_epoch meta_info = { "use_remove_padding": self.config.model.use_remove_padding, "use_dynamic_bsz": self.config.data.use_dynamic_bsz, "max_token_len_per_gpu": self.config.data.max_token_len_per_gpu, "micro_batch_size_per_gpu": self.config.data.micro_batch_size_per_gpu, "temperature": 1.0, "global_batch_size": self.global_batch_size, "pad_mode": self.config.data.pad_mode, "pad_token_id": self.model_config.tokenizer.pad_token_id, } train_time = 0 total_tokens = 0 for epoch in range(start_epoch, self.config.trainer.total_epochs): self.train_sampler.set_epoch(epoch=epoch) aggressive_empty_cache(force_sync=True) log_gpu_memory_usage(f"rank {self.rank}: At start of epoch {epoch}", logger=logger) for step_in_epoch, data in enumerate( tqdm( self.train_dataloader, initial=global_step % self.steps_per_epoch if epoch == start_epoch else 0, total=self.steps_per_epoch, desc=f"Epoch {epoch + 1}/{self.config.trainer.total_epochs}", disable=not is_logging, ) ): global_step += 1 # construct tensordict data = tu.get_tensordict(tensor_dict=data, non_tensor_dict=meta_info) batch_seqlens = self._get_batch_seqlens(data=data) # this is necessary. Otherwise, it is interpreted as NonTensorStack batch_seqlens_ntd = NonTensorData(batch_seqlens) tu.assign_non_tensor(data, update_lr_scheduler=True, global_token_num=batch_seqlens_ntd) # start profile in SPMD mode if global_step == self.start_profile_step: self.training_client.start_profile() # train for on batch output = self.training_client.train_batch(data=data) if global_step == self.end_profile_step: self.training_client.stop_profile() if self.engine.is_mp_src_rank_with_outputs(): metrics = tu.get(output, "metrics") # TODO: we can actual accumulate metrics for N steps and perform aggregate metrics for k in ["loss", "grad_norm", "lr", "mfu"]: if k in metrics.keys(): value = metrics.pop(k) metrics[f"train/{k}"] = value metrics["train/global_tokens"] = torch.sum( torch.tensor(batch_seqlens, device=self.device_name) ).item() total_tokens += metrics["train/global_tokens"] metrics["train/total_tokens(B)"] = total_tokens / 1e9 if self.engine.get_data_parallel_rank() == 0: tracking.log(data=metrics, step=global_step) is_last_step = global_step >= self.total_training_steps is_valid_step = global_step % self.test_freq == 0 is_save_step = global_step % self.save_freq == 0 # early exit or validation step if is_last_step and self.val_dataloader is not None or (self.test_freq > 0 and is_valid_step): # Perform validation val_losses = [] for val_data in self.val_dataloader: val_data = tu.get_tensordict(tensor_dict=val_data, non_tensor_dict=meta_info) output = self.training_client.infer_batch(val_data) if self.engine.is_mp_src_rank_with_outputs(): metrics = tu.get(output, "metrics") val_losses.append(metrics["loss"]) if self.engine.is_mp_src_rank_with_outputs(): val_loss = torch.mean(torch.tensor(val_losses, device=self.device_name)) # average over data parallel group dp_group = self.engine.get_data_parallel_group() if dp_group is not None: torch.distributed.all_reduce(val_loss, op=torch.distributed.ReduceOp.AVG, group=dp_group) if is_logging: metric = {"val/loss": val_loss.detach().item()} tracking.log(data=metric, step=global_step) last_valid_metric = metric torch.distributed.barrier() if is_last_step or (self.save_freq > 0 and is_save_step): aggressive_empty_cache(force_sync=True) self.ckpt_handler.save_checkpoint(step=global_step) if is_last_step: if is_logging: print(f"Total time for train steps: {train_time:.2f}s") print(f"Final validation metrics: {last_valid_metric}") return def run_sft(config): from verl.utils.distributed import initialize_global_process_group initialize_global_process_group() trainer = SFTTrainer(config=config) trainer.fit() destroy_global_process_group() @hydra.main(config_path="config", config_name="sft_trainer_engine", version_base=None) def main(config): # Automatically set `config.trainer.device = npu` when running on Ascend NPU. auto_set_device(config) run_sft(config) def create_sft_dataset(data_paths, data_config, tokenizer, processor, max_samples=-1): """Create a dataset.""" # build dataset # First check if a custom dataset class is specified if data_config.custom_cls.get("path", None): from verl.utils.import_utils import load_extern_object dataset_cls = load_extern_object(data_config.custom_cls.path, data_config.custom_cls.name) else: # Default to multi-turn dataset dataset_cls = MultiTurnSFTDataset # Create datasets based on the selected class dataset = dataset_cls( parquet_files=data_paths, tokenizer=tokenizer, config=data_config, processor=processor, max_samples=max_samples ) return dataset if __name__ == "__main__": main() ================================================ FILE: verl/trainer/sft_trainer_ray.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from functools import partial from tensordict.tensorclass import NonTensorData os.environ["NCCL_DEBUG"] = "WARN" os.environ["TOKENIZERS_PARALLELISM"] = "true" import logging import hydra import ray import torch import torch.distributed from omegaconf import OmegaConf from torch.utils.data import DistributedSampler from torchdata.stateful_dataloader import StatefulDataLoader from tqdm import tqdm from verl.utils import tensordict_utils as tu from verl.utils.checkpoint import CheckpointHandler, OrchestrationMode from verl.utils.dataset.dataset_utils import SFTTensorCollator from verl.utils.dataset.multiturn_sft_dataset import MultiTurnSFTDataset from verl.utils.device import auto_set_device, get_device_name from verl.utils.logger import log_with_rank from verl.utils.tracking import Tracking from verl.workers.engine_workers import TrainingWorker logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_SFT_LOGGING_LEVEL", "WARN")) class SFTTrainer: def __init__( self, config, ): self.config = config self._build_config() self._build_dataset() self._build_dataloader() self._build_engine() self._build_ckpt_handler() # Initialize resume-related variables self.resume_global_step = self.ckpt_handler.load_checkpoint() self.device_name = self.config.trainer.device print(self.config) def _build_ckpt_handler(self): resume_mode = getattr(self.config.trainer, "resume_mode", "auto") resume_from_path = getattr(self.config.trainer, "resume_from_path", None) max_ckpt_to_keep = getattr(self.config.trainer, "max_ckpt_to_keep", None) default_hdfs_dir = getattr(self.config.trainer, "default_hdfs_dir", None) self.ckpt_handler = CheckpointHandler( engine=self.training_client, train_dataloader=self.train_dataloader, default_local_dir=self.config.trainer.default_local_dir, max_ckpt_to_keep=max_ckpt_to_keep, default_hdfs_dir=default_hdfs_dir, resume_mode=resume_mode, resume_from_path=resume_from_path, mode=OrchestrationMode.RAY, ) def _build_config(self): from verl.utils.config import omega_conf_to_dataclass self.model_config = omega_conf_to_dataclass(self.config.model) self.engine_config = omega_conf_to_dataclass(self.config.engine) self.optimizer_config = omega_conf_to_dataclass(self.config.optim) self.checkpoint_config = omega_conf_to_dataclass(self.config.checkpoint) self.profiler_config = omega_conf_to_dataclass(self.config.profiler) # check profile interval self.profiler_interval = self.config.trainer.profile_interval self._validate_profiler_interval() def _validate_profiler_interval(self): assert len(self.profiler_interval) == 2 self.start_profile_step = self.profiler_interval[0] self.end_profile_step = self.profiler_interval[1] assert self.end_profile_step >= self.start_profile_step if self.start_profile_step < 0: assert self.end_profile_step < 0 def _build_engine(self): from verl.workers.engine_workers import TrainingWorkerConfig from verl.workers.utils.losses import sft_loss self.loss_fn = partial(sft_loss, config=None) config = TrainingWorkerConfig( model_type="language_model", model_config=self.model_config, engine_config=self.engine_config, optimizer_config=self.optimizer_config, checkpoint_config=self.checkpoint_config, profiler_config=self.profiler_config, ) wg_kwargs = {} if self.start_profile_step != -1: wg_kwargs["profile_steps"] = list(range(self.start_profile_step, self.end_profile_step + 1)) # Only require nsight worker options when tool is nsys if OmegaConf.select(self.config.profiler, "tool") == "nsys": wg_kwargs["worker_nsight_options"] = OmegaConf.to_container( OmegaConf.select(self.config.global_profiler.global_tool_config.nsys, "worker_nsight_options") ) # create resource pool and worker group from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup n_gpus_per_node = self.config.trainer.n_gpus_per_node nnodes = self.config.trainer.nnodes self.resource_pool = RayResourcePool(process_on_nodes=[n_gpus_per_node] * nnodes) ray_cls_with_init = RayClassWithInitArgs(ray.remote(TrainingWorker), config=config) self.training_client = RayWorkerGroup( resource_pool=self.resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=self.config.trainer.device, **wg_kwargs, ) self.training_client.set_loss_fn(loss_fn=self.loss_fn) self.training_client.reset() def _build_dataset(self): config = self.config tokenizer = self.model_config.tokenizer processor = self.model_config.processor train_dataset = create_sft_dataset( config.data.train_files, config.data, tokenizer, processor=processor, max_samples=config.data.get("train_max_samples", -1), ) if config.data.val_files: val_dataset = create_sft_dataset( config.data.val_files, config.data, tokenizer, processor=processor, max_samples=config.data.get("val_max_samples", -1), ) else: val_dataset = None self.train_dataset, self.val_dataset = train_dataset, val_dataset def _build_dataloader(self): # build dataset config = self.config # build dataloader # Use data parallel rank and size instead of global rank and world size # Set pin_memory_device when pin_memory is enabled. device_name = get_device_name() dp_rank = 0 dp_size = 1 self.train_sampler = DistributedSampler( self.train_dataset, shuffle=True, num_replicas=dp_size, rank=dp_rank, drop_last=True ) self.global_batch_size = config.data.train_batch_size self.train_batch_size_per_dp = self.global_batch_size // dp_size self.collate_fn = SFTTensorCollator(config.data.pad_mode) self.train_dataloader = StatefulDataLoader( dataset=self.train_dataset, batch_size=self.train_batch_size_per_dp, sampler=self.train_sampler, collate_fn=self.collate_fn, num_workers=8, pin_memory=False, drop_last=True, pin_memory_device=device_name, ) if self.val_dataset: self.val_sampler = DistributedSampler( self.val_dataset, shuffle=False, num_replicas=dp_size, rank=dp_rank, drop_last=True ) self.val_dataloader = StatefulDataLoader( dataset=self.val_dataset, batch_size=self.train_batch_size_per_dp, sampler=self.val_sampler, collate_fn=self.collate_fn, num_workers=8, pin_memory=False, drop_last=True, pin_memory_device=device_name, ) else: self.val_dataloader = None # update if self.config.trainer.total_training_steps is not None: self.total_training_steps = self.config.trainer.total_training_steps else: self.total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs self.optimizer_config.total_training_steps = self.total_training_steps self.steps_per_epoch = len(self.train_dataloader) # manage save and test frequency self.save_freq = self.config.trainer.save_freq if self.save_freq == "after_each_epoch": self.save_freq = self.steps_per_epoch self.test_freq = self.config.trainer.test_freq if self.test_freq == "after_each_epoch": self.test_freq = self.steps_per_epoch def _get_batch_seqlens(self, data): # mean over dp group is_nested = data["input_ids"].is_nested if is_nested: batch_seqlens: torch.Tensor = data["input_ids"].offsets().diff() else: batch_seqlens: torch.Tensor = data["attention_mask"].sum(dim=-1) return batch_seqlens def fit(self): tracking = Tracking( project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True), ) global_step = self.resume_global_step # Start from resumed step last_valid_metric = None log_with_rank( f"Total training steps: {self.total_training_steps},", logger=logger, rank=0, log_only_rank_0=True, ) # With StatefulDataLoader, we don't need to manually calculate epochs and steps # The dataloader will automatically resume from where it left off if global_step > 0: log_with_rank( f"StatefulDataLoader will automatically resume from global step: {global_step}", logger=logger, rank=0, log_only_rank_0=True, ) # Calculate which epoch we're starting from for sampler.set_epoch() start_epoch = global_step // self.steps_per_epoch meta_info = { "use_remove_padding": self.config.model.use_remove_padding, "use_dynamic_bsz": self.config.data.use_dynamic_bsz, "max_token_len_per_gpu": self.config.data.max_token_len_per_gpu, "micro_batch_size_per_gpu": self.config.data.micro_batch_size_per_gpu, "temperature": 1.0, "global_batch_size": self.global_batch_size, "pad_mode": self.config.data.pad_mode, "pad_token_id": self.model_config.tokenizer.pad_token_id, } train_time = 0 total_tokens = 0 for epoch in range(start_epoch, self.config.trainer.total_epochs): self.train_sampler.set_epoch(epoch=epoch) for step_in_epoch, data in enumerate( tqdm( self.train_dataloader, initial=global_step % self.steps_per_epoch if epoch == start_epoch else 0, total=self.steps_per_epoch, desc=f"Epoch {epoch + 1}/{self.config.trainer.total_epochs}", ) ): global_step += 1 # construct tensordict data = tu.get_tensordict(tensor_dict=data, non_tensor_dict=meta_info) batch_seqlens = self._get_batch_seqlens(data=data).tolist() # this is necessary. Otherwise, it is interpreted as NonTensorStack batch_seqlens_ntd = NonTensorData(batch_seqlens) tu.assign_non_tensor(data, update_lr_scheduler=True, global_token_num=batch_seqlens_ntd) # start profile in SPMD mode if global_step == self.start_profile_step: self.training_client.start_profile() # train for on batch output = self.training_client.train_batch(data) output = output.get() if global_step == self.end_profile_step: self.training_client.stop_profile() metrics = tu.get(output, "metrics") # TODO: we can actual accumulate metrics for N steps and perform aggregate metrics metrics["train/loss"] = metrics.pop("loss") metrics["train/grad_norm"] = metrics.pop("grad_norm") metrics["train/lr"] = metrics.pop("lr") metrics["train/mfu"] = metrics.pop("mfu") metrics["train/global_tokens"] = torch.sum(torch.tensor(batch_seqlens, device=self.device_name)).item() total_tokens += metrics["train/global_tokens"] metrics["train/total_tokens(B)"] = total_tokens / 1e9 tracking.log(data=metrics, step=global_step) is_last_step = global_step >= self.total_training_steps is_valid_step = global_step % self.test_freq == 0 is_save_step = global_step % self.save_freq == 0 # early exit or validation step if is_last_step and self.val_dataloader is not None or (self.test_freq > 0 and is_valid_step): # Perform validation val_losses = [] for val_data in self.val_dataloader: val_data = tu.get_tensordict(tensor_dict=val_data, non_tensor_dict=meta_info) output = self.training_client.infer_batch(val_data) output = output.get() metrics = tu.get(output, "metrics") val_losses.append(metrics["loss"]) val_loss = torch.mean(torch.tensor(val_losses, device=self.device_name)) metric = {"val/loss": val_loss.detach().item()} tracking.log(data=metric, step=global_step) last_valid_metric = metric if is_last_step or (self.save_freq > 0 and is_save_step): self.ckpt_handler.save_checkpoint(step=global_step) if is_last_step: print(f"Total time for train steps: {train_time:.2f}s") print(f"Final validation metrics: {last_valid_metric}") return def run_sft(config): ray.init() trainer = SFTTrainer(config=config) trainer.fit() @hydra.main(config_path="config", config_name="sft_trainer_engine", version_base=None) def main(config): # Automatically set `config.trainer.device = npu` when running on Ascend NPU. auto_set_device(config) run_sft(config) def create_sft_dataset(data_paths, data_config, tokenizer, processor, max_samples=-1): """Create a dataset.""" # build dataset # First check if a custom dataset class is specified if data_config.custom_cls.get("path", None): from verl.utils.import_utils import load_extern_type dataset_cls = load_extern_type(data_config.custom_cls.path, data_config.custom_cls.name) else: # Default to multi-turn dataset dataset_cls = MultiTurnSFTDataset # Create datasets based on the selected class dataset = dataset_cls( parquet_files=data_paths, tokenizer=tokenizer, config=data_config, processor=processor, max_samples=max_samples ) return dataset if __name__ == "__main__": main() ================================================ FILE: verl/utils/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import config, tokenizer from .config import omega_conf_to_dataclass, validate_config from .groupwise import as_torch_index, group_mean_std from .tokenizer import hf_processor, hf_tokenizer, normalize_token_ids __all__ = ( tokenizer.__all__ + config.__all__ + ["hf_processor", "hf_tokenizer", "normalize_token_ids", "omega_conf_to_dataclass", "validate_config"] + ["as_torch_index", "group_mean_std"] ) ================================================ FILE: verl/utils/activation_offload.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Functionality for CPU offloading of tensors saved for backward pass.""" from __future__ import annotations import functools import logging import os from typing import Any, Optional import torch from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from verl.utils.device import get_torch_device from verl.utils.fsdp_utils import FSDPModule as FSDP2 logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def _get_unique_tensor_key(tensor): key = (tensor.untyped_storage().data_ptr() + tensor.storage_offset(), tensor.dtype) return key class FSDPParameterFilter: def __init__(self): self.model_parameters_storage = set() def __call__(self, tensor): return tensor.untyped_storage().data_ptr() not in self.model_parameters_storage def update_model_parameters(self, model): new_storage = set() for p in model.parameters(): new_storage.add(p.data.untyped_storage().data_ptr()) self.model_parameters_storage = new_storage class CpuOffloadHookWithOffloadHandler: """Context-manager that offloads/recovers tensors through an offload hander. The hook just offloads/recovers the tensor object to the handler through `tensor_push` and `tensor_pop` interface. How the offload-handler manages the offloading, recovering or prefetching timing is transparent to this hook. """ def __init__( self, offload_handler: OffloadHandler, handler_extra_kwargs: Optional[dict[str, Any]] = None, ) -> None: if handler_extra_kwargs is None: handler_extra_kwargs = {} self.offload_handler: OffloadHandler = offload_handler self.handler_extra_kwargs: dict[str, Any] = handler_extra_kwargs self.inside_context = False def __enter__(self): self.inside_context = True torch._C._autograd._push_saved_tensors_default_hooks(self.on_save_for_backward, self.on_get_saved_tensor) def __exit__(self, *args: Any): self.inside_context = False torch._C._autograd._pop_saved_tensors_default_hooks() def on_save_for_backward(self, tensor: torch.Tensor) -> Any: retrieve_identifier = self.offload_handler.tensor_push(tensor, **self.handler_extra_kwargs) return retrieve_identifier def on_get_saved_tensor(self, saved_state: Any) -> torch.Tensor: tensor = self.offload_handler.tensor_pop(saved_state, **self.handler_extra_kwargs) return tensor class OffloadHandler: """A base class for CPU offload-handler.""" def __init__(self) -> None: pass def tensor_push(self, tensor: torch.Tensor, **kwargs) -> Any: """Tensor push.""" raise NotImplementedError( "`tensor_push is not implented in OffloadHandler class. Inherit this class and implement your " "custom tensor_push." ) def tensor_pop(self, tensor_tag: Any, **kwargs): """Tensor pop.""" raise NotImplementedError( "`tensor_pop is not implented in OffloadHandler class. Inherit this class and implement your " "custom tensor_pop." ) class GroupCommitFunction(torch.autograd.Function): """this is a dummy op with output identical to input. However, it is necessary for marking a timepoint for offload handler to accomplish all synchronizations. Implementing it as a function is necessary because we need to actions in both forward and backward. """ @staticmethod def forward(ctx, tensor, cpu_offload_handler): # pylint: disable=missing-function-docstring cpu_offload_handler.on_group_commit_forward() ctx.cpu_offload_handler = cpu_offload_handler # return the identical tensor return tensor @staticmethod def backward(ctx, grad_output): # pylint: disable=missing-function-docstring cpu_offload_handler = ctx.cpu_offload_handler cpu_offload_handler.on_group_commit_backward() return grad_output, None group_prefetch_offload_commit = GroupCommitFunction.apply class SynchronizedGroupOffloadHandler(OffloadHandler): """Offload Handler that offloads/reloads in a synchronized way. The device-to-host and host-to-device copying happen in the same stream as the computation kernels, thus the copying will block computation. """ def __init__(self, num_offload_group, tensor_need_offloading_checker=(lambda _: True)) -> None: super().__init__() self.num_offload_group = num_offload_group self.tensor_need_offloading_checker = tensor_need_offloading_checker self.groupid_reset() def groupid_reset(self): """Groupid reset.""" # Data structures to label saved tensors and book-keep their cpu copies. # Currently, on push, create a new cpu tensor and copies; on pop, copies # the tensor back to gpu and deletes the cpu tensor. # These will increment whenever `group_commit()` is invoked self.current_group, self.tensor_count_current_group = (0, 0) self.torch_tensor_count = 0 self.tensor_tag_to_state = {} def on_group_commit_forward(self): """On group commit forward.""" # finishing up with updating current group and tensor count self.current_group += 1 # increment self.tensor_count_current_group = 0 # reset def on_group_commit_backward(self): """On group commit backward.""" self.current_group -= 1 assert self.current_group >= 0 @staticmethod def offload(src_tensor, pin_memory=True): """Offload.""" cpu_backup = torch.empty( src_tensor.size(), dtype=src_tensor.dtype, layout=src_tensor.layout, device="cpu", pin_memory=pin_memory, ) cpu_backup.copy_(src_tensor, non_blocking=True) state = (src_tensor.device, cpu_backup) return state @staticmethod def reload(state, non_blocking=None): """Reload.""" dev, cpu_backup = state if non_blocking is None: non_blocking = cpu_backup.is_pinned() return cpu_backup.to(dev, non_blocking=non_blocking) def tensor_push(self, tensor: torch.Tensor, **kwargs): """Tensor push.""" # obtain a unique tensor tag tensor_tag = (self.current_group, self.tensor_count_current_group) self.tensor_count_current_group += 1 assert tensor_tag not in self.tensor_tag_to_state if self.current_group < self.num_offload_group and self.tensor_need_offloading_checker(tensor): state = SynchronizedGroupOffloadHandler.offload(tensor) self.tensor_tag_to_state[tensor_tag] = state else: # will be offloaded together after group commit self.tensor_tag_to_state[tensor_tag] = tensor return tensor_tag def tensor_pop(self, tensor_tag, **kwargs): """Tensor pop.""" assert tensor_tag in self.tensor_tag_to_state state = self.tensor_tag_to_state.pop(tensor_tag) if isinstance(state, tuple): tensor = SynchronizedGroupOffloadHandler.reload(state) else: tensor = state return tensor class AsyncDoubleBufferGroupOffloadHandler(SynchronizedGroupOffloadHandler): """Compared to synchronize, this uses more memory because of the buffer but achieves better performance due to the overlapping. D2h and h2d copying are completely hidden behind computation if computation time of a layer is longer than host-device communication time. Bulk offloading with delay and bulk reloading with prefetch are implemented.""" def __init__( self, num_offload_group, # must be <= actual number of groups (number of commits) num_model_group, tensor_need_offloading_checker=(lambda t: True), ) -> None: super().__init__( num_offload_group=num_offload_group, tensor_need_offloading_checker=tensor_need_offloading_checker, ) # Number of layers in the model self.num_layers = num_model_group # Data Structure to maintain reference to activation tensors self.tensor_tag_to_buf = {} # Tracking the number of layers offloaded self.offloaded_group_count = 0 # Core data structure that decides the window for offloading self.layer_window_map = {} self.group_offload_mapping = {} # Logic to make offloading load balance across computation # for optimal CPU/GPU interconnect usage constant = 0 for i in range(self.num_offload_group): self.layer_window_map[i] = ((self.num_layers // self.num_offload_group) * (i + 1)) - 1 if i < (self.num_layers % self.num_offload_group): self.layer_window_map[i] += i + 1 constant = i + 1 else: self.layer_window_map[i] += constant # allocate streams and events for synchronization self.d2h_stream = get_torch_device().Stream() self.h2d_stream = get_torch_device().Stream() def tensor_push(self, tensor: torch.Tensor, **kwargs) -> Any: torch_stray_tensor = isinstance( tensor, torch._subclasses.fake_tensor.FakeTensor | torch._subclasses.functional_tensor.FunctionalTensor, ) need_offload = not torch_stray_tensor need_offload = need_offload and self.tensor_need_offloading_checker(tensor) if need_offload: # obtain a unique tensor tag tensor_tag = (self.current_group, self.tensor_count_current_group) self.tensor_count_current_group += 1 assert tensor_tag not in self.tensor_tag_to_state self.tensor_tag_to_state[tensor_tag] = tensor if self.current_group < self.num_offload_group: self.tensor_tag_to_buf[tensor_tag] = tensor else: tensor_tag = tensor return tensor_tag def tensor_pop(self, tensor_tag, **kwargs): """Tensor pop.""" if isinstance(tensor_tag, torch.Tensor): return tensor_tag assert tensor_tag in self.tensor_tag_to_state tensor = self.tensor_tag_to_state.pop(tensor_tag) self.tensor_tag_to_buf.pop(tensor_tag, None) # the tensor should have been copied back in on_group_commit_backward() # which invokes bulk_reload_group. assert not isinstance(tensor, tuple) return tensor def bulk_offload_group(self, group_to_offload): """Bulk offload group.""" offload_mapping = {} offload_size = 0 with get_torch_device().stream(self.d2h_stream): for tensor_tag, state in self.tensor_tag_to_state.items(): group_id, _ = tensor_tag if group_id == group_to_offload: assert not isinstance(state, tuple) key = _get_unique_tensor_key(state) if key not in offload_mapping: offload_mapping[key] = state # if offload, return the reference to cpu copy self.tensor_tag_to_state[tensor_tag] = (key, state.shape) for key, tensor in offload_mapping.items(): state = SynchronizedGroupOffloadHandler.offload(tensor) offload_size += tensor.numel() * tensor.element_size() offload_mapping[key] = state self.group_offload_mapping[group_to_offload] = offload_mapping def synchronize_on_group_commit_forward(self, current_group): """Synchronize on group commit forward.""" # For the first group, kickstart the offload after we have # the first compute completion if current_group == 0: self.d2h_stream.wait_stream(get_torch_device().current_stream()) self.bulk_offload_group(current_group) # Window map data structure helps us synchronize based on number # of layers offloaded if self.layer_window_map[self.offloaded_group_count] == current_group: # Stream synchronization both ways self.d2h_stream.wait_stream(get_torch_device().current_stream()) get_torch_device().current_stream().wait_stream(self.d2h_stream) # Time to free the activation memory after usage for tensor_tag, _ in self.tensor_tag_to_buf.items(): if tensor_tag[0] == self.offloaded_group_count: self.tensor_tag_to_buf[tensor_tag] = None # Time to offload the next group if self.offloaded_group_count < (self.num_offload_group - 1): self.bulk_offload_group(self.offloaded_group_count + 1) # Increment the offload group count to keep track self.offloaded_group_count += 1 def on_group_commit_forward(self): """This function will cause host device synchronization""" # handle synchronization events self.synchronize_on_group_commit_forward(self.current_group) super().on_group_commit_forward() @torch.no_grad def bulk_reload_group(self, group_to_reload): """Bulk reload group.""" assert group_to_reload < self.num_offload_group with get_torch_device().stream(self.h2d_stream): # move back tensors offload_mapping = self.group_offload_mapping.pop(group_to_reload) assert offload_mapping is not None for key, state in offload_mapping.items(): offload_mapping[key] = SynchronizedGroupOffloadHandler.reload(state) for tensor_label, state in self.tensor_tag_to_state.items(): group_id, _ = tensor_label if group_id == group_to_reload and not isinstance(state, torch.Tensor): assert isinstance(state, tuple), f"{group_id} {state}" key, shape = state recovered_tensor = offload_mapping[key].view(shape) self.tensor_tag_to_state[tensor_label] = recovered_tensor def on_group_commit_backward(self): # first decrement the current group. # after last commit in forward, the group will +1; in backward it -1. # Finally it should be decremented to 0. self.current_group -= 1 assert self.current_group >= 0 # Layer window data structure helps us to reload at right times if self.layer_window_map[self.offloaded_group_count - 1] == self.current_group: # Stream synchronization both ways self.h2d_stream.wait_stream(get_torch_device().current_stream()) get_torch_device().current_stream().wait_stream(self.h2d_stream) # Time to reload the next group self.bulk_reload_group(self.offloaded_group_count - 1) # Decrease the offloading group counter self.offloaded_group_count -= 1 if self.offloaded_group_count > 1 else 0 # Last group computation needs to wait till all the reloads complete if self.current_group == 0: get_torch_device().current_stream().wait_stream(self.h2d_stream) self.offloaded_group_count = 0 def get_activation_offload_context( num_layers: int = 1, model_layers: int = 1, tensor_need_offloading_checker=(lambda t: True) ): cpu_offload_handler = AsyncDoubleBufferGroupOffloadHandler( num_offload_group=num_layers, num_model_group=model_layers, tensor_need_offloading_checker=tensor_need_offloading_checker, ) def group_prefetch_offload_commit_async(tensor): return group_prefetch_offload_commit(tensor, cpu_offload_handler) return ( CpuOffloadHookWithOffloadHandler(offload_handler=cpu_offload_handler), group_prefetch_offload_commit_async, ) class ActivationHandler: def __init__(self, offload_ctx, sync_func, tensor_filter, enable_ckpt): self._offload_ctx = offload_ctx self._sync_func = sync_func self._enable_ckpt = enable_ckpt self._tensor_filter = tensor_filter if enable_ckpt: self.checkpoint_fn = functools.partial( torch.utils.checkpoint.checkpoint, use_reentrant=True, ) def pre_forward(self, module): if module.training: self._offload_ctx.__enter__() self._tensor_filter.update_model_parameters(module) def post_forward(self, module): if module.training: self._offload_ctx.__exit__(None, None, None) def _pack_kwargs(self, *args, **kwargs): kwarg_keys = [] flat_args = list(args) for k, v in kwargs.items(): kwarg_keys.append(k) flat_args.append(v) return tuple(flat_args), tuple(kwarg_keys) def _unpack_kwargs(self, flat_args, kwarg_keys): assert len(kwarg_keys) <= len(flat_args), f"too many keys {len(kwarg_keys)} vs. {len(flat_args)}" if len(kwarg_keys) == 0: return flat_args, {} args = flat_args[: -len(kwarg_keys)] kwargs = dict(zip(kwarg_keys, flat_args[-len(kwarg_keys) :], strict=True)) return args, kwargs def _ckpt_forward(self, forward_method, *args, **kwargs): flat_args, kwarg_keys = self._pack_kwargs(*args, **kwargs) def my_function(*inputs): # unpack back into args and kwargs nonlocal forward_method, kwarg_keys unpacked_args, unpacked_kwargs = self._unpack_kwargs(inputs, kwarg_keys) # run original module return forward_method(*unpacked_args, **unpacked_kwargs) return self.checkpoint_fn( my_function, *flat_args, ) def forward(self, module, forward_method, *args, **kwargs): if not module.training: return forward_method(*args, **kwargs) if not self._enable_ckpt: ret = forward_method(*args, **kwargs) else: ret = self._ckpt_forward(forward_method, *args, **kwargs) binded_tensor = ret if isinstance(ret, tuple): binded_tensor = ret[0] binded_tensor = self._sync_func(binded_tensor) final_ret = binded_tensor if isinstance(ret, tuple): final_ret = (final_ret,) + ret[1:] return final_ret def wrap_module_forward_method(self, module): orig_method = module.forward handler = self @functools.wraps(orig_method) def wrapped_method(model_self, *args, **kwargs): nonlocal handler handler.pre_forward(model_self) out = handler.forward(model_self, orig_method, *args, **kwargs) handler.post_forward(model_self) return out module.forward = wrapped_method.__get__(module, type(module)) def enable_activation_offloading(model, strategy, enable_ckpt=False): """ Enable activation offloading for the model. It groups activations by TransformerLayer and offloads activation groups asynchronously. This means that the offloading of the i-th activation group and the computation of the i+1-th activation group happen at the same time, and there are at most two activation groups in GPU memory. Args: model: the model to enable activation offloading strategy: the training strategy of the model, such as "fsdp" enable_ckpt: whether activation checkpointing(also called gradient checkpointing) has been enabled for the model Note: For best efficiency, activation offloading is usually combined with activation checkpointing. However, this implementation of activation offloading is conflicted with the implementation of activation checkpointing in some training strategies. This function resolves this conflict, and therefore requires the "strategy" and "enable_ckpt" arguments. Returns: """ assert strategy == "fsdp" or strategy == "fsdp2", "activation offloading only supports fsdp strategy" layers = [] def get_layers(module): for name, child in module.named_children(): if not isinstance(child, FSDP | FSDP2): get_layers(child) else: wrapped_module = child if isinstance(child, FSDP): wrapped_module = child._fsdp_wrapped_module # In some cases, torch.nn.Embedding is wrapped with FSDP alone. However, the activation # size of torch.nn.Embedding is small, so it's not necessary to offload it. if not isinstance(wrapped_module, torch.nn.Embedding): layers.append(child) get_layers(model) if len(layers) < 3: logger.warning(f"Find only {len(layers)} fsdp layers, not necessary to enable async activation offloading") return tensor_filter = FSDPParameterFilter() context, sync_func = get_activation_offload_context(len(layers) - 1, len(layers), tensor_filter) if enable_ckpt: # The implementation of activation checkpointing in transformers library is incompatible with # activation offloading, # so it will be disabled, but this implementation supports another version of activation checkpointing, so that # these two features can be enabled at the same time. for module in model.modules(): if hasattr(module, "gradient_checkpointing_disable"): module.gradient_checkpointing_disable() handler = ActivationHandler(context, sync_func, tensor_filter, enable_ckpt) for layer in layers: module = layer if isinstance(layer, FSDP): module = module._fsdp_wrapped_module handler.wrap_module_forward_method(module) ================================================ FILE: verl/utils/attention_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable _index_first_axis, _pad_input, _rearrange, _unpad_input = None, None, None, None def _get_attention_functions() -> tuple[Callable, Callable, Callable, Callable]: """Dynamically import attention functions based on available hardware.""" from verl.utils.device import is_torch_npu_available global _index_first_axis, _pad_input, _rearrange, _unpad_input if is_torch_npu_available(check_device=False): from verl.utils.npu_flash_attn_utils import index_first_axis, pad_input, rearrange, unpad_input else: from flash_attn.bert_padding import index_first_axis, pad_input, rearrange, unpad_input _index_first_axis, _pad_input, _rearrange, _unpad_input = index_first_axis, pad_input, rearrange, unpad_input return _index_first_axis, _pad_input, _rearrange, _unpad_input def index_first_axis(*args, **kwargs): """ Unified entry point for `index_first_axis` across CUDA and NPU backends. Dynamically dispatches to the appropriate device-specific implementation: - On CUDA: `flash_attn.bert_padding.index_first_axis` - On NPU: `transformers.integrations.npu_flash_attention.index_first_axis` (falls back to `transformers.modeling_flash_attention_utils._index_first_axis` in newer versions of transformers). Users can call this function directly without worrying about the underlying device. """ func, *_ = _get_attention_functions() return func(*args, **kwargs) def pad_input(*args, **kwargs): """ Unified entry point for `pad_input` across CUDA and NPU backends. Dynamically dispatches to the appropriate device-specific implementation: - On CUDA: `flash_attn.bert_padding.pad_input` - On NPU: `transformers.integrations.npu_flash_attention.pad_input` (falls back to `transformers.modeling_flash_attention_utils._pad_input` in newer versions of transformers). Users can call this function directly without worrying about the underlying device. """ _, func, *_ = _get_attention_functions() return func(*args, **kwargs) def rearrange(*args, **kwargs): """ Unified entry point for `rearrange` across CUDA and NPU backends. Dynamically dispatches to the appropriate device-specific implementation: - On CUDA: `flash_attn.bert_padding.rearrange` - On NPU: `transformers.integrations.npu_flash_attention.rearrange` (falls back to `einops.rearrange` if no dedicated NPU implementation exists). Users can call this function directly without worrying about the underlying device. """ *_, func, _ = _get_attention_functions() return func(*args, **kwargs) def unpad_input(*args, **kwargs): """ Unified entry point for `unpad_input` across CUDA and NPU backends. Dynamically dispatches to the appropriate device-specific implementation: - On CUDA: `flash_attn.bert_padding.unpad_input` - On NPU: `transformers.integrations.npu_flash_attention.unpad_input` (falls back to `transformers.modeling_flash_attention_utils._unpad_input` in newer versions of transformers). Users can call this function directly without worrying about the underlying device. """ *_, func = _get_attention_functions() return func(*args, **kwargs) __all__ = ["index_first_axis", "pad_input", "rearrange", "unpad_input"] ================================================ FILE: verl/utils/chat_template.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates import logging import os from transformers import PreTrainedTokenizerBase, ProcessorMixin from verl.utils.tokenizer import normalize_token_ids logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def initialize_system_prompt(tokenizer, **apply_chat_template_kwargs) -> list[int]: """ Initialize system prompt tokens for chat templates that support them. Args: tokenizer: The tokenizer with a chat template **apply_chat_template_kwargs: Additional arguments for apply_chat_template Returns: List of token IDs for the system prompt, or empty list if not supported """ token1 = normalize_token_ids( tokenizer.apply_chat_template([{"role": "user", "content": ""}], add_generation_prompt=False, tokenize=True) ) token2 = normalize_token_ids( tokenizer.apply_chat_template([{"role": "user", "content": ""}] * 2, add_generation_prompt=False, tokenize=True) ) # get system prompt tokens system_prompt = token1[: -(len(token2) - len(token1))] return system_prompt def extract_system_prompt_and_generation(tokenizer): token1 = normalize_token_ids( tokenizer.apply_chat_template([{"role": "user", "content": ""}], add_generation_prompt=False, tokenize=True) ) token2 = normalize_token_ids( tokenizer.apply_chat_template([{"role": "user", "content": ""}] * 2, add_generation_prompt=False, tokenize=True) ) # get system prompt tokens system_prompt = token1[: -(len(token2) - len(token1))] # get generate prompt tokens token3 = normalize_token_ids( tokenizer.apply_chat_template([{"role": "user", "content": ""}], add_generation_prompt=True, tokenize=True) ) generate_prompt = token3[len(token1) :] return system_prompt, generate_prompt def apply_chat_template( processor: PreTrainedTokenizerBase | ProcessorMixin, messages: list[dict], *, tokenize: bool = True, add_generation_prompt: bool = True, tools=None, return_dict: bool = False, **kwargs, ) -> list[int] | str: """apply_chat_template to messages with special attention to template requiring at least one user message, e.g. Qwen3.5. Args: processor: tokenizer or processor. messages: list[dict], messages. tokenize: bool, whether to tokenize the output. add_generation_prompt: bool, whether to add generation prompt. tools: list[dict], tools schema. return_dict: bool, whether to return a dict. **kwargs: additional arguments for apply_chat_template. Returns: list[int] | str: tokenized ids or text string. """ try: return processor.apply_chat_template( messages, tokenize=tokenize, add_generation_prompt=add_generation_prompt, tools=tools, return_dict=return_dict, **kwargs, ) except Exception: # Qwen3.5 apply_chat_template needs messages with at least one user message dummy_user_message = [{"role": "user", "content": [{"type": "text", "text": ""}]}] dummy_user_prefix = processor.apply_chat_template( dummy_user_message, tokenize=tokenize, add_generation_prompt=False, tools=tools, return_dict=return_dict, **kwargs, ) output = processor.apply_chat_template( dummy_user_message + messages, tokenize=tokenize, add_generation_prompt=add_generation_prompt, tools=tools, return_dict=return_dict, **kwargs, ) if not tokenize: # tokenize=False return output[len(dummy_user_prefix) :] elif not return_dict: # tokenize=True and return_dict=False if isinstance(output[0], list): # transformers>=5 assert len(output) == 1, "output must be a list[int] or list[list[int]]" dummy_user_prefix = dummy_user_prefix[0] output = output[0] return output[len(dummy_user_prefix) :] else: # tokenize=True and return_dict=True and return_tensors="pt" dummy_user_prefix = dict(dummy_user_prefix) output = dict(output) prefix_len = dummy_user_prefix["input_ids"].shape[1] output["input_ids"] = output["input_ids"][:, prefix_len:] output["attention_mask"] = output["attention_mask"][:, prefix_len:] if "mm_token_type_ids" in output: output["mm_token_type_ids"] = output["mm_token_type_ids"][:, prefix_len:] return output ================================================ FILE: verl/utils/checkpoint/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .checkpoint_handler import CheckpointHandler, OrchestrationMode __all__ = ["CheckpointHandler", "OrchestrationMode"] ================================================ FILE: verl/utils/checkpoint/checkpoint_handler.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: add unit tests import json import logging import os import re from enum import Enum import torch import verl.utils.hdfs_io as hdfs_io from verl.single_controller import WorkerGroup from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path, get_checkpoint_tracker_filename from verl.utils.logger import log_with_rank from verl.workers.engine import BaseEngine def extract_step(path): match = re.search(r"global_step_(\d+)", path) if match: return int(match.group(1)) return None logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_SFT_LOGGING_LEVEL", "WARN")) class OrchestrationMode(Enum): SPMD = 0 RAY = 1 class CheckpointHandler: """ Checkpoint handler handles the path, global_step of a checkpoint folder. Currently, it only works with a single model. We can expand it to support multiple models. It is expected to be used with SPMD style (e.g., torchrun) """ def __init__( self, engine: BaseEngine | WorkerGroup, train_dataloader, *, default_local_dir, max_ckpt_to_keep=None, default_hdfs_dir=None, resume_mode="auto", resume_from_path=None, mode=OrchestrationMode.SPMD, lora_train_meta=None, ): self.default_local_dir = default_local_dir self.max_ckpt_to_keep = max_ckpt_to_keep self.default_hdfs_dir = default_hdfs_dir self.resume_mode = resume_mode self.resume_from_path = resume_from_path self.engine = engine self.train_dataloader = train_dataloader self.mode = mode self.lora_train_meta = lora_train_meta if self.mode == OrchestrationMode.SPMD: self.rank = torch.distributed.get_rank() self.is_mp_src_rank_with_outputs = self.engine.is_mp_src_rank_with_outputs() self.dp_rank = self.engine.get_data_parallel_rank() elif self.mode == OrchestrationMode.RAY: self.rank = 0 self.is_mp_src_rank_with_outputs = True self.dp_rank = 0 else: raise ValueError(f"Unknown {self.mode=}") def save_checkpoint(self, step): """Save checkpoint using FSDPCheckpointManager with improved tracking""" from verl.utils.fs import local_mkdir_safe # Determine checkpoint path local_global_step_folder = os.path.join(self.default_local_dir, f"global_step_{step}") if self.rank == 0: print(f"Saving checkpoint to: {local_global_step_folder}") # Get max checkpoints to keep max_ckpt_to_keep = self.max_ckpt_to_keep # Use checkpoint manager to save self.engine.save_checkpoint( local_path=local_global_step_folder, global_step=step, max_ckpt_to_keep=max_ckpt_to_keep ) # Save dataloader state. Note that we only save the iterator in the train_dataloader. # So it's identical in each dp rank. if self.rank == 0 and self.lora_train_meta is not None: local_mkdir_safe(local_global_step_folder) lora_meta_path = os.path.join(local_global_step_folder, "lora_train_meta.json") with open(lora_meta_path, "w", encoding="utf-8") as f: json.dump(self.lora_train_meta, f, ensure_ascii=False, indent=4) print(f"Saved LoRA rank/alpha metadata to: {lora_meta_path}") if self.is_mp_src_rank_with_outputs: dp_rank = self.dp_rank local_mkdir_safe(local_global_step_folder) dataloader_local_path = os.path.join(local_global_step_folder, f"data_{dp_rank}.pt") # Use StatefulDataLoader's built-in state dict functionality dataloader_state_dict = self.train_dataloader.state_dict() torch.save(dataloader_state_dict, dataloader_local_path) print(f"Saved dataloader state to: {dataloader_local_path}") if self.rank == 0: # Update latest checkpoint tracker (atomic write) tracker_file = get_checkpoint_tracker_filename(self.default_local_dir) temp_tracker_file = tracker_file + ".tmp" with open(temp_tracker_file, "w") as f: f.write(str(step)) os.rename(temp_tracker_file, tracker_file) print(f"Updated checkpoint tracker: {tracker_file}") # Copy to HDFS if configured if self.rank == 0 and self.default_hdfs_dir: hdfs_io.makedirs(self.default_hdfs_dir, exist_ok=True) hdfs_io.copy(src=local_global_step_folder, dst=self.default_hdfs_dir, dirs_exist_ok=True) if self.mode == OrchestrationMode.SPMD: torch.distributed.barrier() def load_checkpoint(self): # Determine resume path based on configuration checkpoint_path = self._determine_resume_path() if checkpoint_path is None: return 0 # extract resume step from checkpoint path resume_step = extract_step(checkpoint_path) if resume_step is None: log_with_rank( f"Warning: Could not extract step number from {checkpoint_path}, starting from step 0", logger=logger, rank=self.rank, level=logging.WARNING, log_only_rank_0=True, ) return 0 self.resume_global_step = resume_step # Use checkpoint manager to load model state self.engine.load_checkpoint(checkpoint_path) # Always load dataloader state for StatefulDataLoader self._load_dataloader_state(checkpoint_path) return resume_step def _load_dataloader_state(self, checkpoint_path: str): """Load dataloader state from checkpoint""" dp_rank = self.dp_rank dataloader_path = os.path.join(checkpoint_path, f"data_{dp_rank}.pt") if os.path.exists(dataloader_path): # Use StatefulDataLoader's built-in state dict functionality dataloader_state_dict = torch.load(dataloader_path, map_location="cpu", weights_only=False) self.train_dataloader.load_state_dict(dataloader_state_dict) log_with_rank( f"Successfully loaded dataloader state from {dataloader_path}", logger=logger, rank=self.rank, log_only_rank_0=True, ) else: log_with_rank( f"Warning: No dataloader state found at {dataloader_path}, will start from scratch", logger=logger, rank=self.rank, level=logging.WARNING, log_only_rank_0=True, ) def _determine_resume_path(self): """Determine the path to resume from based on resume_mode configuration""" resume_mode = self.resume_mode resume_from_path = self.resume_from_path if resume_mode == "disable": return None elif resume_mode == "auto": if resume_from_path is not None: assert os.path.exists(resume_from_path), ( "resume_from_path must be null or an existing path when resume_mode is 'auto'" ) assert "global_step_" in resume_from_path, "resume_from_path must specify the global_steps" return resume_from_path # Try to find the latest checkpoint in the default directory return self._find_latest_checkpoint() elif resume_mode == "resume_path": assert os.path.exists(resume_from_path), ( "resume_from_path must be an existing path when resume_mode is 'resume_path'" ) assert "global_step_" in resume_from_path, "resume_from_path must specify the global_steps" return resume_from_path else: raise ValueError(f"Invalid resume_mode: {resume_mode}. Must be 'auto', 'disable', or 'resume_path'") def _find_latest_checkpoint(self): """Find the latest checkpoint in the default local directory""" checkpoint_dir = self.default_local_dir if not os.path.exists(checkpoint_dir): return None latest_checkpoint = find_latest_ckpt_path(checkpoint_dir) if latest_checkpoint and self.rank == 0: step_num = extract_step(latest_checkpoint) print(f"Found latest checkpoint: {latest_checkpoint} (step {step_num})") return latest_checkpoint ================================================ FILE: verl/utils/checkpoint/checkpoint_manager.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import random import shutil import numpy as np import torch import torch.distributed from omegaconf import DictConfig from transformers import PreTrainedTokenizer, ProcessorMixin from verl.trainer.config import CheckpointConfig from verl.utils.device import get_device_name, get_torch_device class BaseCheckpointManager: """ A checkpoint manager that saves and loads the following states in a SPMD way: - model - optimizer - lr_scheduler - extra_states We save - sharded model states and optimizer states - full lr_scheduler states - huggingface tokenizer and config for ckpt merge """ def __init__( self, model, optimizer: torch.optim.Optimizer, lr_scheduler: torch.optim.lr_scheduler.LRScheduler = None, processing_class: PreTrainedTokenizer | ProcessorMixin = None, checkpoint_config: DictConfig | CheckpointConfig = None, ): self.checkpoint_config = checkpoint_config checkpoint_load_contents = checkpoint_config.get("load_contents", None) if checkpoint_config else None checkpoint_save_contents = checkpoint_config.get("save_contents", None) if checkpoint_config else None if checkpoint_load_contents is None: checkpoint_load_contents = ["model", "optimizer", "extra"] if checkpoint_save_contents is None: checkpoint_save_contents = ["model", "optimizer", "extra"] self.previous_global_step = None self.previous_saved_paths = [] self.model = model self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.processing_class = processing_class self.checkpoint_load_contents = checkpoint_load_contents self.checkpoint_save_contents = checkpoint_save_contents self.rank = torch.distributed.get_rank() self.world_size = torch.distributed.get_world_size() @property def should_save_model(self) -> bool: """ Returns True if 'model' is in checkpoint_save_contents, indicating the model state should be saved. """ return "model" in self.checkpoint_save_contents @property def should_save_optimizer(self) -> bool: """ Returns True if 'optimizer' is in checkpoint_save_contents, indicating the optimizer state should be saved. """ return "optimizer" in self.checkpoint_save_contents @property def should_save_extra(self) -> bool: """ Returns True if 'extra' is in checkpoint_save_contents, indicating the extra state should be saved. """ return "extra" in self.checkpoint_save_contents @property def should_save_hf_model(self) -> bool: """ Returns True if 'hf_model' is in checkpoint_save_contents, indicating the model should be converted to hf model and saved. """ return "hf_model" in self.checkpoint_save_contents @property def should_load_model(self) -> bool: """ Returns True if 'model' is in checkpoint_load_contents, indicating the model state should be loaded. """ return "model" in self.checkpoint_load_contents @property def should_load_optimizer(self) -> bool: """ Returns True if 'optimizer' is in checkpoint_load_contents, indicating the optimizer state should be loaded. """ return "optimizer" in self.checkpoint_load_contents @property def should_load_extra(self) -> bool: """ Returns True if 'extra' is in checkpoint_load_contents, indicating the extra state should be loaded. """ return "extra" in self.checkpoint_load_contents def load_checkpoint(self, local_path: str, hdfs_path: str = None, del_local_after_load: bool = False): raise NotImplementedError def save_checkpoint( self, local_path: str, hdfs_path: str = None, global_step: int = 0, max_ckpt_to_keep: int = None ): raise NotImplementedError @staticmethod def checkpath(local_path: str, hdfs_path: str): assert local_path is not None or hdfs_path is not None, "local_path and hdfs_path cannot be both None" return local_path is not None, local_path if local_path is not None else hdfs_path def remove_previous_save_local_path(self, path): if isinstance(path, str): path = [path] for p in path: abs_path = os.path.abspath(p) print(f"Checkpoint manager remove previous save local path: {abs_path}") if not os.path.exists(abs_path): continue shutil.rmtree(abs_path, ignore_errors=True) def ensure_checkpoint_capacity(self, max_ckpt_to_keep: int): """ Remove old checkpoints to make room for a new one, keeping a safety buffer. With max_ckpt_to_keep=1, this does nothing - we keep the existing checkpoint until the new save completes successfully (handled by register_checkpoint). For max_ckpt_to_keep >= 2, we keep (max_ckpt_to_keep - 1) checkpoints before save. """ if not (max_ckpt_to_keep and isinstance(max_ckpt_to_keep, int) and max_ckpt_to_keep > 1): return if len(self.previous_saved_paths) >= max_ckpt_to_keep: keep_start = len(self.previous_saved_paths) - max_ckpt_to_keep + 1 self.remove_previous_save_local_path(self.previous_saved_paths[:keep_start]) self.previous_saved_paths = self.previous_saved_paths[keep_start:] def register_checkpoint(self, new_path: str, max_ckpt_to_keep: int): """ Register a successfully saved checkpoint and enforce retention limit. Adds the new checkpoint path to tracking and removes excess old checkpoints beyond max_ckpt_to_keep. """ self.previous_saved_paths.append(new_path) if not (max_ckpt_to_keep and isinstance(max_ckpt_to_keep, int) and max_ckpt_to_keep > 0): return if len(self.previous_saved_paths) > max_ckpt_to_keep: keep_start = len(self.previous_saved_paths) - max_ckpt_to_keep self.remove_previous_save_local_path(self.previous_saved_paths[:keep_start]) self.previous_saved_paths = self.previous_saved_paths[keep_start:] @staticmethod def get_rng_state(): rng_state = { "cpu": torch.get_rng_state(), "numpy": np.random.get_state(), "random": random.getstate(), } if get_device_name() != "cpu": rng_state[get_device_name()] = get_torch_device().get_rng_state() return rng_state @staticmethod def load_rng_state(rng_state): torch.set_rng_state(rng_state["cpu"]) np.random.set_state(rng_state["numpy"]) random.setstate(rng_state["random"]) if get_device_name() != "cpu": get_torch_device().set_rng_state(rng_state[get_device_name()]) def find_latest_ckpt_path(path, directory_format="global_step_{}"): """ Return the most recent checkpoint directory based on a tracker file. Args: path (str): Base directory containing the checkpoint tracker. directory_format (str): Template for checkpoint subfolders with one placeholder for the iteration number (default "global_step_{}"). Returns: str or None: Full path to the latest checkpoint directory, or None if the tracker or checkpoint folder is missing. """ if path is None: return None tracker_file = get_checkpoint_tracker_filename(path) if not os.path.exists(tracker_file): if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: print(f"Checkpoint tracker file does not exist: {tracker_file}") return None with open(tracker_file, "rb") as f: iteration = int(f.read().decode()) ckpt_path = os.path.join(path, directory_format.format(iteration)) if not os.path.exists(ckpt_path): print("Checkpoint does not exist: %s", ckpt_path) return None print("Found checkpoint: %s", ckpt_path) return ckpt_path def get_checkpoint_tracker_filename(root_path: str): """ Tracker file rescords the latest chckpoint during training to restart from. """ return os.path.join(root_path, "latest_checkpointed_iteration.txt") def should_save_ckpt_esi(max_steps_duration: float, save_ckpt_duration: float = 60, redundant_time: float = 0) -> bool: """ Determine if checkpoint should be saved based on capacity esi expiration. Args: max_steps_duration: Max estimated time (seconds) required to complete one training step save_ckpt_duration: Estimated time (seconds) required to save checkpoint (default: 60) redundant_time: Additional buffer time (seconds) for unexpected delays (default: 0) """ exp_ts_mlp = os.getenv("MLP_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP") # vemlp exp_ts_aws = os.getenv("SAGEMAKER_CURRENT_CAPACITY_BLOCK_EXPIRATION_TIMESTAMP") # aws if exp_ts_mlp: try: import time remaining = float(exp_ts_mlp) - time.time() except ValueError: return False return ( remaining > 0 and max_steps_duration > 0 and remaining <= save_ckpt_duration + max_steps_duration + redundant_time ) elif exp_ts_aws: from datetime import datetime, timedelta expiration_time = datetime.fromtimestamp(int(exp_ts_aws)) time_difference = expiration_time - datetime.now() threshold_minutes = (save_ckpt_duration + max_steps_duration + redundant_time) / 60 return time_difference < timedelta(minutes=threshold_minutes) else: return False ================================================ FILE: verl/utils/checkpoint/fsdp_checkpoint_manager.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import os import warnings from dataclasses import asdict, dataclass from typing import Optional import torch import torch.distributed from accelerate import init_empty_weights from omegaconf import DictConfig from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictType from transformers import GenerationConfig, PreTrainedTokenizer, ProcessorMixin from transformers.dynamic_module_utils import custom_object_save from verl.utils.device import is_cuda_available from verl.utils.fs import copy_to_local, is_non_local, local_mkdir_safe from verl.utils.fsdp_utils import fsdp_version, get_fsdp_full_state_dict, get_fsdp_state_ctx from verl.utils.logger import log_with_rank from verl.utils.transformers_compat import get_auto_model_for_vision2seq from .checkpoint_manager import BaseCheckpointManager # Setup logging logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "INFO")) @dataclass class FSDPConfig: """Configuration for FSDP checkpointing. Args: FSDP_version (int): Version of FSDP being used. world_size (int): Number of processes in the distributed training setup. """ FSDP_version: int world_size: int class FSDPCheckpointManager(BaseCheckpointManager): """ Manage FSDP checkpointing in SPMD training. - Saves/loads per-rank sharded model & optimizer states - Persists full lr_scheduler and RNG state - Stores HF tokenizer/processor and model/config for unified restore Args: model (FSDP): Wrapped model instance. optimizer (Optimizer): Training optimizer. lr_scheduler (LRScheduler): Learning-rate scheduler. processing_class (PreTrainedTokenizer or ProcessorMixin, optional): Pre-/post-processing artifact handler. checkpoint_contents DictConfig: Configuration for checkpoint contents. - 'load': Components to load; must contain 'model'. Defaults to ['model', 'optimizer', 'extra']. - 'save': Components to save; must contain 'model'. Defaults to ['model', 'optimizer', 'extra']. trust_remote_code: Whether to trust_remote_code when loading the model configuration """ def __init__( self, model: FSDP, optimizer: Optional[torch.optim.Optimizer] = None, lr_scheduler: Optional[torch.optim.lr_scheduler.LRScheduler] = None, processing_class: PreTrainedTokenizer | ProcessorMixin = None, checkpoint_config: DictConfig = None, trust_remote_code: bool = False, **kwargs, ): if processing_class is None and "tokenizer" in kwargs: warnings.warn( "`tokenizer` is deprecated. use `processing_class` instead.", DeprecationWarning, stacklevel=2 ) processing_class = kwargs.pop("tokenizer") super().__init__( model, optimizer, lr_scheduler=lr_scheduler, processing_class=processing_class, checkpoint_config=checkpoint_config, ) self.trust_remote_code = trust_remote_code def load_checkpoint(self, local_path: str, hdfs_path: str = None, del_local_after_load=False): """ Load an FSDP checkpoint for this rank. Downloads and loads: - model and optimizer shards - extra state dict (scheduler + RNG) Args: local_path: Directory with per-rank checkpoint files. hdfs_path: Unused (for API compatibility). del_local_after_load: Remove local files after loading. """ if local_path is None: return # check if the checkpoint_load_contents is valid if self.should_load_model: assert self.model is not None, "model must be provided when checkpoint_contents.load includes ['model']" if self.should_load_optimizer: assert self.optimizer is not None, ( "optimizer must be provided when checkpoint_contents.load includes ['optimizer']" ) # every rank download its own checkpoint state_dict_cfg = ( ShardedStateDictConfig(offload_to_cpu=True if is_cuda_available else False) if self.should_load_model else None ) optim_cfg = ( ShardedOptimStateDictConfig(offload_to_cpu=True if is_cuda_available else False) if self.should_load_optimizer else None ) with get_fsdp_state_ctx(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg): if self.should_load_model: remote_model_path = os.path.join(local_path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt") local_model_path = copy_to_local(remote_model_path) model_state_dict = torch.load(local_model_path, weights_only=False) self.model.load_state_dict(model_state_dict) log_with_rank(f"Loaded model from {remote_model_path}", rank=self.rank, logger=logger) if self.should_load_optimizer: remote_optim_path = os.path.join(local_path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt") local_optim_path = copy_to_local(remote_optim_path) optimizer_state_dict = torch.load(local_optim_path, weights_only=False) self.optimizer.load_state_dict(optimizer_state_dict) log_with_rank(f"Loaded optimizer from {remote_optim_path}", rank=self.rank, logger=logger) if self.should_load_extra: remote_extra_state_path = os.path.join( local_path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt" ) local_extra_state_path = copy_to_local(remote_extra_state_path) extra_state_dict = torch.load(local_extra_state_path, weights_only=False) # recover random state if "rng" in extra_state_dict: # 'rng' may not exist for backward compatibility self.load_rng_state(extra_state_dict["rng"]) log_with_rank(f"Loaded rng from {remote_extra_state_path}", rank=self.rank, logger=logger) lr_scheduler_state_dict = extra_state_dict["lr_scheduler"] if lr_scheduler_state_dict is not None and self.lr_scheduler is not None: self.lr_scheduler.load_state_dict(lr_scheduler_state_dict) log_with_rank(f"Loaded lr_scheduler from {remote_extra_state_path}", rank=self.rank, logger=logger) if self.rank == 0 and del_local_after_load: try: os.remove(local_model_path) if is_non_local(local_model_path) else None os.remove(local_optim_path) if is_non_local(local_optim_path) else None os.remove(local_extra_state_path) if is_non_local(local_extra_state_path) else None except Exception as e: log_with_rank( f"remove local resume ckpt file after loading failed, exception {e} will be ignored", rank=self.rank, logger=logger, ) # wait for everyone to load checkpoints torch.distributed.barrier() def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: int = 0, max_ckpt_to_keep=None): """ Save an FSDP checkpoint for this rank. Writes: - model & optimizer shard files - extra state dict (scheduler + RNG) - HF tokenizer/processor and model/config on rank 0 - optional full HF model under 'huggingface/' if requested Rotates old checkpoints, keeping at most `max_ckpt_to_keep`. Args: local_path: Target directory for checkpoint files. hdfs_path: Unused (for API compatibility). global_step: Current training step (used for bookkeeping). max_ckpt_to_keep: Number of recent checkpoints to retain. """ if local_path is None: return # record the previous global step self.previous_global_step = global_step if self.rank == 0: self.ensure_checkpoint_capacity(max_ckpt_to_keep) local_path = local_mkdir_safe(local_path) torch.distributed.barrier() # check if the checkpoint_save_contents is valid if self.should_save_model: assert self.model is not None, "model must be provided when checkpoint_contents.save includes ['model']" if self.should_save_optimizer: assert self.optimizer is not None, ( "optimizer must be provided when checkpoint_contents.save includes ['optimizer']" ) # every rank will save its own model and optim shard state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True if is_cuda_available else False) optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True if is_cuda_available else False) with warnings.catch_warnings(): warnings.simplefilter("ignore") with get_fsdp_state_ctx(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg): model_path = os.path.join(local_path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt") optim_path = os.path.join(local_path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt") extra_path = os.path.join(local_path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt") if self.should_save_model: model_state_dict = self.model.state_dict() torch.save(model_state_dict, model_path) log_with_rank(f"Saved model to {os.path.abspath(model_path)}", rank=self.rank, logger=logger) if self.should_save_optimizer: optimizer_state_dict = self.optimizer.state_dict() torch.save(optimizer_state_dict, optim_path) log_with_rank(f"Saved optim to {os.path.abspath(optim_path)}", rank=self.rank, logger=logger) if self.should_save_extra: lr_scheduler_state_dict = self.lr_scheduler.state_dict() if self.lr_scheduler is not None else None extra_state_dict = { "lr_scheduler": lr_scheduler_state_dict, "rng": self.get_rng_state(), } torch.save(extra_state_dict, extra_path) log_with_rank(f"Saved extra_state to {os.path.abspath(extra_path)}", rank=self.rank, logger=logger) if self.rank == 0: # Save HF tokenizer/processor and model config on rank 0 to huggingface/ directory, no matter whether # huggingface model is requested to be saved or not. if fsdp_version(self.model) == 1: unwrap_model = self.model._fsdp_wrapped_module else: unwrap_model = self.model hf_config_tokenizer_path = os.path.join(local_path, "huggingface") local_mkdir_safe(hf_config_tokenizer_path) model_config = unwrap_model.config generation_config = None if unwrap_model.can_generate() and hasattr(model_config, "name_or_path") and model_config.name_or_path: try: # Some model's name_or_path is empty if not initialized from pretrained, # in this cases, we don't save generation config. generation_config = GenerationConfig.from_pretrained(model_config.name_or_path) generation_config.save_pretrained(hf_config_tokenizer_path) except Exception: # if the generation config isn't available, we don't save it pass if hasattr(model_config, "auto_map") and None in model_config.auto_map: model_config.auto_map = {k: v for k, v in model_config.auto_map.items() if k is not None} model_config.save_pretrained(hf_config_tokenizer_path) if self.processing_class is not None: self.processing_class.save_pretrained(hf_config_tokenizer_path) log_with_rank( f"Saved model config and tokenizer class to {os.path.abspath(hf_config_tokenizer_path)}", rank=self.rank, logger=logger, log_only_rank_0=True, ) # If we have a custom model, we copy the file defining it in the folder and set the attributes so it can be # loaded from the Hub. if hasattr(model_config, "auto_map"): custom_object_save(unwrap_model, hf_config_tokenizer_path, config=model_config) # Also save runtime FSDP config fsdp_config_path = os.path.join(local_path, "fsdp_config.json") fsdp_config = FSDPConfig( FSDP_version=fsdp_version(self.model), world_size=self.world_size, ) with open(fsdp_config_path, "w") as f: json.dump(asdict(fsdp_config), f, indent=4) # wait for everyone to dump to local torch.distributed.barrier() if self.should_save_hf_model: # Only rank 0 will save hf model and, # offload to cpu to save LLMs which may be too large to fit in one GPU state_dict = get_fsdp_full_state_dict(self.model, offload_to_cpu=True, rank0_only=True) if self.rank == 0: hf_local_path = os.path.join(local_path, "huggingface") os.makedirs(hf_local_path, exist_ok=True) if "ForTokenClassification" in model_config.architectures[0]: from transformers import AutoModelForTokenClassification auto_model_cls = AutoModelForTokenClassification elif "ForCausalLM" in model_config.architectures[0]: from transformers import AutoModelForCausalLM auto_model_cls = AutoModelForCausalLM elif "ForConditionalGeneration" in model_config.architectures[0]: auto_model_cls = get_auto_model_for_vision2seq() else: raise NotImplementedError(f"Unknown architecture {model_config['architectures']}") with init_empty_weights(): save_model = auto_model_cls.from_config( model_config, torch_dtype=torch.bfloat16, trust_remote_code=self.trust_remote_code ) save_model.to_empty(device="cpu") if save_model.can_generate(): if generation_config is not None: save_model.generation_config = generation_config else: print( f"Warning: {self.__class__.__name__}.save_checkpoint: Generation config file not found " f"in, using a generation config created from the model config when saving hf_model." ) save_model.save_pretrained(hf_local_path, state_dict=state_dict) log_with_rank( f"Saved hf_model to {os.path.abspath(hf_local_path)}", rank=self.rank, logger=logger, log_only_rank_0=True, ) del state_dict del save_model # wait for rank0 to dump hf_model to local torch.distributed.barrier() if self.rank == 0: self.register_checkpoint(local_path, max_ckpt_to_keep) ================================================ FILE: verl/utils/checkpoint/megatron_checkpoint_manager.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import json import logging import os import random from collections.abc import Callable from dataclasses import asdict import megatron.core import numpy as np import torch import torch.distributed from megatron.core import dist_checkpointing, mpu, tensor_parallel from megatron.core.dist_checkpointing.mapping import ShardedObject from megatron.core.transformer.enums import AttnBackend from packaging import version from transformers import GenerationConfig from verl.models.weight_loader_registry import get_weight_saver from verl.utils.device import get_device_name, get_torch_device from verl.utils.fs import is_non_local, local_mkdir_safe from verl.utils.logger import log_with_rank from verl.utils.megatron.dist_checkpointing import load_dist_checkpointing, save_dist_checkpointing from verl.utils.megatron_utils import ( get_dist_checkpoint_path, get_hf_model_checkpoint_path, get_transformer_config_checkpoint_path, ) from .checkpoint_manager import BaseCheckpointManager # Setup logging logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "INFO")) mcore_ge_014 = version.parse(megatron.core.__version__) >= version.parse("0.14.0") if not mcore_ge_014: logger.warning( "Detected megatron.core %s, recommend upgrading to >= 0.14.0 for better checkpoint compatibility", megatron.core.__version__, ) class MegatronCheckpointManager(BaseCheckpointManager): """ Checkpoint manager for Megatron-LM distributed training. This class manages the saving and loading of model checkpoints in a Megatron-LM distributed training environment. It handles various aspects of checkpointing including model states, optimizer states, learning rate schedulers, and random number generator states, ensuring compatibility with HuggingFace formats. Key features: - Distributed checkpoint saving and loading using Megatron's dist_checkpointing - Support for tensor parallel, pipeline parallel, and data parallel configurations - Automatic handling of model state dictionaries across multiple pipeline stages - Integration with HuggingFace model configurations and tokenizers - Random number generator state management for reproducibility - Support for both synchronous and asynchronous checkpoint operations The manager automatically handles: - Directory structure creation based on global steps and process ranks - Model configuration and tokenizer saving in HuggingFace format - Optimizer and scheduler state persistence - CUDA RNG state management for deterministic training - Checkpoint cleanup and retention policies Args: model: The Megatron model instance to checkpoint optimizer: The optimizer instance (optional) lr_scheduler: The learning rate scheduler instance (optional) Attributes: model: Reference to the Megatron model being checkpointed optimizer: Reference to the optimizer (if provided) lr_scheduler: Reference to the learning rate scheduler (if provided) rank: Current process rank in the distributed setup Example: ```python checkpoint_manager = MegatronCheckpointManager( model=megatron_model, optimizer=optimizer, lr_scheduler=scheduler ) checkpoint_manager.save_checkpoint( local_path="checkpoints/step_1000", global_step=1000 ) checkpoint_manager.load_checkpoint( local_path="checkpoints/step_1000" ) ``` """ def __init__( self, config, checkpoint_config, model_config, transformer_config, role, model: torch.nn.ModuleList, arch: str, hf_config, param_dtype: torch.dtype, share_embeddings_and_output_weights: bool, processing_class, optimizer, optimizer_scheduler, use_distributed_optimizer: bool, use_checkpoint_opt_param_scheduler: bool = False, use_dist_checkpointing: bool = True, bridge=None, provider=None, peft_cls=None, **kwargs, ): super().__init__( model, optimizer=optimizer, lr_scheduler=optimizer_scheduler, processing_class=processing_class, checkpoint_config=checkpoint_config, ) self.arch = arch self.config = config self.transformer_config = transformer_config self.role = role self.is_value_model = False if self.role in ["reward", "critic"]: self.is_value_model = True self.model_config = model_config self.hf_config = hf_config self.param_dtype = param_dtype self.share_embeddings_and_output_weights = share_embeddings_and_output_weights self.model_path = self.config.model.path self.use_distributed_optimizer = use_distributed_optimizer self.use_checkpoint_opt_param_scheduler = use_checkpoint_opt_param_scheduler self.bridge = bridge self.provider = provider self.vanilla_bridge = self.provider is None self.peft_cls = peft_cls self.rank = torch.distributed.get_rank() # Megatron-Bridge is Okay to load/save HF checkpoint for value model as well self.use_dist_checkpointing = ( use_dist_checkpointing or not self.bridge or (self.vanilla_bridge and self.is_value_model) ) self.use_hf_checkpoint = not self.use_dist_checkpointing self.weight_saver = None if self.bridge is None: self.weight_saver = get_weight_saver(self.arch) def get_rng_state(self, use_dist_ckpt: bool = True, data_parallel_random_init: bool = False): """collect rng state across data parallel ranks""" rng_state = { "random_rng_state": random.getstate(), "np_rng_state": np.random.get_state(), "torch_rng_state": torch.get_rng_state(), "rng_tracker_states": tensor_parallel.get_cuda_rng_tracker().get_states(), } if get_device_name() != "cpu": rng_state[f"{get_device_name()}_rng_state"] = get_torch_device().get_rng_state() rng_state_list = None if torch.distributed.is_initialized() and mpu.get_data_parallel_world_size() > 1 and data_parallel_random_init: rng_state_list = [None for i in range(mpu.get_data_parallel_world_size())] torch.distributed.all_gather_object(rng_state_list, rng_state, group=mpu.get_data_parallel_group()) else: rng_state_list = [rng_state] if use_dist_ckpt: pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() rng_state_list = ShardedObject( "rng_state", rng_state_list, (pp_size, tp_size), (pp_rank, tp_rank), replica_id=mpu.get_data_parallel_rank(with_context_parallel=True), ) return rng_state_list def get_checkpoint_name( self, checkpoints_path, pipeline_parallel=None, tensor_rank=None, pipeline_rank=None, cp_rank=None, expert_parallel=None, expert_rank=None, return_base_dir=True, basename="model.pt", ): """Determine the directory name for this rank's checkpoint.""" # Use both the tensor and pipeline MP rank. if pipeline_parallel is None: pipeline_parallel = mpu.get_pipeline_model_parallel_world_size() > 1 if tensor_rank is None: tensor_rank = mpu.get_tensor_model_parallel_rank() if pipeline_rank is None: pipeline_rank = mpu.get_pipeline_model_parallel_rank() if cp_rank is None: cp_rank = mpu.get_context_parallel_rank() if expert_parallel is None: expert_parallel = mpu.get_expert_model_parallel_world_size() > 1 if expert_rank is None: expert_rank = mpu.get_expert_model_parallel_rank() # Use both the tensor and pipeline MP rank. If using the distributed # optimizer, then the optimizer's path must additionally include the # data parallel rank. # due to the fact that models are identical across cp ranks, cp rank is not used in the checkpoint path if not pipeline_parallel: common_path = os.path.join(checkpoints_path, f"mp_rank_{tensor_rank:02d}") else: common_path = os.path.join(checkpoints_path, f"mp_rank_{tensor_rank:02d}_{pipeline_rank:03d}") if expert_parallel: common_path = common_path + f"_{expert_rank:03d}" os.makedirs(common_path, exist_ok=True) if return_base_dir: return common_path return os.path.join(common_path, basename) def generate_state_dict( self, generate_model: bool = True, generate_optimizer: bool = True, generate_extra: bool = True, is_loading: bool = False, metadata: dict | None = None, ): # For save dist checkpointing state_dict = {} base_metadata = metadata or self._build_sharded_state_dict_metadata() # Should always generate model state dict # All ranks Save Model to reduce memory pressure # Get sharded state dict, notice that state_dict will collect among dp groups, causing memory pressure for vpp_rank, model in enumerate(self.model): if len(self.model) > 1: mpu.set_virtual_pipeline_model_parallel_rank(vpp_rank) key = f"model{vpp_rank}" if len(self.model) > 1 else "model" else: key = "model" if hasattr(model, "module"): model = model.module # GPTModel's sharded_state_dict function when having mtp requires metadata['dp_cp_group'] model_metadata = dict(base_metadata) model_metadata["dp_cp_group"] = mpu.get_data_parallel_group(with_context_parallel=True) kwargs = {"metadata": model_metadata} state_dict[key] = model.sharded_state_dict(**kwargs) # Optimizer State Dict if generate_optimizer: torch.distributed.barrier() sharded_state_dict_kwargs = {"is_loading": is_loading} if base_metadata is not None: # https://github.com/NVIDIA/Megatron-LM/blob/core_v0.14.0/megatron/core/optimizer/distrib_optimizer.py#L1109-L1123 if mcore_ge_014: sharded_state_dict_kwargs["metadata"] = base_metadata optimizer_sharded_states = self.optimizer.sharded_state_dict(state_dict, **sharded_state_dict_kwargs) state_dict["optimizer"] = optimizer_sharded_states if self.lr_scheduler is not None: lr_state_dict = self.lr_scheduler.state_dict() state_dict["lr_scheduler"] = lr_state_dict if not generate_model: state_dict.pop("model", None) # RNG States State Dict if generate_extra: torch.distributed.barrier() rng_state = self.get_rng_state() state_dict["rng_state"] = rng_state return state_dict def _build_sharded_state_dict_metadata(self) -> dict: """Builds metadata used for sharded_state_dict versioning. The whole content metadata is passed to ``sharded_state_dict`` model and optimizer methods and therefore affects only the logic behind sharded_state_dict creation. The content metadata should be minimalistic, ideally flat (or with a single nesting level) and with semantically meaningful flag names (e.g. `distrib_optim_sharding_type`). In particular, a simple integer (or SemVer) versioning flag (e.g. `metadata['version'] = 3.4`) is discouraged, because the metadata serves for all models and optimizers and it's practically impossible to enforce a linearly increasing versioning for this whole space. """ metadata: dict = {} if not mcore_ge_014: # For backward compatibility with Megatron core < v0.14.0 if self.use_distributed_optimizer: metadata["distrib_optim_sharding_type"] = "fully_sharded_model_space" return metadata if self.use_distributed_optimizer: megatron_config = getattr(self.config, self.role, self.config).megatron dist_ckpt_optim_fully_reshardable = megatron_config.dist_ckpt_optim_fully_reshardable distrib_optim_fully_reshardable_mem_efficient = ( megatron_config.distrib_optim_fully_reshardable_mem_efficient ) if dist_ckpt_optim_fully_reshardable: metadata["distrib_optim_sharding_type"] = "fully_reshardable" metadata["distrib_optim_fully_reshardable_mem_efficient"] = ( distrib_optim_fully_reshardable_mem_efficient ) else: metadata["distrib_optim_sharding_type"] = "dp_reshardable" metadata["singleton_local_shards"] = False metadata["chained_optim_avoid_prefix"] = True return metadata def load_rng_states(self, rng_states, data_parallel_random_init=False, use_dist_ckpt=True): # access rng_state for data parallel rank if data_parallel_random_init: rng_states = rng_states[mpu.get_data_parallel_rank()] else: rng_states = rng_states[0] random.setstate(rng_states["random_rng_state"]) np.random.set_state(rng_states["np_rng_state"]) torch.set_rng_state(rng_states["torch_rng_state"]) if get_device_name() != "cpu": get_torch_device().set_rng_state(rng_states[f"{get_device_name()}_rng_state"]) # Check for empty states array if not rng_states["rng_tracker_states"]: raise KeyError tensor_parallel.get_cuda_rng_tracker().set_states(rng_states["rng_tracker_states"]) def load_checkpoint(self, local_path: str, hdfs_path: str = None, del_local_after_load=False): if local_path is not None: assert os.path.exists(local_path), f"Checkpoint path {local_path} does not exist." # For load optimizer dist_ckpt try: import transformer_engine torch.serialization.add_safe_globals([torch.optim.AdamW]) torch.serialization.add_safe_globals([transformer_engine.pytorch.optimizers.fused_adam.FusedAdam]) except Exception: pass dist_checkpoint_path = get_dist_checkpoint_path(local_path) load_content_metadata = getattr(dist_checkpointing, "load_content_metadata", None) if load_content_metadata is None: # For backward compatibility sharded_sd_metadata = None else: sharded_sd_metadata = load_content_metadata(checkpoint_dir=dist_checkpoint_path) if sharded_sd_metadata is None: if self.use_distributed_optimizer: # Backward-compatibility with old checkpoints which don't have content versioning # Can be removed after ending support for MLM optimizer checkpoints with MCore < v0.13 # (for MCore v0.13+ checkpoints `sharded_sd_metadata is not None`) sharded_sd_metadata = { "distrib_optim_sharding_type": "fully_sharded_model_space", } else: sharded_sd_metadata = self._build_sharded_state_dict_metadata() # Get State Dict for loading sharded_state_dict = self.generate_state_dict( self.should_load_model and self.use_dist_checkpointing, self.should_load_optimizer, self.should_load_extra, is_loading=True, metadata=sharded_sd_metadata, ) log_with_rank(f"Generated state dict for loading: {sharded_state_dict.keys()}", rank=self.rank, logger=logger) # Load Dist Checkpointing state_dict = load_dist_checkpointing( sharded_state_dict=sharded_state_dict, ckpt_dir=dist_checkpoint_path, ) if self.should_load_model and self.use_dist_checkpointing: assert "model" in state_dict or any( f"model{vpp_rank}" in state_dict for vpp_rank in range(len(self.model)) ), f"Model state dict not found in {state_dict.keys()}. Please check the checkpoint file {local_path}." for vpp_rank, model in enumerate(self.model): if len(self.model) == 1: model_state_dict = state_dict["model"] else: assert f"model{vpp_rank}" in state_dict, f"model{vpp_rank} not found in state_dict" model_state_dict = state_dict[f"model{vpp_rank}"] mpu.set_virtual_pipeline_model_parallel_rank(vpp_rank) self.model[vpp_rank].load_state_dict(model_state_dict) log_with_rank(f"Loaded sharded model checkpoint from {local_path}", rank=self.rank, logger=logger) # Skip HF checkpoint loading if PEFT is used elif self.should_load_model and self.use_hf_checkpoint and self.peft_cls is None: hf_model_path = get_hf_model_checkpoint_path(local_path) if self.vanilla_bridge: self.bridge.load_weights(self.model, hf_model_path) else: self.bridge.load_hf_weights(self.model, hf_model_path) log_with_rank(f"Loaded HF model checkpoint from {hf_model_path} with bridge", rank=self.rank, logger=logger) # Load PEFT adapter checkpoint if available if self.should_load_model and self.peft_cls is not None: adapter_ckpt_path = os.path.join(local_path, "adapter_checkpoint") if os.path.exists(adapter_ckpt_path): from verl.utils.megatron_peft_utils import load_adapter_checkpoint # TODO: a better format for adapter checkpoint, waiting megatron-bridge support load_adapter_checkpoint( self.model, adapter_ckpt_path, ) log_with_rank( f"Loaded adapter checkpoint from {adapter_ckpt_path}", rank=self.rank, logger=logger, ) else: log_with_rank( f"PEFT config is set but no adapter checkpoint found at {adapter_ckpt_path}", rank=self.rank, logger=logger, ) if self.should_load_optimizer: assert "optimizer" in state_dict, ( f"Optimizer state dict not found in {state_dict.keys()}. Please check the checkpoint file {local_path}." ) optimizer_state_dict = state_dict["optimizer"] self.optimizer.load_state_dict(optimizer_state_dict) log_with_rank(f"Loaded optimizer checkpoint from {local_path}", rank=self.rank, logger=logger) if self.use_checkpoint_opt_param_scheduler: assert "lr_scheduler" in state_dict, ( f"LR scheduler state dict not found in {state_dict.keys()}. Please check the checkpoint file " f"{local_path}." ) lr_scheduler_state_dict = state_dict["lr_scheduler"] if self.lr_scheduler is not None: self.lr_scheduler.load_state_dict(lr_scheduler_state_dict) log_with_rank(f"Loaded LR scheduler checkpoint from {local_path}", rank=self.rank, logger=logger) if self.should_load_extra: assert "rng_state" in state_dict, ( f"RNG state dict not found in {state_dict.keys()}. Please check the checkpoint file {local_path}." ) rng_state = state_dict["rng_state"] self.load_rng_states(rng_state) log_with_rank(f"Loaded RNG states from {local_path}", rank=self.rank, logger=logger) if del_local_after_load: try: os.remove(local_path) if is_non_local(local_path) else None except Exception as e: log_with_rank( f"remove local resume ckpt file after loading failed, exception {e} will be ignored", rank=self.rank, logger=logger, ) def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: int = 0, max_ckpt_to_keep=None): # record the previous global step self.previous_global_step = global_step if not self.checkpoint_config.async_save: self.ensure_checkpoint_capacity(max_ckpt_to_keep) local_path = local_mkdir_safe(local_path) dist_checkpoint_path = get_dist_checkpoint_path(local_path) # Note that model weights, optimizer states, and extra states are generated # together in a state dict, we save them in one time if self.use_dist_checkpointing: # Generate state dict for saving sharded_sd_metadata = self._build_sharded_state_dict_metadata() state_dict = self.generate_state_dict( self.should_save_model, self.should_save_optimizer, self.should_save_extra, metadata=sharded_sd_metadata, ) log_with_rank(f"Generated state dict for saving: {state_dict.keys()}", rank=self.rank, logger=logger) for vpp_rank, model in enumerate(self.model): if len(self.model) > 1: model_i_keys = state_dict[f"model{vpp_rank}"].keys() log_with_rank(f"Generated state dict for saving: {model_i_keys}", rank=self.rank, logger=logger) else: log_with_rank( f"Generated state dict for saving: {state_dict['model'].keys()}", rank=self.rank, logger=logger ) # Start Async save if enabled async_save_request = save_dist_checkpointing( sharded_state_dict=state_dict, ckpt_path=dist_checkpoint_path, async_save=self.checkpoint_config.async_save, content_metadata=sharded_sd_metadata, ) # Synchronize all async save requests if not self.checkpoint_config.async_save: assert async_save_request is None, "Async save request should be None when not using async save." torch.distributed.barrier() else: assert self.use_hf_checkpoint, "When not using distributed checkpointing, use_hf_checkpoint should be True." # Generate optimizer and exra state dicts sharded_sd_metadata = self._build_sharded_state_dict_metadata() state_dict = self.generate_state_dict( generate_model=False, generate_optimizer=self.should_save_optimizer, generate_extra=self.should_save_extra, metadata=sharded_sd_metadata, ) # Save optimizer and extra states to local path # Start Async save if enabled async_save_request = save_dist_checkpointing( sharded_state_dict=state_dict, ckpt_path=dist_checkpoint_path, async_save=self.checkpoint_config.async_save, content_metadata=sharded_sd_metadata, ) # Synchronize all async save requests if not self.checkpoint_config.async_save: assert async_save_request is None, "Async save request should be None when not using async save." torch.distributed.barrier() if self.should_save_model: # Save adapter-only checkpoint if PEFT is enabled if self.peft_cls is not None: from verl.utils.megatron_peft_utils import save_adapter_checkpoint adapter_ckpt_path = os.path.join(local_path, "adapter_checkpoint") # Save adapter weights only (much smaller than full model) save_adapter_checkpoint( self.model, adapter_ckpt_path, self.rank, ) log_with_rank( f"Saved adapter-only checkpoint to {adapter_ckpt_path}", rank=self.rank, logger=logger, log_only_rank_0=True, ) elif self.use_hf_checkpoint: # Use mbridge to save HF model checkpoint log_with_rank(f"Saving HF model checkpoint to {local_path} with bridge", rank=self.rank, logger=logger) hf_ckpt_path = get_hf_model_checkpoint_path(local_path) if self.vanilla_bridge: extended_args = {} mbridge_config = getattr(self.checkpoint_config, "mbridge_config", None) or {} for sig in inspect.signature(self.bridge.save_weights).parameters: if sig == "weights_path" or sig == "models": continue if sig in mbridge_config: extended_args[sig] = mbridge_config[sig] self.bridge.save_weights(self.model, hf_ckpt_path, **extended_args) else: self.bridge.save_hf_weights(self.model, hf_ckpt_path) log_with_rank(f"Saved bridge checkpoint to {hf_ckpt_path}", rank=self.rank, logger=logger) # Only rank 0 saves the hf config and tokenizer to huggingface path # No matter whether we save hf model or not if self.rank == 0: # Save tokenizer hf_config_tokenizer_path = get_hf_model_checkpoint_path(local_path) if self.processing_class is not None: self.processing_class.save_pretrained(hf_config_tokenizer_path) # Save huggingface config self.hf_config.save_pretrained(hf_config_tokenizer_path) if hasattr(self.hf_config, "name_or_path") and self.hf_config.name_or_path: try: generation_config = GenerationConfig.from_pretrained(self.hf_config.name_or_path) generation_config.save_pretrained(hf_config_tokenizer_path) except Exception: # if the generation config isn't available, we don't save it pass log_with_rank( f"Saved Huggingface config and tokenizer to {hf_config_tokenizer_path}", rank=self.rank, logger=logger, log_only_rank_0=True, ) if self.should_save_extra: if self.rank == 0: # Save transformer config print(self.transformer_config) bypass_keys = [ "finalize_model_grads_func", "grad_scale_func", "no_sync_func", "grad_sync_func", "param_sync_func", "generation_config", "_pg_collection", ] backup = {} for k in bypass_keys: if hasattr(self.transformer_config, k): backup[k] = getattr(self.transformer_config, k, None) delattr(self.transformer_config, k) transformer_config_dict = asdict(self.transformer_config) for k in backup: setattr(self.transformer_config, k, backup[k]) to_convert_types = {torch.dtype: str, AttnBackend: str} ignore_types = [Callable] pop_keys = [] for key, value in transformer_config_dict.items(): if type(value) in to_convert_types: transformer_config_dict[key] = to_convert_types[type(value)](value) if type(value) in ignore_types: pop_keys.append(key) if callable(value): pop_keys.append(key) for key in pop_keys: transformer_config_dict.pop(key) transformer_config_path = get_transformer_config_checkpoint_path(local_path) with open(transformer_config_path, "w") as f: json.dump(transformer_config_dict, f, indent=2) if self.should_save_hf_model and not self.use_hf_checkpoint: # wait for everyone to dump to local if self.bridge is not None: hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path) if self.vanilla_bridge: extended_args = {} mbridge_config = getattr(self.checkpoint_config, "mbridge_config", None) or {} for sig in inspect.signature(self.bridge.save_weights).parameters: if sig == "weights_path" or sig == "models": continue if sig in mbridge_config: extended_args[sig] = mbridge_config[sig] self.bridge.save_weights(self.model, hf_model_ckpt_path, **extended_args) else: self.bridge.save_hf_weights(self.model, hf_model_ckpt_path) else: state_dict = self.weight_saver( self.model, self.hf_config, dtype=self.param_dtype, is_value_model=self.is_value_model, tie_word_embeddings=self.share_embeddings_and_output_weights, ) torch.distributed.barrier() if self.rank == 0: hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path) import warnings from accelerate import init_empty_weights with init_empty_weights(), warnings.catch_warnings(): warnings.simplefilter("ignore") if "mistral7b-rm" in self.config.model.path: from transformers import MistralForSequenceClassification model = MistralForSequenceClassification.from_pretrained( self.config.model.path ) # use score head instead of lm_head state_dict["score.weight"] = state_dict["score.weight"] else: from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(self.config.model.path, torch_dtype="auto") model.save_pretrained(hf_model_ckpt_path, state_dict=state_dict) log_with_rank( f"Saved Huggingface config and tokenizer to {hf_model_ckpt_path}", rank=self.rank, logger=logger, log_only_rank_0=True, ) if hdfs_path is not None: log_with_rank( f"Uploading checkpoint to {hdfs_path}", rank=self.rank, logger=logger, log_only_rank_0=True ) from verl.utils import hdfs_io hdfs_io.makedirs(hdfs_path, exist_ok=True) hdfs_io.copy(src=hf_model_ckpt_path, dst=hdfs_path, dirs_exist_ok=True) log_with_rank( f"HDFS checkpoint uploaded to {hdfs_path}", rank=self.rank, logger=logger, log_only_rank_0=True, ) def finalize_save_fn(): # Rank 0 uploads checkpoint to HDFS if hdfs_path is provided log_with_rank( f"Dist checkpointing save completed for {dist_checkpoint_path}", rank=self.rank, logger=logger ) if self.rank == 0: if hdfs_path is not None: log_with_rank(f"Uploading checkpoint to {hdfs_path}", rank=self.rank, logger=logger) from verl.utils import hdfs_io hdfs_io.makedirs(hdfs_path, exist_ok=True) hdfs_io.copy(src=dist_checkpoint_path, dst=hdfs_path, dirs_exist_ok=True) hdfs_io.copy(src=hf_config_tokenizer_path, dst=hdfs_path, dirs_exist_ok=True) # update latest_checkpointed_iteration.txt when async_save is True if self.checkpoint_config.async_save and self.rank == 0: log_with_rank( f"Update latest_checkpointed_iteration.txt to step {global_step}", rank=self.rank, logger=logger, ) local_latest_checkpointed_iteration = os.path.join( os.path.dirname(os.path.dirname(local_path)), "latest_checkpointed_iteration.txt" ) with open(local_latest_checkpointed_iteration, "w") as f: f.write(str(global_step)) self.register_checkpoint(local_path, max_ckpt_to_keep) if self.checkpoint_config.async_save: assert async_save_request is not None, "Async save request should not be None when using async save." async_save_request.add_finalize_fn(finalize_save_fn) from megatron.core.dist_checkpointing.strategies.base import async_calls async_calls.schedule_async_request(async_save_request) else: finalize_save_fn() ================================================ FILE: verl/utils/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import is_dataclass from typing import Any, Optional from omegaconf import DictConfig, ListConfig, OmegaConf __all__ = ["omega_conf_to_dataclass", "validate_config"] def omega_conf_to_dataclass(config: DictConfig | dict, dataclass_type: Optional[type[Any]] = None) -> Any: """ Convert an OmegaConf DictConfig to a dataclass. Args: config: The OmegaConf DictConfig or dict to convert. dataclass_type: The dataclass type to convert to. When dataclass_type is None, the DictConfig must contain _target_ to be instantiated via hydra.instantiate API. Returns: The dataclass instance. """ # Got an empty config if not config: return dataclass_type if dataclass_type is None else dataclass_type() # Got an object if not isinstance(config, DictConfig | ListConfig | dict | list): return config if dataclass_type is None: assert "_target_" in config, ( "When dataclass_type is not provided, config must contain _target_. " "See trainer/config/ppo_trainer.yaml algorithm section for an example. " f"Got config: {config}" ) from hydra.utils import instantiate return instantiate(config, _convert_="partial") if not is_dataclass(dataclass_type): raise ValueError(f"{dataclass_type} must be a dataclass") cfg = OmegaConf.create(config) # in case it's a dict # pop _target_ to avoid hydra instantiate error, as most dataclass do not have _target_ # Updated (vermouth1992) We add _target_ to BaseConfig so that it is compatible. # Otherwise, this code path can't support recursive instantiation. # if "_target_" in cfg: # cfg.pop("_target_") cfg_from_dataclass = OmegaConf.structured(dataclass_type) # let cfg override the existing vals in `cfg_from_dataclass` cfg_merged = OmegaConf.merge(cfg_from_dataclass, cfg) # now convert to `dataclass_type` config_object = OmegaConf.to_object(cfg_merged) return config_object def update_dict_with_config(dictionary: dict, config: DictConfig): for key in dictionary: if hasattr(config, key): dictionary[key] = getattr(config, key) def validate_config( config: DictConfig, use_reference_policy: bool, use_critic: bool, ) -> None: """Validate an OmegaConf DictConfig. Args: config (DictConfig): The OmegaConf DictConfig to validate. use_reference_policy (bool): is ref policy needed use_critic (bool): is critic needed """ # number of GPUs total n_gpus = config.trainer.n_gpus_per_node * config.trainer.nnodes if not config.actor_rollout_ref.actor.use_dynamic_bsz: if config.actor_rollout_ref.actor.strategy == "megatron": model_parallel_size = ( config.actor_rollout_ref.actor.megatron.tensor_model_parallel_size * config.actor_rollout_ref.actor.megatron.pipeline_model_parallel_size ) assert ( n_gpus % (model_parallel_size * config.actor_rollout_ref.actor.megatron.context_parallel_size) == 0 ), ( f"n_gpus ({n_gpus}) must be divisible by model_parallel_size ({model_parallel_size}) times " f"context_parallel_size ({config.actor_rollout_ref.actor.megatron.context_parallel_size})" ) megatron_dp = n_gpus // ( model_parallel_size * config.actor_rollout_ref.actor.megatron.context_parallel_size ) minimal_bsz = megatron_dp * config.actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu else: minimal_bsz = n_gpus # 1. Check total batch size for data correctness real_train_batch_size = config.data.train_batch_size * config.actor_rollout_ref.rollout.n assert real_train_batch_size % minimal_bsz == 0, ( f"real_train_batch_size ({real_train_batch_size}) must be divisible by minimal possible batch size " f"({minimal_bsz})" ) # A helper function to check "micro_batch_size" vs "micro_batch_size_per_gpu" # We throw an error if the user sets both. The new convention is "..._micro_batch_size_per_gpu". def check_mutually_exclusive(mbs, mbs_per_gpu, name: str): """Validate mutually exclusive micro batch size configuration options. Ensures that users don't set both deprecated micro_batch_size and the new micro_batch_size_per_gpu parameters simultaneously. Args: mbs: Deprecated micro batch size parameter value. mbs_per_gpu: New micro batch size per GPU parameter value. name (str): Configuration section name for error messages. Raises: ValueError: If both parameters are set or neither is set. """ settings = { "actor_rollout_ref.ref": "log_prob_micro_batch_size", "actor_rollout_ref.rollout": "log_prob_micro_batch_size", } if name in settings: param = settings[name] param_per_gpu = f"{param}_per_gpu" if mbs is None and mbs_per_gpu is None: raise ValueError(f"[{name}] Please set at least one of '{name}.{param}' or '{name}.{param_per_gpu}'.") if mbs is not None and mbs_per_gpu is not None: raise ValueError( f"[{name}] You have set both '{name}.{param}' AND '{name}.{param_per_gpu}'. Please remove " f"'{name}.{param}' because only '*_{param_per_gpu}' is supported (the former is deprecated)." ) # Actor validation done in ActorConfig.__post_init__ and validate() actor_config = omega_conf_to_dataclass(config.actor_rollout_ref.actor) actor_config.validate(n_gpus, config.data.train_batch_size, config.actor_rollout_ref.model) if not config.actor_rollout_ref.actor.use_dynamic_bsz: if use_reference_policy: # reference: log_prob_micro_batch_size vs. log_prob_micro_batch_size_per_gpu check_mutually_exclusive( config.actor_rollout_ref.ref.log_prob_micro_batch_size, config.actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu, "actor_rollout_ref.ref", ) # The rollout section also has log_prob_micro_batch_size vs. log_prob_micro_batch_size_per_gpu check_mutually_exclusive( config.actor_rollout_ref.rollout.log_prob_micro_batch_size, config.actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu, "actor_rollout_ref.rollout", ) if config.algorithm.use_kl_in_reward and config.actor_rollout_ref.actor.use_kl_loss: print("NOTICE: You have both enabled in-reward kl and kl loss.") # critic if use_critic: critic_config = omega_conf_to_dataclass(config.critic) critic_config.validate(n_gpus, config.data.train_batch_size) if config.data.get("val_batch_size", None) is not None: print( "WARNING: val_batch_size is deprecated." + " Validation datasets are sent to inference engines as a whole batch," + " which will schedule the memory themselves." ) # check eval config if config.actor_rollout_ref.rollout.val_kwargs.do_sample: assert config.actor_rollout_ref.rollout.temperature > 0, ( "validation gen temperature should be greater than 0 when enabling do_sample" ) # check LoRA rank in vLLM lora_config = config.actor_rollout_ref.model.get("lora", {}) lora_rank = lora_config.get("rank", 0) if lora_rank <= 0: lora_rank = config.actor_rollout_ref.model.get("lora_rank", 0) if lora_config.get("merge", False): lora_rank = 0 if lora_rank > 0 and config.actor_rollout_ref.rollout.name == "vllm": from verl.workers.rollout.vllm_rollout.utils import get_vllm_max_lora_rank get_vllm_max_lora_rank(lora_rank) print("[validate_config] All configuration checks passed successfully!") ================================================ FILE: verl/utils/dataset/README.md ================================================ # Dataset Format ## RLHF dataset We combine all the data sources into a single parquet files. We directly organize the prompt into the chat format so that multi-turn chats can be easily incorporated. In the prompt, we may add instruction following texts to guide the model output the answers in a particular format so that we can extract the answers. Math problems ```json { "data_source": "openai/gsm8k", "prompt": [{"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? Let's think step by step and output the final answer after \"####\""}], "ability": "math", "reward_model": { "style": "rule", "ground_truth": ["72"] }, } ``` ================================================ FILE: verl/utils/dataset/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .rl_dataset import RLHFDataset from .rm_dataset import RMDataset __all__ = ["RLHFDataset", "RMDataset"] ================================================ FILE: verl/utils/dataset/dataset_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from enum import Enum import torch from tensordict.tensorclass import NonTensorData class DatasetPadMode(str, Enum): """Padding mode for dataset""" RIGHT = "right" LEFT_RIGHT = "left_right" NO_PADDING = "no_padding" class SFTTensorCollator: """ A custom collate_fn that handles batching of sequences. 1. for variable-length sequences, convert them into NestedTensors. 2. for fixed-length sequences, use default_collate. """ def __init__(self, pad_mode: DatasetPadMode = DatasetPadMode.LEFT_RIGHT): self.pad_mode = pad_mode def __call__(self, batch: list[dict[str, any]]) -> dict[str, any]: if self.pad_mode == DatasetPadMode.NO_PADDING: return self.collate_variable_batch(batch) elif self.pad_mode in [DatasetPadMode.RIGHT, DatasetPadMode.LEFT_RIGHT]: from torch.utils.data import default_collate return default_collate(batch) else: raise NotImplementedError(f"pad_mode {self.pad_mode} not implemented") def collate_variable_batch(self, batch: list[dict[str, any]]) -> dict[str, any]: """ Collates a list of samples into a single batch. Args: batch: A list of dictionary samples from the dataset. Returns: A dictionary representing the batched data, with variable-length sequences converted to NestedTensors. """ final_batch = {} tensor_keys = set().union(*(d.keys() for d in batch)) # Handle tensor values by creating a NestedTensor. for key in tensor_keys: if isinstance(batch[0][key], torch.Tensor): tensors = [item[key] for item in batch] final_batch[key] = torch.nested.as_nested_tensor(tensors, layout=torch.jagged) else: tensors = [NonTensorData(item.get(key)) for item in batch] final_batch[key] = torch.stack(tensors, dim=0) return final_batch ================================================ FILE: verl/utils/dataset/multiturn_sft_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2025 ModelBest Inc. and/or its affiliates # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Multi-turn SFT dataset that supports training on conversation data with multiple turns """ import logging import os import re from functools import wraps from typing import Any, Optional import numpy as np import pandas as pd import torch import torch.nn.functional as F from omegaconf import DictConfig, ListConfig from torch.utils.data import Dataset from transformers import PreTrainedTokenizer, ProcessorMixin from verl.models.transformers.qwen2_vl import get_rope_index from verl.utils import hf_tokenizer from verl.utils.chat_template import apply_chat_template, extract_system_prompt_and_generation from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.dataset.vision_utils import process_image, process_video from verl.utils.fs import copy_local_path_from_hdfs from verl.utils.py_functional import convert_nested_value_to_list_recursive logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def once(func): """Decorator to ensure a function runs only once. Subsequent calls do nothing.""" @wraps(func) def wrapper(*args, **kwargs): if not hasattr(wrapper, "called"): wrapper.called = True return func(*args, **kwargs) return wrapper @once def print_assembled_message(tokenizer, message_list, input_ids, loss_mask, attn_mask, tools): """ Print the message after applying the chat template """ tokenized = tokenizer.apply_chat_template(message_list, add_generation_prompt=False, tokenize=False, tools=tools) sep = "\n\n" str = f"tokenized entire message:\n{tokenized}" str += sep decoded_ids = input_ids.tolist() if hasattr(input_ids, "tolist") else input_ids str += f"tokenized seperately :\n{tokenizer.decode(decoded_ids)}" logger.debug(str) class MultiTurnSFTDataset(Dataset): """ Dataset for multi-turn conversations where each assistant response should be trained Args: data_files (str or list): Path(s) to Parquet file(s). tokenizer (PreTrainedTokenizer): For the tokenization of text to token IDs. config (DictConfig): Options like cache_dir, prompt_key, max_prompt_length, truncation, etc. processor (ProcessorMixin, optional): Multimodal preprocessor for images/videos. max_samples (int, optional): Limit the number of samples. Defaults to -1 (use all). """ def __init__( self, parquet_files: str | list[str], tokenizer: PreTrainedTokenizer, config: DictConfig, processor: Optional[ProcessorMixin] = None, max_samples: int = -1, ): # Set defaults and extract parameters from config if provided config = config or {} self.pad_mode = config.get("pad_mode", "right") assert self.pad_mode in ["right", "no_padding"], ( f"Expect pad_mode to be 'right' or 'no_padding'. Got {self.pad_mode}" ) self.truncation = config.get("truncation", "error") # for right padding self.max_length = config.get("max_length", 1024) # Get messages_key from the new multiturn config structure self.messages_key = config.get("messages_key", "messages") self.image_key = config.get("image_key", "images") self.video_key = config.get("video_key", "videos") self.image_patch_size = config.get( "image_patch_size", processor.image_processor.patch_size if processor else None ) self.tools_key = config.get("tools_key", "tools") self.enable_thinking_key = config.get("enable_thinking_key", "enable_thinking") self.enable_thinking_default = config.get("enable_thinking_default", None) self.apply_chat_template_kwargs = config.get("apply_chat_template_kwargs", {}) self.shuffle = config.get("shuffle", False) self.seed = config.get("seed") self.max_samples = max_samples self.ignore_input_ids_mismatch = config.get("ignore_input_ids_mismatch", False) assert self.truncation in ["error", "left", "right"] if not isinstance(parquet_files, list | ListConfig): parquet_files = [parquet_files] self.parquet_files = parquet_files if isinstance(tokenizer, str): tokenizer = hf_tokenizer(tokenizer) self.tokenizer: PreTrainedTokenizer = tokenizer self.processor = processor self._download() self._read_files_and_process() def _download(self): for i, parquet_file in enumerate(self.parquet_files): self.parquet_files[i] = copy_local_path_from_hdfs(parquet_file, verbose=True) def _read_files_and_process(self): def series_to_item(ls): import numpy import pandas while isinstance(ls, pandas.core.series.Series | numpy.ndarray) and len(ls) == 1: ls = ls[0] return ls dataframes = [] for parquet_file in self.parquet_files: # default loader loads some list as np.ndarray, which fails the tokenizer dataframe = pd.read_parquet(parquet_file, dtype_backend="pyarrow") dataframes.append(dataframe) self.dataframe = pd.concat(dataframes) total = len(self.dataframe) print(f"dataset len: {len(self.dataframe)}") if self.max_samples > 0 and self.max_samples < total: if self.shuffle: rngs_args = (self.seed,) if self.seed is not None else () rng = np.random.default_rng(*rngs_args) indices = rng.choice(total, size=self.max_samples, replace=False) else: indices = np.arange(self.max_samples) self.dataframe = self.dataframe.iloc[indices.tolist()] print(f"selected {self.max_samples} random samples out of {total}") # Extract messages list from dataframe self.messages = self.dataframe[self.messages_key].apply(convert_nested_value_to_list_recursive).tolist() # Extract tools list from dataframe if self.tools_key in self.dataframe.columns: self.tools = self.dataframe[self.tools_key].apply(convert_nested_value_to_list_recursive).tolist() else: self.tools = None # Extract enable_thinking list from dataframe if self.enable_thinking_key in self.dataframe.columns: self.enable_thinking = self.dataframe[self.enable_thinking_key].tolist() else: self.enable_thinking = None # system prompt: <|im_start|>system\nYou are a helpful assistant.<|im_end|>\n # generation prompt: <|im_start|>assistant\n self.system_prompt, self.generation_prompt = extract_system_prompt_and_generation(self.tokenizer) def __len__(self): return len(self.messages) def _process_single_message( self, index: int, message: dict[str, Any], full_message: list, tools: Optional[list[dict[str, Any]]] = None, enable_thinking: Optional[bool] = None, ) -> tuple[list[int], list[int], list[int]]: """ Process a single message and return its tokenized representation. Args: index: turn index in the conversation message: A single message dictionary images: List of images to be used videos: List of videos to be used tools: List of tools to be used enable_thinking: Whether to enable thinking mode Returns: Tuple of (input_ids, loss_mask, attention_mask, dict[str, torch.Tensor]) """ processor = self.processor if self.processor is not None else self.tokenizer apply_chat_template_kwargs = {**self.apply_chat_template_kwargs} if enable_thinking is not None: apply_chat_template_kwargs["enable_thinking"] = enable_thinking inputs = apply_chat_template( processor, messages=[message], tools=tools, add_generation_prompt=False, tokenize=True, return_dict=True, return_tensors="pt", **apply_chat_template_kwargs, ) inputs = dict(inputs) input_ids = inputs.pop("input_ids")[0] attention_mask = inputs.pop("attention_mask")[0] # remove system prompt if exists if index != 0 and message["role"] != "system": input_ids = input_ids[len(self.system_prompt) :] attention_mask = attention_mask[len(self.system_prompt) :] if message["role"] == "assistant": loss_mask = torch.ones_like(attention_mask) # mask out generation prompt if assistant message loss_mask[: len(self.generation_prompt)] = 0 else: loss_mask = torch.zeros_like(attention_mask) return input_ids, loss_mask, attention_mask, inputs def _build_messages(self, example: dict): """Replace and score = score / 4 return score return score else: return format_score def compute_score_subem(solution_str, ground_truth, method="strict", format_score=0.0, score=1.0): """The scoring function for substring exact match (EM). Args: solution_str: the solution text ground_truth: the ground truth method: the method to extract the solution, choices are 'strict' and 'flexible' format_score: the score for the format score: the score for the correct answer """ answer = extract_solution(solution_str=solution_str) do_print = random.randint(1, 64) == 1 if do_print: print("--------------------------------") print(f"Golden answers: {ground_truth['target']}") print(f"Extracted answer: {answer}") print(f"Solution string: {solution_str}") if answer is None: return 0 else: if subem_check(answer, ground_truth["target"]): return score else: return format_score ================================================ FILE: verl/utils/rollout_skip.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path from verl.protocol import DataProto class RolloutSkip: """ RolloutSkip skips sequence generation during rollout by attempting to load previously dumped data. If no dumped data is found, it generates new sequences and saves them to disk. Args: config: The configuration object containing rollout settings. rollout_wg: The worker group that handles the rollout process. Note: When rollout.n or rollout.gen_batch_size differ from previous runs, new sequences will be generated and saved with different filenames. """ print_mark = "[RolloutSkip()]" def __init__(self, config, rollout_wg): self.rollout_config = config.actor_rollout_ref.rollout self.exp_name = config.data.get("experiment_name", "") self.project_name = config.data.get("project_name", "") self.n = int(self.rollout_config.get("n", 0)) self.gbs = int(config.data.get("gen_batch_size", config.data.get("train_batch_size", 0))) self.dumped_dir = Path(self.rollout_config.get("skip_dump_dir", "/tmp/verl/rollout_dump")) self.dumped_dir.mkdir(parents=True, exist_ok=True) # Check if path is in Ray temporary directory if str(self.dumped_dir.absolute()).startswith("/tmp/ray/session"): print( f"\033[33m{self.print_mark} Warning: \nUsing dump path ", f"'{self.dumped_dir.absolute()}' is not recommended ", "as it's located in /tmp/ray/session*\033[0m", flush=True, ) print( f"{self.print_mark} Rollout skip dump path set to: ", f"{self.dumped_dir.absolute()}", flush=True, ) self._rollout_wg = rollout_wg @property def curr_path_dump(self): return self.dumped_dir.joinpath(f"{self.exp_name}_{self.project_name}_GBS{self.gbs}__N{self.n}").absolute() def wrap_generate_sequences(self): try: self._rollout_wg.generate_sequences = wrap_generate_sequences(self, self._rollout_wg) print( f"{self.print_mark} Successfully patched `actor_rollout_wg.generate_sequences()`", flush=True, ) except Exception as e: raise RuntimeError( "{self.print_mark} Failed to patch `actor_rollout_wg.generate_sequences()`", flush=True, ) from e def try_load(self): if not self.curr_path_dump.exists(): print( f"{self.print_mark} No data dump found at {self.curr_path_dump}.", "The trainer will generate and automatically dump the data for this first run.", flush=True, ) return None try: # * Load ret_batch = DataProto.load_from_disk(self.curr_path_dump) print( f"\033[32m{self.print_mark} Successfully load pre-generated data from {self.curr_path_dump}\033[0m", flush=True, ) return ret_batch except Exception as e: print( f"\033[31m{self.print_mark} Failed to load pre-generated data from {self.curr_path_dump}", f"Error: {str(e)}\033[0m", flush=True, ) return None def dump(self, outputs: DataProto): try: outputs.save_to_disk(self.curr_path_dump) print( f"\033[32m{self.print_mark} Successfully dump data in {self.curr_path_dump}\033[0m", flush=True, ) except Exception as e: print( f"\033[31m{self.print_mark} Failed to dump data in {self.curr_path_dump}: {e}\033[0m", flush=True, ) def wrap_generate_sequences(rolloutskip: RolloutSkip, rollout_wg): generate_sequences = rollout_wg.generate_sequences def warp_fn(batch, **kwargs): gen_batch_output = rolloutskip.try_load() if gen_batch_output is None: # * 1. Generation gen_batch_output = generate_sequences(batch, **kwargs) # * 2. Dump rolloutskip.dump(gen_batch_output) return gen_batch_output return warp_fn ================================================ FILE: verl/utils/rollout_trace.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import functools import inspect import os from contextvars import ContextVar from typing import Optional from pydantic import BaseModel from verl.utils.ray_utils import get_event_loop _trace_enabled: ContextVar[bool] = ContextVar("_trace_enabled", default=True) class RolloutTraceConfig: """Configuration for rollout tracing with various backends. Singleton configuration class for managing rollout trace settings across different tracing backends like Weave and MLflow. Args: backend (Optional[str]): Tracing backend to use ('weave', 'mlflow', or None). client (Optional[object]): Client instance for the selected backend. token2text (bool): Whether to convert tokens to text in traces. Defaults to False. project_name (str): Name of the project for tracing. experiment_name (str): Name of the experiment for tracing. max_samples_per_step_per_worker (Optional[int]): Maximum number of unique samples to trace per worker per step. If None, all samples are traced. If set, each worker will randomly select up to this many unique samples to trace (including all their rollouts for GRPO). Total traces = max_samples_per_step_per_worker * num_workers * n_rollouts_per_sample. """ _instance: Optional["RolloutTraceConfig"] = None backend: Optional[str] = None client: Optional[object] = None token2text: bool = False _initialized: bool = False project_name: str = None experiment_name: str = None max_samples_per_step_per_worker: Optional[int] = None def __new__(cls, *args, **kwargs): if cls._instance is None: cls._instance = super().__new__(cls) cls._instance._initialized = False return cls._instance @classmethod def get_instance(cls) -> "RolloutTraceConfig": if cls._instance is None: cls._instance = cls() return cls._instance @classmethod def init( cls, project_name: str, experiment_name: str, backend: str, token2text: bool = False, max_samples_per_step_per_worker: Optional[int] = None, ): config = cls.get_instance() if config._initialized: return config.backend = backend config.token2text = token2text config.project_name = project_name config.experiment_name = experiment_name config.max_samples_per_step_per_worker = max_samples_per_step_per_worker if backend == "weave": import weave config.client = weave.init(project_name) elif backend == "mlflow": import mlflow mlflow.config.enable_async_logging() config.client = mlflow MLFLOW_TRACKING_URI = os.environ.get("MLFLOW_TRACKING_URI", "sqlite:////tmp/mlruns.db") mlflow.set_tracking_uri(MLFLOW_TRACKING_URI) mlflow.set_experiment(project_name) else: config.client = None config._initialized = True @classmethod def get_backend(cls) -> Optional[str]: return cls.get_instance().backend @classmethod def get_client(cls) -> Optional[object]: return cls.get_instance().client @classmethod def enable_token2text(cls) -> Optional[bool]: return cls.get_instance().token2text @classmethod def reset(cls): cls._instance = None @contextlib.contextmanager def rollout_trace_attr( sample_index=None, step=None, rollout_n=None, name="rollout_trace", validate=False, trace: bool = True ): """A context manager to add attributes to a trace for the configured backend. Args: sample_index: Sample index for the trace. step: Training step number. rollout_n: Rollout number (for GRPO with multiple rollouts per sample). name: Name for the trace span (used by mlflow backend). validate: Whether this is a validation run. trace: If False, disables tracing for the duration of the context. """ backend = RolloutTraceConfig.get_backend() should_skip = backend is not None and not trace if should_skip: token = _trace_enabled.set(False) try: yield finally: _trace_enabled.reset(token) return # Build attributes for the trace attributes = {} if backend: if sample_index is not None: attributes["sample_index"] = sample_index if step is not None: attributes["step"] = step if rollout_n is not None: attributes["rollout_n"] = rollout_n attributes["validate"] = validate attributes["experiment_name"] = RolloutTraceConfig.get_instance().experiment_name if not attributes or backend is None: yield return if backend == "weave": import weave with weave.attributes(attributes): yield elif backend == "mlflow": import mlflow with mlflow.start_span(name=name) as span: trace_id = span.trace_id for key, value in attributes.items(): mlflow.set_trace_tag(trace_id, str(key), str(value)) yield else: yield def rollout_trace_op(func): @functools.wraps(func) async def async_wrapper(self, *args, **kwargs): if not _trace_enabled.get(): return await func(self, *args, **kwargs) backend = RolloutTraceConfig.get_backend() enable_token2text = RolloutTraceConfig.enable_token2text() if backend is None: return await func(self, *args, **kwargs) sig = inspect.signature(func) bound_args = sig.bind(self, *args, **kwargs) bound_args.apply_defaults() inputs = dict(bound_args.arguments) del inputs["self"] async def add_token2text(self, result): if hasattr(result, "prompt_ids") and hasattr(self, "tokenizer") and hasattr(self.tokenizer, "decode"): # Use model_dump() for Pydantic models to get a proper copy, # otherwise vars() returns a reference to internal __dict__ which # can cause serialization issues with MLflow if isinstance(result, BaseModel): _result = result.model_dump() else: _result = dict(vars(result)) loop = get_event_loop() if hasattr(result, "prompt_ids"): prompt_text = await loop.run_in_executor(None, self.tokenizer.decode, result.prompt_ids) _result["prompt_text"] = prompt_text if hasattr(result, "response_ids"): response_text = await loop.run_in_executor(None, self.tokenizer.decode, result.response_ids) _result["response_text"] = response_text return _result return result if backend == "weave": tracer = RolloutTraceConfig.get_client() from weave.trace.context import call_context cur_attributes = {**call_context.call_attributes.get()} call = tracer.create_call(op=func.__qualname__, inputs=inputs, attributes=cur_attributes) try: result = await func(self, *args, **kwargs) if enable_token2text: _result = await add_token2text(self, result) tracer.finish_call(call, output=_result) else: tracer.finish_call(call, output=result) return result except Exception as e: tracer.finish_call(call, exception=e) raise e elif backend == "mlflow": import mlflow with mlflow.start_span(name=func.__qualname__) as span: span.set_inputs(inputs) result = await func(self, *args, **kwargs) if enable_token2text: _result = await add_token2text(self, result) span.set_outputs(_result) else: span.set_outputs(result) return result else: return await func(self, *args, **kwargs) @functools.wraps(func) def wrapper(self, *args, **kwargs): if not _trace_enabled.get(): return func(self, *args, **kwargs) backend = RolloutTraceConfig.get_backend() if backend is None: return func(self, *args, **kwargs) sig = inspect.signature(func) bound_args = sig.bind(self, *args, **kwargs) bound_args.apply_defaults() inputs = dict(bound_args.arguments) del inputs["self"] if backend == "weave": tracer = RolloutTraceConfig.get_client() from weave.trace.context import call_context cur_attributes = {**call_context.call_attributes.get()} call = tracer.create_call(op=func.__qualname__, inputs=inputs, attributes=cur_attributes) try: result = func(self, *args, **kwargs) tracer.finish_call(call, output=result) return result except Exception as e: tracer.finish_call(call, exception=e) raise e elif backend == "mlflow": import mlflow return mlflow.trace(func)(self, *args, **kwargs) else: return func(self, *args, **kwargs) return async_wrapper if inspect.iscoroutinefunction(func) else wrapper ================================================ FILE: verl/utils/seqlen_balancing.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import heapq from itertools import chain import torch from torch import distributed as dist from verl.protocol import DataProto from verl.utils import tensordict_utils as tu from verl.utils.device import get_device_name def calculate_workload(seqlen_list: torch.Tensor) -> torch.Tensor: """Calculate approximate computational workload for transformer attention. Estimates FLOPs for dense transformer blocks based on sequence length using the formula: FLOPs ≈ 12 * hidden_size² * seqlen + 2 * hidden_size * seqlen² The constants are calibrated for a 7B model (hidden_size=4096), yielding: workload ∝ 24576 * seqlen + seqlen² Args: seqlen_list: Sequence lengths as a tensor. Returns: torch.Tensor: Estimated workload values proportional to actual FLOPs. Note: The returned values are relative workloads, not actual FLOP counts. Useful for balancing computation across data parallel ranks. """ return 24576 * seqlen_list + seqlen_list**2 def karmarkar_karp(seqlen_list: list[int], k_partitions: int, equal_size: bool) -> list[list[int]]: """Partition items into k groups using the Karmarkar-Karp differencing method. Implements the Largest Differencing Method (LDM) algorithm for balanced multi-way number partitioning. This heuristic produces near-optimal partitions by iteratively combining the sets with the largest difference. Args: seqlen_list: Values to partition (typically sequence lengths or workloads). k_partitions: Number of partitions to create. equal_size: If True, each partition will have exactly len(seqlen_list) / k_partitions items. If False, partitions may have different sizes. Returns: list[list[int]]: List of k partitions, each containing indices into seqlen_list. See Also: https://en.wikipedia.org/wiki/Largest_differencing_method Note: When equal_size=True, len(seqlen_list) must be divisible by k_partitions. """ # see: https://en.wikipedia.org/wiki/Largest_differencing_method class Set: def __init__(self) -> None: self.sum = 0 self.items = [] def add(self, idx: int, val: int): self.items.append((idx, val)) self.sum += val def merge(self, other): for idx, val in other.items: self.items.append((idx, val)) self.sum += val def __lt__(self, other): if self.sum != other.sum: return self.sum < other.sum if len(self.items) != len(other.items): return len(self.items) < len(other.items) return self.items < other.items class State: def __init__(self, items: list[tuple[int, int]], k: int) -> None: self.k = k # sets should always be decreasing order self.sets = [Set() for _ in range(k)] assert len(items) in [1, k], f"{len(items)} not in [1, {k}]" for i, (idx, seqlen) in enumerate(items): self.sets[i].add(idx=idx, val=seqlen) self.sets = sorted(self.sets, reverse=True) def get_partitions(self): partitions = [] for i in range(len(self.sets)): cur_partition = [] for idx, _ in self.sets[i].items: cur_partition.append(idx) partitions.append(cur_partition) return partitions def merge(self, other): for i in range(self.k): self.sets[i].merge(other.sets[self.k - 1 - i]) self.sets = sorted(self.sets, reverse=True) @property def spread(self) -> int: return self.sets[0].sum - self.sets[-1].sum def __lt__(self, other): # least heap, let the state with largest spread to be popped first, # if the spread is the same, let the state who has the largest set # to be popped first. if self.spread != other.spread: return self.spread > other.spread return self.sets[0] > other.sets[0] def __repr__(self) -> str: repr_str = "[" for i in range(self.k): if i > 0: repr_str += "," repr_str += "{" for j, (_, seqlen) in enumerate(self.sets[i].items): if j > 0: repr_str += "," repr_str += str(seqlen) repr_str += "}" repr_str += "]" return repr_str sorted_seqlen_list = sorted([(seqlen, i) for i, seqlen in enumerate(seqlen_list)]) states_pq = [] if equal_size: assert len(seqlen_list) % k_partitions == 0, f"{len(seqlen_list)} % {k_partitions} != 0" for offset in range(0, len(sorted_seqlen_list), k_partitions): items = [] for i in range(k_partitions): seqlen, idx = sorted_seqlen_list[offset + i] items.append((idx, seqlen)) heapq.heappush(states_pq, State(items=items, k=k_partitions)) else: for seqlen, idx in sorted_seqlen_list: heapq.heappush(states_pq, State(items=[(idx, seqlen)], k=k_partitions)) while len(states_pq) > 1: state0 = heapq.heappop(states_pq) state1 = heapq.heappop(states_pq) # merge states state0.merge(state1) heapq.heappush(states_pq, state0) final_state = states_pq[0] partitions = final_state.get_partitions() if equal_size: for i, partition in enumerate(partitions): assert len(partition) * k_partitions == len(seqlen_list), ( f"{len(partition)} * {k_partitions} != {len(seqlen_list)}" ) return partitions def greedy_partition(seqlen_list: list[int], k_partitions: int, equal_size: bool) -> list[list[int]]: """Partition items into k groups using a greedy assignment strategy. Assigns each item to the partition with the smallest current sum, iterating through items in order. Simpler but typically less optimal than Karmarkar-Karp. Args: seqlen_list: Values to partition (typically sequence lengths or workloads). k_partitions: Number of partitions to create. equal_size: If True, adds a bias to ensure equal partition sizes. Requires len(seqlen_list) to be divisible by k_partitions. Returns: list[list[int]]: List of k partitions, each containing indices into seqlen_list. Note: When equal_size=True, a large bias is added to encourage equal distribution of items before considering the actual values. """ bias = sum(seqlen_list) + 1 if equal_size else 0 sorted_seqlen = [(seqlen + bias, i) for i, seqlen in enumerate(seqlen_list)] partitions = [[] for _ in range(k_partitions)] partition_sums = [0 for _ in range(k_partitions)] for seqlen, i in sorted_seqlen: min_idx = None for j in range(k_partitions): if min_idx is None or partition_sums[j] < partition_sums[min_idx]: min_idx = j partitions[min_idx].append(i) partition_sums[min_idx] += seqlen if equal_size: for i, partition in enumerate(partitions): assert len(partition) * k_partitions == len(seqlen_list), ( f"{len(partition)} * {k_partitions} != {len(seqlen_list)}" ) return partitions def get_seqlen_balanced_partitions(seqlen_list: list[int], k_partitions: int, equal_size: bool): """ Calculates partitions of indices from seqlen_list such that the sum of sequence lengths in each partition is balanced. Uses the Karmarkar-Karp differencing method. This is useful for balancing workload across devices or batches, especially when dealing with variable sequence lengths. Args: seqlen_list (List[int]): A list of sequence lengths for each item. k_partitions (int): The desired number of partitions. equal_size (bool): If True, ensures that each partition has the same number of items. Requires len(seqlen_list) to be divisible by k_partitions. If False, partitions can have varying numbers of items, focusing only on balancing the sum of sequence lengths. Returns: List[List[int]]: A list containing k_partitions lists. Each inner list contains the original indices of the items assigned to that partition. The indices within each partition list are sorted. Raises: AssertionError: If len(seqlen_list) < k_partitions. AssertionError: If equal_size is True and len(seqlen_list) is not divisible by k_partitions. AssertionError: If any resulting partition is empty. """ assert len(seqlen_list) >= k_partitions, f"number of items:[{len(seqlen_list)}] < k_partitions:[{k_partitions}]" def _check_and_sort_partitions(partitions): assert len(partitions) == k_partitions, f"{len(partitions)} != {k_partitions}" seen_idx = set() sorted_partitions = [None] * k_partitions for i, partition in enumerate(partitions): assert len(partition) > 0, f"the {i}-th partition is empty" for idx in partition: seen_idx.add(idx) sorted_partitions[i] = sorted(partition) assert seen_idx == set(range(len(seqlen_list))) return sorted_partitions partitions = karmarkar_karp(seqlen_list=seqlen_list, k_partitions=k_partitions, equal_size=equal_size) return _check_and_sort_partitions(partitions) def log_seqlen_unbalance(seqlen_list: list[int], partitions: list[list[int]], prefix): """ Calculate and log metrics related to sequence length imbalance before and after partitioning. Args: seqlen_list (List[int]): A list of sequence lengths for each item. partitions (List[List[int]]): A list of partitions, where each inner list contains indices from seqlen_list assigned to that partition. prefix (str): A prefix to be added to each metric key in the returned dictionary. Returns: dict: A dictionary containing metrics related to sequence length imbalance. """ # Get the number of partitions k_partition = len(partitions) # assert len(seqlen_list) % k_partition == 0 batch_size = len(seqlen_list) // k_partition min_sum_seqlen = None max_sum_seqlen = None total_sum_seqlen = 0 # Iterate over each batch of sequence lengths for offset in range(0, len(seqlen_list), batch_size): cur_sum_seqlen = sum(seqlen_list[offset : offset + batch_size]) if min_sum_seqlen is None or cur_sum_seqlen < min_sum_seqlen: min_sum_seqlen = cur_sum_seqlen if max_sum_seqlen is None or cur_sum_seqlen > max_sum_seqlen: max_sum_seqlen = cur_sum_seqlen total_sum_seqlen += cur_sum_seqlen balanced_sum_seqlen_list = [] for partition in partitions: cur_sum_seqlen_balanced = sum([seqlen_list[i] for i in partition]) balanced_sum_seqlen_list.append(cur_sum_seqlen_balanced) # print("balanced_sum_seqlen_list: ", balanced_sum_seqlen_list) min_sum_seqlen_balanced = min(balanced_sum_seqlen_list) max_sum_seqlen_balanced = max(balanced_sum_seqlen_list) return { f"{prefix}/min": min_sum_seqlen, f"{prefix}/max": max_sum_seqlen, f"{prefix}/minmax_diff": max_sum_seqlen - min_sum_seqlen, f"{prefix}/balanced_min": min_sum_seqlen_balanced, f"{prefix}/balanced_max": max_sum_seqlen_balanced, f"{prefix}/mean": total_sum_seqlen / len(partitions), } def ceildiv(a: int, b: int) -> int: """Compute ceiling division of a by b. Returns the smallest integer greater than or equal to a/b. Uses the identity: ceil(a/b) = floor((a + b - 1) / b) = -(-a // b) Args: a: Dividend (numerator). b: Divisor (denominator), must be non-zero. Returns: int: Ceiling of a divided by b. Example: >>> ceildiv(7, 3) # ceil(7/3) = ceil(2.33) = 3 3 >>> ceildiv(6, 3) # ceil(6/3) = ceil(2.0) = 2 2 """ return -(a // -b) def roundup_divisible(a: int, b: int) -> int: """Round up a to the nearest multiple of b. Returns the smallest multiple of b that is >= a. Args: a: Value to round up. b: Divisor to round to (must be positive). Returns: int: Smallest multiple of b that is >= a. Example: >>> roundup_divisible(7, 4) # nearest multiple of 4 >= 7 is 8 8 >>> roundup_divisible(8, 4) # 8 is already a multiple of 4 8 """ return ((a + b - 1) // b) * b def rearrange_micro_batches( batch, max_token_len, dp_group=None, num_batches_divided_by=None, same_micro_num_in_dp=True, min_num_micro_batch=None, use_dynamic_bsz_balance=True, force_group_size=1, ): """ Split a batch into micro-batches by total token count, with optional DP sync and padding. Args: batch (TensorDict): must include "attention_mask" (B*S); other fields are sliced similarly. max_token_len (int): max sum of attention_mask per micro-batch. dp_group (optional): torch.distributed group for data-parallel sync. num_batches_divided_by (optional): virtual pipeline parallel size, for megatron. same_micro_num_in_dp (bool): if True and dp_group set, pad all ranks to the same count. min_num_micro_batch (int, optional): force at least this many splits (pads empty ones). use_dynamic_bsz_balance (bool, optional): balance the computational workload between micro-batches force_group_size (int, optional): force consecutive samples to be in the same micro-batch (for RM training). Returns: List[TensorDict]: the micro-batches. List[List[int]]: index lists mapping each micro-batch back to original positions. """ # this is per local micro_bsz input_ids = batch["input_ids"] if input_ids.is_nested: seq_len_effective: torch.Tensor = input_ids.offsets().diff() max_seq_len = max(seq_len_effective) else: max_seq_len = batch["attention_mask"].shape[-1] seq_len_effective: torch.Tensor = batch["attention_mask"].sum(dim=1) assert max_token_len >= max_seq_len, ( f"max_token_len must be greater than the sequence length. Got {max_token_len=} and {max_seq_len=}" ) # Validate force_group_size batch_size = len(seq_len_effective) assert batch_size % force_group_size == 0, ( f"Batch size {batch_size} must be divisible by force_group_size {force_group_size}" ) total_seqlen = seq_len_effective.sum().item() # NOTE: num_microbatches <= batch_size, so take the min of this two. # When force_group_size > 1, we work with groups instead of individual samples num_groups = batch_size // force_group_size num_micro_batches = min(num_groups, ceildiv(total_seqlen, max_token_len)) if min_num_micro_batch is not None: # used to support pp num_micro_batches = max(min_num_micro_batch, num_micro_batches) if dist.is_initialized() and same_micro_num_in_dp and dp_group is not None: num_micro_batches = torch.tensor([num_micro_batches], device=get_device_name()) dist.all_reduce(num_micro_batches, op=dist.ReduceOp.MAX, group=dp_group) num_micro_batches = num_micro_batches.cpu().item() if num_batches_divided_by is not None: num_micro_batches = roundup_divisible(num_micro_batches, num_batches_divided_by) assert num_micro_batches <= num_groups # upcast to int64 to avoid potential overflow im `calculate_workload` computation. seq_len_effective = seq_len_effective.long() # When force_group_size > 1, aggregate workloads by groups if force_group_size > 1: # Calculate workload for each group (sum of workloads of samples in the group) workloads_per_sample = calculate_workload(seq_len_effective) workloads_per_sample_grouped = workloads_per_sample.view(num_groups, force_group_size) group_workloads = workloads_per_sample_grouped.sum(dim=1).cpu().tolist() # Partition groups instead of individual samples micro_bsz_group_idx = get_seqlen_balanced_partitions(group_workloads, num_micro_batches, equal_size=False) # Convert group indices back to sample indices micro_bsz_idx = [] for group_partition in micro_bsz_group_idx: sample_partition = [] for group_idx in group_partition: start_idx = group_idx * force_group_size sample_partition.extend(range(start_idx, start_idx + force_group_size)) micro_bsz_idx.append(sample_partition) workloads = group_workloads else: # Original logic for force_group_size == 1 # note that seq_len_effective is a GPU tensor. We need to make it a list to avoid D2H! workloads = calculate_workload(seq_len_effective).cpu().tolist() micro_bsz_idx = get_seqlen_balanced_partitions(workloads, num_micro_batches, equal_size=False) if use_dynamic_bsz_balance: # Use the sum of squared sequence lengths to approximate attention computation workload if force_group_size > 1: # For grouped samples, use group workloads for sorting micro_bsz_idx.sort( key=lambda partition: ( sum(workloads[idx // force_group_size] for idx in partition[::force_group_size]), partition[0] if partition else 0, ), reverse=True, ) else: micro_bsz_idx.sort( key=lambda partition: ( sum(workloads[idx] for idx in partition), partition[0] if partition else 0, ), reverse=True, ) # Place smaller micro-batches at both ends to reduce the bubbles exposed during the warm-up and cool-down. micro_bsz_idx = micro_bsz_idx[::2][::-1] + micro_bsz_idx[1::2] micro_batches = [] for partition in micro_bsz_idx: curr_micro_batch = tu.index_select_tensor_dict(batch, partition) micro_batches.append(curr_micro_batch) return micro_batches, micro_bsz_idx def get_reverse_idx(idx_map): """ Build the inverse of an index mapping. Args: idx_map (Sequence[int]): Sequence where idx_map[i] = j. Returns: List[int]: Inverse mapping list such that output[j] = i for each i. """ reverse_idx_map = copy.deepcopy(idx_map) for i, idx in enumerate(idx_map): reverse_idx_map[idx] = i return reverse_idx_map def prepare_dynamic_batch( data: DataProto, max_token_len: int, dp_group=None, num_batches_divided_by=None, same_micro_num_in_dp=True, min_num_micro_batch=None, use_dynamic_bsz_balance=True, ) -> tuple[list[DataProto], list[list[int]]]: """ Prepare a batch for dynamic batching. Args: data (DataProto): The input data. max_token_len (int): The maximum token length for dynamic batching. Returns: Tuple[List[DataProto], List[List[int]]]: A tuple containing a list of DataProto objects and a list of index lists. """ batch, batch_idx_list = rearrange_micro_batches( data.batch, max_token_len=max_token_len, dp_group=dp_group, num_batches_divided_by=num_batches_divided_by, same_micro_num_in_dp=same_micro_num_in_dp, min_num_micro_batch=min_num_micro_batch, use_dynamic_bsz_balance=use_dynamic_bsz_balance, ) micro_batches = [] for i, batch_idx in enumerate(batch_idx_list): tensors = dict(batch[i]) non_tensors = {key: value[batch_idx] for key, value in data.non_tensor_batch.items()} meta_info = copy.deepcopy(data.meta_info) micro_batches.append(DataProto.from_dict(tensors, non_tensors, meta_info=meta_info)) return micro_batches, batch_idx_list def restore_dynamic_batch(data: torch.Tensor, batch_idx_list: list[list[int]]) -> torch.Tensor: """ Restore a batch from dynamic batching. Args: data (torch.Tensor): The input data. batch_idx_list (List[List[int]]): The list of index lists. Returns: torch.Tensor: The restored data. """ indices = list(chain.from_iterable(batch_idx_list)) batch_size = data.shape[0] assert len(indices) == batch_size, f"{len(indices)} vs. {batch_size}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) if data.is_nested: data_lst = data.unbind() tensors = [data_lst[i] for i in revert_indices] reverted_data = torch.nested.as_nested_tensor(tensors, layout=torch.jagged) else: reverted_data = data[revert_indices] return reverted_data def get_group_balanced_partitions( seqlen_list: list[int], uid_list: list, k_partitions: int, ) -> list[list[int]]: """ Partition samples into k groups while keeping samples with the same uid together. Args: seqlen_list: List of sequence lengths for each sample. uid_list: List of uids identifying which samples share the same prefix. Samples with the same uid will be kept together. k_partitions: Number of partitions (typically world_size). Returns: List of k lists, each containing sample indices assigned to that partition. Samples with the same uid are guaranteed to be in the same partition. """ assert len(seqlen_list) == len(uid_list), "seqlen_list and uid_list must have same length" # Build groups: each group contains indices of samples with the same uid # Assumes samples with same uid are contiguous groups = [] # List of (group_indices, group_total_seqlen) current_uid = None current_indices = [] current_seqlen = 0 for i, (seqlen, uid) in enumerate(zip(seqlen_list, uid_list, strict=False)): if uid != current_uid: if current_indices: groups.append((current_indices, current_seqlen)) current_uid = uid current_indices = [i] current_seqlen = seqlen else: current_indices.append(i) current_seqlen += seqlen # Don't forget the last group if current_indices: groups.append((current_indices, current_seqlen)) num_groups = len(groups) assert num_groups >= k_partitions, ( f"Number of uid groups ({num_groups}) must be >= k_partitions ({k_partitions}). " f"Consider reducing world_size or increasing batch_size." ) # Calculate workload for each group (as integers for partitioning) group_workloads = [] for indices, total_seqlen in groups: # Use sum of individual workloads for more accurate estimation workload = sum(int(calculate_workload(torch.tensor([seqlen_list[i]])).item()) for i in indices) group_workloads.append(workload) # Use Karmarkar-Karp to partition groups # equal_size=True ensures each partition gets the same number of groups, # which is required when each group has the same number of samples (rollout.n) group_partitions = get_seqlen_balanced_partitions( seqlen_list=group_workloads, k_partitions=k_partitions, equal_size=True, ) # Convert group partitions to sample partitions sample_partitions = [] for group_partition in group_partitions: sample_indices = [] for group_idx in group_partition: sample_indices.extend(groups[group_idx][0]) sample_partitions.append(sorted(sample_indices)) return sample_partitions ================================================ FILE: verl/utils/sglang/sglang_fp8_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.utils.fp8_utils import FP8QuantizerHelper class SGLangFP8QuantizerHelper(FP8QuantizerHelper): def __init__(self, quant_config): super().__init__(quant_config) ================================================ FILE: verl/utils/tensordict_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from typing import Any, Iterable import torch from tensordict import TensorDict from tensordict.tensorclass import NonTensorData, NonTensorStack def assign_non_tensor_data(tensor_dict: TensorDict, key, val): """Assign a single non-tensor value to a TensorDict. Wraps the value in NonTensorData so it can be stored alongside tensors in the TensorDict. Use this for scalar metadata or simple non-tensor values. Args: tensor_dict: The TensorDict to assign to. key: The key under which to store the value. val: Any non-tensor value to store (e.g., string, int, dict). Raises: AssertionError: If tensor_dict is not a TensorDict. Example: >>> td = TensorDict({"obs": torch.randn(3, 4)}, batch_size=[3]) >>> assign_non_tensor_data(td, "experiment_name", "run_001") """ assert isinstance(tensor_dict, TensorDict), "input dict must be a TensorDict" tensor_dict[key] = NonTensorData(val) def assign_non_tensor_stack(tensor_dict: TensorDict, key, val: list): """Assign a list with potentially nested structures (lists, dicts, etc.) to TensorDict. This function handles complex nested data structures like: - Lists of lists: [[], [0.5, 0.8], [0.9]] - Lists of dicts: [{"acc": 1.0}, {"acc": 0.0}] - Lists of lists of dicts: [[{"content": "...", "role": "user"}]] These structures are wrapped in NonTensorStack so TensorDict can handle them correctly. Args: tensor_dict: The TensorDict to assign to key: The key to assign the value under val: A list containing potentially nested structures Example: >>> td = TensorDict({}, batch_size=[]) >>> turn_scores = [[], [0.5, 0.8], [0.9]] >>> assign_non_tensor_stack(td, "turn_scores", turn_scores) >>> # Now td["turn_scores"] contains the nested data """ # Convert list to NonTensorStack to handle nested structures # This wraps each item in NonTensorData to preserve complex objects # TODO(petersh6): can convert back to val directly if we are not accessing .data from the NonTensorStack assert isinstance(tensor_dict, TensorDict), "input dict must be a TensorDict" tensor_dict[key] = NonTensorStack.from_list([NonTensorData(item) for item in val]) def assign_non_tensor(tensor_dict: TensorDict, **kwargs): """Assign non-tensor data to a TensorDict. Automatically detects if the value is a list with nested structures and uses the appropriate assignment method (NonTensorData for simple values, NonTensorStack for lists with nested structures). Args: tensor_dict: The TensorDict to assign to **kwargs: Key-value pairs where values can be: - Simple values (stored as NonTensorData) - Lists with nested structures (stored as NonTensorStack) Example: >>> td = TensorDict({"obs": torch.randn(3, 4)}, batch_size=[3]) >>> assign_non_tensor( ... tensor_dict=td, ... metadata="experiment_1", # Simple value ... turn_scores=[[], [0.5, 0.8], [0.9]] # Nested list ... ) """ assert isinstance(tensor_dict, TensorDict), "input dict must be a TensorDict" for key, val in kwargs.items(): if isinstance(val, (NonTensorData | NonTensorStack)): tensor_dict[key] = val elif isinstance(val, list): # For lists, use NonTensorStack assign_non_tensor_stack(tensor_dict=tensor_dict, key=key, val=val) else: # For non-list values, use NonTensorData assign_non_tensor_data(tensor_dict=tensor_dict, key=key, val=val) return tensor_dict def unwrap_non_tensor_data(data): """Unwrap a NonTensorData object to get the underlying value. If the input is a NonTensorData wrapper, extracts and returns the underlying data. Otherwise, returns the input unchanged. Args: data: Either a NonTensorData object or any other value. Returns: The unwrapped data if input was NonTensorData, otherwise the original input unchanged. Example: >>> wrapped = NonTensorData("hello") >>> unwrap_non_tensor_data(wrapped) 'hello' >>> unwrap_non_tensor_data(42) # Non-wrapped value 42 """ if isinstance(data, NonTensorData): return data.data return data def get_non_tensor_data(data: TensorDict, key: str, default): """Retrieve and unwrap non-tensor data from a TensorDict. Fetches the value for the given key from the TensorDict and automatically unwraps it if it's stored as NonTensorData. Args: data: The TensorDict to retrieve from. key: The key to look up. default: Value to return if the key is not found. Returns: The unwrapped value if the key exists and was wrapped in NonTensorData, the raw value if it wasn't wrapped, or the default if key not found. Example: >>> td = TensorDict({}, batch_size=[]) >>> assign_non_tensor_data(td, "config", {"lr": 0.01}) >>> get_non_tensor_data(td, "config", None) {'lr': 0.01} >>> get_non_tensor_data(td, "missing", "default_value") 'default_value' """ output = data.get(key, default) return unwrap_non_tensor_data(output) def concat_nested_tensors(tensors: list[torch.Tensor]) -> torch.Tensor: """Concatenate multiple nested tensors along the batch dimension. Takes a list of nested tensors with jagged layout and concatenates them into a single nested tensor. Each input tensor must have 2 or more dimensions and be contiguous. Args: tensors: List of nested tensors to concatenate. All tensors must be nested, contiguous, and have 2 or more dimensions. Returns: A new nested tensor with jagged layout containing all rows from the input tensors concatenated along dimension 0. Raises: AssertionError: If any tensor is not nested, not contiguous, or doesn't have 2 or more dimensions. Example: >>> t1 = torch.nested.as_nested_tensor([torch.randn(3), torch.randn(5)], layout=torch.jagged) >>> t2 = torch.nested.as_nested_tensor([torch.randn(2), torch.randn(4)], layout=torch.jagged) >>> result = concat_nested_tensors([t1, t2]) >>> # result contains 4 rows: lengths [3, 5, 2, 4] """ for tensor in tensors: assert tensor.is_nested and tensor.is_contiguous() unbind_tensors = [] for tensor in tensors: assert len(tensor.shape) >= 2, f"nested tensor must have 2 or more dimensions. Got {tensor.shape}" unbind_tensor = tensor.unbind(0) unbind_tensors.extend(list(unbind_tensor)) tensor = torch.nested.as_nested_tensor(unbind_tensors, layout=torch.jagged) return tensor def concat_tensordict_with_none_bsz(data: list[TensorDict]): """Handle concatenation of TensorDicts with empty batch size. For TensorDicts that contain only metadata (NonTensorData) with no batch dimension, returns the first TensorDict as the concatenation result. Args: data: List of TensorDicts, each with empty batch_size (batch_size=[]). Returns: The first TensorDict from the list, as metadata concatenation simply preserves the first instance. Raises: AssertionError: If any TensorDict has a non-empty batch_size. Note: This is used internally by concat_tensordict when handling TensorDicts that contain only non-tensor metadata. """ for d in data: assert len(d.batch_size) == 0 # directly return the first meta info return data[0] def concat_tensordict(data: list[TensorDict]) -> TensorDict: """Concatenate multiple TensorDicts along dimension zero. Combines a list of TensorDicts into a single TensorDict by concatenating all tensors along the batch dimension (dim=0). Handles nested tensors specially by unbinding and rebinding them. Args: data: List of TensorDicts to concatenate. All TensorDicts must have the same keys and the same set of nested tensor keys. Returns: A new TensorDict containing concatenated tensors from all inputs. Raises: AssertionError: If data is empty or if TensorDicts have inconsistent nested tensor keys. Note: - For TensorDicts with empty batch_size, returns the first one - Nested tensors are handled specially via concat_nested_tensors - Regular tensors use TensorDict.cat for efficient concatenation """ assert len(data) > 0, "Must have at least one tensordict" # Find nested tensor keys from the first tensordict nested_tensor_keys = {key for key, value in data[0].items() if isinstance(value, torch.Tensor) and value.is_nested} if not nested_tensor_keys: if len(data[0].batch_size) == 0: return concat_tensordict_with_none_bsz(data) # if batch size is None (only contain NonTensorData) return TensorDict.cat(data, dim=0) # Create a list of tensordicts containing only non-nested tensors for concatenation regular_tds = [] for td in data: current_nested_keys = {k for k, v in td.items() if isinstance(v, torch.Tensor) and v.is_nested} assert current_nested_keys == nested_tensor_keys, "All tensordicts must have the same set of nested tensors." # Create a new TensorDict with non-nested items without modifying the original regular_items = {k: v for k, v in td.items() if k not in nested_tensor_keys} regular_tds.append(TensorDict(regular_items, batch_size=td.batch_size, device=td.device)) # Concatenate the regular tensordicts output = TensorDict.cat(regular_tds, dim=0) # Concatenate and add nested tensors to the output for key in nested_tensor_keys: nested_tensors_to_concat = [td[key] for td in data] output[key] = concat_nested_tensors(nested_tensors_to_concat) return output def chunk_tensordict(td: TensorDict, chunks: int) -> list[TensorDict]: """Split a TensorDict into equal-sized chunks with special nested tensor handling. Divides a TensorDict into the specified number of chunks along the batch dimension. Handles NestedTensors specially since TensorDict.chunk() doesn't support jagged tensors. Args: td: The TensorDict to split. chunks: Number of chunks to create. Must evenly divide len(td). Returns: List of TensorDicts, each containing a portion of the original data. Raises: AssertionError: If td is not a TensorDict or if its length is not evenly divisible by chunks. Note: PyTorch ``unbind(dim=0)`` on 3D+ jagged NestedTensors has a bug where ``split_with_sizes`` is applied to the wrong dimension of the internal ``_values`` tensor. For example, mRoPE ``position_ids`` with per-sample shape ``(4, seq_len)`` becomes a 3D jagged NestedTensor ``[B, *(ragged=4), seq_len]``; ``_values`` is ``[B*4, seq_len]`` and ``unbind`` erroneously splits dimension 1 (``seq_len``) instead of dimension 0, causing:: RuntimeError: split_with_sizes expects split_sizes to sum exactly to , but got split_sizes=[4, 4, ...] 2D jagged NestedTensors (e.g. ``input_ids``, ``loss_mask``) are unaffected — ``unbind(dim=0)`` works correctly for them. The workaround: try ``unbind`` first (fast path for 2D); on failure, fall back to ``to_padded_tensor`` → ``chunk`` → reconstruct per-chunk NestedTensors using the original ragged lengths from ``offsets``. See https://github.com/pytorch/pytorch/issues/153238 """ assert isinstance(td, TensorDict) and len(td) % chunks == 0, ( f"expecting td with length divisible by chunks, but got {len(td)} and {chunks}" ) chunk_size = len(td) // chunks nested_keys = {key for key, val in td.items() if isinstance(val, torch.Tensor) and val.is_nested} new_td = TensorDict( {k: v for k, v in td.items() if k not in nested_keys}, batch_size=td.batch_size, device=td.device ) tds = new_td.chunk(chunks=chunks) for key in nested_keys: nt = td[key] try: tensors = nt.unbind(dim=0) except RuntimeError: padded = nt.to_padded_tensor(0) padded_chunks = padded.chunk(chunks, dim=0) offsets = nt.offsets() lengths = offsets.diff().tolist() for i, chunk_td in enumerate(tds): chunk_lengths = lengths[i * chunk_size : (i + 1) * chunk_size] chunk_tensors = [padded_chunks[i][j, :seq_len] for j, seq_len in enumerate(chunk_lengths)] chunk_td[key] = torch.nested.as_nested_tensor(chunk_tensors, layout=torch.jagged) continue for i, chunk_td in enumerate(tds): chunk_td[key] = torch.nested.as_nested_tensor( tensors[i * chunk_size : (i + 1) * chunk_size], layout=torch.jagged ) return tds def get_tensordict(tensor_dict: dict[str, torch.Tensor | list], non_tensor_dict: dict = None) -> TensorDict: """Create a TensorDict from tensors and non-tensor data. Automatically handles nested structures in lists by converting them to NonTensorStack. This enables support for: - Lists of lists: [[], [0.5, 0.8], [0.9]] - Lists of dicts: [{"acc": 1.0}, {"acc": 0.0}] - Lists of lists of dicts: [[{"content": "...", "role": "user"}]] Args: tensor_dict: Dictionary of tensors and lists to include in the TensorDict non_tensor_dict: Dictionary of metadata to store as NonTensorData Returns: TensorDict with proper handling of nested structures Example: >>> td = get_tensordict( ... tensor_dict={ ... "obs": torch.randn(3, 4), ... "turn_scores": [[], [0.5, 0.8], [0.9]] # Nested list ... }, ... non_tensor_dict={"experiment": "test"} ... ) """ tensor_dict = tensor_dict.copy() if non_tensor_dict is None: non_tensor_dict = {} batch_size = None for key, val in tensor_dict.items(): if isinstance(val, torch.Tensor) and val.is_nested: assert val.is_contiguous(), "Nested tensors must be contiguous. Try setting layout=torch.jagged" assert val.layout == torch.jagged, "Nested tensors must be jagged." # Skip validation for NonTensorStack as it's already properly formatted if isinstance(val, NonTensorStack): if batch_size is None: batch_size = len(val) else: assert len(val) == batch_size, ( f"Batch size of NonTensorStack {key} is not consistent with other tensors. " f"Expected {batch_size}, got {len(val)}" ) continue if isinstance(val, list): for v in val: assert not isinstance(v, torch.Tensor), ( "Passing a list makes the data NonTensorStack, " "which doesn't support torch.Tensor. Please convert to numpy first" ) # Convert to NonTensorStack to handle nested structures tensor_dict[key] = NonTensorStack.from_list([NonTensorData(item) for item in val]) assert isinstance(val, torch.Tensor | list) if batch_size is None: batch_size = val.size(0) if isinstance(val, torch.Tensor) else len(val) else: val_batch_size = val.size(0) if isinstance(val, torch.Tensor) else len(val) assert val_batch_size == batch_size, ( f"Batch size of tensor {key} is not consistent with other tensors. " f"Expected {batch_size}, got {val_batch_size}" ) if batch_size is None: batch_size = [] else: batch_size = [batch_size] for key, val in non_tensor_dict.items(): assert key not in tensor_dict tensor_dict[key] = NonTensorData(val) return TensorDict(source=tensor_dict, batch_size=batch_size) def index_select_tensor_dict(batch: TensorDict, indices: torch.Tensor | list[int]) -> TensorDict: """Select rows from a TensorDict using indices. Creates a new TensorDict containing only the rows specified by indices. Handles regular tensors, nested tensors, NonTensorStack, and NonTensorData appropriately. Args: batch: The TensorDict to index into. Can be None. indices: 1D tensor or list of integers specifying which rows to select. Returns: A new TensorDict containing only the selected rows, or None if batch was None. Raises: AssertionError: If indices is not 1-dimensional. Note: - Regular tensors are indexed directly - Nested tensors are unbound, indexed, and rebound - NonTensorStack is indexed by batch dimension - NonTensorData (scalar metadata) is preserved unchanged """ if isinstance(indices, list): indices = torch.tensor(indices) assert indices.dim() == 1, "indices must be a 1D tensor" data_dict = {} batch_size = indices.shape[0] if batch is not None: for key, tensor in batch.items(): if isinstance(tensor, torch.Tensor) and not tensor.is_nested: data_dict[key] = tensor[indices] elif isinstance(tensor, torch.Tensor) and tensor.is_nested: tensor_lst = tensor.unbind() # for performance data_dict[key] = torch.nested.as_nested_tensor( [tensor_lst[idx] for idx in indices], layout=torch.jagged ) else: # This handles NonTensorStack (indexable by batch dim) and NonTensorData (scalar metadata). if tensor.shape: data_dict[key] = tensor[indices] else: data_dict[key] = tensor selected_batch = TensorDict(source=data_dict, batch_size=batch_size) else: selected_batch = None return selected_batch def union_tensor_dict(tensor_dict1: TensorDict, tensor_dict2: TensorDict) -> TensorDict: """Merge two TensorDicts, adding keys from the second to the first. Performs an in-place union of two TensorDicts. Keys from tensor_dict2 that don't exist in tensor_dict1 are added. Keys that exist in both must have identical values. Args: tensor_dict1: The base TensorDict to merge into (modified in-place). tensor_dict2: The TensorDict whose keys will be added to tensor_dict1. Returns: The modified tensor_dict1 containing the union of both TensorDicts. Raises: AssertionError: If batch sizes don't match, or if a key exists in both TensorDicts with different values. Example: >>> td1 = TensorDict({"a": torch.tensor([1, 2])}, batch_size=[2]) >>> td2 = TensorDict({"b": torch.tensor([3, 4])}, batch_size=[2]) >>> result = union_tensor_dict(td1, td2) >>> list(result.keys()) ['a', 'b'] """ assert tensor_dict1.batch_size == tensor_dict2.batch_size, ( f"Two tensor dict must have identical batch size. Got {tensor_dict1.batch_size} and {tensor_dict2.batch_size}" ) for key in tensor_dict2.keys(): if key not in tensor_dict1.keys(): # Note that there is a difference between tensor_dict2[key] and tensor_dict2.get(key) tensor_dict1[key] = tensor_dict2.get(key) else: if isinstance(tensor_dict2[key], torch.Tensor): assert tensor_dict1[key].equal(tensor_dict2[key]), ( f"{key} in tensor_dict1 and tensor_dict2 are not the same object" ) else: # non-tensor assert tensor_dict1[key] == tensor_dict2[key], ( f"{key} in tensor_dict1 and tensor_dict2 are not the same object" ) return tensor_dict1 def make_iterator(tensordict: TensorDict, mini_batch_size, epochs, seed=None, dataloader_kwargs=None): """Create an iterator that yields mini-batches from a TensorDict. Wraps a TensorDict in a DataLoader-style iterator that yields mini-batches for the specified number of epochs. Useful for training loops. Args: tensordict: The TensorDict to iterate over. mini_batch_size: Size of each mini-batch. Must evenly divide the TensorDict's batch size. epochs: Number of times to iterate through the entire dataset. seed: Optional random seed for reproducible shuffling. dataloader_kwargs: Optional dict of additional kwargs to pass to the underlying DataLoader (e.g., shuffle=True, num_workers=4). Returns: An iterator that yields TensorDict mini-batches. Raises: AssertionError: If batch size is not divisible by mini_batch_size. Example: >>> td = TensorDict({"obs": torch.randn(100, 4)}, batch_size=[100]) >>> for batch in make_iterator(td, mini_batch_size=10, epochs=2): ... # batch is a TensorDict with batch_size=[10] ... pass """ from torch.utils.data import DataLoader assert tensordict.batch_size[0] % mini_batch_size == 0, f"{tensordict.batch_size[0]} % {mini_batch_size} != 0" # we can directly create a dataloader from TensorDict if dataloader_kwargs is None: dataloader_kwargs = {} if seed is not None: generator = torch.Generator() generator.manual_seed(seed) else: generator = None assert isinstance(dataloader_kwargs, dict) idx_lst = torch.arange(tensordict.shape[0]) train_dataloader = DataLoader( dataset=idx_lst, batch_size=mini_batch_size, collate_fn=lambda x: x, generator=generator, **dataloader_kwargs ) def get_data(): for _ in range(epochs): for idx in train_dataloader: yield index_select_tensor_dict(tensordict, idx) return iter(get_data()) def assert_tensordict_eq(tensordict1: TensorDict, tensordict2: TensorDict): """Assert that two TensorDicts are equal. Performs a deep equality check between two TensorDicts, verifying that they have the same keys with identical values. Handles nested tensors by comparing their unbound components. Args: tensordict1: First TensorDict to compare. tensordict2: Second TensorDict to compare. Raises: AssertionError: If the TensorDicts differ in keys, value types, or value contents. The error message indicates what differs. Note: - Regular tensors are compared element-wise - Nested tensors are unbound and compared component by component - Non-tensor values are compared with standard equality """ tensordict1_key_set = set(tensordict1.keys()) tensordict2_key_set = set(tensordict2.keys()) assert tensordict1_key_set == tensordict2_key_set, ( f"key set diffs. Got {tensordict2_key_set=} vs {tensordict1_key_set=}" ) for key in tensordict1.keys(): val = tensordict1[key] val2 = tensordict2[key] assert type(val) is type(val2), f"The type of {key} must be the same. Got {type(val)} vs {type(val2)}" if isinstance(val, torch.Tensor): if val.is_nested: assert val.is_nested and val2.is_nested, ( f"Both tensors must be nested tensors. {val.is_nested=}, {val2.is_nested=}" ) t1, t2 = val.unbind(), val2.unbind() assert len(t1) == len(t2), f"Nested tensor should have the same lengths. {len(t1)=} vs {len(t2)=}" for c1, c2 in zip(t1, t2, strict=True): assert torch.equal(c1, c2), f"Nested tensor components have different values. {c1=} vs {c2=}" else: assert torch.all(torch.eq(val, val2)).item() else: assert val == val2 def get(tensordict: TensorDict, key: str, default=None) -> Any: """Get a value from a TensorDict with automatic unwrapping. Retrieves a value from the TensorDict and automatically converts it to a Python-native format: - Tensors are returned as-is - NonTensorStack is converted to a Python list - NonTensorData is unwrapped to its underlying value Args: tensordict: The TensorDict to retrieve from. key: The key to look up. default: Value to return if the key doesn't exist. Defaults to None. Returns: The value for the key in its native format, or default if not found. Example: >>> td = get_tensordict({"obs": torch.randn(3, 4), "labels": ["a", "b", "c"]}) >>> get(td, "obs") # Returns torch.Tensor >>> get(td, "labels") # Returns ["a", "b", "c"] as a list >>> get(td, "missing", "default") # Returns "default" """ if key not in tensordict: return default output = tensordict.get(key) if isinstance(output, torch.Tensor): return output elif isinstance(output, NonTensorStack): return output.tolist() else: assert isinstance(output, NonTensorData) return output.data def get_keys(tensordict: TensorDict, keys: Iterable[str]) -> TensorDict: """Extract a subset of keys from a TensorDict into a new TensorDict. Creates a new TensorDict containing only the specified keys. Values are properly categorized as tensor or non-tensor data. Args: tensordict: The source TensorDict. keys: Iterable of key names to extract. Returns: A new TensorDict containing only the specified keys with their values. Raises: KeyError: If any key in keys doesn't exist in the tensordict. Example: >>> td = get_tensordict({"a": torch.randn(3), "b": torch.randn(3), "c": torch.randn(3)}) >>> subset = get_keys(td, ["a", "c"]) >>> list(subset.keys()) ['a', 'c'] """ tensor_output = {} non_tensor_output = {} for key in keys: if key not in tensordict.keys(): raise KeyError(f"key {key} not in tensordict") output = tensordict.get(key) if isinstance(output, torch.Tensor): tensor_output[key] = output elif isinstance(output, NonTensorStack): tensor_output[key] = output.tolist() else: assert isinstance(output, NonTensorData) non_tensor_output[key] = output.data return get_tensordict(tensor_output, non_tensor_output) def pop(tensordict: TensorDict, key: str, default=None) -> Any: """Remove and return a value from a TensorDict with automatic unwrapping. Removes the specified key from the TensorDict and returns its value, automatically converting to Python-native format (same as get()). Args: tensordict: The TensorDict to pop from. key: The key to remove and return. default: Value to return if the key doesn't exist. Defaults to None. Returns: The value for the key in its native format, or default if not found. The key is removed from the TensorDict. Example: >>> td = get_tensordict({"obs": torch.randn(3, 4), "labels": ["a", "b", "c"]}) >>> labels = pop(td, "labels") # Returns ["a", "b", "c"], removes from td >>> "labels" in td.keys() False """ _sentinel = object() output = tensordict.pop(key, _sentinel) if output is _sentinel: return default if isinstance(output, torch.Tensor): return output elif isinstance(output, NonTensorStack): return output.tolist() else: assert isinstance(output, NonTensorData) return output.data def pop_keys(tensordict: TensorDict, keys: Iterable[str]) -> TensorDict: """Remove multiple keys from a TensorDict and return them as a new TensorDict. Removes the specified keys from the source TensorDict and creates a new TensorDict containing those keys and their values. Args: tensordict: The source TensorDict to pop from (modified in-place). keys: Iterable of key names to remove and return. Returns: A new TensorDict containing the popped keys and their values. Raises: KeyError: If any key in keys doesn't exist in the tensordict. Example: >>> td = get_tensordict({"a": torch.randn(3), "b": torch.randn(3), "c": torch.randn(3)}) >>> popped = pop_keys(td, ["a", "c"]) >>> list(td.keys()) # Only 'b' remains ['b'] >>> list(popped.keys()) ['a', 'c'] """ tensor_output = {} non_tensor_output = {} for key in keys: if key not in tensordict.keys(): raise KeyError(f"key {key} not in tensordict") output = tensordict.get(key) if isinstance(output, torch.Tensor): tensor_output[key] = tensordict.pop(key) elif isinstance(output, NonTensorStack): tensor_output[key] = tensordict.pop(key).tolist() else: assert isinstance(output, NonTensorData) non_tensor_output[key] = tensordict.pop(key) return get_tensordict(tensor_output, non_tensor_output) def pad_to_divisor(data: TensorDict, size_divisor: int): """Pad a TensorDict's batch dimension to be divisible by a given divisor. If the TensorDict's length is not evenly divisible by size_divisor, pads the batch dimension by repeating elements from the beginning. Useful for ensuring even distribution across workers in distributed training. Args: data: The TensorDict to pad. size_divisor: The divisor that the padded length must be divisible by. Returns: tuple: A tuple containing: - data (TensorDict): The padded TensorDict (or original if no padding needed) - pad_size (int): Number of elements added as padding (0 if none) Raises: AssertionError: If data is not a TensorDict. Example: >>> td = TensorDict({"obs": torch.randn(10, 4)}, batch_size=[10]) >>> padded, pad_size = pad_to_divisor(td, 4) >>> len(padded) # 12 (next multiple of 4 after 10) 12 >>> pad_size 2 """ assert isinstance(data, TensorDict), "data must be a TensorDict" if len(data) % size_divisor != 0: pad_size = size_divisor - len(data) % size_divisor padding_protos = [] remaining_pad = pad_size while remaining_pad > 0: take_size = min(remaining_pad, len(data)) padding_protos.append(data[:take_size]) remaining_pad -= take_size data_padded = torch.cat([data] + padding_protos) else: if len(data) == 0: logging.warning("padding a DataProto with no item, no changed made") pad_size = 0 data_padded = data return data_padded, pad_size def unpad(data: TensorDict, pad_size): """Remove padding from a TensorDict. Reverses the effect of pad_to_divisor by removing the specified number of elements from the end of the TensorDict. Args: data: The padded TensorDict. pad_size: Number of padding elements to remove. If 0, returns data unchanged. Returns: The TensorDict with padding removed, equivalent to data[:-pad_size]. Example: >>> td = TensorDict({"obs": torch.randn(12, 4)}, batch_size=[12]) >>> unpadded = unpad(td, pad_size=2) >>> len(unpadded) 10 """ if pad_size != 0: data = data[:-pad_size] return data def contiguous(data: TensorDict) -> TensorDict: """Call contiguous on a tensor dict. The contiguous function of tensordict lib will make NonTensorStack. This function will always return a new tensordict Args: data: The input tensordict Returns: a tensordict that is contiguous """ tensor_dict = {} non_tensor_dict = {} for key in data.keys(): val = data.get(key) if isinstance(val, NonTensorData): non_tensor_dict[key] = val elif isinstance(val, NonTensorStack): tensor_dict[key] = val else: assert isinstance(val, torch.Tensor), f"Expect val to be a torch.Tensor. Got {type(val)}" tensor_dict[key] = val.contiguous() return get_tensordict(tensor_dict=tensor_dict, non_tensor_dict=non_tensor_dict) def maybe_fix_3d_position_ids(data: TensorDict): # note for tensordict with pickle/unpickle. nested tensor in tensordict after consolidate and pickle/unpickle # will incur indexing error for ragged tensor. This only happens when using 3D position ids in VLMs. # This is likely a bug in tensordict. As a workaround, we manually set _ragged_index. if "position_ids" in data.keys() and data["position_ids"].dim() == 3 and data["position_ids"].is_nested: data["position_ids"]._ragged_idx = 2 ================================================ FILE: verl/utils/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utils for tokenization.""" import types import warnings __all__ = ["hf_tokenizer", "hf_processor", "normalize_token_ids"] def normalize_token_ids(tokenized_output) -> list[int]: """Normalize tokenizer outputs into a flat ``list[int]``. This handles Transformers 4/5 differences where ``apply_chat_template(tokenize=True)`` may return either ``list[int]`` or a ``BatchEncoding``/mapping with ``input_ids``. """ token_ids = tokenized_output if isinstance(tokenized_output, dict): if "input_ids" in tokenized_output: token_ids = tokenized_output["input_ids"] elif hasattr(tokenized_output, "input_ids"): token_ids = tokenized_output.input_ids if hasattr(token_ids, "tolist"): token_ids = token_ids.tolist() if isinstance(token_ids, tuple): token_ids = list(token_ids) if isinstance(token_ids, list) and len(token_ids) == 1 and isinstance(token_ids[0], list | tuple): token_ids = list(token_ids[0]) if not isinstance(token_ids, list): raise TypeError(f"token_ids must be list-like token ids, got {type(token_ids).__name__}: {token_ids!r}") normalized_ids = [] for idx, token_id in enumerate(token_ids): if hasattr(token_id, "item"): token_id = token_id.item() try: normalized_ids.append(int(token_id)) except (TypeError, ValueError) as e: raise TypeError(f"token_id must be int-convertible, got {type(token_id).__name__}: {token_id!r}") from e return normalized_ids def set_pad_token_id(tokenizer): """Set pad_token_id to eos_token_id if it is None. Args: tokenizer (transformers.PreTrainedTokenizer): The tokenizer to be set. """ if tokenizer.pad_token_id is None: tokenizer.pad_token_id = tokenizer.eos_token_id warnings.warn(f"tokenizer.pad_token_id is None. Now set to {tokenizer.eos_token_id}", stacklevel=1) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token warnings.warn(f"tokenizer.pad_token is None. Now set to {tokenizer.eos_token}", stacklevel=1) def hf_tokenizer(name_or_path, correct_pad_token=True, correct_gemma2=True, **kwargs): """Create a huggingface pretrained tokenizer which correctness handles eos and pad tokens. Args: name (str): The name of the tokenizer. correct_pad_token (bool): Whether to correct the pad token id. correct_gemma2 (bool): Whether to correct the gemma2 tokenizer. Returns: transformers.PreTrainedTokenizer: The pretrained tokenizer. """ from transformers import AutoTokenizer if correct_gemma2 and isinstance(name_or_path, str) and "gemma-2-2b-it" in name_or_path: # the EOS token in gemma2 is ambiguious, which may worsen RL performance. # https://huggingface.co/google/gemma-2-2b-it/commit/17a01657f5c87135bcdd0ec7abb4b2dece04408a warnings.warn( "Found gemma-2-2b-it tokenizer. Set eos_token and eos_token_id to and 107.", stacklevel=1 ) kwargs["eos_token"] = "" kwargs["eos_token_id"] = 107 tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs) if correct_pad_token: set_pad_token_id(tokenizer) return tokenizer def hf_processor(name_or_path, **kwargs): """Create a huggingface processor to process multimodal data. Args: name_or_path (str): The name of the processor. Returns: Optional[transformers.ProcessorMixin]: The pretrained multimodal processor. Returns ``None`` for text-only models (including AutoProcessor fallbacks to tokenizer backends such as ``TokenizersBackend``). """ from transformers import AutoConfig, AutoProcessor, PreTrainedTokenizerBase try: processor = AutoProcessor.from_pretrained(name_or_path, **kwargs) # In newer transformers, AutoProcessor may legitimately fall back to a # tokenizer backend (e.g. TokenizersBackend) for text-only models. # Treat it as "no multimodal processor" and let callers use hf_tokenizer. if isinstance(processor, PreTrainedTokenizerBase): return None config = AutoConfig.from_pretrained(name_or_path, **kwargs) # Bind vlm model's get_rope_index method to processor processor.config = config model_class = None match processor.__class__.__name__: case "Qwen2VLProcessor": from transformers.models.qwen2_vl import Qwen2VLModel model_class = Qwen2VLModel case "Qwen2_5_VLProcessor": from transformers.models.qwen2_5_vl import Qwen2_5_VLModel model_class = Qwen2_5_VLModel case "Qwen3VLProcessor": from transformers.models.qwen3_vl import Qwen3VLModel model_class = Qwen3VLModel case "Glm4vImageProcessor": from transformers.models.glm4v import Glm4vModel model_class = Glm4vModel case "MllamaProcessor": pass # MllamaProcessor and MllamaModel doesn't have get_rope_index property case _: raise ValueError(f"Unsupported processor type: {processor.__class__.__name__}") if model_class is not None: processor.get_rope_index = types.MethodType(model_class.get_rope_index, processor) if hasattr(model_class, "get_vision_position_ids"): processor.get_vision_position_ids = types.MethodType(model_class.get_vision_position_ids, processor) except Exception as e: processor = None # TODO(haibin.lin): try-catch should be removed after adding transformer version req to setup.py to avoid # silent failure warnings.warn(f"Failed to create processor: {e}. This may affect multimodal processing", stacklevel=1) # Avoid load tokenizer, see: # https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/auto/processing_auto.py#L344 if processor is not None and "Processor" not in processor.__class__.__name__: processor = None return processor ================================================ FILE: verl/utils/torch_dtypes.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Adapted from Cruise. """ import torch HALF_LIST = [16, "16", "fp16", "float16", torch.float16] FLOAT_LIST = [32, "32", "fp32", "float32", torch.float32] BFLOAT_LIST = ["bf16", "bfloat16", torch.bfloat16] class PrecisionType: """Type of precision used. >>> PrecisionType.HALF == 16 True >>> PrecisionType.HALF in (16, "16") True """ HALF = "16" FLOAT = "32" FULL = "64" BFLOAT = "bf16" MIXED = "mixed" @staticmethod def supported_type(precision: str | int) -> bool: return any(x == precision for x in PrecisionType) @staticmethod def supported_types() -> list[str]: return [x.value for x in PrecisionType] @staticmethod def is_fp16(precision): return precision in HALF_LIST @staticmethod def is_fp32(precision): return precision in FLOAT_LIST @staticmethod def is_bf16(precision): return precision in BFLOAT_LIST @staticmethod def to_dtype(precision): if precision in HALF_LIST: return torch.float16 elif precision in FLOAT_LIST: return torch.float32 elif precision in BFLOAT_LIST: return torch.bfloat16 else: raise RuntimeError(f"unexpected precision: {precision}") @staticmethod def to_str(precision): if precision == torch.float16: return "fp16" elif precision == torch.float32: return "fp32" elif precision == torch.bfloat16: return "bf16" else: raise RuntimeError(f"unexpected precision: {precision}") ================================================ FILE: verl/utils/torch_functional.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contain small torch utilities """ import math from contextlib import contextmanager from typing import Optional import torch import torch.distributed import torch.nn.functional as F from tensordict import TensorDict from torch import nn from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR from transformers import PreTrainedTokenizer from verl.utils.device import get_device_name, get_torch_device try: from flash_attn.ops.triton.cross_entropy import cross_entropy_loss FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE = True except ImportError: FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE = False try: import torch_npu NPU_CROSS_ENTROPY_LOSS_AVAILABLE = hasattr(torch_npu, "npu_cross_entropy_loss") except ImportError: NPU_CROSS_ENTROPY_LOSS_AVAILABLE = False def gather_from_labels(data: torch.Tensor, label: torch.Tensor) -> torch.Tensor: """Gather values from data tensor at positions specified by label indices. Selects elements from the last dimension of `data` based on indices in `label`. Commonly used to extract log-probabilities for specific token IDs from a vocabulary distribution. Args: data: Input tensor of shape (..., vocab_size) containing values to gather from. label: Index tensor of shape (...,) with values in range [0, vocab_size). Returns: torch.Tensor: Gathered values with shape (...,), same as label shape. Example: >>> logits = torch.randn(2, 3, 100) # [batch, seq, vocab] >>> labels = torch.randint(0, 100, (2, 3)) # [batch, seq] >>> gathered = gather_from_labels(logits, labels) # [batch, seq] """ output = torch.gather(data, -1, label.unsqueeze(-1)).squeeze(-1) return output def logprobs_from_logits(logits, labels, inplace_backward=True): """ Compute per-token log-probabilities for the given labels. Uses a Flash-Attention–based cross-entropy (if available) for efficient backward, otherwise falls back to a standard log-softmax+gather approach. See: https://github.com/pytorch/pytorch/issues/563#issuecomment-330103591 Args: logits (Tensor): Model outputs of shape (..., vocab_size). labels (LongTensor): True class indices of shape matching logits[..., :-1]. inplace_backward (bool): If True and Flash-Attn is available, perform backward in-place. Returns: Tensor: Log-probabilities of the target labels, shape logits.shape[:-1]. """ if FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE: batch_dim = logits.shape[:-1] last_dim = logits.shape[-1] logits = logits.reshape(-1, last_dim) labels = labels.reshape(-1) output = logprobs_from_logits_flash_attn(logits, labels, inplace_backward=inplace_backward) output = output.view(*batch_dim) elif NPU_CROSS_ENTROPY_LOSS_AVAILABLE: output = logprobs_from_logits_torch_npu(logits, labels) else: output = logprobs_from_logits_v2(logits, labels) return output def logprobs_from_logits_flash_attn( logits: torch.Tensor, labels: torch.Tensor, inplace_backward: bool = True ) -> torch.Tensor: """Compute log-probabilities using Flash Attention's optimized cross-entropy. Uses the Flash Attention library's Triton-based cross-entropy implementation for efficient computation on NVIDIA GPUs. Args: logits: Model output logits of shape (batch_size, vocab_size). labels: Target token indices of shape (batch_size,). inplace_backward: If True, perform backward pass in-place for memory efficiency. Returns: torch.Tensor: Log-probabilities for target labels, shape (batch_size,). Raises: AssertionError: If flash-attn version < 2.4.3 (different return format). """ output = cross_entropy_loss(logits, labels, inplace_backward=inplace_backward) assert isinstance(output, tuple), ( "please make sure flash-attn>=2.4.3 where cross_entropy_loss returns Tuple[losses, z_losses]." ) return -output[0] def logprobs_from_logits_torch_npu(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: """Compute log-probabilities using Ascend NPU's optimized cross-entropy. Uses torch_npu's native cross-entropy implementation for efficient computation on Huawei Ascend NPU devices. Args: logits: Model output logits of shape (..., vocab_size). labels: Target token indices of shape (...,). Returns: torch.Tensor: Log-probabilities for target labels, same shape as labels. """ batch_dim = logits.shape[:-1] logits = logits.reshape(-1, logits.shape[-1]) loss, _, _, _ = torch_npu.npu_cross_entropy_loss(logits, labels.reshape(-1), reduction="none") return -loss.view(*batch_dim) def logprobs_from_logits_naive(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: """Compute log-probabilities using standard log-softmax approach. Simple implementation using PyTorch's log_softmax followed by gathering. Less memory-efficient than specialized implementations but works on all devices. Args: logits: Model output logits of shape (..., vocab_size). labels: Target token indices of shape (...,). Returns: torch.Tensor: Log-probabilities for target labels, same shape as labels. """ logp = F.log_softmax(logits, dim=-1) logpy = gather_from_labels(logp, labels) return logpy def logprobs_from_logits_v2(logits: torch.FloatTensor, labels: torch.Tensor) -> torch.Tensor: """Memory-efficient log-probability computation using row-wise processing. Computes log-probabilities by processing one row at a time to reduce peak memory consumption. Uses logsumexp for float32/float64, falls back to log_softmax for bfloat16 due to numerical stability concerns. The mathematical identity used is: log_softmax(x_i) = x_i - logsumexp(x) Args: logits: Model output logits of shape (batch_size, seq_len, vocab_size) or (batch_size, vocab_size). labels: Target token indices matching logits shape without vocab dimension. Returns: torch.Tensor: Log-probabilities for target labels. Note: This implementation trades compute for memory by iterating over batch dimension, making it suitable for large vocabulary sizes. """ if logits.dtype in [torch.float32, torch.float64]: logits_labels = torch.gather(logits, dim=-1, index=labels.unsqueeze(-1)).squeeze(-1) # loop to reduce peak mem consumption logsumexp_values = torch.stack([torch.logsumexp(logit, dim=-1) for logit in logits]) logprobs_labels = logits_labels - logsumexp_values # log_softmax(x_i) = x_i - logsumexp(x) else: # logsumexp approach is unstable with bfloat16, fall back to slightly less efficent approach logprobs_labels = [] for row_logits, row_labels in zip(logits, labels, strict=True): # loop to reduce peak mem consumption row_logprobs = F.log_softmax(row_logits, dim=-1) row_logprobs_labels = row_logprobs.gather(dim=-1, index=row_labels.unsqueeze(-1)).squeeze(-1) logprobs_labels.append(row_logprobs_labels) logprobs_labels = torch.stack(logprobs_labels) return logprobs_labels def clip_by_value(x: torch.Tensor, tensor_min: torch.Tensor, tensor_max: torch.Tensor) -> torch.Tensor: """Clip tensor values to a range defined by tensor bounds. Extension of torch.clamp that supports tensor-valued min/max bounds instead of only scalar bounds. Args: x: Input tensor to clip. tensor_min: Minimum bound tensor (broadcastable to x). tensor_max: Maximum bound tensor (broadcastable to x). Returns: torch.Tensor: Clipped tensor with values in [tensor_min, tensor_max]. See Also: https://github.com/pytorch/pytorch/issues/2793#issuecomment-428784713 """ clipped = torch.max(torch.min(x, tensor_max), tensor_min) return clipped def entropy_from_logits(logits: torch.Tensor) -> torch.Tensor: """Calculate Shannon entropy from unnormalized logits. Computes H(p) = -sum(p * log(p)) using the numerically stable formula: entropy = logsumexp(logits) - sum(softmax(logits) * logits) Args: logits: Unnormalized log-probabilities of shape (..., vocab_size). Returns: torch.Tensor: Entropy values with shape (...,), one per distribution. """ pd = torch.nn.functional.softmax(logits, dim=-1) entropy = torch.logsumexp(logits, dim=-1) - torch.sum(pd * logits, dim=-1) return entropy def entropy_from_logits_with_chunking(logits: torch.Tensor, chunk_size: int = 2048) -> torch.Tensor: """Memory-efficient entropy calculation using chunked processing. Computes entropy by processing the batch in chunks to reduce peak memory usage. Useful for large batch sizes or when memory is constrained. Args: logits: Unnormalized log-probabilities of shape (batch_size, vocab_size). chunk_size: Number of samples to process at once. Defaults to 2048. Returns: torch.Tensor: Entropy values with shape (batch_size,). Note: Converts chunks to float32 for numerical stability during computation. """ entropy = torch.zeros(logits.shape[0], device=logits.device) for i in range(0, logits.shape[0], chunk_size): logits_chunk = logits[i : i + chunk_size].float() pd_chunk = torch.nn.functional.softmax(logits_chunk, dim=-1) entropy_chunk = torch.logsumexp(logits_chunk, dim=-1) - torch.sum(pd_chunk * logits_chunk, dim=-1) entropy[i : i + chunk_size] = entropy_chunk return entropy def masked_sum(values: torch.Tensor, mask: torch.Tensor, axis: int | tuple[int, ...] | None = None) -> torch.Tensor: """Compute sum of tensor values where mask is True. NaN values outside the mask are replaced with zeros to prevent contaminating the sum. Args: values: Input tensor containing values to sum. mask: Boolean or numeric mask tensor (same shape as values). Non-zero values indicate elements to include. axis: Dimension(s) along which to sum. None sums all elements. Returns: torch.Tensor: Sum of masked values, reduced along specified axis. """ # If NaNs exist out of mask, replace NaNs in values with a value that # won't affect the sum (e.g., 0 for masked regions) valid_values = torch.where(mask.bool(), values, 0.0) return (valid_values * mask).sum(axis=axis) def masked_mean(values, mask, axis=None): """ Compute the mean of `values` over elements selected by `mask`. Args: values (Tensor): Input tensor. mask (Tensor): Boolean or numeric mask of the same shape as `values`. axis (int or tuple of int, optional): Dimension(s) along which to compute the mean. Defaults to None (over all elements). Returns: Tensor: Masked mean, with shape equal to `values` reduced over `axis`. """ s = masked_sum(values, mask, axis) return s / (mask.sum(axis=axis) + 1e-8) def masked_var(values, mask, unbiased=True): """Compute variance of tensor with masked values.""" mean = masked_mean(values, mask) centered_values = values - mean variance = masked_mean(centered_values**2, mask) if unbiased: mask_sum = mask.sum() if mask_sum == 0: raise ValueError("At least one element in the mask has to be 1.") # note that if mask_sum == 1, then there is a division by zero issue # to avoid it you just need to use a larger minibatch_size if mask_sum == 1: raise ValueError("The sum of the mask is one, which can cause a division by zero.") bessel_correction = mask_sum / (mask_sum - 1) variance = variance * bessel_correction return variance def masked_whiten(values, mask, shift_mean=True): """ Whiten `values` by normalizing with mean and variance computed over `mask`. Args: values (torch.Tensor): Input tensor. mask (torch.Tensor): Boolean tensor of same shape, selects elements for stats. shift_mean (bool): If True (default), output is zero-mean; if False, the original mean is re-added after scaling. Returns: torch.Tensor: Whitened tensor of same shape as `values`. """ mean, var = masked_mean(values, mask), masked_var(values, mask) whitened = (values - mean) * torch.rsqrt(var + 1e-8) if not shift_mean: whitened += mean return whitened def get_response_mask(response_id: torch.Tensor, eos_token: int | list[int] = 2, dtype=torch.int64): """ end of sentence token can be int or list: 1 or [1, 2] e.g. response_id = torch.tensor([[20, 10, 34, 1, 0, 0, 0], [78, 0, 76, 2, 1, 0, 0], [23, 98, 1, 0, 0, 0, 0], [33, 3, 98, 45, 1, 0, 0]]) #eos_token=1 response_mask: tensor([[1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0]]) #eos_token=[1,2] response_mask: tensor([[1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0]]) """ eos_mask = torch.isin(response_id, torch.tensor(eos_token, device=response_id.device)).int() return (eos_mask.cumsum(dim=1) - eos_mask).eq(0).to(dtype) def compute_grad_norm(model: nn.Module) -> float: """Compute the squared L2 norm of all gradients in a model. Sums the squared values of all gradient tensors across all parameters. Useful for monitoring gradient magnitudes during training. Args: model: PyTorch model with computed gradients. Returns: float: Sum of squared gradient values (not the square root). Note: Returns the squared norm, not the norm itself. To get the actual L2 norm, take the square root of the returned value. """ total_grad_square = 0 for param in model.parameters(): if param.grad is not None: total_grad_square += torch.sum(torch.square(param.grad.detach())).item() return total_grad_square def broadcast_dict_tensor(tensors: dict[str, torch.Tensor] | TensorDict, src: int, group) -> None: """Broadcast all tensors in a dictionary from source rank to all ranks. Iterates over all tensors in the dictionary and broadcasts each one from the source rank to all other ranks in the process group. Args: tensors: Dictionary or TensorDict containing tensors to broadcast. src: Source rank from which to broadcast. group: Process group for the broadcast operation. Note: This implementation broadcasts tensors one at a time. Could be optimized to use a single broadcast with packed tensors. """ for key in tensors.sorted_keys: torch.distributed.broadcast(tensors[key], src=src, group=group, async_op=False) def allgather_dict_tensors( tensors: dict[str, torch.Tensor] | TensorDict, size: int, group, dim: int = 0 ) -> dict[str, torch.Tensor] | TensorDict: """Gather tensors from all ranks and concatenate them. Performs all_gather on each tensor in the dictionary and concatenates the results along the specified dimension. Args: tensors: Dictionary or TensorDict containing tensors to gather. size: Number of ranks in the process group. group: Process group for the all_gather operation. dim: Dimension along which to concatenate gathered tensors. Defaults to 0. Returns: Dictionary or TensorDict (matching input type) with gathered and concatenated tensors. Each tensor's size along `dim` is multiplied by `size`. Note: This implementation gathers tensors one at a time synchronously. Could be optimized using async ops or packed all_gather. """ if isinstance(tensors, TensorDict): is_tensor_dict = True tensors_as_dict = tensors.to_dict() else: tensors_as_dict = tensors is_tensor_dict = False output = {} sorted_keys = sorted(tensors_as_dict.keys()) for key in sorted_keys: val = tensors_as_dict[key] output[key] = [torch.empty_like(val) for _ in range(size)] torch.distributed.all_gather(output[key], val, group=group, async_op=False) output[key] = torch.cat(output[key], dim=dim) if is_tensor_dict: output = TensorDict(source=output, batch_size=tensors.batch_size[0] * size) return output def allgather_dict_into_dict(data: dict, group=None) -> dict: """allgather a dict into a dict of list Args: data: a dict group: the process group to allgather Returns: dict containing a list of the results from allgather """ assert isinstance(data, dict), f"Expect data to be a dictionary, Got {type(data)}" group_size = torch.distributed.get_world_size(group=group) final_metrics = {} all_metrics_lst = [None for _ in range(group_size)] torch.distributed.all_gather_object(all_metrics_lst, data, group=group) for all_metrics in all_metrics_lst: for key, val in all_metrics.items(): if key not in final_metrics: final_metrics[key] = [] final_metrics[key].append(val) return final_metrics def split_dict_tensor_into_batches(tensors: TensorDict, batch_size) -> list[TensorDict]: assert tensors.batch_size[0] % batch_size == 0, ( f"input data batch size: {tensors.batch_size[0]}, split batch size: {batch_size}" ) return tensors.split(batch_size) def pad_2d_list_to_length(response, pad_token_id, max_length=None): """ pad a 2D list (e.g. responses, logprobs) to a 2D tensor. """ response_length = max(len(sub_list) for sub_list in response) target_length = max_length if max_length is not None and max_length > response_length else response_length padded_response = [tuple(sub_list) + (pad_token_id,) * (target_length - len(sub_list)) for sub_list in response] tensor = torch.tensor(padded_response) return tensor def pad_sequence_to_length(tensors, max_seq_len, pad_token_id, left_pad=False): """ pad a 2D tensors (e.g. responses, logprobs) in the last dim to max_seq_length. input shape: [bs, seq_length] output shape: [bs, max_seq_length] """ if tensors.shape[-1] >= max_seq_len: return tensors # (0, max_seq_len - tensors.shape[-1]) means right pad to max_seq_length and no left pad pad_tuple = (max_seq_len - tensors.shape[-1], 0) if left_pad else (0, max_seq_len - tensors.shape[-1]) return F.pad(tensors, pad_tuple, "constant", pad_token_id) def postprocess_data( input_ids: torch.Tensor, attention_mask: torch.Tensor, max_length: int, pad_token_id: int, left_pad=True, truncation="error", ): """Process tokenizer outputs to consistent shapes via padding/truncation. Args: input_ids: Token indices [batch_size, seq_len] attention_mask: Mask [batch_size, seq_len] max_length: Target sequence length pad_token_id: Padding token ID left_pad: Pad left if True truncation: "left", "right", "middle" or "error" Returns: (input_ids, attention_mask) padded/truncated to max_length """ assert truncation in ["left", "right", "middle", "error"] assert input_ids.ndim == 2 sequence_length = input_ids.shape[-1] if sequence_length < max_length: input_ids = pad_sequence_to_length( input_ids, max_seq_len=max_length, pad_token_id=pad_token_id, left_pad=left_pad ) attention_mask = pad_sequence_to_length( attention_mask, max_seq_len=max_length, pad_token_id=0, left_pad=left_pad ) elif sequence_length > max_length: if truncation == "left": # actually, left truncation may not be reasonable input_ids = input_ids[:, -max_length:] attention_mask = attention_mask[:, -max_length:] elif truncation == "right": input_ids = input_ids[:, :max_length] attention_mask = attention_mask[:, :max_length] elif truncation == "middle": left_half = max_length // 2 right_half = max_length - left_half input_ids = torch.cat([input_ids[:, :left_half], input_ids[:, -right_half:]], dim=-1) attention_mask = torch.cat([attention_mask[:, :left_half], attention_mask[:, -right_half:]], dim=-1) elif truncation == "error": raise NotImplementedError(f"{sequence_length=} is larger than {max_length=}") else: raise NotImplementedError(f"Unknown truncation method {truncation}") return input_ids, attention_mask def tokenize_and_postprocess_data( prompt: str, tokenizer: PreTrainedTokenizer, max_length: int, pad_token_id: int, left_pad=True, truncation="error" ): """Tokenize text and process outputs to consistent tensor shapes. Args: prompt: Input text to tokenize tokenizer: HuggingFace tokenizer instance max_length: Target sequence length pad_token_id: Padding token ID left_pad: Pad left if True truncation: Truncation strategy ("left"/"right"/"error") Returns: Tuple of (input_ids, attention_mask) from postprocess_data """ input_data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False) input_ids = input_data["input_ids"] attention_mask = input_data["attention_mask"] return postprocess_data(input_ids, attention_mask, max_length, pad_token_id, left_pad, truncation) def remove_pad_token(input_ids: torch.Tensor, attention_mask: torch.Tensor): """Remove the pad token. Args: input_ids shape: [bs, seq_length] attention_mask shape: [bs, seq_length] Returns: no_padding_batch(List[List[int]]): contains the rmpad token ids per query. """ no_padding_batch = [] for ids, mask in zip(input_ids, attention_mask, strict=True): no_padding_batch.append((ids[len(ids) - mask.sum() :]).cpu().numpy().tolist()) return no_padding_batch def log_probs_from_logits_response(input_ids, logits, response_length): """Compute the response log_probs from full logits. Note that logits = model(input_ids) Args: input_ids: [batch_size, seqlen] logits: [batch_size, seqlen, vocab_size] Returns: response_log_prob: """ response_logits = logits[:, -response_length - 1 : -1] response = input_ids[:, -response_length:] response_log_prob = logprobs_from_logits(logits=response_logits, labels=response) return response_log_prob def log_probs_from_logits_response_rmpad(input_ids, attention_mask, logits_rmpad, response_length): """Compute the log_probs from logits with rmpad logits and pad input. Note that logits_rmpad = model(input_ids_rmpad). For each sentences, there is a shift between logits and input_ids. The reason for this function to is to compute logprobs_from_logits in rmpad mode because it is memory-intensive for large vocab_size Args: input_ids: [batch_size, seqlen] attention_mask: [batch_size, seqlen] logits_rmpad: [total_nnz, vocab_size] response_length: int """ from flash_attn.bert_padding import pad_input, unpad_input batch_size, seqlen = input_ids.shape input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask=attention_mask) input_ids_rmpad = input_ids_rmpad.squeeze(-1) input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0) full_log_probs_rmpad = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # (total_nnz,) full_output = pad_input( hidden_states=full_log_probs_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen ) output = full_output.squeeze(-1)[:, -response_length - 1 : -1] # [batch_size, response_length] return output def log_probs_from_logits_all_rmpad(input_ids_rmpad, logits_rmpad, indices, batch_size, seqlen, response_length): """Compute the log_probs from logits with rmpad input_ids and logits. Note that logits_rmpad = model(input_ids_rmpad). For each sentences, there is a shift between logits and input_ids. The reason for this function to is to compute logprobs_from_logits in rmpad mode because it is memory-intensive for large vocab_size Args: input_ids_rmpad: [1, total_nnz] logits_rmpad: [total_nnz, vocab_size] indices: [total_nnz] batch_size: int seqlen: int response_length: int """ if get_device_name() == "cuda": from flash_attn.bert_padding import pad_input elif get_device_name() == "npu": from verl.utils.attention_utils import pad_input input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # transpose back to [total_nnz, 1] input_ids_rmpad = input_ids_rmpad.squeeze(-1) input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0) full_log_probs_rmpad = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # (total_nnz,) full_output = pad_input( hidden_states=full_log_probs_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen ) output = full_output.squeeze(-1)[:, -response_length - 1 : -1] # [batch_size, response_length] return output def post_process_logits(input_ids, logits, temperature, top_k, top_p): if temperature != 1.0: logits = logits.div_(temperature) # inplace operation to avoid OOM # TODO: add them back # if top_k is not None and top_k > 0: # logits = TopKLogitsWarper(top_k=top_k)(input_ids, logits) # if top_p is not None and top_p < 1.0 and top_p > 0.0: # logits = TopPLogitsWarper(top_p=top_p)(input_ids, logits) return logits def calculate_sum_pi_squared_from_logits(logits: torch.Tensor): """ Compute exact sum of squared probabilities from logits. Formula: Σπ² = exp(logsumexp(2*logits) - 2*logsumexp(logits)) Used for optimal baseline variance reduction as described in "What Matters for Model Merging at Scale?" (arXiv:2410.03617) Args: logits: Logits tensor (..., vocab_size). Returns: Sum of squared probabilities tensor (...). """ return torch.exp(torch.logsumexp(2.0 * logits, dim=-1) - 2.0 * torch.logsumexp(logits, dim=-1)) """ Optimizer related """ def get_cosine_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, min_lr_ratio: float = 0.0, num_cycles: float = 0.5, last_epoch: int = -1, init_lr_ratio: float = None, zero_indexed_step: bool = True, ): """ Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer (:class:`~torch.optim.Optimizer`): The optimizer for which to schedule the learning rate. num_warmup_steps (:obj:`int`): The number of steps for the warmup phase. num_training_steps (:obj:`int`): The total number of training steps. min_lr_ratio (:obj:`float`, `optional`, defaults to 0.0): The minimum lr ratio w.r.t the maximum. num_cycles (:obj:`float`, `optional`, defaults to 0.5): The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). last_epoch (:obj:`int`, `optional`, defaults to -1): The index of the last epoch when resuming training. init_lr_ratio (:obj:`float`, `optional`, defaults to None): The initial lr ratio w.r.t the maximum. zero_indexed_step (:obj:`bool`, `optional`, defaults to True): Whether the LR schedule uses 0-indexed steps. If True (default), step counting starts at 0. If False (used by torchtitan), step counting starts at 1. Return: :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ min_lr_ratio = 0.0 if min_lr_ratio is None else min_lr_ratio assert min_lr_ratio >= 0 and min_lr_ratio <= 1.0 coef = (1 - min_lr_ratio) * 0.5 intercept = (1 + min_lr_ratio) * 0.5 init_lr_ratio = 0.0 if init_lr_ratio is None else init_lr_ratio assert init_lr_ratio >= 0 and init_lr_ratio <= 1.0 def lr_lambda(current_step): if not zero_indexed_step: current_step += 1 if current_step < num_warmup_steps: return init_lr_ratio + (1.0 - init_lr_ratio) * (float(current_step) / float(max(1, num_warmup_steps))) progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) x = math.cos(math.pi * float(num_cycles) * 2.0 * progress) return max(min_lr_ratio, x * coef + intercept) return LambdaLR(optimizer, lr_lambda, last_epoch) def get_constant_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1, ): """ Create a constant LR schedule with a linear warmup phase. Args: optimizer (Optimizer): Wrapped optimizer. num_warmup_steps (int): Number of steps to ramp up the LR from 0 to initial value. last_epoch (int, optional): The index of the last epoch when resuming training. Defaults to -1. Returns: LambdaLR: Scheduler that increases LR linearly during warmup, then holds it constant. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1.0, num_warmup_steps)) return 1.0 return LambdaLR(optimizer, lr_lambda, last_epoch) def prepare_decoder_attention_mask(attention_mask, input_shape, inputs_embeds): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) def get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) def get_wsd_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, min_lr_ratio: float = 0.0, num_cycles: float = 0.5, last_epoch: int = -1, stable_ratio: float = 0.9, ): """ Create a Warmup-Stable-Decay learning rate scheduler. The schedule follows three phases: 1. Warmup: Learning rate increases linearly from 0 to the initial LR 2. Stable: Learning rate remains constant at the initial LR 3. Decay: Learning rate decreases following a cosine curve to min_lr_ratio * initial LR Args: optimizer (:class:`~torch.optim.Optimizer`): The optimizer for which to schedule the learning rate. num_warmup_steps (:obj:`int`): The number of steps for the warmup phase. num_training_steps (:obj:`int`): The total number of training steps. min_lr_ratio (:obj:`float`, `optional`, defaults to 0.0): The minimum learning rate ratio w.r.t the initial learning rate. num_cycles (:obj:`float`, `optional`, defaults to 0.5): The number of waves in the cosine schedule during decay phase. last_epoch (:obj:`int`, `optional`, defaults to -1): The index of the last epoch when resuming training. stable_ratio (:obj:`float`, `optional`, defaults to 0.0): The ratio of non-warmup steps that should maintain a constant learning rate. Set to 0.0 to behave exactly like cosine schedule. Return: :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ remaining_steps = max(0, num_training_steps - num_warmup_steps) num_stable_steps = int(remaining_steps * stable_ratio) num_decay_steps = remaining_steps - num_stable_steps def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) if current_step < num_warmup_steps + num_stable_steps: return 1.0 if current_step < num_training_steps: progress = float(current_step - num_warmup_steps - num_stable_steps) / float(max(1, num_decay_steps)) value = max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))) return (1.0 - min_lr_ratio) * value + min_lr_ratio return min_lr_ratio return LambdaLR(optimizer, lr_lambda, last_epoch) @contextmanager def check_device_is_available(): """ Some modules must be imported after CUDA is initialized. Such as sglang's sharding manager. This context manager checks if CUDA is available and raises an error if it is not. """ if not get_torch_device().is_available(): raise RuntimeError("Device {} must be initialized before importing this module.".format(get_device_name())) yield def distributed_mean_max_min_std(local_tensor, compute_max=True, compute_min=True, compute_std=True): """Compute distributed statistics across all processes. Args: local_tensor: Tensor containing local values compute_max: Include maximum value calculation compute_min: Include minimum value calculation compute_std: Include standard deviation calculation Returns: Tuple containing (mean, max, min, std) in this order. None for disabled metrics. """ # Sum the local tensor across all processes local_sum = torch.sum(local_tensor) local_num = torch.tensor(torch.numel(local_tensor), device=get_device_name()) torch.distributed.all_reduce(local_sum, op=torch.distributed.ReduceOp.SUM) torch.distributed.all_reduce(local_num, op=torch.distributed.ReduceOp.SUM) global_mean = local_sum / local_num if compute_max: local_max = torch.max(local_tensor) torch.distributed.all_reduce(local_max, op=torch.distributed.ReduceOp.MAX) else: local_max = None if compute_min: local_min = torch.min(local_tensor) torch.distributed.all_reduce(local_min, op=torch.distributed.ReduceOp.MIN) else: local_min = None if compute_std: square_diff = torch.sum(torch.pow(local_tensor - global_mean, 2)) torch.distributed.all_reduce(square_diff, op=torch.distributed.ReduceOp.SUM) global_std = torch.sqrt(square_diff / (local_num - 1)) else: global_std = None return global_mean, local_max, local_min, global_std def distributed_masked_mean(local_tensor, local_mask): """Compute global mean of non-masked elements across distributed processes. Args: local_tensor (torch.Tensor): Input tensor with local values local_mask (torch.Tensor): Binary mask (1=valid, 0=ignore) matching local_tensor shape Returns: torch.Tensor: Global mean of all valid elements across processes """ local_tensor = local_tensor * local_mask local_sum = torch.sum(local_tensor) local_num = torch.sum(local_mask) torch.distributed.all_reduce(local_sum, op=torch.distributed.ReduceOp.SUM) torch.distributed.all_reduce(local_num, op=torch.distributed.ReduceOp.SUM) global_mean = local_sum / local_num return global_mean def expand_as_nested(tensor: torch.Tensor, nested_tensor: torch.Tensor) -> torch.Tensor: """ Args: tensor: a tensor with shape (bsz,) nested_tensor: a nested tensor with shape (bsz, xxx) Returns: a tensor with the same shape as nested_tensor """ assert nested_tensor.is_nested, "nested_tensor must be nested" assert tensor.shape[0] == nested_tensor.shape[0], ( f"The batch shape must be the same. Got {tensor.shape[0]} vs {nested_tensor.shape[0]}" ) assert len(tensor.shape) == 1, "The ndim of tensor must be 1" assert len(nested_tensor.shape) == 2, "The ndim of nested_tensor must be 2" offsets = nested_tensor.offsets() seqlens = offsets.diff() output = torch.repeat_interleave(tensor, seqlens, dim=0) output = torch.nested.nested_tensor_from_jagged(values=output, offsets=offsets) return output @contextmanager def use_original_torch_compile(): """torch.compile might be replaced by mindspeed on NPU, this contextmanager can revert torch.compile temporarily. """ try: from mindspeed.patch_utils import MindSpeedPatchesManager compile_patch = None for patch in MindSpeedPatchesManager.patches_info.values(): if patch.orig_module_name == "torch" and patch.orig_func_name == "compile": if patch.is_applied(): compile_patch = patch break if compile_patch is not None: compile_patch.remove_patch() yield compile_patch.apply_patch() else: yield except Exception: yield ================================================ FILE: verl/utils/tracking.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A unified tracking interface that supports logging data to different backend """ import dataclasses import json import logging import os from enum import Enum from functools import partial from pathlib import Path from typing import Any import orjson logger = logging.getLogger(__name__) MLFLOW_MAX_ATTEMPTS = 3 MLFLOW_SLEEP_SECONDS = 5 class Tracking: """A unified tracking interface for logging experiment data to multiple backends. This class provides a centralized way to log experiment metrics, parameters, and artifacts to various tracking backends including WandB, MLflow, SwanLab, TensorBoard, and console. Attributes: supported_backend: List of supported tracking backends. logger: Dictionary of initialized logger instances for each backend. """ supported_backend = [ "wandb", "mlflow", "swanlab", "vemlp_wandb", "tensorboard", "console", "clearml", "trackio", "file", ] def __init__(self, project_name, experiment_name, default_backend: str | list[str] = "console", config=None): if isinstance(default_backend, str): default_backend = [default_backend] for backend in default_backend: if backend == "tracking": import warnings warnings.warn("`tracking` logger is deprecated. use `wandb` instead.", DeprecationWarning, stacklevel=2) else: assert backend in self.supported_backend, f"{backend} is not supported" self.logger = {} if "tracking" in default_backend or "wandb" in default_backend: import os import wandb settings = None if config and config["trainer"].get("wandb_proxy", None): settings = wandb.Settings(https_proxy=config["trainer"]["wandb_proxy"]) entity = os.environ.get("WANDB_ENTITY", None) wandb.init(project=project_name, name=experiment_name, entity=entity, config=config, settings=settings) self.logger["wandb"] = wandb if "trackio" in default_backend: import trackio trackio.init(project=project_name, name=experiment_name, config=config) self.logger["trackio"] = trackio if "mlflow" in default_backend: import os import time import mlflow for _mlflow_attempt in range(1, MLFLOW_MAX_ATTEMPTS + 1): try: MLFLOW_TRACKING_URI = os.environ.get("MLFLOW_TRACKING_URI", "sqlite:////tmp/mlruns.db") logger.info("Using MLFlow tracking URI: %s", MLFLOW_TRACKING_URI) mlflow.set_tracking_uri(MLFLOW_TRACKING_URI) # Some cloud providers like Azure ML or Databricks automatically set MLFLOW_RUN_ID # If set, attach to the existing run instead of creating a new one run_id = os.environ.get("MLFLOW_RUN_ID") if run_id: mlflow.start_run(run_id=run_id) else: # Project_name is actually experiment_name in MLFlow # If experiment does not exist, will create a new experiment experiment = mlflow.set_experiment(project_name) mlflow.start_run(experiment_id=experiment.experiment_id, run_name=experiment_name) mlflow.log_params(_compute_mlflow_params_from_objects(config)) self.logger["mlflow"] = _MlflowLoggingAdapter() break # Success except Exception as e: logger.warning( "MLflow initialization attempt %d/%d failed: %s", _mlflow_attempt, MLFLOW_MAX_ATTEMPTS, e ) if _mlflow_attempt < MLFLOW_MAX_ATTEMPTS: time.sleep(MLFLOW_SLEEP_SECONDS) else: logger.warning("All MLflow initialization attempts failed. Proceeding without MLflow tracking.") if "swanlab" in default_backend: import os import swanlab SWANLAB_API_KEY = os.environ.get("SWANLAB_API_KEY", None) SWANLAB_LOG_DIR = os.environ.get("SWANLAB_LOG_DIR", "swanlog") SWANLAB_MODE = os.environ.get("SWANLAB_MODE", "cloud") if SWANLAB_API_KEY: swanlab.login(SWANLAB_API_KEY) # NOTE: previous login information will be overwritten if config is None: config = {} # make sure config is not None, otherwise **config will raise error swanlab.init( project=project_name, experiment_name=experiment_name, config={"FRAMEWORK": "verl", **config}, logdir=SWANLAB_LOG_DIR, mode=SWANLAB_MODE, ) self.logger["swanlab"] = swanlab if "vemlp_wandb" in default_backend: import os import volcengine_ml_platform from volcengine_ml_platform import wandb as vemlp_wandb volcengine_ml_platform.init( ak=os.environ["VOLC_ACCESS_KEY_ID"], sk=os.environ["VOLC_SECRET_ACCESS_KEY"], region=os.environ["MLP_TRACKING_REGION"], ) vemlp_wandb.init( project=project_name, name=experiment_name, config=config, sync_tensorboard=True, ) self.logger["vemlp_wandb"] = vemlp_wandb if "tensorboard" in default_backend: self.logger["tensorboard"] = _TensorboardAdapter(project_name, experiment_name) if "console" in default_backend: from verl.utils.logger import LocalLogger self.console_logger = LocalLogger(print_to_console=True) self.logger["console"] = self.console_logger if "clearml" in default_backend: self.logger["clearml"] = ClearMLLogger(project_name, experiment_name, config) if "file" in default_backend: self.logger["file"] = FileLogger(project_name, experiment_name) def log(self, data, step, backend=None): for default_backend, logger_instance in self.logger.items(): if backend is None or default_backend in backend: logger_instance.log(data=data, step=step) def __del__(self): if "wandb" in self.logger: self.logger["wandb"].finish(exit_code=0) if "swanlab" in self.logger: self.logger["swanlab"].finish() if "vemlp_wandb" in self.logger: self.logger["vemlp_wandb"].finish(exit_code=0) if "tensorboard" in self.logger: self.logger["tensorboard"].finish() if "clearml" in self.logger: self.logger["clearml"].finish() if "trackio" in self.logger: self.logger["trackio"].finish() if "file" in self.logger: self.logger["file"].finish() class ClearMLLogger: def __init__(self, project_name: str, experiment_name: str, config): self.project_name = project_name self.experiment_name = experiment_name import clearml self._task: clearml.Task = clearml.Task.init( task_name=experiment_name, project_name=project_name, continue_last_task=True, output_uri=False, ) self._task.connect_configuration(config, name="Hyperparameters") def _get_logger(self): return self._task.get_logger() def log(self, data, step): import numpy as np import pandas as pd # logs = self._rewrite_logs(data) logger = self._get_logger() for k, v in data.items(): title, series = k.split("/", 1) if isinstance(v, int | float | np.floating | np.integer): logger.report_scalar( title=title, series=series, value=v, iteration=step, ) elif isinstance(v, pd.DataFrame): logger.report_table( title=title, series=series, table_plot=v, iteration=step, ) else: logger.warning( f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}". This ' f"invocation of ClearML logger's function is incorrect so this attribute was dropped. " ) def finish(self): self._task.close() class FileLogger: def __init__(self, project_name: str, experiment_name: str): self.project_name = project_name self.experiment_name = experiment_name self.filepath = os.getenv("VERL_FILE_LOGGER_PATH", None) if self.filepath is None: root_path = os.path.expanduser(os.getenv("VERL_FILE_LOGGER_ROOT", ".")) directory = os.path.join(root_path, self.project_name) os.makedirs(directory, exist_ok=True) self.filepath = os.path.join(directory, f"{self.experiment_name}.jsonl") print(f"Creating file logger at {self.filepath}") self.fp = open(self.filepath, "wb", buffering=0) def log(self, data, step): data = {"step": step, "data": data} self.fp.write(orjson.dumps(data, option=orjson.OPT_SERIALIZE_NUMPY) + b"\n") def finish(self): self.fp.close() class _TensorboardAdapter: def __init__(self, project_name, experiment_name): import os from torch.utils.tensorboard import SummaryWriter tensorboard_dir = os.environ.get("TENSORBOARD_DIR", f"tensorboard_log/{project_name}/{experiment_name}") os.makedirs(tensorboard_dir, exist_ok=True) print(f"Saving tensorboard log to {tensorboard_dir}.") self.writer = SummaryWriter(tensorboard_dir) def log(self, data, step): for key in data: self.writer.add_scalar(key, data[key], step) def finish(self): self.writer.close() class _MlflowLoggingAdapter: def __init__(self): import logging import re self.logger = logging.getLogger(__name__) # Suppress noisy "Found credentials from IAM Role" on every MLflow request logging.getLogger("botocore.credentials").setLevel(logging.WARNING) # MLflow metric key validation logic: # https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py#L157C12-L157C44 # Only characters allowed: slashes, alphanumerics, underscores, periods, dashes, colons, # and spaces. self._invalid_chars_pattern = re.compile( r"[^/\w.\- :]" ) # Allowed: slashes, alphanumerics, underscores, periods, dashes, colons, and spaces. self._consecutive_slashes_pattern = re.compile(r"/+") self._sanitized_key_cache = {} def _sanitize_key(self, key): if key in self._sanitized_key_cache: return self._sanitized_key_cache[key] or key # First replace @ with _at_ for backward compatibility sanitized = key.replace("@", "_at_") # Replace consecutive slashes with a single slash (MLflow treats them as file paths) sanitized = self._consecutive_slashes_pattern.sub("/", sanitized) # Then replace any other invalid characters with _ sanitized = self._invalid_chars_pattern.sub("_", sanitized) if sanitized == key: self._sanitized_key_cache[key] = None else: self.logger.warning("[MLflow] Metric key '%s' sanitized to '%s' due to invalid characters.", key, sanitized) self._sanitized_key_cache[key] = sanitized return sanitized def log(self, data, step): import mlflow results = {self._sanitize_key(k): v for k, v in data.items()} mlflow.log_metrics(metrics=results, step=step) def _compute_mlflow_params_from_objects(params) -> dict[str, Any]: if params is None: return {} return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep="/") def _transform_params_to_json_serializable(x, convert_list_to_dict: bool): _transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict) if dataclasses.is_dataclass(x): return _transform(dataclasses.asdict(x)) if isinstance(x, dict): return {k: _transform(v) for k, v in x.items()} if isinstance(x, list): if convert_list_to_dict: return {"list_len": len(x)} | {f"{i}": _transform(v) for i, v in enumerate(x)} else: return [_transform(v) for v in x] if isinstance(x, Path): return str(x) if isinstance(x, Enum): return x.value return x def _flatten_dict(raw: dict[str, Any], *, sep: str) -> dict[str, Any]: import pandas as pd ans = pd.json_normalize(raw, sep=sep).to_dict(orient="records")[0] assert isinstance(ans, dict) return ans @dataclasses.dataclass class ValidationGenerationsLogger: project_name: str = None experiment_name: str = None def log(self, loggers, samples, step): if "wandb" in loggers: self.log_generations_to_wandb(samples, step) if "swanlab" in loggers: self.log_generations_to_swanlab(samples, step) if "mlflow" in loggers: self.log_generations_to_mlflow(samples, step) if "clearml" in loggers: self.log_generations_to_clearml(samples, step) if "tensorboard" in loggers: self.log_generations_to_tensorboard(samples, step) if "vemlp_wandb" in loggers: self.log_generations_to_vemlp_wandb(samples, step) def log_generations_to_vemlp_wandb(self, samples, step): from volcengine_ml_platform import wandb as vemlp_wandb self._log_generations_to_wandb(samples, step, vemlp_wandb) def log_generations_to_wandb(self, samples, step): import wandb self._log_generations_to_wandb(samples, step, wandb) def _log_generations_to_wandb(self, samples, step, wandb): """Log samples to wandb as a table""" # Create column names for all samples columns = ["step"] + sum( [[f"input_{i + 1}", f"output_{i + 1}", f"score_{i + 1}"] for i in range(len(samples))], [] ) if not hasattr(self, "validation_table"): # Initialize the table on first call self.validation_table = wandb.Table(columns=columns) # Create a new table with same columns and existing data # Workaround for https://github.com/wandb/wandb/issues/2981#issuecomment-1997445737 new_table = wandb.Table(columns=columns, data=self.validation_table.data) # Add new row with all data row_data = [] row_data.append(step) for sample in samples: row_data.extend(sample) new_table.add_data(*row_data) # Update reference and log if wandb.run is not None: wandb.log({"val/generations": new_table}, step=step) self.validation_table = new_table def log_generations_to_swanlab(self, samples, step): """Log samples to swanlab as text""" import swanlab swanlab_table = swanlab.echarts.Table() # Create column names headers = ["step", "input", "output", "score"] swanlab_row_list = [[step, *sample] for sample in samples] swanlab_table.add(headers=headers, rows=swanlab_row_list) # Log to swanlab swanlab.log({"val/generations": swanlab_table}, step=step) def log_generations_to_mlflow(self, samples, step): """Log validation generation to mlflow as artifacts""" # https://mlflow.org/docs/latest/api_reference/python_api/mlflow.html?highlight=log_artifact#mlflow.log_artifact import tempfile import mlflow try: with tempfile.TemporaryDirectory() as tmp_dir: validation_gen_step_file = Path(tmp_dir, f"val_step{step}.json") row_data = [] for sample in samples: data = {"input": sample[0], "output": sample[1], "score": sample[2]} row_data.append(data) with open(validation_gen_step_file, "w") as file: json.dump(row_data, file) mlflow.log_artifact(validation_gen_step_file) except Exception as e: print(f"WARNING: save validation generation file to mlflow failed with error {e}") def log_generations_to_clearml(self, samples, step): """Log validation generation to clearml as table""" import clearml import pandas as pd task: clearml.Task | None = clearml.Task.current_task() if task is None: return table = [ { "step": step, "input": sample[0], "output": sample[1], "score": sample[2], } for sample in samples ] logger = task.get_logger() logger.report_table( series="Validation generations", title="Validation", table_plot=pd.DataFrame.from_records(table), iteration=step, ) def log_generations_to_tensorboard(self, samples, step): """Log samples to tensorboard as text""" # Initialize tensorboard writer if not exists if not hasattr(self, "writer"): from torch.utils.tensorboard import SummaryWriter # Use the same directory structure as _TensorboardAdapter if self.project_name and self.experiment_name: default_dir = os.path.join("tensorboard_log", self.project_name, self.experiment_name) else: default_dir = "tensorboard_log" tensorboard_dir = os.environ.get("TENSORBOARD_DIR", default_dir) os.makedirs(tensorboard_dir, exist_ok=True) self.writer = SummaryWriter(log_dir=tensorboard_dir) # Format the samples data into readable text text_content = f"**Generation Results - Step {step}**\n\n" for i, sample in enumerate(samples): text_content += f"### Sample {i + 1}\n" # Assuming sample contains [input, output, score] if len(sample) >= 3: input_text, output_text, score = sample[0], sample[1], sample[2] text_content += f"**Input:** {input_text}\n\n" text_content += f"**Output:** {output_text}\n\n" text_content += f"**Score:** {score}\n\n" else: # Handle cases where sample format might be different text_content += f"**Data:** {sample}\n\n" text_content += "---\n\n" # Log to tensorboard as text self.writer.add_text("val/generations", text_content, step) # Flush to ensure data is written self.writer.flush() ================================================ FILE: verl/utils/transformers_compat.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Compatibility utilities for different versions of transformers library. """ import importlib.metadata from functools import lru_cache from typing import Optional from packaging import version # Handle version compatibility for flash_attn_supports_top_left_mask # This function was added in newer versions of transformers try: from transformers.modeling_flash_attention_utils import flash_attn_supports_top_left_mask except ImportError: # For older versions of transformers that don't have this function # Default to False as a safe fallback for older versions def flash_attn_supports_top_left_mask(): """Fallback implementation for older transformers versions. Returns False to disable features that require this function. """ return False @lru_cache def is_transformers_version_in_range(min_version: Optional[str] = None, max_version: Optional[str] = None) -> bool: try: # Get the installed version of the transformers library transformers_version_str = importlib.metadata.version("transformers") except importlib.metadata.PackageNotFoundError as e: raise ModuleNotFoundError("The `transformers` package is not installed.") from e transformers_version = version.parse(transformers_version_str) lower_bound_check = True if min_version is not None: lower_bound_check = version.parse(min_version) <= transformers_version upper_bound_check = True if max_version is not None: upper_bound_check = transformers_version <= version.parse(max_version) return lower_bound_check and upper_bound_check @lru_cache def get_auto_model_for_vision2seq(): """Return the available VL auto model class across transformers versions.""" try: # Prefer the newer class when available. In transformers 4.x this class has # a broader mapping than AutoModelForVision2Seq, and AutoModelForVision2Seq # is deprecated for removal in v5. from transformers import AutoModelForImageTextToText except ImportError: from transformers import AutoModelForVision2Seq return AutoModelForVision2Seq return AutoModelForImageTextToText ================================================ FILE: verl/utils/trtllm/trtllm_fp8_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.utils.fp8_utils import FP8QuantizerHelper class TRTLLMFP8QuantizerHelper(FP8QuantizerHelper): def __init__(self, quant_config): super().__init__(quant_config) ================================================ FILE: verl/utils/ulysses.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities for DeepSpeed Ulysses Sequence Parallelism. DeepSpeed Ulysses Paper: https://arxiv.org/abs/2309.14509 Inspired from: https://github.com/deepspeedai/DeepSpeed/blob/master/deepspeed/sequence/layer.py """ from typing import Any, Optional import torch import torch.distributed as dist from torch import Tensor from torch.distributed import ProcessGroup _ULYSSES_SEQUENCE_PARALLEL_GROUP = None def set_ulysses_sequence_parallel_group(group: dist.ProcessGroup): """ Set ulysses sequence parallel process group. """ global _ULYSSES_SEQUENCE_PARALLEL_GROUP _ULYSSES_SEQUENCE_PARALLEL_GROUP = group def get_ulysses_sequence_parallel_group() -> Optional[dist.ProcessGroup]: """ Get ulysses sequence parallel process group. """ global _ULYSSES_SEQUENCE_PARALLEL_GROUP return _ULYSSES_SEQUENCE_PARALLEL_GROUP def get_ulysses_sequence_parallel_world_size(group: ProcessGroup = None) -> int: """ Get ulysses sequence parallel world size. """ group = get_ulysses_sequence_parallel_group() if group is None else group return dist.get_world_size(group) if group else 1 def get_ulysses_sequence_parallel_rank(group: ProcessGroup = None) -> int: """ Get ulysses sequence parallel rank. """ group = get_ulysses_sequence_parallel_group() if group is None else group return dist.get_rank(group) if group else 0 def gather_seq_scatter_heads( x: Tensor, seq_dim: int, head_dim: int, unpadded_dim_size: int = 0, group: ProcessGroup = None, ) -> Tensor: """ A func to sync embedding input with alltoall in sequence parallel gather sequence dimension and scatter head dim: e.g. seq_dim: 1, head_dim: 2 [bsz, seq/n, h, ...] -> [bsz, seq, h/n, ...] """ group = get_ulysses_sequence_parallel_group() if group is None else group if not group: return x sp_world = get_ulysses_sequence_parallel_world_size(group) x = SeqAllToAll.apply(group, x, head_dim, seq_dim) if unpadded_dim_size and unpadded_dim_size % sp_world != 0: padding_size = x.size(seq_dim) - unpadded_dim_size x = _unpad_tensor(x, seq_dim, padding_size) return x def gather_heads_scatter_seq(x: Tensor, head_dim: int, seq_dim: int, group: ProcessGroup = None) -> Tensor: """ A func to sync attention result with alltoall in sequence parallel gather head dimension and scatter seq dim: e.g. seq_dim: 1, head_dim: 2 [bsz, seq, h/n, ...] -> [bsz, seq/n, h, ...] """ group = get_ulysses_sequence_parallel_group() if group is None else group if not group: return x dim_size = x.size(seq_dim) sp_world = get_ulysses_sequence_parallel_world_size(group) if dim_size % sp_world != 0: padding_size = sp_world - (dim_size % sp_world) x = _pad_tensor(x, seq_dim, padding_size) return SeqAllToAll.apply(group, x, seq_dim, head_dim, False) def _pad_tensor(x: Tensor, dim: int, padding_size: int) -> Tensor: shape = list(x.shape) shape[dim] = padding_size pad = torch.zeros(shape, dtype=x.dtype, device=x.device) return torch.cat([x, pad], dim=dim) def _unpad_tensor(x: Tensor, dim: int, padding_size: int) -> Tensor: slc = [slice(None)] * len(x.shape) slc[dim] = slice(0, -padding_size) return x[tuple(slc)] def slice_input_tensor(x: Tensor, dim: int, padding: bool = True, group: ProcessGroup = None) -> Tensor: group = get_ulysses_sequence_parallel_group() if group is None else group sp_world_size = dist.get_world_size(group) sp_rank = get_ulysses_sequence_parallel_rank() dim_size = x.size(dim) # pad before slice if padding and dim_size % sp_world_size: padding_size = sp_world_size - (dim_size % sp_world_size) x = _pad_tensor(x, dim, padding_size) # slice the input tensor parts = x.size(dim) // sp_world_size slc = [slice(None)] * len(x.shape) slc[dim] = slice(sp_rank * parts, (sp_rank + 1) * parts) return x[tuple(slc)].contiguous() def all_to_all_tensor( local_input: Tensor, scatter_dim: int, gather_dim: int, group: Optional[dist.ProcessGroup] = None, async_op: bool = False, ): group = get_ulysses_sequence_parallel_group() if group is None else group seq_world_size = dist.get_world_size(group) input_list = [t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)] output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)] comm = dist.all_to_all(output_list, input_list, group=group, async_op=async_op) if async_op: def wait(): comm.wait() return torch.cat(output_list, dim=gather_dim).contiguous() return wait return torch.cat(output_list, dim=gather_dim).contiguous() def all_gather_tensor(local_tensor: Tensor, group: Optional[dist.ProcessGroup] = None, async_op: bool = False): group = get_ulysses_sequence_parallel_group() if group is None else group sp_world_size = dist.get_world_size(group=group) output_shape = list(local_tensor.shape) output_shape[0] = output_shape[0] * sp_world_size output = torch.empty(output_shape, dtype=local_tensor.dtype, device=local_tensor.device) dist.all_gather_into_tensor(output, local_tensor, group=group, async_op=async_op) return output class SeqAllToAll(torch.autograd.Function): @staticmethod def forward( ctx: Any, group: dist.ProcessGroup, local_input: Tensor, scatter_dim: int, gather_dim: int, async_op: bool = False, ) -> Tensor: ctx.group = group ctx.scatter_dim = scatter_dim ctx.gather_dim = gather_dim ctx.async_op = async_op return all_to_all_tensor(local_input, scatter_dim, gather_dim, group, async_op) @staticmethod def backward(ctx: Any, *grad_output: Tensor) -> tuple[None, Tensor, None, None]: input_t = torch.cat(grad_output[1:], dim=ctx.gather_dim).contiguous() if ctx.async_op else grad_output[0] return ( None, all_to_all_tensor(input_t, ctx.gather_dim, ctx.scatter_dim, ctx.group, False), None, None, None, None, ) class Gather(torch.autograd.Function): @staticmethod def forward( ctx: Any, group: dist.ProcessGroup, local_tensor: Tensor, gather_dim: int, grad_scaler: bool = True, async_op=False, ) -> Tensor: ctx.group = group ctx.gather_dim = gather_dim ctx.grad_scaler = grad_scaler ctx.async_op = async_op sp_world_size = dist.get_world_size(group=group) ctx.sp_world_size = sp_world_size sp_rank = dist.get_rank(group=group) ctx.sp_rank = sp_rank local_shape = list(local_tensor.size()) split_size = local_shape[0] part_size = local_shape[gather_dim] # store original size ctx.part_size = part_size output = all_gather_tensor(local_tensor, group, async_op) return torch.cat(output.split(split_size, dim=0), dim=gather_dim) @staticmethod def backward(ctx: Any, grad_output: Tensor) -> Any: if ctx.grad_scaler: grad_output = grad_output * ctx.sp_world_size return ( None, grad_output.split(ctx.part_size, dim=ctx.gather_dim)[ctx.sp_rank].contiguous(), None, None, None, None, ) def gather_outpus_and_unpad(*args, **kwargs): raise RuntimeError( "please use verl.utils.ulysses.gather_outputs_and_unpad instead of verl.utils.ulysses.gather_outpus_and_unpad" ) def gather_outputs_and_unpad( x: Tensor, gather_dim: int, unpad_dim: int = None, padding_size: int = 0, grad_scaler: bool = True, group: Optional[dist.ProcessGroup] = None, ): """ Gather a tensor across a process group and optionally unpad its padded elements. Args: x (Tensor): Input tensor to gather. gather_dim (int): Dimension along which to gather across ranks. unpad_dim (int, optional): Dimension from which to remove padding. If None, no unpadding. padding_size (int): Number of padding elements to remove on `unpad_dim`. Defaults to 0. grad_scaler (bool): Whether to apply gradient scaling during gather. Defaults to True. group (ProcessGroup, optional): Process group for gathering. If None, uses `get_ulysses_sequence_parallel_group()`. If still None, returns `x` unchanged. Returns: Tensor: The gathered tensor, with padding removed if requested. """ group = get_ulysses_sequence_parallel_group() if group is None else group if group is None: return x x = Gather.apply(group, x, gather_dim, grad_scaler) if unpad_dim is not None: assert isinstance(padding_size, int), "padding size is not given or is not an integer" if padding_size == 0: return x x = _unpad_tensor(x, unpad_dim, padding_size) return x def ulysses_pad( input_ids_rmpad: torch.Tensor, position_ids_rmpad: Optional[torch.Tensor] = None, sp_size: int = 1, pad_value=0 ): if position_ids_rmpad is not None: assert position_ids_rmpad.size(-2) == 1 assert input_ids_rmpad.size(-1) == position_ids_rmpad.size(-1) if sp_size <= 1: return input_ids_rmpad, position_ids_rmpad, 0 _, total_seq_len = input_ids_rmpad.shape pad_size = (sp_size - total_seq_len % sp_size) % sp_size if pad_size > 0: input_ids_rmpad = torch.nn.functional.pad(input_ids_rmpad, (0, pad_size), value=pad_value) if position_ids_rmpad is not None: pad_pos_ids = torch.arange(pad_size, device=position_ids_rmpad.device).unsqueeze(0) if position_ids_rmpad.dim() == 3: pad_pos_ids = pad_pos_ids.unsqueeze(0).repeat(position_ids_rmpad.size(0), 1, 1) position_ids_rmpad = torch.cat((position_ids_rmpad, pad_pos_ids), dim=-1) return input_ids_rmpad, position_ids_rmpad, pad_size def ulysses_pad_and_slice_inputs( input_ids_rmpad: torch.Tensor, position_ids_rmpad: Optional[torch.Tensor] = None, sp_size: int = 1, skip_position_ids_rmpad: bool = False, pad_value=0, ): """ Pad and slice input_ids to be divisible by sp_size Pad position_ids to be divisible by sp_size. Note both input_ids_rmpad and position_ids_rmpad will be padded and sliced. The is the utility of pre-forward for ulysses sequence parallelism Args: input_ids_rmpad: shape of [bsz, seqlen] position_ids_rmpad: shape of [bsz, seqlen], where bsz must be 1 sp_size (int): ulysses sequence parallelism size skip_position_ids_rmpad: whether to skip position_ids_rmpad for VeOmniEngine Returns: torch.Tensor: padded and sliced input_ids torch.Tensor: padded and sliced position_ids int: pad size """ input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad( input_ids_rmpad, position_ids_rmpad, sp_size, pad_value=pad_value ) input_ids_rmpad = slice_input_tensor(input_ids_rmpad, dim=1, padding=False) if position_ids_rmpad is not None and not skip_position_ids_rmpad: position_ids_rmpad = slice_input_tensor(position_ids_rmpad, dim=1, padding=False) return input_ids_rmpad, position_ids_rmpad, pad_size def validate_ulysses_config(num_heads, ulysses_sequence_size): if ulysses_sequence_size > 1: assert num_heads % ulysses_sequence_size == 0, ( f"num_heads ({num_heads}) must be divisible by ulysses sequence size({ulysses_sequence_size})" ) ================================================ FILE: verl/utils/vllm/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .npu_vllm_patch import check_vllm_ascend_before_server_launch from .utils import TensorLoRARequest, VLLMHijack, is_version_ge # The contents of vllm/patch.py should not be imported here, because the contents of # patch.py should be imported after the vllm LLM instance is created. Therefore, # wait until you actually start using it before importing the contents of # patch.py separately. __all__ = [ "TensorLoRARequest", "VLLMHijack", "is_version_ge", "check_vllm_ascend_before_server_launch", ] ================================================ FILE: verl/utils/vllm/npu_vllm_patch.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Copyright 2025 The Qwen Team and The HuggingFace Inc. team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from functools import wraps from verl.utils.device import is_torch_npu_available def vllm_ascend_v011_select_moe_comm_method_wrapper(fn): @wraps(fn) def wrapper(self, num_tokens, with_prefill): moe_comm_method = fn(self, num_tokens, with_prefill) from vllm_ascend.ascend_forward_context import MoECommType from vllm_ascend.utils import AscendSocVersion, enable_sp, get_ascend_soc_version soc_version = get_ascend_soc_version() # AscendSocVersion.A2 is not support MC2 in Single-card multi-process scenario now. if soc_version in {AscendSocVersion.A2} and moe_comm_method == MoECommType.MC2: quant_type = getattr(self.vllm_config.model_config.hf_config, "moe_quantize", None) # Currently, w4a8_dynamic does not support allgatherep if quant_type == "w4a8_dynamic": moe_comm_method = MoECommType.ALLTOALL else: moe_comm_method = MoECommType.ALLGATHER if with_prefill: if enable_sp(): moe_comm_method = MoECommType.ALLGATHER else: moe_comm_method = MoECommType.NAIVE_MULTICAST return moe_comm_method return wrapper def vllm_ascend_v011_matmul_and_reduce_wrapper(fn): @wraps(fn) def wrapper(self, *args, **kwargs): from vllm_ascend.utils import AscendSocVersion, get_ascend_soc_version soc_version = get_ascend_soc_version() # AscendSocVersion.A2 is not support MC2 in Single-card multi-process scenario now. if soc_version in {AscendSocVersion.A2}: from vllm.forward_context import get_forward_context try: forward_context = get_forward_context() forward_context.mmrs_fusion = False except AssertionError: # forward_context.mmrs_fusion will be false in matmul_and_reduce func. pass return fn(self, *args, **kwargs) return wrapper def check_vllm_ascend_before_server_launch(): import torch_npu import vllm def _is_ascend_soc_version_A2_v011_local(): from vllm_ascend.utils import AscendSocVersion soc_version = torch_npu.npu.get_soc_version() if 220 <= soc_version <= 225: _ascend_soc_version = AscendSocVersion.A2 elif 250 <= soc_version <= 255: _ascend_soc_version = AscendSocVersion.A3 else: _ascend_soc_version = AscendSocVersion.UNDEFINED return _ascend_soc_version == AscendSocVersion.A2 def _is_ascend_soc_version_A2_v013_local(): from vllm_ascend.utils import AscendDeviceType soc_version = torch_npu.npu.get_soc_version() if 220 <= soc_version <= 225: cur_device_type = AscendDeviceType.A2 elif 250 <= soc_version <= 255: cur_device_type = AscendDeviceType.A3 elif 200 <= soc_version <= 205: cur_device_type = AscendDeviceType._310P elif soc_version == 260: cur_device_type = AscendDeviceType.A5 else: raise RuntimeError(f"Can not support soc_version: {soc_version}.") return cur_device_type == AscendDeviceType.A2 if vllm.__version__ == "0.11.0": is_A2 = _is_ascend_soc_version_A2_v011_local() elif vllm.__version__ == "0.13.0": is_A2 = _is_ascend_soc_version_A2_v013_local() else: is_A2 = False if is_A2: VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE = bool(int(os.getenv("VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE", "0"))) if VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE: raise AssertionError( "AscendSocVersion.A2 is not support VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE \ in Single-card multi-process scenario now. " ) def vllm_ascend_v013_select_moe_comm_method_wrapper(fn): @wraps(fn) def wrapper(*args, **kwargs): moe_comm_method = fn(*args, **kwargs) from vllm_ascend.ascend_forward_context import MoECommType from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type ascend_device_type = get_ascend_device_type() # AscendSocVersion.A2 is not support MC2 in Single-card multi-process scenario now. if ascend_device_type in {AscendDeviceType.A2} and moe_comm_method == MoECommType.MC2: moe_comm_method = MoECommType.ALLGATHER return moe_comm_method return wrapper def vllm_ascend_v013_matmul_and_reduce_wrapper(fn): @wraps(fn) def wrapper(self, *args, **kwargs): from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type ascend_device_type = get_ascend_device_type() # AscendSocVersion.A2 is not support MC2 in Single-card multi-process scenario now. if ascend_device_type in {AscendDeviceType.A2}: from vllm.forward_context import get_forward_context try: forward_context = get_forward_context() forward_context.mmrs_fusion = False except AssertionError: # forward_context.mmrs_fusion will be false in matmul_and_reduce func. pass return fn(self, *args, **kwargs) return wrapper def patch_vllm013_rotary_emb(): from vllm.model_executor.layers.rotary_embedding.common import ApplyRotaryEmb def vllm013_npu_rotary_embedding_init_impl( self, enforce_enable: bool = False, is_neox_style: bool = True, enable_fp32_compute: bool = False, ) -> None: super(ApplyRotaryEmb, self).__init__(enforce_enable) self.is_neox_style = is_neox_style self.enable_fp32_compute = enable_fp32_compute self.apply_rotary_emb_flash_attn = None ApplyRotaryEmb.__init__ = vllm013_npu_rotary_embedding_init_impl if is_torch_npu_available(check_device=False): import vllm from packaging import version _VLLM_VERSION = version.parse(vllm.__version__) if _VLLM_VERSION >= version.parse("0.13.0"): # Disable flash_attn in RotaryEmbedding (NPU) when VLLM >= 0.13 patch_vllm013_rotary_emb() VERL_NPU_ENABLE_A2_PATCH_VLLM_ASCEND_MC2 = bool(int(os.getenv("VERL_NPU_ENABLE_A2_PATCH_VLLM_ASCEND_MC2", "1"))) if VERL_NPU_ENABLE_A2_PATCH_VLLM_ASCEND_MC2: # only support vllm 0.13 and 0.11 now. if _VLLM_VERSION >= version.parse("0.13.0"): from vllm_ascend import ascend_forward_context from vllm_ascend.ops.linear_op import SequenceRowParallelOp ascend_forward_context.select_moe_comm_method = vllm_ascend_v013_select_moe_comm_method_wrapper( ascend_forward_context.select_moe_comm_method ) SequenceRowParallelOp.matmul_and_reduce = vllm_ascend_v013_matmul_and_reduce_wrapper( SequenceRowParallelOp.matmul_and_reduce ) elif _VLLM_VERSION >= version.parse("0.11.0"): from vllm_ascend.ops.linear_op import SequenceRowParallelOp from vllm_ascend.worker.model_runner_v1 import NPUModelRunner NPUModelRunner._select_moe_comm_method = vllm_ascend_v011_select_moe_comm_method_wrapper( NPUModelRunner._select_moe_comm_method ) SequenceRowParallelOp.matmul_and_reduce = vllm_ascend_v011_matmul_and_reduce_wrapper( SequenceRowParallelOp.matmul_and_reduce ) ================================================ FILE: verl/utils/vllm/patch.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # To support different vLLM versions, we add the model into SUPPORTED_MOE_MODELS separately to avoid triggering # unsupported issues. SUPPORTED_MOE_MODELS = [] try: from vllm.model_executor.models.deepseek_v2 import DeepseekV2ForCausalLM, DeepseekV3ForCausalLM SUPPORTED_MOE_MODELS.append(DeepseekV2ForCausalLM) SUPPORTED_MOE_MODELS.append(DeepseekV3ForCausalLM) except ImportError: pass try: from vllm.model_executor.models.mixtral import MixtralForCausalLM SUPPORTED_MOE_MODELS.append(MixtralForCausalLM) except ImportError: pass try: from vllm.model_executor.models.qwen2_moe import Qwen2MoeForCausalLM SUPPORTED_MOE_MODELS.append(Qwen2MoeForCausalLM) except ImportError: pass try: from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM SUPPORTED_MOE_MODELS.append(Qwen3MoeForCausalLM) except ImportError: pass try: from vllm.model_executor.models.qwen3_vl_moe import Qwen3MoeLLMForCausalLM SUPPORTED_MOE_MODELS.append(Qwen3MoeLLMForCausalLM) except ImportError: pass try: from vllm.model_executor.models.qwen3_next import Qwen3NextForCausalLM SUPPORTED_MOE_MODELS.append(Qwen3NextForCausalLM) except ImportError: pass try: from vllm.model_executor.models.kimi_vl import KimiVLForConditionalGeneration SUPPORTED_MOE_MODELS.append(KimiVLForConditionalGeneration) except ImportError: pass def patch_vllm_moe_model_weight_loader(model): # this is a work around to load the weight of vllm fused moe model # it is from a bug from vllm 0.8.2 # all the weights are supposed to have a weight_loader, but the moe weights # do not have a weight_loader, so we need to patch it # (True, 'model.embed_tokens.weight') # (True, 'model.layers.0.self_attn.qkv_proj.weight') # (True, 'model.layers.0.self_attn.qkv_proj.bias') # (True, 'model.layers.0.self_attn.o_proj.weight') # (True, 'model.layers.0.mlp.gate.weight') # (True, 'model.layers.0.mlp.shared_expert.gate_up_proj.weight') # (True, 'model.layers.0.mlp.shared_expert.down_proj.weight') # (False, 'model.layers.0.mlp.shared_expert_gate.weight') use default # (False, 'model.layers.0.input_layernorm.weight') use default # (False, 'model.layers.0.post_attention_layernorm.weight') use default # (False, 'model.layers.0.mlp.experts.w13_weight') use mlp.experts.weight_loader # (False, 'model.layers.0.mlp.experts.w2_weight') use mlp.experts.weight_loader # Early return if no MOE models are supported if not SUPPORTED_MOE_MODELS: return original_model_type = type(model) if hasattr(model, "runnable") and "ACLGraphWrapper" in str(original_model_type): model = model.runnable original_model_type = type(model) # Define MLP attribute mapping for different model types MLP_ATTR_MAPPING = {} try: from vllm.model_executor.models.mixtral import MixtralForCausalLM MLP_ATTR_MAPPING[MixtralForCausalLM] = "block_sparse_moe" except ImportError: pass DEFAULT_MLP_ATTR = "mlp" # Get inner model (either model.model or model.language_model) inner_model = getattr(model, "model", None) or getattr(model, "language_model", None) if inner_model is None: raise ValueError("The provided model does not have a valid 'model' or 'language_model' attribute.") if not isinstance(model, tuple(SUPPORTED_MOE_MODELS)) and not isinstance(inner_model, tuple(SUPPORTED_MOE_MODELS)): return # TODO(@leisuzz): class Qwen3MoeLLMForCausalLM is not available if VLLM version < 0.11.0, # will update the 'if statement' with 'isinstance' when verl commonly use VLLM version >= 0.11.0 if type(inner_model).__name__ == "Qwen3MoeLLMForCausalLM": inner_model = inner_model.model # Reassign inner_model in Qwen3-vl for layer_idx, layer in enumerate(inner_model.layers): mlp_attr = MLP_ATTR_MAPPING.get(original_model_type, DEFAULT_MLP_ATTR) mlp = getattr(layer, mlp_attr, None) if not mlp: continue experts = getattr(mlp, "experts", None) if not experts or not hasattr(experts, "weight_loader"): continue # Patch the weight loaders for name, param in mlp.named_parameters(): if "w13_weight" in name or "w2_weight" in name: param.weight_loader = experts.weight_loader ================================================ FILE: verl/utils/vllm/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from msgspec import field from packaging import version as vs try: from vllm.lora.lora_model import LoRAModel except ImportError: from vllm.lora.models import LoRAModel from vllm.lora.request import LoRARequest from vllm.lora.utils import get_adapter_absolute_path from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from verl.third_party.vllm import get_version class TensorLoRARequest(LoRARequest): peft_config: dict = field(default=None) lora_tensors: dict = field(default=None) class VLLMHijack: @staticmethod def hijack(): def hijack__load_adapter(self, lora_request: TensorLoRARequest) -> LoRAModel: """ based on vllm.lora.worker_manager.WorkerLoRAManager._load_adapter, support load adapter with lora tensors Reason: VLLM does not support adding LoRA from tensors directly. It only supports adding LoRA via file paths. To synchronize the LoRA tensors of the actor model, we need to find a workaround to enable VLLM to load memory-based LoRA tensors. """ try: supported_lora_modules = self._adapter_manager.supported_lora_modules packed_modules_mapping = self._adapter_manager.packed_modules_mapping expected_lora_modules: list[str] = [] for module in supported_lora_modules: if module in packed_modules_mapping: expected_lora_modules.extend(packed_modules_mapping[module]) else: expected_lora_modules.append(module) expected_lora_modules = list(set(expected_lora_modules)) lora_tensors = None from vllm.lora.peft_helper import PEFTHelper if isinstance(lora_request, TensorLoRARequest): peft_config = lora_request.peft_config lora_tensors = lora_request.lora_tensors peft_helper = PEFTHelper.from_dict(peft_config) else: lora_path = get_adapter_absolute_path(lora_request.lora_path) peft_helper = PEFTHelper.from_local_dir(lora_path, self.max_position_embeddings) # Validates the LoRA configuration against requirements before # loading weights, throwing an exception if validation fails. peft_helper.validate_legal(self.lora_config) # For some models like Qwen2VL, we need to use hf_to_vllm_mapper # to ensure correct loading of lora weights. model = self._adapter_manager.model hf_to_vllm_mapper = None if hasattr(model, "hf_to_vllm_mapper") and model.hf_to_vllm_mapper is not None: hf_to_vllm_mapper = model.hf_to_vllm_mapper lora_request_kwargs = { "peft_helper": peft_helper, "lora_model_id": lora_request.lora_int_id, "device": "cpu", "dtype": self.lora_config.lora_dtype, "weights_mapper": hf_to_vllm_mapper, } if hasattr(self, "embedding_padding_modules"): lora_request_kwargs["embedding_modules"] = self.embedding_modules lora_request_kwargs["embedding_padding_modules"] = self.embedding_padding_modules else: lora_request_kwargs["model_vocab_size"] = self.vocab_size if hasattr(self.lora_config, "lora_extra_vocab_size"): lora_request_kwargs["target_embedding_padding"] = ( self.vocab_size + self.lora_config.lora_extra_vocab_size ) if isinstance(lora_request, TensorLoRARequest): lora = self._lora_model_cls.from_lora_tensors( tensors=lora_tensors, **lora_request_kwargs, ) else: lora = self._lora_model_cls.from_local_checkpoint( lora_path, expected_lora_modules, **lora_request_kwargs, ) except Exception: raise if getattr(lora, "extra_vocab_size", 0) > getattr(self.lora_config, "lora_extra_vocab_size", 0): raise ValueError( f"LoRA added vocab size {lora.extra_vocab_size} is greater than lora_extra_vocab_size " f"{self.lora_config.lora_extra_vocab_size}." ) return lora def do_hijack(target_cls, target_method_name, hooking_method): setattr(target_cls, target_method_name, hooking_method) do_hijack(LRUCacheWorkerLoRAManager, "_load_adapter", hijack__load_adapter) def is_version_ge(pkg: str = "vllm", minver: str = "0.7.3"): """check if the package version is greater than or equal to the minimum version""" return vs.parse(get_version(pkg)) >= vs.parse(minver) ================================================ FILE: verl/utils/vllm/vllm_fp8_utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from dataclasses import dataclass, field from unittest.mock import patch import torch import vllm from packaging import version try: from vllm.model_executor.layers.fused_moe.layer import FusedMoE from vllm.model_executor.layers.linear import LinearBase except ImportError as e: raise ImportError("FP8 quantization not available") from e from verl.utils.kernel.fp8_kernel import scaled_fp8_blockwise logger = logging.getLogger(__name__) # Ref: https://github.com/NVIDIA-NeMo/RL/commit/bc24887c72a6e1b2699a228bc87c588546dfe6b7 @dataclass() class FP8State: # A cache of fp8 parameter names, we can check this cache to see if a # param name corresponds to a fp8 weight seen_params: set = field(default_factory=lambda: set()) fp8_param_names: set = field(default_factory=lambda: set()) vllm_patches: list = field(default_factory=lambda: []) fp8_state: FP8State = FP8State() def is_fp8_model(vllm_config): from vllm.model_executor.layers.quantization.fp8 import Fp8Config if hasattr(vllm_config, "quant_config") and isinstance(vllm_config.quant_config, Fp8Config): return True return False def get_module_from_param_name(model, name: str): # Split the name into parts (e.g., 'layers', '0', 'self_attn', 'q_proj', 'weight') # The module path is all but the last part (the parameter's own name) path_parts = name.split(".") module_path = path_parts[:-1] # Replace with the fused model name packed_modules_mapping = model.packed_modules_mapping reversed_mapping = { original_name: fused_name for fused_name, original_names_list in packed_modules_mapping.items() for original_name in original_names_list } if module_path[-1] in reversed_mapping.keys(): module_path[-1] = reversed_mapping[module_path[-1]] current_module = model try: # Traverse the model hierarchy for part in module_path: if isinstance(current_module, FusedMoE): return current_module elif isinstance(current_module, torch.nn.ModuleList): current_module = current_module[int(part)] else: current_module = getattr(current_module, part) except (AttributeError, IndexError, ValueError) as e: print(f"Warning: Could not find module for parameter '{name}'. Error: {e}") return current_module def is_fp8_weight(name, model): if name not in fp8_state.seen_params: fp8_state.seen_params.add(name) # Filter out bias params if name.endswith("weight"): module = get_module_from_param_name(model, name) # We currently only quantize linear layers if (isinstance(module, LinearBase) and module.weight.dtype == torch.float8_e4m3fn) or ( isinstance(module, FusedMoE) and module.w13_weight.dtype == torch.float8_e4m3fn and module.w2_weight.dtype == torch.float8_e4m3fn ): fp8_state.fp8_param_names.add(name) return name in fp8_state.fp8_param_names def quant_weights(weights, model, quant_config, dtype=torch.bfloat16): """Quantize weights to FP8 format using a memory-efficient generator. Args: weights: Generator or iterable of (name, tensor) pairs model: The model to check for FP8 weight names quant_config: Quantization configuration with weight_block_size dtype: Data type for intermediate computation (default: bfloat16) Yields: Tuples of (name, tensor) for each weight and its scale """ if quant_config.weight_block_size is None: raise ValueError("Currently only support blockwise quantization, please set weight_block_size in quant_config") # vLLM v0.11-v0.12 renamed weight_scale_inv → weight_scale in process_weights_after_loading, # so load_weights expects "_scale" suffix. v0.14+ keeps weight_scale_inv, so expects "_scale_inv". _use_scale_not_scale_inv = version.parse("0.11.0") <= version.parse(vllm.__version__) < version.parse("0.14.0") for k, v in weights: if not is_fp8_weight(k, model): yield (k, v) continue # Cast the weight into fp8 and its scale factor if torch.distributed.get_rank() == 0: logger.debug(f"Quantizing to FP8 blockwise: {k}") param_lp, param_scale = scaled_fp8_blockwise( v.to(dtype), weight_block_size=quant_config.weight_block_size, ) param_scale = param_scale.squeeze(-1) # Yield the quantized weight yield (k, param_lp) # Yield the scale with appropriate naming based on vLLM version if _use_scale_not_scale_inv and "expert" not in k: yield (k + "_scale", param_scale) else: yield (k + "_scale_inv", param_scale) # Explicitly delete original tensor reference to help GC del v, param_lp, param_scale def load_quanted_weights(weights, model_runner): model = model_runner.model quant_config = model_runner.vllm_config.quant_config vllm_dtype = model_runner.vllm_config.model_config.dtype weights_quantized = quant_weights(weights, model, quant_config, dtype=vllm_dtype) # Monkey patch the param class to their subclass, as certain models # will check the param type to call the proper weightloader for name, param in model.named_parameters(): if hasattr(param, "subclass_type"): param.orig_type = param.__class__ param.__class__ = param.subclass_type # Finally load the weights into vllm loaded_params = model.load_weights(weights_quantized) # Undo the type change above to the original type for name, param in model.named_parameters(): if hasattr(param, "subclass_type"): param.__class__ = param.orig_type return loaded_params def process_weights_after_loading_for_vllm10(self, layer) -> None: """This function is used to process the weights after loading for a Linear layer, it is used for vllm v0.10 Compared to the original process_weights_after_loading in vllm, we just avoid creation of new torch.nn.Parameter objects, because that removes the weight_loader attribute which we need for refit. """ logger.debug("Applying patch process_weights_after_loading") try: from vllm.model_executor.parameter import ( BlockQuantScaleParameter, ModelWeightParameter, ) except Exception: print("error") from torch.nn import Parameter def _create_param_from_subclass_attributes(custom_param): param = Parameter(custom_param.data, requires_grad=False) base_param_dir = dir(torch.nn.Parameter) custom_param_dir = dir(custom_param) # Find the attributes that are unique to the custom parameter custom_attributes = [ attr for attr in custom_param_dir if attr not in base_param_dir and not attr.startswith("__") ] # Set the custom attributes into the base parameter object for attr in custom_attributes: setattr(param, attr, getattr(custom_param, attr)) param.subclass_type = type(custom_param) return param assert self.block_quant and self.quant_config.is_checkpoint_fp8_serialized assert self.quant_config.activation_scheme == "dynamic" weight = layer.weight.data weight_scale_inv = layer.weight_scale_inv.data weight = self._maybe_pad_weight(weight) layer.weight = _create_param_from_subclass_attributes( ModelWeightParameter( data=weight, output_dim=0, input_dim=1, weight_loader=layer.weight.weight_loader, ) ) layer.weight_scale_inv = _create_param_from_subclass_attributes( BlockQuantScaleParameter( data=weight_scale_inv, output_dim=0, input_dim=1, weight_loader=layer.weight_scale_inv.weight_loader, ) ) def process_weights_after_loading_for_vllm11(self, layer) -> None: """This function is used to process the weights after loading for a Linear layer, it is used for vllm 0.11 Compared to the original process_weights_after_loading in vllm, we just avoid creation of new torch.nn.Parameter objects, because that removes the weight_loader attribute which we need for refit. """ from torch.nn import Parameter from vllm.model_executor.layers.quantization.utils.fp8_utils import ( maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy, ) from vllm.model_executor.parameter import ( BlockQuantScaleParameter, ModelWeightParameter, ) assert self.block_quant and self.quant_config.is_checkpoint_fp8_serialized assert self.quant_config.activation_scheme == "dynamic" def _create_param_from_subclass_attributes(custom_param): param = Parameter(custom_param.data, requires_grad=False) base_param_dir = dir(torch.nn.Parameter) custom_param_dir = dir(custom_param) # Find the attributes that are unique to the custom parameter custom_attributes = [ attr for attr in custom_param_dir if attr not in base_param_dir and not attr.startswith("__") ] # Set the custom attributes into the base parameter object for attr in custom_attributes: setattr(param, attr, getattr(custom_param, attr)) param.subclass_type = type(custom_param) return param weight_scale = layer.weight_scale_inv if hasattr(layer, "weight_scale_inv") else layer.weight_scale weight, weight_scale = process_fp8_weight_block_strategy(layer.weight, weight_scale) layer.weight = _create_param_from_subclass_attributes( ModelWeightParameter( data=weight.data, output_dim=0, input_dim=1, weight_loader=layer.weight.weight_loader, ) ) layer.weight_scale = _create_param_from_subclass_attributes( BlockQuantScaleParameter( data=weight_scale.data, output_dim=0, input_dim=1, weight_loader=layer.weight_scale_inv.weight_loader, ) ) del layer.weight_scale_inv if version.parse(vllm.__version__) == version.parse("0.11.0"): maybe_post_process_fp8_weight_block(layer, self.cutlass_block_fp8_supported) else: maybe_post_process_fp8_weight_block(layer) def process_weights_after_loading_for_vllm14(self, layer) -> None: """process_weights_after_loading for vLLM >= 0.14. Starting from v0.14, vLLM keeps the scale parameter as `weight_scale_inv` (instead of renaming it to `weight_scale` like v0.11-v0.12), and `apply()` accesses `layer.weight_scale_inv`. We preserve `weight_loader` and `subclass_type` attributes so that refit (repeated weight sync) works. """ from torch.nn import Parameter from vllm.model_executor.layers.quantization.utils.fp8_utils import ( maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy, ) from vllm.model_executor.parameter import ( BlockQuantScaleParameter, ModelWeightParameter, ) assert self.block_quant and self.quant_config.is_checkpoint_fp8_serialized assert self.quant_config.activation_scheme == "dynamic" def _create_param_from_subclass_attributes(custom_param): param = Parameter(custom_param.data, requires_grad=False) base_param_dir = dir(torch.nn.Parameter) custom_param_dir = dir(custom_param) custom_attributes = [ attr for attr in custom_param_dir if attr not in base_param_dir and not attr.startswith("__") ] for attr in custom_attributes: setattr(param, attr, getattr(custom_param, attr)) param.subclass_type = type(custom_param) return param weight, weight_scale_inv = process_fp8_weight_block_strategy(layer.weight, layer.weight_scale_inv) layer.weight = _create_param_from_subclass_attributes( ModelWeightParameter( data=weight.data, output_dim=0, input_dim=1, weight_loader=layer.weight.weight_loader, ) ) layer.weight_scale_inv = _create_param_from_subclass_attributes( BlockQuantScaleParameter( data=weight_scale_inv.data, output_dim=0, input_dim=1, weight_loader=layer.weight_scale_inv.weight_loader, ) ) # vLLM v0.17 removed the `else: register_parameter("input_scale", None)` from # create_weights() for dynamic activation, but apply() still accesses layer.input_scale. # Since block_quant always uses dynamic activation, ensure the attribute exists. if not hasattr(layer, "input_scale"): layer.input_scale = None maybe_post_process_fp8_weight_block(layer) def process_weights_after_loading_moe_for_vllm10(self, layer) -> None: """This function is used to process the weights after loading for a FusedMoE layer, it is used for vllm v0.10""" from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled from vllm.model_executor.layers.quantization.fp8 import _is_col_major, _swap_w13_to_w31 from vllm.model_executor.layers.quantization.utils.fp8_utils import ( get_col_major_tma_aligned_tensor, requant_weight_ue8m0_inplace, ) from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled() assert self.quant_config.activation_scheme == "dynamic" if self.flashinfer_moe_enabled: w13_weight = _swap_w13_to_w31(layer.w13_weight.data) w13_weight_scale_inv = _swap_w13_to_w31(layer.w13_weight_scale_inv.data) w2_weight = layer.w2_weight.data w2_weight_scale_inv = layer.w2_weight_scale_inv.data else: w13_weight = layer.w13_weight.data w13_weight_scale_inv = layer.w13_weight_scale_inv.data w2_weight = layer.w2_weight w2_weight_scale_inv = layer.w2_weight_scale_inv from torch.nn import Parameter def _create_param_from_subclass_attributes(custom_data, custom_weight): param = Parameter(custom_data, requires_grad=False) base_param_dir = dir(torch.nn.Parameter) custom_weight_dir = dir(custom_weight) # Find the attributes that are unique to the custom parameter custom_attributes = [ attr for attr in custom_weight_dir if attr not in base_param_dir and not attr.startswith("__") ] # Set the custom attributes into the base parameter object for attr in custom_attributes: setattr(param, attr, getattr(custom_weight, attr)) return param layer.w13_weight = _create_param_from_subclass_attributes(w13_weight, layer.w13_weight) layer.w13_weight_scale_inv = _create_param_from_subclass_attributes( w13_weight_scale_inv, layer.w13_weight_scale_inv ) layer.w2_weight = _create_param_from_subclass_attributes(w2_weight, layer.w2_weight) layer.w2_weight_scale_inv = _create_param_from_subclass_attributes(w2_weight_scale_inv, layer.w2_weight_scale_inv) # DeepGemm scales need to be transposed and aligned. We try to do # it ahead of time for performance reasons. if self.allow_deep_gemm and not is_blackwell_deep_gemm_used(): # Lazy import to avoid CUDA initialization problems. if _is_col_major(layer.w13_weight_scale_inv): layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous() if _is_col_major(layer.w2_weight_scale_inv): layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous() if is_blackwell_deep_gemm_used(): assert layer.weight_block_size is not None # Re-quantise the expert weights so their scales are UE8M0. block_sz = tuple(layer.weight_block_size) requant_weight_ue8m0_inplace( layer.w13_weight.data, layer.w13_weight_scale_inv.data, block_sz, ) requant_weight_ue8m0_inplace( layer.w2_weight.data, layer.w2_weight_scale_inv.data, block_sz, ) if _is_col_major(layer.w13_weight_scale_inv): layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous() if _is_col_major(layer.w2_weight_scale_inv): layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous() def process_weights_after_loading_moe_for_vllm11(self, layer) -> None: """This function is used to process the weights after loading for a FusedMoE layer, it is used for vllm 0.11""" from vllm.model_executor.layers.quantization.utils.flashinfer_utils import ( swap_w13_to_w31, ) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( expert_weight_is_col_major, requant_weight_ue8m0_inplace, ) from vllm.utils.deep_gemm import ( get_col_major_tma_aligned_tensor, is_deep_gemm_e8m0_used, ) try: from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled() except ImportError: from vllm._aiter_ops import rocm_aiter_ops self.rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled() assert self.block_quant and self.quant_config.is_checkpoint_fp8_serialized assert self.quant_config.activation_scheme == "dynamic" if self.flashinfer_moe_backend is not None: layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data) layer.w13_weight_scale_inv.data = swap_w13_to_w31(layer.w13_weight_scale_inv.data) if self.allow_deep_gemm and not is_deep_gemm_e8m0_used(): if expert_weight_is_col_major(layer.w13_weight_scale_inv): layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv) if expert_weight_is_col_major(layer.w2_weight_scale_inv): layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv) if is_deep_gemm_e8m0_used(): assert layer.weight_block_size is not None # Re-quantise the expert weights so their scales are UE8M0. block_sz = tuple(layer.weight_block_size) requant_weight_ue8m0_inplace( layer.w13_weight.data, layer.w13_weight_scale_inv.data, block_sz, ) requant_weight_ue8m0_inplace( layer.w2_weight.data, layer.w2_weight_scale_inv.data, block_sz, ) # Ensure column-major TMA alignment expected by DeepGEMM. if expert_weight_is_col_major(layer.w13_weight_scale_inv): layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv) if expert_weight_is_col_major(layer.w2_weight_scale_inv): layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv) def process_weights_after_loading_moe_for_vllm14(self, layer) -> None: # removed the reentrancy guard here for refit from vllm.model_executor.layers.fused_moe.oracle.fp8 import ( convert_to_fp8_moe_kernel_format, make_fp8_moe_kernel, ) # Allow for accessing weights and scales in standard way. w13 = layer.w13_weight w2 = layer.w2_weight w13_scale = getattr(layer, f"w13_{self.weight_scale_name}") w2_scale = getattr(layer, f"w2_{self.weight_scale_name}") w13_input_scale = layer.w13_input_scale w2_input_scale = layer.w2_input_scale # Shuffle weights to runtime format and setup kernel. w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format( fp8_backend=self.fp8_backend, layer=layer, w13=w13, w2=w2, w13_scale=w13_scale, w2_scale=w2_scale, w13_input_scale=w13_input_scale, w2_input_scale=w2_input_scale, ) from torch.nn import Parameter def _create_param_from_subclass_attributes(custom_data, custom_weight): param = Parameter(custom_data, requires_grad=False) base_param_dir = dir(torch.nn.Parameter) custom_weight_dir = dir(custom_weight) # Find the attributes that are unique to the custom parameter custom_attributes = [ attr for attr in custom_weight_dir if attr not in base_param_dir and not attr.startswith("__") ] # Set the custom attributes into the base parameter object for attr in custom_attributes: setattr(param, attr, getattr(custom_weight, attr)) return param # Replace parameters with updated versions. Note that this helper # function ensures the replacement is compatible with RL weight reloads. layer.w13_weight = _create_param_from_subclass_attributes(w13, layer.w13_weight) layer.w2_weight = _create_param_from_subclass_attributes(w2, layer.w2_weight) layer.w13_weight_scale_inv = _create_param_from_subclass_attributes(w13_scale, layer.w13_weight_scale_inv) layer.w2_weight_scale_inv = _create_param_from_subclass_attributes(w2_scale, layer.w2_weight_scale_inv) self.moe_quant_config = self.get_fused_moe_quant_config(layer) if self.moe_quant_config: assert self.experts_cls is not None self.moe_kernel = make_fp8_moe_kernel( moe_quant_config=self.moe_quant_config, moe_config=self.moe, fp8_backend=self.fp8_backend, experts_cls=self.experts_cls, routing_tables=layer._maybe_init_expert_routing_tables(), shared_experts=layer.shared_experts, ) def apply_vllm_fp8_patches(): logger.info("Applying vllm fp8 patches for blockwise quantization") vllm_ver = version.parse(vllm.__version__) # Linear patch: v0.14+ keeps weight_scale_inv, v0.11-v0.12 renames to weight_scale func1_path = "vllm.model_executor.layers.quantization.fp8.Fp8LinearMethod.process_weights_after_loading" if vllm_ver >= version.parse("0.14.0"): linear_patch_fn = process_weights_after_loading_for_vllm14 elif vllm_ver >= version.parse("0.11.0"): linear_patch_fn = process_weights_after_loading_for_vllm11 else: linear_patch_fn = process_weights_after_loading_for_vllm10 patcher1 = patch(func1_path, linear_patch_fn) patcher1.start() # MoE patch func2_path = "vllm.model_executor.layers.quantization.fp8.Fp8MoEMethod.process_weights_after_loading" if vllm_ver >= version.parse("0.14.0"): moe_patch_fn = process_weights_after_loading_moe_for_vllm14 elif vllm_ver >= version.parse("0.11.0"): moe_patch_fn = process_weights_after_loading_moe_for_vllm11 else: moe_patch_fn = process_weights_after_loading_moe_for_vllm10 patcher2 = patch(func2_path, moe_patch_fn) patcher2.start() ================================================ FILE: verl/version/version ================================================ 0.8.0.dev ================================================ FILE: verl/workers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/workers/actor/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BasePPOActor from .dp_actor import DataParallelPPOActor __all__ = ["BasePPOActor", "DataParallelPPOActor"] ================================================ FILE: verl/workers/actor/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The base class for Actor """ from abc import ABC, abstractmethod import torch from verl import DataProto __all__ = ["BasePPOActor"] class BasePPOActor(ABC): def __init__(self, config): """The base class for PPO actor Args: config (DictConfig): a config passed to the PPOActor. We expect the type to be DictConfig (https://omegaconf.readthedocs.io/), but it can be any namedtuple in general. """ super().__init__() self.config = config @abstractmethod def compute_log_prob(self, data: DataProto) -> torch.Tensor: """Compute logits given a batch of data. Args: data (DataProto): a batch of data represented by DataProto. It must contain key ```input_ids```, ```attention_mask``` and ```position_ids```. Returns: DataProto: a DataProto containing the key ```log_probs``` """ pass @abstractmethod def update_policy(self, data: DataProto) -> dict: """Update the policy with an iterator of DataProto Args: data (DataProto): an iterator over the DataProto that returns by ```make_minibatch_iterator``` Returns: Dict: a dictionary contains anything. Typically, it contains the statistics during updating the model such as ```loss```, ```grad_norm```, etc,. """ pass ================================================ FILE: verl/workers/actor/dp_actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Single Process Actor """ import logging import os import torch from torch import nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.tensor import DTensor import verl.utils.torch_functional as verl_F from verl import DataProto from verl.trainer.ppo.core_algos import agg_loss, get_policy_loss_fn, kl_penalty from verl.utils.attention_utils import index_first_axis, pad_input, rearrange, unpad_input from verl.utils.device import get_device_id, get_device_name from verl.utils.fsdp_utils import FSDPModule, fsdp2_clip_grad_norm_ from verl.utils.profiler import GPUMemoryLogger from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import prepare_dynamic_batch, restore_dynamic_batch from verl.utils.torch_dtypes import PrecisionType from verl.utils.torch_functional import logprobs_from_logits from verl.utils.ulysses import gather_outputs_and_unpad, ulysses_pad, ulysses_pad_and_slice_inputs from verl.workers.actor import BasePPOActor from verl.workers.config import ActorConfig __all__ = ["DataParallelPPOActor"] logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class DataParallelPPOActor(BasePPOActor): """FSDP DataParallel PPO Actor or Ref worker Args: config (ActorConfig): Actor config actor_module (nn.Module): Actor or ref module actor_optimizer (torch.optim.Optimizer, optional): Actor optimizer. Defaults to None. """ def __init__(self, config: ActorConfig, actor_module: nn.Module, actor_optimizer: torch.optim.Optimizer = None): """When optimizer is None, it is Reference Policy""" super().__init__(config) self.actor_module = actor_module self.actor_optimizer = actor_optimizer role = "Ref" if actor_optimizer is None else "Actor" self.use_remove_padding = self.config.get("use_remove_padding", False) if torch.distributed.get_rank() == 0: print(f"{role} use_remove_padding={self.use_remove_padding}") self.use_fused_kernels = self.config.get("use_fused_kernels", False) if torch.distributed.get_rank() == 0: print(f"{role} use_fused_kernels={self.use_fused_kernels}") self.ulysses_sequence_parallel_size = self.config.ulysses_sequence_parallel_size self.use_ulysses_sp = self.ulysses_sequence_parallel_size > 1 self.use_dynamic_bsz = self.config.get("use_dynamic_bsz", False) self.use_prefix_grouper = self.config.get("use_prefix_grouper", False) if torch.distributed.get_rank() == 0: print(f"{role} use_prefix_grouper={self.use_prefix_grouper}") if self.config.entropy_from_logits_with_chunking: entropy_from_logits = verl_F.entropy_from_logits_with_chunking else: entropy_from_logits = verl_F.entropy_from_logits self.compute_entropy_from_logits = ( torch.compile(entropy_from_logits, dynamic=True) if self.config.get("use_torch_compile", True) # use torch compile by default else entropy_from_logits ) self.device_name = get_device_name() self.param_dtype = PrecisionType.to_dtype(self.config.fsdp_config.get("dtype", "bfloat16")) if self.param_dtype == torch.float16: from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler self.scaler = ShardedGradScaler(growth_interval=400) else: self.scaler = None # Sum of squared probabilities computation (for optimal_token_baseline) # Only initialize if calculate_sum_pi_squared config is enabled if self.config.get("calculate_sum_pi_squared", False): self.calculate_sum_pi_squared_from_logits = ( torch.compile(verl_F.calculate_sum_pi_squared_from_logits, dynamic=True) if self.config.get("use_torch_compile", True) else verl_F.calculate_sum_pi_squared_from_logits ) assert not (self.use_fused_kernels or self.use_prefix_grouper), ( "calculate_sum_pi_squared is not supported with " f"{self.use_fused_kernels=} or {self.use_prefix_grouper=} for now." ) def _forward_micro_batch( self, micro_batch: dict[str, torch.Tensor], temperature: float, calculate_entropy: bool = False ) -> dict[str, torch.Tensor]: """ Returns: dict[str, torch.Tensor]: log_probs: (bs, response_len) if calculate_entropy is True: entropys: (bs, response_len) if calculate_sum_pi_squared is False: sum_pi_squared: (bs, response_len) """ calculate_sum_pi_squared = self.config.get("calculate_sum_pi_squared", False) sum_pi_squared_checkpointing = self.config.get("sum_pi_squared_checkpointing", False) # PrefixGrouper path for shared-prefix optimization if self.use_prefix_grouper: can_use_pg = ( not self.use_remove_padding and not self.use_ulysses_sp and not self.use_fused_kernels and not self.use_dynamic_bsz ) if can_use_pg and "response_mask" in micro_batch and "uid" in micro_batch: from verl.trainer.ppo.prefix_grouper_utils import forward_micro_batch_with_prefix_grouper return forward_micro_batch_with_prefix_grouper( micro_batch=micro_batch, model=self.actor_module, temperature=temperature, calculate_entropy=calculate_entropy, device_name=self.device_name, param_dtype=self.param_dtype, use_chunking_entropy=self.config.get("entropy_from_logits_with_chunking", False), ) response_length = micro_batch["responses"].size(-1) multi_modal_inputs = {} if "multi_modal_inputs" in micro_batch.keys(): from verl.utils.model import extract_multi_modal_inputs multi_modal_inputs = extract_multi_modal_inputs(micro_batch["multi_modal_inputs"]) with torch.autocast(device_type=self.device_name, dtype=self.param_dtype): input_ids = micro_batch["input_ids"] batch_size, seqlen = input_ids.shape attention_mask = micro_batch["attention_mask"] position_ids = micro_batch["position_ids"] entropy = None if position_ids.dim() == 3: # qwen2vl mrope position_ids = position_ids.transpose(0, 1) # (bsz, 4, seqlen) -> (4, bsz, seqlen) if self.use_remove_padding: input_ids_rmpad, indices, cu_seqlens, *_ = unpad_input( input_ids.unsqueeze(-1), attention_mask ) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary if position_ids.dim() == 3: position_ids_rmpad = ( index_first_axis(rearrange(position_ids, "c b s ... -> (b s) c ..."), indices) .transpose(0, 1) .unsqueeze(1) ) # (4, bsz, seqlen) -> (4, 1, bsz * seqlen) else: position_ids_rmpad = index_first_axis( rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices ).transpose(0, 1) is_mask_all_zero = attention_mask.sum() == 0 if is_mask_all_zero: input_ids_rmpad = torch.zeros( (1, self.ulysses_sequence_parallel_size), device=input_ids.device, dtype=input_ids.dtype, ) if position_ids.dim() == 3: position_ids_rmpad = torch.zeros( (position_ids.shape[0], 1, self.ulysses_sequence_parallel_size), device=position_ids.device, dtype=position_ids.dtype, ) else: position_ids_rmpad = torch.zeros( (1, self.ulysses_sequence_parallel_size), device=position_ids.device, dtype=position_ids.dtype, ) if "image_bound" in multi_modal_inputs: from verl.utils.dataset.vision_utils import process_multi_modal_inputs_for_minicpmo multi_modal_inputs = process_multi_modal_inputs_for_minicpmo( input_ids, attention_mask, position_ids, cu_seqlens, multi_modal_inputs ) # for compute the log_prob input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=1) # (1, total_nnz) # pad and slice the inputs if sp > 1 if self.use_ulysses_sp: is_vlm_model = hasattr( getattr(self.actor_module, "module", self.actor_module).config, "vision_config" ) if is_vlm_model: # vlm model's inputs will be sliced after embedding input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad( input_ids_rmpad, position_ids_rmpad=position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size, ) else: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs( input_ids_rmpad, position_ids_rmpad=position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size, ) input_ids_rmpad_rolled, _, _ = ulysses_pad_and_slice_inputs( input_ids_rmpad_rolled, position_ids_rmpad=None, sp_size=self.ulysses_sequence_parallel_size, ) input_ids_rmpad_rolled = input_ids_rmpad_rolled.squeeze(0) # ((total_nnz / sp) + pad) # only pass input_ids and position_ids to enable flash_attn_varlen extra_args = {} if self.use_fused_kernels: extra_args["temperature"] = temperature extra_args["return_dict"] = True output = self.actor_module( input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, **multi_modal_inputs, use_cache=False, **extra_args, ) # prevent model thinks we are generating if self.use_fused_kernels: log_probs = output.log_probs.squeeze(0) # (total_nnz,) entropy_rmpad = output.entropy.squeeze(0) # (total_nnz,) else: logits_rmpad = output.logits.squeeze(0) # (total_nnz, vocab_size) logits_rmpad.div_(temperature) # if use_sp: ((total_nnz / sp) + pad) ; if not use_sp: (batch, seqlen) inplace_backward = True if calculate_entropy: inplace_backward = False log_probs = logprobs_from_logits( logits=logits_rmpad, labels=input_ids_rmpad_rolled, inplace_backward=inplace_backward, ) # compute entropy if calculate_entropy: # ((total_nnz / sp) + pad) entropy_rmpad = ( self.compute_entropy_from_logits(logits_rmpad) if not self.config.entropy_checkpointing else torch.utils.checkpoint.checkpoint(self.compute_entropy_from_logits, logits_rmpad) ) # Compute sum_pi_squared if requested (for optimal_token_baseline) if calculate_sum_pi_squared: sum_pi_squared_rmpad = ( self.calculate_sum_pi_squared_from_logits(logits_rmpad) if not sum_pi_squared_checkpointing else torch.utils.checkpoint.checkpoint( self.calculate_sum_pi_squared_from_logits, logits_rmpad ) ) # gather log_prob if sp > 1 if self.use_ulysses_sp: # gather and unpad for the ulysses sp log_probs = gather_outputs_and_unpad( log_probs, gather_dim=0, unpad_dim=0, padding_size=pad_size, ) if calculate_entropy: entropy_rmpad = gather_outputs_and_unpad( entropy_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size, ) if calculate_sum_pi_squared: sum_pi_squared_rmpad = gather_outputs_and_unpad( sum_pi_squared_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size ) if is_mask_all_zero: log_probs = log_probs[:0] if calculate_entropy: entropy_rmpad = entropy_rmpad[:0] # pad back to (bsz, seqlen) if calculate_entropy: full_entropy = pad_input( hidden_states=entropy_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen, ) if calculate_sum_pi_squared: full_sum_pi_squared = pad_input( hidden_states=sum_pi_squared_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen, ) full_log_probs = pad_input( hidden_states=log_probs.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen, ) # only return response part: if calculate_entropy: entropy = full_entropy.squeeze(-1)[:, -response_length - 1 : -1] # (bsz, response_length) if calculate_sum_pi_squared: # (bsz, response_length) sum_pi_squared = full_sum_pi_squared.squeeze(-1)[:, -response_length - 1 : -1] log_probs = full_log_probs.squeeze(-1)[:, -response_length - 1 : -1] # (bsz, response_length) else: # not using rmpad and no ulysses sp extra_args = {} if self.use_fused_kernels: extra_args["temperature"] = temperature extra_args["return_dict"] = True output = self.actor_module( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, **multi_modal_inputs, use_cache=False, **extra_args, ) # prevent model thinks we are generating if self.use_fused_kernels: log_probs = output.log_probs[:, -response_length - 1 : -1] entropy = output.entropy[:, -response_length - 1 : -1] # (bsz, response_length) else: logits = output.logits logits.div_(temperature) logits = logits[:, -response_length - 1 : -1, :] # (bsz, response_length, vocab_size) log_probs = logprobs_from_logits(logits, micro_batch["responses"]) if calculate_entropy: if not self.config.entropy_checkpointing: entropy = verl_F.entropy_from_logits(logits) # (bsz, response_length) else: entropy = torch.utils.checkpoint.checkpoint(verl_F.entropy_from_logits, logits) # Compute sum_pi_squared if requested (for optimal_token_baseline) if calculate_sum_pi_squared: sum_pi_squared = ( self.calculate_sum_pi_squared_from_logits(logits) if not sum_pi_squared_checkpointing else torch.utils.checkpoint.checkpoint(self.calculate_sum_pi_squared_from_logits, logits) ) outputs = {"log_probs": log_probs} if calculate_entropy: outputs["entropys"] = entropy if calculate_sum_pi_squared: outputs["sum_pi_squared"] = sum_pi_squared return outputs def _optimizer_step(self): assert self.config.grad_clip is not None if self.scaler is not None: self.scaler.unscale_(self.actor_optimizer) if isinstance(self.actor_module, FSDP): grad_norm = self.actor_module.clip_grad_norm_(max_norm=self.config.grad_clip) elif isinstance(self.actor_module, FSDPModule): grad_norm = fsdp2_clip_grad_norm_(self.actor_module.parameters(), max_norm=self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.actor_module.parameters(), max_norm=self.config.grad_clip) if isinstance(grad_norm, DTensor): grad_norm = grad_norm.full_tensor() # if grad_norm is not finite, skip the update if self.scaler is not None: self.scaler.step(self.actor_optimizer) self.scaler.update() else: if not torch.isfinite(grad_norm): print(f"WARN: rank {torch.distributed.get_rank()} grad_norm is not finite: {grad_norm}") self.actor_optimizer.zero_grad() else: self.actor_optimizer.step() # Clear cached weight scales for QAT (weights changed) if getattr(self.actor_module, "_qat_fuse_enabled", False): from verl.utils.qat import invalidate_all_scales invalidate_all_scales(self.actor_module) return grad_norm @GPUMemoryLogger(role="dp actor", logger=logger) def compute_log_prob(self, data: DataProto, calculate_entropy: bool = False) -> dict[str, torch.Tensor]: """Compute the log probability of the responses given input_ids, attention_mask and position_ids Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``. ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64. ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Returns: dict[str, torch.Tensor]: a dict containing keys - ``log_probs``: tensor of shape [batch_size, response_length]. torch.float32. - ``entropys``: tensor of shape [batch_size, response_length]. torch.float32. - ``sum_pi_squared``: tensor of shape [batch_size, response_length]. torch.float32. """ calculate_sum_pi_squared = self.config.get("calculate_sum_pi_squared", False) # set to eval self.actor_module.eval() micro_batch_size = data.meta_info["micro_batch_size"] temperature = data.meta_info["temperature"] # temperature must be in the data.meta_info to avoid silent error use_dynamic_bsz = data.meta_info["use_dynamic_bsz"] pad_token_id = data.meta_info.get("pad_token_id", 0) has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() select_keys = ["responses", "input_ids", "attention_mask", "position_ids"] non_tensor_select_keys = ["multi_modal_inputs"] if has_multi_modal_inputs else [] if self.use_prefix_grouper: select_keys += [k for k in ["prompts", "response_mask"] if k in data.batch] if "uid" in data.non_tensor_batch: non_tensor_select_keys.append("uid") data = data.select(batch_keys=select_keys, non_tensor_batch_keys=non_tensor_select_keys) if use_dynamic_bsz: max_token_len = data.meta_info["max_token_len"] * self.ulysses_sequence_parallel_size micro_batches, batch_idx_list = prepare_dynamic_batch(data, max_token_len=max_token_len) else: micro_batches = data.split(micro_batch_size) log_probs_lst = [] entropy_lst = [] sum_pi_squared_lst = [] for micro_batch in micro_batches: micro_batch = micro_batch.to(get_device_id()) model_inputs = {**micro_batch.batch, **micro_batch.non_tensor_batch, "pad_token_id": pad_token_id} with torch.no_grad(): outputs = self._forward_micro_batch( model_inputs, temperature=temperature, calculate_entropy=calculate_entropy ) log_probs_lst.append(outputs["log_probs"]) if calculate_entropy: entropy_lst.append(outputs["entropys"]) if calculate_sum_pi_squared: sum_pi_squared_lst.append(outputs["sum_pi_squared"]) log_probs = torch.concat(log_probs_lst, dim=0) if calculate_entropy: entropys = torch.concat(entropy_lst, dim=0) if calculate_sum_pi_squared: sum_pi_squared = torch.concat(sum_pi_squared_lst, dim=0) if use_dynamic_bsz: log_probs = restore_dynamic_batch(log_probs, batch_idx_list) if calculate_entropy: entropys = restore_dynamic_batch(entropys, batch_idx_list) if calculate_sum_pi_squared: sum_pi_squared = restore_dynamic_batch(sum_pi_squared, batch_idx_list) outputs = {"log_probs": log_probs} if calculate_entropy: outputs["entropys"] = entropys if calculate_sum_pi_squared: outputs["sum_pi_squared"] = sum_pi_squared return outputs @GPUMemoryLogger(role="dp actor", logger=logger) def update_policy(self, data: DataProto): # make sure we are in training mode self.actor_module.train() temperature = data.meta_info["temperature"] # temperature must be in the data.meta_info to avoid silent error pad_token_id = data.meta_info.get("pad_token_id", 0) select_keys = [ "responses", "response_mask", "input_ids", "attention_mask", "position_ids", "old_log_probs", "advantages", ] if self.use_prefix_grouper and "prompts" in data.batch.keys(): select_keys.append("prompts") if self.config.use_kl_loss: select_keys.append("ref_log_prob") # Include pre-computed IS weights if present in batch # Weights are computed centrally in trainer and added to batch when algorithm.rollout_is=True if "rollout_is_weights" in data.batch.keys(): select_keys.append("rollout_is_weights") # Include rollout_log_probs for computing rollout_corr metrics in bypass mode if "rollout_log_probs" in data.batch.keys(): select_keys.append("rollout_log_probs") has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() non_tensor_select_keys = [] if has_multi_modal_inputs: non_tensor_select_keys.append("multi_modal_inputs") if self.use_prefix_grouper and "uid" in data.non_tensor_batch.keys(): non_tensor_select_keys.append("uid") data = data.select(batch_keys=select_keys, non_tensor_batch_keys=non_tensor_select_keys) # Split to make minibatch iterator for updating the actor # See PPO paper for details. https://arxiv.org/abs/1707.06347 mini_batches = data.split(self.config.ppo_mini_batch_size) on_policy = len(mini_batches) == 1 and self.config.ppo_epochs == 1 metrics = { "actor/pg_loss": 0.0, "actor/kl_loss": 0.0, } for _ in range(self.config.ppo_epochs): for batch_idx, mini_batch in enumerate(mini_batches): if self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, _ = prepare_dynamic_batch(mini_batch, max_token_len=max_token_len) else: self.gradient_accumulation = ( self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu ) micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu) self.actor_optimizer.zero_grad() for micro_batch in micro_batches: micro_batch = micro_batch.to(get_device_id()) micro_batch_metrics = {} model_inputs = {**micro_batch.batch, **micro_batch.non_tensor_batch, "pad_token_id": pad_token_id} response_mask = model_inputs["response_mask"] old_log_prob = model_inputs["old_log_probs"] advantages = model_inputs["advantages"] entropy_coeff = self.config.entropy_coeff loss_agg_mode = self.config.loss_agg_mode calculate_entropy = self.config.calculate_entropy or (entropy_coeff != 0) if self.config.use_dynamic_bsz: loss_scale_factor = response_mask.shape[0] / self.config.ppo_mini_batch_size else: loss_scale_factor = 1 / self.gradient_accumulation # all return: (bsz, response_length) outputs = self._forward_micro_batch( model_inputs, temperature=temperature, calculate_entropy=calculate_entropy ) log_prob = outputs["log_probs"] entropy = outputs["entropys"] if calculate_entropy else None # for fully_async_policy if hasattr(self.config, "use_rollout_log_probs") and self.config.use_rollout_log_probs: old_log_prob = model_inputs["old_log_probs"] else: if on_policy: old_log_prob = log_prob.detach() else: old_log_prob = model_inputs["old_log_probs"] loss_mode = self.config.policy_loss.get("loss_mode", "vanilla") # vanilla -> verl.trainer.ppo.core_algos.compute_policy_loss_vanilla # Extract pre-computed rollout correction weights if present # Weights are computed centrally in trainer and added when algorithm.rollout_is=True rollout_is_weights = model_inputs.get("rollout_is_weights", None) # gpg -> verl.trainer.ppo.core_algos.compute_policy_loss_gpg # clip_cov -> verl.trainer.ppo.core_algos.compute_policy_loss_clip_cov policy_loss_fn = get_policy_loss_fn(loss_mode) # Compute policy loss (any function is expected to return 2 values) pg_loss, pg_metrics = policy_loss_fn( old_log_prob=old_log_prob, log_prob=log_prob, advantages=advantages, response_mask=response_mask, loss_agg_mode=loss_agg_mode, config=self.config, rollout_is_weights=rollout_is_weights, ) micro_batch_metrics.update(pg_metrics) # Skip if using bypass_mode loss (metrics already computed in pg_metrics) rollout_log_prob = model_inputs.get("rollout_log_probs", None) if loss_mode != "bypass_mode" and rollout_log_prob is not None: # Compute metrics using CURRENT policy π_θ vs π_rollout # Tracks evolving off-policy gap as π_θ updates during mini-batch training from verl.trainer.ppo.rollout_corr_helper import compute_rollout_corr_metrics_from_logprobs rollout_corr_metrics = compute_rollout_corr_metrics_from_logprobs( log_prob=log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, ) micro_batch_metrics.update(rollout_corr_metrics) policy_loss = pg_loss if calculate_entropy and entropy is not None: entropy_agg = agg_loss(loss_mat=entropy, loss_mask=response_mask, loss_agg_mode=loss_agg_mode) micro_batch_metrics["actor/entropy"] = entropy_agg.detach().item() if entropy_coeff != 0: policy_loss -= entropy_agg * entropy_coeff if self.config.use_kl_loss: ref_log_prob = model_inputs["ref_log_prob"] # compute kl loss kld = kl_penalty( logprob=log_prob, ref_logprob=ref_log_prob, kl_penalty=self.config.kl_loss_type ) kl_loss = agg_loss(loss_mat=kld, loss_mask=response_mask, loss_agg_mode=loss_agg_mode) policy_loss = policy_loss + kl_loss * self.config.kl_loss_coef metrics["actor/kl_loss"] += kl_loss.detach().item() * loss_scale_factor micro_batch_metrics["actor/kl_coef"] = self.config.kl_loss_coef if self.config.use_dynamic_bsz: # relative to the dynamic bsz loss = policy_loss * loss_scale_factor else: loss = policy_loss * loss_scale_factor if self.scaler is not None: self.scaler.scale(loss).backward() else: loss.backward() metrics["actor/pg_loss"] += pg_loss.detach().item() * loss_scale_factor append_to_dict(metrics, micro_batch_metrics) grad_norm = self._optimizer_step() mini_batch_metrics = {"actor/grad_norm": grad_norm.detach().item()} append_to_dict(metrics, mini_batch_metrics) self.actor_optimizer.zero_grad() return metrics ================================================ FILE: verl/workers/actor/megatron_actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Megatron Actor. In megatron actor, the differences are: 1. We only make minibatch Note that our model doesn't have to be `MegatronModule` because we don't share embedding in the last layer """ import itertools import logging import os from functools import partial from typing import Iterable import torch import torch.distributed from megatron.core import parallel_state as mpu from megatron.core.distributed import finalize_model_grads # from megatron.core.optimizer import DistributedOptimizer from megatron.core.optimizer import DistributedOptimizer from megatron.core.pipeline_parallel import get_forward_backward_func from omegaconf import OmegaConf from torch import nn from verl import DataProto from verl.trainer.ppo.core_algos import agg_loss, get_policy_loss_fn, kl_penalty from verl.utils.device import get_device_id, get_torch_device from verl.utils.megatron.pipeline_parallel import make_batch_generator from verl.utils.megatron.router_replay_patch import RouterReplay, RouterReplayAction from verl.utils.megatron.router_replay_utils import ( RouterReplayHelper, merge_router_topk_indices, pp_gather, reorder_and_merge_vpp_layers, set_router_replay_data, ) from verl.utils.megatron.tensor_parallel import vocab_parallel_entropy, vocab_parallel_log_probs_from_logits from verl.utils.megatron_utils import get_megatron_mtp_loss, get_model_config, unwrap_model from verl.utils.profiler import GPUMemoryLogger from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import get_reverse_idx, rearrange_micro_batches from verl.utils.torch_functional import broadcast_dict_tensor from verl.workers.actor import BasePPOActor from verl.workers.config import MtpConfig __all__ = ["MegatronPPOActor"] logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class MegatronPPOActor(BasePPOActor): def __init__( self, config, model_config, hf_config, tf_config, actor_module: nn.ModuleList, actor_optimizer: DistributedOptimizer, mtp_config: MtpConfig = None, ): """MeagtronPPOActor class. This class implements the simple PPO logics when the model is built with Megatron. Args: config (OmegaConf): the basic config that contains the hyper-parameters of PPO Actor. It must contain ``ppo_micro_batch_size_per_gpu``: micro batch size when updating ppo. ``ppo_mini_batch_size``: minibatch size when updating ppo using the batch data. ``ppo_epochs``: number of epochs to update the actor using the batch data. ``shuffle``: whether to shuffle the data after each ppo epoch. ``clip_ratio``: clip ratio of the ppo algorithm. See https://arxiv.org/abs/1707.06347. ``entropy_coeff``: entropy coefficient of the PPO loss. See https://arxiv.org/abs/1707.06347. model_config (OmegaConf): model configuration. It must contains ``model_config.vocab_size`` and ``model_config.hidden_size`` hf_config (PretrainedConfig): huggingface config tf_config (TransformerConfig): mcore transformer config mtp_config (MtpConfig): mtp config, default None actor_module (nn.ModuleList): actor module is a ModuleList that contains a list of nn.Module in this pp stage. each nn.Module in this rank holds a vpp module chunk. See https://arxiv.org/pdf/2104.04473.pdf for more details. The actor module has some constraints to follow in order to use the updating logics implemented here 1. It must implement unpad_input before any computation and pad_input after all the computation. Remove padding is an optimization that removes the padding tokens. See unpad_input and pad_input function in flash-attn (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/bert_padding.py). 2. Each pp stage must return the hidden state with the same shape [total_nnz, 1, hidden_size], where total_nnz is the number of valid tokens in this batch. If sequence parallel is enabled, the size of the hidden state is [total_nnz // tp, 1, hidden_size]. actor_optimizer (DistributedOptimizer): currently, we only support DistributedOptimizer in Megatron. It implements zero1 optimizer that shards the optimizer state across dp ranks. >>> from megatron.training import get_model >>> from megatron.optimizer import get_megatron_optimizer >>> actor_module = get_model(megatron_actor_model_provider, wrap_with_ddp=True) >>> actor_module = nn.ModuleList(actor_module) >>> actor_optimizer = get_megatron_optimizer(actor_module) >>> actor = MegatronPPOActor(config=config, >>> model_config=actor_model_config, >>> hf_config=hf_config, >>> tf_config=tf_config, >>> actor_module=actor_module, >>> actor_optimizer=actor_optimizer) """ super().__init__(config) self._validate_config(config) self.model_config = model_config self.hf_config = hf_config self.tf_config = tf_config self.mtp_config = mtp_config self.actor_module = actor_module self.actor_optimizer: DistributedOptimizer = actor_optimizer if self.mtp_config: assert self.mtp_config.enable, "MTP requires mtp_config.enable to be True" self.use_fused_kernels = self.config.get("use_fused_kernels", False) if getattr(self.mtp_config, "enable", False) and self.use_fused_kernels: self.use_fused_kernels = False logger.warning_once( "MTP is not compatible with fused kernels for now. Automatically disable use_fused_kernels." ) if self.use_fused_kernels and not getattr(self.config, "overlap_moe_expert_parallel_comm", False): # do not patch if overlap_moe_expert_parallel_comm is enabled logger.warning_once( "Recommend to disable use_fused_kernels since the fused kernel's performance is broken for triton>=3.3" "Unless you are using a very old version of triton < 3.3" ) from verl.models.mcore.model_forward_fused import patch_fused_forward for model in self.actor_module: patch_fused_forward(model) else: from verl.models.mcore.mtp_patch import patch_postprocess for model in self.actor_module: if self.mtp_config: from verl.models.mcore.mtp_patch import patch_mtp_layer_get_embeddings patch_postprocess(model) if self.mtp_config.detach_encoder: patch_mtp_layer_get_embeddings(model) self.optimizer_step_args = OmegaConf.create( { "skip_grad": None, "overlap_dp_param_comm": False, "overlap_dp_grad_comm": False, "gradient_accumulation_steps": 1, "sequence_parallel": self.tf_config.sequence_parallel, "DDP_impl": "local", "layernorm_allreduce_bucket_threshold": 0, "reduce_grads_use_alltoall": False, } ) self.router_replay = self.config.router_replay self.enable_routing_replay = self.router_replay.mode != "disabled" if self.enable_routing_replay: self.mini_layer_topk_idx_list = [] config = get_model_config(self.actor_module[0]) print(config) config.finalize_model_grads_func = finalize_model_grads def _validate_config(self, config) -> None: """Validate config options not implemented for Megatron backend""" assert config.get("ulysses_sequence_parallel_size", 1) == 1 if config.get("shuffle", False): assert config.data_loader_seed is not None, "If shuffle dataloader, seed must be manually set" if config.megatron.tensor_model_parallel_size == 1: print("[Warining] Because actor tp size == 1, set sp to False") config.megatron.sequence_parallel = False self.config = config @GPUMemoryLogger(role="megatron actor", logger=logger) def compute_log_prob(self, data: DataProto, calculate_entropy=False) -> torch.Tensor: """Compute the log probability of the responses given input_ids, attention_mask and position_ids Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``. ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64. ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Returns: DataProto: torch.Tensor: the log_prob tensor """ prev_modes = [m.training for m in self.actor_module] for module in self.actor_module: module.eval() use_dynamic_bsz = data.meta_info.get("use_dynamic_bsz", False) micro_batch_size = data.meta_info.get("micro_batch_size", None) max_token_len = data.meta_info.get("max_token_len", None) if use_dynamic_bsz: assert max_token_len is not None, "max_token_len must be set when use_dynamic_bsz is True" max_token_len = max_token_len * self.config.megatron.context_parallel_size else: assert micro_batch_size is not None, ( "micro batch size is needed for forward compute when use_dynamic_bsz is False" ) def compute_logprobs_fn(output, data, use_dynamic_bsz=False, indices=None): response = data["responses"] response_length = response.size(1) log_probs = output["log_probs"][:, -response_length - 1 : -1].contiguous() return {"log_probs": log_probs} # We make recompute_old_log_prob by default here. # TODO (zhangchi.usc1992): actually, this function should only return log_prob and this logic should be # handled by user outside recompute_old_log_prob = self.config.get("recompute_old_log_prob", True) entropys = torch.Tensor() if recompute_old_log_prob: select_keys = ["responses", "input_ids", "attention_mask", "position_ids"] if self.enable_routing_replay and self.config.router_replay.mode == "R3": assert "routed_experts" in data.batch.keys(), "routed_experts must be in data.batch.keys()" select_keys.append("routed_experts") batch = data.select(batch_keys=select_keys).batch input_ids = batch["input_ids"] batch_size = input_ids.size(0) response = batch["responses"] response_length = response.size(1) with torch.no_grad(): output = self.forward_backward_batch( data, forward_only=True, post_process_fn=compute_logprobs_fn, calculate_entropy=calculate_entropy, use_dynamic_bsz=use_dynamic_bsz, micro_batch_size=micro_batch_size, max_token_len=max_token_len, ) if mpu.is_pipeline_last_stage(ignore_virtual=True): # only on last rank. It should be on every tp rank if calculate_entropy: log_probs = [o[0]["log_probs"] for o in output["output"]] # (bs, seq_size) else: log_probs = [o["log_probs"] for o in output["output"]] # (bs, seq_size) log_probs = torch.cat(log_probs, dim=0).to(torch.float32) if use_dynamic_bsz: indices = output["indices"] indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == log_probs.size(0), f"{len(indices)} vs. {log_probs.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) log_probs = log_probs[revert_indices] else: log_probs = torch.empty( size=(batch_size, response_length), dtype=torch.float32, device=input_ids.device ) log_probs = log_probs.to(get_device_id()) # broadcast across pp ranks torch.distributed.broadcast( tensor=log_probs, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), async_op=False, ) log_probs = log_probs.to("cpu") if calculate_entropy: # Note that o[0] is metrics, o[1] is entropy if mpu.is_pipeline_last_stage(ignore_virtual=True): entropys = torch.cat([o[1] for o in output["output"]], dim=0) entropys = entropys.to(torch.float32) if use_dynamic_bsz: indices = output["indices"] indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == entropys.size(0), f"{len(indices)} vs. {entropys.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) entropys = entropys[revert_indices] else: entropys = torch.empty( size=(batch_size, response_length), dtype=torch.float32, device=input_ids.device ) # broadcast across pp ranks entropys = entropys.to(get_device_id()) torch.distributed.broadcast( tensor=entropys, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), async_op=False, ) entropys = entropys.to("cpu") layers_topk_idx = None if RouterReplayHelper.is_r2_record_action(self.tf_config): # (bs, max_seq_len/response_len,local_layer_num,topk) layers_topk_idx = output["mini_layer_topk_idx_tensor"].to(torch.uint8) if use_dynamic_bsz: indices = output["indices"] indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == layers_topk_idx.size(0), f"{len(indices)} vs. {layers_topk_idx.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) layers_topk_idx = layers_topk_idx[revert_indices] layers_topk_idx = pp_gather(layers_topk_idx, self.tf_config) # add empty cache after each compute get_torch_device().empty_cache() for module, mode in zip(self.actor_module, prev_modes, strict=False): module.train(mode) return log_probs, entropys, layers_topk_idx def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]: """Make minibatch iterator for updating the actor Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64, where ``sequence_length = prompt_length + response_length`` ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64 ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64 ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Note that responses = input_ids[:, -response_length:] ``old_log_probs``: tensor of shape [batch_size, response_length]. torch.float32. The log probability of responses. ``advantages``: tensor of shape [batch_size, response_length]. torch.float32. The advantages of responses. See PPO paper for details. https://arxiv.org/abs/1707.06347 Returns: """ select_keys = [ "responses", "input_ids", "attention_mask", "response_mask", "position_ids", "old_log_probs", "advantages", ] if self.config.use_kl_loss: select_keys.append("ref_log_prob") # Include pre-computed IS weights if present in batch # Weights are computed centrally in trainer and added to batch when algorithm.rollout_is=True if "rollout_is_weights" in data.batch.keys(): select_keys.append("rollout_is_weights") # Include rollout_log_probs for computing rollout_corr metrics in bypass mode if "rollout_log_probs" in data.batch.keys(): select_keys.append("rollout_log_probs") self.has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() # router replay if self.enable_routing_replay: select_keys.append("routed_experts") if self.has_multi_modal_inputs: data = data.select(select_keys, ["multi_modal_inputs"]) else: data = data.select(batch_keys=select_keys) return data.make_iterator( mini_batch_size=self.config.ppo_mini_batch_size, epochs=self.config.ppo_epochs, seed=self.config.data_loader_seed, dataloader_kwargs={"shuffle": self.config.shuffle}, ) def forward_backward_batch( self, data: DataProto, forward_only=False, post_process_fn=None, calculate_entropy=False, use_dynamic_bsz=False, micro_batch_size=None, max_token_len=None, mini_batch_size=None, ): """ We assume: - The model takes input: (input_ids, attention_mask, position_ids). No rmpad for the input - The communication shape is (total_nnz_pad_to_sp // tp_size, 1, hidden_size) if sequence parallel is enabled """ # broadcast from last pp rank to all other pp ranks # TODO: actually, we just need to control the sampling order. data.to(get_device_id()) data.batch = data.batch.contiguous() mini_batch = data broadcast_dict_tensor( mini_batch.batch, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), ) mini_batch.to("cpu") # split into micro-batches mini_batch.batch["attention_mask"] = mini_batch.batch["attention_mask"].to(bool) self.has_multi_modal_inputs = "multi_modal_inputs" in mini_batch.non_tensor_batch.keys() if self.has_multi_modal_inputs: mini_batch.batch["multi_modal_inputs"] = mini_batch.non_tensor_batch["multi_modal_inputs"] mini_batch.batch["multi_modal_inputs_idx"] = torch.Tensor( list(range(len(mini_batch.non_tensor_batch["multi_modal_inputs"]))) ).to(torch.int64) if mini_batch.batch["position_ids"].dim() == 3: # qwen2vl mrope [bs, 3, seq_len] mini_batch.batch["position_ids"] = mini_batch.batch["position_ids"][ :, 0 ] # mcore patch recompute qwen2vl's pos ids during forward indices = None temperature = data.meta_info["temperature"] if use_dynamic_bsz: assert max_token_len is not None, "max_token_len must be set when use_dynamic_bsz is True" dp_group = mpu.get_data_parallel_group() vpp_size = mpu.get_virtual_pipeline_model_parallel_world_size() if vpp_size is not None and vpp_size > 1: microbatch_group_size_per_vp_stage = self.tf_config.microbatch_group_size_per_vp_stage micro_batches, indices = rearrange_micro_batches( batch=mini_batch.batch, num_batches_divided_by=microbatch_group_size_per_vp_stage, max_token_len=max_token_len, dp_group=dp_group, ) assert len(micro_batches) % self.tf_config.microbatch_group_size_per_vp_stage == 0, ( f"micro_batches {micro_batches} must be divisible by microbatch_group_size_per_vp_stage " f"{microbatch_group_size_per_vp_stage} for megatron backend" ) else: micro_batches, indices = rearrange_micro_batches( batch=mini_batch.batch, max_token_len=max_token_len, dp_group=dp_group ) total_seqlen = max_token_len else: assert micro_batch_size is not None, ( "micro_batch_size is needed to be passed in when not using dynamic batch size" ) micro_batches = mini_batch.batch.split(micro_batch_size) seq_len = micro_batches[0]["input_ids"].shape[1] total_seqlen = micro_batch_size * seq_len # compute input shapes for pp stages n_micro_batch = len(micro_batches) forward_backward_func = get_forward_backward_func() def loss_func(output, data, meta_info): # For memory efficiency # We move calculation of entropy to compute_log_probs, forward_only == True log_probs = None entropy = None if isinstance(output, dict): log_probs = output["log_probs"] if "entropy" in output: entropy = output["entropy"] else: assert isinstance(output, torch.Tensor) log_probs = output device = log_probs.device metrics = {} if forward_only: if post_process_fn is None: pass # metrics["logits"] = output else: stats = post_process_fn(output, data) metrics.update(stats) if not calculate_entropy: return torch.tensor(1.0, device=device), metrics responses = data["responses"] response_length = responses.size(1) response_mask = data["response_mask"].to(bool) loss_agg_mode = self.config.loss_agg_mode # compute policy loss log_prob = log_probs[:, -response_length - 1 : -1].contiguous() ret_entropy = None stats = {} if not forward_only: old_log_prob = data["old_log_probs"] advantages = data["advantages"] entropy_coeff = self.config.entropy_coeff loss_agg_mode = self.config.loss_agg_mode loss_mode = self.config.policy_loss.get("loss_mode", "vanilla") policy_loss_fn = get_policy_loss_fn(loss_mode) # Extract pre-computed rollout correction weights if present # Weights are computed centrally in trainer and added when algorithm.rollout_is=True rollout_is_weights = data.get("rollout_is_weights", None) pg_loss, pg_metrics = policy_loss_fn( old_log_prob=old_log_prob, log_prob=log_prob, advantages=advantages, response_mask=response_mask, loss_agg_mode=loss_agg_mode, config=self.config, rollout_is_weights=rollout_is_weights, ) stats.update(pg_metrics) # Skip if using bypass_mode loss (metrics already computed in pg_metrics) rollout_log_prob = data.get("rollout_log_probs", None) if loss_mode != "bypass_mode" and rollout_log_prob is not None: # Compute metrics using CURRENT policy π_θ vs π_rollout # Tracks evolving off-policy gap as π_θ updates during mini-batch training from verl.trainer.ppo.rollout_corr_helper import compute_rollout_corr_metrics_from_logprobs rollout_corr_metrics = compute_rollout_corr_metrics_from_logprobs( log_prob=log_prob, rollout_log_prob=rollout_log_prob, response_mask=response_mask, ) stats.update(rollout_corr_metrics) stats["actor/pg_loss"] = pg_loss.detach().item() policy_loss = pg_loss if calculate_entropy: entropy = output["entropy"][:, -response_length - 1 : -1].contiguous() if not forward_only: entropy_loss = agg_loss(loss_mat=entropy, loss_mask=response_mask, loss_agg_mode=loss_agg_mode) entropy_coeff = meta_info["entropy_coeff"] policy_loss = pg_loss - entropy_coeff * entropy_loss else: ret_entropy = entropy if forward_only: policy_loss = torch.tensor(1.0, device=device) else: if self.config.use_kl_loss: ref_log_prob = data["ref_log_prob"] # compute kl loss kld = kl_penalty(logprob=log_prob, ref_logprob=ref_log_prob, kl_penalty=self.config.kl_loss_type) kl_loss = agg_loss(loss_mat=kld, loss_mask=response_mask, loss_agg_mode=self.config.loss_agg_mode) policy_loss = policy_loss + kl_loss * self.config.kl_loss_coef metrics["actor/kl_loss"] = kl_loss.detach().item() metrics["actor/kl_coef"] = self.config.kl_loss_coef # return loss and stats append_to_dict(metrics, stats) return policy_loss, [metrics, ret_entropy] def forward_step(batch_iter, model, return_schedule_plan: bool = False): """ Args: batch_iter: the batch iterator model: the model return_schedule_plan: whether to return the schedule plan, for 1f1b overlap """ if return_schedule_plan: assert self.tf_config.overlap_moe_expert_parallel_comm, ( "overlap_moe_expert_parallel_comm must be enabled to return the schedule plan" ) # TODO: Fix this assert not calculate_entropy, "calculate_entropy must be disabled to return the schedule plan" from megatron.core.models.gpt.gpt_model import GPTModel assert isinstance(model, GPTModel), "model must be a GPTModel" assert self.use_fused_kernels, "use_fused_kernels must be enabled to return the schedule plan" # TODO: support VLM with MoE from verl.models.mcore.model_forward_1f1b_overlap import gptmodel_forward_1f1b_overlap batch = next(batch_iter) batch = batch.to(get_device_id()) batch = batch.contiguous() input_ids = batch["input_ids"] attention_mask = batch["attention_mask"].to(bool) position_ids = batch["position_ids"] unwrapped_model = unwrap_model(model) if hasattr(unwrapped_model, "vp_stage"): vp_rank = unwrapped_model.vp_stage else: vp_rank = 0 multi_modal_inputs = {} if "multi_modal_inputs" in batch: from verl.utils.model import extract_multi_modal_inputs indices = batch.get("multi_modal_inputs_idx", None) multi_modal_inputs = extract_multi_modal_inputs(batch["multi_modal_inputs"], indices) responses = batch["responses"] response_length = responses.size(1) label = position_ids.clone() label[:, -response_length - 1 : -1] = responses label_mask = attention_mask.clone() label_mask[:, : -response_length - 1] = False label_mask[:, -1] = False if RouterReplayHelper.is_replay_backward_action(self.tf_config, vp_rank): router_instance_list = RouterReplayHelper.get_micro_batch_router_list(self.tf_config, vp_rank) for router in router_instance_list: router.set_router_replay_action(RouterReplayAction.REPLAY_FORWARD) if RouterReplayHelper.is_replay_forward_action(self.tf_config, vp_rank): layers_topk_idx = batch["routed_experts"] set_router_replay_data(layers_topk_idx, attention_mask, self.tf_config, vp_rank) from verl.models.mcore import get_mcore_forward_fn, get_mcore_forward_fused_fn if self.use_fused_kernels: forward_fn = get_mcore_forward_fused_fn(self.hf_config) if return_schedule_plan: forward_fn = gptmodel_forward_1f1b_overlap # return dict of [logits, entropy] output = forward_fn( model=model, input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, labels=label, labels_mask=label_mask, temperature=temperature, multi_modal_inputs=multi_modal_inputs, ) else: forward_fn = get_mcore_forward_fn(self.hf_config) def logits_processor(logits, label, label_mask): assert logits.shape[:2] == label.shape[:2] assert label.shape == label_mask.shape logits.div_(temperature) ret = {} if calculate_entropy: logits_bak = logits.clone() # # disable the hint until the fused_kernel is optimized for triton>=3.3 # logger.warning_once( # "For memory-efficient computation, enable fused kernels via " # "`actor_rollout_ref.model.use_fused_kernels=True`. " # "The current `clone()` operation ensures correctness but increases memory usage." # ) entropy = vocab_parallel_entropy(logits) ret["entropy"] = entropy else: logits_bak = logits log_probs = vocab_parallel_log_probs_from_logits(logits_bak, label) log_probs = log_probs.masked_fill(~label_mask, 0.0) ret["log_probs"] = log_probs return ret logits_processor_args = {"label": label, "label_mask": label_mask} output = forward_fn( model=model, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, multi_modal_inputs=multi_modal_inputs, logits_processor=logits_processor, logits_processor_args=logits_processor_args, data_format="thd" if self.config.megatron.use_remove_padding else "bshd", mtp_config=None if forward_only else self.mtp_config, ) if forward_only: meta_info = None else: clip_ratio_c = self.config.get("clip_ratio_c", 3.0) meta_info = { "clip_ratio": self.config.clip_ratio, "entropy_coeff": self.config.entropy_coeff, "clip_ratio_c": clip_ratio_c, } if RouterReplayHelper.is_r2_record_action(self.tf_config, vp_rank): merge_router_topk_indices( attention_mask, input_ids, self.mini_layer_topk_idx_list, self.tf_config, vp_rank ) if RouterReplayHelper.is_replay_forward_action(self.tf_config, vp_rank): router_instance_list = RouterReplayHelper.get_micro_batch_router_list(self.tf_config, vp_rank) for router in router_instance_list: router.set_router_replay_action(RouterReplayAction.REPLAY_BACKWARD) return output, partial(loss_func, data=batch, meta_info=meta_info) # batch should be a list of batches inside micro-batches batch_generator = make_batch_generator(micro_batches, vpp_size=len(self.actor_module)) # TODO: we may use the new schedule instead # for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size) if mpu.get_pipeline_model_parallel_world_size() > 1: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.actor_module, num_microbatches=n_micro_batch, seq_length=total_seqlen, # no use when input_shapes was set micro_batch_size=1, # no use when input_shapes was set forward_only=forward_only, ) else: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.actor_module, num_microbatches=n_micro_batch, seq_length=total_seqlen, # in use for pp = 1 micro_batch_size=1, # in use for pp = 1 forward_only=forward_only, ) # loss_reduces contains the stats returned from loss_func if self.has_multi_modal_inputs: data.batch.pop("multi_modal_inputs") data.batch.pop("multi_modal_inputs_idx") data.non_tensor_batch.pop("multi_modal_inputs") losses_reduced = {"output": losses_reduced} if use_dynamic_bsz: losses_reduced["indices"] = indices if RouterReplayHelper.is_r2_record_action(self.tf_config): if self.tf_config.virtual_pipeline_model_parallel_size is not None: # config = self.actor_module[0].module.module.config vp_size = len(self.actor_module) microbatch_group_size_per_vp_stage = self.tf_config.microbatch_group_size_per_vp_stage bs = n_micro_batch losses_reduced["mini_layer_topk_idx_tensor"] = reorder_and_merge_vpp_layers( self.mini_layer_topk_idx_list, bs, vp_size, microbatch_group_size_per_vp_stage ) else: losses_reduced["mini_layer_topk_idx_tensor"] = torch.cat(self.mini_layer_topk_idx_list, dim=0) self.mini_layer_topk_idx_list = [] # Collect and pass MTP metrics to losses_reduced if not forward_only and self.mtp_config and self.mtp_config.enable_train: metrics = get_megatron_mtp_loss(n_micro_batch) losses_reduced["mtp_losses"] = [metrics] return losses_reduced @GPUMemoryLogger(role="megatron actor", logger=logger) def update_policy(self, dataloader: Iterable[DataProto], enable_mtp: bool = False) -> dict: """Update the policy with an iterator of DataProto Args: dataloader (Iterable[DataProto]): an iterator over the DataProto that returns by ``make_minibatch_iterator`` The keys of each data batch is described in the make_minibatch_iterator. enable_mtp (bool, optional): whether to enable MTP communication Returns: Dict: a dictionary containing the statistics. Note that the statistics are only valid in the last pp stage and users have to combine the output in each dp rank manually. """ metrics = {} for data in dataloader: if self.config.router_replay.mode in ["R2", "R3"]: RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD) self.actor_optimizer.zero_grad() # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm for chunk in self.actor_module: # if use distributed optimizer, zero grad buffer will be handled by optimizer chunk.zero_grad_buffer() calculate_entropy = self.config.entropy_coeff != 0 if data.meta_info.get("micro_batch_size", None) is not None: micro_batch_size = data.meta_info["micro_batch_size"] else: micro_batch_size = self.config.ppo_micro_batch_size_per_gpu max_token_len = None if self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.config.megatron.context_parallel_size metric_micro_batch = self.forward_backward_batch( data, calculate_entropy=calculate_entropy, use_dynamic_bsz=self.config.use_dynamic_bsz, micro_batch_size=micro_batch_size, max_token_len=max_token_len, mini_batch_size=self.config.ppo_mini_batch_size, ) mtp_losses = metric_micro_batch.get("mtp_losses", None) if mtp_losses is not None: # mtp_losses is now in format: [{"mtp_losses/mtp_1_loss": [value1], "mtp_losses/mtp_2_loss": [value2]}] for mtp_metrics_dict in mtp_losses: append_to_dict(metrics, mtp_metrics_dict) metric_micro_batch = metric_micro_batch["output"] for metric in metric_micro_batch: # Note that o[0] is metrics, o[1] is entropy, o[2] is response_mask append_to_dict(metrics, metric[0]) # append the metric from this micro-batch to global metrics. update_successful, grad_norm, num_zeros_in_grad = self.actor_optimizer.step() data = {"actor/grad_norm": grad_norm} append_to_dict(metrics, data) if update_successful: # allgather already execute in optimizer.step in new megatron pass else: raise NotImplementedError if self.config.router_replay.mode in ["R2", "R3"]: RouterReplay.clear_global_router_replay_action() RouterReplay.clear_global_indices() self.actor_optimizer.zero_grad() get_torch_device().empty_cache() return metrics ================================================ FILE: verl/workers/config/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import actor, critic, engine, model, optimizer, reward, rollout from .actor import * # noqa: F401 from .critic import * # noqa: F401 from .engine import * # noqa: F401 from .model import * # noqa: F401 from .optimizer import * # noqa: F401 from .reward import * # noqa: F401 from .rollout import * # noqa: F401 __all__ = ( actor.__all__ + critic.__all__ + reward.__all__ + engine.__all__ + optimizer.__all__ + rollout.__all__ + model.__all__ ) ================================================ FILE: verl/workers/config/actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass, field from typing import Any, Optional from omegaconf import MISSING from verl.base_config import BaseConfig from verl.trainer.config import CheckpointConfig, RolloutCorrectionConfig from verl.utils.profiler.config import ProfilerConfig from verl.utils.qat import QATConfig from .engine import FSDPEngineConfig, McoreEngineConfig, TorchtitanEngineConfig, VeOmniEngineConfig from .model import HFModelConfig from .optimizer import OptimizerConfig __all__ = [ "PolicyLossConfig", "RouterReplayConfig", "ActorConfig", "FSDPActorConfig", "McoreActorConfig", "VeOmniActorConfig", "QATConfig", "TorchTitanActorConfig", ] @dataclass class RouterReplayConfig(BaseConfig): """Configuration for router replay in MoE models. This configuration controls the routing behavior for Mixture of Experts (MoE) models, allowing for deterministic training through route recording and replay. Args: mode (str): Router replay mode. Options: 'disabled', 'R2', 'R3'. - 'disabled': No router replay functionality - 'R2': Use Router Replay routing strategy - 'R3': Use Rollout Router Replay routing strategy record_file (Optional[str]): File path to save recorded routing decisions. Required when mode is 'record', 'R2', or 'R3'. replay_file (Optional[str]): File path to load recorded routing decisions for replay. Required when mode is 'replay'. """ mode: str = "disabled" record_file: Optional[str] = None replay_file: Optional[str] = None def __post_init__(self): """Validate router replay configuration.""" valid_modes = ["disabled", "R2", "R3"] if self.mode not in valid_modes: raise ValueError(f"Invalid router_replay mode: {self.mode}. Must be one of {valid_modes}") @dataclass class PolicyLossConfig(BaseConfig): """Configuration for policy loss computation. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: loss_mode (str): Loss function mode. Options: 'vanilla', 'clip-cov', 'kl-cov', 'gpg'. clip_cov_ratio (float): Ratio of tokens to be clipped for clip-cov loss. clip_cov_lb (float): Lower bound for clip-cov loss. clip_cov_ub (float): Upper bound for clip-cov loss. kl_cov_ratio (float): Ratio of tokens to be applied KL penalty for kl-cov loss. ppo_kl_coef (float): KL divergence penalty coefficient. rollout_correction (RolloutCorrectionConfig): Configuration for rollout correction. """ loss_mode: str = "vanilla" clip_cov_ratio: float = 0.0002 clip_cov_lb: float = 1.0 clip_cov_ub: float = 5.0 kl_cov_ratio: float = 0.0002 ppo_kl_coef: float = 0.1 rollout_correction: RolloutCorrectionConfig = field(default_factory=RolloutCorrectionConfig) @dataclass class ActorConfig(BaseConfig): """Configuration for actor model training. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: strategy (str): Training strategy. Must be specified. ppo_mini_batch_size (int): Mini-batch size for PPO training. ppo_micro_batch_size (Optional[int]): Micro-batch size for PPO training. If None, uses ppo_micro_batch_size_per_gpu. ppo_micro_batch_size_per_gpu (Optional[int]): Micro-batch size per GPU for PPO training. use_dynamic_bsz (bool): Whether to use dynamic batch sizing. ppo_max_token_len_per_gpu (int): Maximum token length per GPU for PPO training. clip_ratio (float): PPO clipping ratio for policy loss. clip_ratio_low (float): Lower bound for PPO clipping ratio. clip_ratio_high (float): Upper bound for PPO clipping ratio. policy_loss (PolicyLossConfig): Configuration for policy loss computation. clip_ratio_c (float): Clipping ratio for critic loss. loss_agg_mode (str): Loss aggregation mode. Options: 'token-mean', 'sample-mean'. loss_scale_factor (Optional[int]): Scale factor for 'seq-mean-token-sum-norm' loss aggregation mode. If None, uses response_length. Set to a constant to ensure consistent normalization. entropy_coeff (float): Entropy coefficient for regularization. tau_pos (float): Positive tau for SAPO smoothing (>= 1.0 keeps rewards stable). tau_neg (float): Negative tau for SAPO smoothing (> tau_pos for asymmetry). use_kl_loss (bool): Whether to use KL divergence loss. use_torch_compile (bool): Whether to use torch.compile for optimization. kl_loss_coef (float): KL divergence loss coefficient. kl_loss_type (str): Type of KL loss to use. ppo_epochs (int): Number of PPO epochs per training step. shuffle (bool): Whether to shuffle data during training. checkpoint (CheckpointConfig): Configuration for checkpointing. optim (OptimizerConfig): Configuration for optimizer. use_fused_kernels (bool): Whether to use custom fused kernels (e.g., FlashAttention, fused MLP). data_loader_seed (int): Seed for data loader. If None, uses global seed. router_replay (RouterReplayConfig): Configuration for router replay in MoE models. """ _mutable_fields = BaseConfig._mutable_fields | { "ppo_mini_batch_size", "ppo_micro_batch_size", "ppo_micro_batch_size_per_gpu", "ppo_infer_micro_batch_size_per_gpu", "engine", "model_config", } strategy: str = MISSING ppo_mini_batch_size: int = 256 ppo_micro_batch_size: Optional[int] = None # deprecate ppo_micro_batch_size_per_gpu: Optional[int] = None ppo_infer_micro_batch_size_per_gpu: Optional[int] = None use_dynamic_bsz: bool = False ppo_max_token_len_per_gpu: int = 16384 ppo_infer_max_token_len_per_gpu: int = 16384 clip_ratio: float = 0.2 clip_ratio_low: float = 0.2 clip_ratio_high: float = 0.2 freeze_vision_tower: bool = False policy_loss: PolicyLossConfig = field(default_factory=PolicyLossConfig) clip_ratio_c: float = 3.0 loss_agg_mode: str = "token-mean" loss_scale_factor: Optional[int] = None entropy_coeff: float = 0 tau_pos: float = 1.0 tau_neg: float = 1.05 calculate_entropy: bool = False use_kl_loss: bool = False # Whether to enable PrefixGrouper-based shared-prefix forward use_prefix_grouper: bool = False use_torch_compile: bool = True kl_loss_coef: float = 0.001 kl_loss_type: str = "low_var_kl" ppo_epochs: int = 1 shuffle: bool = False data_loader_seed: int = 1 checkpoint: CheckpointConfig = field(default_factory=CheckpointConfig) optim: OptimizerConfig = field(default_factory=OptimizerConfig) use_fused_kernels: bool = False profiler: ProfilerConfig = field(default_factory=ProfilerConfig) engine: BaseConfig = field(default_factory=BaseConfig) rollout_n: int = MISSING # must be override by sampling config model_config: HFModelConfig = field(default_factory=BaseConfig) router_replay: RouterReplayConfig = field(default_factory=RouterReplayConfig) # Store global batch info for loss aggregation: # dp_size: data parallel size # batch_num_tokens: number of valid tokens in global batch # global_batch_size: global batch size global_batch_info: dict = field(default_factory=dict) def __post_init__(self): """Validate actor configuration parameters.""" assert self.strategy != MISSING assert self.rollout_n != MISSING if not self.use_dynamic_bsz: if self.ppo_micro_batch_size is not None and self.ppo_micro_batch_size_per_gpu is not None: raise ValueError( "[actor] You have set both 'actor.ppo_micro_batch_size' AND 'actor.ppo_micro_batch_size_per_gpu'. " "Please remove 'actor.ppo_micro_batch_size' because only '*_ppo_micro_batch_size_per_gpu' is " "supported (the former is deprecated)." ) else: assert not (self.ppo_micro_batch_size is None and self.ppo_micro_batch_size_per_gpu is None), ( "[actor] Please set at least one of 'actor.ppo_micro_batch_size' or " "'actor.ppo_micro_batch_size_per_gpu' if use_dynamic_bsz is not enabled." ) valid_loss_agg_modes = [ "token-mean", "seq-mean-token-sum", "seq-mean-token-mean", "seq-mean-token-sum-norm", ] if self.loss_agg_mode not in valid_loss_agg_modes: raise ValueError(f"Invalid loss_agg_mode: {self.loss_agg_mode}") def validate(self, n_gpus: int, train_batch_size: int, model_config: dict = None): """Validate actor configuration with runtime parameters.""" if not self.use_dynamic_bsz: if train_batch_size < self.ppo_mini_batch_size: raise ValueError( f"train_batch_size ({train_batch_size}) must be >= " f"actor.ppo_mini_batch_size ({self.ppo_mini_batch_size})" ) sp_size = getattr(self, "ulysses_sequence_parallel_size", 1) if self.ppo_micro_batch_size is not None: if self.ppo_mini_batch_size % self.ppo_micro_batch_size != 0: raise ValueError( f"ppo_mini_batch_size ({self.ppo_mini_batch_size}) must be divisible by " f"ppo_micro_batch_size ({self.ppo_micro_batch_size})" ) if self.ppo_micro_batch_size * sp_size < n_gpus: raise ValueError( f"ppo_micro_batch_size ({self.ppo_micro_batch_size}) * " f"ulysses_sequence_parallel_size ({sp_size}) must be >= n_gpus ({n_gpus})" ) @staticmethod def _check_mutually_exclusive(mbs, mbs_per_gpu, name: str): """Validate mutually exclusive micro batch size configuration options.""" param = "ppo_micro_batch_size" param_per_gpu = f"{param}_per_gpu" if mbs is None and mbs_per_gpu is None: raise ValueError(f"[{name}] Please set at least one of '{name}.{param}' or '{name}.{param_per_gpu}'.") if mbs is not None and mbs_per_gpu is not None: raise ValueError( f"[{name}] You have set both '{name}.{param}' AND '{name}.{param_per_gpu}'. Please remove " f"'{name}.{param}' because only '*_{param_per_gpu}' is supported (the former is deprecated)." ) @dataclass class McoreActorConfig(ActorConfig): """Configuration for Megatron actor models. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: strategy (str): Training strategy set to 'megatron' for Megatron parallelism. load_weight (bool): Whether to load model weights from checkpoint. megatron (dict[str, Any]): Configuration for Megatron parallelism settings. profile (dict[str, Any]): Configuration for profiling settings. """ strategy: str = "megatron" load_weight: bool = True megatron: McoreEngineConfig = field(default_factory=McoreEngineConfig) profile: dict[str, Any] = field(default_factory=dict) use_rollout_log_probs: bool = False def __post_init__(self): """Validate FSDP actor configuration parameters.""" super().__post_init__() self.engine = self.megatron @dataclass class FSDPActorConfig(ActorConfig): """Configuration for FSDP actor models. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: strategy (str): Training strategy set to 'fsdp' for Fully Sharded Data Parallel. grad_clip (float): Gradient clipping threshold. ulysses_sequence_parallel_size (int): [DEPRECATED] Ulysses sequence parallel size for long sequences. entropy_from_logits_with_chunking (bool): Whether to compute entropy from logits with chunking for memory efficiency. entropy_checkpointing (bool): Whether to use gradient checkpointing for entropy computation. fsdp_config (dict[str, Any]): Configuration for FSDP settings. use_remove_padding (bool): Whether to remove padding tokens in inputs during training """ strategy: str = "fsdp" grad_clip: float = 1.0 ulysses_sequence_parallel_size: int = 1 entropy_from_logits_with_chunking: bool = False entropy_checkpointing: bool = False fsdp_config: FSDPEngineConfig = field(default_factory=FSDPEngineConfig) use_remove_padding: bool = False use_rollout_log_probs: bool = False calculate_sum_pi_squared: bool = False sum_pi_squared_checkpointing: bool = False qat: QATConfig = field(default_factory=QATConfig) def __post_init__(self): """Validate FSDP actor configuration parameters.""" super().__post_init__() self.engine = self.fsdp_config # backward compatibility if self.ulysses_sequence_parallel_size > 1: self.fsdp_config.ulysses_sequence_parallel_size = self.ulysses_sequence_parallel_size def validate(self, n_gpus: int, train_batch_size: int, model_config: dict = None): """Validate FSDP actor configuration with runtime parameters.""" super().validate(n_gpus, train_batch_size, model_config) if self.strategy in {"fsdp", "fsdp2"} and self.ulysses_sequence_parallel_size > 1: if model_config and not model_config.get("use_remove_padding", False): raise ValueError( "When using sequence parallelism for actor/ref policy, you must enable `use_remove_padding`." ) @dataclass class VeOmniActorConfig(ActorConfig): """Configuration for VeOmni actor models. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: strategy (str): Training strategy set to 'veomni' for VeOmni parallelism. veomni (dict[str, Any]): Configuration for VeOmni settings. use_remove_padding (bool): Whether to remove padding tokens in inputs during training """ strategy: str = "veomni" veomni: VeOmniEngineConfig = field(default_factory=VeOmniEngineConfig) use_remove_padding: bool = False use_rollout_log_probs: bool = False def __post_init__(self): """Validate VeOmni actor configuration parameters.""" super().__post_init__() self.engine = self.veomni @dataclass class TorchTitanActorConfig(ActorConfig): """Configuration for TorchTitan actor models. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: strategy (str): Training strategy set to 'torchtitan' for TorchTitan parallelism. torchtitan (TorchtitanEngineConfig): Configuration for TorchTitan engine settings. use_remove_padding (bool): Whether to remove padding tokens in inputs during training use_rollout_log_probs (bool): Whether to use log probabilities from rollout engine """ strategy: str = "torchtitan" torchtitan: TorchtitanEngineConfig = field(default_factory=TorchtitanEngineConfig) use_remove_padding: bool = False use_rollout_log_probs: bool = False def __post_init__(self): """Validate TorchTitan actor configuration parameters.""" super().__post_init__() self.engine = self.torchtitan ================================================ FILE: verl/workers/config/critic.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from dataclasses import dataclass, field from typing import Optional from omegaconf import MISSING from verl.base_config import BaseConfig from verl.trainer.config import BaseModelConfig, CheckpointConfig from verl.utils.profiler import ProfilerConfig from .engine import FSDPEngineConfig, McoreEngineConfig, TorchtitanEngineConfig from .model import HFModelConfig from .optimizer import OptimizerConfig __all__ = ["CriticConfig", "FSDPCriticConfig", "McoreCriticConfig", "TorchTitanCriticConfig", "FSDPCriticModelCfg"] @dataclass class CriticConfig(BaseConfig): """Configuration for critic model training. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: strategy (str): Strategy used for critic model training (fsdp, fsdp2, megatron). ppo_micro_batch_size_per_gpu (int): Local per-GPU micro batch size. rollout_n (int): Number of rollouts per update (mirrors actor rollout_n). optim (Dict[str, Any]): Optimizer configuration including lr, weight_decay, etc. model (Dict[str, Any]): Model configuration including path, tokenizer_path, etc. ppo_mini_batch_size (int): PPO mini-batch size per update. ppo_micro_batch_size (Optional[int]): Global micro batch size (deprecated). use_dynamic_bsz (bool): Whether to automatically adjust batch size at runtime. ppo_max_token_len_per_gpu (int): Max tokens per GPU in one PPO batch. forward_max_token_len_per_gpu (int): Max token length per GPU in forward pass. ppo_epochs (int): Number of PPO epochs per batch. shuffle (bool): Shuffle training data across PPO epochs. cliprange_value (float): PPO value function clipping range. loss_agg_mode (str): Loss aggregation mode. checkpoint (Dict[str, Any]): Checkpoint configuration. profiler (Dict[str, Any]): Profiler configuration. enable (Optional[bool]): Whether to enable the critic. """ _mutable_fields = BaseConfig._mutable_fields | { "ppo_micro_batch_size_per_gpu", "ppo_mini_batch_size", "ppo_micro_batch_size", "model_config", } strategy: str = MISSING ppo_micro_batch_size_per_gpu: Optional[int] = None enable: Optional[bool] = None rollout_n: int = 1 ppo_mini_batch_size: int = 1 use_dynamic_bsz: bool = False ppo_max_token_len_per_gpu: int = 32768 # deprecate this forward_max_token_len_per_gpu: int = 32768 ppo_infer_micro_batch_size_per_gpu: Optional[int] = None ppo_infer_max_token_len_per_gpu: int = 32768 ppo_epochs: int = 1 data_loader_seed: int = 1 shuffle: bool = True cliprange_value: float = 0.5 loss_agg_mode: str = "token-mean" ppo_micro_batch_size: Optional[int] = None engine: BaseConfig = field(default_factory=BaseConfig) optim: OptimizerConfig = field(default_factory=OptimizerConfig) # deprecate model to favor model_config model: BaseModelConfig = field(default_factory=BaseModelConfig) model_config: HFModelConfig = None checkpoint: CheckpointConfig = field(default_factory=CheckpointConfig) profiler: ProfilerConfig = field(default_factory=ProfilerConfig) def __post_init__(self): """Validate critic configuration parameters.""" assert self.strategy != MISSING if self.model_config is None: warnings.warn("using model in Critic Config is deprecated, please use model_config instead", stacklevel=2) self.model_config = HFModelConfig( path=self.model.path, tokenizer_path=self.model.tokenizer_path, override_config=self.model.override_config, external_lib=self.model.external_lib, trust_remote_code=self.model.trust_remote_code, ) if not self.use_dynamic_bsz: self._check_mutually_exclusive(self.ppo_micro_batch_size, self.ppo_micro_batch_size_per_gpu, "critic") if self.ppo_micro_batch_size is not None: if self.ppo_mini_batch_size % self.ppo_micro_batch_size != 0: raise ValueError( f"[critic] ppo_mini_batch_size ({self.ppo_mini_batch_size}) must be divisible by " f"ppo_micro_batch_size ({self.ppo_micro_batch_size})" ) def validate(self, n_gpus: int, train_batch_size: int): """Validate critic configuration with runtime parameters. Args: n_gpus: Total number of GPUs available train_batch_size: Training batch size from data config """ if not self.use_dynamic_bsz: if train_batch_size < self.ppo_mini_batch_size: raise ValueError( f"train_batch_size ({train_batch_size}) must be >= " f"critic.ppo_mini_batch_size ({self.ppo_mini_batch_size})" ) @staticmethod def _check_mutually_exclusive(mbs, mbs_per_gpu, name: str): """Validate mutually exclusive micro batch size configuration options. Ensures that users don't set both deprecated micro_batch_size and the new micro_batch_size_per_gpu parameters simultaneously. Args: mbs: Deprecated micro batch size parameter value. mbs_per_gpu: New micro batch size per GPU parameter value. name (str): Configuration section name for error messages. Raises: ValueError: If both parameters are set or neither is set. """ param = "micro_batch_size" param_per_gpu = f"{param}_per_gpu" if mbs is None and mbs_per_gpu is None: raise ValueError(f"[{name}] Please set at least one of '{name}.{param}' or '{name}.{param_per_gpu}'.") if mbs is not None and mbs_per_gpu is not None: raise ValueError( f"[{name}] You have set both '{name}.{param}' AND '{name}.{param_per_gpu}'. Please remove " f"'{name}.{param}' because only '*_{param_per_gpu}' is supported (the former is deprecated)." ) @dataclass class McoreCriticConfig(CriticConfig): """Configuration for Megatron-based critic model training. The inheritance from CriticConfig provides all base critic configuration plus Megatron-specific settings. Args: nccl_timeout (int): NCCL timeout in seconds for distributed operations. megatron (Dict[str, Any]): Megatron-specific parallelism settings. load_weight (bool): Whether to load initial weights. """ strategy: str = "megatron" nccl_timeout: int = 600 megatron: McoreEngineConfig = field(default_factory=McoreEngineConfig) load_weight: bool = True def validate(self, n_gpus: int, train_batch_size: int): """Validate Megatron critic configuration with runtime parameters.""" super().validate(n_gpus, train_batch_size) @dataclass class FSDPCriticConfig(CriticConfig): """Configuration for FSDP-based critic model training. The inheritance from CriticConfig provides all base critic configuration plus FSDP-specific settings. Args: forward_micro_batch_size (int): Forward-only batch size during inference (global). forward_micro_batch_size_per_gpu (int): Forward-only batch size during inference (per GPU). ulysses_sequence_parallel_size (int): [DEPRECATED] Ulysses sequence parallel size for long sequences. grad_clip (float): Gradient clipping for critic updates. """ _mutable_fields = CriticConfig._mutable_fields | { "forward_micro_batch_size", "forward_micro_batch_size_per_gpu", } strategy: str = "fsdp" forward_micro_batch_size: int = 1 forward_micro_batch_size_per_gpu: int = 1 ulysses_sequence_parallel_size: int = 1 grad_clip: float = 1.0 def __post_init__(self): """Validate FSDP critic configuration parameters.""" super().__post_init__() if self.strategy in {"fsdp", "fsdp2"}: if self.ulysses_sequence_parallel_size > 1: if not self.model.get("use_remove_padding", False): raise ValueError( "When using sequence parallelism for critic, you must enable `use_remove_padding`." ) def validate(self, n_gpus: int, train_batch_size: int): """Validate FSDP critic configuration with runtime parameters.""" super().validate(n_gpus, train_batch_size) if not self.use_dynamic_bsz: sp_size = self.ulysses_sequence_parallel_size if self.ppo_micro_batch_size is not None: if self.ppo_micro_batch_size * sp_size < n_gpus: raise ValueError( f"critic.ppo_micro_batch_size ({self.ppo_micro_batch_size}) * " f"ulysses_sequence_parallel_size ({sp_size}) must be >= n_gpus ({n_gpus})" ) @dataclass class TorchTitanCriticConfig(CriticConfig): """Configuration for TorchTitan-based critic model training. The inheritance from CriticConfig provides all base critic configuration plus TorchTitan-specific settings. Args: strategy (str): Training strategy set to 'torchtitan' for TorchTitan parallelism. torchtitan (TorchtitanEngineConfig): Configuration for TorchTitan engine settings. """ strategy: str = "torchtitan" torchtitan: TorchtitanEngineConfig = field(default_factory=TorchtitanEngineConfig) def __post_init__(self): """Validate TorchTitan critic configuration parameters.""" super().__post_init__() self.engine = self.torchtitan @dataclass class FSDPCriticModelCfg(BaseModelConfig): """FSDP-enabled critic model configuration. Inherits base critic settings and adds distributed-memory and LoRA options. Args: use_shm (bool): Whether to use shared memory for loading the model. enable_activation_offload (bool): Offload activations to CPU to reduce GPU memory usage. use_remove_padding (bool): Use remove-padding optimization (saves compute). enable_gradient_checkpointing (bool): Enable gradient checkpointing for memory efficiency. fsdp_config (FSDPEngineConfig): FSDP-specific configuration block. lora_rank (int): Set to positive value to enable LoRA (e.g., 32). lora_alpha (int): LoRA scaling factor. target_modules (Union[str, List[str]]): LoRA target modules: "all-linear" or list of layer names. """ use_shm: bool = False enable_activation_offload: bool = False use_remove_padding: bool = False enable_gradient_checkpointing: bool = True fsdp_config: FSDPEngineConfig = field(default_factory=FSDPEngineConfig) lora_rank: int = 0 lora_alpha: int = 16 target_modules: str | list[str] = "all-linear" # TiledMLP configuration for memory-efficient MLP computation tiled_mlp: dict = field(default_factory=lambda: {"enabled": False, "num_shards": 4}) ================================================ FILE: verl/workers/config/engine.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from dataclasses import dataclass, field from typing import Any, Callable, Literal, Optional from verl.base_config import BaseConfig from verl.trainer.config import CheckpointConfig from ...utils.profiler import ProfilerConfig from .model import HFModelConfig from .optimizer import OptimizerConfig __all__ = [ "FSDPEngineConfig", "McoreEngineConfig", "TrainingWorkerConfig", "TorchtitanEngineConfig", "VeOmniEngineConfig", "AutomodelEngineConfig", "EngineConfig", "EngineRouterReplayConfig", "QATEngineConfig", ] # TODO: rename to RouterReplayConfig after removing the legacy implementation @dataclass class EngineRouterReplayConfig(BaseConfig): """Configuration for router replay in MoE models. This configuration controls the routing behavior for Mixture of Experts (MoE) models, allowing for deterministic training through route recording and replay. Args: mode (str): Router replay mode. Options: 'disabled', 'R2', 'R3'. - 'disabled': No router replay functionality - 'R2': Use Router Replay routing strategy - 'R3': Use Rollout Router Replay routing strategy record_file (Optional[str]): File path to save recorded routing decisions. Required when mode is 'record', 'R2', or 'R3'. replay_file (Optional[str]): File path to load recorded routing decisions for replay. Required when mode is 'replay'. """ mode: str = "disabled" record_file: Optional[str] = None replay_file: Optional[str] = None def __post_init__(self): """Validate router replay configuration.""" valid_modes = ["disabled", "R2", "R3"] if self.mode not in valid_modes: raise ValueError(f"Invalid router_replay mode: {self.mode}. Must be one of {valid_modes}") @dataclass class EngineConfig(BaseConfig): _mutable_fields = BaseConfig._mutable_fields | { "use_dynamic_bsz", "max_token_len_per_gpu", "micro_batch_size_per_gpu", "infer_max_token_len_per_gpu", "infer_micro_batch_size_per_gpu", "use_fused_kernels", "use_remove_padding", "forward_only", "param_offload", } # whether to offload param param_offload: bool = False # whether to offload optimizer optimizer_offload: bool = False # whether to offload grad grad_offload: bool = False # whether the engine is forward only (e.g., ref policy) forward_only: bool = False # the strategy (backend) strategy: str = None # model dtype dtype: str = "bfloat16" # ["bfloat16", "float16"] # whether to use dynamic bsz use_dynamic_bsz: bool = True # for training max_token_len_per_gpu: int = None micro_batch_size_per_gpu: int = None # for inference infer_max_token_len_per_gpu: int = None infer_micro_batch_size_per_gpu: int = None # whether use fuse lm head kernel use_fused_kernels: bool = False # TODO (this may conflict with the one in model config) use_remove_padding: bool = True seed: int = 42 full_determinism: bool = False router_replay: EngineRouterReplayConfig = field(default_factory=EngineRouterReplayConfig) def __post_init__(self): pass # TODO: turn on this check after we reorg config # if self.use_dynamic_bsz: # assert self.max_token_len_per_gpu is not None # else: # assert self.micro_batch_size_per_gpu is not None @dataclass class McoreEngineConfig(EngineConfig): """Configuration for Megatron parallelism. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: param_offload (bool): Whether to offload parameters to CPU. grad_offload (bool): Whether to offload gradients to CPU. optimizer_offload (bool): Whether to offload optimizer states to CPU. tensor_model_parallel_size (int): Tensor model parallel size. expert_model_parallel_size (int): Expert model parallel size for MoE models. expert_tensor_parallel_size (Optional[int]): Expert tensor parallel size for MoE models. pipeline_model_parallel_size (int): Pipeline model parallel size. virtual_pipeline_model_parallel_size (Optional[int]): Virtual pipeline model parallel size for interleaved scheduling. context_parallel_size (int): Context parallel size for long sequences. sequence_parallel (bool): Whether to enable sequence parallelism. use_distributed_optimizer (bool): Whether to use distributed optimizer. use_dist_checkpointing (bool): Whether to use distributed checkpointing. dist_checkpointing_path (Optional[str]): Path for distributed checkpointing. dist_ckpt_optim_fully_reshardable (bool): Use fully reshardable optimizer checkpoints. distrib_optim_fully_reshardable_mem_efficient (bool): Use memory-efficient fully reshardable format. seed (int): Random seed for reproducibility. override_ddp_config (dict[str, Any]): Override configuration for DDP. override_transformer_config (dict[str, Any]): Override configuration for transformer. use_mbridge (bool): Whether to use MBridge for communication. dtype (str): Mixed precision training param dtype, default "bfloat16" """ # sequence_parallel is not listed as a frozen field for auto-correction purpose _mutable_fields = EngineConfig._mutable_fields | {"sequence_parallel"} # mcore parallelism tensor_model_parallel_size: int = 1 expert_model_parallel_size: int = 1 expert_tensor_parallel_size: Optional[int] = None pipeline_model_parallel_size: int = 1 virtual_pipeline_model_parallel_size: Optional[int] = None context_parallel_size: int = 1 sequence_parallel: bool = True use_distributed_optimizer: bool = True use_dist_checkpointing: bool = False dist_checkpointing_path: Optional[str] = None dist_checkpointing_prefix: str = "" dist_ckpt_optim_fully_reshardable: bool = False distrib_optim_fully_reshardable_mem_efficient: bool = False override_ddp_config: dict[str, Any] = field(default_factory=dict) override_transformer_config: dict[str, Any] = field(default_factory=dict) override_mcore_model_config: dict[str, Any] = field(default_factory=dict) use_mbridge: bool = True vanilla_mbridge: bool = True strategy: str = "megatron" def __post_init__(self) -> None: super().__post_init__() """config validation logics go here""" assert self.strategy == "megatron" assert self.dtype in ["bfloat16", "float16"], f"dtype {self.dtype} not supported" if self.tensor_model_parallel_size == 1: warnings.warn("set sequence parallel to false as TP size is 1", stacklevel=2) self.sequence_parallel = False @dataclass class QATEngineConfig(BaseConfig): """Configuration for QAT (Quantization-Aware Training) within an engine. Args: enable (bool): Whether to enable QAT, default False mode (str): Quantization mode, "w4a16" or "w4a4", default "w4a16" group_size (int): Group size for blockwise quantization, default 16 ignore_patterns (list[str]): Module name patterns to exclude from quantization activation_observer (str): Observer strategy for activation global_scale (W4A4 only) quantization_config_path (Optional[str]): Path to quantization config JSON for vLLM """ enable: bool = False mode: str = "w4a16" group_size: int = 16 ignore_patterns: list[str] = field(default_factory=lambda: ["lm_head", "embed_tokens", "re:.*mlp.gate$"]) activation_observer: str = "static_minmax" quantization_config_path: Optional[str] = None @dataclass class FSDPEngineConfig(EngineConfig): """Configuration for FSDP (Fully Sharded Data Parallel). The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: wrap_policy (Dict[str, Any]): Configuration for FSDP wrap policy. param_offload (bool): Whether to offload parameters to CPU, default False optimizer_offload (bool): Whether to offload optimizer states to CPU, default False offload_policy (bool): Whether to offload policy model parameters, default False reshard_after_forward (bool): Whether to reshard parameters after forward pass, default True fsdp_size (int): FSDP group size. -1 means use all available GPUs. forward_prefetch (bool): Whether to prefetch parameters for next forward pass, default False model_dtype (str): Model data type used to initialize the transformers model. default "fp32" use_orig_params (bool): Whether to use original parameters when initialize FSDP1, default False seed (int): Random seed for reproducibility. full_determinism (bool): If true, enable_full_determinism is called to ensure reproducible results in distributed training. Important: this will negatively impact performance, so only use it for debugging. mixed_precision (Optional[dict[str, Any]]): Mixed precision configuration for FSDP, default None dtype (str): Mixed precision training param dtype, default "bfloat16" qat (QATEngineConfig): QAT configuration, default disabled """ # ulysses_sequence_parallel_size is mutable for backward compatibility _mutable_fields = EngineConfig._mutable_fields | {"ulysses_sequence_parallel_size"} # fsdp specific flags wrap_policy: dict[str, Any] = field(default_factory=dict) offload_policy: bool = False reshard_after_forward: bool = True fsdp_size: int = -1 forward_prefetch: bool = False model_dtype: str = "fp32" use_orig_params: bool = False mixed_precision: Optional[dict[str, Any]] = None ulysses_sequence_parallel_size: int = 1 entropy_from_logits_with_chunking: bool = False use_torch_compile: bool = True entropy_checkpointing: bool = False strategy: str = "fsdp" qat: QATEngineConfig = field(default_factory=QATEngineConfig) def __post_init__(self): super().__post_init__() assert self.strategy in ["fsdp", "fsdp2"], f"strategy {self.strategy} not supported" @dataclass class VeOmniEngineConfig(EngineConfig): """Configuration for VeOmni. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: wrap_policy (Dict[str, Any]): Configuration for FSDP wrap policy. param_offload (bool): Whether to offload parameters to CPU, default False optimizer_offload (bool): Whether to offload optimizer states to CPU, default False offload_policy (bool): Whether to offload policy model parameters, default False reshard_after_forward (bool): Whether to reshard parameters after forward pass, default True fsdp_size (int): FSDP group size. -1 means use all available GPUs, default -1 ulysses_parallel_size (int): Ulysses sequence parallel size, default 1 expert_parallel_size (int): Expert parallel size, default 1 init_device (str): Device to initialize model weights. 1. `cpu`: Init parameters on CPU in rank0 only. 2. `cuda`: Init parameters on GPU. 3. `meta`: Init parameters on meta. 4. `npu`: Init parameters on Ascend NPU. default "meta" enable_full_shard (bool): Enable fully shard for FSDP training (ZeRO-3), default False enable_fsdp_offload (bool): Enable CPU offload for FSDP1, default False enable_reentrant (bool): Use reentrant gradient checkpointing, default False attn_implementation (str): Attention implementation to use. 1. `eager` 2. `sdpa` 3. `flash_attention_2` 4. `flash_attention_3` 5. `veomni_flash_attention_2_with_sp` 6. `veomni_flash_attention_3_with_sp` 7. `native-sparse` default "flash_attention_2" Note: In case VeOmni add more attn_implementation, please check https://github.com/ByteDance-Seed/VeOmni/ moe_implementation (str): MoE implementation to use. 1. `eager` 2. `fused` default "fused" Note: In case VeOmni add more moe_implementation, please check https://github.com/ByteDance-Seed/VeOmni/ force_use_huggingface (bool): Force loading model from huggingface, default False activation_gpu_limit (float): When enabling activation offload, `activation_gpu_limit` GB activations are allowed to reserve on GPU, default 0.0 basic_modules (list[str]): List of basic modules to use, default None forward_prefetch (bool): Whether to prefetch parameters for next forward pass, default False model_dtype (str): Model data type used to initialize the transformers model. default "fp32" use_orig_params (bool): Whether to use original parameters when initialize FSDP1, default False seed (int): Random seed for reproducibility. full_determinism (bool): If true, enable_full_determinism is called to ensure reproducible results in distributed training. Important: this will negatively impact performance, so only use it for debugging. mixed_precision (Optional[dict[str, Any]]): Mixed precision configuration for FSDP, default None """ wrap_policy: dict[str, Any] = field(default_factory=dict) offload_policy: bool = False reshard_after_forward: bool = True forward_prefetch: bool = False use_orig_params: bool = False entropy_from_logits_with_chunking: bool = False use_torch_compile: bool = True entropy_checkpointing: bool = False strategy: str = "veomni" fsdp_size: int = -1 ulysses_parallel_size: int = 1 expert_parallel_size: int = 1 seed: int = 42 full_determinism: bool = False mixed_precision: bool = False init_device: str = "meta" enable_full_shard: bool = False ckpt_manager: Literal["dcp"] = "dcp" load_checkpoint_path: Optional[str] = None enable_fsdp_offload: bool = False enable_reentrant: bool = False attn_implementation: str = "flash_attention_2" moe_implementation: str = "fused" force_use_huggingface: bool = False activation_gpu_limit: float = 0.0 basic_modules: Optional[list[str]] = field(default_factory=list) def __post_init__(self): super().__post_init__() assert self.strategy in ["veomni"], f"strategy {self.strategy} not supported" @dataclass class TorchtitanEngineConfig(EngineConfig): """Configuration for Torchtitan. The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config. Args: wrap_policy (Dict[str, Any]): Configuration for FSDP wrap policy. reshard_after_forward (Literal["default", "always", "never"]): The policy for applying `reshard_after_forward` within an FSDP setup, default "default" forward_prefetch (bool): Whether to prefetch parameters for next forward pass, default False use_orig_params (bool): Whether to use original parameters when initialize FSDP, default False mixed_precision (bool): Mixed precision configuration for FSDP, default False offload_policy (bool): Whether to offload policy model parameters, default False data_parallel_size (int): Data parallel group size, default 1 data_parallel_replicate_size (int): Data parallel replicate size, default 1 data_parallel_shard_size (int): Data parallel shard degree, default 1 tensor_parallel_size (int): Tensor parallel size, default 1 expert_parallel_size (int): Expert parallel size, default 1 expert_tensor_parallel_size (int): Expert tensor parallel size, default 1 pipeline_parallel_size (int): Pipeline parallel size, default 1 context_parallel_size (int): Context parallel size, default 1 attn_type (str): Attention type for torchtitan's model (e.g., "sdpa", "flex", "varlen"), default "flex" strategy (str): Strategy to use for distributed training, default "torchtitan" seed (int): Random seed for reproducibility. full_determinism (bool): If true, enable_full_determinism is called to ensure reproducible results in distributed training. Important: this will negatively impact performance, so only use it for debugging. """ wrap_policy: dict[str, Any] = field(default_factory=dict) reshard_after_forward: Literal["default", "always", "never"] = "default" forward_prefetch: bool = False use_orig_params: bool = False mixed_precision: bool = False offload_policy: bool = False use_torch_compile: bool = True entropy_from_logits_with_chunking: bool = False entropy_checkpointing: bool = False data_parallel_size: int = 1 data_parallel_replicate_size: int = 1 data_parallel_shard_size: int = 1 tensor_parallel_size: int = 1 expert_parallel_size: int = 1 expert_tensor_parallel_size: int = 1 pipeline_parallel_size: int = 1 context_parallel_size: int = 1 attn_type: str = "flex" max_seq_len: Optional[int] = None strategy: str = "torchtitan" seed: int = 42 full_determinism: bool = False def __post_init__(self): super().__post_init__() assert self.strategy in ["torchtitan"], f"strategy {self.strategy} not supported" @dataclass class AutomodelEngineConfig(EngineConfig): """Configuration for Automodel (nemo_automodel) backend. The Automodel backend uses NeMoAutoModelForCausalLM for model loading and supports FSDP2, MegatronFSDP, and DDP distributed strategies with optional TP, CP, and EP parallelism. Args: strategy (str): Backend strategy identifier, must be "automodel". distributed_strategy (str): Distributed training strategy: "fsdp2", "megatron_fsdp", or "ddp". tp_size (int): Tensor parallel size. pp_size (int): Pipeline parallel size (only pp_size=1 supported initially). cp_size (int): Context parallel size. ep_size (int): Expert parallel size for MoE models. dp_replicate_size (int): Data-parallel replicate size for HSDP. 1 = pure sharding. sequence_parallel (bool): Enable sequence parallelism in the TP plan. defer_fsdp_grad_sync (bool): Defer FSDP gradient sync to the final micro-batch. activation_checkpointing (bool): Whether to enable activation checkpointing. enable_fp8 (bool): Whether to enable FP8 training. enable_compile (bool): Whether to enable torch.compile for the model. model_dtype (str): Model data type for loading weights. "fp32" loads in float32 (matching FSDP golden), "auto" uses the dtype from the model config. attn_implementation (str): Attention implementation to use ("sdpa", "flash_attention_2", "eager", "te"). Backend settings (nemo_automodel BackendConfig): backend_config (dict): Dict of kwargs passed directly to nemo_automodel.components.models.common.BackendConfig(**backend_config). Controls how model layers are implemented (TE vs PyTorch) and MoE dispatch. See automodel.yaml for all predefined keys with defaults. Key fields: attn (str): Attention backend. "te" = TransformerEngine fused attention, "sdpa" = PyTorch scaled dot-product attention. Default: "sdpa". linear (str): Linear layer backend. "te" = TE fused linear (with FP8 support), "torch" = standard PyTorch linear. Default: "te". rms_norm (str): RMSNorm backend. "te" = TE fused RMSNorm, "torch" = PyTorch, "torch_fp32" = PyTorch in FP32 (better numerical stability for MoE). Default: "torch_fp32". rope_fusion (bool): Enable fused RoPE kernel (requires CP=1). Default: true. experts (str): MoE expert computation backend. "gmm" = grouped_gemm (requires pip install grouped_gemm), "torch_mm" = torch._grouped_mm (no external dependency), "te" = TE GroupedLinear. Default: "gmm". dispatcher (str): MoE token dispatch strategy. "torch" = standard all-gather + local compute, "deepep" = DeepEP optimized all-to-all (higher throughput). Default: "torch". Note: "deepep" with experts="gmm" matches the legacy enable_deepep=True behavior. enable_fsdp_optimizations (bool): Enable FSDP-specific optimizations in Automodel. Default: false. enable_hf_state_dict_adapter (bool): Enable HuggingFace state dict adapter for checkpoint compatibility. Default: true. fake_balanced_gate (bool): Use fake balanced gating for debugging. Default: false. fake_gate_noise (float): Noise added to fake balanced gate. Default: 0.0. gate_precision: Gate computation precision. Default: null (auto). Full reference: nemo_automodel/components/models/common/backend_config.py MoE / Expert Parallelism settings: moe_config (dict): Dict of kwargs passed directly to nemo_automodel.components.moe.parallelizer.MoEParallelizerConfig(**moe_config). Controls MoE parallelization behavior within FSDP2. See automodel.yaml for all predefined keys with defaults. Key fields: ignore_router_for_ac (bool): Exclude router from activation checkpointing. Default: false. reshard_after_forward (bool): Reshard expert params after forward pass (trades compute for memory). Default: false. lm_head_precision: Precision for the LM head. Default: null (auto). wrap_outer_model (bool): Whether to FSDP-wrap the outermost model module. Default: true. Full reference: nemo_automodel/components/moe/parallelizer.py Mixed precision policy (FSDP2): mp_param_dtype (str): Parameter dtype for FSDP2 mixed precision policy. mp_reduce_dtype (str): Reduce dtype for FSDP2 mixed precision policy. mp_output_dtype (str): Output dtype for FSDP2 mixed precision policy. Entropy computation: entropy_from_logits_with_chunking (bool): Whether to use chunked entropy computation. use_torch_compile (bool): Whether to use torch.compile for entropy computation. entropy_checkpointing (bool): Whether to use checkpointing for entropy computation. """ strategy: str = "automodel" distributed_strategy: str = "fsdp2" # Parallelism sizes tp_size: int = 1 pp_size: int = 1 cp_size: int = 1 ep_size: int = 1 dp_replicate_size: int = 1 sequence_parallel: bool = False defer_fsdp_grad_sync: bool = True # Model settings activation_checkpointing: bool = False enable_fp8: bool = False enable_compile: bool = False model_dtype: str = "fp32" attn_implementation: str = "flash_attention_2" # Backend settings backend_config: dict = field(default_factory=dict) # MoE settings moe_config: dict = field(default_factory=dict) # Mixed precision policy mp_param_dtype: str = "bf16" mp_reduce_dtype: str = "fp32" mp_output_dtype: str = "bf16" # Entropy computation entropy_from_logits_with_chunking: bool = False use_torch_compile: bool = True entropy_checkpointing: bool = False def __post_init__(self): super().__post_init__() assert self.strategy == "automodel", f"strategy must be 'automodel', got {self.strategy}" assert self.distributed_strategy in ["fsdp2", "megatron_fsdp", "ddp"], ( f"distributed_strategy {self.distributed_strategy} not supported" ) assert self.pp_size == 1, "Pipeline parallelism (pp_size > 1) is not yet supported for automodel backend" @dataclass class TrainingWorkerConfig(BaseConfig): model_type: str = None # model type (language_model/value_model) model_config: HFModelConfig = None engine_config: EngineConfig = None optimizer_config: OptimizerConfig = None checkpoint_config: CheckpointConfig = None profiler_config: ProfilerConfig = None # automatically select engine and optimizer function. # This function takes model config and the device name as parameter. # Users can pass in a higher-order function to take more parameters auto_select_engine_optim_fn: Callable[["HFModelConfig", str], tuple["EngineConfig", "OptimizerConfig"]] = None ================================================ FILE: verl/workers/config/megatron_peft.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PEFT configuration of Megatron for VERL.""" def get_peft_cls(model_config, bridge, provider, dtype=None): """Get PEFT class from model config. Args: model_config: Model configuration object. bridge: Megatron-Bridge AutoBridge instance. provider: Provider instance. Returns: PEFT configuration object (LoRAConfig, CanonicalLoRAConfig, DoRAConfig) or None. """ peft_cls = None if not hasattr(model_config, "lora"): return peft_cls lora_cfg = model_config.lora # Only enable if rank > 0 if lora_cfg.get("rank", 0) <= 0: return peft_cls assert bridge is not None and provider is not None, "LoRA/PEFT only supported via Megatron-Bridge" from verl.models.mcore.bridge import CanonicalLoRA, DoRA, LoRA, VLMLoRA lora_dtype = lora_cfg.get("dtype", dtype) if lora_dtype is not None: from verl.utils.torch_dtypes import PrecisionType lora_dtype = PrecisionType.to_dtype(lora_dtype) lora_type = lora_cfg.get("type", "lora") if lora_type == "lora": peft_cls = LoRA( target_modules=lora_cfg.get("target_modules", ["linear_qkv", "linear_proj", "linear_fc1", "linear_fc2"]), dim=lora_cfg.get("rank"), alpha=lora_cfg.get("alpha", 32), dropout=lora_cfg.get("dropout", 0.0), dropout_position=lora_cfg.get("dropout_position", "pre"), lora_A_init_method=lora_cfg.get("lora_A_init_method", "xavier"), lora_B_init_method=lora_cfg.get("lora_B_init_method", "zero"), a2a_experimental=lora_cfg.get("a2a_experimental", False), lora_dtype=lora_dtype, exclude_modules=lora_cfg.get("exclude_modules", []), ) if lora_type == "vlm_lora": peft_cls = VLMLoRA( target_modules=lora_cfg.get("target_modules", ["linear_qkv", "linear_proj", "linear_fc1", "linear_fc2"]), dim=lora_cfg.get("rank"), alpha=lora_cfg.get("alpha", 32), dropout=lora_cfg.get("dropout", 0.0), dropout_position=lora_cfg.get("dropout_position", "pre"), lora_A_init_method=lora_cfg.get("lora_A_init_method", "xavier"), lora_B_init_method=lora_cfg.get("lora_B_init_method", "zero"), a2a_experimental=lora_cfg.get("a2a_experimental", False), lora_dtype=lora_dtype, freeze_vision_model=lora_cfg.get("freeze_vision_model", True), freeze_vision_projection=lora_cfg.get("freeze_vision_projection", True), freeze_language_model=lora_cfg.get("freeze_language_model", True), exclude_modules=lora_cfg.get("exclude_modules", []), ) elif lora_type == "canonical_lora": peft_cls = CanonicalLoRA( target_modules=lora_cfg.get( "target_modules", [ "linear_q", "linear_k", "linear_v", "linear_proj", "linear_fc1_up", "linear_fc1_gate", "linear_fc2", ], ), dim=lora_cfg.get("rank"), alpha=lora_cfg.get("alpha", 32), dropout=lora_cfg.get("dropout", 0.0), dropout_position=lora_cfg.get("dropout_position", "pre"), lora_A_init_method=lora_cfg.get("lora_A_init_method", "xavier"), lora_B_init_method=lora_cfg.get("lora_B_init_method", "zero"), exclude_modules=lora_cfg.get("exclude_modules", []), ) elif lora_type == "dora": peft_cls = DoRA( target_modules=lora_cfg.get("target_modules", ["linear_qkv", "linear_proj", "linear_fc1", "linear_fc2"]), dim=lora_cfg.get("rank"), alpha=lora_cfg.get("alpha", 32), dropout=lora_cfg.get("dropout", 0.0), dropout_position=lora_cfg.get("dropout_position", "pre"), lora_A_init_method=lora_cfg.get("lora_A_init_method", "xavier"), lora_B_init_method=lora_cfg.get("lora_B_init_method", "zero"), exclude_modules=lora_cfg.get("exclude_modules", []), ) print( f"Enabling {lora_type.upper()} with rank={lora_cfg.get('rank')}, " f"alpha={lora_cfg.get('alpha')}, dropout={lora_cfg.get('dropout')}" ) return peft_cls __all__ = [ "get_peft_cls", ] ================================================ FILE: verl/workers/config/model.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass, field from typing import Any, Optional from omegaconf import MISSING from transformers import AutoConfig from verl.base_config import BaseConfig from verl.utils import hf_processor, hf_tokenizer from verl.utils.fs import copy_to_local from verl.utils.import_utils import import_external_libs from verl.utils.model import get_generation_config, update_model_config __all__ = ["HFModelConfig", "MtpConfig"] @dataclass class MtpConfig(BaseConfig): """ Configuration for MTP model. enable: Enable loading and saving of MTP parameters, but do not use them enable_train: Whether to enable using MTP parameters during training enable_rollout: Whether to enable using MTP parameters during rollout Training parameters: detach_encoder: Whether to detach encoder parameters during MTP training mtp_loss_scaling_factor: Loss scaling factor during MTP training vLLM rollout parameters: method: "mtp" num-speculative-tokens: 1 SGLang rollout parameters: speculative-algorithm: EAGLE speculative-num-steps: 3 speculative-eagle-topk: 1 speculative-num-draft-tokens: 4 """ enable: bool = False enable_train: bool = False enable_rollout: bool = False detach_encoder: bool = False mtp_loss_scaling_factor: float = 0.1 speculative_algorithm: str = "EAGLE" speculative_num_steps: int = 3 speculative_eagle_topk: int = 1 speculative_num_draft_tokens: int = 4 method: str = "mtp" num_speculative_tokens: int = 1 @dataclass class HFModelConfig(BaseConfig): # note that we separate model_path, model_config_path and tokenizer_path in case they are different _mutable_fields = { "hf_config_path", "tokenizer_path", "hf_config", "generation_config", "tokenizer", "processor", "local_path", "architectures", "local_hf_config_path", "local_tokenizer_path", "mtp", } path: str = MISSING local_path: Optional[str] = None hf_config_path: Optional[str] = None local_hf_config_path: Optional[str] = None tokenizer_path: Optional[str] = None local_tokenizer_path: Optional[str] = None # whether to load tokenizer. This is useful when we only want to load model config load_tokenizer: bool = True hf_config: Any = None generation_config: Any = None tokenizer: Any = None processor: Any = None # whether to use shared memory use_shm: bool = False trust_remote_code: bool = False # custom chat template for the model custom_chat_template: Optional[str] = None external_lib: Optional[str] = None override_config: dict = field(default_factory=dict) enable_gradient_checkpointing: bool = True enable_activation_offload: bool = False use_remove_padding: bool = True # TODO: unify fsdp and megatron lora config # fsdp lora related. We may setup a separate config later lora_rank: int = 0 lora_alpha: int = 16 target_modules: Optional[Any] = "all-linear" # allow both "all-linear" and ["q_proj","k_proj"] target_parameters: Optional[list[str]] = None # for lora adapter on nn.Parameter exclude_modules: Optional[str] = None # megatron lora config lora: dict[str, Any] = field(default_factory=dict) # path to pre-trained LoRA adapter to load for continued training lora_adapter_path: Optional[str] = None use_liger: bool = False use_fused_kernels: bool = False fused_kernel_options: dict = field(default_factory=dict) # TiledMLP configuration for memory-efficient MLP computation tiled_mlp: dict = field(default_factory=lambda: {"enabled": False, "num_shards": 4}) architectures: Optional[list[str]] = None mtp: MtpConfig = field(default_factory=MtpConfig) def __post_init__(self): import_external_libs(self.external_lib) if self.hf_config_path is None: self.hf_config_path = self.path if self.tokenizer_path is None: self.tokenizer_path = self.path self.local_path = copy_to_local(self.path, use_shm=self.use_shm) # construct tokenizer if self.load_tokenizer: self.local_tokenizer_path = copy_to_local(self.tokenizer_path, use_shm=self.use_shm) self.tokenizer = hf_tokenizer(self.local_tokenizer_path, trust_remote_code=self.trust_remote_code) self.processor = hf_processor(self.local_tokenizer_path, trust_remote_code=self.trust_remote_code) # For base models (e.g. Qwen3.5-2b-Base), the processor may not have a chat_template # while the tokenizer does. Sync it so that processor.apply_chat_template() works. if ( self.processor is not None and not getattr(self.processor, "chat_template", None) and getattr(self.tokenizer, "chat_template", None) ): self.processor.chat_template = self.tokenizer.chat_template if self.custom_chat_template is not None: if self.processor is not None: self.processor.chat_template = self.custom_chat_template else: self.tokenizer.chat_template = self.custom_chat_template self.local_hf_config_path = copy_to_local(self.hf_config_path, use_shm=self.use_shm) self.generation_config = get_generation_config( self.local_hf_config_path, trust_remote_code=self.trust_remote_code ) # construct hf_config attn_implementation = self.override_config.get("attn_implementation", "flash_attention_2") self.hf_config = AutoConfig.from_pretrained( self.local_hf_config_path, trust_remote_code=self.trust_remote_code, attn_implementation=attn_implementation ) override_config_kwargs = {} if self.tokenizer is not None: override_config_kwargs.update( { "bos_token_id": self.tokenizer.bos_token_id, "eos_token_id": self.tokenizer.eos_token_id, "pad_token_id": self.tokenizer.pad_token_id, } ) # TODO: (vermouth1992). self.config.model in megatron differs from that of fsdp in the override_config. override_config = ( self.override_config["model_config"] if "model_config" in self.override_config else self.override_config ) override_config_kwargs.update(override_config) update_model_config(self.hf_config, override_config_kwargs=override_config_kwargs) self.share_embeddings_and_output_weights = getattr(self.hf_config, "tie_word_embeddings", False) # get model architectures self.architectures = getattr(self.hf_config, "architectures", None) assert self.architectures is not None and len(self.architectures) == 1, ( "Expect only one architecture, got {}".format(self.architectures) ) # per model patch if getattr(self.hf_config, "model_type", None) == "kimi_vl": self.hf_config.text_config.topk_method = "greedy" # Ensure target_modules is a str or list[str] (only if not None) if self.target_modules is not None: if not isinstance(self.target_modules, (str | list)): raise TypeError( "target_modules must be a string or a list of strings, " f"but got {type(self.target_modules).__name__}" ) if isinstance(self.target_modules, list): for x in self.target_modules: if not isinstance(x, str): raise TypeError( f"All elements in target_modules list must be strings, but found {type(x).__name__}" ) def get_processor(self): return self.processor if self.processor is not None else self.tokenizer ================================================ FILE: verl/workers/config/optimizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from dataclasses import dataclass from typing import Optional from omegaconf import MISSING from verl.base_config import BaseConfig __all__ = [ "OptimizerConfig", "FSDPOptimizerConfig", "McoreOptimizerConfig", "build_optimizer", "VeOmniOptimizerConfig", "TorchtitanOptimizerConfig", "AutomodelOptimizerConfig", ] @dataclass class OptimizerConfig(BaseConfig): """Base optimizer configuration. Args: lr (float): learning rate. Must be specified. lr_warmup_steps_ratio (float): Warmup steps ratio; total steps will be injected at runtime. total_training_steps (int): Total training steps (must be overridden at runtime). weight_decay (float): Weight decay factor. lr_warmup_steps (Optional[int]): Number of warmup steps; None delegates to lr_warmup_steps_ratio. """ _mutable_fields = {"clip_grad", "total_training_steps", "lr_warmup_steps"} lr: float = 1e-3 lr_warmup_steps_ratio: float = 0.0 total_training_steps: int = -1 weight_decay: float = 0.01 lr_warmup_steps: Optional[int] = -1 betas: tuple[float, float] = (0.9, 0.999) clip_grad: float = 1.0 # deprecate grad_clip grad_clip: Optional[float] = None def __post_init__(self): assert self.lr != MISSING if self.grad_clip is not None: warnings.warn("`grad_clip` is deprecated, use `clip_grad` instead.", DeprecationWarning, stacklevel=2) self.clip_grad = self.grad_clip @dataclass class VeOmniOptimizerConfig(OptimizerConfig): """VeOmni optimizer configuration extending base OptimizerConfig. Args: optimizer (str): Optimizer name; default is "adamw". lr (float): Learning rate. lr_min (float): Minimum learning rate. lr_start (float): Starting learning rate for warmup. lr_decay_ratio (float): LR decay ratio. lr_scheduler_type (str): LR scheduler type: "constant" or "cosine". """ _mutable_fields = OptimizerConfig._mutable_fields.copy() optimizer: str = "adamw" lr_min: float = 0.0 lr_start: float = 0.0 lr_decay_ratio: float = 1.0 lr_scheduler_type: str = "constant" override_optimizer_config: Optional[dict] = None @dataclass class FSDPOptimizerConfig(OptimizerConfig): """FSDP optimizer configuration extending base OptimizerConfig. Args: optimizer (str): Optimizer class name (e.g., "AdamW", "AdamW8bit", "_AdamW"). optimizer_impl (str): Module path to import optimizer from (e.g., "torch.optim", "torchao.optim", "bitsandbytes.optim"). lr (float): Learning rate. min_lr_ratio (Optional[float]): Minimum LR ratio for cosine schedule. lr_scheduler_type (str): LR scheduler type: "constant" or "cosine". num_cycles (float): Number of cosine cycles in LR schedule. zero_indexed_step (bool): Whether the LR schedule uses 0-indexed steps. If True (default), step counting starts at 0. If False, step counting starts at 1. """ _mutable_fields = OptimizerConfig._mutable_fields.copy() _mutable_fields.add("lr_scheduler_type") optimizer: str = "AdamW" optimizer_impl: str = "torch.optim" min_lr_ratio: Optional[float] = None # deprecate warmup_style warmup_style: Optional[str] = None lr_scheduler_type: str = "constant" num_cycles: float = 0.5 override_optimizer_config: Optional[dict] = None zero_indexed_step: bool = True def __post_init__(self): if self.warmup_style is not None: assert self.warmup_style in ["constant", "cosine"] warnings.warn( "`warmup_style` is deprecated, use `lr_scheduler_type` instead.", DeprecationWarning, stacklevel=2 ) self.lr_scheduler_type = self.warmup_style assert self.lr_scheduler_type in ["constant", "cosine"] return super().__post_init__() @dataclass class McoreOptimizerConfig(OptimizerConfig): """Mcore optimizer configuration extending base OptimizerConfig. Args: optimizer (str): Optimizer name; default is "adam". lr (float): Learning rate. clip_grad (float): Gradient clipping norm. lr_warmup_init (float): Initial learning rate for warmup; defaults to 0.0. lr_decay_steps (Optional[int]): Number of decay steps. lr_decay_style (str): LR decay style: "constant", "linear", "cosine", or "inverse_square_root". min_lr (float): Minimum learning rate. weight_decay_incr_style (str): Weight decay increment style: "constant" or "cosine". lr_wsd_decay_style (str): Weight-standard-deviation decay style: "constant", "exponential", or "cosine". lr_wsd_decay_steps (Optional[int]): Number of steps for weight-standard-deviation decay. use_checkpoint_opt_param_scheduler (bool): Whether to use checkpoint optimizer parameter scheduler. """ optimizer: str = "adam" lr_warmup_init: float = 0.0 lr_decay_steps: Optional[int] = None lr_decay_style: str = "linear" min_lr: float = 0.0 weight_decay_incr_style: str = "constant" lr_wsd_decay_style: str = "exponential" lr_wsd_decay_steps: Optional[int] = None use_checkpoint_opt_param_scheduler: bool = False override_optimizer_config: Optional[dict] = None @dataclass class TorchtitanOptimizerConfig(OptimizerConfig): """Torchtitan optimizer configuration extending base OptimizerConfig. Args: name (str): Optimizer name; default is "AdamW". eps (float): Epsilon value for AdamW optimizer, default 1e-8. decay_type (str): Weight decay type: "linear", "sqrt", or "cosine". min_lr_factor (float): Minimum learning rate factor. """ name: str = "AdamW" eps: float = 1e-8 decay_type: str = "linear" min_lr_factor: float = 0.0 @dataclass class AutomodelOptimizerConfig(OptimizerConfig): """Automodel optimizer configuration extending base OptimizerConfig. Uses the same optimizer building mechanism as FSDP (dynamic import from optimizer_impl). LR scheduling is handled by Automodel's OptimizerParamScheduler. Args: optimizer (str): Optimizer class name (e.g., "AdamW"). optimizer_impl (str): Module path to import optimizer from (e.g., "torch.optim"). lr (float): Learning rate (maps to max_lr in OptimizerParamScheduler). init_lr_ratio (Optional[float]): Initial LR ratio for warmup start (init_lr = lr * init_lr_ratio). min_lr_ratio (Optional[float]): Minimum LR ratio after decay (min_lr = lr * min_lr_ratio). lr_scheduler_type (str): LR decay style: "constant", "cosine", "linear", or "inverse-square-root". wd_incr_style (str): Weight decay increment style: "constant", "linear", or "cosine". num_cycles (float): Kept for backward compatibility (unused by Automodel scheduler). zero_indexed_step (bool): Kept for backward compatibility (unused by Automodel scheduler). """ _mutable_fields = OptimizerConfig._mutable_fields.copy() _mutable_fields.add("lr_scheduler_type") optimizer: str = "AdamW" optimizer_impl: str = "torch.optim" init_lr_ratio: Optional[float] = 0.1 min_lr_ratio: Optional[float] = 0.01 lr_scheduler_type: str = "cosine" wd_incr_style: str = "constant" num_cycles: float = 0.5 zero_indexed_step: bool = True # Common optimizer kwargs eps: float = 1e-8 master_weights: bool = False store_param_remainders: bool = False exp_avg_dtype: Optional[str] = None # "fp32", "bf16", "fp16", or "torch.float32" etc. exp_avg_sq_dtype: Optional[str] = None # "fp32", "bf16", "fp16", or "torch.float32" etc. master_weight_dtype: Optional[str] = None # "fp32", "bf16", "fp16", or "torch.float32" etc. override_optimizer_config: Optional[dict] = None def __post_init__(self): assert self.lr_scheduler_type in ["constant", "cosine", "linear", "inverse-square-root"] return super().__post_init__() def build_optimizer(parameters, config: FSDPOptimizerConfig): """Build an optimizer based on the configuration. Dynamically imports and instantiates an optimizer class from the specified module. Args: parameters: Model parameters to optimize config: FSDPOptimizerConfig with optimizer settings Returns: Optimizer instance Examples: # PyTorch AdamW config.optimizer_impl = "torch.optim" config.optimizer = "AdamW" # TorchAO AdamW with bf16 stochastic rounding config.optimizer_impl = "torchao.optim" config.optimizer = "_AdamW" config.override_optimizer_config = {"bf16_stochastic_round": True} # BitsAndBytes AdamW 8bit config.optimizer_impl = "bitsandbytes.optim" config.optimizer = "AdamW8bit" """ import importlib optimizer_args = { "lr": config.lr, "weight_decay": config.weight_decay, } optimizer_name_lower = config.optimizer.lower() if "adam" in optimizer_name_lower or "ademamix" in optimizer_name_lower: optimizer_args["betas"] = config.betas if config.override_optimizer_config is not None: optimizer_args.update(config.override_optimizer_config) try: module = importlib.import_module(config.optimizer_impl) optimizer_cls = getattr(module, config.optimizer) except ImportError as e: raise ImportError( f"Failed to import module '{config.optimizer_impl}'. Make sure the package is installed. Error: {e}" ) from e except AttributeError as e: raise AttributeError( f"Optimizer '{config.optimizer}' not found in module '{config.optimizer_impl}'. " f"Available optimizers: {dir(module)}" ) from e return optimizer_cls(parameters, **optimizer_args) ================================================ FILE: verl/workers/config/reward.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from dataclasses import dataclass, field from typing import Optional from verl.base_config import BaseConfig from verl.trainer.config.config import ModuleConfig from .rollout import RolloutConfig __all__ = ["SandboxFusionConfig", "RewardConfig", "RewardModelConfig"] logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @dataclass class RewardManagerConfig(BaseConfig): """Configuration for reward manager. A reward manager defines the mechanism of computing rule-based reward and handling different reward sources. Args: source (str): Source of the reward manager. Options: ``"register"``, ``"importlib"``. Default: ``"register"``. name (str, optional): - When ``source`` is ``"register"``, the name is used in `get_reward_manager_cls(name)``. See ``verl/experimental/reward/reward_manager.py`` for options. Default: ``"naive"``. - When ``source`` is ``"importlib"``, the name is used in ``getattr(module, name)``, e.g., ``"DAPORewardManager"``. module (ModuleConfig, optional): Optional configuration for the external module defining the reward manager, """ source: str = "register" name: str = "naive" module: Optional[ModuleConfig] = field(default_factory=ModuleConfig) def __post_init__(self): super().__post_init__() if self.source == "register": from verl.experimental.reward_loop.reward_manager.registry import REWARD_MANAGER assert self.name in REWARD_MANAGER, ( f"Reward manager is not registered: {self.name=} ,{REWARD_MANAGER.keys()=}" ) elif self.source == "importlib": # NOTE: The existence is not checked since it depends on which machine the config is initialized on. assert self.module is not None and self.module.path is not None, ( "When source is importlib, module.path should be set." ) @dataclass class SandboxFusionConfig(BaseConfig): """Configuration for cloud/local sandbox fusion. Args: url (Optional[str]): Cloud/local function URL for sandbox execution. max_concurrent (int): Max concurrent requests allowed to sandbox. memory_limit_mb (int): Max memory limit for each sandbox process in MB. """ url: Optional[str] = None max_concurrent: int = 64 memory_limit_mb: int = 1024 @dataclass class RewardModelConfig(BaseConfig): _mutable_fields = BaseConfig._mutable_fields enable: bool = False enable_resource_pool: bool = False n_gpus_per_node: int = 0 nnodes: int = 0 model_path: Optional[str] = None inference: RolloutConfig = field(default_factory=RolloutConfig) @dataclass class RewardConfig(BaseConfig): _mutable_fields = BaseConfig._mutable_fields # reward manager args num_workers: int = 8 reward_manager: RewardManagerConfig = field(default_factory=RewardManagerConfig) # reward model args reward_model: RewardModelConfig = field(default_factory=RewardModelConfig) # sandbox fusion args sandbox_fusion: SandboxFusionConfig = field(default_factory=SandboxFusionConfig) ================================================ FILE: verl/workers/config/rollout.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from dataclasses import dataclass, field from typing import Optional from omegaconf import MISSING from verl.base_config import BaseConfig from verl.utils.profiler import ProfilerConfig from verl.workers.config.model import MtpConfig __all__ = [ "SamplingConfig", "MultiTurnConfig", "CustomAsyncServerConfig", "AgentLoopConfig", "TraceConfig", "ServerConfig", "PrometheusConfig", "RolloutConfig", "CheckpointEngineConfig", ] @dataclass class SamplingConfig(BaseConfig): temperature: float = 1.0 top_k: int = -1 top_p: float = 1.0 do_sample: bool = True n: int = 1 @dataclass class MultiTurnConfig(BaseConfig): _mutable_fields = {"max_assistant_turns", "max_user_turns"} enable: bool = False max_assistant_turns: Optional[int] = None tool_config_path: Optional[str] = None max_user_turns: Optional[int] = None max_parallel_calls: int = 1 max_tool_response_length: int = 256 tool_response_truncate_side: str = "middle" interaction_config_path: Optional[str] = None use_inference_chat_template: bool = False tokenization_sanity_check_mode: str = "strict" format: str = "hermes" num_repeat_rollouts: Optional[int] = None @dataclass class CustomAsyncServerConfig(BaseConfig): path: Optional[str] = None name: Optional[str] = None @dataclass class AgentLoopConfig(BaseConfig): num_workers: int = 8 default_agent_loop: str = "single_turn_agent" agent_loop_config_path: Optional[str] = None custom_async_server: CustomAsyncServerConfig = field(default_factory=CustomAsyncServerConfig) # Fully qualified class name for custom AgentLoopManager (e.g., "mypackage.module.MyManager"). # Security: This class will be dynamically imported via importlib. Only use trusted class paths. agent_loop_manager_class: Optional[str] = None @dataclass class TraceConfig(BaseConfig): project_name: Optional[str] = None experiment_name: Optional[str] = None backend: Optional[str] = None token2text: bool = False max_samples_per_step_per_worker: Optional[int] = None def __post_init__(self): if self.max_samples_per_step_per_worker is not None and self.max_samples_per_step_per_worker < 0: raise ValueError("`max_samples_per_step_per_worker` must be a non-negative integer or null.") @dataclass class ServerConfig(BaseConfig): """ Configuration for SGLang server when running in server mode """ timeout: float = 60.0 max_attempts: int = 3 retry_delay: float = 2.0 max_connections: int = 1000 max_start_wait_time: float = 300.0 @dataclass class PrometheusConfig(BaseConfig): """ Configuration for Prometheus server """ # whether enable prometheus on server mode rollout enable: bool = False # Port number that Prometheus listens on, default is 9090 port: int = 9090 # Path to Prometheus configuration file file: str = "/tmp/ray/session_latest/metrics/prometheus/prometheus.yml" # Specify served_model_name to avoid displaying overly long model paths in Grafana served_model_name: Optional[str] = None @dataclass class CheckpointEngineConfig(BaseConfig): """ Configuration for checkpoint engine to update weights from trainer to rollout """ # Backend for checkpoint engine: naive, nccl, nixl, hccl backend: Optional[str] = "naive" # Bucket size in MB to transfer multiple weights at one time update_weights_bucket_megabytes: int = 2048 # Additional keyword arguments for checkpoint engine engine_kwargs: dict = field(default_factory=dict) @dataclass class RolloutConfig(BaseConfig): _mutable_fields = {"max_model_len", "load_format", "expert_parallel_size", "moe_tensor_parallel_size"} name: Optional[str] = MISSING mode: str = "async" nnodes: int = 0 n_gpus_per_node: int = 8 temperature: float = 1.0 top_k: int = -1 top_p: float = 1.0 do_sample: bool = True n: int = 1 repetition_penalty: float = 1.0 # Early termination threshold for multi-turn rollout in sglang. # Abort remaining requests when (1 - over_sample_rate) * total_requests are completed. over_sample_rate: float = 0.0 prompt_length: int = 512 response_length: int = 512 dtype: str = "bfloat16" gpu_memory_utilization: float = 0.5 ignore_eos: bool = False enforce_eager: bool = True cudagraph_capture_sizes: Optional[list] = None free_cache_engine: bool = True data_parallel_size: int = 1 expert_parallel_size: int = 1 tensor_model_parallel_size: int = 2 pipeline_model_parallel_size: int = 1 moe_tensor_parallel_size: int = 1 max_num_batched_tokens: int = 8192 logprobs_mode: Optional[str] = "processed_logprobs" scheduling_policy: Optional[str] = "fcfs" # TODO: enable train_kwargs # train_sampling_config: SamplingConfig = field(default_factory=SamplingConfig) val_kwargs: SamplingConfig = field(default_factory=SamplingConfig) max_model_len: Optional[int] = None max_num_seqs: int = 1024 # note that the logprob computation should belong to the actor log_prob_micro_batch_size: Optional[int] = None log_prob_micro_batch_size_per_gpu: Optional[int] = None log_prob_use_dynamic_bsz: bool = False log_prob_max_token_len_per_gpu: int = 16384 disable_log_stats: bool = True multi_stage_wake_up: bool = False engine_kwargs: dict = field(default_factory=dict) calculate_log_probs: bool = False agent: AgentLoopConfig = field(default_factory=AgentLoopConfig) trace: TraceConfig = field(default_factory=TraceConfig) multi_turn: MultiTurnConfig = field(default_factory=MultiTurnConfig) # Server configuration for sglang server mode server: ServerConfig = field(default_factory=ServerConfig) # Use Prometheus to collect and monitor rollout statistics prometheus: PrometheusConfig = field(default_factory=PrometheusConfig) # Extension point for custom configurations custom: Optional[dict] = None # Checkpoint Engine config for update weights from trainer to rollout checkpoint_engine: CheckpointEngineConfig = field(default_factory=CheckpointEngineConfig) skip_rollout: bool = False skip_dump_dir: str = "/tmp/rollout_dump" profiler: Optional[ProfilerConfig] = None enable_chunked_prefill: bool = True enable_prefix_caching: bool = True load_format: str = "dummy" layered_summon: bool = False layer_name_map: dict = field(default_factory=dict) sglang_engine_mode: str = "local" limit_images: Optional[int] = None skip_tokenizer_init: bool = False quantization: Optional[str] = None quantization_config_file: Optional[str] = None enable_rollout_routing_replay: bool = False enable_sleep_mode: bool = True mtp: MtpConfig = field(default_factory=MtpConfig) qat: Optional[dict] = None def __post_init__(self): """Validate the rollout config""" # Deprecation warning for mode field - only async mode is supported if self.mode == "sync": raise ValueError( "Rollout mode 'sync' has been removed. Please set " "`actor_rollout_ref.rollout.mode=async` or remove the mode setting entirely." ) if self.mode != "async": warnings.warn( f"Unknown rollout mode '{self.mode}'. Only 'async' mode is supported. " "The 'mode' field is deprecated and will be removed in a future version.", DeprecationWarning, stacklevel=2, ) if self.name != "trtllm" and self.expert_parallel_size > 1: assert self.expert_parallel_size == (self.tensor_model_parallel_size * self.data_parallel_size), ( "expert_parallel_size must be equal to tensor_model_parallel_size * data_parallel_size" ) if self.moe_tensor_parallel_size is not None and self.moe_tensor_parallel_size > 1: assert self.name == "trtllm", "moe_tensor_parallel_size is only supported for trtllm" if self.name == "trtllm": # If either expert_parallel_size or moe_tensor_parallel_size is at default 1, # convert to None so TensorRT-LLM treats it as unspecified. # When both unspecified: moe_ep_size=1, moe_tp_size=moe_world_size (no EP, all TP). # When only one set: the other is auto-derived from tensor_model_parallel_size. if self.expert_parallel_size is not None and self.expert_parallel_size == 1: self.expert_parallel_size = None if self.moe_tensor_parallel_size is not None and self.moe_tensor_parallel_size == 1: self.moe_tensor_parallel_size = None if self.expert_parallel_size is not None and self.moe_tensor_parallel_size is not None: assert self.moe_tensor_parallel_size * self.expert_parallel_size == self.tensor_model_parallel_size, ( "moe_tensor_parallel_size * expert_parallel_size must equal tensor_model_parallel_size " f"(got {self.moe_tensor_parallel_size} * {self.expert_parallel_size} = " f"{self.moe_tensor_parallel_size * self.expert_parallel_size}, " f"tensor_model_parallel_size={self.tensor_model_parallel_size})" ) if self.pipeline_model_parallel_size > 1: if self.name == "vllm" or self.name == "sglang" or self.name == "trtllm": raise NotImplementedError( f"Current rollout {self.name=} not implemented pipeline_model_parallel_size > 1 yet." ) ================================================ FILE: verl/workers/critic/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BasePPOCritic from .dp_critic import DataParallelPPOCritic __all__ = ["BasePPOCritic", "DataParallelPPOCritic"] ================================================ FILE: verl/workers/critic/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Base class for a critic """ from abc import ABC, abstractmethod import torch from verl import DataProto __all__ = ["BasePPOCritic"] class BasePPOCritic(ABC): def __init__(self, config): super().__init__() self.config = config @abstractmethod def compute_values(self, data: DataProto) -> torch.Tensor: """Compute values""" pass @abstractmethod def update_critic(self, data: DataProto): """Update the critic""" pass ================================================ FILE: verl/workers/critic/dp_critic.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement a multiprocess PPOCritic """ import logging import os import torch import torch.distributed from torch import nn, optim from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from verl import DataProto from verl.trainer.ppo import core_algos from verl.utils.attention_utils import index_first_axis, pad_input, rearrange, unpad_input from verl.utils.device import get_device_id, get_device_name from verl.utils.fsdp_utils import FSDPModule, fsdp2_clip_grad_norm_ from verl.utils.profiler import GPUMemoryLogger from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import prepare_dynamic_batch, restore_dynamic_batch from verl.utils.torch_functional import masked_mean from verl.utils.ulysses import gather_outputs_and_unpad, ulysses_pad_and_slice_inputs from verl.workers.critic import BasePPOCritic logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class DataParallelPPOCritic(BasePPOCritic): def __init__(self, config, critic_module: nn.Module, critic_optimizer: optim.Optimizer): super().__init__(config=config) self.critic_module = critic_module self.critic_optimizer = critic_optimizer self.use_remove_padding = self.config.model.get("use_remove_padding", False) print(f"Critic use_remove_padding={self.use_remove_padding}") self.ulysses_sequence_parallel_size = self.config.get("ulysses_sequence_parallel_size", 1) self.device_name = get_device_name() def _forward_micro_batch(self, micro_batch): response_length = micro_batch["responses"].size(-1) multi_modal_inputs = {} if "multi_modal_inputs" in micro_batch.keys(): from verl.utils.model import extract_multi_modal_inputs multi_modal_inputs = extract_multi_modal_inputs(micro_batch["multi_modal_inputs"]) with torch.autocast(device_type=self.device_name, dtype=torch.bfloat16): input_ids = micro_batch["input_ids"] batch, seqlen = input_ids.shape attention_mask = micro_batch["attention_mask"] position_ids = micro_batch["position_ids"] if position_ids.dim() == 3: # qwen2vl mrope position_ids = position_ids.transpose(0, 1) if self.use_remove_padding: input_ids_rmpad, indices, *_ = unpad_input( input_ids.unsqueeze(-1), attention_mask ) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary if position_ids.dim() == 3: position_ids_rmpad = ( index_first_axis(rearrange(position_ids, "c b s ... -> (b s) c ..."), indices) .transpose(0, 1) .unsqueeze(1) ) # (4, bsz, seqlen) -> (4, 1, bsz * seqlen) else: position_ids_rmpad = index_first_axis( rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices ).transpose(0, 1) # pad and slice the inputs if sp > 1 if self.ulysses_sequence_parallel_size > 1: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs( input_ids_rmpad, position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size ) # only pass input_ids and position_ids to enable flash_attn_varlen output = self.critic_module( input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, **multi_modal_inputs, use_cache=False, ) # prevent model thinks we are generating if hasattr(self.critic_module, "v_head"): # For trl.AutoModelForCausalLMWithValueHead values_rmpad = output[2].squeeze(0).unsqueeze(-1) else: values_rmpad = output.logits values_rmpad = values_rmpad.squeeze(0) # (total_nnz) # gather output if sp > 1 if self.ulysses_sequence_parallel_size > 1: values_rmpad = gather_outputs_and_unpad( values_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size ) # pad it back values = pad_input(values_rmpad, indices=indices, batch=batch, seqlen=seqlen).squeeze(-1) values = values[:, -response_length - 1 : -1] else: output = self.critic_module( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, **multi_modal_inputs, use_cache=False, ) # prevent model thinks we are generating if hasattr(self.critic_module, "v_head"): # For trl.AutoModelForCausalLMWithValueHead values = output[2] else: values = output.logits values = values[:, -response_length - 1 : -1].squeeze(-1) return values def _optimizer_step(self): assert self.config.grad_clip is not None if isinstance(self.critic_module, FSDP): grad_norm = self.critic_module.clip_grad_norm_(self.config.grad_clip) elif isinstance(self.critic_module, FSDPModule): grad_norm = fsdp2_clip_grad_norm_(self.critic_module.parameters(), max_norm=self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.critic_module.parameters(), max_norm=self.config.grad_clip) # if grad_norm is not finite, skip the update if not torch.isfinite(grad_norm): print(f"WARN: grad_norm is not finite: {grad_norm}") self.critic_optimizer.zero_grad() else: self.critic_optimizer.step() return grad_norm @GPUMemoryLogger(role="dp critic", logger=logger) def compute_values(self, data: DataProto) -> torch.Tensor: self.critic_module.eval() micro_batch_size = data.meta_info["micro_batch_size"] use_dynamic_bsz = data.meta_info["use_dynamic_bsz"] has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() select_keys = ( ["responses", "input_ids", "response_mask", "attention_mask", "position_ids"] if "response_mask" in data.batch else ["responses", "input_ids", "attention_mask", "position_ids"] ) non_tensor_select_keys = ["multi_modal_inputs"] if has_multi_modal_inputs else [] data = data.select(batch_keys=select_keys, non_tensor_batch_keys=non_tensor_select_keys) if use_dynamic_bsz: max_token_len = data.meta_info["max_token_len"] * self.ulysses_sequence_parallel_size micro_batches, batch_idx_list = prepare_dynamic_batch(data, max_token_len=max_token_len) else: micro_batches = data.split(micro_batch_size) values_lst = [] for micro_batch in micro_batches: micro_batch = micro_batch.to(get_device_id()) model_inputs = {**micro_batch.batch, **micro_batch.non_tensor_batch} with torch.no_grad(): values = self._forward_micro_batch(model_inputs) values_lst.append(values) values = torch.concat(values_lst, dim=0) if use_dynamic_bsz: values = restore_dynamic_batch(values, batch_idx_list) if "response_mask" in data.batch: response_mask = data.batch["response_mask"] response_mask = response_mask.to(values.device) values = values * response_mask # Only action tokens have values return values @GPUMemoryLogger(role="dp critic", logger=logger) def update_critic(self, data: DataProto): # make sure we are in training mode self.critic_module.train() metrics = { "critic/vf_loss": 0.0, } select_keys = ["input_ids", "responses", "response_mask", "attention_mask", "position_ids", "values", "returns"] has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() non_tensor_select_keys = ["multi_modal_inputs"] if has_multi_modal_inputs else [] data = data.select(batch_keys=select_keys, non_tensor_batch_keys=non_tensor_select_keys) # Split to make minibatch iterator for updating the actor # See PPO paper for details. https://arxiv.org/abs/1707.06347 mini_batches = data.split(self.config.ppo_mini_batch_size) for _ in range(self.config.ppo_epochs): for batch_idx, mini_batch in enumerate(mini_batches): if self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, _ = prepare_dynamic_batch(mini_batch, max_token_len=max_token_len) else: self.gradient_accumulation = ( self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu ) micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu) self.critic_optimizer.zero_grad() for micro_batch in micro_batches: micro_batch = micro_batch.to(get_device_id()) micro_batch_metrics = {} model_inputs = {**micro_batch.batch, **micro_batch.non_tensor_batch} response_mask = model_inputs["response_mask"] values = model_inputs["values"] returns = model_inputs["returns"] vpreds = self._forward_micro_batch(model_inputs) vf_loss, vf_clipfrac = core_algos.compute_value_loss( vpreds=vpreds, values=values, returns=returns, response_mask=response_mask, cliprange_value=self.config.cliprange_value, loss_agg_mode=self.config.loss_agg_mode, ) if self.config.use_dynamic_bsz: # relative to the dynamic bsz loss_scale_factor = response_mask.shape[0] / self.config.ppo_mini_batch_size loss = vf_loss * loss_scale_factor else: loss_scale_factor = 1 / self.gradient_accumulation loss = vf_loss * loss_scale_factor loss.backward() micro_batch_metrics.update( { "critic/vf_clipfrac": vf_clipfrac.detach().item(), "critic/vpred_mean": masked_mean(vpreds, response_mask).detach().item(), } ) metrics["critic/vf_loss"] += vf_loss.detach().item() * loss_scale_factor append_to_dict(metrics, micro_batch_metrics) grad_norm = self._optimizer_step() mini_batch_metrics = {"critic/grad_norm": grad_norm.detach().item()} append_to_dict(metrics, mini_batch_metrics) self.critic_optimizer.zero_grad() return metrics ================================================ FILE: verl/workers/critic/megatron_critic.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement a multiprocess PPOCritic """ import itertools import logging import os from functools import partial from typing import Iterable import torch import torch.distributed from megatron.core import parallel_state as mpu from megatron.core.optimizer import DistributedOptimizer, OptimizerConfig from megatron.core.pipeline_parallel import get_forward_backward_func from omegaconf import OmegaConf from torch import nn from verl import DataProto from verl.trainer.ppo import core_algos from verl.utils.device import get_device_id, get_torch_device from verl.utils.megatron.pipeline_parallel import make_batch_generator from verl.utils.profiler import GPUMemoryLogger from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import get_reverse_idx, rearrange_micro_batches from verl.utils.torch_functional import broadcast_dict_tensor, masked_mean from verl.workers.critic import BasePPOCritic logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class MegatronPPOCritic(BasePPOCritic): def __init__( self, config, model_config, hf_config, tf_config, critic_module: nn.ModuleList, critic_optimizer: DistributedOptimizer, critic_optimizer_config: OptimizerConfig, ): super().__init__(config=config) self._validate_config(config) self.model_config = model_config self.hf_config = hf_config # huggingface config self.tf_config = tf_config # mcore transformer config self.critic_module = critic_module self.critic_optimizer = critic_optimizer self.critic_optimizer_config = critic_optimizer_config # we create a separate nametuple for optimizer step so that global args won't affect it. self.optimizer_step_args = OmegaConf.create( { "skip_grad": None, "overlap_dp_param_comm": False, "overlap_dp_grad_comm": False, "gradient_accumulation_steps": 1, "sequence_parallel": self.tf_config.sequence_parallel, "DDP_impl": "local", "layernorm_allreduce_bucket_threshold": 0, "reduce_grads_use_alltoall": False, } ) def _validate_config(self, config) -> None: """Validate config options not implemented for Megatron backend""" assert config.get("ulysses_sequence_parallel_size", 1) == 1 if config.shuffle: assert config.data_loader_seed is not None, "If shuffle dataloader, seed must be manually set" self.config = config @GPUMemoryLogger("megatron critic", logger=logger) def compute_values(self, data: DataProto) -> DataProto: prev_modes = [m.training for m in self.critic_module] for module in self.critic_module: module.eval() responses = data.batch["responses"] attention_mask = data.batch["attention_mask"] use_dynamic_bsz = data.meta_info.get("use_dynamic_bsz", False) micro_batch_size = data.meta_info.get("micro_batch_size", None) max_token_len = data.meta_info.get("max_token_len", None) assert micro_batch_size is not None, "micro batch size is needed for forward compute" if use_dynamic_bsz: assert max_token_len is not None, "max_token_len must be set when use_dynamic_bsz is True" max_token_len = max_token_len * self.config.megatron.context_parallel_size response_length = responses.size(1) with torch.no_grad(): output = self.forward_backward_batch( data=data, forward_only=True, use_dynamic_bsz=use_dynamic_bsz, micro_batch_size=micro_batch_size, max_token_len=max_token_len, mini_batch_size=None, ) if mpu.is_pipeline_last_stage(ignore_virtual=True): # only on last rank. It should be on every tp rank values = [o["vpreds"] for o in output["output"]] # (bs, seq_size, vocal_size) values = torch.cat(values, dim=0).to(torch.float32) if use_dynamic_bsz: indices = output["indices"] indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == values.size(0), f"{len(indices)} vs. {values.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) values = values[revert_indices] else: values = torch.empty_like(attention_mask, dtype=torch.float32) # each tp ranks should contain the same value values = values[ :, -response_length - 1 : -1 ] # Values are predicted at the ends of prefixes, e.g., the last prompt token response_mask = attention_mask[:, -response_length:] values = values * response_mask # Only action tokens have values values = values.contiguous() # sync among pp ranks values = values.to(get_device_id()) torch.distributed.broadcast( tensor=values, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), ) values = values.to("cpu") # add empty cache after each compute get_torch_device().empty_cache() for module, mode in zip(self.critic_module, prev_modes, strict=False): module.train(mode) return values def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]: select_keys = ["input_ids", "responses", "attention_mask", "position_ids", "values", "returns"] data = data.select(batch_keys=select_keys) return data.make_iterator( mini_batch_size=self.config.ppo_mini_batch_size, epochs=self.config.ppo_epochs, seed=self.config.data_loader_seed, dataloader_kwargs={"shuffle": self.config.shuffle}, ) def forward_backward_batch( self, data: DataProto, forward_only=False, use_dynamic_bsz=False, micro_batch_size=None, max_token_len=None, mini_batch_size=None, ): # broadcast from last pp rank to all other pp ranks data.to(get_device_id()) mini_batch = data mini_batch.batch = mini_batch.batch.contiguous() broadcast_dict_tensor( mini_batch.batch, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), ) mini_batch.to("cpu") # split into micro-batches mini_batch.batch["attention_mask"] = mini_batch.batch["attention_mask"].to(bool) indices = None if use_dynamic_bsz: assert max_token_len is not None, "max_token_len must be set when use_dynamic_bsz is True" dp_group = mpu.get_data_parallel_group() vpp_size = mpu.get_virtual_pipeline_model_parallel_world_size() if vpp_size is not None and vpp_size > 1: microbatch_group_size_per_vp_stage = self.tf_config.microbatch_group_size_per_vp_stage micro_batches, indices = rearrange_micro_batches( batch=mini_batch.batch, num_batches_divided_by=microbatch_group_size_per_vp_stage, max_token_len=max_token_len, dp_group=dp_group, ) assert len(micro_batches) % self.tf_config.microbatch_group_size_per_vp_stage == 0, ( f"micro_batches {micro_batches} must be divisible by microbatch_group_size_per_vp_stage " f"{microbatch_group_size_per_vp_stage} for megatron backend" ) else: micro_batches, indices = rearrange_micro_batches( batch=mini_batch.batch, max_token_len=max_token_len, dp_group=dp_group ) total_seqlen = max_token_len else: assert micro_batch_size is not None, ( "micro_batch_size is needed to be passed in when not using dynamic batch size" ) micro_batches = mini_batch.batch.split(micro_batch_size) seq_len = micro_batches[0]["input_ids"].shape[1] total_seqlen = micro_batch_size * seq_len n_micro_batch = len(micro_batches) forward_backward_func = get_forward_backward_func() def loss_func(output, data, meta_info): nonlocal use_dynamic_bsz if forward_only: return torch.tensor(1.0, device=output.device), {"vpreds": output} responses = data["responses"] attention_mask = data["attention_mask"] values = data["values"] returns = data["returns"] response_length = responses.size(1) response_mask = attention_mask[:, -response_length:] cliprange_value = self.config.cliprange_value vpreds = output # (bs, sequence_length) vpreds = vpreds[:, -response_length - 1 : -1] vf_loss, vf_clipfrac = core_algos.compute_value_loss( vpreds=vpreds, values=values, returns=returns, response_mask=response_mask, cliprange_value=cliprange_value, loss_agg_mode=self.config.loss_agg_mode, ) stats = { "critic/vf_loss": vf_loss.detach().item(), "critic/vf_clipfrac": vf_clipfrac.detach().item(), "critic/vpred_mean": masked_mean(vpreds, response_mask).detach().item(), } return vf_loss, stats def forward_step(batch_iter, model): batch = next(batch_iter) batch = batch.to(get_device_id()) batch = batch.contiguous() input_ids = batch["input_ids"] attention_mask = batch["attention_mask"] position_ids = batch["position_ids"] from verl.models.mcore import get_mcore_forward_fn forward_fn = get_mcore_forward_fn(self.hf_config) output = forward_fn( model, input_ids, attention_mask, position_ids, {}, # multi_modal_inputs value_model=True, ) return output, partial(loss_func, data=batch, meta_info={}) # batch should be a list of batches inside micro-batches batch_generator = make_batch_generator(micro_batches, vpp_size=len(self.critic_module)) # TODO: we may use the new schedule instead # for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size) if mpu.get_pipeline_model_parallel_world_size() > 1: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.critic_module, num_microbatches=n_micro_batch, seq_length=total_seqlen, # no use when input_shapes was set micro_batch_size=1, # no use when input_shapes was set forward_only=forward_only, ) else: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.critic_module, num_microbatches=n_micro_batch, seq_length=total_seqlen, # in use for pp = 1 micro_batch_size=1, # in use for pp = 1 forward_only=forward_only, ) # loss_reduces contains the stats returned from loss_func losses_reduced = {"output": losses_reduced} if use_dynamic_bsz: losses_reduced["indices"] = indices return losses_reduced @GPUMemoryLogger("megatron critic", logger=logger) def update_critic(self, dataloader: Iterable[DataProto]): metrics = {} for data in dataloader: self.critic_optimizer.zero_grad() # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm for chunk in self.critic_module: chunk.zero_grad_buffer() micro_batch_size = self.config.ppo_micro_batch_size_per_gpu max_token_len = None if self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.config.megatron.context_parallel_size metric_micro_batch = self.forward_backward_batch( data, forward_only=False, use_dynamic_bsz=self.config.use_dynamic_bsz, micro_batch_size=micro_batch_size, max_token_len=max_token_len, mini_batch_size=self.config.ppo_mini_batch_size, ) metric_micro_batch = metric_micro_batch["output"] update_successful, grad_norm, num_zeros_in_grad = self.critic_optimizer.step() learning_rate = self.critic_optimizer.param_groups[-1]["lr"] data = {"critic/grad_norm": grad_norm, "critic/lr": learning_rate} append_to_dict(metrics, data) if update_successful: # allgather already execute in optimizer.step in new megatron pass else: raise NotImplementedError for metric in metric_micro_batch: append_to_dict(metrics, metric) # append the metric from this micro-batch to global metrics. # add empty cache after each compute get_torch_device().empty_cache() return metrics ================================================ FILE: verl/workers/engine/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BaseEngine, EngineRegistry from .fsdp import FSDPEngine, FSDPEngineWithLMHead __all__ = [ "BaseEngine", "EngineRegistry", "FSDPEngine", "FSDPEngineWithLMHead", ] try: from .torchtitan import TorchTitanEngine, TorchTitanEngineWithLMHead __all__ += ["TorchTitanEngine", "TorchTitanEngineWithLMHead"] except ImportError: TorchTitanEngine = None TorchTitanEngineWithLMHead = None try: from .veomni import VeOmniEngine, VeOmniEngineWithLMHead __all__ += ["VeOmniEngine", "VeOmniEngineWithLMHead"] except ImportError: VeOmniEngine = None VeOmniEngineWithLMHead = None try: from .automodel import AutomodelEngine, AutomodelEngineWithLMHead __all__ += ["AutomodelEngine", "AutomodelEngineWithLMHead"] except ImportError: AutomodelEngine = None AutomodelEngineWithLMHead = None # Mindspeed must be imported before Megatron to ensure the related monkey patches take effect as expected try: from .mindspeed import MindspeedEngineWithLMHead __all__ += ["MindspeedEngineWithLMHead"] except ImportError: MindspeedEngineWithLMHead = None try: from .megatron import MegatronEngine, MegatronEngineWithLMHead __all__ += ["MegatronEngine", "MegatronEngineWithLMHead"] except ImportError: MegatronEngine = None MegatronEngineWithLMHead = None ================================================ FILE: verl/workers/engine/automodel/__init__.py ================================================ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .transformer_impl import AutomodelEngine, AutomodelEngineWithLMHead __all__ = [ "AutomodelEngine", "AutomodelEngineWithLMHead", ] ================================================ FILE: verl/workers/engine/automodel/transformer_impl.py ================================================ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Automodel (nemo_automodel) engine for verl SFT training. This engine delegates model building, parallelization, optimizer sharding, LR scheduling, gradient clipping, and checkpointing to Automodel's infrastructure while using verl's training loop, data pipeline, and loss function. """ import gc import logging import os from contextlib import nullcontext from typing import Any, Callable, Optional import torch import torch.distributed from huggingface_hub.constants import HF_HUB_CACHE from nemo_automodel.components.checkpoint.checkpointing import Checkpointer, CheckpointingConfig from nemo_automodel.components.optim.scheduler import OptimizerParamScheduler from nemo_automodel.components.training.utils import ( prepare_for_final_backward, prepare_for_grad_accumulation, scale_grads_and_clip_grad_norm, ) from tensordict import TensorDict from torch.distributed.tensor import DTensor import verl.utils.torch_functional as verl_F from verl.trainer.config import CheckpointConfig from verl.utils import tensordict_utils as tu from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.debug import log_gpu_memory_usage from verl.utils.device import get_device_id, get_device_name from verl.utils.model import convert_weight_keys, extract_multi_modal_inputs from verl.utils.torch_functional import logprobs_from_logits from verl.workers.config import AutomodelEngineConfig, AutomodelOptimizerConfig, HFModelConfig from ..base import BaseEngine, BaseEngineCtx, EngineRegistry from ..utils import enable_full_determinism, postprocess_batch_func, prepare_micro_batches from .utils import ( build_automodel_model, build_distributed_config_from_engine_config, get_dp_group_size, get_dp_rank, get_pp_rank, get_tp_rank, load_automodel_model_to_gpu, load_automodel_optimizer, maybe_fully_shard_optimizer, offload_automodel_model_to_cpu, offload_automodel_optimizer, ) logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class AutomodelEngine(BaseEngine): """Engine implementation using Automodel for distributed training.""" def __init__( self, model_config: HFModelConfig, engine_config: AutomodelEngineConfig, optimizer_config: AutomodelOptimizerConfig, checkpoint_config: CheckpointConfig, **kwargs, ): super().__init__() self.model_config = model_config self.engine_config = engine_config self.optimizer_config = optimizer_config self.checkpoint_config = checkpoint_config self.mode = None self.rank = torch.distributed.get_rank() # Apply compatibility patches early in the process from nemo_automodel._transformers.utils import apply_cache_compatibility_patches from nemo_automodel.shared.te_patches import apply_te_patches apply_cache_compatibility_patches() apply_te_patches() world_size = torch.distributed.get_world_size() self.distributed_config, self.device_mesh, self.moe_mesh = build_distributed_config_from_engine_config( self.engine_config, world_size ) if self.engine_config.full_determinism: enable_full_determinism(seed=self.engine_config.seed) self._is_offload_param = self.engine_config.param_offload self._is_offload_optimizer = self.engine_config.optimizer_offload if self.engine_config.entropy_from_logits_with_chunking: entropy_from_logits = verl_F.entropy_from_logits_with_chunking else: entropy_from_logits = verl_F.entropy_from_logits self.compute_entropy_from_logits = ( torch.compile(entropy_from_logits, dynamic=True) if self.engine_config.use_torch_compile else entropy_from_logits ) @property def is_param_offload_enabled(self) -> bool: return self._is_offload_param @property def is_optimizer_offload_enabled(self) -> bool: return self._is_offload_optimizer def initialize(self): """Build the model, optimizer, LR scheduler, and checkpointer using Automodel infrastructure.""" self.module = build_automodel_model( self.model_config, self.engine_config, self.distributed_config, self.device_mesh, self.moe_mesh ) log_gpu_memory_usage("After Automodel model build", logger=logger) if not self.engine_config.forward_only: self.optimizer = self._build_optimizer(self.module) # maybe shard optimizer for MegatronFSDP maybe_fully_shard_optimizer(self.module, self.optimizer, self.distributed_config) self.lr_scheduler = self._build_lr_scheduler(self.optimizer) else: self.optimizer = None self.lr_scheduler = None self._build_checkpointer() self.to( device="cpu", model=self._is_offload_param, optimizer=self._is_offload_optimizer, grad=self._is_offload_param, ) log_gpu_memory_usage("After offload model/optimizer/grad during init", logger=logger) torch.cuda.empty_cache() def _build_optimizer(self, module): """Build optimizer via Automodel's build_optimizer.""" from nemo_automodel.components.config.loader import ConfigNode from nemo_automodel.recipes.llm.train_ft import build_optimizer as automodel_build_optimizer config = self.optimizer_config opt_dict = { "_target_": f"{config.optimizer_impl}.{config.optimizer}", "lr": config.lr, "weight_decay": config.weight_decay, "eps": config.eps, "betas": list(config.betas), } if config.master_weights: opt_dict["master_weights"] = config.master_weights if config.store_param_remainders: opt_dict["store_param_remainders"] = config.store_param_remainders _short_to_torch = {"bf16": "torch.bfloat16", "fp32": "torch.float32", "fp16": "torch.float16"} for attr in ("exp_avg_dtype", "exp_avg_sq_dtype", "master_weight_dtype"): val = getattr(config, attr, None) if val is not None: opt_dict[attr] = _short_to_torch.get(val, val) if config.override_optimizer_config: opt_dict.update(config.override_optimizer_config) cfg_opt = ConfigNode(opt_dict) optimizers = automodel_build_optimizer(module, cfg_opt, self.distributed_config, self.device_mesh) assert len(optimizers) == 1, f"Expected 1 optimizer, got {len(optimizers)}" return optimizers[0] def _build_lr_scheduler(self, optimizer): cfg = self.optimizer_config total_steps = cfg.total_training_steps num_warmup_steps = cfg.lr_warmup_steps if num_warmup_steps <= 0: num_warmup_steps = int(cfg.lr_warmup_steps_ratio * total_steps) base_lr = cfg.lr init_lr_ratio = cfg.init_lr_ratio if cfg.init_lr_ratio is not None else 0.1 min_lr_ratio = cfg.min_lr_ratio if cfg.min_lr_ratio is not None else 0.01 if self.rank == 0: print( f"Automodel LR Scheduler: total_steps={total_steps}, warmup={num_warmup_steps}, " f"decay_style={cfg.lr_scheduler_type}, init_lr={base_lr * init_lr_ratio:.2e}, " f"max_lr={base_lr:.2e}, min_lr={base_lr * min_lr_ratio:.2e}" ) scheduler = OptimizerParamScheduler( optimizer=optimizer, init_lr=base_lr * init_lr_ratio, max_lr=base_lr, min_lr=base_lr * min_lr_ratio, lr_warmup_steps=num_warmup_steps, lr_decay_steps=total_steps, lr_decay_style=cfg.lr_scheduler_type, start_wd=cfg.weight_decay, end_wd=cfg.weight_decay, wd_incr_steps=total_steps, wd_incr_style=getattr(cfg, "wd_incr_style", "constant"), ) return scheduler def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False) -> Any: batch_num_tokens = data["loss_mask"].sum().to(get_device_id()) torch.distributed.all_reduce( batch_num_tokens, op=torch.distributed.ReduceOp.SUM, group=self.get_data_parallel_group() ) tu.assign_non_tensor(data, batch_num_tokens=batch_num_tokens.item()) tu.assign_non_tensor(data, dp_size=self.get_data_parallel_size()) micro_batches, indices = prepare_micro_batches( data=data, dp_group=self.get_data_parallel_group(), same_micro_num_in_dp=True ) output_lst = [] ctx = torch.no_grad() if forward_only else nullcontext() if not forward_only: prepare_for_grad_accumulation([self.module]) # Set MoE aux loss backward scale to counteract FSDP's gradient allreduce. if self.engine_config.ep_size > 1: from nemo_automodel.components.moe.megatron.moe_utils import MoEAuxLossAutoScaler MoEAuxLossAutoScaler.main_loss_backward_scale = torch.tensor( float(get_dp_group_size(self.device_mesh, include_cp=True)) ) num_micro_batches = len(micro_batches) for i, micro_batch in enumerate(micro_batches): # Signal final backward for MoE if not forward_only and i == num_micro_batches - 1: prepare_for_final_backward([self.module]) with ctx: loss, meta_info = self.forward_step(micro_batch, loss_function=loss_function, forward_only=forward_only) if not forward_only: loss.backward() output_lst.append(meta_info) return postprocess_batch_func(output_lst=output_lst, indices=indices, data=data) def forward_step(self, micro_batch: TensorDict, loss_function, forward_only): raise NotImplementedError("forward_step must be implemented in subclass") def optimizer_zero_grad(self): self.optimizer.zero_grad() def optimizer_step(self): grad_norm = scale_grads_and_clip_grad_norm( max_grad_norm=self.optimizer_config.clip_grad, model_parts=[self.module], norm_type=2.0, pp_enabled=False, device_mesh=self.device_mesh, moe_mesh=self.moe_mesh, ep_axis_name="ep" if self.moe_mesh is not None and "ep" in self.moe_mesh.mesh_dim_names else None, pp_axis_name=None, foreach=True, num_label_tokens=None, dp_group_size=get_dp_group_size(self.device_mesh, include_cp=True), ) if isinstance(grad_norm, torch.Tensor): grad_norm_val = grad_norm.item() else: grad_norm_val = float(grad_norm) # If grad_norm is not finite, skip the update if not torch.isfinite(torch.tensor(grad_norm_val)): print(f"WARN: grad_norm is not finite: {grad_norm_val}") self.optimizer.zero_grad() else: self.optimizer.step() if hasattr(self.module, "update_moe_gate_bias"): self.module.update_moe_gate_bias() return grad_norm_val def lr_scheduler_step(self): """Step Automodel's OptimizerParamScheduler and return current LR.""" self.lr_scheduler.step(increment=1) lr = self.optimizer.param_groups[0]["lr"] return lr def get_data_parallel_rank(self): if self.device_mesh is not None: return self.device_mesh.get_local_rank("dp") return torch.distributed.get_rank() def get_data_parallel_size(self): if self.device_mesh is not None: return self.device_mesh["dp"].size() return torch.distributed.get_world_size() def get_data_parallel_group(self): if self.device_mesh is not None: return self.device_mesh.get_group(mesh_dim="dp") return torch.distributed.group.WORLD def is_mp_src_rank_with_outputs(self): if self.device_mesh is not None and "tp" in self.device_mesh.mesh_dim_names: if self.device_mesh["tp"].size() > 1: return self.device_mesh.get_local_rank("tp") == 0 return True def train_mode(self, **kwargs): return AutomodelTrainModeCtx(self, **kwargs) def eval_mode(self, **kwargs): return AutomodelEvalModeCtx(self, **kwargs) def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): super().to(device=device, model=model, optimizer=optimizer, grad=grad) if self.engine_config.forward_only: return device_name = get_device_name() assert device in (device_name, "cpu") if device == device_name: if model: load_automodel_model_to_gpu(self.module) if optimizer and self.optimizer is not None: load_automodel_optimizer(self.optimizer, get_device_id()) gc.collect() elif device == "cpu": if model: offload_automodel_model_to_cpu(self.module) if optimizer and self.optimizer is not None: offload_automodel_optimizer(self.optimizer) else: raise ValueError(f"Invalid device type: {device}") def _build_checkpointer(self): ckpt_config = CheckpointingConfig( enabled=True, checkpoint_dir="checkpoints/", model_save_format="safetensors", model_cache_dir=HF_HUB_CACHE, model_repo_id=self.model_config.path, save_consolidated=True, is_peft=False, ) self.checkpointer = Checkpointer( config=ckpt_config, dp_rank=get_dp_rank(self.device_mesh, include_cp=True), tp_rank=get_tp_rank(self.device_mesh), pp_rank=get_pp_rank(self.device_mesh), moe_mesh=self.moe_mesh, ) def save_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, global_step: int = 0, max_ckpt_to_keep: Optional[int] = None, **kwargs, ) -> None: """Save model, optimizer, and LR scheduler using Automodel's Checkpointer.""" origin_module_device = next(self.module.parameters()).device.type if self._is_offload_param or origin_module_device == "cpu": load_automodel_model_to_gpu(self.module) # Save model weights self.checkpointer.save_model(self.module, local_path) # Save optimizer and LR scheduler state if self.optimizer is not None: scheduler_list = [self.lr_scheduler] if self.lr_scheduler is not None else None self.checkpointer.save_optimizer(self.optimizer, self.module, local_path, scheduler=scheduler_list) torch.distributed.barrier() if self._is_offload_param: offload_automodel_model_to_cpu(self.module) def load_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, del_local_after_load: int = True, **kwargs ) -> None: """Load model, optimizer, and LR scheduler using Automodel's Checkpointer.""" if self._is_offload_param: load_automodel_model_to_gpu(self.module) model_path = os.path.join(local_path, "model") if not os.path.isdir(model_path): model_path = local_path self.checkpointer.load_model(self.module, model_path) if self.optimizer is not None: scheduler_list = [self.lr_scheduler] if self.lr_scheduler is not None else None self.checkpointer.load_optimizer(self.optimizer, self.module, local_path, scheduler=scheduler_list) torch.distributed.barrier() if self._is_offload_param: offload_automodel_model_to_cpu(self.module) if self._is_offload_optimizer and self.optimizer is not None: offload_automodel_optimizer(self.optimizer) def get_per_tensor_param(self, **kwargs): load_automodel_model_to_gpu(self.module) params = self.module.state_dict() params = convert_weight_keys(params, getattr(self.module, "_fsdp_wrapped_module", self.module)) if self._is_offload_param: offload_automodel_model_to_cpu(self.module) def param_generator(): for name, param in params.items(): unsharded_tensor = param.full_tensor() if isinstance(param, DTensor) else param yield name, unsharded_tensor return param_generator(), None class AutomodelEvalModeCtx(BaseEngineCtx): def __init__(self, engine: AutomodelEngine, **kwargs): super().__init__(engine=engine, mode="eval", **kwargs) def __enter__(self): assert isinstance(self.engine, AutomodelEngine) super().__enter__() self.engine.module.eval() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, AutomodelEngine) # Reshard the root FSDP module if hasattr(self.engine.module, "reshard"): self.engine.module.reshard() super().__exit__(exc_type, exc_value, traceback) class AutomodelTrainModeCtx(BaseEngineCtx): def __init__(self, engine: AutomodelEngine, **kwargs): super().__init__(engine=engine, mode="train", **kwargs) def __enter__(self): assert isinstance(self.engine, AutomodelEngine) super().__enter__() self.engine.module.train() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, AutomodelEngine) self.engine.optimizer_zero_grad() super().__exit__(exc_type, exc_value, traceback) @EngineRegistry.register(model_type="language_model", backend=["automodel"], device=["cuda"]) class AutomodelEngineWithLMHead(AutomodelEngine): """Automodel engine for language model with LM head training.""" def prepare_model_inputs(self, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) use_fused_kernels = tu.get_non_tensor_data(data=micro_batch, key="use_fused_kernels", default=False) temperature = micro_batch["temperature"] temperature_item = temperature if use_fused_kernels: assert not isinstance(temperature, torch.Tensor), ( "use_fused_kernels does not support per sample temperature yet" ) assert pad_mode == DatasetPadMode.NO_PADDING, f"pad_mode {pad_mode} not supported" multi_modal_inputs = extract_multi_modal_inputs(micro_batch.get("multi_modal_inputs", [])) input_ids = micro_batch["input_ids"] position_ids = micro_batch["position_ids"] if not isinstance(temperature, torch.Tensor): temperature = torch.tensor([temperature] * input_ids.shape[0], device=input_ids.device) temperature = temperature.to(torch.float32) assert temperature.shape[0] == input_ids.shape[0] output_args = {} if use_remove_padding: temperature_rmpad = verl_F.expand_as_nested(temperature, input_ids).values() temperature_rmpad = temperature_rmpad.unsqueeze(0) if pad_mode == DatasetPadMode.NO_PADDING: input_ids_rmpad = input_ids.values().unsqueeze(0) if position_ids.dim() == 3: position_ids_rmpad = position_ids.values().unsqueeze(1) else: position_ids_rmpad = position_ids.values().unsqueeze(0) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=1) input_ids_rmpad_rolled = input_ids_rmpad_rolled.squeeze(0) temperature_rmpad = temperature_rmpad.squeeze(0) output_args["input_ids_rmpad_rolled"] = input_ids_rmpad_rolled output_args["temperature_rmpad"] = temperature_rmpad model_inputs = { "input_ids": input_ids_rmpad, "attention_mask": None, "position_ids": position_ids_rmpad, } # For TE attention backend, pass cu_seqlens if self.engine_config.attn_implementation == "te": cu_seqlens = input_ids.offsets().to(torch.int32) max_seqlen = cu_seqlens.diff().max().item() model_inputs["qkv_format"] = "thd" model_inputs["cu_seqlens"] = cu_seqlens.unsqueeze(0) model_inputs["max_seqlen"] = max_seqlen else: if pad_mode == DatasetPadMode.NO_PADDING: input_ids = micro_batch["input_ids"] position_ids = micro_batch["position_ids"] loss_mask = micro_batch["loss_mask"] pad_token_id = tu.get_non_tensor_data(data=micro_batch, key="pad_token_id", default=0) batch_size = micro_batch.batch_size[0] seq_len_effective = input_ids.offsets().diff() max_seq_len = max(seq_len_effective) input_ids_rmpad_rolled = torch.roll(input_ids.values(), shifts=-1, dims=0) output_args["input_ids_rmpad_rolled"] = input_ids_rmpad_rolled output_args["temperature"] = temperature input_ids = torch.nested.to_padded_tensor( input_ids, padding=pad_token_id, output_size=(batch_size, max_seq_len) ) if position_ids.dim() == 3: position_ids = torch.nested.to_padded_tensor( position_ids, padding=0, output_size=(batch_size, 4, max_seq_len) ).transpose(0, 1) else: position_ids = torch.nested.to_padded_tensor( position_ids, padding=0, output_size=(batch_size, max_seq_len) ) attention_mask_list = [torch.ones_like(t, dtype=torch.int32) for t in loss_mask] attention_mask = torch.nested.as_nested_tensor(attention_mask_list, layout=torch.jagged) attention_mask = torch.nested.to_padded_tensor( attention_mask, padding=0, output_size=(batch_size, max_seq_len) ) model_inputs = { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, } else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") extra_args = {} if use_fused_kernels: extra_args["temperature"] = temperature_item extra_args["return_dict"] = True model_inputs.update(multi_modal_inputs) model_inputs.update(extra_args) return model_inputs, output_args def prepare_model_outputs(self, output, output_args, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) use_fused_kernels = tu.get_non_tensor_data(data=micro_batch, key="use_fused_kernels", default=False) calculate_entropy = tu.get_non_tensor_data(data=micro_batch, key="calculate_entropy", default=False) if isinstance(output, torch.Tensor): from types import SimpleNamespace output = SimpleNamespace(logits=output) model_output = {} input_ids = micro_batch["input_ids"] if use_remove_padding: input_ids_rmpad_rolled = output_args["input_ids_rmpad_rolled"] temperature_rmpad = output_args["temperature_rmpad"] if use_fused_kernels: log_probs = output.log_probs.squeeze(0) entropy_rmpad = output.entropy.squeeze(0) else: logits_rmpad = output.logits.squeeze(0) # With TP, logits are DTensors sharded on vocab dim; gather for log_softmax. if isinstance(logits_rmpad, DTensor): logits_rmpad = logits_rmpad.full_tensor() logits_rmpad = logits_rmpad / temperature_rmpad.clamp(min=1e-8).unsqueeze(-1).to(logits_rmpad.dtype) inplace_backward = True if calculate_entropy: inplace_backward = False log_probs = logprobs_from_logits( logits=logits_rmpad, labels=input_ids_rmpad_rolled, inplace_backward=inplace_backward, ) if calculate_entropy: if not self.engine_config.entropy_checkpointing: entropy_rmpad = self.compute_entropy_from_logits(logits_rmpad) else: entropy_rmpad = torch.utils.checkpoint.checkpoint( self.compute_entropy_from_logits, logits_rmpad ) if pad_mode == DatasetPadMode.NO_PADDING: cu_seqlens = input_ids.offsets() log_probs = torch.nested.nested_tensor_from_jagged(log_probs, cu_seqlens) if calculate_entropy: entropy = torch.nested.nested_tensor_from_jagged(entropy_rmpad, cu_seqlens) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") else: response_length = tu.get_non_tensor_data(data=micro_batch, key="max_response_length", default=1024) if use_fused_kernels: log_probs = output.log_probs[:, -response_length - 1 : -1] entropy = output.entropy[:, -response_length - 1 : -1] else: logits = output.logits # With TP, logits are DTensors sharded on vocab dim; gather for log_softmax. if isinstance(logits, DTensor): logits = logits.full_tensor() temperature = output_args["temperature"] temperature = temperature.unsqueeze(-1).unsqueeze(-1) logits = logits / temperature.clamp(min=1e-8).to(logits.dtype) if calculate_entropy: if not self.engine_config.entropy_checkpointing: entropy = verl_F.entropy_from_logits(logits) else: entropy = torch.utils.checkpoint.checkpoint(verl_F.entropy_from_logits, logits) if pad_mode == DatasetPadMode.NO_PADDING: cu_seqlens = input_ids.offsets() seq_lengths = cu_seqlens.diff() starts = torch.zeros_like(seq_lengths, dtype=torch.int64) logits = torch.nested.narrow(logits, 1, starts, seq_lengths, layout=torch.jagged) logits_rmpad = torch.cat([t for t in logits.unbind()]) input_ids_rmpad_rolled = output_args["input_ids_rmpad_rolled"] log_probs = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) log_probs = torch.nested.nested_tensor_from_jagged(log_probs, cu_seqlens) if calculate_entropy: entropy = torch.nested.narrow(entropy, 1, starts, seq_lengths, layout=torch.jagged) entropy_rmpad = torch.cat([t for t in entropy.unbind()]) entropy = torch.nested.nested_tensor_from_jagged(entropy_rmpad, cu_seqlens) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") model_output["log_probs"] = log_probs if calculate_entropy: model_output["entropy"] = entropy return model_output def forward_step(self, micro_batch: TensorDict, loss_function, forward_only): """Run forward pass, compute loss, and return outputs.""" device_name = get_device_name() micro_batch = micro_batch.to(get_device_id()) model_inputs, output_args = self.prepare_model_inputs(micro_batch=micro_batch) with torch.autocast(device_type=device_name, dtype=torch.bfloat16): raw_output = self.module( **model_inputs, use_cache=False, ) model_output = self.prepare_model_outputs( output=raw_output, output_args=output_args, micro_batch=micro_batch ) if loss_function is not None: loss, metrics = loss_function( model_output=model_output, data=micro_batch, dp_group=self.get_data_parallel_group() ) else: assert forward_only, "forward_only must be True when loss_function is None" loss = torch.tensor(1.0, device=device_name) metrics = {} output = { "model_output": model_output, "loss": loss.detach().item(), "metrics": metrics, } return loss, output ================================================ FILE: verl/workers/engine/automodel/utils.py ================================================ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utility functions for the Automodel engine integration.""" import torch import torch.distributed from verl.utils.device import get_device_id, get_torch_device def get_dp_rank(device_mesh, include_cp=False): """Get data-parallel rank from device mesh.""" if device_mesh is None: return 0 if include_cp and "cp" in device_mesh.mesh_dim_names and device_mesh["cp"].size() > 1: return device_mesh.get_local_rank("dp_cp") return device_mesh.get_local_rank("dp") def get_tp_rank(device_mesh): """Get tensor-parallel rank from device mesh.""" if device_mesh is None or "tp" not in device_mesh.mesh_dim_names or device_mesh["tp"].size() == 1: return 0 return device_mesh.get_local_rank("tp") def get_pp_rank(device_mesh): """Get pipeline-parallel rank from device mesh.""" if device_mesh is None or "pp" not in device_mesh.mesh_dim_names or device_mesh["pp"].size() == 1: return 0 return device_mesh.get_local_rank("pp") def get_dp_group_size(device_mesh, include_cp=False): """Get data-parallel group size from device mesh.""" if device_mesh is None: return torch.distributed.get_world_size() if include_cp and "cp" in device_mesh.mesh_dim_names and device_mesh["cp"].size() > 1: return device_mesh["dp_cp"].size() if "dp" in device_mesh.mesh_dim_names: return device_mesh["dp"].size() return torch.distributed.get_world_size() def maybe_fully_shard_optimizer(model, optimizer, distributed_config): """Call fully_shard_optimizer for MegatronFSDP strategy.""" from nemo_automodel.components.distributed.config import MegatronFSDPConfig if isinstance(distributed_config, MegatronFSDPConfig) and torch.distributed.get_world_size() > 1: from megatron_fsdp.fully_shard import fully_shard_optimizer fully_shard_optimizer(model, optimizer) def build_distributed_config_from_engine_config(engine_config, world_size): """Build v5 distributed config, device_mesh, and moe_mesh from engine config. Args: engine_config: AutomodelEngineConfig instance. world_size: Total number of processes in the job. Returns: Tuple of (distributed_config, device_mesh, moe_mesh). """ from nemo_automodel.components.distributed.config import DDPConfig, FSDP2Config, MegatronFSDPConfig from nemo_automodel.components.distributed.mesh_utils import create_device_mesh strategy = engine_config.distributed_strategy if strategy == "fsdp2": from torch.distributed.fsdp import MixedPrecisionPolicy from verl.utils.torch_dtypes import PrecisionType mp_policy = MixedPrecisionPolicy( param_dtype=PrecisionType.to_dtype(engine_config.mp_param_dtype), reduce_dtype=PrecisionType.to_dtype(engine_config.mp_reduce_dtype), output_dtype=PrecisionType.to_dtype(engine_config.mp_output_dtype), cast_forward_inputs=True, ) distributed_config = FSDP2Config( sequence_parallel=engine_config.sequence_parallel, mp_policy=mp_policy, activation_checkpointing=engine_config.activation_checkpointing, defer_fsdp_grad_sync=engine_config.defer_fsdp_grad_sync, ) elif strategy == "megatron_fsdp": distributed_config = MegatronFSDPConfig( activation_checkpointing=engine_config.activation_checkpointing, ) elif strategy == "ddp": distributed_config = DDPConfig( activation_checkpointing=engine_config.activation_checkpointing, ) else: raise ValueError(f"Unsupported distributed_strategy: {strategy}") device_mesh, moe_mesh = create_device_mesh( distributed_config, tp_size=engine_config.tp_size, pp_size=engine_config.pp_size, cp_size=engine_config.cp_size, ep_size=engine_config.ep_size, dp_replicate_size=engine_config.dp_replicate_size, world_size=world_size, ) return distributed_config, device_mesh, moe_mesh def build_automodel_model(model_config, engine_config, distributed_config, device_mesh, moe_mesh): """Build a model using NeMoAutoModelForCausalLM.from_pretrained(). Args: model_config: HFModelConfig with model path and settings. engine_config: AutomodelEngineConfig with distributed settings. distributed_config: FSDP2Config, MegatronFSDPConfig, or DDPConfig instance. device_mesh: Pre-created device mesh (or None for DDP). moe_mesh: Pre-created MoE mesh (or None). Returns: A HuggingFace model with Automodel's distributed infrastructure applied. """ from nemo_automodel._transformers.auto_model import NeMoAutoModelForCausalLM kwargs = {} if engine_config.enable_fp8: from nemo_automodel.components.quantization.fp8 import FP8Config kwargs["fp8_config"] = FP8Config() if engine_config.enable_compile: from nemo_automodel.components.utils.compile_utils import CompileConfig kwargs["compile_config"] = CompileConfig() # Qwen/Llama with ep_size<=1: use HF implementation. from transformers import AutoConfig _cfg = AutoConfig.from_pretrained(model_config.path, trust_remote_code=model_config.trust_remote_code) _arch = (getattr(_cfg, "architectures", None) or [""])[0].lower() if engine_config.ep_size <= 1 and ("qwen" in _arch or "llama" in _arch): kwargs["force_hf"] = True if engine_config.backend_config and not kwargs.get("force_hf", False): from nemo_automodel.components.models.common.utils import BackendConfig backend_kwargs = dict(engine_config.backend_config) kwargs["backend"] = BackendConfig(**backend_kwargs) # MoE config for MoEParallelizerConfig if engine_config.ep_size > 1: from nemo_automodel.components.moe.config import MoEParallelizerConfig moe_kwargs = dict(engine_config.moe_config) if engine_config.moe_config else {} if hasattr(distributed_config, "mp_policy"): moe_kwargs.setdefault("mp_policy", distributed_config.mp_policy) kwargs["moe_config"] = MoEParallelizerConfig(**moe_kwargs) kwargs["attn_implementation"] = engine_config.attn_implementation from verl.utils.torch_dtypes import PrecisionType kwargs["torch_dtype"] = PrecisionType.to_dtype(engine_config.model_dtype) model = NeMoAutoModelForCausalLM.from_pretrained( pretrained_model_name_or_path=model_config.path, device_mesh=device_mesh, moe_mesh=moe_mesh, distributed_config=distributed_config, activation_checkpointing=engine_config.activation_checkpointing, trust_remote_code=model_config.trust_remote_code, **kwargs, ) return model @torch.no_grad() def offload_automodel_model_to_cpu(model, empty_cache=True): """Offload an FSDP2-wrapped model to CPU (reshard, move to CPU, optional cache clear).""" from torch.distributed.fsdp._fully_shard._fsdp_common import TrainingState from torch.distributed.fsdp._fully_shard._fsdp_state import _get_module_fsdp_state for module in model.modules(): state = _get_module_fsdp_state(module) if state is None: continue fsdp_param_group = state._fsdp_param_group if fsdp_param_group is None: continue fsdp_param_group._training_state = TrainingState.IDLE model.reshard() model.cpu() if empty_cache: get_torch_device().empty_cache() @torch.no_grad() def load_automodel_model_to_gpu(model): """Load model back to GPU.""" device = get_device_id() model.to(device, non_blocking=True) @torch.no_grad() def offload_automodel_optimizer(optimizer): """Offload optimizer state to CPU.""" if not optimizer.state: return for param_group in optimizer.param_groups: for param in param_group["params"]: state = optimizer.state[param] for key, value in state.items(): if isinstance(value, torch.Tensor): state[key] = value.to("cpu", non_blocking=True) @torch.no_grad() def load_automodel_optimizer(optimizer, device_id): """Load optimizer state back to GPU.""" if not optimizer.state: return for param_group in optimizer.param_groups: for param in param_group["params"]: state = optimizer.state[param] for key, value in state.items(): if isinstance(value, torch.Tensor): state[key] = value.to(device_id, non_blocking=True) ================================================ FILE: verl/workers/engine/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The abstract base class defining the interface for model training engines. """ from abc import abstractmethod from contextlib import nullcontext from typing import Any, Callable, ContextManager, Generator, Optional import torch from tensordict import TensorDict from verl.utils.device import get_device_name from verl.utils.tensordict_utils import maybe_fix_3d_position_ids class BaseEngine: """ Abstract base class defining the interface for model training engines. Interface is subject to change before release. Engine implementations must subclass BaseEngine and provide concrete behavior for all methods. """ def initialize(self): """ Instantiate or load the model, optimizer, and learning rate scheduler. Should prepare all components necessary for training or evaluation. """ raise NotImplementedError @property @abstractmethod def is_param_offload_enabled(self) -> bool: """Whether parameter offloading is enabled.""" raise NotImplementedError @property @abstractmethod def is_optimizer_offload_enabled(self) -> bool: """Whether optimizer offloading is enabled.""" raise NotImplementedError def train_mode(self, **kwargs): """ Context manager entry for switching the engine and model into training mode. Usage: with engine.train_mode(): # runs in training mode """ raise NotImplementedError def eval_mode(self, **kwargs): """ Context manager entry for switching the engine and model into evaluation mode. Usage: with engine.eval_mode(): # runs in evaluation mode """ raise NotImplementedError def optimizer_zero_grad(self): """ Zero the gradients of the optimizer. """ raise NotImplementedError def optimizer_step(self): """ Perform an optimization step using the optimizer. """ raise NotImplementedError def lr_scheduler_step(self): """ Advance the learning rate scheduler by one step. Returns: current_lr (float or list[float]): Updated learning rate(s). """ raise NotImplementedError def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False) -> Any: """ Perform a forward pass and optionally a backward pass on a batch of data. Args: data: The input data for the forward pass, typically containing tensors and metadata. loss_function: The loss function to optimize. See `verl.workers.roles.utils.losses` for examples. forward_only: If True, perform only the forward pass. If False, perform forward and backward pass. Returns: Any: The output of the forward pass, which can be used for loss computation or other purposes. """ raise NotImplementedError def train_batch(self, data: TensorDict, loss_function: Callable) -> Any: """ Perform a training step on a batch of data. Args: data: The input data for training, typically containing tensors and metadata. loss_function: A function that computes the loss and metrics given a batch and predictions. Returns: dict[str, torch.Tensor]: A dictionary containing the aggregated training metrics for the batch. """ maybe_fix_3d_position_ids(data) self.optimizer_zero_grad() outputs = self.forward_backward_batch(data, loss_function, forward_only=False) grad_norm = self.optimizer_step() if self.is_mp_src_rank_with_outputs(): assert "grad_norm" not in outputs["metrics"] outputs["metrics"]["grad_norm"] = grad_norm return outputs def infer_batch(self, data: TensorDict, loss_function: Optional[Callable] = None) -> Any: """ Perform inference on a batch of data. Args: data: The input data for inference, typically containing tensors and metadata. Returns: Any: The output of the inference, which can be used for predictions or other purposes. """ # see comments from train_batch maybe_fix_3d_position_ids(data) with torch.no_grad(): outputs = self.forward_backward_batch(data, loss_function, forward_only=True) return outputs def get_per_tensor_param(self) -> tuple[Generator[tuple[str, torch.Tensor], None, None], Optional[dict]]: """ Get a generator that yields per-tensor parameters and optional peft config. Returns: Generator[tuple[str, torch.Tensor]]: A generator that yields tuples of parameter names and tensors. Optional[dict]: Optional peft config. """ raise NotImplementedError def get_data_parallel_size(self): raise NotImplementedError def get_data_parallel_rank(self): raise NotImplementedError def get_data_parallel_group(self): raise NotImplementedError def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): """ Move model parameters, optimizer states, or both to the specified device. Args: device: Target device identifier. model: If True, move the model. optimizer: If True, move the optimizer states. grad: If True, move the gradient buffer. """ if not model: assert not optimizer and not grad, "Model must be moved to device along with optimizer and grad" def save_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, global_step: int = 0, max_ckpt_to_keep: Optional[int] = None, **kwargs, ) -> None: """ Save model, optimizer, and scheduler states to a checkpoint. Args: local_path: Local filesystem path to save checkpoint. hdfs_path: Optional HDFS path to copy checkpoint. global_step: Integer training step number for naming. max_ckpt_to_keep: Maximum number of recent checkpoints to retain. **kwargs: Arbitrary keyword arguments. """ raise NotImplementedError def load_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, del_local_after_load: bool = True, **kwargs ) -> None: """ Load model, optimizer, and scheduler states from a checkpoint. Args: local_path: Local filesystem path of the checkpoint. hdfs_path: Optional HDFS path where checkpoint is stored. del_local_after_load: Whether to delete local copy after loading. **kwargs: Arbitrary keyword arguments. """ raise NotImplementedError def is_mp_src_rank_with_outputs(self): """ Whether the current rank is the first rank in model parallel group that contains model outputs """ raise NotImplementedError def disable_adapter(self) -> ContextManager: """ Disable all adapters temporarily under the context in the model for LoRA """ return nullcontext() class BaseEngineCtx: def __init__(self, engine: BaseEngine, mode, **kwargs): """Base Engine context that handles load and offload Args: engine: **kwargs: """ self.engine = engine self.mode = mode assert self.mode in ("train", "eval") self.disable_auto_offload = kwargs.pop("disable_auto_offload", False) def _context_switch(self, device): if self.disable_auto_offload: return if self.mode == "eval": self.engine.to(device=device, model=self.engine.is_param_offload_enabled, optimizer=False, grad=False) elif self.mode == "train": self.engine.to( device=device, model=self.engine.is_param_offload_enabled, optimizer=self.engine.is_optimizer_offload_enabled, grad=self.engine.is_param_offload_enabled, ) def __enter__(self): self._context_switch(get_device_name()) self.engine.mode = self.mode def __exit__(self, exc_type, exc_val, exc_tb): self._context_switch("cpu") self.engine.mode = None class EngineRegistry: """ A registry for managing and instantiating different types of training engines. This class uses a dictionary to store engine classes, mapping a string key to each class. It provides a decorator `register` to add new engines to the registry and a `new` method to create an instance of a registered engine. """ _engines = {} @classmethod def register(cls, model_type: str, backend: list[str] | str, device: list[str] | str = "cuda"): """ A class method decorator that registers an engine class with a given key. This allows for dynamic instantiation of engine classes by their registered key. Args: model_type (str): The type of the model backend (list[str] | str): The backend to use for the model type device (list[str] | str): The device type (e.g., "cuda", "npu", "cpu") this engine supports, default is "cuda" Returns: A decorator function that takes an engine class and registers it. """ def decorator(engine_class): assert issubclass(engine_class, BaseEngine) if model_type not in cls._engines: cls._engines[model_type] = {} backends = backend if isinstance(backend, list) else [backend] devices = device if isinstance(device, list) else [device] for current_backend in backends: for current_device in devices: if current_backend not in cls._engines[model_type]: cls._engines[model_type][current_backend] = {} if current_device not in cls._engines[model_type][current_backend]: cls._engines[model_type][current_backend][current_device] = engine_class return engine_class return decorator @classmethod def get_engine_cls(cls, model_type: str, backend: str): assert model_type in cls._engines, f"Unknown model_type: {model_type}" assert backend in cls._engines[model_type], f"Unknown backend: {backend}" device = get_device_name() assert device in cls._engines[model_type][backend], ( f"Unknown device: {device} for model_type: {model_type} and backend: {backend}" ) return cls._engines[model_type][backend][device] @classmethod def new(cls, model_type, backend, *args, **kwargs): """ Function to create a new training engine instance based on the provided config. Args: key: A configuration object containing the engine key and other settings. *args: Variable length argument list. **kwargs: Arbitrary keyword arguments. Returns: engine: An instance of the training engine corresponding to the config. Raises: NotImplementedError: If the engine key in the config does not match any known engines. """ engine_cls = cls.get_engine_cls(model_type, backend) return engine_cls(*args, **kwargs) ================================================ FILE: verl/workers/engine/fsdp/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .transformer_impl import FSDPEngine, FSDPEngineWithLMHead __all__ = ["FSDPEngine", "FSDPEngineWithLMHead"] ================================================ FILE: verl/workers/engine/fsdp/transformer_impl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The concrete Engine implementation using PyTorch FullyShardedDataParallel (FSDP) """ import gc import logging import os import warnings from contextlib import nullcontext from typing import Callable, ContextManager, Optional import torch import torch.distributed from peft import LoraConfig, TaskType, get_peft_model from tensordict import TensorDict from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.api import FullStateDictConfig, ShardedStateDictConfig, StateDictType from torch.distributed.tensor import DTensor import verl.utils.torch_functional as verl_F from verl.models.transformers.monkey_patch import apply_monkey_patch from verl.trainer.config import CheckpointConfig from verl.utils import tensordict_utils as tu from verl.utils.activation_offload import enable_activation_offloading from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.debug import log_gpu_memory_usage from verl.utils.device import get_device_id, get_device_name from verl.utils.fsdp_utils import ( CPUOffloadPolicy, FSDPModule, MixedPrecisionPolicy, apply_fsdp2, collect_lora_params, fsdp2_clip_grad_norm_, fsdp2_load_full_state_dict, fsdp_version, get_fsdp_wrap_policy, get_init_weight_context_manager, init_fn, load_fsdp_model_to_gpu, load_fsdp_optimizer, merged_lora_context, normalize_peft_param_name, offload_fsdp_model_to_cpu, offload_fsdp_optimizer, replace_lora_wrapper, ) from verl.utils.model import convert_weight_keys, extract_multi_modal_inputs from verl.utils.py_functional import convert_to_regular_types from verl.utils.torch_functional import logprobs_from_logits from verl.utils.ulysses import ( gather_outputs_and_unpad, get_ulysses_sequence_parallel_group, set_ulysses_sequence_parallel_group, ulysses_pad, ulysses_pad_and_slice_inputs, ) from verl.workers.config import FSDPEngineConfig, FSDPOptimizerConfig, HFModelConfig from ..base import BaseEngine, BaseEngineCtx, EngineRegistry from ..utils import enable_full_determinism, postprocess_batch_func, prepare_micro_batches from .utils import create_device_mesh, get_sharding_strategy logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) device_name = get_device_name() class FSDPEngine(BaseEngine): """ Concrete Engine implementation using PyTorch FullyShardedDataParallel (FSDP). Supports model sharding, activation/optimizer offloading, LoRA, and sequence parallelism. """ def __init__( self, model_config: HFModelConfig, engine_config: FSDPEngineConfig, optimizer_config: FSDPOptimizerConfig, checkpoint_config: CheckpointConfig, ): """ Initialize the FSDPEngine. Sets up distributed device meshes, LoRA, and offload policies based on config. Args: config: Configuration object with FSDP and model settings. """ super().__init__() self.model_config = model_config self.engine_config = engine_config self.optimizer_config = optimizer_config self.checkpoint_config = checkpoint_config self.mode = None self.rank = torch.distributed.get_rank() # Apply NPU patches for FSDP backend from .utils import apply_npu_fsdp_patches apply_npu_fsdp_patches() # build device mesh for Ulysses Sequence Parallel self.use_remove_padding = self.model_config.use_remove_padding self._init_device_mesh() if self.engine_config.full_determinism: enable_full_determinism(seed=self.engine_config.seed) # set FSDP offload params self._is_offload_param = self.engine_config.param_offload self._is_offload_optimizer = self.engine_config.optimizer_offload self._is_lora = self.model_config.lora_rank > 0 # QAT (Quantization-Aware Training) self._qat_config = getattr(self.engine_config, "qat", None) self._qat_enabled = self._qat_config is not None and getattr(self._qat_config, "enable", False) if self._qat_enabled: logger.info(f"QAT enabled: mode={self._qat_config.mode}, group_size={self._qat_config.group_size}") if self.engine_config.entropy_from_logits_with_chunking: entropy_from_logits = verl_F.entropy_from_logits_with_chunking else: entropy_from_logits = verl_F.entropy_from_logits self.compute_entropy_from_logits = ( torch.compile(entropy_from_logits, dynamic=True) if self.engine_config.use_torch_compile # use torch compile by default else entropy_from_logits ) @property def is_param_offload_enabled(self) -> bool: return self._is_offload_param @property def is_optimizer_offload_enabled(self) -> bool: return self._is_offload_optimizer def is_mp_src_rank_with_outputs(self): if self.ulysses_device_mesh is not None: is_collect = self.ulysses_device_mesh["sp"].get_local_rank() == 0 else: is_collect = True return is_collect def initialize(self): """ Build the model, optimizer, and learning rate scheduler under FSDP. Applies device, dtype, and precision configurations, including mixed precision. Sets up checkpoint manager and FLOPs counter. """ # This is used to import external_lib into the huggingface systems self._build_model_optimizer() self.checkpoint_manager = FSDPCheckpointManager( model=self.module, optimizer=self.optimizer, lr_scheduler=self.lr_scheduler, processing_class=self.model_config.get_processor(), checkpoint_config=self.checkpoint_config, trust_remote_code=self.model_config.trust_remote_code, ) self.to( device="cpu", model=self._is_offload_param, optimizer=self._is_offload_optimizer, grad=self._is_offload_param, ) log_gpu_memory_usage("After offload model/optimizer/grad during init", logger=logger) def _init_device_mesh(self): world_size = torch.distributed.get_world_size() from torch.distributed.device_mesh import init_device_mesh fsdp_size = self.engine_config.fsdp_size self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=fsdp_size) self.ulysses_device_mesh = None self.ulysses_parallel_group = None self.ulysses_sequence_parallel_size = self.engine_config.ulysses_sequence_parallel_size dp_size = self.get_data_parallel_size() if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh( device_name, mesh_shape=(dp_size, self.ulysses_sequence_parallel_size), mesh_dim_names=["dp", "sp"] ) self.ulysses_parallel_group = self.ulysses_device_mesh["sp"].get_group() self.use_ulysses_sp = self.ulysses_sequence_parallel_size > 1 def _build_module(self): from verl.utils.model import get_hf_auto_model_class from verl.utils.torch_dtypes import PrecisionType torch_dtype = self.engine_config.model_dtype if torch_dtype is None: # if it is training, we force torch_dtype to fp32 torch_dtype = torch.float32 if not self.engine_config.forward_only else torch.bfloat16 torch_dtype = PrecisionType.to_dtype(torch_dtype) init_context = get_init_weight_context_manager( use_meta_tensor=not self.model_config.hf_config.tie_word_embeddings, mesh=self.device_mesh ) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") auto_class = get_hf_auto_model_class(hf_config=self.model_config.hf_config) module = auto_class.from_pretrained( pretrained_model_name_or_path=self.model_config.local_path, torch_dtype=torch_dtype, config=self.model_config.hf_config, trust_remote_code=self.model_config.trust_remote_code, ) use_liger = self.model_config.use_liger # Apply Liger kernel to the model if use_liger is set to True if use_liger: from liger_kernel.transformers.monkey_patch import _apply_liger_kernel_to_instance _apply_liger_kernel_to_instance(model=module) fused_kernel_options = self.model_config.fused_kernel_options fused_kernels_backend = ( fused_kernel_options.get("impl_backend", None) if fused_kernel_options is not None else None ) use_fused_kernels = self.model_config.use_fused_kernels apply_monkey_patch( model=module, use_remove_padding=self.use_remove_padding, ulysses_sp_size=self.ulysses_sequence_parallel_size, use_fused_kernels=use_fused_kernels, fused_kernels_backend=fused_kernels_backend, ) # some parameters may not in torch_dtype module.to(torch_dtype) if self.model_config.enable_gradient_checkpointing: module.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) return module def _build_lora_module(self, module): module.enable_input_require_grads() lora_adapter_path = getattr(self.model_config, "lora_adapter_path", None) if lora_adapter_path is not None: from peft import PeftModel from verl.utils.fs import copy_to_local print(f"Loading pre-trained LoRA adapter to from: {lora_adapter_path}") # Copy adapter to local if needed local_adapter_path = copy_to_local(lora_adapter_path, use_shm=self.model_config.use_shm) module = PeftModel.from_pretrained(module, local_adapter_path, is_trainable=True) peft_config = module.peft_config["default"] # Ensure task_type is TaskType enum, not string if isinstance(peft_config.task_type, str): peft_config.task_type = TaskType.CAUSAL_LM else: # Convert config to regular Python types before creating PEFT model lora_config = { "task_type": TaskType.CAUSAL_LM, "r": self.model_config.lora_rank, "lora_alpha": self.model_config.lora_alpha, "target_modules": convert_to_regular_types(self.model_config.target_modules), "target_parameters": convert_to_regular_types(self.model_config.target_parameters), "exclude_modules": convert_to_regular_types(self.model_config.exclude_modules), "bias": "none", } module = get_peft_model(module, LoraConfig(**lora_config)) return module def _build_fsdp_module(self, module): # TODO(ziheng): need to improve from torch.distributed.fsdp import CPUOffload, MixedPrecision from verl.utils.torch_dtypes import PrecisionType mixed_precision_config = self.engine_config.mixed_precision if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get("param_dtype", "bf16")) reduce_dtype = PrecisionType.to_dtype(mixed_precision_config.get("reduce_dtype", "fp32")) buffer_dtype = PrecisionType.to_dtype(mixed_precision_config.get("buffer_dtype", "fp32")) else: param_dtype = torch.bfloat16 reduce_dtype = torch.float32 buffer_dtype = torch.float32 mixed_precision = MixedPrecision(param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype) auto_wrap_policy = get_fsdp_wrap_policy( module=module, config=self.engine_config.wrap_policy, is_lora=self.model_config.lora_rank > 0, ) fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) # Note: We force turn off CPUOffload because it causes incorrect results when using grad accumulation if self.engine_config.strategy == "fsdp": # cpu_offload: # - actor: None # - critic: None # - ref: CPUOffload(offload_params=True) # We force reference policy to use CPUOffload to save memory. # We force turn off CPUOffload for actor because it causes incorrect results when using grad accumulation cpu_offload = None if self.engine_config.forward_only: cpu_offload = CPUOffload(offload_params=True) self._is_offload_param = False self._is_offload_optimizer = False module = FSDP( module, param_init_fn=init_fn, auto_wrap_policy=auto_wrap_policy, device_id=get_device_id(), sharding_strategy=sharding_strategy, mixed_precision=mixed_precision, sync_module_states=True, device_mesh=self.device_mesh, forward_prefetch=self.engine_config.forward_prefetch, use_orig_params=self.engine_config.use_orig_params, cpu_offload=cpu_offload, ) elif self.engine_config.strategy == "fsdp2": # - actor: offload_policy # - critic: offload_policy # - ref: CPUOffloadPolicy(pin_memory=True) assert CPUOffloadPolicy is not None, "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)" mp_policy = MixedPrecisionPolicy( param_dtype=param_dtype, reduce_dtype=reduce_dtype, cast_forward_inputs=True ) offload_policy = None if self.engine_config.offload_policy or self.engine_config.forward_only: self._is_offload_param = False self._is_offload_optimizer = False offload_policy = CPUOffloadPolicy(pin_memory=True) fsdp_kwargs = { "mesh": fsdp_mesh, "mp_policy": mp_policy, "offload_policy": offload_policy, "reshard_after_forward": self.engine_config.reshard_after_forward, } full_state = module.state_dict() apply_fsdp2(module, fsdp_kwargs, self.engine_config) fsdp2_load_full_state_dict(module, full_state, fsdp_mesh, offload_policy) else: raise NotImplementedError(f"Unknown strategy {self.engine_config.strategy}") if self.model_config.enable_activation_offload: enable_gradient_checkpointing = self.model_config.enable_gradient_checkpointing enable_activation_offloading(module, self.engine_config.strategy, enable_gradient_checkpointing) if torch.distributed.get_world_size() == 1 and fsdp_version(module) == 1: FSDP.set_state_dict_type( module, state_dict_type=StateDictType.FULL_STATE_DICT, state_dict_config=FullStateDictConfig(), ) elif fsdp_version(module) == 1: FSDP.set_state_dict_type( module, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig(), ) return module def _build_optimizer(self, module): from verl.workers.config.optimizer import build_optimizer optimizer = build_optimizer(module.parameters(), self.optimizer_config) return optimizer def _build_lr_scheduler(self, optimizer): from verl.utils.torch_functional import get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup optim_config = self.optimizer_config total_steps = optim_config.total_training_steps num_warmup_steps = optim_config.lr_warmup_steps lr_scheduler_type = optim_config.lr_scheduler_type min_lr_ratio = optim_config.min_lr_ratio num_cycles = optim_config.num_cycles zero_indexed_step = optim_config.zero_indexed_step if num_warmup_steps <= 0: num_warmup_steps_ratio = optim_config.lr_warmup_steps_ratio num_warmup_steps = int(num_warmup_steps_ratio * total_steps) if self.rank == 0: print(f"Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}") if lr_scheduler_type == "constant": lr_scheduler = get_constant_schedule_with_warmup(optimizer=optimizer, num_warmup_steps=num_warmup_steps) elif lr_scheduler_type == "cosine": lr_scheduler = get_cosine_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps, min_lr_ratio=min_lr_ratio, num_cycles=num_cycles, zero_indexed_step=zero_indexed_step, ) else: raise NotImplementedError(f"LR scheduler type {lr_scheduler_type} is not supported") return lr_scheduler def _apply_qat(self, module): """Apply QAT transformations to the model before FSDP wrapping.""" from verl.utils.qat.core import apply_qat, enable_qat_fuse module = apply_qat( module, { "enable": self._qat_config.enable, "mode": self._qat_config.mode, "group_size": self._qat_config.group_size, "ignore_patterns": list(self._qat_config.ignore_patterns), "activation_observer": self._qat_config.activation_observer, }, ) enable_qat_fuse(module) if self._qat_config.mode == "w4a4": self._restore_w4a4_input_scales(module, self.model_config.local_path) return module def _restore_w4a4_input_scales(self, model, model_path): """Restore input_global_scale and input_amax from checkpoint for W4A4 mode.""" import glob from safetensors import safe_open safetensor_files = glob.glob(f"{model_path}/model*.safetensors") loaded_count = 0 for sf_path in safetensor_files: with safe_open(sf_path, framework="pt") as f: for key in f.keys(): if "input_global_scale" in key: module_path = key.replace(".input_global_scale", "") amax_key = f"{module_path}.input_amax" module = model for part in module_path.split("."): module = module[int(part)] if part.isdigit() else getattr(module, part) scale_val = f.get_tensor(key) val = scale_val.item() if scale_val.numel() == 1 else scale_val.max().item() module.input_global_scale.fill_(val) amax_val = f.get_tensor(amax_key) amax = amax_val.item() if amax_val.numel() == 1 else amax_val.max().item() module.input_amax.fill_(amax) loaded_count += 1 logger.info(f"[QAT W4A4] Restored {loaded_count} input_global_scale/input_amax from {model_path}") def _build_model_optimizer(self): from verl.utils.model import print_model_size # Load base model with specified configuration and dtype module = self._build_module() # Apply LoRA adapters if low-rank adaptation is enabled if self._is_lora: module = self._build_lora_module(module) # Apply QAT before FSDP wrapping (training only) if self._qat_enabled and not self.engine_config.forward_only: module = self._apply_qat(module) # Synchronize all distributed processes before proceeding torch.distributed.barrier() if self.rank == 0: print_model_size(module) log_gpu_memory_usage("After init model from HF AutoModel", logger=logger) # Wrap model with FSDP for distributed training (sharding, mixed precision, etc.) log_gpu_memory_usage("Before FSDP", logger=None) module = self._build_fsdp_module(module) log_gpu_memory_usage("After FSDP", logger=None) if not self.engine_config.forward_only: # Initialize optimizer with model parameters and config settings optimizer = self._build_optimizer(module) # Create learning rate scheduler with warmup and decay settings lr_scheduler = self._build_lr_scheduler(optimizer) else: optimizer = None lr_scheduler = None self.module = module self.optimizer = optimizer self.lr_scheduler = lr_scheduler def train_mode(self, **kwargs): """ Return a context manager that switches to training mode with FSDP-specific handling. Includes parameter and optimizer offload entry/exit. """ return EngineTrainModeCtx(self, **kwargs) def eval_mode(self, **kwargs): """ Return a context manager that switches to evaluation mode with FSDP-specific handling. Includes activation offload entry/exit. """ return EngineEvalModeCtx(self, **kwargs) def get_data_parallel_rank(self): if self.ulysses_device_mesh is not None: return self.ulysses_device_mesh["dp"].get_local_rank() else: return torch.distributed.get_rank() def get_data_parallel_size(self): return torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size def get_data_parallel_group(self): if self.ulysses_device_mesh is not None: return self.ulysses_device_mesh.get_group(mesh_dim="dp") else: return torch.distributed.group.WORLD def get_model_parallel_group(self): raise NotImplementedError def get_context_parallel_group(self): raise NotImplementedError def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False) -> list[TensorDict]: # note that the global_batch_size should include data on all the dp tu.assign_non_tensor(data, sp_size=self.ulysses_sequence_parallel_size) # compute num_tokens in global batch for loss normalization batch_num_tokens = data["loss_mask"].sum().to(get_device_id()) torch.distributed.all_reduce( batch_num_tokens, op=torch.distributed.ReduceOp.SUM, group=self.get_data_parallel_group() ) tu.assign_non_tensor(data, batch_num_tokens=batch_num_tokens.item()) tu.assign_non_tensor(data, dp_size=self.get_data_parallel_size()) micro_batches, indices = prepare_micro_batches( data=data, dp_group=self.get_data_parallel_group(), same_micro_num_in_dp=True ) output_lst = [] ctx = torch.no_grad() if forward_only else nullcontext() for micro_batch in micro_batches: with ctx: loss, meta_info = self.forward_step(micro_batch, loss_function=loss_function, forward_only=forward_only) if not forward_only: loss.backward() output_lst.append(meta_info) # postprocess and return return postprocess_batch_func(output_lst=output_lst, indices=indices, data=data) def forward_step(self, micro_batch: TensorDict, loss_function, forward_only): raise NotImplementedError("forward_step must be implemented in subclass") def optimizer_zero_grad(self): """ Zero gradients and enforce FSDP grad-clipping logic. """ self.optimizer.zero_grad() def optimizer_step(self): """ Clip gradients, skip update if non-finite, and step optimizer. Returns: grad_norm (float): Norm of gradients before clipping. """ assert self.optimizer_config.clip_grad is not None if isinstance(self.module, FSDP): grad_norm = self.module.clip_grad_norm_(self.optimizer_config.clip_grad) elif isinstance(self.module, FSDPModule): grad_norm = fsdp2_clip_grad_norm_(self.module.parameters(), max_norm=self.optimizer_config.clip_grad) else: grad_norm = torch.nn.utils.clip_grad_norm_( self.module.parameters(), max_norm=self.optimizer_config.clip_grad ) if isinstance(grad_norm, DTensor): grad_norm = grad_norm.full_tensor() # if grad_norm is not finite, skip the update if not torch.isfinite(grad_norm): print(f"WARN: grad_norm is not finite: {grad_norm}") self.optimizer.zero_grad() else: self.optimizer.step() if self._qat_enabled: from verl.utils.qat.core import invalidate_all_scales invalidate_all_scales(self.module) return grad_norm.item() def lr_scheduler_step(self): """ Advance FSDP scheduler and return updated learning rate. """ self.lr_scheduler.step() lr = self.lr_scheduler.get_last_lr()[0] # only return the first group return lr def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): """ Move FSDP model and/or optimizer to CPU or GPU with offload support. Note that this function executes irrespective of offload config. It serves as manual control """ super().to(device=device, model=model, optimizer=optimizer, grad=grad) if self.engine_config.forward_only: # force cpu_offload return device_name = get_device_name() assert device in (device_name, "cpu") if device == device_name: if model: load_fsdp_model_to_gpu(self.module) if optimizer and self.optimizer is not None: load_fsdp_optimizer(self.optimizer, device) gc.collect() elif device == "cpu": if model: offload_fsdp_model_to_cpu(self.module) if optimizer and self.optimizer is not None: offload_fsdp_optimizer(self.optimizer) else: raise ValueError(f"Invalid device type: {device}") def save_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, global_step: int = 0, max_ckpt_to_keep: Optional[int] = None, **kwargs, ) -> None: """ Save FSDP checkpoint, handling parameter offload as needed. """ origin_module_device = next(self.module.parameters()).device.type if self._is_offload_param or origin_module_device == "cpu": load_fsdp_model_to_gpu(self.module) self.checkpoint_manager.save_checkpoint( local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep ) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.module) def load_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, del_local_after_load: int = True, **kwargs ) -> None: """ Load FSDP checkpoint, restoring parameters and optimizer state. """ import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.module) self.checkpoint_manager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.module) if self._is_offload_optimizer: offload_fsdp_optimizer(self.optimizer) def get_per_tensor_param(self, layered_summon=False, base_sync_done=False, **kwargs): log_gpu_memory_usage("Before load_fsdp_model_to_gpu", logger=logger) load_fsdp_model_to_gpu(self.module) log_gpu_memory_usage("After load_fsdp_model_to_gpu", logger=logger) peft_config = None merge_lora = self.model_config.lora.get("merge", False) peft_model = getattr(self.module, "_fsdp_wrapped_module", self.module) if hasattr(peft_model, "peft_config"): # LoRA if not merge_lora: peft_config = peft_model.peft_config.get("default", None) params = collect_lora_params( module=self.module, layered_summon=layered_summon, base_sync_done=base_sync_done, ) if not base_sync_done: params = {replace_lora_wrapper(k, peft_config): v for k, v in params.items()} else: # merge lora with merged_lora_context(self.module, backup_adapters=True): params = self.module.state_dict() params = normalize_peft_param_name(params) else: params = self.module.state_dict() params = convert_weight_keys(params, getattr(self.module, "_fsdp_wrapped_module", self.module)) log_gpu_memory_usage("Before offload_fsdp_model_to_cpu", logger=logger) if self._is_offload_param: offload_fsdp_model_to_cpu(self.module) log_gpu_memory_usage("After offload_fsdp_model_to_cpu", logger=logger) if peft_config is not None and base_sync_done: per_tensor_param = params.items() else: device = get_device_id() # used when fsdp2 set cpu_offload_policy # TODO: cast fp32 to bf16 to reduce weight sync overhead, need more fine-grained control, e.g MoE gate per_tensor_param = ( ( name, param.to(device, non_blocking=True).full_tensor().to(torch.bfloat16, non_blocking=True) if isinstance(param, DTensor) else param, ) for name, param in params.items() ) if self._qat_enabled: from verl.utils.qat.quantizer import QATQuantizer from verl.utils.torch_dtypes import PrecisionType mixed_precision_config = self.engine_config.mixed_precision if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get("param_dtype", "bf16")) else: param_dtype = torch.bfloat16 quantizer = QATQuantizer( mode=self._qat_config.mode, group_size=self._qat_config.group_size, ignore_patterns=list(self._qat_config.ignore_patterns), device=torch.device(get_device_id()), param_dtype=param_dtype, ) per_tensor_param = quantizer.quantize_with_fusion( per_tensor_param, target_device=torch.device("cpu"), ) peft_config_dict = peft_config.to_dict() if peft_config is not None else None return per_tensor_param, peft_config_dict def disable_adapter(self) -> ContextManager: return self.module.disable_adapter() class EngineEvalModeCtx(BaseEngineCtx): def __init__(self, engine: FSDPEngine, **kwargs): super().__init__(engine=engine, mode="eval", **kwargs) def __enter__(self): assert isinstance(self.engine, FSDPEngine) super().__enter__() self.prev_sp_group = get_ulysses_sequence_parallel_group() set_ulysses_sequence_parallel_group(self.engine.ulysses_parallel_group) self.engine.module.eval() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, FSDPEngine) set_ulysses_sequence_parallel_group(self.prev_sp_group) # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.engine.engine_config.fsdp_size > 1: if fsdp_version(self.engine.module) == 1: self.engine.module._handle.reshard(True) elif fsdp_version(self.engine.module) == 2: self.engine.module.reshard() super().__exit__(exc_type, exc_value, traceback) class EngineTrainModeCtx(BaseEngineCtx): def __init__(self, engine: FSDPEngine, **kwargs): super().__init__(engine=engine, mode="train", **kwargs) def __enter__(self): assert isinstance(self.engine, FSDPEngine) super().__enter__() self.prev_sp_group = get_ulysses_sequence_parallel_group() set_ulysses_sequence_parallel_group(self.engine.ulysses_parallel_group) self.engine.module.train() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, FSDPEngine) set_ulysses_sequence_parallel_group(self.prev_sp_group) self.engine.optimizer_zero_grad() super().__exit__(exc_type, exc_value, traceback) @EngineRegistry.register(model_type="language_model", backend=["fsdp", "fsdp2"], device=["cuda", "npu"]) class FSDPEngineWithLMHead(FSDPEngine): def prepare_model_inputs(self, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) use_fused_kernels = tu.get_non_tensor_data(data=micro_batch, key="use_fused_kernels", default=False) temperature = micro_batch["temperature"] temperature_item = temperature if use_fused_kernels: assert not isinstance(temperature, torch.Tensor), ( "use_fused_kernels does not support per sample temperature yet" ) assert pad_mode == DatasetPadMode.NO_PADDING, f"pad_mode {pad_mode} not supported" multi_modal_inputs = extract_multi_modal_inputs(micro_batch.get("multi_modal_inputs", [])) input_ids = micro_batch["input_ids"] position_ids = micro_batch["position_ids"] if not isinstance(temperature, torch.Tensor): temperature = torch.tensor([temperature] * input_ids.shape[0], device=input_ids.device) temperature = temperature.to(torch.float32) assert temperature.shape[0] == input_ids.shape[0] # args used to get outputs output_args = {} if use_remove_padding: # support per sample temperature # temperature (bsz,) # input_ids (bsz, j1) temperature_rmpad = verl_F.expand_as_nested(temperature, input_ids).values() # (total_nnz,) temperature_rmpad = temperature_rmpad.unsqueeze(0) # (1, total_nnz) if pad_mode == DatasetPadMode.NO_PADDING: input_ids_rmpad = input_ids.values().unsqueeze(0) # (1, total_nnz) if position_ids.dim() == 3: position_ids_rmpad = position_ids.values().unsqueeze(1) # (4, 1, total_nnz) else: position_ids_rmpad = position_ids.values().unsqueeze(0) # (1, total_nnz) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") # for compute the log_prob input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=1) # (1, total_nnz) # pad and slice the inputs if sp > 1 if self.use_ulysses_sp: is_vlm_model = hasattr(getattr(self.module, "module", self.module).config, "vision_config") if is_vlm_model: # vlm model's inputs will be sliced after embedding input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad( input_ids_rmpad, position_ids_rmpad=position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size, ) else: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs( input_ids_rmpad, position_ids_rmpad=position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size, skip_position_ids_rmpad=True if self.__class__.__name__ == "VeOmniEngineWithLMHead" else False, ) input_ids_rmpad_rolled, _, _ = ulysses_pad_and_slice_inputs( input_ids_rmpad_rolled, position_ids_rmpad=None, sp_size=self.ulysses_sequence_parallel_size, ) temperature_rmpad, _, _ = ulysses_pad_and_slice_inputs( temperature_rmpad, position_ids_rmpad=None, sp_size=self.ulysses_sequence_parallel_size, pad_value=1 ) output_args["pad_size"] = pad_size input_ids_rmpad_rolled = input_ids_rmpad_rolled.squeeze(0) # ((total_nnz / sp) + pad) temperature_rmpad = temperature_rmpad.squeeze(0) output_args["input_ids_rmpad_rolled"] = input_ids_rmpad_rolled output_args["temperature_rmpad"] = temperature_rmpad # only pass input_ids and position_ids to enable flash_attn_varlen model_inputs = { "input_ids": input_ids_rmpad, "attention_mask": None, "position_ids": position_ids_rmpad, } else: if pad_mode == DatasetPadMode.NO_PADDING: input_ids = micro_batch["input_ids"] position_ids = micro_batch["position_ids"] loss_mask = micro_batch["loss_mask"] pad_token_id = tu.get_non_tensor_data(data=micro_batch, key="pad_token_id", default=0) batch_size = micro_batch.batch_size[0] seq_len_effective = input_ids.offsets().diff() max_seq_len = max(seq_len_effective) input_ids_rmpad_rolled = torch.roll(input_ids.values(), shifts=-1, dims=0) output_args["input_ids_rmpad_rolled"] = input_ids_rmpad_rolled # we store the per sample temperature output_args["temperature"] = temperature input_ids = torch.nested.to_padded_tensor( input_ids, padding=pad_token_id, output_size=(batch_size, max_seq_len) ) if position_ids.dim() == 3: position_ids = torch.nested.to_padded_tensor( position_ids, padding=0, output_size=(batch_size, 4, max_seq_len) ).transpose(0, 1) # (4, batch_size, max_seq_len) else: position_ids = torch.nested.to_padded_tensor( position_ids, padding=0, output_size=(batch_size, max_seq_len) ) attention_mask_list = [torch.ones_like(t, dtype=torch.int32) for t in loss_mask] attention_mask = torch.nested.as_nested_tensor(attention_mask_list, layout=torch.jagged) attention_mask = torch.nested.to_padded_tensor( attention_mask, padding=0, output_size=(batch_size, max_seq_len) ) model_inputs = { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, } else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") extra_args = {} if use_fused_kernels: extra_args["temperature"] = temperature_item extra_args["return_dict"] = True model_inputs.update(multi_modal_inputs) model_inputs.update(extra_args) return model_inputs, output_args def prepare_model_outputs(self, output, output_args, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) use_fused_kernels = tu.get_non_tensor_data(data=micro_batch, key="use_fused_kernels", default=False) calculate_entropy = tu.get_non_tensor_data(data=micro_batch, key="calculate_entropy", default=False) model_output = {} input_ids = micro_batch["input_ids"] if use_remove_padding: input_ids_rmpad_rolled = output_args["input_ids_rmpad_rolled"] temperature_rmpad = output_args["temperature_rmpad"] if use_fused_kernels: # temperature is singleton log_probs = output.log_probs.squeeze(0) # (total_nnz,) entropy_rmpad = output.entropy.squeeze(0) # (total_nnz,) else: logits_rmpad = output.logits.squeeze(0) # (total_nnz, vocab_size) logits_rmpad.div_(temperature_rmpad.clamp(min=1e-8).unsqueeze(-1).to(logits_rmpad.dtype)) # if use_sp: ((total_nnz / sp) + pad) ; if not use_sp: (batch, seqlen) inplace_backward = True if calculate_entropy: inplace_backward = False log_probs = logprobs_from_logits( logits=logits_rmpad, labels=input_ids_rmpad_rolled, inplace_backward=inplace_backward, ) # compute entropy if calculate_entropy: if not self.engine_config.entropy_checkpointing: entropy_rmpad = self.compute_entropy_from_logits(logits_rmpad) # ((total_nnz / sp) + pad) else: entropy_rmpad = torch.utils.checkpoint.checkpoint( self.compute_entropy_from_logits, logits_rmpad ) # gather log_prob if sp > 1 if self.use_ulysses_sp: pad_size = output_args["pad_size"] # gather and unpad for the ulysses sp log_probs = gather_outputs_and_unpad( log_probs, gather_dim=0, unpad_dim=0, padding_size=pad_size, ) if calculate_entropy: entropy_rmpad = gather_outputs_and_unpad( entropy_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size, ) if pad_mode == DatasetPadMode.NO_PADDING: cu_seqlens = input_ids.offsets() # (bsz, j1), for each sample, is the length of each sample: [real_prompt length + real_response length] log_probs = torch.nested.nested_tensor_from_jagged(log_probs, cu_seqlens) if calculate_entropy: entropy = torch.nested.nested_tensor_from_jagged(entropy_rmpad, cu_seqlens) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") else: # not using rmpad and no ulysses sp response_length = tu.get_non_tensor_data(data=micro_batch, key="max_response_length", default=1024) if use_fused_kernels: log_probs = output.log_probs[:, -response_length - 1 : -1] entropy = output.entropy[:, -response_length - 1 : -1] # (bsz, response_length) else: logits = output.logits # (bsz, response_length, vocab_size) temperature = output_args["temperature"] # (bsz,) temperature = temperature.unsqueeze(-1).unsqueeze(-1) logits.div_(temperature.clamp(min=1e-8).to(logits.dtype)) if calculate_entropy: if not self.engine_config.entropy_checkpointing: entropy = verl_F.entropy_from_logits(logits) else: entropy = torch.utils.checkpoint.checkpoint(verl_F.entropy_from_logits, logits) if pad_mode == DatasetPadMode.NO_PADDING: cu_seqlens = input_ids.offsets() seq_lengths = cu_seqlens.diff() starts = torch.zeros_like(seq_lengths, dtype=torch.int64) logits = torch.nested.narrow(logits, 1, starts, seq_lengths, layout=torch.jagged) logits_rmpad = torch.cat([t for t in logits.unbind()]) input_ids_rmpad_rolled = output_args["input_ids_rmpad_rolled"] log_probs = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # (bsz, j1), for each sample, length of each sample: [real_prompt_length + real_response_length] log_probs = torch.nested.nested_tensor_from_jagged(log_probs, cu_seqlens) if calculate_entropy: entropy = torch.nested.narrow(entropy, 1, starts, seq_lengths, layout=torch.jagged) entropy_rmpad = torch.cat([t for t in entropy.unbind()]) entropy = torch.nested.nested_tensor_from_jagged(entropy_rmpad, cu_seqlens) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") model_output["log_probs"] = log_probs if calculate_entropy: model_output["entropy"] = entropy return model_output def forward_step(self, micro_batch: TensorDict, loss_function, forward_only): device_name = get_device_name() # actually, we should avoid assigning like this... micro_batch = micro_batch.to(get_device_id()) model_inputs, output_args = self.prepare_model_inputs(micro_batch=micro_batch) with torch.autocast(device_type=device_name, dtype=torch.bfloat16): raw_output = self.module( **model_inputs, use_cache=False, ) # prevent model thinks we are generating model_output = self.prepare_model_outputs( output=raw_output, output_args=output_args, micro_batch=micro_batch ) if loss_function is not None: loss, metrics = loss_function( model_output=model_output, data=micro_batch, dp_group=self.get_data_parallel_group() ) else: assert forward_only, "forward_only must be True when loss_function is None" loss = torch.tensor(1.0, device=device_name) metrics = {} output = { "model_output": model_output, "loss": loss.detach().item(), "metrics": metrics, } return loss, output @EngineRegistry.register(model_type="value_model", backend=["fsdp", "fsdp2"], device=["cuda", "npu"]) class FSDPEngineWithValueHead(FSDPEngineWithLMHead): """ The only difference between critic and actor is how the raw model output is processed """ def prepare_model_outputs(self, output, output_args, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) input_ids = micro_batch["input_ids"] if use_remove_padding: if hasattr(self.module, "v_head"): # For trl.AutoModelForCausalLMWithValueHead values_rmpad = output[2].squeeze(0).unsqueeze(-1) else: values_rmpad = output.logits values_rmpad = values_rmpad.squeeze(0) # (total_nnz, 1) # critic model arch is like Qwen3ForTokenClassfication and num_labels=1 # so we squeeze the last dimension here to get the value for each token values_rmpad = values_rmpad.squeeze(-1) # gather output if sp > 1 if self.use_ulysses_sp: pad_size = output_args["pad_size"] values_rmpad = gather_outputs_and_unpad(values_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size) if pad_mode == DatasetPadMode.NO_PADDING: cu_seqlens = input_ids.offsets() # (bsz, j1), for each sample, is the length of each sample: [real_prompt length + real_response length] values = torch.nested.nested_tensor_from_jagged(values_rmpad, cu_seqlens) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") else: if hasattr(self.module, "v_head"): # For trl.AutoModelForCausalLMWithValueHead values = output[2] else: values = output.logits if pad_mode == DatasetPadMode.NO_PADDING: cu_seqlens = input_ids.offsets() seq_lengths = cu_seqlens.diff() starts = torch.zeros_like(seq_lengths, dtype=torch.int64) values = torch.nested.narrow(values, 1, starts, seq_lengths, layout=torch.jagged) values_rmpad = torch.cat([t for t in values.unbind()]) # (bsz, j1), for each sample, length of each sample: [real_prompt_length + real_response_length] values = torch.nested.nested_tensor_from_jagged(values_rmpad, cu_seqlens) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") return {"values": values} ================================================ FILE: verl/workers/engine/fsdp/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import torch from torch.distributed.device_mesh import init_device_mesh from verl.utils.device import get_device_name, is_npu_available logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def apply_npu_fsdp_patches(): """Apply NPU patches for FSDP backend if NPU is available.""" if is_npu_available: try: import verl.models.transformers.npu_patch # noqa if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0: logger.info("Applied NPU patches for FSDP backend") except Exception as e: logger.warning(f"Failed to apply NPU patches: {e}") def create_device_mesh(world_size, fsdp_size): """ Create a device mesh for distributed training based on the world size and FSDP size. Args: world_size (int): Total number of processes in the distributed training setup. fsdp_size (int): Size of the Fully Sharded Data Parallel (FSDP) group. Returns: torch.distributed.device_mesh.DeviceMesh: The initialized device mesh. """ device_name = get_device_name() if fsdp_size < 0 or fsdp_size >= world_size: device_mesh = init_device_mesh(device_name, mesh_shape=(world_size,), mesh_dim_names=["fsdp"]) else: device_mesh = init_device_mesh( device_name, mesh_shape=(world_size // fsdp_size, fsdp_size), mesh_dim_names=["ddp", "fsdp"] ) return device_mesh def get_sharding_strategy(device_mesh): """ Determine the appropriate sharding strategy based on the number of dimensions of the device mesh. Args: device_mesh (torch.distributed.device_mesh.DeviceMesh): The device mesh used for distributed training. Returns: torch.distributed.fsdp.ShardingStrategy: The sharding strategy to be used with FSDP. Raises: NotImplementedError: If the number of dimensions of the device mesh is neither 1 nor 2. """ from torch.distributed.fsdp import ShardingStrategy if device_mesh.ndim == 1: sharding_strategy = ShardingStrategy.FULL_SHARD elif device_mesh.ndim == 2: sharding_strategy = ShardingStrategy.HYBRID_SHARD else: raise NotImplementedError(f"Get device mesh ndim={device_mesh.ndim}, but only support 1 or 2") return sharding_strategy ================================================ FILE: verl/workers/engine/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # HACK Avoid cpu worker trigger cuda jit error import os from verl.utils.device import is_cuda_available if not is_cuda_available and "TORCH_CUDA_ARCH_LIST" not in os.environ: os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0" from .transformer_impl import MegatronEngine, MegatronEngineWithLMHead # noqa: E402 if not is_cuda_available: del os.environ["TORCH_CUDA_ARCH_LIST"] __all__ = ["MegatronEngine", "MegatronEngineWithLMHead"] ================================================ FILE: verl/workers/engine/megatron/transformer_impl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from functools import partial from typing import Any, Callable, ContextManager, Iterator, Optional import torch import torch.distributed from megatron.core import parallel_state as mpu from megatron.core.pipeline_parallel import get_forward_backward_func from omegaconf import OmegaConf from tensordict import TensorDict import verl.utils.torch_functional as verl_F from verl.models.mcore import get_mcore_forward_fused_no_padding_fn, get_mcore_weight_converter from verl.trainer.config import CheckpointConfig from verl.utils import tensordict_utils as tu from verl.utils.checkpoint.megatron_checkpoint_manager import MegatronCheckpointManager from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.debug import log_gpu_memory_usage from verl.utils.device import get_device_id, get_device_name from verl.utils.megatron.pipeline_parallel import make_batch_generator from verl.utils.megatron.router_replay_patch import RouterReplay, RouterReplayAction, apply_router_replay_patch from verl.utils.megatron.router_replay_utils import ( RouterReplayHelper, merge_router_topk_indices, pp_gather, reorder_and_merge_vpp_layers, set_router_replay_data, ) from verl.utils.megatron.tensor_parallel import vocab_parallel_entropy, vocab_parallel_log_probs_from_logits from verl.utils.megatron_peft_utils import add_base_layer_suffix, build_peft_config_for_vllm from verl.utils.megatron_utils import ( check_mtp_config, get_megatron_module_device, get_megatron_mtp_loss, load_megatron_model_to_gpu, load_megatron_optimizer, offload_megatron_model_to_cpu, offload_megatron_optimizer, patch_engine_mtp, register_megatron_training_hooks, unwrap_model, ) from verl.utils.model import extract_multi_modal_inputs, load_mcore_dist_weights from verl.utils.seqlen_balancing import restore_dynamic_batch from verl.workers.config import HFModelConfig, McoreEngineConfig, McoreOptimizerConfig from ..base import BaseEngine, BaseEngineCtx, EngineRegistry from ..utils import postprocess_batch_func, prepare_micro_batches from .utils import set_random_seed logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class MegatronEngine(BaseEngine): def __init__( self, model_config: HFModelConfig, engine_config: McoreEngineConfig, optimizer_config: McoreOptimizerConfig, checkpoint_config: CheckpointConfig, ): super().__init__() self.model_config = model_config self.engine_config = engine_config self.optimizer_config = optimizer_config self.checkpoint_config = checkpoint_config assert self.engine_config.use_mbridge, "use_mbridge must be True" self._init_device_mesh() set_random_seed(seed=self.engine_config.seed) self._is_offload_param = self.engine_config.param_offload self._is_offload_grad = self.engine_config.grad_offload self._is_offload_optimizer = self.engine_config.optimizer_offload self.mode = None self.layer_name_mapping = { "qkv_layer_name": "self_attention.linear_qkv.", "gate_proj_layer_name": "linear_fc1.", } self.weight_converter = None # Router replay configuration for MoE models self.enable_routing_replay = self.engine_config.router_replay.mode != "disabled" logger.info(f"enable_routing_replay in MegatronEngine: {self.enable_routing_replay}") if self.enable_routing_replay: apply_router_replay_patch() self.mini_layer_topk_idx_list = [] def _init_device_mesh(self): # TODO: set different parallelism for actor, critic, ref if mpu.is_initialized(): return mpu.initialize_model_parallel( tensor_model_parallel_size=self.engine_config.tensor_model_parallel_size, pipeline_model_parallel_size=self.engine_config.pipeline_model_parallel_size, virtual_pipeline_model_parallel_size=self.engine_config.virtual_pipeline_model_parallel_size, use_sharp=False, context_parallel_size=self.engine_config.context_parallel_size, expert_model_parallel_size=self.engine_config.expert_model_parallel_size, expert_tensor_parallel_size=self.engine_config.expert_tensor_parallel_size, nccl_communicator_config_path=None, ) def _build_tf_config(self): from verl.utils.megatron_utils import mapping_string_to_attn_backend from verl.utils.torch_dtypes import PrecisionType check_mtp_config(self.model_config, self.engine_config) self.param_dtype = PrecisionType.to_dtype(self.engine_config.dtype) self.dtype = PrecisionType.to_dtype(self.param_dtype) override_transformer_config = mapping_string_to_attn_backend({**self.engine_config.override_transformer_config}) if self.enable_routing_replay: override_transformer_config["enable_routing_replay"] = True self.provider = None self.vanilla_bridge = self.engine_config.vanilla_mbridge if self.vanilla_bridge: from verl.models.mcore.mbridge import AutoBridge bridge = AutoBridge.from_config(self.model_config.hf_config, dtype=self.param_dtype) bridge.set_extra_args(**override_transformer_config) tf_config = bridge.config tf_config.fp16 = self.param_dtype == torch.float16 tf_config.bf16 = self.param_dtype == torch.bfloat16 else: from verl.models.mcore.bridge import AutoBridge # Use Megatron-Bridge to convert HF config to Megatron config bridge = AutoBridge.from_hf_pretrained( self.model_config.local_path, trust_remote_code=self.model_config.trust_remote_code ) # Get Megatron provider and configure it provider = bridge.to_megatron_provider(load_weights=False) # In case of invalid overrides, we need to make sure some critical params are set correctly provider.params_dtype = self.param_dtype # Ensure dtype settings propagate to Megatron-Bridge/TE provider.fp16 = self.param_dtype == torch.float16 provider.bf16 = self.param_dtype == torch.bfloat16 # Pass distributed info provider.tensor_model_parallel_size = self.engine_config.tensor_model_parallel_size provider.pipeline_model_parallel_size = self.engine_config.pipeline_model_parallel_size provider.expert_model_parallel_size = self.engine_config.expert_model_parallel_size provider.expert_tensor_parallel_size = self.engine_config.expert_tensor_parallel_size provider.virtual_pipeline_model_parallel_size = self.engine_config.virtual_pipeline_model_parallel_size provider.context_parallel_size = self.engine_config.context_parallel_size provider.sequence_parallel = self.engine_config.sequence_parallel # Match verl implementation (need variable_seq_lengths) from megatron.core.transformer.enums import AttnBackend provider.attention_backend = AttnBackend.flash provider.variable_seq_lengths = True provider.moe_token_dispatcher_type = "alltoall" provider.moe_router_load_balancing_type = "none" # Apply transformer config overrides for key, value in override_transformer_config.items(): setattr(provider, key, value) provider.finalize() self.provider = provider tf_config = None # Will be set after model creation self.bridge = bridge if not self.bridge: self.weight_converter = get_mcore_weight_converter(self.model_config.hf_config, self.dtype) if torch.distributed.get_rank() == 0: if tf_config is not None: print(f"TF config: {tf_config}") self.tf_config = tf_config from verl.workers.config.megatron_peft import get_peft_cls self.peft_cls = get_peft_cls( model_config=self.model_config, bridge=self.bridge, provider=self.provider, dtype=self.param_dtype ) def _build_megatron_module(self): from verl.utils.megatron_utils import McoreModuleWrapperConfig, make_megatron_module from verl.utils.model import print_model_size # TODO: add more cases is_value_model = ( "ForTokenClassification" in self.model_config.architectures[0] or "ForSequenceClassification" in self.model_config.architectures[0] ) self.is_value_model = is_value_model if self.engine_config.forward_only: wrap_with_ddp = False else: wrap_with_ddp = True wrap_config = McoreModuleWrapperConfig( is_value_model=is_value_model, # actor is not value model share_embeddings_and_output_weights=self.model_config.share_embeddings_and_output_weights, wrap_with_ddp=wrap_with_ddp, use_distributed_optimizer=self.engine_config.use_distributed_optimizer, ) module, updated_tf_config = make_megatron_module( wrap_config=wrap_config, tf_config=self.tf_config, hf_config=self.model_config.hf_config, bridge=self.bridge, provider=self.provider, override_model_config=self.engine_config.override_mcore_model_config, override_ddp_config=self.engine_config.override_ddp_config, peft_cls=self.peft_cls, peft_config=self.model_config.get("lora", None), ) self.tf_config = updated_tf_config print(f"module: {len(module)}") if self.engine_config.use_dist_checkpointing: load_mcore_dist_weights(module, self.engine_config.dist_checkpointing_path, is_value_model=is_value_model) else: if self.vanilla_bridge: self.bridge.load_weights(module, self.model_config.local_path) else: allowed_mismatched_params = [] if self.is_value_model: allowed_mismatched_params = ["output_layer.weight"] self.bridge.load_hf_weights( module, self.model_config.local_path, allowed_mismatched_params=allowed_mismatched_params ) if torch.distributed.get_rank() == 0: print_model_size(module[0]) if self.enable_routing_replay: print(f"routing replay layers: {len(RouterReplay.router_instances)}") return module def _maybe_enable_fused_kernels(self): if not self.engine_config.use_fused_kernels: return if self.is_value_model or self.model_config.mtp.enable: logger.warning_once( "Fused kernels are not supported for value models or when MTP is enabled in Megatron engine; disabling." ) self.engine_config.use_fused_kernels = False return from verl.models.mcore.model_forward_fused import patch_fused_forward for model in self.module: patch_fused_forward(model) def _build_optimizer(self): from verl.utils.megatron.optimizer import get_megatron_optimizer, init_megatron_optim_config optim_config_megatron = init_megatron_optim_config( self.optimizer_config, use_distributed_optimizer=self.engine_config.use_distributed_optimizer, fp16=self.param_dtype == torch.float16, ) optimizer = get_megatron_optimizer(model=self.module, config=optim_config_megatron) register_megatron_training_hooks(self.module, optimizer) return optimizer def _build_lr_scheduler(self): from verl.utils.megatron.optimizer import get_megatron_optimizer_param_scheduler optimizer_scheduler = get_megatron_optimizer_param_scheduler( optimizer=self.optimizer, config=self.optimizer_config ) return optimizer_scheduler @property def is_param_offload_enabled(self) -> bool: return self._is_offload_param @property def is_optimizer_offload_enabled(self) -> bool: return self._is_offload_optimizer def is_mp_src_rank_with_outputs(self): return ( mpu.get_tensor_model_parallel_rank() == 0 and mpu.get_pipeline_model_parallel_rank() == mpu.get_pipeline_model_parallel_world_size() - 1 and mpu.get_context_parallel_rank() == 0 ) def initialize(self): self._build_tf_config() self.module = self._build_megatron_module() self._maybe_enable_fused_kernels() if self.model_config.mtp.enable: patch_engine_mtp(self.module, self.model_config) # For forward_only, we don't need optimizer, lr_scheduler, checkpoint_mananager if self.engine_config.forward_only: self.optimizer = None self.lr_scheduler = None self.to(device="cpu", model=self._is_offload_param, optimizer=False, grad=False) log_gpu_memory_usage("After offload model during init (forward_only)", logger=logger) return self.optimizer = self._build_optimizer() self.lr_scheduler = self._build_lr_scheduler() full_reshardable = self.engine_config.dist_ckpt_optim_fully_reshardable mem_eff = self.engine_config.distrib_optim_fully_reshardable_mem_efficient tmp_config = OmegaConf.create( { "model": {"path": self.model_config.local_path}, "megatron": { "dist_ckpt_optim_fully_reshardable": full_reshardable, "distrib_optim_fully_reshardable_mem_efficient": mem_eff, }, } ) role = "actor" if not self.is_value_model else "critic" self.checkpoint_mananager = MegatronCheckpointManager( config=tmp_config, checkpoint_config=self.checkpoint_config, model_config=self.model_config.hf_config, transformer_config=self.tf_config, role=role, model=self.module, arch=self.model_config.architectures[0], hf_config=self.model_config.hf_config, param_dtype=self.param_dtype, share_embeddings_and_output_weights=self.model_config.share_embeddings_and_output_weights, processing_class=self.model_config.get_processor(), optimizer=self.optimizer, optimizer_scheduler=self.lr_scheduler, use_distributed_optimizer=self.engine_config.use_distributed_optimizer, use_checkpoint_opt_param_scheduler=self.optimizer_config.use_checkpoint_opt_param_scheduler, bridge=self.bridge, provider=self.provider, peft_cls=self.peft_cls, use_dist_checkpointing=self.engine_config.use_dist_checkpointing, ) self.to( device="cpu", model=self._is_offload_param, optimizer=self._is_offload_optimizer, grad=self._is_offload_param, ) log_gpu_memory_usage("After offload model/optimizer/grad during init", logger=logger) def train_mode(self, **kwargs): """ Context manager entry for switching the engine and model into training mode. Usage: with engine.train_mode(): # runs in training mode """ return EngineTrainModeCtx(self, **kwargs) def eval_mode(self, **kwargs): """ Context manager entry for switching the engine and model into evaluation mode. Usage: with engine.eval_mode(): # runs in evaluation mode """ return EngineEvalModeCtx(self, **kwargs) def optimizer_zero_grad(self): """ Zero out gradients of all parameters before starting a new backward pass. """ self.optimizer.zero_grad() # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm for chunk in self.module: # if use distributed optimizer, zero grad buffer will be handled by optimizer chunk.zero_grad_buffer() def optimizer_step(self): """ Perform an optimization step to update model parameters based on accumulated gradients. Returns: grad_norm (float): The norm of the gradients before clipping or update. """ update_successful, grad_norm, num_zeros_in_grad = self.optimizer.step() if update_successful: # allgather already execute in optimizer.step in new megatron pass else: raise NotImplementedError("Megatron optimizer step failed. This should not happen") return grad_norm def lr_scheduler_step(self): """ Advance the learning rate scheduler by one step. Returns: current_lr (float or list[float]): Updated learning rate(s). """ from verl.utils.megatron.optimizer import get_megatron_last_lr self.lr_scheduler.step(1) return get_megatron_last_lr(self.optimizer) def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): """ Move model parameters, optimizer states, or both to the specified device. Note that this function executes irrespective of offload config. It serves as manual control Args: device: Target device identifier. model: If True, move the model. optimizer: If True, move the optimizer states. """ super().to(device=device, model=model, optimizer=optimizer, grad=grad) device_name = get_device_name() assert device in (device_name, "cpu") if device == device_name: if model: load_megatron_model_to_gpu(self.module, load_grad=grad) if optimizer and self.optimizer is not None: load_megatron_optimizer(self.optimizer) elif device == "cpu": if model: offload_megatron_model_to_cpu(self.module) if optimizer and self.optimizer is not None: offload_megatron_optimizer(self.optimizer) else: raise ValueError(f"Invalid device type: {device}") def get_data_parallel_rank(self): return mpu.get_data_parallel_rank() def get_data_parallel_size(self): return mpu.get_data_parallel_world_size() def get_data_parallel_group(self): return mpu.get_data_parallel_group() def get_model_parallel_group(self): return mpu.get_model_parallel_group() def get_context_parallel_group(self): return mpu.get_context_parallel_group() def save_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, global_step: int = 0, max_ckpt_to_keep: Optional[int] = None, **kwargs, ) -> None: """ Save model, optimizer, and scheduler states to a checkpoint. Args: local_path: Local filesystem path to save checkpoint. hdfs_path: Optional HDFS path to copy checkpoint. global_step: Integer training step number for naming. max_ckpt_to_keep: Maximum number of recent checkpoints to retain. """ origin_module_device = get_megatron_module_device(self.module) if self._is_offload_param or origin_module_device == "cpu": load_megatron_model_to_gpu(self.module, load_grad=True) self.checkpoint_mananager.save_checkpoint( local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep ) torch.distributed.barrier() if self._is_offload_param: offload_megatron_model_to_cpu(self.module) def load_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, del_local_after_load: bool = True, **kwargs ) -> None: """ Load model, optimizer, and scheduler states from a checkpoint. Args: local_path: Local filesystem path of the checkpoint. hdfs_path: Optional HDFS path where checkpoint is stored. del_local_after_load: Whether to delete local copy after loading. """ if self._is_offload_param: load_megatron_model_to_gpu(self.module) self.checkpoint_mananager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) if self._is_offload_param: offload_megatron_model_to_cpu(self.module) if self._is_offload_optimizer: offload_megatron_optimizer(self.optimizer) def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False) -> Any: tu.assign_non_tensor(data, sp_size=self.engine_config.context_parallel_size) # compute num_tokens in global batch for loss normalization batch_num_tokens = data["loss_mask"].sum().to(get_device_id()) torch.distributed.all_reduce( batch_num_tokens, op=torch.distributed.ReduceOp.SUM, group=self.get_data_parallel_group() ) tu.assign_non_tensor(data, batch_num_tokens=batch_num_tokens.item()) tu.assign_non_tensor(data, dp_size=self.get_data_parallel_size()) vpp_size = mpu.get_virtual_pipeline_model_parallel_world_size() if vpp_size is not None and vpp_size > 1: num_batches_divided_by = self.tf_config.microbatch_group_size_per_vp_stage else: num_batches_divided_by = None micro_batches, indices = prepare_micro_batches( data=data, dp_group=self.get_data_parallel_group(), num_batches_divided_by=num_batches_divided_by, same_micro_num_in_dp=True, min_num_micro_batch=None, ) if num_batches_divided_by is not None: assert len(micro_batches) % num_batches_divided_by == 0, ( f"micro_batches {micro_batches} must be divisible by num_batches_divided_by " f"{num_batches_divided_by} for megatron backend" ) # compute input shapes for pp stages n_micro_batch = len(micro_batches) for micro_batch in micro_batches: tu.assign_non_tensor(micro_batch, num_micro_batch=n_micro_batch) forward_backward_func = get_forward_backward_func() postprocess_micro_batch_func = partial( self.postprocess_micro_batch_func, forward_only=forward_only, loss_function=loss_function, ) tu.assign_non_tensor(data, num_micro_batch=n_micro_batch) forward_step = partial(self.forward_step, postprocess_micro_batch_func=postprocess_micro_batch_func) enable_routing_replay = tu.get_non_tensor_data(data, key="enable_routing_replay", default=False) if enable_routing_replay: # Set to REPLAY mode: for R3 mode or actor update phase in R2 mode RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD) if forward_only and self.engine_config.router_replay.mode == "R2": # In R2 mode, forward_only calls (e.g., compute_log_probs) need to record routing information RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD) # batch should be a list of batches inside micro-batches batch_generator = make_batch_generator(micro_batches, vpp_size=len(self.module)) # TODO: we may use the new schedule instead # for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size) losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.module, num_microbatches=n_micro_batch, seq_length=1, # the communication shape is obtained via p2p comm micro_batch_size=1, # the communication shape is obtained via p2p comm forward_only=forward_only, ) if self.model_config.mtp.enable and self.is_mp_src_rank_with_outputs(): # add mtp_losses metrics = get_megatron_mtp_loss(n_micro_batch) if "metrics" not in losses_reduced[0]: losses_reduced[0]["metrics"] = {} losses_reduced[0]["metrics"].update(metrics) if RouterReplayHelper.is_r2_record_action(self.tf_config): if self.tf_config.virtual_pipeline_model_parallel_size is not None: # config = self.actor_module[0].module.module.config vp_size = len(self.module) microbatch_group_size_per_vp_stage = self.tf_config.microbatch_group_size_per_vp_stage bs = n_micro_batch topk_idx_td = reorder_and_merge_vpp_layers( self.mini_layer_topk_idx_list, bs, vp_size, microbatch_group_size_per_vp_stage ) else: tensors = [tensor for nt in self.mini_layer_topk_idx_list for tensor in nt.unbind()] topk_idx_td = torch.nested.as_nested_tensor(tensors, layout=torch.jagged) self.mini_layer_topk_idx_list = [] layers_topk_idx = pp_gather(topk_idx_td.to(torch.uint8), self.tf_config) use_dynamic_bsz = tu.get_non_tensor_data(data=data, key="use_dynamic_bsz", default=True) if use_dynamic_bsz and indices is not None: layers_topk_idx = restore_dynamic_batch(layers_topk_idx, indices) output = {} if mpu.is_pipeline_last_stage(ignore_virtual=True): output = postprocess_batch_func(output_lst=losses_reduced, indices=indices, data=data) if RouterReplayHelper.is_r2_record_action(self.tf_config): output["model_output"]["routed_experts"] = layers_topk_idx if enable_routing_replay: RouterReplay.clear_global_indices() RouterReplay.clear_global_router_replay_action() return output def get_per_tensor_param(self, base_sync_done=False, **kwargs): peft_config = None non_merge_lora_sync = self.peft_cls is not None and not self.model_config.lora.get("merge", False) adapter_only = base_sync_done and non_merge_lora_sync # when lora adapter only, we only load adapter weights when base sync is done, otherwise load all weights load_megatron_model_to_gpu(self.module, load_grad=False, load_frozen_params=not adapter_only) if self.vanilla_bridge: per_tensor_param = self.bridge.export_weights(self.module) elif adapter_only: # Only export adapter weights peft_config = build_peft_config_for_vllm(self.model_config.lora) per_tensor_param = self.bridge.export_adapter_weights(self.module) else: per_tensor_param = self.bridge.export_hf_weights(self.module) if non_merge_lora_sync: per_tensor_param = add_base_layer_suffix( per_tensor_param, model_type=self.model_config.hf_config.model_type ) return per_tensor_param, peft_config def disable_adapter(self) -> ContextManager: return self.peft_cls.disable_adapter(self.module) def forward_step(self, batch_iter, model, postprocess_micro_batch_func): raise NotImplementedError("forward_step must be implemented in subclass") def postprocess_micro_batch_func(self, output, data: TensorDict, forward_only: bool, loss_function): raise NotImplementedError("postprocess_micro_batch_func must be implemented in subclass") class EngineEvalModeCtx(BaseEngineCtx): def __init__(self, engine: MegatronEngine, **kwargs): super().__init__(engine=engine, mode="eval", **kwargs) def __enter__(self): assert isinstance(self.engine, MegatronEngine) super().__enter__() # mcore module is a list of model chunk in each vpp stage for module in self.engine.module: module.eval() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, MegatronEngine) super().__exit__(exc_type, exc_value, traceback) class EngineTrainModeCtx(BaseEngineCtx): def __init__(self, engine: MegatronEngine, **kwargs): super().__init__(engine=engine, mode="train", **kwargs) def __enter__(self): assert isinstance(self.engine, MegatronEngine) super().__enter__() # mcore module is a list of model chunk in each vpp stage for module in self.engine.module: module.train() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, MegatronEngine) self.engine.optimizer_zero_grad() super().__exit__(exc_type, exc_value, traceback) @EngineRegistry.register(model_type="language_model", backend="megatron") class MegatronEngineWithLMHead(MegatronEngine): def prepare_model_inputs(self, batch: TensorDict): input_ids = batch["input_ids"] loss_mask = batch["loss_mask"].to(bool) multi_modal_inputs = extract_multi_modal_inputs(batch.get("multi_modal_inputs", [])) routed_experts = batch.get("routed_experts", None) return { "input_ids": input_ids, "loss_mask": loss_mask, "multi_modal_inputs": multi_modal_inputs, "routed_experts": routed_experts, } def prepare_model_outputs(self, output: dict, data: TensorDict): calculate_entropy = tu.get_non_tensor_data(data, key="calculate_entropy", default=False) log_prob = output["log_probs"] model_output = {"log_probs": log_prob} if calculate_entropy: entropy = output["entropy"] model_output["entropy"] = entropy return model_output def forward_step(self, batch_iter: Iterator[TensorDict], model, postprocess_micro_batch_func): batch: TensorDict = next(batch_iter) batch = batch.to(get_device_id()) use_fused_kernels = tu.get_non_tensor_data(batch, key="use_fused_kernels", default=False) calculate_entropy = tu.get_non_tensor_data(batch, key="calculate_entropy", default=False) pad_mode = tu.get_non_tensor_data(batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) temperature = batch["temperature"] model_inputs = self.prepare_model_inputs(batch) input_ids = model_inputs["input_ids"] multi_modal_inputs = model_inputs["multi_modal_inputs"] loss_mask = model_inputs["loss_mask"] unwrapped_model = unwrap_model(model) if hasattr(unwrapped_model, "vp_stage"): vp_rank = unwrapped_model.vp_stage else: vp_rank = 0 if RouterReplayHelper.is_replay_backward_action(self.tf_config, vp_rank): router_instance_list = RouterReplayHelper.get_micro_batch_router_list(self.tf_config, vp_rank) for router in router_instance_list: router.set_router_replay_action(RouterReplayAction.REPLAY_FORWARD) if RouterReplayHelper.is_replay_forward_action(self.tf_config, vp_rank): layers_topk_idx = model_inputs["routed_experts"] set_router_replay_data(layers_topk_idx, None, self.tf_config, vp_rank) if pad_mode == DatasetPadMode.NO_PADDING: label = input_ids.clone() else: raise NotImplementedError(f"Pad mode {pad_mode} is not supported for megatron engine") from verl.models.mcore import get_mcore_forward_no_padding_fn if use_fused_kernels: if not self.engine_config.use_remove_padding: logger.warning_once( "Fused kernels require `use_remove_padding=True` for Megatron engine. Falling back to non-fused." ) use_fused_kernels = False elif isinstance(temperature, torch.Tensor): if temperature.numel() != 1: logger.warning_once( "Fused kernels do not support per-sample temperature. Falling back to non-fused." ) use_fused_kernels = False else: temperature_value = float(temperature.item()) else: temperature_value = float(temperature) if use_fused_kernels: fused_forward_fn = get_mcore_forward_fused_no_padding_fn(self.model_config.hf_config) output = fused_forward_fn( model=model, input_ids=input_ids, labels=label, multi_modal_inputs=multi_modal_inputs, temperature=temperature_value, calculate_entropy=calculate_entropy, pad_token_id=self.model_config.tokenizer.pad_token_id, ) else: if not isinstance(temperature, torch.Tensor): temperature = torch.tensor([temperature] * input_ids.shape[0], device=input_ids.device) temperature = temperature.to(torch.float32) assert temperature.shape[0] == input_ids.shape[0] temperature = verl_F.expand_as_nested(temperature, input_ids) # (bsz, j1) forward_fn = get_mcore_forward_no_padding_fn(self.model_config.hf_config) def logits_processor(logits, label, temperature): assert logits.shape[:2] == label.shape[:2] # avoid non-positive temperature such as padding temperature[temperature <= 0] = 1e-8 assert torch.all(temperature > 0).item(), f"temperature tensor must be positive. Got {temperature}" logits.div_(temperature.unsqueeze(dim=-1).to(logits.dtype)) ret = {} if calculate_entropy: logits_bak = logits.clone() # # disable the hint until the fused_kernel is optimized for triton>=3.3 # if torch.distributed.get_rank() == 0: # logger.warning_once( # "For memory-efficient computation, enable fused kernels via " # "`actor_rollout_ref.model.use_fused_kernels=True`. " # "The current `clone()` operation ensures correctness but increases memory usage." # ) entropy = vocab_parallel_entropy(logits) ret["entropy"] = entropy else: logits_bak = logits log_probs = vocab_parallel_log_probs_from_logits(logits_bak, label) ret["log_probs"] = log_probs return ret logits_processor_args = {"label": label, "temperature": temperature, "loss_mask": loss_mask} output = forward_fn( model, input_ids, multi_modal_inputs, logits_processor=logits_processor, logits_processor_args=logits_processor_args, vision_model=hasattr(self.model_config.hf_config, "vision_config"), pad_token_id=self.model_config.tokenizer.pad_token_id, data_format="thd" if self.engine_config.use_remove_padding else "bshd", mtp_enable_train=self.model_config.mtp.enable and self.model_config.mtp.enable_train, ) # Router replay: record routing decisions for R2 mode if RouterReplayHelper.is_r2_record_action(self.tf_config, vp_rank): merge_router_topk_indices(None, input_ids, self.mini_layer_topk_idx_list, self.tf_config, vp_rank) # Router replay: switch to backward replay mode for next backward pass if RouterReplayHelper.is_replay_forward_action(self.tf_config, vp_rank): router_instance_list = RouterReplayHelper.get_micro_batch_router_list(self.tf_config, vp_rank) for router in router_instance_list: router.set_router_replay_action(RouterReplayAction.REPLAY_BACKWARD) return output, partial(postprocess_micro_batch_func, data=batch) def postprocess_micro_batch_func(self, output, data: TensorDict, forward_only: bool, loss_function): # For memory efficiency # We move calculation of entropy to compute_log_probs, forward_only == True device = data["input_ids"].device model_output = self.prepare_model_outputs(output, data) if loss_function is not None: loss, metrics = loss_function(model_output=model_output, data=data, dp_group=self.get_data_parallel_group()) # scale loss by num_micro_batch because megatron will scale loss # by n_micro_batch inside pp schedule scaled_loss = loss * data["num_micro_batch"] else: assert forward_only, "forward_only must be True when loss_function is None" loss = torch.tensor(1.0, device=device) scaled_loss = loss metrics = {} output = { "model_output": model_output, "loss": loss.detach().item(), "metrics": metrics, } # return loss and stats return scaled_loss, output @EngineRegistry.register(model_type="value_model", backend="megatron") class MegatronEngineWithValueHead(MegatronEngineWithLMHead): # for value head def forward_step(self, batch_iter, model, postprocess_micro_batch_func): batch: TensorDict = next(batch_iter) batch = batch.to(get_device_id()) model_inputs = self.prepare_model_inputs(batch) input_ids = model_inputs["input_ids"] multi_modal_inputs = model_inputs["multi_modal_inputs"] from verl.models.mcore import get_mcore_forward_no_padding_fn forward_fn = get_mcore_forward_no_padding_fn(self.model_config.hf_config) output = forward_fn( model, input_ids, multi_modal_inputs, value_model=True, vision_model=hasattr(self.model_config.hf_config, "vision_config"), pad_token_id=self.model_config.tokenizer.pad_token_id, enable_mtp=self.model_config.mtp.enable_train, ) return output, partial(postprocess_micro_batch_func, data=batch) def prepare_model_outputs(self, output: dict | torch.Tensor, data: TensorDict): return {"values": output} ================================================ FILE: verl/workers/engine/megatron/utils.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.utils.device import get_torch_device def set_random_seed(seed): import random import numpy as np import torch torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) if get_torch_device().device_count() > 0: from megatron.core import tensor_parallel tensor_parallel.model_parallel_cuda_manual_seed(seed) # FIXME: torch cumsum not support deterministic (used in vllm sampler), # https://github.com/pytorch/pytorch/issues/89492 # torch.use_deterministic_algorithms(True, warn_only=True) # os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' ================================================ FILE: verl/workers/engine/mindspeed/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .transformer_impl import MindspeedEngineWithLMHead __all__ = ["MindspeedEngineWithLMHead"] ================================================ FILE: verl/workers/engine/mindspeed/transformer_impl.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os try: from mindspeed.megatron_adaptor import repatch except ImportError: repatch = None from verl.trainer.config import CheckpointConfig from verl.workers.config import HFModelConfig, McoreEngineConfig, McoreOptimizerConfig from ..base import EngineRegistry from ..megatron import MegatronEngineWithLMHead logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) @EngineRegistry.register(model_type="language_model", backend="megatron", device="npu") class MindspeedEngineWithLMHead(MegatronEngineWithLMHead): def __init__( self, model_config: HFModelConfig, engine_config: McoreEngineConfig, optimizer_config: McoreOptimizerConfig, checkpoint_config: CheckpointConfig, ): super().__init__(model_config, engine_config, optimizer_config, checkpoint_config) repatch_config = self.engine_config.get("override_transformer_config", {}) repatch_config["use_flash_attn"] = True if self.engine_config.context_parallel_size > 1: repatch_config["context_parallel_size"] = self.engine_config.context_parallel_size repatch(repatch_config) ================================================ FILE: verl/workers/engine/torchtitan/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .transformer_impl import TorchTitanEngine, TorchTitanEngineWithLMHead __all__ = ["TorchTitanEngine", "TorchTitanEngineWithLMHead"] ================================================ FILE: verl/workers/engine/torchtitan/transformer_impl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The concrete Engine implementation using PyTorch TorchTitan parallelism (FSDP2 + TP + PP) """ import gc import importlib import logging import os import re from contextlib import nullcontext from typing import Any, Callable, Optional import torch import torch.distributed from tensordict import TensorDict from torch.distributed.checkpoint.state_dict import get_model_state_dict from torch.distributed.tensor import DTensor from torchtitan.components.checkpoint import CheckpointManager from torchtitan.components.lr_scheduler import LRSchedulersContainer from torchtitan.components.optimizer import OptimizersContainer from torchtitan.config import CompileConfig, ParallelismConfig, TrainingConfig from torchtitan.distributed import utils as dist_utils from torchtitan.distributed.context_parallel import prepare_context_parallel_input from torchtitan.distributed.parallel_dims import ParallelDims from torchtitan.train import Trainer import verl.utils.torch_functional as verl_F from verl.trainer.config import CheckpointConfig from verl.utils import tensordict_utils as tu from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.debug import log_gpu_memory_usage from verl.utils.device import get_device_id, get_device_name from verl.utils.fsdp_utils import ( load_fsdp_model_to_gpu, load_fsdp_optimizer, offload_fsdp_model_to_cpu, offload_fsdp_optimizer, ) from verl.utils.model import extract_multi_modal_inputs from verl.utils.torch_functional import logprobs_from_logits from verl.workers.config import HFModelConfig, TorchtitanEngineConfig, TorchtitanOptimizerConfig from verl.workers.engine.torchtitan.utils import ( NoOpDataLoader, derive_torchtitan_name_and_flavor, enable_fsdp_gradient_division, get_attention_masks, iter_per_tensor_params_ep, ) from ..base import BaseEngine, BaseEngineCtx, EngineRegistry from ..utils import enable_full_determinism, postprocess_batch_func, prepare_micro_batches logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) device_name = get_device_name() class TorchTitanEngine(BaseEngine): """ Concrete Engine implementation using PyTorch TorchTitan parallelism. Supports model sharding with FSDP2, tensor parallelism, activation/optimizer offloading, LoRA, and sequence parallelism following the TorchTitan design. """ def __init__( self, model_config: HFModelConfig, engine_config: TorchtitanEngineConfig, optimizer_config: TorchtitanOptimizerConfig, checkpoint_config: CheckpointConfig, ): """ Initialize the TorchTitanEngine. Sets up distributed device meshes for tensor and data parallelism, LoRA, and offload policies. Args: model_config: Configuration for HuggingFace model. engine_config: Configuration for FSDP/TorchTitan engine (uses FSDP2). optimizer_config: Configuration for optimizer. checkpoint_config: Configuration for checkpointing. """ super().__init__() self.model_config = model_config self.engine_config = engine_config self.optimizer_config = optimizer_config self.checkpoint_config = checkpoint_config # Derive torchtitan model name and flavor from HF config torchtitan_name, torchtitan_flavor = derive_torchtitan_name_and_flavor(self.model_config.hf_config) # Get ModelSpec from model registry model_module = importlib.import_module(f"torchtitan.models.{torchtitan_name}") model_spec = model_module.model_registry(torchtitan_flavor) # Override attn_backend on the model config if needed attn_type = self.engine_config.attn_type if hasattr(model_spec.model, "layer") and hasattr(model_spec.model.layer, "attention"): model_spec.model.layer.attention.attn_backend = attn_type optimizer = OptimizersContainer.Config( name=self.optimizer_config.name, lr=self.optimizer_config.lr, eps=self.optimizer_config.eps, beta1=self.optimizer_config.betas[0], beta2=self.optimizer_config.betas[1], weight_decay=self.optimizer_config.weight_decay, ) total_steps = self.optimizer_config.total_training_steps lr_warmup_steps = self.optimizer_config.lr_warmup_steps if lr_warmup_steps is None or lr_warmup_steps <= 0: lr_warmup_steps = int(self.optimizer_config.lr_warmup_steps_ratio * total_steps) lr_scheduler = LRSchedulersContainer.Config( warmup_steps=lr_warmup_steps, decay_type=self.optimizer_config.decay_type, min_lr_factor=self.optimizer_config.min_lr_factor, ) parallelism = ParallelismConfig( data_parallel_replicate_degree=self.engine_config.data_parallel_replicate_size, data_parallel_shard_degree=self.engine_config.data_parallel_shard_size, fsdp_reshard_after_forward=self.engine_config.reshard_after_forward, tensor_parallel_degree=self.engine_config.tensor_parallel_size, pipeline_parallel_degree=self.engine_config.pipeline_parallel_size, context_parallel_degree=self.engine_config.context_parallel_size, expert_parallel_degree=self.engine_config.expert_parallel_size, expert_tensor_parallel_degree=self.engine_config.expert_tensor_parallel_size, ) checkpoint = CheckpointManager.Config( enable=True, initial_load_in_hf=True, initial_load_model_only=True, initial_load_path=model_config.path, ) compile_config = CompileConfig(enable=self.engine_config.use_torch_compile) training_kwargs = {} if self.engine_config.max_seq_len is not None: training_kwargs["seq_len"] = self.engine_config.max_seq_len if self.engine_config.offload_policy or self.engine_config.forward_only: training = TrainingConfig(enable_cpu_offload=True, **training_kwargs) else: training = TrainingConfig(**training_kwargs) # Construct Torchtitan's Trainer.Config self.config = Trainer.Config( model_spec=model_spec, hf_assets_path=self.model_config.path, optimizer=optimizer, lr_scheduler=lr_scheduler, parallelism=parallelism, checkpoint=checkpoint, compile=compile_config, training=training, # Use a no-op dataloader since verl has its own data loading dataloader=NoOpDataLoader.Config(), ) self.trainer = Trainer(self.config) self._init_device_mesh() # Re-enable FSDP's gradient division for verl's loss scaling. # TorchTitan disables gradient division by default (for global token normalization), # but verl's loss function multiplies by dp_size to compensate for gradient averaging. if self.engine_config.data_parallel_shard_size > 1: dp_size = self.get_data_parallel_size() for model_part in self.trainer.model_parts: enable_fsdp_gradient_division(model_part, dp_size) if self.engine_config.full_determinism: enable_full_determinism(seed=self.engine_config.seed) # set FSDP offload params self._is_offload_param = self.engine_config.param_offload self._is_offload_optimizer = self.engine_config.optimizer_offload if self.engine_config.entropy_from_logits_with_chunking: entropy_from_logits = verl_F.entropy_from_logits_with_chunking else: entropy_from_logits = verl_F.entropy_from_logits self.compute_entropy_from_logits = ( torch.compile(entropy_from_logits, dynamic=True) if self.engine_config.use_torch_compile else entropy_from_logits ) @property def is_param_offload_enabled(self) -> bool: return self._is_offload_param @property def is_optimizer_offload_enabled(self) -> bool: return self._is_offload_optimizer def is_mp_src_rank_with_outputs(self): """ Whether the current rank is the first rank in model parallel group that contains model outputs """ is_collect = True # TP: outputs are on TP rank 0 if self.parallel_dims.tp > 1: tp_mesh = self.parallel_dims.get_optional_mesh("tp") is_collect = is_collect and (tp_mesh.get_local_rank() == 0) # PP: outputs are on the last PP rank if self.parallel_dims.pp > 1: pp_mesh = self.parallel_dims.get_optional_mesh("pp") is_collect = is_collect and (pp_mesh.get_local_rank() == self.parallel_dims.pp - 1) # CP: outputs are on CP rank 0 if self.parallel_dims.cp > 1: cp_mesh = self.parallel_dims.get_optional_mesh("cp") is_collect = is_collect and (cp_mesh.get_local_rank() == 0) return is_collect def initialize(self): """ Build the model, optimizer, and learning rate scheduler with TorchTitan parallelism. Applies device, dtype, and precision configurations, including mixed precision. Sets up checkpoint manager. """ self.module = self.trainer.model_parts self.checkpointer = self.trainer.checkpointer # load initial HF weights self.checkpointer.load() if not self.engine_config.forward_only: self.optimizer = self.trainer.optimizers self.lr_scheduler = self.trainer.lr_schedulers else: self.optimizer = None self.lr_scheduler = None self.to( device="cpu", model=self._is_offload_param, optimizer=self._is_offload_optimizer, grad=self._is_offload_param, ) log_gpu_memory_usage("After offload model/optimizer/grad during init", logger=logger) def _init_device_mesh(self): """Initialize the device mesh for TorchTitan style parallelism.""" world_size = torch.distributed.get_world_size() self.parallel_dims = ParallelDims( dp_shard=self.engine_config.data_parallel_shard_size, dp_replicate=self.engine_config.data_parallel_replicate_size, cp=self.engine_config.context_parallel_size, tp=self.engine_config.tensor_parallel_size, pp=self.engine_config.pipeline_parallel_size, ep=self.engine_config.expert_parallel_size, etp=self.engine_config.expert_tensor_parallel_size, world_size=world_size, ) self.device_mesh = self.parallel_dims.build_mesh() def train_mode(self, **kwargs): """Return a context manager for training mode.""" return EngineTrainModeCtx(self, **kwargs) def eval_mode(self, **kwargs): """Return a context manager for evaluation mode.""" return EngineEvalModeCtx(self, **kwargs) def get_data_parallel_rank(self): mesh = self._get_data_parallel_mesh() return 0 if mesh is None else mesh.get_local_rank() def get_data_parallel_size(self): return self.engine_config.data_parallel_shard_size * self.engine_config.data_parallel_replicate_size def get_data_parallel_group(self): mesh = self._get_data_parallel_mesh() if mesh is not None: return mesh.get_group() # If world_size == dp_size (e.g. single GPU, or all ranks are DP), # return WORLD so that collective ops in _postprocess_output # (allgather_dict_into_dict, all_reduce) still run and produce the # correct metric aggregation format. if torch.distributed.get_world_size() == self.get_data_parallel_size(): return torch.distributed.group.WORLD return None def get_model_parallel_group(self): raise NotImplementedError def get_context_parallel_group(self): raise NotImplementedError def _get_data_parallel_mesh(self): """Get the data parallel mesh, handling hybrid/fully/replicate shard modes.""" mesh = self.parallel_dims.get_optional_mesh(["dp_replicate", "fsdp"]) if mesh is None: mesh = self.parallel_dims.get_optional_mesh("fsdp") if mesh is None: mesh = self.parallel_dims.get_optional_mesh("dp_replicate") return mesh def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False): """Perform forward and optionally backward pass on a batch.""" tu.assign_non_tensor(data, sp_size=self.engine_config.tensor_parallel_size) # Compute num_tokens in global batch for loss normalization batch_num_tokens = data["loss_mask"].sum().to(get_device_id()) dp_group = self.get_data_parallel_group() if dp_group is not None: torch.distributed.all_reduce(batch_num_tokens, op=torch.distributed.ReduceOp.SUM, group=dp_group) tu.assign_non_tensor(data, batch_num_tokens=batch_num_tokens.item()) tu.assign_non_tensor(data, dp_size=self.get_data_parallel_size()) micro_batches, indices = prepare_micro_batches( data=data, dp_group=self.get_data_parallel_group(), same_micro_num_in_dp=True, ) output_lst = [] ctx = torch.no_grad() if forward_only else nullcontext() for micro_batch in micro_batches: with ctx: loss, output = self.forward_step(micro_batch, loss_function=loss_function, forward_only=forward_only) if not forward_only: loss.backward() output_lst.append(output) return postprocess_batch_func(output_lst=output_lst, indices=indices, data=data) def model_forward_step( self, *, inputs: torch.Tensor, extra_inputs: dict[str, torch.Tensor] | None = None, extra_kwargs: dict[str, torch.Tensor] | None = None, ) -> torch.Tensor: """ Perform a forward pass through the trainer model without backward. """ model_parts = self.module parallel_dims = self.parallel_dims if parallel_dims.pp_enabled: raise NotImplementedError( "Pipeline parallelism is not yet supported in model_forward_step. " "This will be implemented in a follow-up PR." ) else: # Non-PP forward assert len(model_parts) == 1 with self.trainer.train_context(): with self.trainer.maybe_enable_amp: pred = model_parts[0](inputs, **extra_inputs, **extra_kwargs) if isinstance(pred, DTensor): pred = pred.full_tensor() return pred def forward_step(self, micro_batch: TensorDict, loss_function, forward_only): raise NotImplementedError("forward_step must be implemented in subclass") def optimizer_zero_grad(self): """Zero gradients.""" self.optimizer.zero_grad() def optimizer_step(self): """Perform optimizer step with gradient clipping.""" grad_norm = dist_utils.clip_grad_norm_( [p for m in self.module for p in m.parameters()], self.config.training.max_norm, foreach=True, pp_mesh=self.parallel_dims.get_optional_mesh("pp"), ep_enabled=self.parallel_dims.ep_enabled, ) # if grad_norm is not finite, skip the update if not torch.isfinite(grad_norm): logger.warning(f"grad_norm is not finite: {grad_norm}") self.optimizer.zero_grad() else: self.optimizer.step() return grad_norm.item() def lr_scheduler_step(self): """Advance learning rate scheduler.""" self.lr_scheduler.step() lr = self.lr_scheduler.schedulers[0].get_last_lr()[0] return lr def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): """Move model and/or optimizer to CPU or GPU.""" super().to(device=device, model=model, optimizer=optimizer, grad=grad) if self.engine_config.forward_only: return device_name = get_device_name() assert device in (device_name, "cpu") if device == device_name: if model: for module in self.module: load_fsdp_model_to_gpu(module) if optimizer and self.optimizer is not None: load_fsdp_optimizer(self.optimizer, device) gc.collect() elif device == "cpu": if model: for module in self.module: offload_fsdp_model_to_cpu(module) if optimizer and self.optimizer is not None: offload_fsdp_optimizer(self.optimizer) else: raise ValueError(f"Invalid device type: {device}") def save_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, global_step: int = 0, max_ckpt_to_keep: Optional[int] = None, **kwargs, ) -> None: """Save checkpoint.""" if self._is_offload_param: for module in self.module: load_fsdp_model_to_gpu(module) # Override TorchTitan's folder to use verl's path parent_dir = os.path.dirname(local_path) self.checkpointer.folder = parent_dir if max_ckpt_to_keep is not None: self.checkpointer.keep_latest_k = max_ckpt_to_keep self.checkpointer.save(curr_step=global_step) torch.distributed.barrier() if self._is_offload_param: for module in self.module: offload_fsdp_model_to_cpu(module) def load_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, del_local_after_load: int = True, **kwargs ) -> None: """Load checkpoint.""" if self._is_offload_param: for module in self.module: load_fsdp_model_to_gpu(module) # Override TorchTitan's folder to use verl's path parent_dir = os.path.dirname(local_path) self.checkpointer.folder = parent_dir # Extract step number from path (verl uses global_step_N format) match = re.search(r"global_step_(\d+)", local_path) if match: step = int(match.group(1)) self.checkpointer.load(step=step) else: # Fallback to latest self.checkpointer.load(step=-1) torch.distributed.barrier() if self._is_offload_param: for module in self.module: offload_fsdp_model_to_cpu(module) if self._is_offload_optimizer: offload_fsdp_optimizer(self.optimizer) def get_per_tensor_param(self, **kwargs): for module in self.module: load_fsdp_model_to_gpu(module) # Collect state dicts from all model parts params = {} for module in self.module: module_params = get_model_state_dict(module) params.update(module_params) if self._is_offload_param: for module in self.module: offload_fsdp_model_to_cpu(module) # Convert TorchTitan key names to HuggingFace key names (expected by vLLM) sd_adapter = self.checkpointer.sd_adapter if sd_adapter is not None: params = sd_adapter.to_hf(params) # When weight tying is enabled, the sd_adapter skips lm_head.weight during # to_hf() conversion (since it's the same tensor as embed_tokens.weight in # the torchtitan model). But vLLM needs lm_head.weight explicitly, so we # add it back as a reference to embed_tokens.weight. if "model.embed_tokens.weight" in params and "lm_head.weight" not in params: params["lm_head.weight"] = params["model.embed_tokens.weight"] device = get_device_id() # used when fsdp2 set cpu_offload_policy # When Expert Parallel (EP) is used, sd_adapter.to_hf() only produces # individual expert weights for the locally-owned experts (e.g., 16 out of # 128 with EP=8). vLLM needs ALL experts. We gather the missing experts # by all-gathering each expert weight across the EP process group. if self.parallel_dims.ep_enabled: ep_mesh = self.parallel_dims.get_optional_mesh("ep") ep_group = ep_mesh.get_group() ep_size = self.parallel_dims.ep per_tensor_param = iter_per_tensor_params_ep(params, device, ep_group, ep_size) else: # TODO: cast fp32 to bf16 to reduce weight sync overhead, need more fine-grained control, e.g MoE gate per_tensor_param = ( ( name, param.to(device, non_blocking=True).full_tensor().to(torch.bfloat16, non_blocking=True) if isinstance(param, DTensor) else param, ) for name, param in params.items() ) # TODO: support Torchtitan PEFT return per_tensor_param, None class EngineEvalModeCtx(BaseEngineCtx): def __init__(self, engine: TorchTitanEngine, **kwargs): super().__init__(engine=engine, mode="eval", **kwargs) def __enter__(self): assert isinstance(self.engine, TorchTitanEngine) super().__enter__() for module in self.engine.module: module.eval() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, TorchTitanEngine) # Reshard the root FSDP module if self.engine.engine_config.data_parallel_shard_size > 1: for module in self.engine.module: module.reshard() super().__exit__(exc_type, exc_value, traceback) class EngineTrainModeCtx(BaseEngineCtx): def __init__(self, engine: TorchTitanEngine, **kwargs): super().__init__(engine=engine, mode="train", **kwargs) def __enter__(self): assert isinstance(self.engine, TorchTitanEngine) super().__enter__() for module in self.engine.module: module.train() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, TorchTitanEngine) self.engine.optimizer_zero_grad() super().__exit__(exc_type, exc_value, traceback) @EngineRegistry.register(model_type="language_model", backend=["torchtitan"], device=["cuda", "npu"]) class TorchTitanEngineWithLMHead(TorchTitanEngine): """TorchTitan engine implementation for language models with LM head.""" def prepare_model_inputs(self, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) assert pad_mode == DatasetPadMode.NO_PADDING, f"pad_mode {pad_mode} not supported" multi_modal_inputs = extract_multi_modal_inputs(micro_batch.get("multi_modal_inputs", [])) input_ids = micro_batch["input_ids"] position_ids = micro_batch["position_ids"] output_args = {} if use_remove_padding: input_ids = input_ids.values().unsqueeze(0) if position_ids.dim() == 3: position_ids = position_ids.values().unsqueeze(1) else: position_ids = position_ids.values().unsqueeze(0) labels = torch.roll(input_ids, shifts=-1, dims=1) attn_type = self.trainer.model_config.layer.attention.attn_backend attention_mask = get_attention_masks( input_batch=input_ids, positions=position_ids, attn_type=attn_type, ) else: loss_mask = micro_batch["loss_mask"] pad_token_id = tu.get_non_tensor_data(data=micro_batch, key="pad_token_id", default=0) batch_size = micro_batch.batch_size[0] max_seq_len = max(input_ids.offsets().diff()) labels = torch.roll(input_ids.values(), shifts=-1, dims=0) input_ids = torch.nested.to_padded_tensor( input_ids, padding=pad_token_id, output_size=(batch_size, max_seq_len) ) if position_ids.dim() == 3: position_ids = torch.nested.to_padded_tensor( position_ids, padding=0, output_size=(batch_size, 4, max_seq_len) ).transpose(0, 1) else: position_ids = torch.nested.to_padded_tensor( position_ids, padding=0, output_size=(batch_size, max_seq_len) ) attention_mask_list = [torch.ones_like(t, dtype=torch.int32) for t in loss_mask] attention_mask = torch.nested.as_nested_tensor(attention_mask_list, layout=torch.jagged) attention_mask = torch.nested.to_padded_tensor( attention_mask, padding=0, output_size=(batch_size, max_seq_len) ) extra_inputs = { "positions": position_ids, } # For arguments, like attention_masks, we have to put them in a separate # dict as extra_inputs are not forwarded to other stages in PP, but # extra_kwargs are. extra_kwargs: dict[str, Any] = {"attention_masks": attention_mask} if self.parallel_dims.cp_enabled: input_ids, labels, extra_kwargs = prepare_context_parallel_input( input_ids, labels, extra_kwargs, self.parallel_dims.get_mesh("cp"), self.trainer.device, self.trainer.config.parallelism.context_parallel_load_balancer, ) # TODO(jessicazhong): multimodal is not yet supported for Torchtitan engine extra_inputs.update(multi_modal_inputs) output_args["labels"] = labels return input_ids, extra_inputs, extra_kwargs, output_args def prepare_model_outputs(self, logits, output_args, micro_batch: TensorDict): use_remove_padding = tu.get_non_tensor_data(data=micro_batch, key="use_remove_padding", default=True) pad_mode = tu.get_non_tensor_data(data=micro_batch, key="pad_mode", default=DatasetPadMode.NO_PADDING) assert pad_mode == DatasetPadMode.NO_PADDING, f"pad_mode {pad_mode} not supported" temperature = micro_batch["temperature"] calculate_entropy = tu.get_non_tensor_data(data=micro_batch, key="calculate_entropy", default=False) labels = output_args["labels"] model_output = {} input_ids = micro_batch["input_ids"] cu_seqlens = input_ids.offsets() if use_remove_padding: labels = labels.squeeze(0) logits_rmpad = logits.squeeze(0) # PyTorch's autograd doesn't allow in-place modification of views when gradients need to flow back logits_rmpad = logits_rmpad / temperature inplace_backward = True if calculate_entropy: inplace_backward = False log_probs = logprobs_from_logits( logits=logits_rmpad, labels=labels, inplace_backward=inplace_backward, ) if calculate_entropy: if not self.engine_config.entropy_checkpointing: entropy_rmpad = self.compute_entropy_from_logits(logits_rmpad) else: entropy_rmpad = torch.utils.checkpoint.checkpoint(self.compute_entropy_from_logits, logits_rmpad) log_probs = torch.nested.nested_tensor_from_jagged(log_probs.squeeze(0), cu_seqlens) if calculate_entropy: entropy = torch.nested.nested_tensor_from_jagged(entropy_rmpad, cu_seqlens) else: logits.div_(temperature) if calculate_entropy: if not self.engine_config.entropy_checkpointing: entropy = verl_F.entropy_from_logits(logits) else: entropy = torch.utils.checkpoint.checkpoint(verl_F.entropy_from_logits, logits) seq_lengths = cu_seqlens.diff() starts = torch.zeros_like(seq_lengths, dtype=torch.int64) logits = torch.nested.narrow(logits, 1, starts, seq_lengths, layout=torch.jagged) logits_rmpad = torch.cat([t for t in logits.unbind()]) log_probs = logprobs_from_logits(logits=logits_rmpad, labels=output_args["labels"]) log_probs = torch.nested.nested_tensor_from_jagged(log_probs, cu_seqlens) if calculate_entropy: entropy = torch.nested.narrow(entropy, 1, starts, seq_lengths, layout=torch.jagged) entropy_rmpad = torch.cat([t for t in entropy.unbind()]) entropy = torch.nested.nested_tensor_from_jagged(entropy_rmpad, cu_seqlens) model_output["log_probs"] = log_probs if calculate_entropy: model_output["entropy"] = entropy return model_output def forward_step(self, micro_batch: TensorDict, loss_function, forward_only): device_name = get_device_name() micro_batch = micro_batch.to(get_device_id()) input_ids, extra_inputs, extra_kwargs, output_args = self.prepare_model_inputs(micro_batch=micro_batch) with torch.autocast(device_type=device_name, dtype=torch.bfloat16): logits = self.model_forward_step(inputs=input_ids, extra_inputs=extra_inputs, extra_kwargs=extra_kwargs) model_output = self.prepare_model_outputs(logits=logits, output_args=output_args, micro_batch=micro_batch) if loss_function is not None: loss, metrics = loss_function( model_output=model_output, data=micro_batch, dp_group=self.get_data_parallel_group() ) else: assert forward_only, "forward_only must be True when loss_function is None" loss = torch.tensor(1.0, device=device_name) metrics = {} output = { "model_output": model_output, "loss": loss.detach().item(), "metrics": metrics, } return loss, output ================================================ FILE: verl/workers/engine/torchtitan/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import logging import re from collections import defaultdict from collections.abc import Generator, Iterator from dataclasses import dataclass from typing import Any import torch import torch.distributed import torch.nn as nn from torch.distributed._composable.fsdp import FSDPModule from torch.distributed.tensor import DTensor from torch.nn.attention.flex_attention import _mask_mod_signature, and_masks from torchtitan.components.dataloader import BaseDataLoader from torchtitan.models.common.attention import ( AttentionMasksType, VarlenMetadata, create_attention_mask, get_causal_mask_mod, ) logger = logging.getLogger(__name__) class NoOpDataLoader(BaseDataLoader): """A no-op dataloader for use when verl manages its own data loading. Satisfies the BaseDataLoader interface required by torchtitan's Trainer but does nothing. Its __iter__ yields nothing, and state_dict / load_state_dict are no-ops. """ @dataclass(kw_only=True, slots=True) class Config(BaseDataLoader.Config): pass def __init__(self, **kwargs): pass def __iter__(self) -> Iterator[tuple[dict[str, torch.Tensor], torch.Tensor]]: return iter([]) def state_dict(self): return {} def load_state_dict(self, state_dict): pass # Mapping from HuggingFace model_type to torchtitan model name. # Torchtitan models not mapped here: # - flux: diffusion model, not applicable to verl's RL/SFT workflows # - llama3_ft: fault-tolerant variant of llama3, same HF models (mapped via "llama") _HF_MODEL_TYPE_TO_TORCHTITAN_NAME = { "qwen2": "qwen3", "qwen3": "qwen3", "qwen2_moe": "qwen3", "qwen3_moe": "qwen3", "llama": "llama3", "llama4": "llama4", "deepseek_v3": "deepseek_v3", "gpt_oss": "gpt_oss", } def derive_torchtitan_name_and_flavor(hf_config) -> tuple[str, str]: """Derive torchtitan model name and flavor from a HuggingFace config. The name is mapped from ``hf_config.model_type``. The flavor is found by matching architecture parameters (dim, n_layers, vocab_size) against the known flavors registered in the torchtitan model package. Args: hf_config: A HuggingFace AutoConfig object. Returns: A ``(name, flavor)`` tuple. Raises: ValueError: If model_type is unsupported or no matching flavor is found. """ model_type = getattr(hf_config, "model_type", None) if model_type is None: raise ValueError("HuggingFace config does not have 'model_type' field") name = _HF_MODEL_TYPE_TO_TORCHTITAN_NAME.get(model_type) if name is None: raise ValueError( f"Cannot derive torchtitan model name from HF model_type '{model_type}'. " f"Supported types: {list(_HF_MODEL_TYPE_TO_TORCHTITAN_NAME.keys())}." ) # Import the model package and find the configs dict model_module = importlib.import_module(f"torchtitan.models.{name}") model_configs = None for attr in dir(model_module): obj = getattr(model_module, attr) if isinstance(obj, dict) and attr.endswith("_configs"): model_configs = obj break if model_configs is None: raise ValueError( f"Could not find model configs dict in torchtitan.models.{name}. " f"Expected a dict attribute ending with '_configs'." ) hidden_size = hf_config.hidden_size num_layers = hf_config.num_hidden_layers vocab_size = hf_config.vocab_size for flavor_name, model_cfg in model_configs.items(): if ( getattr(model_cfg, "dim", None) == hidden_size and getattr(model_cfg, "n_layers", None) == num_layers and getattr(model_cfg, "vocab_size", None) == vocab_size ): logger.info( f"Auto-derived torchtitan name='{name}', flavor='{flavor_name}' from HF model_type='{model_type}'" ) return name, flavor_name raise ValueError( f"No matching torchtitan flavor found for model_type='{model_type}' " f"(hidden_size={hidden_size}, num_hidden_layers={num_layers}, " f"vocab_size={vocab_size}). " f"Available flavors for '{name}': {list(model_configs.keys())}." ) def enable_fsdp_gradient_division(model: nn.Module, dp_size: int) -> None: """ Re-enable FSDP's automatic gradient division. TorchTitan calls disable_fsdp_gradient_division() which sets gradient_divide_factor=1.0. This re-enables it by setting the factor to the specified dp_size, so gradients are averaged across FSDP ranks. This is needed for verl's loss scaling (loss * dp_size) to work correctly. Args: model: The model (or model part) to enable gradient division on. dp_size: The data parallel size to use as the gradient divide factor. """ for module in model.modules(): if isinstance(module, FSDPModule): module.set_gradient_divide_factor(float(dp_size)) def get_attention_masks( input_batch: torch.Tensor, positions: torch.Tensor, attn_type: str, ) -> AttentionMasksType: match attn_type: case "flex": return _get_flex_attention_masks( input_batch, positions, ) case "varlen": return _create_varlen_metadata_for_document( input_batch, positions, ) case _: raise TypeError("Only varlen and flex attn masks are supported") def _get_document_mask_mod(positions: torch.Tensor) -> _mask_mod_signature: # Detect boundaries from position resets first_dummy_value = positions[:, :1] - 1 position_diff = torch.diff(positions, prepend=first_dummy_value, dim=-1) sequence_indices = (position_diff != 1).cumsum(-1) # [batch, seq] def document_mask(b: torch.Tensor, h: torch.Tensor, q_idx: torch.Tensor, kv_idx: torch.Tensor) -> torch.Tensor: return sequence_indices[b, q_idx] == sequence_indices[b, kv_idx] return document_mask def _get_flex_attention_masks( input_batch: torch.Tensor, positions: torch.Tensor, ) -> AttentionMasksType: mask_mods = [get_causal_mask_mod()] B = input_batch.shape[0] mask_mods.append(_get_document_mask_mod(positions=positions)) return create_attention_mask(and_masks(*mask_mods), B, None, input_batch.shape[1], input_batch.shape[1]) def _create_varlen_metadata_for_document(input_batch: torch.Tensor, positions: torch.Tensor) -> VarlenMetadata: """ Creates cumulative sequence length indices needed for variable length attention Args: input_batch: Input token IDs with shape [batch, seq]. positions: Position IDs with shape [batch, seq]. Boundaries detected where position diff != 1 (i.e., position resets). Returns: VarlenMetadata containing cumulative sequence length indices for q, k, and max_seq_len """ batch_size, seq_len = input_batch.shape device = input_batch.device # Detect boundaries from position resets (where diff != 1) first_dummy_value = positions[:, :1] - 1 position_diff = torch.diff(positions, prepend=first_dummy_value, dim=-1) # boundary_mask[b, i] is True if position i starts a new document boundary_mask = position_diff != 1 # [batch, seq] boundary_mask[:, 0] = True cu_seqlens_list, all_seq_lengths = [], [] offset = 0 for b in range(batch_size): # Find positions where new documents start boundary_positions = boundary_mask[b].nonzero(as_tuple=True)[0].to(torch.int32) sample_cu_seqlens = torch.cat( [ boundary_positions, torch.tensor([seq_len], dtype=torch.int32, device=device), ] ) sample_cu_seqlens = torch.unique_consecutive(sample_cu_seqlens) seq_lengths = torch.diff(sample_cu_seqlens) all_seq_lengths.append(seq_lengths) cu_seqlens_adjusted = sample_cu_seqlens[:-1] + offset cu_seqlens_list.append(cu_seqlens_adjusted) offset += seq_len packed_cu_seqlens = torch.cat(cu_seqlens_list + [torch.tensor([offset], dtype=torch.int32, device=device)]) max_seqlen = 0 if len(all_seq_lengths) > 0: all_seq_lengths = torch.cat(all_seq_lengths) # device to host sync but only done once per model forward max_seqlen = all_seq_lengths.max().item() return VarlenMetadata( cu_seq_q=packed_cu_seqlens, cu_seq_k=packed_cu_seqlens, max_q=max_seqlen, max_k=max_seqlen, ) # Regex to parse: model.layers.{L}.mlp.experts.{E}.{weight_suffix} _EXPERT_PATTERN = re.compile(r"\.layers\.(\d+)\..*\.experts\.(\d+)\.(.*)") def _parse_expert_name(name: str) -> tuple[int, int, str] | None: """Parse layer_id, expert_id, weight_suffix from expert param name.""" match = _EXPERT_PATTERN.search(name) if match: return int(match.group(1)), int(match.group(2)), match.group(3) return None def _make_expert_name_template(name: str) -> str: """Convert 'model.layers.0.mlp.experts.3.w1' -> 'model.layers.0.mlp.experts.{}.w1'""" return _EXPERT_PATTERN.sub(lambda m: f".layers.{m.group(1)}.mlp.experts.{{}}.{m.group(3)}", name) def iter_per_tensor_params_ep( params: dict[str, Any], device: int, ep_group: torch.distributed.ProcessGroup, ep_size: int, ) -> Generator[tuple[str, torch.Tensor], None, None]: """Yield (name, tensor) pairs for weight sync with Expert Parallel. Gathers expert weights across EP ranks one (layer, weight_type) group at a time to avoid OOM from materializing all experts simultaneously. Non-expert params are yielded first (with FSDP full_tensor() if needed), then expert params are all-gathered per group and yielded individually. Args: params: HF-format state dict with per-expert keys. Expert keys must follow the pattern ``model.layers.{L}.mlp.experts.{E}.{suffix}``. device: device ID to place tensors on. ep_group: The EP process group for all-gather. ep_size: Number of EP ranks. """ expert_params: dict[tuple[int, str], dict[int, tuple[str, Any]]] = defaultdict(dict) non_expert_params: list[tuple[str, Any]] = [] for name, param in params.items(): parsed = _parse_expert_name(name) if "mlp.experts." in name else None if parsed is None: non_expert_params.append((name, param)) else: layer_id, expert_id, weight_suffix = parsed expert_params[(layer_id, weight_suffix)][expert_id] = (name, param) params.clear() # Yield non-expert params for name, param in non_expert_params: if isinstance(param, DTensor): yield name, param.to(device, non_blocking=True).full_tensor().to(torch.bfloat16, non_blocking=True) else: yield name, param del non_expert_params # Yield expert params with all-gather for (layer_id, weight_suffix), experts_dict in sorted(expert_params.items()): sorted_expert_ids = sorted(experts_dict.keys()) # Stack local expert weights local_weights = [] for eid in sorted_expert_ids: _, param = experts_dict[eid] if isinstance(param, DTensor): param = param.to(device, non_blocking=True).full_tensor() else: param = param.to(device, non_blocking=True) local_weights.append(param) name_template = _make_expert_name_template(experts_dict[sorted_expert_ids[0]][0]) local_stacked = torch.stack(local_weights, dim=0) # All-gather across EP ranks gathered_list = [torch.empty_like(local_stacked) for _ in range(ep_size)] torch.distributed.all_gather(gathered_list, local_stacked, group=ep_group) all_experts = torch.cat(gathered_list, dim=0) for expert_id in range(all_experts.shape[0]): yield name_template.format(expert_id), all_experts[expert_id].to(torch.bfloat16).clone() del local_weights, local_stacked, gathered_list, all_experts torch.cuda.empty_cache() ================================================ FILE: verl/workers/engine/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import random import numpy as np import torch from tensordict import TensorDict from verl.utils import tensordict_utils as tu from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.device import is_npu_available from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import rearrange_micro_batches, restore_dynamic_batch def enable_full_determinism(seed: int): """ Helper function for reproducibility in distributed training. See https://pytorch.org/docs/stable/notes/randomness.html for details. """ os.environ["PYTHONHASHSEED"] = str(seed) os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" os.environ["NCCL_DETERMINISTIC"] = "1" os.environ["FLASH_ATTENTION_DETERMINISTIC"] = "1" if is_npu_available: # The environment variable required to enable deterministic mode on Ascend NPUs. os.environ["NCCL_DETERMINISTIC"] = "true" os.environ["CLOSE_MATMUL_K_SHIFT"] = "1" random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.use_deterministic_algorithms(True, warn_only=True) # Enable CUDNN deterministic mode torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.enabled = False if is_npu_available: torch.npu.manual_seed(seed) torch.npu.manual_seed_all(seed) def prepare_micro_batches( data: TensorDict, dp_group=None, num_batches_divided_by=None, same_micro_num_in_dp=True, min_num_micro_batch=None, use_dynamic_bsz_balance=True, ): """ Prepare micro batches from data. """ use_dynamic_bsz = tu.get_non_tensor_data(data=data, key="use_dynamic_bsz", default=True) sp_size = tu.get_non_tensor_data(data=data, key="sp_size", default=1) force_group_size = tu.get_non_tensor_data(data=data, key="force_group_size", default=1) if use_dynamic_bsz: assert "max_token_len_per_gpu" in data.keys(), "max_token_len_per_gpu must be set when use_dynamic_bsz is True" max_token_len_per_gpu = data["max_token_len_per_gpu"] max_token_len = max_token_len_per_gpu * sp_size micro_batches, batch_idx_list = rearrange_micro_batches( data, max_token_len=max_token_len, dp_group=dp_group, num_batches_divided_by=num_batches_divided_by, same_micro_num_in_dp=same_micro_num_in_dp, min_num_micro_batch=min_num_micro_batch, use_dynamic_bsz_balance=use_dynamic_bsz_balance, force_group_size=force_group_size, ) else: total_data_size = len(data) micro_batch_size_per_gpu = data["micro_batch_size_per_gpu"] assert total_data_size % (force_group_size * micro_batch_size_per_gpu) == 0, ( "data size must be divisible by force_group_size * micro_batch_size_per_gpu" ) micro_batches = tu.chunk_tensordict(data, total_data_size // (micro_batch_size_per_gpu * force_group_size)) batch_idx_list = None return micro_batches, batch_idx_list def postprocess_batch_func(output_lst, indices, data: TensorDict): """postprocess the output of a forward_backward_batch. output_lst is a list of dict containing outputs for each micro-batch reorder entropy and outputs. Return None for other pp ranks only on last rank. It should be on every tp rank each losses_reduced contains 1. model_output, 2. loss, 3. metrics. """ use_dynamic_bsz = tu.get_non_tensor_data(data=data, key="use_dynamic_bsz", default=True) pad_mode = tu.get_non_tensor_data(data=data, key="pad_mode", default=DatasetPadMode.NO_PADDING) assert pad_mode == DatasetPadMode.NO_PADDING, "postprocess_batch_func only support NO_PADDING pad_mode" # losses_reduced is a list of dict containing outputs for each micro-batch # reorder entropy and outputs. Return None for other pp ranks # only on last rank. It should be on every tp rank # losses_reduced contains 1. model_output, 2. loss, 3. metrics. # We perform reverse model_output = {} losses = [] aggregated_metrics = {} # model output for o in output_lst: if "model_output" in o: for key, val in o["model_output"].items(): if key not in model_output: model_output[key] = [] model_output[key].append(val) # concat results from micro batches for key, val in model_output.items(): if pad_mode == DatasetPadMode.NO_PADDING: tensors = [tensor for nt in model_output[key] for tensor in nt.unbind()] model_output[key] = torch.nested.as_nested_tensor(tensors, layout=torch.jagged) else: raise NotImplementedError(f"pad_mode {pad_mode} not implemented") # reverse with dynamic bsz if use_dynamic_bsz: model_output[key] = restore_dynamic_batch(model_output[key], indices) # loss for o in output_lst: if "loss" in o: losses.append(o["loss"]) # metrics for o in output_lst: if "metrics" in o: metrics = o["metrics"] append_to_dict(aggregated_metrics, metrics) output = { "model_output": model_output, "loss": losses, "metrics": aggregated_metrics, } return output ================================================ FILE: verl/workers/engine/veomni/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .transformer_impl import VeOmniEngine, VeOmniEngineWithLMHead __all__ = ["VeOmniEngine", "VeOmniEngineWithLMHead"] ================================================ FILE: verl/workers/engine/veomni/transformer_impl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from dataclasses import dataclass, field from typing import Any, Callable, Optional, Sequence import torch import torch.distributed as dist from tensordict import TensorDict from torch.distributed.tensor import DTensor from veomni.distributed import parallel_state from veomni.distributed.offloading import build_activation_offloading_context from veomni.distributed.torch_parallelize import build_parallelize_model from veomni.models.auto import build_foundation_model from veomni.optim import build_lr_scheduler, build_optimizer import verl.utils.torch_functional as verl_F from verl.trainer.config import CheckpointConfig from verl.utils import tensordict_utils as tu from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.utils.device import get_device_id, get_device_name from verl.utils.fsdp_utils import fsdp_version from verl.utils.model import convert_weight_keys from verl.utils.profiler import log_gpu_memory_usage from verl.utils.ulysses import ( get_ulysses_sequence_parallel_group, set_ulysses_sequence_parallel_group, ) from verl.workers.config import HFModelConfig, VeOmniEngineConfig, VeOmniOptimizerConfig from ..base import BaseEngineCtx, EngineRegistry from ..fsdp.transformer_impl import FSDPEngine, FSDPEngineWithLMHead from ..utils import enable_full_determinism, postprocess_batch_func, prepare_micro_batches from .utils import ( MOE_PARAM_HANDERS, VL_TYPE2INDEX, load_veomni_model_to_gpu, load_veomni_optimizer, offload_veomni_model_to_cpu, offload_veomni_optimizer, ) logger = logging.getLogger(__file__) class VeOmniEngine(FSDPEngine): def __init__( self, model_config: HFModelConfig, engine_config: VeOmniEngineConfig, optimizer_config: VeOmniOptimizerConfig, checkpoint_config: CheckpointConfig, **kwargs, ): """ Initialize the VeOmniEngine. Sets up distributed device meshes, LoRA, and offload policies based on config. Args: config: Configuration object with VeOmni and model settings. """ self.model_config = model_config self.engine_config = engine_config self.optimizer_config = optimizer_config self.checkpoint_config = checkpoint_config # VeOmniEngine only supports fsdp2. self.data_parallel_mode = "fsdp2" self.rank = dist.get_rank() fsdp_size = self.engine_config.fsdp_size world_size = dist.get_world_size() dp_size = world_size // self.engine_config.ulysses_parallel_size if fsdp_size < 0 or fsdp_size >= dp_size: data_parallel_replicate_size = 1 data_parallel_shard_size = dp_size else: if dp_size % fsdp_size != 0: raise ValueError( f"Data parallel size ({dp_size}) must be divisible by fsdp_size ({fsdp_size}). " "Please adjust your parallel configuration." ) data_parallel_replicate_size = dp_size // fsdp_size data_parallel_shard_size = fsdp_size parallel_state.init_parallel_state( dp_size=dp_size, dp_replicate_size=data_parallel_replicate_size, dp_shard_size=data_parallel_shard_size, ep_size=self.engine_config.expert_parallel_size, ulysses_size=self.engine_config.ulysses_parallel_size, dp_mode=self.data_parallel_mode, ) if self.engine_config.full_determinism: enable_full_determinism(seed=self.engine_config.seed) self.use_remove_padding = self.model_config.use_remove_padding self._is_offload_param = self.engine_config.param_offload self._is_offload_optimizer = self.engine_config.optimizer_offload self._is_lora = self.model_config.lora_rank > 0 self.use_ulysses_sp = parallel_state.get_parallel_state().sp_enabled self.ulysses_sequence_parallel_size = self.engine_config.ulysses_parallel_size if self.use_ulysses_sp: self.ulysses_parallel_group = parallel_state.get_parallel_state().device_mesh["sp"].get_group() else: self.ulysses_parallel_group = None if self.engine_config.entropy_from_logits_with_chunking: entropy_from_logits = verl_F.entropy_from_logits_with_chunking else: entropy_from_logits = verl_F.entropy_from_logits self.compute_entropy_from_logits = ( torch.compile(entropy_from_logits, dynamic=True) if self.engine_config.use_torch_compile # use torch compile by default else entropy_from_logits ) def initialize(self): """ Build the model, optimizer, and learning rate scheduler under VeOmni. Applies device, dtype, and precision configurations, including mixed precision. Sets up checkpoint manager and FLOPs counter. """ self._build_model_optimizer() self.checkpoint_manager = FSDPCheckpointManager( model=self.module, optimizer=self.optimizer, lr_scheduler=self.lr_scheduler, processing_class=self.model_config.get_processor(), checkpoint_config=self.checkpoint_config, trust_remote_code=self.model_config.trust_remote_code, ) self.to( device="cpu", model=self._is_offload_param, optimizer=self._is_offload_optimizer, grad=self._is_offload_optimizer, ) log_gpu_memory_usage("After offload model/optimizer/grad during init", logger=logger) def _build_optimizer(self, module): optimizer = build_optimizer( module, lr=self.optimizer_config.lr, betas=self.optimizer_config.betas, weight_decay=self.optimizer_config.weight_decay, optimizer_type=self.optimizer_config.optimizer, ) get_optimizer_pre_hook = getattr(module, "get_optimizer_pre_hook", None) if get_optimizer_pre_hook is not None: optimizer_pre_hook = get_optimizer_pre_hook(module, module.config, self.data_parallel_mode) optimizer.register_step_pre_hook(optimizer_pre_hook) return optimizer def _build_lr_scheduler(self, optimizer): optim_config = self.optimizer_config lr_scheduler = build_lr_scheduler( optimizer, train_steps=optim_config.total_training_steps, lr=optim_config.lr, lr_min=optim_config.lr_min, lr_decay_style=optim_config.lr_scheduler_type, lr_decay_ratio=optim_config.lr_decay_ratio, lr_warmup_ratio=optim_config.lr_warmup_steps_ratio, lr_start=optim_config.lr_start, ) return lr_scheduler def _build_model_optimizer(self): # Load base model with specified configuration and dtype module = build_foundation_model( config_path=self.model_config.hf_config_path, weights_path=self.model_config.path, torch_dtype="float32" if self.engine_config.mixed_precision else "bfloat16", attn_implementation=self.engine_config.attn_implementation, moe_implementation=self.engine_config.moe_implementation, init_device=self.engine_config.init_device, ) log_gpu_memory_usage("After load base model", logger=logger) # Applies parallel strategies to the model. log_gpu_memory_usage("Before parallelize model", logger=logger) module = build_parallelize_model( module, init_device=self.engine_config.init_device, weights_path=self.model_config.path, enable_full_shard=self.engine_config.enable_full_shard, enable_mixed_precision=self.engine_config.mixed_precision, enable_gradient_checkpointing=self.model_config.enable_gradient_checkpointing, enable_fsdp_offload=self.engine_config.enable_fsdp_offload, basic_modules=module._no_split_modules + self.engine_config.basic_modules, enable_reentrant=self.engine_config.enable_reentrant, enable_forward_prefetch=self.engine_config.forward_prefetch, ) log_gpu_memory_usage("After parallelize model", logger=logger) if not self.engine_config.forward_only: # Initialize optimizer with model parameters and config settings optimizer = self._build_optimizer(module) # Create learning rate scheduler with warmup and decay settings lr_scheduler = self._build_lr_scheduler(optimizer) else: optimizer = None lr_scheduler = None self.module = module self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.model_fwd_context, self.model_bwd_context = build_activation_offloading_context( self.model_config.enable_activation_offload, self.model_config.enable_gradient_checkpointing, self.engine_config.activation_gpu_limit, ) def optimizer_step(self): """ Perform an optimization step using the optimizer. """ if hasattr(self.module, "clip_grad_norm_"): grad_norm = self.module.clip_grad_norm_(self.optimizer_config.clip_grad) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.module.parameters(), self.optimizer_config.clip_grad) if isinstance(grad_norm, DTensor): grad_norm = grad_norm.full_tensor() # if grad_norm is not finite, skip the update if not torch.isfinite(grad_norm): print(f"WARN: grad_norm is not finite: {grad_norm}") self.optimizer.zero_grad() else: self.optimizer.step() return grad_norm.item() def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forward_only=False) -> Any: """ Perform a forward pass and optionally a backward pass on a batch of data. Args: data: The input data for the forward pass, typically containing tensors and metadata. loss_function: The loss function to optimize. See `verl.workers.roles.utils.losses` for examples. forward_only: If True, perform only the forward pass. If False, perform forward and backward pass. Returns: Any: The output of the forward pass, which can be used for loss computation or other purposes. """ tu.assign_non_tensor(data, sp_size=parallel_state.get_parallel_state().ulysses_size) # compute num_tokens in global batch for loss normalization batch_num_tokens = data["loss_mask"].sum().to(get_device_id()) torch.distributed.all_reduce( batch_num_tokens, op=torch.distributed.ReduceOp.SUM, group=self.get_data_parallel_group() ) tu.assign_non_tensor(data, batch_num_tokens=batch_num_tokens.item()) tu.assign_non_tensor(data, dp_size=self.get_data_parallel_size()) micro_batches, indices = prepare_micro_batches( data=data, dp_group=self.get_data_parallel_group(), same_micro_num_in_dp=True ) output_lst = [] for micro_batch in micro_batches: with self.model_fwd_context: loss, meta_info = self.forward_step(micro_batch, loss_function=loss_function, forward_only=forward_only) if not forward_only: with self.model_bwd_context: loss.backward() output_lst.append(meta_info) return postprocess_batch_func(output_lst=output_lst, indices=indices, data=data) def get_data_parallel_rank(self): return parallel_state.get_parallel_state().device_mesh.get_local_rank("dp") def get_data_parallel_size(self): return torch.distributed.get_world_size() // parallel_state.get_parallel_state().ulysses_size def get_data_parallel_group(self): if parallel_state.get_parallel_state().ulysses_size > 1: return parallel_state.get_parallel_state().device_mesh.get_group(mesh_dim="dp") else: return torch.distributed.group.WORLD def get_model_parallel_group(self): raise NotImplementedError def get_context_parallel_group(self): raise NotImplementedError def is_mp_src_rank_with_outputs(self): """ Whether the current rank is the first rank in model parallel group that contains model outputs """ if parallel_state.get_parallel_state().ulysses_size > 1: is_collect = parallel_state.get_parallel_state().device_mesh["ulysses"].get_local_rank() == 0 else: is_collect = True return is_collect def train_mode(self, **kwargs): """ Return a context manager that switches to training mode with VeOmni-specific handling. Includes parameter and optimizer offload entry/exit. """ return EngineTrainModeCtx(self, **kwargs) def eval_mode(self, **kwargs): """ Return a context manager that switches to evaluation mode with VeOmni-specific handling. Includes activation offload entry/exit. """ return EngineEvalModeCtx(self, **kwargs) def to(self, device: str, model: bool = True, optimizer: bool = True, grad: bool = True): """ Move model parameters, optimizer states, or both to the specified device. Note that this function executes irrespective of offload config. It serves as manual control. Args: device: Target device identifier. model: If True, move the model. optimizer: If True, move the optimizer states. """ super(FSDPEngine, self).to(device=device, model=model, optimizer=optimizer, grad=grad) device_name = get_device_name() assert device in (device_name, "cpu") if device == device_name: if model: load_veomni_model_to_gpu(self.module) if optimizer and self.optimizer is not None: load_veomni_optimizer(self.optimizer, device) elif device == "cpu": if model: offload_veomni_model_to_cpu(self.module) if optimizer and self.optimizer is not None: offload_veomni_optimizer(self.optimizer) else: raise ValueError(f"Invalid device type: {device}") def save_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, global_step: int = 0, max_ckpt_to_keep: Optional[int] = None, **kwargs, ) -> None: """ Save VeOmni checkpoint, handling parameter offload as needed. """ origin_module_device = next(self.module.parameters()).device.type if self._is_offload_param or origin_module_device == "cpu": load_veomni_model_to_gpu(self.module) self.checkpoint_manager.save_checkpoint( local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep ) torch.distributed.barrier() if self._is_offload_param: offload_veomni_model_to_cpu(self.module) def load_checkpoint( self, local_path: str, hdfs_path: Optional[str] = None, del_local_after_load: int = True, **kwargs ) -> None: """ Load VeOmni checkpoint, restoring parameters and optimizer state. """ if self._is_offload_param: load_veomni_model_to_gpu(self.module) self.checkpoint_manager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) torch.distributed.barrier() if self._is_offload_param: offload_veomni_model_to_cpu(self.module) if self._is_offload_optimizer: offload_veomni_optimizer(self.optimizer) def get_per_tensor_param(self, **kwargs): load_veomni_model_to_gpu(self.module) params = self.module.state_dict() params = convert_weight_keys(params, getattr(self.module, "_fsdp_wrapped_module", self.module)) if self._is_offload_param: offload_veomni_model_to_cpu(self.module) device = get_device_id() ps = parallel_state.get_parallel_state() model_type = getattr(self.module.config, "model_type", "default") process_func = MOE_PARAM_HANDERS.get(model_type, lambda n, t: iter([(n, t)])) def param_generator(): for name, param in params.items(): unsharded_tensor = param.full_tensor() if isinstance(param, DTensor) else param is_expert_layer = "mlp.experts." in name is_proj = any(p in name for p in ["down_proj", "gate_proj", "up_proj", "gate_up_proj"]) if is_expert_layer and is_proj and ps.ep_enabled: output_shape = list(unsharded_tensor.shape) output_shape[0] *= ps.ep_size stacked_tensor = torch.empty(output_shape, dtype=unsharded_tensor.dtype, device=device) # all gather expert tensors [32, H, I] -> [128, H, I] torch.distributed.all_gather_into_tensor(stacked_tensor, unsharded_tensor, group=ps.ep_group) yield from process_func(name, stacked_tensor) del stacked_tensor else: if is_expert_layer: yield from process_func(name, unsharded_tensor) else: yield name, unsharded_tensor # TODO: support VeOmni LoRA return param_generator(), None class EngineEvalModeCtx(BaseEngineCtx): def __init__(self, engine: VeOmniEngine, **kwargs): super().__init__(engine=engine, mode="eval", **kwargs) def __enter__(self): assert isinstance(self.engine, VeOmniEngine) super().__enter__() self.prev_sp_group = get_ulysses_sequence_parallel_group() set_ulysses_sequence_parallel_group(self.engine.ulysses_parallel_group) self.engine.module.train() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, VeOmniEngine) set_ulysses_sequence_parallel_group(self.prev_sp_group) # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if parallel_state.get_parallel_state().dp_shard_size > 1: if fsdp_version(self.engine.module) == 1: self.engine.module._handle.reshard(True) elif fsdp_version(self.engine.module) == 2: self.engine.module.reshard() super().__exit__(exc_type, exc_value, traceback) class EngineTrainModeCtx(BaseEngineCtx): def __init__(self, engine: VeOmniEngine, **kwargs): super().__init__(engine=engine, mode="train", **kwargs) def __enter__(self): assert isinstance(self.engine, VeOmniEngine) super().__enter__() self.prev_sp_group = get_ulysses_sequence_parallel_group() set_ulysses_sequence_parallel_group(self.engine.ulysses_parallel_group) # TODO: Switch to eval mode after Integrating the CI environment # VeOmni (ref: https://github.com/ByteDance-Seed/VeOmni/pull/421) self.engine.module.train() def __exit__(self, exc_type, exc_value, traceback): assert isinstance(self.engine, VeOmniEngine) set_ulysses_sequence_parallel_group(self.prev_sp_group) self.engine.optimizer_zero_grad() super().__exit__(exc_type, exc_value, traceback) @dataclass class OmniSequenceShardCollator: """ Data collator to chunk inputs along the sequence length. """ # features to slice sequence dimension sp_slice_features: dict[str, int] = field( default_factory=lambda: { "input_ids": -1, "labels": -1, "pixel_values": 0, "pixel_values_videos": 0, }, metadata={"help": "features to slice sequence dimension."}, ) # features to padding sequence dimension padding_features: dict[str, int] = field( default_factory=lambda: { "pixel_values": 0, }, metadata={"help": "features to padding sequence dimension."}, ) # padding scale for padding features padding_scale: dict[str, int] = field( default_factory=lambda: {"pixel_values": 4}, metadata={"help": "padding scale for padding features."} ) def __post_init__(self): self.sp_size = parallel_state.get_parallel_state().sp_size self.sp_rank = parallel_state.get_parallel_state().sp_rank def sp_slice(self, feature: torch.Tensor, dim: int = -1) -> dict[str, "torch.Tensor"]: seq_length = feature.size(dim) sp_chunk_size = (seq_length + self.sp_size - 1) // self.sp_size return feature.narrow(dim, self.sp_rank * sp_chunk_size, sp_chunk_size) def sp_padding( self, tensor: "torch.Tensor", dim: int = -1, pad_value: int = 0, pad_scale: int = 1 ) -> "torch.Tensor": """ Pads a tensor with pad_length to aligns tensor with sp size. """ seq_length = tensor.size(dim) scale_sp_size = self.sp_size * pad_scale sp_chunk_size = (seq_length + scale_sp_size - 1) // scale_sp_size pad_size = sp_chunk_size * scale_sp_size - seq_length if pad_size == 0: return tensor pad_shape = list(tensor.shape) pad_shape[dim] = pad_size pad = torch.full(pad_shape, fill_value=pad_value, dtype=tensor.dtype, device=tensor.device) return torch.cat((tensor, pad), dim=dim) def __call__(self, batch: Sequence[dict[str, "torch.Tensor"]]) -> dict[str, "torch.Tensor"]: for key in batch.keys(): if key in self.padding_features.keys(): batch[key] = self.sp_padding( batch[key], dim=self.sp_slice_features.get(key, -1), pad_value=self.padding_features[key], pad_scale=self.padding_scale.get(key, 1), ) # sp slice for key in batch.keys(): if key in self.sp_slice_features.keys(): batch[key] = self.sp_slice(batch[key], dim=self.sp_slice_features[key]) return batch @EngineRegistry.register(model_type="language_model", backend=["veomni"], device=["cuda", "npu"]) class VeOmniEngineWithLMHead(VeOmniEngine, FSDPEngineWithLMHead): def prepare_model_inputs(self, micro_batch: TensorDict): # TODO: Cannot work properly for qwen_vl ulysses model_inputs, output_args = super().prepare_model_inputs(micro_batch) input_ids_rmpad = model_inputs["input_ids"] if self.module.config.model_type in VL_TYPE2INDEX.keys(): image_mask = input_ids_rmpad == VL_TYPE2INDEX[self.module.config.model_type]["IMAGE_INPUT_INDEX"] video_mask = input_ids_rmpad == VL_TYPE2INDEX[self.module.config.model_type]["VIDEO_INPUT_INDEX"] model_inputs.update({"image_mask": image_mask, "video_mask": video_mask}) if parallel_state.get_parallel_state().sp_enabled: omni_sequence_shard_collator = OmniSequenceShardCollator() omni_sequence_shard_collator(model_inputs) return model_inputs, output_args ================================================ FILE: verl/workers/engine/veomni/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from verl.utils.device import get_device_id, get_torch_device VL_TYPE2INDEX = { "qwen2_5_vl": { "IMAGE_INPUT_INDEX": 151655, "VIDEO_INPUT_INDEX": 151656, }, "qwen3_vl": { "IMAGE_INPUT_INDEX": 151655, "VIDEO_INPUT_INDEX": 151656, }, "qwen3_vl_moe": { "IMAGE_INPUT_INDEX": 151655, "VIDEO_INPUT_INDEX": 151656, }, } @torch.no_grad() def offload_veomni_model_to_cpu(model, empty_cache: bool = True): from torch.distributed.fsdp._fully_shard._fsdp_common import TrainingState from torch.distributed.fsdp._fully_shard._fsdp_state import _get_module_fsdp_state for module in model.modules(): state = _get_module_fsdp_state(module) if state is None: continue fsdp_param_group = state._fsdp_param_group if fsdp_param_group is None: continue fsdp_param_group._training_state = TrainingState.IDLE model.reshard() model.cpu() if empty_cache: get_torch_device().empty_cache() @torch.no_grad() def load_veomni_model_to_gpu(model): device = get_device_id() model.to(device) @torch.no_grad() def offload_veomni_optimizer(optimizer): optimizers = [] # Check if this is a MultiOptimizer (for ep and non-ep parameters when ep+fsdp2 is enabled) if hasattr(optimizer, "_is_multi_optimizer") and optimizer._is_multi_optimizer: optimizers.extend(optimizer.optimizers_dict.values()) else: optimizers.append(optimizer) for opt in optimizers: if not opt.state: continue for param_group in opt.param_groups: for param in param_group["params"]: state = opt.state[param] for key, value in state.items(): if isinstance(value, torch.Tensor): state[key] = value.to("cpu", non_blocking=True) @torch.no_grad() def load_veomni_optimizer(optimizer, device_id): optimizers = [] # Check if this is a MultiOptimizer (for ep and non-ep parameters when ep+fsdp2 is enabled) if hasattr(optimizer, "_is_multi_optimizer") and optimizer._is_multi_optimizer: optimizers.extend(optimizer.optimizers_dict.values()) else: optimizers.append(optimizer) for opt in optimizers: if not opt.state: continue for param_group in opt.param_groups: for param in param_group["params"]: state = opt.state[param] for key, value in state.items(): if isinstance(value, torch.Tensor): state[key] = value.to(device_id, non_blocking=True) def _map_moe_params_qwen3_moe(name, tensor): for i in range(tensor.size(0)): new_key = name.replace("mlp.experts.", f"mlp.experts.{i}.") + ".weight" yield new_key, tensor[i].to(get_device_id(), non_blocking=True) MOE_PARAM_HANDERS = { "qwen3_moe": _map_moe_params_qwen3_moe, } ================================================ FILE: verl/workers/engine_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools import logging import os from contextlib import nullcontext from copy import deepcopy from functools import partial from itertools import chain import torch from codetiming import Timer from omegaconf import DictConfig, open_dict from tensordict import NonTensorData, TensorDict from torch.distributed.device_mesh import init_device_mesh from verl.checkpoint_engine import CheckpointEngineRegistry from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.utils import tensordict_utils as tu from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import get_device_name, set_expandable_segments from verl.utils.distributed import initialize_global_process_group_ray from verl.utils.flops_counter import FlopsCounter from verl.utils.memory_utils import aggressive_empty_cache from verl.utils.metric.utils import Metric from verl.utils.profiler import DistProfiler, DistProfilerExtension, ProfilerConfig, log_gpu_memory_usage from verl.utils.py_functional import append_to_dict from verl.utils.tensordict_utils import maybe_fix_3d_position_ids from verl.utils.torch_functional import allgather_dict_into_dict from verl.workers.config import ActorConfig, HFModelConfig, MtpConfig, RolloutConfig, TrainingWorkerConfig from verl.workers.rollout.base import BaseRollout, get_rollout_class from verl.workers.utils.losses import ppo_loss logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def _with_routing_replay_flag(enabled: bool): """Decorator to set 'enable_routing_replay' flag on the data TensorDict.""" def decorator(func): @functools.wraps(func) def wrapper(self, data: TensorDict, *args, **kwargs): if self.enable_routing_replay: tu.assign_non_tensor_data(data, "enable_routing_replay", enabled) return func(self, data, *args, **kwargs) return wrapper return decorator class TrainingWorker(Worker, DistProfilerExtension): """ TrainingWorker provides a Tinker-like API (https://thinkingmachines.ai/tinker/) as a RayWorkerGroup to a single controller. Currently, we only provide more coarse grained APIs, and do not provide exact APIs as Tinker does. But this can be added in the future. """ def __init__(self, config: TrainingWorkerConfig): Worker.__init__(self) from verl.workers.engine import BaseEngine, EngineRegistry initialize_global_process_group_ray(timeout_second=None) self.config = config self.model_config = self.config.model_config self.engine_config = self.config.engine_config self.optimizer_config = self.config.optimizer_config self.checkpoint_config = self.config.checkpoint_config self.device_name = get_device_name() if self.engine_config is None: assert self.optimizer_config is None if self.config.auto_select_engine_optim_fn is None: raise ValueError( "engine_config is not provided and auto_select_engine_optim_fn is not set. " "Cannot determine engine backend." ) # Support automatically select engine backend given model config self.engine_config, self.optimizer_config = self.config.auto_select_engine_optim_fn( self.model_config, self.device_name ) # we use the one defined in model # TODO: this is not elegant and should refactor later self.engine_config.use_remove_padding = self.model_config.use_remove_padding self.engine_config.use_fused_kernels = self.model_config.use_fused_kernels # TODO: add DistProfilerExtension self.profiler_config = self.config.profiler_config if self.profiler_config is not None: self.profiler_tool_config = self.profiler_config.tool_config.get(self.profiler_config.tool, {}) else: self.profiler_tool_config = None DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=self.profiler_config, tool_config=self.profiler_tool_config) ) self.engine: BaseEngine = EngineRegistry.new( model_type=self.config.model_type, backend=self.engine_config.strategy, model_config=self.model_config, engine_config=self.engine_config, optimizer_config=self.optimizer_config, checkpoint_config=self.checkpoint_config, ) # build dispatch info self._register_dispatch_collect_info( mesh_name="train", dp_rank=self.engine.get_data_parallel_rank(), is_collect=self.engine.is_mp_src_rank_with_outputs(), ) self.flops_counter = FlopsCounter(self.model_config.hf_config) self.loss_fn = None @register(dispatch_mode=Dispatch.ONE_TO_ALL) def to(self, device, model=True, optimizer=True, grad=True): """Manual control of load/offload""" assert device in ["cpu", "device"] if device == "device": device = get_device_name() self.engine.to(device=device, model=model, optimizer=optimizer, grad=grad) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def set_loss_fn(self, loss_fn): self.loss_fn = loss_fn @register(dispatch_mode=Dispatch.ONE_TO_ALL) def reset(self): """ Reset the model engine to the initial state. If the engine is not initialized, we initialize it. Otherwise, reload ckpt and reset states """ self.engine.initialize() def _postprocess_output(self, output, *, global_token_num, delta_time, forward_only, images_seqlens): """ Args: output: a dictionary containing loss, model_outputs and metrics Returns: """ # TODO: whether to log memory # metrics["perf/max_memory_allocated_gb"] = get_torch_device().max_memory_allocated() / (1024 ** 3) # metrics["perf/max_memory_reserved_gb"] = get_torch_device().max_memory_reserved() / (1024 ** 3) # metrics["perf/cpu_memory_used_gb"] = psutil.virtual_memory().used / (1024 ** 3) metrics: dict = output.pop("metrics") # perform all gather in dp group to ensure that it's correct. # Here each metric in metrics can be a list (micro-batch metrics) or a singleton # we should always sum the loss of each micro-batch as we scale by global_bsz/global_token loss = torch.sum(torch.tensor(output.pop("loss"), device=self.device_name)) dp_group = self.engine.get_data_parallel_group() if dp_group is not None: torch.distributed.all_reduce(loss, op=torch.distributed.ReduceOp.AVG, group=dp_group) loss = loss.item() # For grad_norm, we do not perform all reduce because it is already been done when clipping grad grad_norm = metrics.pop("grad_norm", None) lr = metrics.pop("lr", None) # For other metrics, we perform all gather in dp group (only if DP > 1) if dp_group is not None: final_metrics = allgather_dict_into_dict(data=metrics, group=dp_group) else: final_metrics = metrics final_metrics["loss"] = loss if grad_norm is not None: final_metrics["grad_norm"] = grad_norm if lr is not None: final_metrics["lr"] = lr # TODO: confirm the mtp loss IS same across dp for k, v in final_metrics.items(): if k.startswith("mtp_losses"): flatten_v = [sublist[0] for sublist in v] # sublist should be single element final_metrics[k] = sum(flatten_v) / len(flatten_v) # compute mfu if global_token_num is not None: estimated_flops, promised_flops = self.flops_counter.estimate_flops( global_token_num, delta_time, images_seqlens=images_seqlens ) final_metrics["mfu"] = estimated_flops / promised_flops / torch.distributed.get_world_size() if forward_only: final_metrics["mfu"] /= 3.0 # model outputs model_output = output.pop("model_output", {}) # We only return final_metrics final_output = tu.get_tensordict(tensor_dict=model_output, non_tensor_dict={"metrics": final_metrics}) return final_output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train"), blocking=False) def train_mini_batch(self, data: TensorDict) -> TensorDict: """Split a batch into N mini-batches run for multiple epochs Args: data: Returns: """ maybe_fix_3d_position_ids(data) batch_size_per_dp = data.shape[0] disable_auto_offload = tu.pop(data, key="disable_auto_offload", default=False) mini_batch_size = tu.pop(data, key="mini_batch_size", default=None) num_mini_batch = tu.pop(data, key="num_mini_batch", default=None) epochs = tu.pop(data, key="epochs", default=1) seed = tu.pop(data, key="seed", default=42) dataloader_kwargs = tu.pop(data, key="dataloader_kwargs", default={}) assert mini_batch_size is not None or num_mini_batch is not None if mini_batch_size is None: assert batch_size_per_dp % num_mini_batch == 0, f"Got {batch_size_per_dp=} and {num_mini_batch=}" mini_batch_size_per_gpu = batch_size_per_dp // num_mini_batch else: assert mini_batch_size % self.engine.get_data_parallel_size() == 0, ( f"Got {mini_batch_size=} and {self.engine.get_data_parallel_size()=}" ) mini_batch_size_per_gpu = mini_batch_size // self.engine.get_data_parallel_size() # make iterator dataloader = tu.make_iterator( data, mini_batch_size=mini_batch_size_per_gpu, epochs=epochs, seed=seed + self.engine.get_data_parallel_rank(), dataloader_kwargs=dataloader_kwargs, ) with ( self.engine.train_mode(disable_auto_offload=disable_auto_offload), Timer(name="train_batch", logger=None), ): # update output_lst = [] total_num_iterations = data.shape[0] // mini_batch_size_per_gpu * epochs for batch_idx, mini_batch_td in enumerate(dataloader): # add global token num global_token_num = mini_batch_td["input_ids"].offsets().diff().tolist() # (total_nnz,) # allgather from dp rank global_token_num_output = [None] * self.engine.get_data_parallel_size() torch.distributed.all_gather_object( global_token_num_output, global_token_num, self.engine.get_data_parallel_group() ) global_token_num = [x for xs in global_token_num_output for x in xs] tu.assign_non_tensor( mini_batch_td, global_token_num=NonTensorData(global_token_num), update_lr_scheduler=batch_idx == total_num_iterations - 1, disable_auto_offload=True, ) actor_output = self.train_batch(mini_batch_td) output_lst.append(actor_output) if self.engine.is_mp_src_rank_with_outputs(): actor_output = [tu.get(output, "metrics") for output in output_lst] metrics = {} for output in actor_output: for key, val in output.items(): # flattn dp and micro batch if isinstance(val, list): output[key] = ( Metric.aggregate_dp(val) if isinstance(val[0], Metric) else list(chain.from_iterable(val)) ) append_to_dict(metrics, output) output = tu.get_tensordict(tensor_dict={}, non_tensor_dict={"metrics": metrics}).cpu() else: output = None return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train"), blocking=False) def train_batch(self, data: TensorDict) -> TensorDict: assert self.loss_fn is not None, "loss function can't be None when calling train_batch" assert not self.engine_config.forward_only, "Can't run `train_batch` when forward_only is in the engine config." # global_token_num should be a list of number of tokens of each seq in this batch global_token_num = tu.get(data, key="global_token_num") disable_auto_offload = tu.get(data, key="disable_auto_offload", default=False) images_seqlens = tu.get(data, key="images_seqlens", default=None) # inject engineering parameters if not specified default_keys = dict( use_remove_padding=self.model_config.use_remove_padding, use_dynamic_bsz=self.engine_config.use_dynamic_bsz, max_token_len_per_gpu=self.engine_config.max_token_len_per_gpu, micro_batch_size_per_gpu=self.engine_config.micro_batch_size_per_gpu, use_fused_kernels=self.engine_config.use_fused_kernels, ) for key, val in default_keys.items(): if key not in data.keys(): tu.assign_non_tensor(data, **{key: val}) with ( self.engine.train_mode(disable_auto_offload=disable_auto_offload), Timer(name="train_batch", logger=None) as timer, ): output = self.engine.train_batch(data, loss_function=self.loss_fn) # containing loss, model_output and metrics # for training, we only care about loss and metrics delta_time = timer.last update_lr_scheduler = tu.get(data, key="update_lr_scheduler", default=False) # update lr scheduler if update_lr_scheduler: lr = self.engine.lr_scheduler_step() else: lr = None if self.engine.is_mp_src_rank_with_outputs(): # we don't need model_output in training. Maybe we change out mind later output.pop("model_output") if lr is not None: output["metrics"]["lr"] = lr final_output = self._postprocess_output( output, global_token_num=global_token_num, delta_time=delta_time, forward_only=False, images_seqlens=images_seqlens, ).cpu() else: final_output = None return final_output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train"), blocking=False) def infer_batch(self, data: TensorDict) -> TensorDict: # add mfu calculator global_token_num = tu.get(data, key="global_token_num") compute_loss = tu.get(data, key="compute_loss", default=True) disable_auto_offload = tu.get(data, key="disable_auto_offload", default=False) no_lora_adapter = tu.pop(data, key="no_lora_adapter", default=False) images_seqlens = tu.get(data, key="images_seqlens", default=None) default_keys = dict( use_remove_padding=self.model_config.use_remove_padding, use_dynamic_bsz=self.engine_config.use_dynamic_bsz, max_token_len_per_gpu=self.engine_config.infer_max_token_len_per_gpu, micro_batch_size_per_gpu=self.engine_config.infer_micro_batch_size_per_gpu, use_fused_kernels=self.engine_config.use_fused_kernels, ) for key, val in default_keys.items(): if key not in data.keys(): tu.assign_non_tensor(data, **{key: val}) # for sft training, we need to compute loss in eval loss_function = self.loss_fn if compute_loss else None with ( self.engine.eval_mode(disable_auto_offload=disable_auto_offload), Timer(name="eval_batch", logger=None) as timer, ): adapter_ctx = self.engine.disable_adapter() if no_lora_adapter else nullcontext() with adapter_ctx: output = self.engine.infer_batch(data, loss_function=loss_function) delta_time = timer.last if self.engine.is_mp_src_rank_with_outputs(): final_output = self._postprocess_output( output, global_token_num=global_token_num, delta_time=delta_time, forward_only=True, images_seqlens=images_seqlens, ).cpu() else: final_output = None return final_output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): return self.engine.save_checkpoint(local_path, hdfs_path, global_step, max_ckpt_to_keep) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, hdfs_path=None, del_local_after_load=False): return self.engine.load_checkpoint(local_path, hdfs_path, del_local_after_load) class ActorRolloutRefWorker(Worker, DistProfilerExtension): """Hybrid worker that includes actor model, rollout and optional ref model. For standalone actor or rollout, use ActorWorker or BaseRollout respectively. NOTE: ActorRolloutRefWorker no longer support spmd mode and run native server mode. """ def __init__(self, config: DictConfig, role: str, **kwargs): Worker.__init__(self) self.config = config self.role = role self.actor: TrainingWorker = None self.ref: TrainingWorker = None self.rollout: BaseRollout = None assert self.role in ["actor", "rollout", "ref", "actor_rollout", "actor_rollout_ref"] self._is_actor = self.role in ["actor", "actor_rollout", "actor_rollout_ref"] self._is_rollout = self.role in ["rollout", "actor_rollout", "actor_rollout_ref"] self._is_ref = self.role in ["ref", "actor_rollout_ref"] if self._is_actor: omega_profiler_config = config.actor.get("profiler", {}) elif self._is_rollout: # NOTE: In colocation mode, rollout config may not take effect (follow the actor config) # This is for extendability in AsyncRL cases omega_profiler_config = config.rollout.get("profiler", {}) else: omega_profiler_config = config.ref.get("profiler", {}) profiler_config = omega_conf_to_dataclass(omega_profiler_config, dataclass_type=ProfilerConfig) if omega_profiler_config.get("tool", None) in ["npu", "nsys", "torch", "torch_memory"]: tool_config = omega_conf_to_dataclass( omega_profiler_config.get("tool_config", {}).get(omega_profiler_config.get("tool")) ) else: tool_config = None self.enable_routing_replay = ( self.config.actor.strategy == "megatron" and self.config.actor.megatron.router_replay.mode != "disabled" ) DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=profiler_config, tool_config=tool_config) ) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def set_loss_fn(self, loss_fn): self.actor.set_loss_fn(loss_fn=loss_fn) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def to(self, device, model=True, optimizer=True, grad=True): """Manual control of load/offload""" self.actor.to(device=device, model=model, optimizer=optimizer, grad=grad) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): model_config: HFModelConfig = omega_conf_to_dataclass(self.config.model) # 1. build reference model if "ref" in self.role: # TODO: align ref config with actor config with open_dict(self.config.ref): self.config.ref.ppo_mini_batch_size = self.config.actor.ppo_mini_batch_size self.config.ref.ppo_micro_batch_size = self.config.ref.pop("log_prob_micro_batch_size", None) self.config.ref.ppo_micro_batch_size_per_gpu = self.config.ref.pop( "log_prob_micro_batch_size_per_gpu", None ) self.config.ref.use_dynamic_bsz = self.config.ref.pop("log_prob_use_dynamic_bsz", False) self.config.ref.ppo_max_token_len_per_gpu = self.config.ref.pop("log_prob_max_token_len_per_gpu", None) ref_config: ActorConfig = omega_conf_to_dataclass(self.config.ref) # The ref model does not need to enable MTP; force it to false. ref_config.model_config = deepcopy(model_config) ref_config.model_config.mtp = MtpConfig(enable=False) # construct TrainingWorkerConfig ref_training_config = TrainingWorkerConfig( model_type="language_model", model_config=ref_config.model_config, engine_config=ref_config.engine, optimizer_config=ref_config.optim, checkpoint_config=ref_config.checkpoint, ) # assign engine configs ref_training_config.engine_config.use_dynamic_bsz = self.config.ref.use_dynamic_bsz ref_training_config.engine_config.infer_max_token_len_per_gpu = self.config.ref.ppo_max_token_len_per_gpu ref_training_config.engine_config.infer_micro_batch_size_per_gpu = ( self.config.ref.ppo_micro_batch_size_per_gpu ) ref_training_config.engine_config.use_remove_padding = model_config.use_remove_padding self.ref = TrainingWorker(config=ref_training_config) self.ref.reset() self.set_dispatch_collect(mesh_name="ref", **self.ref.get_dispatch_collect()) # 2. build actor model if "actor" in self.role: actor_config: ActorConfig = omega_conf_to_dataclass(self.config.actor) actor_config.model_config = model_config actor_training_config = TrainingWorkerConfig( model_type="language_model", model_config=actor_config.model_config, engine_config=actor_config.engine, optimizer_config=actor_config.optim, checkpoint_config=actor_config.checkpoint, ) assert self.config.actor.use_dynamic_bsz == self.config.rollout.log_prob_use_dynamic_bsz # assign engine configs actor_training_config.engine_config.use_dynamic_bsz = self.config.actor.use_dynamic_bsz actor_training_config.engine_config.infer_max_token_len_per_gpu = ( self.config.rollout.log_prob_max_token_len_per_gpu ) actor_training_config.engine_config.infer_micro_batch_size_per_gpu = ( self.config.rollout.log_prob_micro_batch_size_per_gpu ) actor_training_config.engine_config.max_token_len_per_gpu = self.config.actor.ppo_max_token_len_per_gpu actor_training_config.engine_config.micro_batch_size_per_gpu = ( self.config.actor.ppo_micro_batch_size_per_gpu ) actor_training_config.engine_config.use_remove_padding = model_config.use_remove_padding if self.config.actor.use_dynamic_bsz: assert self.config.rollout.log_prob_max_token_len_per_gpu is not None assert self.config.actor.ppo_max_token_len_per_gpu is not None else: assert self.config.rollout.log_prob_micro_batch_size_per_gpu is not None assert self.config.actor.ppo_micro_batch_size_per_gpu is not None self.loss_fn = partial(ppo_loss, config=actor_config) self.actor = TrainingWorker(config=actor_training_config) self.actor.reset() self.actor.set_loss_fn(self.loss_fn) self.set_dispatch_collect(mesh_name="actor", **self.actor.get_dispatch_collect()) # 3. build rollout engine if "rollout" in self.role: rollout_config: RolloutConfig = omega_conf_to_dataclass(self.config.rollout) # TODO: move rollout_device_mesh into ServerAdapter # 3.1 build rollout device mesh (sglang need only) infer_tp = rollout_config.tensor_model_parallel_size * rollout_config.data_parallel_size infer_pp = rollout_config.pipeline_model_parallel_size infer_world_size = infer_tp * infer_pp dp = self.world_size // infer_world_size assert self.world_size % infer_world_size == 0, ( f"rollout world_size: {self.world_size} is not divisible by infer_world_size: {infer_world_size}" ) rollout_device_mesh = init_device_mesh( get_device_name(), mesh_shape=(dp, infer_tp, infer_pp), mesh_dim_names=["dp", "infer_tp", "infer_pp"] ) # 3.2 initialize rollout engine rollout_cls: type[BaseRollout] = get_rollout_class(rollout_config.name, rollout_config.mode) self.rollout = rollout_cls( config=rollout_config, model_config=model_config, device_mesh=rollout_device_mesh ) # used for LoRA self.base_sync_done: bool = "dummy" not in self.config.rollout.load_format self.layered_summon = self.config.rollout.get("layered_summon", False) self.peft_merge: bool = model_config.lora.get("merge", False) # 4. build checkpoint engine if "actor" in self.role: checkpoint_engine_config = omega_conf_to_dataclass(self.config.rollout.checkpoint_engine) backend = checkpoint_engine_config.backend bucket_size = checkpoint_engine_config.update_weights_bucket_megabytes << 20 engine_kwargs = checkpoint_engine_config.engine_kwargs.get(backend, {}) self.checkpoint_engine = CheckpointEngineRegistry.new( backend, is_master=(torch.distributed.get_rank() == 0), bucket_size=bucket_size, **engine_kwargs ) # Free cached GPU memory so colocated vLLM processes can see it via cudaMemGetInfo aggressive_empty_cache(force_sync=True) @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="ref")) @DistProfiler.annotate(color="olive", role="ref_compute_log_prob") @_with_routing_replay_flag(enabled=False) def compute_ref_log_prob(self, data: TensorDict) -> TensorDict: output = self.ref.infer_batch(data=data) return output.cpu() if output is not None else None @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @DistProfiler.annotate(color="blue", role="actor_compute_log_prob") @_with_routing_replay_flag(enabled=True) def compute_log_prob(self, data: TensorDict) -> TensorDict: output = self.actor.infer_batch(data) return output.cpu() if output is not None else None @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @DistProfiler.annotate(color="red", role="actor_update") @_with_routing_replay_flag(enabled=True) def update_actor(self, data: TensorDict) -> TensorDict: output = self.actor.train_mini_batch(data=data) return output.cpu() if output is not None else None @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, hdfs_path=None, del_local_after_load=False): assert "actor" in self.role, "load_checkpoint only support actor role" self.actor.load_checkpoint(local_path, hdfs_path, del_local_after_load) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): assert "actor" in self.role, "save_checkpoint only support actor role" self.actor.save_checkpoint(local_path, hdfs_path, global_step, max_ckpt_to_keep) @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) async def update_weights(self, global_steps: int = None): """Update weights from trainer to rollout. 1. For sync training with colocated trainer and rollout, update rollout directly from model engine. - before update_weights: rollout should be in sleep mode. - after update_weights: rollout should be in wake_up mode. 2. For async training with disaggregated trainer and rollout, send_weights only by checkpoint engine. """ # 0. send_weights only for async training with disaggregated trainer and rollout if self.config.rollout.checkpoint_engine.backend != "naive": per_tensor_param, _ = self.actor.engine.get_per_tensor_param() await self.checkpoint_engine.send_weights(per_tensor_param) return set_expandable_segments(False) log_gpu_memory_usage("Before resume weights", logger=logger) # 1. resume weights and update weights if self.config.rollout.free_cache_engine: await self.rollout.resume(tags=["weights"]) log_gpu_memory_usage("After resume weights", logger=logger) # 2. get per tensor generator from engine, this will load model to gpu per_tensor_param, peft_config = self.actor.engine.get_per_tensor_param( layered_summon=self.layered_summon, base_sync_done=True ) await self.rollout.update_weights( per_tensor_param, peft_config=peft_config, base_sync_done=True, global_steps=global_steps ) do_lora_base_sync = False if not self.peft_merge and peft_config is not None: # set sleep level for LoRA adapter weights only sync # TODO: make this configurable so that users with small # main memory can trade sync time to avoid OOM self.rollout.sleep_level = 1 do_lora_base_sync = (not self.base_sync_done) or ( self.rollout.sleep_level != 1 and self.config.rollout.free_cache_engine ) if do_lora_base_sync: per_tensor_base_params, _ = self.actor.engine.get_per_tensor_param( layered_summon=self.layered_summon, base_sync_done=False ) await self.rollout.update_weights(per_tensor_base_params, peft_config=peft_config, base_sync_done=False) log_gpu_memory_usage("After update_weights", logger=logger) # 3. offload model to cpu self.actor.engine.to("cpu", model=True, optimizer=False, grad=False) aggressive_empty_cache(force_sync=True) # 4. resume kv_cache if self.config.rollout.free_cache_engine: await self.rollout.resume(tags=["kv_cache"]) log_gpu_memory_usage("After resume kv_cache", logger=logger) self.base_sync_done = True set_expandable_segments(True) @register(dispatch_mode=Dispatch.DP_COMPUTE, blocking=False) def execute_checkpoint_engine(self, method: str, *args, **kwargs): """Execute checkpoint engine method. Args: method (str): Checkpoint engine method name. *args: Variable length argument list. **kwargs: Arbitrary keyword arguments. """ return getattr(self.checkpoint_engine, method)(*args, **kwargs) ================================================ FILE: verl/workers/fsdp_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The main entry point to run the PPO algorithm """ import datetime import json import logging import os import warnings from dataclasses import asdict import psutil import torch import torch.distributed import torch.distributed as dist from codetiming import Timer from omegaconf import DictConfig, OmegaConf, open_dict from omegaconf.errors import ConfigAttributeError from peft import LoraConfig, TaskType, get_peft_model from safetensors.torch import save_file from torch.distributed.device_mesh import init_device_mesh from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.api import FullStateDictConfig, ShardedStateDictConfig, StateDictType try: # for torch 2.5+ from torch.distributed.tensor import DTensor except ImportError: from torch.distributed._tensor import DTensor from verl import DataProto from verl.models.transformers.monkey_patch import apply_monkey_patch from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.utils import hf_processor, hf_tokenizer from verl.utils.activation_offload import enable_activation_offloading from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import ( get_device_id, get_device_name, get_nccl_backend, get_torch_device, set_expandable_segments, ) from verl.utils.flops_counter import FlopsCounter from verl.utils.fs import copy_to_local from verl.utils.fsdp_utils import ( CPUOffloadPolicy, MixedPrecisionPolicy, apply_fsdp2, collect_lora_params, fsdp2_load_full_state_dict, fsdp_version, get_fsdp_wrap_policy, get_init_weight_context_manager, get_shard_placement_fn, init_fn, layered_summon_lora_params, load_fsdp_model_to_gpu, load_fsdp_optimizer, offload_fsdp_model_to_cpu, offload_fsdp_optimizer, replace_lora_wrapper, ) from verl.utils.import_utils import import_external_libs from verl.utils.memory_utils import aggressive_empty_cache from verl.utils.model import convert_weight_keys from verl.utils.profiler import DistProfiler, DistProfilerExtension, ProfilerConfig, log_gpu_memory_usage, simple_timer from verl.utils.profiler.performance import reduce_timing, topk_reduce_ratio_min_max from verl.utils.py_functional import convert_to_regular_types # QAT support from verl.utils.qat import apply_qat, enable_qat_fuse from verl.utils.ray_utils import get_event_loop from verl.utils.transformers_compat import get_auto_model_for_vision2seq from verl.workers.config import FSDPCriticConfig, FSDPEngineConfig, HFModelConfig, RolloutConfig from verl.workers.config.optimizer import build_optimizer from verl.workers.rollout import get_rollout_class from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) device_name = get_device_name() def create_device_mesh(world_size, fsdp_size): if fsdp_size < 0 or fsdp_size >= world_size: device_mesh = init_device_mesh(device_name, mesh_shape=(world_size,), mesh_dim_names=["fsdp"]) else: device_mesh = init_device_mesh( device_name, mesh_shape=(world_size // fsdp_size, fsdp_size), mesh_dim_names=["ddp", "fsdp"] ) return device_mesh def get_sharding_strategy(device_mesh, zero3_enable=True): from torch.distributed.fsdp import ShardingStrategy if zero3_enable: fsdp_strategy = ShardingStrategy.FULL_SHARD hsdp_strategy = ShardingStrategy.HYBRID_SHARD else: fsdp_strategy = ShardingStrategy.SHARD_GRAD_OP hsdp_strategy = ShardingStrategy._HYBRID_SHARD_ZERO2 if device_mesh.ndim == 1: sharding_strategy = fsdp_strategy elif device_mesh.ndim == 2: sharding_strategy = hsdp_strategy else: raise NotImplementedError(f"Get device mesh ndim={device_mesh.ndim}, but only support 1 or 2") return sharding_strategy def get_vl_model_vision_tower(vl_model_instance): """ Util to extract Vision Tower from a VL model instance """ if hasattr(vl_model_instance, "model") and hasattr(vl_model_instance.model, "visual"): # transformers >= 4.52.0 return vl_model_instance.model.visual elif hasattr(vl_model_instance, "visual"): # transformers < 4.52.0 return vl_model_instance.visual return None class ActorRolloutRefWorker(Worker, DistProfilerExtension): """ This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on the config.rollout """ def __init__(self, config: DictConfig, role: str, **kwargs): Worker.__init__(self) self.config = config import torch.distributed if not torch.distributed.is_initialized(): rank = int(os.environ.get("RANK", 0)) world_size = int(os.environ.get("WORLD_SIZE", 1)) torch.distributed.init_process_group( backend=f"cpu:gloo,{get_device_name()}:{get_nccl_backend()}", rank=rank, world_size=world_size, timeout=datetime.timedelta(seconds=self.config.get("nccl_timeout", 600)), init_method=os.environ.get("DIST_INIT_METHOD", None), ) # Apply NPU patches for FSDP backend from verl.workers.engine.fsdp.utils import apply_npu_fsdp_patches apply_npu_fsdp_patches() # build device mesh for FSDP world_size = torch.distributed.get_world_size() # TODO(sgm): support FSDP hybrid shard for larger model self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=self.config.actor.fsdp_config.fsdp_size) # build device mesh for Ulysses Sequence Parallel self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.actor.get("ulysses_sequence_parallel_size", 1) dp = world_size // self.ulysses_sequence_parallel_size if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh( device_name, mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=["dp", "sp"] ) # create training dispatch if self.ulysses_device_mesh is not None: is_collect = self.ulysses_device_mesh["sp"].get_local_rank() == 0 self._register_dispatch_collect_info( "actor", dp_rank=self.ulysses_device_mesh["dp"].get_local_rank(), is_collect=is_collect ) else: self._register_dispatch_collect_info("actor", dp_rank=self.rank, is_collect=True) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) self._lora_rank = self.config.model.get("lora_rank", 0) self._is_lora = self.config.model.get("lora_adapter_path") is not None or self._lora_rank > 0 self.role = role assert self.role in ["actor", "rollout", "ref", "actor_rollout", "actor_rollout_ref"] self._is_actor = self.role in ["actor", "actor_rollout", "actor_rollout_ref"] self._is_rollout = self.role in ["rollout", "actor_rollout", "actor_rollout_ref"] self._is_ref = self.role in ["ref", "actor_rollout_ref"] self.use_orig_params = self.config.actor.fsdp_config.get("use_orig_params", False) # TODO(haibin.lin): # As of now the type of config is DictConfig, if we assign config.profiler with ProfilerConfig, # it will actually convert the ProfilerConfig dataclass back to a DictConfig. # We can still use ProfilerConfig for testing purpose (tests/utils/test_nvtx_profile.py) # as they provides DictConfig-like interface # The benefit of creating the dataclass config is to perform validation during __post_init__ if self._is_actor: omega_profiler_config = config.actor.get("profiler", {}) elif self._is_rollout: # NOTE: In colocation mode, rollout config may not take effect (follow the actor config) # This is for extendability in AsyncRL cases omega_profiler_config = config.rollout.get("profiler", {}) elif self._is_ref: omega_profiler_config = config.ref.get("profiler", {}) else: raise ValueError( f"Invalid role {self.role}, should be one of " "['actor', 'rollout', 'ref', 'actor_rollout', 'actor_rollout_ref']" ) # omega_profiler_config is DictConfig # profiler_config is a ProfilerConfig dataclass profiler_config = omega_conf_to_dataclass(omega_profiler_config, dataclass_type=ProfilerConfig) if omega_profiler_config.get("tool", None) in ["npu", "nsys", "torch", "torch_memory"]: tool_config = omega_conf_to_dataclass( omega_profiler_config.get("tool_config", {}).get(omega_profiler_config.get("tool")) ) else: tool_config = None DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=profiler_config, tool_config=tool_config) ) self._is_offload_param = False self._is_offload_optimizer = False if self._is_actor: self._is_offload_param = self.config.actor.fsdp_config.get("param_offload", False) self._is_offload_optimizer = self.config.actor.fsdp_config.get("optimizer_offload", False) elif self._is_ref: # TODO: it seems that manual offload is slowly than FSDP offload self._is_offload_param = self.config.ref.fsdp_config.get("param_offload", False) # normalize config if self._is_actor: self.config.actor.ppo_mini_batch_size *= self.config.rollout.n self.config.actor.ppo_mini_batch_size //= self.device_mesh.size() // self.ulysses_sequence_parallel_size assert self.config.actor.ppo_mini_batch_size > 0, ( f"ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be larger than 0 after " f"normalization" ) # micro bsz if self.config.actor.ppo_micro_batch_size is not None: self.config.actor.ppo_micro_batch_size //= ( self.device_mesh.size() // self.ulysses_sequence_parallel_size ) self.config.actor.ppo_micro_batch_size_per_gpu = self.config.actor.ppo_micro_batch_size if self.config.actor.ppo_micro_batch_size_per_gpu is not None: assert self.config.actor.ppo_mini_batch_size % self.config.actor.ppo_micro_batch_size_per_gpu == 0, ( f"normalized ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be divisible by " f"ppo_micro_batch_size_per_gpu {self.config.actor.ppo_micro_batch_size_per_gpu}" ) assert self.config.actor.ppo_mini_batch_size // self.config.actor.ppo_micro_batch_size_per_gpu > 0, ( f"normalized ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be larger than " f"ppo_micro_batch_size_per_gpu {self.config.actor.ppo_micro_batch_size_per_gpu}" ) # normalize rollout config if self._is_rollout and self.config.rollout.log_prob_micro_batch_size is not None: self.config.rollout.log_prob_micro_batch_size //= ( self.device_mesh.size() // self.ulysses_sequence_parallel_size ) self.config.rollout.log_prob_micro_batch_size_per_gpu = self.config.rollout.log_prob_micro_batch_size # normalize ref config if self._is_ref and self.config.ref.log_prob_micro_batch_size is not None: self.config.ref.log_prob_micro_batch_size //= self.device_mesh.size() // self.ulysses_sequence_parallel_size self.config.ref.log_prob_micro_batch_size_per_gpu = self.config.ref.log_prob_micro_batch_size def _init_qat_config(self): """Initialize QAT configuration from actor.qat.""" try: self.qat_config = self.config.actor.qat self._qat_enabled = self.qat_config.enable if self._qat_enabled: logger.info( f"QAT enabled: mode={self.qat_config.mode}, config_path={self.qat_config.quantization_config_path}" ) except (AttributeError, KeyError, ConfigAttributeError): # QAT config not provided, disable QAT self._qat_enabled = False self.qat_config = None def _restore_w4a4_input_scales(self, model, model_path): """Restore input_global_scale and input_amax from checkpoint for W4A4 mode.""" import glob from safetensors import safe_open safetensor_files = glob.glob(f"{model_path}/model*.safetensors") loaded_count = 0 for sf_path in safetensor_files: with safe_open(sf_path, framework="pt") as f: for key in f.keys(): if "input_global_scale" in key: module_path = key.replace(".input_global_scale", "") amax_key = f"{module_path}.input_amax" module = model for part in module_path.split("."): module = getattr(module, part) scale_val = f.get_tensor(key) val = scale_val.item() if scale_val.numel() == 1 else scale_val.max().item() module.input_global_scale.fill_(val) amax_val = f.get_tensor(amax_key) amax = amax_val.item() if amax_val.numel() == 1 else amax_val.max().item() module.input_amax.fill_(amax) loaded_count += 1 if self.rank == 0: logger.info(f"[W4A4] Loaded {loaded_count} input scales from checkpoint") def _build_model_optimizer( self, model_path, fsdp_config: FSDPEngineConfig, optim_config, override_model_config, use_remove_padding=False, use_fused_kernels=False, enable_gradient_checkpointing=False, trust_remote_code=False, use_liger=False, role="actor", enable_activation_offload=False, use_prefix_grouper=False, use_tiled_mlp=False, tiled_mlp_shards=4, ): from torch.distributed.fsdp import CPUOffload, MixedPrecision from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, ) try: from transformers import AutoModelForVision2Seq except ImportError: AutoModelForVision2Seq = None try: from transformers import AutoModelForImageTextToText except ImportError: AutoModelForImageTextToText = AutoModelForVision2Seq from verl.utils.model import get_generation_config, print_model_size, update_model_config from verl.utils.torch_dtypes import PrecisionType AutoModelForVision2Seq = get_auto_model_for_vision2seq() assert role in ["actor", "ref"] # TiledMLP requires FSDP2 for correct gradient computation if use_tiled_mlp and self.config.actor.strategy == "fsdp": raise ValueError("TiledMLP requires FSDP2. Set `actor_rollout_ref.actor.strategy=fsdp2`.") log_gpu_memory_usage(f"Before init {role} from HF AutoModel", logger=logger) local_path = model_path # note that we have to create model in fp32. Otherwise, the optimizer is in bf16, which is incorrect # TODO(zhangchi.usc1992): 1. support create from random initialized model. 2. Support init with FSDP directly self.tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) self.processor = hf_processor(local_path, trust_remote_code=trust_remote_code) if self.config.model.get("custom_chat_template", None) is not None: if self.processor is not None: self.processor.chat_template = self.config.model.custom_chat_template else: self.tokenizer.chat_template = self.config.model.custom_chat_template torch_dtype = fsdp_config.get("model_dtype", None) if torch_dtype is None: torch_dtype = torch.float32 if self._is_actor else torch.bfloat16 else: torch_dtype = PrecisionType.to_dtype(torch_dtype) # override model kwargs attn_implementation = override_model_config.get("attn_implementation", "flash_attention_2") actor_model_config = AutoConfig.from_pretrained( local_path, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation ) # TODO: VL models use VisionAttention, which directly uses flash_attention in transformers>=4.53 # which will be patched by _ulysses_flash_attention_forward, but errorly misses position_ids # Maybe support Ulysses in VisionAttention in the future and remove this patch if self.ulysses_sequence_parallel_size > 1 and hasattr(actor_model_config, "vision_config"): actor_model_config.vision_config._attn_implementation = "eager" # patch for qwen2.5-vl: when using flash_attention_3, set vision tower to use flash_attention_2 # because the vision tower does not support flash_attention_3 if ( getattr(actor_model_config, "model_type", None) == "qwen2_5_vl" and attn_implementation == "flash_attention_3" and hasattr(actor_model_config, "vision_config") ): actor_model_config.vision_config._attn_implementation = "flash_attention_2" # patch for kimi-vl if getattr(actor_model_config, "model_type", None) == "kimi_vl": actor_model_config.text_config.topk_method = "greedy" self.generation_config = get_generation_config(local_path, trust_remote_code=trust_remote_code) override_config_kwargs = { "bos_token_id": self.tokenizer.bos_token_id, "eos_token_id": self.tokenizer.eos_token_id, "pad_token_id": self.tokenizer.pad_token_id, } if self.config.model.get("mtp", {}).get("enable", False): raise NotImplementedError("Right now, MTP is not supported in FSDP") else: if hasattr(actor_model_config, "num_nextn_predict_layers"): actor_model_config.num_nextn_predict_layers = 0 override_config_kwargs.update(override_model_config) update_model_config(actor_model_config, override_config_kwargs=override_config_kwargs) if self.rank == 0: print(f"Model config after override: {actor_model_config}") # NOTE(fix me): tie_word_embedding causes meta_tensor init to hang init_context = get_init_weight_context_manager( use_meta_tensor=not actor_model_config.tie_word_embeddings, mesh=self.device_mesh ) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") has_remote_code = hasattr(actor_model_config, "auto_map") and any( actor_model_config.architectures[0] in val for val in actor_model_config.auto_map.values() ) if has_remote_code: auto_class = next( k for k, v in actor_model_config.auto_map.items() if actor_model_config.architectures[0] in v ) match auto_class: case "AutoModelForVision2Seq": actor_module_class = AutoModelForVision2Seq case "AutoModelForCausalLM": actor_module_class = AutoModelForCausalLM case "AutoModelForImageTextToText": actor_module_class = AutoModelForImageTextToText case _: actor_module_class = AutoModel else: if type(actor_model_config) in AutoModelForVision2Seq._model_mapping.keys(): actor_module_class = AutoModelForVision2Seq elif type(actor_model_config) in AutoModelForCausalLM._model_mapping.keys(): actor_module_class = AutoModelForCausalLM elif type(actor_model_config) in AutoModelForImageTextToText._model_mapping.keys(): actor_module_class = AutoModelForImageTextToText else: actor_module_class = AutoModel actor_module = actor_module_class.from_pretrained( pretrained_model_name_or_path=local_path, torch_dtype=torch_dtype, config=actor_model_config, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation, ) # Apply Liger kernel to the model if use_liger is set to True if use_liger: from liger_kernel.transformers.monkey_patch import _apply_liger_kernel_to_instance _apply_liger_kernel_to_instance(model=actor_module) fused_kernel_options = self.config.model.get("fused_kernel_options", None) fused_kernels_backend = ( fused_kernel_options.get("impl_backend", None) if fused_kernel_options is not None else None ) apply_monkey_patch( model=actor_module, use_remove_padding=use_remove_padding, ulysses_sp_size=self.ulysses_sequence_parallel_size, use_fused_kernels=use_fused_kernels, fused_kernels_backend=fused_kernels_backend, use_prefix_grouper=use_prefix_grouper, use_tiled_mlp=use_tiled_mlp, tiled_mlp_shards=tiled_mlp_shards, ) # some parameters may not in torch_dtype. TODO(zhangchi.usc1992) remove this after we switch to fsdp2 actor_module.to(torch_dtype) if enable_gradient_checkpointing: actor_module.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) if self._is_lora: print("Applying LoRA to actor module") actor_module.enable_input_require_grads() lora_adapter_path = self.config.model.get("lora_adapter_path") if lora_adapter_path is not None: from peft import PeftModel print(f"Loading pre-trained LoRA adapter to {role} from: {lora_adapter_path}") # Copy adapter to local if needed local_adapter_path = copy_to_local(lora_adapter_path, use_shm=self.config.model.get("use_shm", False)) actor_module = PeftModel.from_pretrained(actor_module, local_adapter_path, is_trainable=True) peft_config = actor_module.peft_config["default"] # Ensure task_type is TaskType enum, not string if isinstance(peft_config.task_type, str): peft_config.task_type = TaskType.CAUSAL_LM else: # Convert config to regular Python types before creating PEFT model lora_config = { "task_type": TaskType.CAUSAL_LM, "r": self.config.model.lora_rank, "lora_alpha": self.config.model.lora_alpha, "target_modules": convert_to_regular_types(self.config.model.target_modules), "exclude_modules": convert_to_regular_types(self.config.model.exclude_modules), "bias": "none", } actor_module = get_peft_model(actor_module, LoraConfig(**lora_config)) self.use_orig_params = fsdp_config.get("use_orig_params", False) if self.config.actor.get("freeze_vision_tower", False): vision_tower = get_vl_model_vision_tower(actor_module) if vision_tower is not None: vision_tower.requires_grad_(False) self.use_orig_params = True if self.rank == 0: print("[actor model] Vision tower is set to not trainable.") else: if self.rank == 0: print("[actor model] No vision tower found.") # Apply QAT before FSDP wrapping (actor only) if role == "actor" and self._qat_enabled: actor_module = apply_qat(actor_module, self.qat_config) enable_qat_fuse(actor_module) if self.qat_config.mode == "w4a4": self._restore_w4a4_input_scales(actor_module, self.config.model.path) torch.distributed.barrier() if self.rank == 0: print_model_size(actor_module) log_gpu_memory_usage(f"After init {role} from HF AutoModel", logger=logger) # We wrap FSDP for rollout as well mixed_precision_config = fsdp_config.get("mixed_precision", None) if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get("param_dtype", "bf16")) reduce_dtype = PrecisionType.to_dtype(mixed_precision_config.get("reduce_dtype", "fp32")) buffer_dtype = PrecisionType.to_dtype(mixed_precision_config.get("buffer_dtype", "fp32")) else: param_dtype = PrecisionType.to_dtype(fsdp_config.dtype) reduce_dtype = torch.float32 buffer_dtype = torch.float32 mixed_precision = MixedPrecision(param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype) # Store param_dtype for QAT quantizer self._param_dtype = param_dtype auto_wrap_policy = get_fsdp_wrap_policy( module=actor_module, config=fsdp_config.get("wrap_policy", None), is_lora=self._is_lora, ) # if self._is_rollout and self.config.rollout.name == "hf": # # TODO(zhangchi.usc1992, shengguangming) fix me. # Current, auto_wrap_policy causes HFRollout to hang in Gemma # auto_wrap_policy = None if self.rank == 0: print(f"wrap_policy: {auto_wrap_policy}") fsdp_mesh = self.device_mesh fsdp_enable_zero3 = fsdp_config.reshard_after_forward sharding_strategy = get_sharding_strategy(fsdp_mesh, fsdp_enable_zero3) # TODO: add transformer policy # We force reference policy to use CPUOffload to save memory. # We force turn off CPUOffload for actor because it causes incorrect results when using grad accumulation cpu_offload = None if role == "actor" else CPUOffload(offload_params=True) fsdp_strategy = self.config.actor.strategy if fsdp_strategy == "fsdp": actor_module_fsdp = FSDP( actor_module, cpu_offload=cpu_offload, param_init_fn=init_fn, auto_wrap_policy=auto_wrap_policy, device_id=get_device_id(), sharding_strategy=sharding_strategy, # zero3 mixed_precision=mixed_precision, sync_module_states=True, device_mesh=self.device_mesh, use_orig_params=self.use_orig_params, forward_prefetch=fsdp_config.get("forward_prefetch", False), ) elif fsdp_strategy == "fsdp2": assert CPUOffloadPolicy is not None, "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)" mp_policy = MixedPrecisionPolicy( param_dtype=param_dtype, reduce_dtype=reduce_dtype, cast_forward_inputs=True ) if role == "actor" and fsdp_config.offload_policy: cpu_offload = CPUOffloadPolicy(pin_memory=True) self._is_offload_param = False self._is_offload_optimizer = False else: cpu_offload = None if role == "actor" else CPUOffloadPolicy(pin_memory=True) fsdp_kwargs = { "mesh": fsdp_mesh, "mp_policy": mp_policy, "offload_policy": cpu_offload, "reshard_after_forward": fsdp_config.reshard_after_forward, "shard_placement_fn": get_shard_placement_fn(fsdp_size=self.device_mesh.shape[-1]), } full_state = actor_module.state_dict() apply_fsdp2(actor_module, fsdp_kwargs, fsdp_config) fsdp2_load_full_state_dict(actor_module, full_state, fsdp_mesh, cpu_offload) actor_module_fsdp = actor_module else: raise NotImplementedError(f"not implement {fsdp_strategy}") if enable_activation_offload: enable_activation_offloading(actor_module_fsdp, fsdp_strategy, enable_gradient_checkpointing) log_gpu_memory_usage(f"After {role} FSDP init", logger=logger) # TODO: add more optimizer args into config if role == "actor" and optim_config is not None: from verl.utils.torch_functional import get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup actor_optimizer = build_optimizer(actor_module_fsdp.parameters(), optim_config) total_steps = optim_config.get("total_training_steps", 0) num_warmup_steps = int(optim_config.get("lr_warmup_steps", -1)) lr_scheduler_type = optim_config.get("lr_scheduler_type", "constant") min_lr_ratio = optim_config.get("min_lr_ratio", 0.0) num_cycles = optim_config.get("num_cycles", 0.5) if num_warmup_steps < 0: num_warmup_steps_ratio = optim_config.get("lr_warmup_steps_ratio", 0.0) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) if self.rank == 0: print(f"Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}") if lr_scheduler_type == "constant": actor_lr_scheduler = get_constant_schedule_with_warmup( optimizer=actor_optimizer, num_warmup_steps=num_warmup_steps ) elif lr_scheduler_type == "cosine": actor_lr_scheduler = get_cosine_schedule_with_warmup( optimizer=actor_optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps, min_lr_ratio=min_lr_ratio, num_cycles=num_cycles, ) else: raise NotImplementedError(f"LR scheduler type {lr_scheduler_type} is not supported") log_gpu_memory_usage(f"After {role} optimizer init", logger=logger) else: actor_optimizer = None actor_lr_scheduler = None return actor_module_fsdp, actor_optimizer, actor_lr_scheduler, actor_model_config def _build_rollout(self, trust_remote_code=False): from torch.distributed.device_mesh import init_device_mesh # 1. parse rollout and huggingface model config rollout_config: RolloutConfig = omega_conf_to_dataclass(self.config.rollout) model_config: HFModelConfig = omega_conf_to_dataclass(self.config.model, dataclass_type=HFModelConfig) self.model_config = model_config # 2. build rollout device mesh infer_tp = self.config.rollout.tensor_model_parallel_size * self.config.rollout.data_parallel_size infer_pp = self.config.rollout.pipeline_model_parallel_size infer_world_size = infer_tp * infer_pp dp = self.world_size // infer_world_size assert self.world_size % infer_world_size == 0, ( f"rollout world_size: {self.world_size} is not divisible by infer_world_size: {infer_world_size}" ) rollout_device_mesh = init_device_mesh( device_name, mesh_shape=(dp, infer_tp, infer_pp), mesh_dim_names=["dp", "infer_tp", "infer_pp"] ) rollout_name = self.config.rollout.name self.rollout_device_mesh = rollout_device_mesh if rollout_name == "hf": self._register_dispatch_collect_info("rollout", dp_rank=self.rank, is_collect=True) else: is_collect = ( rollout_device_mesh["infer_tp"].get_local_rank() == 0 and rollout_device_mesh["infer_pp"].get_local_rank() == 0 ) self._register_dispatch_collect_info( "rollout", dp_rank=rollout_device_mesh["dp"].get_local_rank(), is_collect=is_collect ) # 4. build rollout model log_gpu_memory_usage(f"Before building {self.config.rollout.name} rollout", logger=logger) self.rollout = get_rollout_class(rollout_config.name, rollout_config.mode)( config=rollout_config, model_config=model_config, device_mesh=rollout_device_mesh ) log_gpu_memory_usage(f"After building {self.config.rollout.name} rollout", logger=logger) # Full params if torch.distributed.get_world_size() == 1 and fsdp_version(self.actor_module_fsdp) == 1: FSDP.set_state_dict_type( self.actor_module_fsdp, state_dict_type=StateDictType.FULL_STATE_DICT, state_dict_config=FullStateDictConfig(), ) elif fsdp_version(self.actor_module_fsdp) == 1: FSDP.set_state_dict_type( self.actor_module_fsdp, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig(), ) # used for LoRA self.base_sync_done: bool = "dummy" not in self.config.rollout.load_format self.layered_summon = self.config.rollout.get("layered_summon", False) # 5. switch to trainer mode # NOTE: It's critical that hybrid engine in trainer mode initially to load checkpoint. # For async mode, we can't call run_until_complete here, so we will switch to trainer mode in AgentLoopManager. # Note: sync mode is deprecated and rejected in RolloutConfig.__post_init__ async def rollout_mode(self): """Context switch hybridengine to rollout mode.""" aggressive_empty_cache(force_sync=True) log_gpu_memory_usage("Before load_fsdp_model_to_gpu", logger=logger) if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) log_gpu_memory_usage("After load_fsdp_model_to_gpu", logger=logger) peft_config = None peft_model = getattr(self.actor_module_fsdp, "_fsdp_wrapped_module", self.actor_module_fsdp) if hasattr(peft_model, "peft_config"): # LoRA peft_config = peft_model.peft_config.get("default", None) params = collect_lora_params( module=self.actor_module_fsdp, layered_summon=self.config.rollout.get("layered_summon", False), base_sync_done=self.base_sync_done, ) if not self.base_sync_done: params = {replace_lora_wrapper(k, peft_config): v for k, v in params.items()} else: params = self.actor_module_fsdp.state_dict() params = convert_weight_keys( params, getattr(self.actor_module_fsdp, "_fsdp_wrapped_module", self.actor_module_fsdp) ) # Special handling for LoRA with sleep_level=2: # When sleep_level=2, base model weights are destroyed during each sleep cycle. # separately collect and update LoRA weights and base model weights through their respective interfaces. # Here: params contains LoRA weights, base_model_params contains base model weights. # Only needed if the rollout engine actually sleeps/frees weights (free_cache_engine=True). if ( peft_config is not None and getattr(self.rollout, "sleep_level", None) == 2 and self.config.rollout.free_cache_engine ): base_model_params = collect_lora_params( module=self.actor_module_fsdp, layered_summon=self.layered_summon, base_sync_done=False, ) base_model_params = {replace_lora_wrapper(k, peft_config): v for k, v in base_model_params.items()} base_model_params = convert_weight_keys( base_model_params, getattr(self.actor_module_fsdp, "_fsdp_wrapped_module", self.actor_module_fsdp) ) log_gpu_memory_usage("Before offload_fsdp_model_to_cpu", logger=logger) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) log_gpu_memory_usage("After offload_fsdp_model_to_cpu", logger=logger) set_expandable_segments(False) if peft_config is not None and self.base_sync_done: per_tensor_param = params.items() if isinstance(params, dict) else params # Fixed: handle dict case else: device = get_device_id() # used when fsdp2 set cpu_offload_policy per_tensor_param = ( (name, param.to(device, non_blocking=True).full_tensor() if isinstance(param, DTensor) else param) for name, param in params.items() ) # QAT: quantize weights before sending to vLLM if self._qat_enabled: from verl.utils.qat.quantizer import QATQuantizer quantizer = QATQuantizer( mode=self.qat_config.mode, group_size=self.qat_config.group_size, ignore_patterns=self.qat_config.ignore_patterns, device=torch.device(get_device_id()), param_dtype=self._param_dtype, ) per_tensor_param = quantizer.quantize_with_fusion( per_tensor_param, target_device=torch.device("cpu"), ) aggressive_empty_cache(force_sync=True) if self.config.rollout.free_cache_engine: await self.rollout.resume(tags=["weights"]) log_gpu_memory_usage("After resume weights", logger=logger) if ( peft_config is not None and getattr(self.rollout, "sleep_level", None) == 2 and self.config.rollout.free_cache_engine ): per_tensor_base_params = ( (name, param.to(device, non_blocking=True).full_tensor() if isinstance(param, DTensor) else param) for name, param in base_model_params.items() ) await self.rollout.update_weights(per_tensor_base_params, base_sync_done=False) del base_model_params, per_tensor_base_params await self.rollout.update_weights(per_tensor_param, peft_config=peft_config, base_sync_done=self.base_sync_done) log_gpu_memory_usage("After update_weights", logger=logger) del params, per_tensor_param aggressive_empty_cache(force_sync=True) if self.config.rollout.free_cache_engine: await self.rollout.resume(tags=["kv_cache"]) log_gpu_memory_usage("After resume kv_cache", logger=logger) self.base_sync_done = True set_expandable_segments(True) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): from verl.workers.actor import DataParallelPPOActor # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get("external_lib", None)) # Initialize QAT config before _build_model_optimizer self._init_qat_config() override_model_config = OmegaConf.to_container(OmegaConf.create(self.config.model.get("override_config", {}))) use_remove_padding = self.config.model.get("use_remove_padding", False) use_shm = self.config.model.get("use_shm", False) use_fused_kernels = self.config.model.get("use_fused_kernels", False) if self._is_actor or self._is_rollout: # we need the model for actor and rollout if self._is_actor: optim_config = self.config.actor.optim fsdp_config = omega_conf_to_dataclass(self.config.actor.fsdp_config) else: optim_config = None fsdp_config = FSDPEngineConfig() local_path = copy_to_local(self.config.model.path, use_shm=use_shm) # TiledMLP configuration for memory-efficient MLP computation tiled_mlp_config = self.config.model.get("tiled_mlp", {}) use_tiled_mlp = tiled_mlp_config.get("enabled", False) tiled_mlp_shards = tiled_mlp_config.get("num_shards", 4) ( self.actor_module_fsdp, self.actor_optimizer, self.actor_lr_scheduler, self.actor_model_config, ) = self._build_model_optimizer( model_path=local_path, fsdp_config=fsdp_config, optim_config=optim_config, override_model_config=override_model_config, use_remove_padding=use_remove_padding, use_fused_kernels=use_fused_kernels, enable_gradient_checkpointing=self.config.model.get("enable_gradient_checkpointing", False), trust_remote_code=self.config.model.get("trust_remote_code", False), use_liger=self.config.model.get("use_liger", False), role="actor", enable_activation_offload=self.config.model.get("enable_activation_offload", False), use_prefix_grouper=self.config.actor.get("use_prefix_grouper", False), use_tiled_mlp=use_tiled_mlp, tiled_mlp_shards=tiled_mlp_shards, ) # get the original unwrapped module if fsdp_version(self.actor_module_fsdp) == 1: self.actor_module = self.actor_module_fsdp._fsdp_wrapped_module if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) log_gpu_memory_usage("After offload actor model during init", logger=logger) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) log_gpu_memory_usage("After offload actor optimizer during init", logger=logger) if self._is_actor: actor_cfg = omega_conf_to_dataclass(self.config.actor) self.actor = DataParallelPPOActor( config=actor_cfg, actor_module=self.actor_module_fsdp, actor_optimizer=self.actor_optimizer ) if self._is_rollout: self._build_rollout(trust_remote_code=self.config.model.get("trust_remote_code", False)) if self._is_ref: ref_model_path = self.config.model.path ref_model = self.config.ref.get("model", None) if ref_model is not None: ref_model_path = ref_model.get("path", self.config.model.path) if self.rank == 0: print("reference model:", ref_model_path) local_path = copy_to_local(ref_model_path, use_shm=use_shm) use_prefix_grouper = hasattr(self.config, "actor") and self.config.actor.get("use_prefix_grouper", False) # TiledMLP for ref model: use ref config if specified, otherwise use actor config ref_tiled_mlp_config = self.config.ref.get("tiled_mlp", None) if ref_tiled_mlp_config is None: ref_tiled_mlp_config = self.config.model.get("tiled_mlp", {}) ref_use_tiled_mlp = ref_tiled_mlp_config.get("enabled", False) ref_tiled_mlp_shards = ref_tiled_mlp_config.get("num_shards", 4) self.ref_module_fsdp = self._build_model_optimizer( model_path=local_path, fsdp_config=omega_conf_to_dataclass(self.config.ref.fsdp_config), optim_config=None, override_model_config=override_model_config, use_remove_padding=use_remove_padding, use_fused_kernels=use_fused_kernels, trust_remote_code=self.config.model.get("trust_remote_code", False), use_liger=self.config.model.get("use_liger", False), role="ref", use_prefix_grouper=use_prefix_grouper, use_tiled_mlp=ref_use_tiled_mlp, tiled_mlp_shards=ref_tiled_mlp_shards, )[0] OmegaConf.set_struct(self.config.ref, True) with open_dict(self.config.ref): self.config.ref.use_remove_padding = use_remove_padding self.config.ref.use_fused_kernels = use_fused_kernels if use_prefix_grouper: self.config.ref.use_prefix_grouper = use_prefix_grouper self.ref_policy = DataParallelPPOActor(config=self.config.ref, actor_module=self.ref_module_fsdp) if self._is_actor: self.flops_counter = FlopsCounter(self.actor_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.actor_module_fsdp, optimizer=self.actor.actor_optimizer, lr_scheduler=self.actor_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer, checkpoint_config=self.config.actor.checkpoint, trust_remote_code=self.config.model.get("trust_remote_code", False), ) if not self._is_actor and self._is_rollout: # If ActorRolloutRefWorker is initialized as a standalone rollout, # create a checkpoint manager for FSDP model to allow loading FSDP checkpoints for rollout. checkpoint_contents = OmegaConf.create({"load_contents": ["model"], "save_contents": []}) self.checkpoint_manager = FSDPCheckpointManager( model=self.actor_module_fsdp, optimizer=None, lr_scheduler=None, processing_class=self.processor if self.processor is not None else self.tokenizer, checkpoint_config=checkpoint_contents, ) # Free cached GPU memory so colocated vLLM processes can see it via cudaMemGetInfo aggressive_empty_cache(force_sync=True) @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @DistProfiler.annotate(color="red", role="actor_update") def update_actor(self, data: DataProto): assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) if self._is_offload_optimizer: load_fsdp_optimizer(optimizer=self.actor_optimizer, device_id=get_device_id()) with self.ulysses_sharding_manager: data = data.to("cpu") # data will to device with each micro batch on actor.update_policy data.meta_info.setdefault("pad_token_id", self.tokenizer.pad_token_id) # perform training with Timer(name="update_policy", logger=None) as timer: metrics = self.actor.update_policy(data=data) delta_time = timer.last global_num_tokens = data.meta_info["global_token_num"] images_seqlens = data.meta_info.get("images_seqlens", None) estimated_flops, promised_flops = self.flops_counter.estimate_flops( global_num_tokens, delta_time, images_seqlens=images_seqlens ) metrics["perf/mfu/actor"] = ( estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size ) metrics["perf/max_memory_allocated_gb"] = get_torch_device().max_memory_allocated() / (1024**3) metrics["perf/max_memory_reserved_gb"] = get_torch_device().max_memory_reserved() / (1024**3) metrics["perf/cpu_memory_used_gb"] = psutil.virtual_memory().used / (1024**3) lr = self.actor_lr_scheduler.get_last_lr()[0] metrics["actor/lr"] = lr.item() if torch.is_tensor(lr) else lr self.actor_lr_scheduler.step() # TODO: here, we should return all metrics output = DataProto(meta_info={"metrics": metrics}) output = output.to("cpu") if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) log_gpu_memory_usage("After offload actor model during update_actor", logger=logger) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) log_gpu_memory_usage("After offload actor optimizer during update_actor", logger=logger) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="rollout")) @DistProfiler.annotate(color="red", role="rollout_generate") def generate_sequences(self, prompts: DataProto): # Support all hardwares assert self._is_rollout prompts = prompts.to(get_device_id()) meta_info = { "eos_token_id": self.generation_config.eos_token_id if self.generation_config is not None else self.tokenizer.eos_token_id, "pad_token_id": self.generation_config.pad_token_id if self.generation_config is not None else self.tokenizer.pad_token_id, } prompts.meta_info.update(meta_info) timing_generate = {} if self._is_actor: # For rollout only, we do not switch context. loop = get_event_loop() loop.run_until_complete(self.rollout_mode()) log_gpu_memory_usage("After switch to rollout mode", logger=logger) with simple_timer("generate_sequences", timing_generate): output = self.rollout.generate_sequences(prompts=prompts) if self._is_actor: loop.run_until_complete(self.trainer_mode()) log_gpu_memory_usage("After switch to trainer mode", logger=logger) # We calculate the average timing across all ranks # to make sure meta_info["timing"] is the same timing_generate_topk_ratio, timing_generate_min, timing_generate_max = topk_reduce_ratio_min_max( timing_generate["generate_sequences"] ) timing_generate = reduce_timing(timing_generate) timing_generate.update( { "generation_timing/max": timing_generate_max, "generation_timing/min": timing_generate_min, "generation_timing/topk_ratio": timing_generate_topk_ratio, } ) output.meta_info["timing"] = timing_generate output = output.to("cpu") # clear kv cache get_torch_device().empty_cache() return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @DistProfiler.annotate(color="blue", role="actor_compute_log_prob") def compute_log_prob(self, data: DataProto): # when is_lora is True, we use the actor without lora applied to calculate the log_prob # which is mostly used for ref log_prob calculation assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) # Support all hardwares from contextlib import nullcontext is_lora = data.meta_info.pop("is_lora", False) adapter_ctx = self.actor.actor_module.disable_adapter() if is_lora else nullcontext() # we should always recompute old_log_probs when it is HybridEngine config_source = self.config.ref if is_lora else self.config.rollout data.meta_info["micro_batch_size"] = config_source.log_prob_micro_batch_size_per_gpu data.meta_info["max_token_len"] = config_source.log_prob_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = config_source.log_prob_use_dynamic_bsz data.meta_info["temperature"] = self.config.rollout.temperature data.meta_info.setdefault("pad_token_id", self.tokenizer.pad_token_id) # perform recompute log_prob calculate_entropy = not is_lora with self.ulysses_sharding_manager: with adapter_ctx: outputs = self.actor.compute_log_prob(data=data, calculate_entropy=calculate_entropy) if not is_lora: tensors = {"old_log_probs": outputs["log_probs"]} else: tensors = {"ref_log_prob": outputs["log_probs"]} if calculate_entropy: tensors["entropys"] = outputs["entropys"] if "sum_pi_squared" in outputs: tensors["sum_pi_squared"] = outputs["sum_pi_squared"] output = DataProto.from_dict( tensors=tensors, meta_info={"temperature": self.config.rollout.temperature}, ) output = output.to("cpu") # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.world_size > 1 and fsdp_version(self.actor.actor_module) == 1: self.actor.actor_module._handle.reshard(True) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) log_gpu_memory_usage("After offload actor model during compute_log_prob", logger=logger) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @DistProfiler.annotate(color="olive", role="ref_compute_log_prob") def compute_ref_log_prob(self, data: DataProto): if self._is_lora: # if _is_lora, actor without lora applied is the ref data.meta_info["is_lora"] = True return self.compute_log_prob(data) assert self._is_ref # else: # otherwise, the class have a standalone ref model micro_batch_size = self.config.ref.log_prob_micro_batch_size_per_gpu data.meta_info["micro_batch_size"] = micro_batch_size data.meta_info["temperature"] = self.config.rollout.temperature data.meta_info["max_token_len"] = self.config.ref.log_prob_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = self.config.ref.log_prob_use_dynamic_bsz data.meta_info.setdefault("pad_token_id", self.tokenizer.pad_token_id) with self.ulysses_sharding_manager: data = data.to("cpu") # data will to device with each micro batch on ref.compute_log_prob outputs = self.ref_policy.compute_log_prob(data=data, calculate_entropy=False) output = DataProto.from_dict(tensors={"ref_log_prob": outputs["log_probs"]}) output = output.to("cpu") # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.world_size > 1: if fsdp_version(self.ref_policy.actor_module) == 1: self.ref_policy.actor_module._handle.reshard(True) elif fsdp_version(self.ref_policy.actor_module) == 2: self.ref_policy.actor_module.reshard() return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): from verl.utils.logger import log_with_rank # only support save and load ckpt for actor assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) self.checkpoint_manager.save_checkpoint( local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep ) dist.barrier() if self._is_lora and hasattr(getattr(self, "actor_module", self.actor_module_fsdp), "peft_config"): lora_save_path = os.path.join(local_path, "lora_adapter") peft_model = getattr(self, "actor_module", self.actor_module_fsdp) peft_config = {} if dist.get_rank() == 0: os.makedirs(lora_save_path, exist_ok=True) peft_config = asdict(peft_model.peft_config.get("default", {})) peft_config["task_type"] = peft_config["task_type"].value peft_config["peft_type"] = peft_config["peft_type"].value peft_config["target_modules"] = list(peft_config["target_modules"]) try: if fsdp_version(self.actor_module_fsdp) > 0: self.actor_module_fsdp = self.actor_module_fsdp.to(get_device_name()) lora_params = layered_summon_lora_params(self.actor_module_fsdp) if dist.get_rank() == 0: save_file(lora_params, os.path.join(lora_save_path, "adapter_model.safetensors")) with open(os.path.join(lora_save_path, "adapter_config.json"), "w", encoding="utf-8") as f: json.dump(peft_config, f, ensure_ascii=False, indent=4) except Exception as e: log_with_rank( f"Save LoRA Adapter Error ({e})", rank=dist.get_rank(), logger=logger, log_only_rank_0=True ) dist.barrier() log_with_rank( f"[rank-{self.rank}]: Saved LoRA adapter to: {lora_save_path}", rank=dist.get_rank(), logger=logger, log_only_rank_0=True, ) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, hdfs_path=None, del_local_after_load=False): assert self._is_actor or (not self._is_actor and self._is_rollout), ( f"Checkpoint loading is only supported for Actor or standalone Rollout Workers, but got " f"{self._is_actor} and {self._is_rollout}" ) # No checkpoint to load, just offload the model and optimizer to CPU if local_path is None: if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) if self._is_offload_optimizer: offload_fsdp_optimizer(self.actor_optimizer) return if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) self.checkpoint_manager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) if self._is_offload_optimizer: offload_fsdp_optimizer(self.actor_optimizer) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def start_profile(self, **kwargs) -> None: """Start profiling for the current rank in the current training step.""" self.profiler.start(**kwargs) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def stop_profile(self) -> None: """Stop profiling for the current rank in the current training step.""" self.profiler.stop() @register(dispatch_mode=Dispatch.ONE_TO_ALL) def dump_memory_snapshot(self, tag: str = "manual", sub_dir: str = None) -> None: """Manually trigger a CUDA memory snapshot dump on all ranks.""" # Memory snapshot is now handled by the profiler system # This method is kept for backward compatibility but delegates to profiler if hasattr(self, "profiler") and hasattr(self.profiler, "_impl"): try: # Try to use the profiler's memory snapshot functionality if hasattr(self.profiler._impl, "sampler"): out_dir = OmegaConf.select(self.config, "actor.profiler.save_path") or "." self.profiler._impl.sampler.dump_memory_snapshot(out_dir=out_dir, tag=tag, sub_dir=sub_dir) except Exception: # silently ignore if profiler doesn't support memory snapshots pass class CriticWorker(Worker, DistProfilerExtension): def __init__(self, config: FSDPCriticConfig): Worker.__init__(self) omega_profiler_config = config.get("profiler", {}) profiler_config = omega_conf_to_dataclass(omega_profiler_config, dataclass_type=ProfilerConfig) if omega_profiler_config.get("tool", None) in ["npu", "nsys", "torch", "torch_memory"]: tool_config = omega_conf_to_dataclass( omega_profiler_config.get("tool_config", {}).get(omega_profiler_config.get("tool")) ) else: tool_config = None DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=profiler_config, tool_config=tool_config) ) import torch.distributed self.config = config if not torch.distributed.is_initialized(): torch.distributed.init_process_group( backend=get_nccl_backend(), timeout=datetime.timedelta(seconds=self.config.get("nccl_timeout", 600)), init_method=os.environ.get("DIST_INIT_METHOD", None), ) self.config: FSDPCriticConfig = config # build device mesh for Ulysses Sequence Parallel world_size = torch.distributed.get_world_size() from torch.distributed.device_mesh import init_device_mesh fsdp_size = self.config.model.fsdp_config.fsdp_size self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=fsdp_size) self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.get("ulysses_sequence_parallel_size", 1) dp = world_size // self.ulysses_sequence_parallel_size if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh( device_name, mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=["dp", "sp"] ) # create training dispatch if self.ulysses_device_mesh is not None: is_collect = self.ulysses_device_mesh["sp"].get_local_rank() == 0 self._register_dispatch_collect_info( "critic", dp_rank=self.ulysses_device_mesh["dp"].get_local_rank(), is_collect=is_collect ) else: self._register_dispatch_collect_info("critic", dp_rank=self.rank, is_collect=True) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) # set FSDP offload params self._is_offload_param = self.config.model.fsdp_config.param_offload self._is_offload_optimizer = self.config.model.fsdp_config.optimizer_offload # normalize config self.config.ppo_mini_batch_size *= self.config.rollout_n self.config.ppo_mini_batch_size //= torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size if self.config.ppo_micro_batch_size is not None: self.config.ppo_micro_batch_size //= ( torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size ) self.config.forward_micro_batch_size //= ( torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size ) self.config.ppo_micro_batch_size_per_gpu = self.config.ppo_micro_batch_size self.config.forward_micro_batch_size_per_gpu = self.config.forward_micro_batch_size if self.config.ppo_micro_batch_size_per_gpu is not None: assert self.config.ppo_mini_batch_size % self.config.ppo_micro_batch_size_per_gpu == 0, ( f"normalized ppo_mini_batch_size {self.config.ppo_mini_batch_size} should be divisible by " f"ppo_micro_batch_size_per_gpu {self.config.ppo_micro_batch_size_per_gpu}" ) assert self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu > 0, ( f"normalized ppo_mini_batch_size {self.config.ppo_mini_batch_size} should be larger than " f"ppo_micro_batch_size_per_gpu {self.config.ppo_micro_batch_size_per_gpu}" ) self._is_lora = ( self.config.model.get("lora_adapter_path") is not None or self.config.model.get("lora_rank", 0) > 0 ) self.use_orig_params = self.config.model.fsdp_config.get("use_orig_params", False) def _build_critic_model_optimizer(self, config: FSDPCriticConfig): # the following line is necessary from torch.distributed.fsdp import MixedPrecision from verl.utils.model import load_valuehead_model, print_model_size from verl.utils.torch_dtypes import PrecisionType use_shm = config.model.get("use_shm", False) local_path = copy_to_local(config.model.path, use_shm=use_shm) # note that the tokenizer between actor and critic may be different. So override tokenizer info with actor info # using random initialized model from any architecture. May not be the same as Actor. tokenizer_path = copy_to_local(config.model.tokenizer_path, use_shm=use_shm) self.tokenizer = hf_tokenizer(tokenizer_path, trust_remote_code=config.model.get("trust_remote_code", False)) self.processor = hf_processor(tokenizer_path, trust_remote_code=config.model.get("trust_remote_code", False)) if self.config.model.get("custom_chat_template", None) is not None: if self.processor is not None: self.processor.chat_template = self.config.model.custom_chat_template else: self.tokenizer.chat_template = self.config.model.custom_chat_template override_config = OmegaConf.to_container(OmegaConf.create(self.config.model.get("override_config", {}))) override_config_kwargs = { "bos_token_id": self.tokenizer.bos_token_id, "eos_token_id": self.tokenizer.eos_token_id, "pad_token_id": self.tokenizer.pad_token_id, } override_config_kwargs.update(override_config) if self.rank == 0: print(f"Critic overriding config {override_config_kwargs}") torch_dtype = self.config.model.fsdp_config.get("model_dtype", "fp32") torch_dtype = PrecisionType.to_dtype(torch_dtype) from transformers import AutoConfig # override model kwargs attn_implementation = override_config.get("attn_implementation", "flash_attention_2") critic_model_config = AutoConfig.from_pretrained( local_path, attn_implementation=attn_implementation, trust_remote_code=config.model.get("trust_remote_code", False), ) # TODO: VL models use VisionAttention, which directly uses flash_attention in transformers>=4.53 # which will be patched by _ulysses_flash_attention_forward, but errorly misses position_ids # Maybe support Ulysses in VisionAttention in the future and remove this patch if self.ulysses_sequence_parallel_size > 1 and hasattr(critic_model_config, "vision_config"): critic_model_config.vision_config._attn_implementation = "eager" critic_model_config.num_labels = 1 # patch for kimi-vl if getattr(critic_model_config, "model_type", None) == "kimi_vl": critic_model_config.text_config.topk_method = "greedy" init_context = get_init_weight_context_manager( use_meta_tensor=not critic_model_config.tie_word_embeddings, mesh=self.device_mesh ) # TiledMLP configuration for memory-efficient MLP computation tiled_mlp_config = config.model.get("tiled_mlp", {}) use_tiled_mlp = tiled_mlp_config.get("enabled", False) tiled_mlp_shards = tiled_mlp_config.get("num_shards", 4) # TiledMLP requires FSDP2 for correct gradient computation if use_tiled_mlp and config.strategy == "fsdp": raise ValueError("TiledMLP requires FSDP2. Set `critic.strategy=fsdp2`.") with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") critic_model_config.classifier_dropout = 0.0 critic_model_config.hidden_dropout = "0" critic_model_config.summary_dropout_prob = 0.0 critic_module = load_valuehead_model( local_path, torch_dtype, critic_model_config, config.model.get("trust_remote_code", False), ) use_remove_padding = config.model.get("use_remove_padding", False) apply_monkey_patch( model=critic_module, use_remove_padding=use_remove_padding, ulysses_sp_size=self.ulysses_sequence_parallel_size, use_tiled_mlp=use_tiled_mlp, tiled_mlp_shards=tiled_mlp_shards, ) # some parameters may not in torch_dtype critic_module.to(torch_dtype) if config.model.get("enable_gradient_checkpointing", False): critic_module.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) if self._is_lora: print("Applying LoRA to critic module") critic_module.enable_input_require_grads() # Check if we should load a pre-trained LoRA adapter lora_adapter_path = self.config.model.get("lora_adapter_path") if lora_adapter_path is not None: from peft import PeftModel print(f"Loading pre-trained LoRA adapter to critic from: {lora_adapter_path}") # Copy adapter to local if needed local_adapter_path = copy_to_local(lora_adapter_path, use_shm=self.config.model.get("use_shm", False)) critic_module = PeftModel.from_pretrained(critic_module, local_adapter_path, is_trainable=True) peft_config = critic_module.peft_config["default"] # Ensure task_type is TaskType enum, not string # Use TOKEN_CLS for Critic since it's loaded as AutoModelForTokenClassification if isinstance(peft_config.task_type, str): peft_config.task_type = TaskType.TOKEN_CLS else: # Convert config to regular Python types before creating PEFT model # Use TOKEN_CLS for Critic since it's loaded as AutoModelForTokenClassification lora_config = { "task_type": TaskType.TOKEN_CLS, "r": self.config.model.lora_rank, "lora_alpha": self.config.model.lora_alpha, "target_modules": convert_to_regular_types(self.config.model.target_modules), "bias": "none", } critic_module = get_peft_model(critic_module, LoraConfig(**lora_config)) if self.rank == 0: print_model_size(critic_module) self.critic_model_config = critic_model_config fsdp_config = self.config.model.fsdp_config mixed_precision_config = fsdp_config.get("mixed_precision", None) if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get("param_dtype", "bf16")) reduce_dtype = PrecisionType.to_dtype(mixed_precision_config.get("reduce_dtype", "fp32")) buffer_dtype = PrecisionType.to_dtype(mixed_precision_config.get("buffer_dtype", "fp32")) else: param_dtype = torch.bfloat16 reduce_dtype = torch.float32 buffer_dtype = torch.float32 mixed_precision = MixedPrecision(param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype) auto_wrap_policy = get_fsdp_wrap_policy( module=critic_module, config=self.config.model.fsdp_config.wrap_policy, is_lora=self._is_lora, ) log_gpu_memory_usage("Before critic FSDP", logger=None) fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) self.use_orig_params = fsdp_config.get("use_orig_params", False) if self.config.model.get("freeze_vision_tower", False): vision_tower = get_vl_model_vision_tower(critic_module) if vision_tower is not None: vision_tower.requires_grad_(False) self.use_orig_params = True if self.rank == 0: print("[critic model] Vision tower is set to not trainable.") else: if self.rank == 0: print("[critic model] No vision tower found.") # Note: We force turn off CPUOffload for critic because it causes incorrect results when using grad accumulation if config.strategy == "fsdp": critic_module = FSDP( critic_module, param_init_fn=init_fn, use_orig_params=self.use_orig_params, auto_wrap_policy=auto_wrap_policy, device_id=get_device_id(), sharding_strategy=sharding_strategy, mixed_precision=mixed_precision, sync_module_states=True, forward_prefetch=self.config.model.fsdp_config.forward_prefetch, device_mesh=self.device_mesh, cpu_offload=None, ) elif config.strategy == "fsdp2": assert CPUOffloadPolicy is not None, "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)" mp_policy = MixedPrecisionPolicy( param_dtype=param_dtype, reduce_dtype=reduce_dtype, cast_forward_inputs=True ) offload_policy = None if fsdp_config.offload_policy: self._is_offload_param = False self._is_offload_optimizer = False offload_policy = CPUOffloadPolicy(pin_memory=True) fsdp_kwargs = { "mesh": fsdp_mesh, "mp_policy": mp_policy, "offload_policy": offload_policy, "reshard_after_forward": fsdp_config.reshard_after_forward, "shard_placement_fn": get_shard_placement_fn(fsdp_size=self.device_mesh.shape[-1]), } full_state = critic_module.state_dict() apply_fsdp2(critic_module, fsdp_kwargs, fsdp_config) fsdp2_load_full_state_dict(critic_module, full_state, fsdp_mesh, offload_policy) else: raise NotImplementedError(f"Unknown strategy {config.strategy}") if config.model.get("enable_activation_offload", False): enable_gradient_checkpointing = config.model.get("enable_gradient_checkpointing", False) enable_activation_offloading(critic_module, config.strategy, enable_gradient_checkpointing) log_gpu_memory_usage("After critic FSDP", logger=None) critic_optimizer = build_optimizer(critic_module.parameters(), config.optim) total_steps = config.optim.get("total_training_steps", 0) num_warmup_steps = int(config.optim.get("lr_warmup_steps", -1)) lr_scheduler_type = config.optim.get("lr_scheduler_type", "constant") if num_warmup_steps < 0: num_warmup_steps_ratio = config.optim.get("lr_warmup_steps_ratio", 0.0) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) if self.rank == 0: print(f"Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}") from verl.utils.torch_functional import get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup if lr_scheduler_type == "constant": critic_lr_scheduler = get_constant_schedule_with_warmup( optimizer=critic_optimizer, num_warmup_steps=num_warmup_steps ) elif lr_scheduler_type == "cosine": min_lr_ratio = config.optim.get("min_lr_ratio", 0.0) num_cycles = config.optim.get("num_cycles", 0.5) critic_lr_scheduler = get_cosine_schedule_with_warmup( optimizer=critic_optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps, min_lr_ratio=min_lr_ratio, num_cycles=num_cycles, ) else: raise NotImplementedError(f"LR scheduler type {lr_scheduler_type} is not supported") return critic_module, critic_optimizer, critic_lr_scheduler @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get("external_lib", None)) from verl.workers.critic import DataParallelPPOCritic self.critic_module, self.critic_optimizer, self.critic_lr_scheduler = self._build_critic_model_optimizer( self.config ) if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) log_gpu_memory_usage("After offload critic model during init", logger=logger) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.critic_optimizer) log_gpu_memory_usage("After offload critic optimizer during init", logger=logger) self.critic = DataParallelPPOCritic( config=self.config, critic_module=self.critic_module, critic_optimizer=self.critic_optimizer ) self.flops_counter = FlopsCounter(self.critic_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.critic_module, optimizer=self.critic_optimizer, lr_scheduler=self.critic_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer, checkpoint_config=self.config.checkpoint, trust_remote_code=self.config.model.get("trust_remote_code", False), ) @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="critic")) @DistProfiler.annotate(color="cyan", role="compute_values") def compute_values(self, data: DataProto): if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) micro_batch_size = self.config.forward_micro_batch_size_per_gpu data.meta_info["micro_batch_size"] = micro_batch_size data.meta_info["max_token_len"] = self.config.forward_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = self.config.use_dynamic_bsz # perform forward computation with self.ulysses_sharding_manager: data = data.to("cpu") # data will to device with each micro batch on critic.compute_values values = self.critic.compute_values(data=data) output = DataProto.from_dict(tensors={"values": values}) output = output.to("cpu") if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="critic")) @DistProfiler.annotate(color="pink", role="critic_update") def update_critic(self, data: DataProto): if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) if self._is_offload_optimizer: load_fsdp_optimizer(optimizer=self.critic_optimizer, device_id=get_device_id()) # perform forward computation with self.ulysses_sharding_manager: data = data.to("cpu") # data will to device with each micro batch on critic.update_critic with Timer(name="update_critic", logger=None) as timer: metrics = self.critic.update_critic(data=data) delta_time = timer.last global_num_tokens = data.meta_info["global_token_num"] estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time) metrics["perf/mfu/critic"] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size lr = self.critic_lr_scheduler.get_last_lr()[0] metrics["critic/lr"] = lr self.critic_lr_scheduler.step() output = DataProto(batch=None, meta_info={"metrics": metrics}) if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.critic_optimizer) output = output.to("cpu") return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) self.checkpoint_manager.save_checkpoint( local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep ) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, hdfs_path=None, del_local_after_load=True): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) self.checkpoint_manager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(self.critic_optimizer) # ================================= Async related workers ================================= class AsyncActorRolloutRefWorker(ActorRolloutRefWorker): @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) async def update_weights(self, global_steps: int = None): await self.rollout_mode() return True ================================================ FILE: verl/workers/megatron_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The main entry point to run the PPO algorithm """ import datetime import logging import os import time import psutil import torch import torch.distributed from codetiming import Timer from omegaconf import DictConfig, OmegaConf try: from verl.workers.engine.mindspeed.transformer_impl import repatch except ImportError: repatch = None from contextlib import nullcontext from megatron.core import parallel_state as mpu from verl import DataProto from verl.models.mcore import get_mcore_weight_converter from verl.single_controller.base import Worker from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register from verl.utils import hf_tokenizer from verl.utils.checkpoint.megatron_checkpoint_manager import MegatronCheckpointManager from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import ( get_device_id, get_device_name, get_nccl_backend, get_torch_device, set_expandable_segments, ) from verl.utils.distributed import set_numa_affinity from verl.utils.flops_counter import FlopsCounter from verl.utils.fs import copy_to_local from verl.utils.megatron.router_replay_patch import RouterReplay, RouterReplayAction, apply_router_replay_patch from verl.utils.megatron_peft_utils import add_base_layer_suffix, build_peft_config_for_vllm from verl.utils.megatron_utils import ( load_megatron_model_to_gpu, load_megatron_optimizer, offload_megatron_model_to_cpu, offload_megatron_optimizer, per_tensor_generator, register_megatron_training_hooks, ) from verl.utils.memory_utils import aggressive_empty_cache from verl.utils.model import get_hf_model_path, load_mcore_dist_weights, load_megatron_gptmodel_weights from verl.utils.profiler import ( DistProfiler, DistProfilerExtension, GPUMemoryLogger, ProfilerConfig, log_gpu_memory_usage, simple_timer, ) from verl.utils.profiler.performance import reduce_timing, topk_reduce_ratio_min_max from verl.utils.ray_utils import get_event_loop from verl.utils.torch_functional import use_original_torch_compile from verl.workers.actor.megatron_actor import MegatronPPOActor from verl.workers.config import HFModelConfig, McoreCriticConfig, RolloutConfig from verl.workers.critic.megatron_critic import MegatronPPOCritic from verl.workers.rollout import get_rollout_class logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) def set_random_seed(seed, only_rollout=False): import random import numpy as np import torch torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) if not only_rollout and get_torch_device().device_count() > 0: from megatron.core import tensor_parallel tensor_parallel.model_parallel_cuda_manual_seed(seed) # FIXME: torch cumsum not support deterministic (used in vllm sampler), # https://github.com/pytorch/pytorch/issues/89492 # torch.use_deterministic_algorithms(True, warn_only=True) # os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' class MegatronWorker(Worker): def _init_hf_config_and_tf_config( self, model_path, tokenizer_or_path, dtype, override_model_config, override_transformer_config, trust_remote_code=False, megatron_config=None, enable_mtp=False, ): from transformers import AutoConfig from verl.models.mcore import hf_to_mcore_config from verl.utils import hf_processor from verl.utils.model import update_model_config # Step 1: initialize the tokenizer self.local_path = copy_to_local(model_path) if tokenizer_or_path is None: self.tokenizer = hf_tokenizer(self.local_path, trust_remote_code=trust_remote_code) self.processor = hf_processor(self.local_path, trust_remote_code=trust_remote_code) elif isinstance(tokenizer_or_path, str): self.tokenizer = hf_tokenizer(copy_to_local(tokenizer_or_path), trust_remote_code=trust_remote_code) self.processor = hf_processor(copy_to_local(tokenizer_or_path), trust_remote_code=trust_remote_code) else: self.tokenizer = tokenizer_or_path self.processor = tokenizer_or_path if self.config.model.get("custom_chat_template", None) is not None: if self.processor is not None: self.processor.chat_template = self.config.model.custom_chat_template else: self.tokenizer.chat_template = self.config.model.custom_chat_template # Step 2: get the hf hf_config = AutoConfig.from_pretrained(self.local_path, trust_remote_code=trust_remote_code) # Step 3: override the hf config override_config_kwargs = { "bos_token_id": self.tokenizer.bos_token_id, "eos_token_id": self.tokenizer.eos_token_id, "pad_token_id": self.tokenizer.pad_token_id, } override_config_kwargs.update(override_model_config.get("model_config", {})) self.share_embeddings_and_output_weights = getattr(hf_config, "tie_word_embeddings", False) # only actor need enable mtp if enable_mtp: assert hf_config.num_nextn_predict_layers > 0, "MTP requires at least one nextn_predict_layer" assert megatron_config.use_mbridge, "MTP requires use_mbridge to be True" override_transformer_config["mtp_loss_scaling_factor"] = self.config.model.mtp.mtp_loss_scaling_factor else: if hasattr(hf_config, "num_nextn_predict_layers"): hf_config.num_nextn_predict_layers = 0 self.enable_mtp = enable_mtp update_model_config(hf_config, override_config_kwargs=override_config_kwargs) self.architectures = getattr(hf_config, "architectures", None) if self.rank == 0: print(f"Model config after override: {hf_config}") from verl.models.mcore.config_converter import mapping_string_to_attn_backend # todo: remove this line after mcore adopt mbridge 0.15, now for compatibility override_transformer_config = mapping_string_to_attn_backend(override_transformer_config) fp16 = dtype == torch.float16 bf16 = dtype == torch.bfloat16 if fp16: assert megatron_config.use_mbridge, "fp16 mode requires use_mbridge to be True" self.provider = None self.vanilla_bridge = megatron_config.get("vanilla_mbridge", True) if megatron_config.use_mbridge: if self.vanilla_bridge: from verl.models.mcore.mbridge import AutoBridge bridge = AutoBridge.from_config(hf_config, dtype=dtype) bridge.set_extra_args(**override_transformer_config) tf_config = bridge.config tf_config.fp16 = fp16 tf_config.bf16 = bf16 else: from verl.models.mcore.bridge import AutoBridge # Use Megatron-Bridge to convert HF config to Megatron config bridge = AutoBridge.from_hf_pretrained(self.local_path, trust_remote_code=trust_remote_code) # Get Megatron provider and configure it provider = bridge.to_megatron_provider(load_weights=False) # In case of invalid overrides, we need to make sure some critical params are set correctly provider.params_dtype = dtype # Ensure dtype settings propagate to Megatron-Bridge/TE provider.fp16 = fp16 provider.bf16 = bf16 # Pass distributed info provider.tensor_model_parallel_size = megatron_config.tensor_model_parallel_size provider.pipeline_model_parallel_size = megatron_config.pipeline_model_parallel_size provider.expert_model_parallel_size = megatron_config.expert_model_parallel_size provider.expert_tensor_parallel_size = megatron_config.expert_tensor_parallel_size provider.virtual_pipeline_model_parallel_size = megatron_config.virtual_pipeline_model_parallel_size provider.context_parallel_size = megatron_config.context_parallel_size provider.sequence_parallel = megatron_config.sequence_parallel # Match verl implementation (need variable_seq_lengths) from megatron.core.transformer.enums import AttnBackend provider.attention_backend = AttnBackend.flash provider.variable_seq_lengths = True provider.moe_token_dispatcher_type = "alltoall" provider.moe_router_load_balancing_type = "none" # Apply transformer config overrides for key, value in override_transformer_config.items(): setattr(provider, key, value) provider.finalize() self.provider = provider tf_config = None # Will be set after model creation self.bridge = bridge else: tf_config = hf_to_mcore_config(hf_config, dtype, **override_transformer_config) self.bridge = None if torch.distributed.get_rank() == 0: if tf_config is not None: print(f"TF config: {tf_config}") self.hf_config = hf_config self.tf_config = tf_config # Get PEFT config from model.lora if specified from verl.workers.config.megatron_peft import get_peft_cls self.peft_cls = get_peft_cls( model_config=self.config.model, bridge=self.bridge, provider=self.provider, dtype=dtype ) class ActorRolloutRefWorker(MegatronWorker, DistProfilerExtension): """ This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on the config.rollout """ def __init__(self, config: DictConfig, role: str, **kwargs): Worker.__init__(self) self.config = config if repatch is not None: # NPU MindSpeed patch, will be refactored with MindSpeedEngine. repatch(self.config.actor.megatron.get("override_transformer_config", {})) self.role = role assert self.role in ["actor", "rollout", "ref", "actor_rollout", "actor_rollout_ref"] self._is_actor = self.role in ["actor", "actor_rollout", "actor_rollout_ref"] self._is_rollout = self.role in ["rollout", "actor_rollout", "actor_rollout_ref"] self._is_ref = self.role in ["ref", "actor_rollout_ref"] # NOTE(sgm): We utilize colocate WorkerGroup by default. # As a result, Workers for different model share the same process. # Therefore, we only require one distribute initialization. # To utilize different parallel strategy in different models: # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models, # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385 if not torch.distributed.is_initialized(): set_numa_affinity() rank = int(os.environ["LOCAL_RANK"]) torch.distributed.init_process_group( backend=f"cpu:gloo,{get_device_name()}:{get_nccl_backend()}", timeout=datetime.timedelta(seconds=self.config.get("nccl_timeout", 600)), init_method=os.environ.get("DIST_INIT_METHOD", None), ) get_torch_device().set_device(rank) if self._is_actor or self._is_ref: mpu.initialize_model_parallel( tensor_model_parallel_size=self.config.actor.megatron.tensor_model_parallel_size, pipeline_model_parallel_size=self.config.actor.megatron.pipeline_model_parallel_size, virtual_pipeline_model_parallel_size=self.config.actor.megatron.virtual_pipeline_model_parallel_size, use_sharp=False, context_parallel_size=self.config.actor.megatron.context_parallel_size, expert_model_parallel_size=self.config.actor.megatron.expert_model_parallel_size, expert_tensor_parallel_size=self.config.actor.megatron.expert_tensor_parallel_size, nccl_communicator_config_path=None, ) if self._is_actor or self._is_ref: is_collect = ( mpu.get_tensor_model_parallel_rank() == 0 and mpu.get_pipeline_model_parallel_rank() == mpu.get_pipeline_model_parallel_world_size() - 1 and mpu.get_context_parallel_rank() == 0 ) self._register_dispatch_collect_info( mesh_name="actor", dp_rank=mpu.get_data_parallel_rank(), is_collect=is_collect ) only_rollout = self._is_rollout and not self._is_actor self.enable_routing_replay = False if self._is_actor: self.router_replay = self.config.actor.router_replay self.enable_routing_replay = self.router_replay.mode != "disabled" if self.enable_routing_replay: apply_router_replay_patch() set_random_seed(seed=self.config.actor.megatron.seed, only_rollout=only_rollout) if self._is_actor: omega_profiler_config = config.actor.get("profiler", {}) elif self._is_rollout: # NOTE: In colocation mode, rollout config may not take effect (follow the actor config) # This is for extendability in AsyncRL cases omega_profiler_config = config.rollout.get("profiler", {}) elif self._is_ref: omega_profiler_config = config.ref.get("profiler", {}) else: raise ValueError( f"Invalid role {self.role}, should be one of " "['actor', 'rollout', 'ref', 'actor_rollout', 'actor_rollout_ref']" ) # omega_profiler_config is DictConfig # profiler_config is a ProfilerConfig dataclass profiler_config = omega_conf_to_dataclass(omega_profiler_config, dataclass_type=ProfilerConfig) if omega_profiler_config.get("tool", None) in ["npu", "nsys", "torch", "torch_memory"]: tool_config = omega_conf_to_dataclass( omega_profiler_config.get("tool_config", {}).get(omega_profiler_config.get("tool")) ) else: tool_config = None DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=profiler_config, tool_config=tool_config) ) # TODO(sgm): Currently, we only support reference model param offload # will support other offload later self._is_offload_param = False self._is_offload_grad = False self._is_offload_optimizer = False # Initialize LoRA-related attributes (will be updated in _build_rollout if needed) self.base_sync_done = False self.peft_merge = False # normalize config if self._is_actor: self.config.actor.ppo_mini_batch_size *= self.config.rollout.n self.config.actor.ppo_mini_batch_size //= mpu.get_data_parallel_world_size() if self.config.actor.get("ppo_micro_batch_size", None): self.config.actor.ppo_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.rollout.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.actor.ppo_micro_batch_size_per_gpu = self.config.actor.ppo_micro_batch_size self.config.rollout.log_prob_micro_batch_size_per_gpu = self.config.rollout.log_prob_micro_batch_size self._is_offload_param = self.config.actor.megatron.get("param_offload", False) self._is_offload_grad = self.config.actor.megatron.get("grad_offload", False) self._is_offload_optimizer = self.config.actor.megatron.get("optimizer_offload", False) elif self._is_ref: if self.config.ref.get("log_prob_micro_batch_size", None): self.config.ref.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.ref.log_prob_micro_batch_size_per_gpu = self.config.ref.log_prob_micro_batch_size else: assert self.config.ref.get("log_prob_micro_batch_size_per_gpu", None) is not None, ( "Please note that in the ref policy configuration, `log_prob_micro_batch_size_per_gpu` and " "`log_prob_micro_batch_size` should not be None at the same time." ) self._ref_is_offload_param = self.config.ref.megatron.get("param_offload", False) def _build_model_optimizer( self, model_path, optim_config, override_model_config, override_transformer_config, override_ddp_config=None ): from verl.utils.megatron.optimizer import ( get_megatron_optimizer, get_megatron_optimizer_param_scheduler, init_megatron_optim_config, ) from verl.utils.megatron_utils import McoreModuleWrapperConfig, make_megatron_module from verl.utils.model import get_generation_config, print_model_size self._init_hf_config_and_tf_config( model_path, self.config.model.get("tokenizer_path") or model_path, self.dtype, override_model_config, override_transformer_config, self.config.model.get("trust_remote_code", False), self.config.actor.megatron if not self._is_ref else self.config.ref.megatron, self.config.model.get("mtp", {}).get("enable", False), ) self.generation_config = get_generation_config( self.local_path, self.config.model.get("trust_remote_code", False), ) if self._is_actor or self._is_rollout: wrap_config = McoreModuleWrapperConfig( is_value_model=False, # actor is not value model share_embeddings_and_output_weights=self.share_embeddings_and_output_weights, wrap_with_ddp=True, use_distributed_optimizer=self.config.actor.megatron.use_distributed_optimizer, ) actor_module, updated_tf_config = make_megatron_module( wrap_config=wrap_config, tf_config=self.tf_config, hf_config=self.hf_config, bridge=self.bridge, provider=self.provider, override_model_config=override_model_config, override_ddp_config=override_ddp_config, peft_cls=self.peft_cls, peft_config=self.config.model.get("lora", None), ) self.tf_config = updated_tf_config print(f"actor_module: {len(actor_module)}") if self.config.actor.load_weight: if self.config.actor.megatron.use_dist_checkpointing: load_mcore_dist_weights( actor_module, self.config.actor.megatron.dist_checkpointing_path, is_value_model=False, prefix=self.config.actor.megatron.dist_checkpointing_prefix, ) else: if self.bridge is not None: local_model_path = get_hf_model_path(self.config) if self.vanilla_bridge: self.bridge.load_weights(actor_module, local_model_path) else: self.bridge.load_hf_weights(actor_module, local_model_path) else: load_megatron_gptmodel_weights( self.config, self.hf_config, actor_module, params_dtype=self.dtype, is_value_model=False ) if self.rank == 0: print_model_size(actor_module[0]) log_gpu_memory_usage("After MegatronPPOActor init", logger=logger) elif self._is_ref: wrap_config = McoreModuleWrapperConfig( is_value_model=False, # ref is not value model share_embeddings_and_output_weights=self.share_embeddings_and_output_weights, wrap_with_ddp=False, use_distributed_optimizer=self.config.ref.megatron.use_distributed_optimizer, ) ref_module, updated_tf_config = make_megatron_module( wrap_config=wrap_config, tf_config=self.tf_config, hf_config=self.hf_config, bridge=self.bridge, provider=self.provider, override_model_config=override_model_config, ) self.tf_config = updated_tf_config if self.config.ref.load_weight: # should align with the actor: assert self.config.actor.load_weight == self.config.ref.load_weight print("load ref weight start") if self.config.ref.megatron.use_dist_checkpointing: load_mcore_dist_weights( ref_module, self.config.ref.megatron.dist_checkpointing_path, is_value_model=False, prefix=self.config.ref.megatron.dist_checkpointing_prefix, ) else: if self.bridge is not None: local_model_path = get_hf_model_path(self.config) if self.vanilla_bridge: self.bridge.load_weights(ref_module, local_model_path) else: self.bridge.load_hf_weights(ref_module, local_model_path) else: load_megatron_gptmodel_weights( self.config, self.hf_config, ref_module, params_dtype=self.dtype, is_value_model=False ) log_gpu_memory_usage("After ref module init", logger=logger) return ref_module, self.hf_config # TODO: add more optimizer args into config if self._is_actor: optim_config_megatron = init_megatron_optim_config( optim_config, use_distributed_optimizer=wrap_config.use_distributed_optimizer, fp16=self.dtype == torch.float16, ) actor_optimizer = get_megatron_optimizer(model=actor_module, config=optim_config_megatron) actor_optimizer_scheduler = get_megatron_optimizer_param_scheduler( optimizer=actor_optimizer, config=optim_config ) else: optim_config = None actor_optimizer = None actor_optimizer_scheduler = None log_gpu_memory_usage("After actor optimizer init", logger=logger) register_megatron_training_hooks(actor_module, actor_optimizer) return actor_module, actor_optimizer, actor_optimizer_scheduler, self.hf_config, optim_config def _build_rollout(self, trust_remote_code=False): from torch.distributed.device_mesh import init_device_mesh # 1. parse rollout and huggingface model config rollout_config: RolloutConfig = omega_conf_to_dataclass(self.config.rollout) model_config: HFModelConfig = omega_conf_to_dataclass(self.config.model) # 2. build rollout device mesh infer_tp = self.config.rollout.tensor_model_parallel_size * self.config.rollout.data_parallel_size infer_pp = self.config.rollout.pipeline_model_parallel_size infer_world_size = infer_tp * infer_pp dp = self.world_size // infer_world_size assert self.world_size % infer_world_size == 0, ( f"rollout world_size: {self.world_size} is not divisible by infer_world_size: {infer_world_size}" ) rollout_device_mesh = init_device_mesh( get_device_name(), mesh_shape=(dp, infer_tp, infer_pp), mesh_dim_names=["dp", "infer_tp", "infer_pp"] ) self.rollout_device_mesh = rollout_device_mesh is_collect = ( rollout_device_mesh["infer_tp"].get_local_rank() == 0 and rollout_device_mesh["infer_pp"].get_local_rank() == 0 ) self._register_dispatch_collect_info( "rollout", dp_rank=rollout_device_mesh["dp"].get_local_rank(), is_collect=is_collect ) # 4. build rollout model log_gpu_memory_usage(f"Before building {self.config.rollout.name} rollout", logger=logger) self.rollout = get_rollout_class(rollout_config.name, rollout_config.mode)( config=rollout_config, model_config=model_config, device_mesh=rollout_device_mesh ) log_gpu_memory_usage(f"After building {self.config.rollout.name} rollout", logger=logger) # Initialize base_sync_done for LoRA self.base_sync_done: bool = "dummy" not in self.config.rollout.load_format self.peft_merge: bool = model_config.lora.get("merge", False) # 5. switch to trainer mode # NOTE: It's critical that hybrid engine in trainer mode initially to load checkpoint. # For async mode, we can't call run_until_complete here, so we will switch to trainer mode in AgentLoopManager. # Note: sync mode is deprecated and rejected in RolloutConfig.__post_init__ @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): if self.config.model.get("external_lib", None) is not None: # This is used to import external_lib into the huggingface systems import importlib importlib.import_module(self.config.model.external_lib) from verl.utils.torch_dtypes import PrecisionType override_model_config = OmegaConf.to_container(OmegaConf.create(self.config.model.get("override_config", {}))) if self._is_actor: override_transformer_config = OmegaConf.to_container( OmegaConf.create(self.config.actor.megatron.get("override_transformer_config", {})) ) if self.enable_routing_replay: override_transformer_config["enable_routing_replay"] = True override_ddp_config = OmegaConf.to_container( OmegaConf.create(self.config.actor.megatron.get("override_ddp_config", {})) ) elif self._is_ref: override_transformer_config = OmegaConf.to_container( OmegaConf.create(self.config.ref.megatron.get("override_transformer_config", {})) ) else: override_transformer_config = {} self.param_dtype = PrecisionType.to_dtype(self.config.actor.megatron.dtype) log_gpu_memory_usage("Before init actor model and optimizer", logger=logger) self.dtype = PrecisionType.to_dtype(self.param_dtype) if self._is_actor: # we need the model for actor and rollout optim_config = self.config.actor.optim if self._is_actor else None ( self.actor_module, self.actor_optimizer, self.actor_optimizer_scheduler, self.actor_model_config, self.actor_optim_config, ) = self._build_model_optimizer( model_path=self.config.model.path, optim_config=optim_config, override_model_config=override_model_config, override_transformer_config=override_transformer_config, override_ddp_config=override_ddp_config, ) if self._is_offload_param: offload_megatron_model_to_cpu(self.actor_module) log_gpu_memory_usage("After offload actor params and grad during init", logger=logger) if self._is_offload_optimizer: offload_megatron_optimizer(self.actor_optimizer) log_gpu_memory_usage("After offload actor optimizer during init", logger=logger) if self._is_actor: actor_cfg = omega_conf_to_dataclass(self.config.actor) self.actor = MegatronPPOActor( config=actor_cfg, model_config=self.actor_model_config, hf_config=self.hf_config, tf_config=self.tf_config, actor_module=self.actor_module, actor_optimizer=self.actor_optimizer, mtp_config=self.config.model.mtp if self.config.model.mtp.enable else None, ) print(f"routing replay layers: {len(RouterReplay.router_instances)}") log_gpu_memory_usage("After MegatronPPOActor init", logger=logger) if self._is_rollout: with use_original_torch_compile(): self._build_rollout(trust_remote_code=self.config.model.get("trust_remote_code", False)) log_gpu_memory_usage("After rollout init", logger=logger) if self._is_ref: self.ref_module, self.ref_model_config = self._build_model_optimizer( model_path=self.config.model.path, optim_config=None, override_model_config=override_model_config, override_transformer_config=override_transformer_config, ) log_gpu_memory_usage("After ref model init", logger=logger) self.ref_policy = MegatronPPOActor( config=self.config.ref, model_config=self.ref_model_config, hf_config=self.hf_config, tf_config=self.tf_config, actor_module=self.ref_module, actor_optimizer=None, ) if self._ref_is_offload_param: offload_megatron_model_to_cpu(self.ref_module) log_gpu_memory_usage("After offload ref params during init", logger=logger) if self._is_actor: self.flops_counter = FlopsCounter(self.actor_model_config) self.checkpoint_mananager = MegatronCheckpointManager( config=self.config, checkpoint_config=self.config.actor.checkpoint, model_config=self.actor_model_config, transformer_config=self.tf_config, role="actor", model=self.actor_module, arch=self.architectures[0], hf_config=self.hf_config, param_dtype=self.param_dtype, share_embeddings_and_output_weights=self.share_embeddings_and_output_weights, processing_class=self.processor if self.processor is not None else self.tokenizer, optimizer=self.actor_optimizer, optimizer_scheduler=self.actor_optimizer_scheduler, use_distributed_optimizer=self.config.actor.megatron.use_distributed_optimizer, use_checkpoint_opt_param_scheduler=self.config.actor.optim.use_checkpoint_opt_param_scheduler, bridge=self.bridge, provider=self.provider, use_dist_checkpointing=self.config.actor.megatron.use_dist_checkpointing, peft_cls=self.peft_cls, ) self.layer_name_mapping = { "qkv_layer_name": "self_attention.linear_qkv.", "gate_proj_layer_name": "linear_fc1.", } self.weight_converter = None if not self.config.actor.megatron.use_mbridge: self.weight_converter = get_mcore_weight_converter(self.actor_model_config, self.dtype) # Free cached GPU memory so colocated vLLM processes can see it via cudaMemGetInfo aggressive_empty_cache(force_sync=True) log_gpu_memory_usage("After init_model finish", logger=logger) async def rollout_mode(self): """Context switch hybridengine to rollout mode.""" aggressive_empty_cache(force_sync=True) set_expandable_segments(False) if self._is_offload_param: load_megatron_model_to_gpu(self.actor.actor_module, load_grad=False) log_gpu_memory_usage("After load actor params during rollout_mode", logger=logger) # Build peft_config for vLLM LoRA support peft_config = None do_lora_base_sync = False if not self.peft_merge and self.peft_cls is not None: peft_config = build_peft_config_for_vllm(self.config.model.get("lora", {})) # set sleep level for LoRA adapter weights only sync # TODO: make this configurable so that users with small # main memory can trade sync time to avoid OOM self.rollout.sleep_level = 1 do_lora_base_sync = (not self.base_sync_done) or ( self.rollout.sleep_level != 1 and self.config.rollout.free_cache_engine ) if self.bridge is not None: if self.vanilla_bridge: per_tensor_param = self.bridge.export_weights(self.actor.actor_module) elif not self.peft_merge and self.peft_cls is not None: # Only export adapter weights per_tensor_param = self.bridge.export_adapter_weights(self.actor.actor_module) else: per_tensor_param = self.bridge.export_hf_weights(self.actor.actor_module) else: per_tensor_param = per_tensor_generator( self.actor.actor_module, self.actor_model_config, self.weight_converter, self.tf_config, self.layer_name_mapping, ) if self.config.rollout.free_cache_engine: await self.rollout.resume(tags=["weights"]) if do_lora_base_sync: # Base layer sync per_tensor_param_lora_base = self.bridge.export_hf_weights( self.actor.actor_module, merge_adapter_weights=False ) await self.rollout.update_weights( add_base_layer_suffix(per_tensor_param_lora_base, model_type=self.hf_config.model_type), peft_config=peft_config, base_sync_done=False, ) # Mark base sync as done after first successful sync self.base_sync_done = True await self.rollout.update_weights(per_tensor_param, peft_config=peft_config, base_sync_done=True) if self._is_offload_param: offload_megatron_model_to_cpu(self.actor.actor_module) aggressive_empty_cache(force_sync=True) if self.config.rollout.free_cache_engine: await self.rollout.resume(tags=["kv_cache"]) set_expandable_segments(True) @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @GPUMemoryLogger(role="update_actor", logger=logger) @DistProfiler.annotate(color="red", role="actor_update") def update_actor(self, data: DataProto): assert self._is_actor if self._is_offload_param: load_megatron_model_to_gpu(self.actor_module) log_gpu_memory_usage("After load actor params and grad during update_actor", logger=logger) if self._is_offload_optimizer: load_megatron_optimizer(self.actor_optimizer) log_gpu_memory_usage("After load actor optimizer during update_actor", logger=logger) micro_batch_size = self.config.actor.ppo_micro_batch_size_per_gpu data.meta_info["micro_batch_size"] = micro_batch_size dataloader = self.actor.make_minibatch_iterator(data=data) with Timer(name="update_policy", logger=None) as timer: metrics = self.actor.update_policy(dataloader=dataloader) delta_time = timer.last global_num_tokens = data.meta_info["global_token_num"] images_seqlens = data.meta_info.get("images_seqlens", None) estimated_flops, promised_flops = self.flops_counter.estimate_flops( global_num_tokens, delta_time, images_seqlens=images_seqlens ) metrics["perf/mfu/actor"] = estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size metrics["perf/max_memory_allocated_gb"] = get_torch_device().max_memory_allocated() / (1024**3) metrics["perf/max_memory_reserved_gb"] = get_torch_device().max_memory_reserved() / (1024**3) metrics["perf/cpu_memory_used_gb"] = psutil.virtual_memory().used / (1024**3) from verl.utils.megatron.optimizer import get_megatron_last_lr metrics["actor/lr"] = get_megatron_last_lr(self.actor_optimizer) self.actor_optimizer_scheduler.step(1) # TODO: here, we should return all metrics output = DataProto(meta_info={"metrics": metrics}) output = output.to("cpu") if self._is_offload_param: offload_megatron_model_to_cpu(self.actor_module) log_gpu_memory_usage("After offload actor params and grad during update_actor", logger=logger) if self._is_offload_optimizer: offload_megatron_optimizer(self.actor_optimizer) log_gpu_memory_usage("After offload actor optimizer during update_actor", logger=logger) aggressive_empty_cache(force_sync=True) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="rollout")) @GPUMemoryLogger(role="generate_sequences", logger=logger) @DistProfiler.annotate(color="red", role="rollout_generate") def generate_sequences(self, prompts: DataProto): assert self._is_rollout prompts = prompts.to(get_device_name()) meta_info = { "eos_token_id": self.generation_config.eos_token_id if self.generation_config is not None else self.tokenizer.eos_token_id, "pad_token_id": self.generation_config.pad_token_id if self.generation_config is not None else self.tokenizer.pad_token_id, } prompts.meta_info.update(meta_info) if self._is_offload_optimizer: offload_megatron_optimizer(self.actor_optimizer) timing_generate = {} if self._is_actor: # For rollout only, we do not switch context. loop = get_event_loop() loop.run_until_complete(self.rollout_mode()) log_gpu_memory_usage("After switch to rollout mode", logger=logger) with simple_timer("generate_sequences", timing_generate): output = self.rollout.generate_sequences(prompts=prompts) if self._is_actor: loop.run_until_complete(self.trainer_mode()) log_gpu_memory_usage("After switch to trainer mode", logger=logger) # We calculate the average timing across all ranks # to make sure meta_info["timing"] is the same timing_generate_topk_ratio, timing_generate_min, timing_generate_max = topk_reduce_ratio_min_max( timing_generate["generate_sequences"] ) timing_generate = reduce_timing(timing_generate) timing_generate.update( { "generation_timing/max": timing_generate_max, "generation_timing/min": timing_generate_min, "generation_timing/topk_ratio": timing_generate_topk_ratio, } ) output.meta_info["timing"] = timing_generate output = output.to("cpu") # clear kv cache aggressive_empty_cache(force_sync=True) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @GPUMemoryLogger(role="compute_ref_log_prob", logger=logger) @DistProfiler.annotate(color="olive", role="ref_compute_log_prob") def compute_ref_log_prob(self, data: DataProto): if self.peft_cls is not None: # if is lora, actor without lora applied is the ref data.meta_info["is_lora"] = True return self.compute_log_prob(data) assert self._is_ref if self._ref_is_offload_param: load_megatron_model_to_gpu(self.ref_module, load_grad=False) log_gpu_memory_usage("After load ref params and grad during compute_ref_log_prob", logger=logger) micro_batch_size = self.config.ref.log_prob_micro_batch_size_per_gpu data.meta_info["micro_batch_size"] = micro_batch_size data.meta_info["max_token_len"] = self.config.ref.log_prob_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = self.config.ref.log_prob_use_dynamic_bsz data.meta_info["temperature"] = self.config.rollout.temperature output, _, _ = self.ref_policy.compute_log_prob(data=data, calculate_entropy=False) output = DataProto.from_dict(tensors={"ref_log_prob": output}) output = output.to("cpu") if self._ref_is_offload_param: offload_megatron_model_to_cpu(self.ref_module) log_gpu_memory_usage("After offload ref params and grad during compute_ref_log_prob", logger=logger) aggressive_empty_cache(force_sync=True) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor")) @GPUMemoryLogger(role="compute_log_prob", logger=logger) @DistProfiler.annotate(color="blue", role="actor_compute_log_prob") def compute_log_prob(self, data: DataProto): assert self._is_actor if self._is_offload_param: load_megatron_model_to_gpu(self.actor_module, load_grad=False) log_gpu_memory_usage("After load actor params and grad during compute_log_prob", logger=logger) is_lora = data.meta_info.pop("is_lora", False) adapter_ctx = self.peft_cls.disable_adapter(self.actor_module) if is_lora else nullcontext() # we should always recompute old_log_probs when it is HybridEngine config_source = self.config.ref if is_lora else self.config.rollout data.meta_info["micro_batch_size"] = config_source.log_prob_micro_batch_size_per_gpu data.meta_info["max_token_len"] = config_source.log_prob_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = config_source.log_prob_use_dynamic_bsz data.meta_info["temperature"] = self.config.rollout.temperature if self.enable_routing_replay and self.config.actor.router_replay.mode == "R2": RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD) if self.enable_routing_replay and self.config.actor.router_replay.mode == "R3": RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD) with adapter_ctx: output, entropys, layers_topk_idx = self.actor.compute_log_prob(data=data, calculate_entropy=not is_lora) tensors = {"ref_log_prob": output} if is_lora else {"old_log_probs": output} if not is_lora: tensors["entropys"] = entropys output = DataProto.from_dict( tensors=tensors, meta_info={"temperature": self.config.rollout.temperature}, ) if self.config.actor.router_replay.mode == "R2": output.batch["routed_experts"] = layers_topk_idx if self.config.actor.router_replay.mode in ["R2", "R3"]: RouterReplay.clear_global_indices() RouterReplay.clear_global_router_replay_action() output = output.to("cpu") # clear kv cache if self._is_offload_param: offload_megatron_model_to_cpu(self.actor_module) log_gpu_memory_usage("After offload actor params and grad during compute_log_prob", logger=logger) aggressive_empty_cache(force_sync=True) return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, checkpoint_path, hdfs_path=None, del_local_after_load=True): # No checkpoint to load, just offload the model and optimizer to CPU if checkpoint_path is None: if self._is_offload_param: offload_megatron_model_to_cpu(self.actor_module) if self._is_offload_optimizer: offload_megatron_optimizer(self.actor_optimizer) log_gpu_memory_usage("After offload actor params and optimizer during load_checkpoint", logger=logger) return if self._is_offload_param: load_megatron_model_to_gpu(self.actor_module) self.checkpoint_mananager.load_checkpoint( local_path=checkpoint_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) if self._is_offload_param: offload_megatron_model_to_cpu(self.actor_module) if self._is_offload_optimizer: offload_megatron_optimizer(self.actor_optimizer) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_pretrained_model(self, checkpoint_path, del_local_after_load=True): pass @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, checkpoint_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): if self._is_offload_param: load_megatron_model_to_gpu(self.actor_module) if self.checkpoint_mananager.checkpoint_config.async_save and self._is_offload_optimizer: load_megatron_optimizer(self.actor_optimizer) self.checkpoint_mananager.save_checkpoint( local_path=checkpoint_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep ) torch.distributed.barrier() if self._is_offload_param: offload_megatron_model_to_cpu(self.actor_module) if self.checkpoint_mananager.checkpoint_config.async_save and self._is_offload_optimizer: offload_megatron_optimizer(self.actor_optimizer) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def async_calls_finalize_fn_exec(self, blocking=False): from megatron.core.dist_checkpointing.strategies.base import async_calls async_calls.maybe_finalize_async_calls(blocking=blocking) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def start_profile(self, **kwargs) -> None: """Start profiling for the current rank in the current training step.""" self.profiler.start(**kwargs) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def stop_profile(self) -> None: """Stop profiling for the current rank in the current training step.""" self.profiler.stop() @register(dispatch_mode=Dispatch.ONE_TO_ALL) def dump_memory_snapshot(self, tag: str = "manual", sub_dir: str = None) -> None: """Manually trigger a CUDA memory snapshot dump on all ranks.""" # Memory snapshot is now handled by the profiler system # This method is kept for backward compatibility but delegates to profiler if hasattr(self, "profiler") and hasattr(self.profiler, "_impl"): try: # Try to use the profiler's memory snapshot functionality if hasattr(self.profiler._impl, "sampler"): out_dir = OmegaConf.select(self.config, "actor.profiler.save_path") or "." self.profiler._impl.sampler.dump_memory_snapshot(out_dir=out_dir, tag=tag, sub_dir=sub_dir) except Exception as e: # Log a warning if memory snapshot fails. This might be expected if the profiler doesn't support it. logger.warning(f"Failed to dump memory snapshot: {e}") class AsyncActorRolloutRefWorker(ActorRolloutRefWorker): @register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False) async def update_weights(self, global_steps: int = None): await self.rollout_mode() return True class CriticWorker(MegatronWorker, DistProfilerExtension): def __init__(self, config: McoreCriticConfig): Worker.__init__(self) omega_profiler_config = config.get("profiler", {}) profiler_config = omega_conf_to_dataclass(omega_profiler_config, dataclass_type=ProfilerConfig) if omega_profiler_config.get("tool", None) in ["npu", "nsys", "torch", "torch_memory"]: tool_config = omega_conf_to_dataclass( omega_profiler_config.get("tool_config", {}).get(omega_profiler_config.get("tool")) ) else: tool_config = None DistProfilerExtension.__init__( self, DistProfiler(rank=self.rank, config=profiler_config, tool_config=tool_config) ) self.config: McoreCriticConfig = config # NOTE(sgm): We utilize colocate WorkerGroup by default. # As a result, Workers for different model share the same process. # Therefore, we only require one distribute initialization. # To utilize different parallel strategy in different models: # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models, # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385 if not torch.distributed.is_initialized(): set_numa_affinity() rank = int(os.environ["LOCAL_RANK"]) torch.distributed.init_process_group( backend=get_nccl_backend(), timeout=datetime.timedelta(seconds=self.config.get("nccl_timeout", 600)), init_method=os.environ.get("DIST_INIT_METHOD", None), ) get_torch_device().set_device(rank) mpu.initialize_model_parallel( tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size, pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size, virtual_pipeline_model_parallel_size=self.config.megatron.virtual_pipeline_model_parallel_size, use_sharp=False, context_parallel_size=self.config.megatron.context_parallel_size, expert_model_parallel_size=self.config.megatron.expert_model_parallel_size, expert_tensor_parallel_size=self.config.megatron.expert_tensor_parallel_size, nccl_communicator_config_path=None, ) is_collect = ( mpu.get_tensor_model_parallel_rank() == 0 and mpu.get_pipeline_model_parallel_rank() == mpu.get_pipeline_model_parallel_world_size() - 1 and mpu.get_context_parallel_rank() == 0 ) self._register_dispatch_collect_info( mesh_name="critic", dp_rank=mpu.get_data_parallel_rank(), is_collect=is_collect ) set_random_seed(seed=self.config.megatron.seed) # set FSDP offload params self._is_offload_param = self.config.megatron.param_offload self._is_offload_optimizer = self.config.megatron.optimizer_offload # normalize config self.config.ppo_mini_batch_size *= self.config.rollout_n self.config.ppo_mini_batch_size //= mpu.get_data_parallel_world_size() if self.config.get("ppo_micro_batch_size", None): self.config.ppo_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.ppo_micro_batch_size_per_gpu = self.config.ppo_micro_batch_size # TODO(sgm): support critic model offload def _build_critic_model_optimizer( self, model_path, optim_config, override_model_config, override_transformer_config, override_ddp_config ): from verl.utils.megatron.optimizer import ( get_megatron_optimizer, get_megatron_optimizer_param_scheduler, init_megatron_optim_config, ) from verl.utils.megatron_utils import McoreModuleWrapperConfig, make_megatron_module from verl.utils.model import print_model_size self._init_hf_config_and_tf_config( model_path, self.config.model.get("tokenizer_path") or model_path, self.dtype, override_model_config, override_transformer_config, self.config.model.get("trust_remote_code", False), self.config.megatron, ) wrap_config = McoreModuleWrapperConfig( is_value_model=True, # critic is value model share_embeddings_and_output_weights=False, wrap_with_ddp=True, use_distributed_optimizer=self.config.megatron.use_distributed_optimizer, ) critic_module, updated_tf_config = make_megatron_module( wrap_config=wrap_config, tf_config=self.tf_config, hf_config=self.hf_config, bridge=self.bridge, provider=self.provider, override_model_config=override_model_config, override_ddp_config=override_ddp_config, peft_cls=self.peft_cls, peft_config=self.config.model.get("lora", None), ) self.tf_config = updated_tf_config # note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp). # but here, we do not use pp (vpp) yet. For simplicity, we remove the list # critic_module = nn.ModuleList(critic_module) if self.config.load_weight: t0 = time.time() if self.config.megatron.use_dist_checkpointing: load_mcore_dist_weights( critic_module, self.config.megatron.dist_checkpointing_path, is_value_model=True, prefix=self.config.megatron.dist_checkpointing_prefix, ) else: if self.bridge is not None: local_model_path = get_hf_model_path(self.config) if self.vanilla_bridge: self.bridge.load_weights(critic_module, local_model_path) else: self.bridge.load_hf_weights( critic_module, local_model_path, allowed_mismatched_params=["output_layer.weight"] ) else: load_megatron_gptmodel_weights( self.config, self.hf_config, critic_module, params_dtype=self.dtype, is_value_model=True ) t1 = time.time() if torch.distributed.get_rank() == 0: print(f"critic load_weight time: {t1 - t0}") if self.rank == 0: print_model_size(critic_module[0]) # TODO: add more optimizer args into config optim_config_megatron = init_megatron_optim_config( optim_config, use_distributed_optimizer=wrap_config.use_distributed_optimizer, fp16=self.dtype == torch.float16, ) critic_optimizer = get_megatron_optimizer(model=critic_module, config=optim_config_megatron) critic_optimizer_scheduler = get_megatron_optimizer_param_scheduler( optimizer=critic_optimizer, config=optim_config ) get_torch_device().empty_cache() register_megatron_training_hooks(critic_module, critic_optimizer) return critic_module, critic_optimizer, critic_optimizer_scheduler, self.hf_config, optim_config @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # create critic from verl.utils.torch_dtypes import PrecisionType if self.config.model.get("external_lib", None) is not None: # This is used to import external_lib into the huggingface systems import importlib importlib.import_module(self.config.model.external_lib) override_model_config = OmegaConf.to_container(OmegaConf.create(self.config.model.get("override_config", {}))) override_transformer_config = OmegaConf.to_container( OmegaConf.create(self.config.megatron.get("override_transformer_config", {})) ) override_ddp_config = OmegaConf.to_container( OmegaConf.create(self.config.megatron.get("override_ddp_config", {})) ) self.param_dtype = PrecisionType.to_dtype(self.config.megatron.dtype) self.dtype = PrecisionType.to_dtype(self.param_dtype) ( self.critic_module, self.critic_optimizer, self.critic_optimizer_scheduler, self.critic_model_config, critic_optimizer_config, ) = self._build_critic_model_optimizer( model_path=self.config.model.path, optim_config=self.config.optim, override_model_config=override_model_config, override_transformer_config=override_transformer_config, override_ddp_config=override_ddp_config, ) if self._is_offload_param: offload_megatron_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_megatron_optimizer(self.critic_optimizer) self.critic = MegatronPPOCritic( config=self.config, model_config=self.critic_model_config, hf_config=self.hf_config, tf_config=self.tf_config, critic_module=self.critic_module, critic_optimizer=self.critic_optimizer, critic_optimizer_config=critic_optimizer_config, ) self.flops_counter = FlopsCounter(self.critic_model_config) self.checkpoint_mananager = MegatronCheckpointManager( config=self.config, checkpoint_config=self.config.checkpoint, model_config=self.critic_model_config, transformer_config=self.tf_config, role="critic", model=self.critic_module, arch=self.architectures[0], hf_config=self.hf_config, param_dtype=self.param_dtype, share_embeddings_and_output_weights=False, processing_class=self.processor if self.processor is not None else self.tokenizer, optimizer=self.critic_optimizer, optimizer_scheduler=self.critic_optimizer_scheduler, use_distributed_optimizer=self.config.megatron.use_distributed_optimizer, use_checkpoint_opt_param_scheduler=self.config.optim.use_checkpoint_opt_param_scheduler, bridge=self.bridge, provider=self.provider, use_dist_checkpointing=self.config.megatron.use_dist_checkpointing, peft_cls=self.peft_cls, ) @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="critic")) @DistProfiler.annotate(color="cyan", role="compute_values") def compute_values(self, data: DataProto): micro_batch_size = self.config.ppo_micro_batch_size_per_gpu data.meta_info["micro_batch_size"] = micro_batch_size data.meta_info["max_token_len"] = self.config.forward_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = self.config.use_dynamic_bsz data = data.to(get_device_id()) if self._is_offload_param: load_megatron_model_to_gpu(self.critic_module) values = self.critic.compute_values(data=data) output = DataProto.from_dict(tensors={"values": values}) output = output.to("cpu") if self._is_offload_param: offload_megatron_model_to_cpu(self.critic_module) return output @register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="critic")) @DistProfiler.annotate(color="pink", role="critic_update") def update_critic(self, data: DataProto): data = data.to(get_device_id()) if self._is_offload_param: load_megatron_model_to_gpu(self.critic_module) if self._is_offload_optimizer: load_megatron_optimizer(self.critic_optimizer) dataloader = self.critic.make_minibatch_iterator(data) with Timer(name="update_critic", logger=None) as timer: metrics = self.critic.update_critic(dataloader=dataloader) delta_time = timer.last global_num_tokens = data.meta_info["global_token_num"] estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time) metrics["perf/mfu/critic"] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size from verl.utils.megatron.optimizer import get_megatron_last_lr metrics["critic/lr"] = get_megatron_last_lr(self.critic_optimizer) self.critic_optimizer_scheduler.step(1) output = DataProto(batch=None, meta_info={"metrics": metrics}) if self._is_offload_param: offload_megatron_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_megatron_optimizer(self.critic_optimizer) output = output.to("cpu") return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, checkpoint_path, hdfs_path=None, del_local_after_load=True): if self._is_offload_param: load_megatron_model_to_gpu(self.critic_module) self.checkpoint_mananager.load_checkpoint( local_path=checkpoint_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) if self._is_offload_param: offload_megatron_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_megatron_optimizer(self.critic_optimizer) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, checkpoint_path, hdfs_path=None, global_steps=0, max_ckpt_to_keep=None): if self._is_offload_param: load_megatron_model_to_gpu(self.critic_module) self.checkpoint_mananager.save_checkpoint( local_path=checkpoint_path, hdfs_path=hdfs_path, global_step=global_steps, max_ckpt_to_keep=max_ckpt_to_keep ) if self._is_offload_param: offload_megatron_model_to_cpu(self.critic_module) ================================================ FILE: verl/workers/reward_manager/__init__.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .registry import get_reward_manager_cls, register # noqa: I001 from .batch import BatchRewardManager from .dapo import DAPORewardManager from .naive import NaiveRewardManager from .prime import PrimeRewardManager # Note(haibin.lin): no need to include all reward managers here in case of complicated dependencies __all__ = [ "BatchRewardManager", "DAPORewardManager", "NaiveRewardManager", "PrimeRewardManager", "register", "get_reward_manager_cls", ] # Import experimental reward managers to ensure they are registered try: from verl.experimental.reward_loop.reward_manager.limited import RateLimitedRewardManager # noqa: F401 __all__.append("RateLimitedRewardManager") except ImportError: pass # Optional dependency, may not be available ================================================ FILE: verl/workers/reward_manager/abstract.py ================================================ # Copyright 2023-2025 SGLang Team # Copyright Amazon.com, Inc. or its affiliates. # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from typing import Any, Callable import torch from verl.protocol import DataProto RawRewardFn = Callable[..., Any] class AbstractRewardManager(ABC): @abstractmethod def __init__( self, tokenizer: Any, num_examine: int, compute_score: RawRewardFn | None, reward_fn_key: str = "data_source", **kwargs: Any, ): pass @abstractmethod def __call__( self, data: DataProto, return_dict: bool = False, ) -> torch.Tensor | dict[str, Any]: pass def _extract_reward_from_rm_scores( self, data: DataProto, return_dict: bool = False ) -> torch.Tensor | dict[str, Any] | None: """ Extract reward from already-computed rm_scores if available. This has been deprecated. Args: data: DataProto object containing the batch data return_dict: Whether to return a dictionary with reward_tensor and reward_extra_info Returns: If rm_scores exists: - If return_dict=True: dict with "reward_tensor" and "reward_extra_info" - If return_dict=False: torch.Tensor of rm_scores If rm_scores doesn't exist: None """ if "rm_scores" not in data.batch.keys(): return None if return_dict: reward_extra_keys = data.meta_info.get("reward_extra_keys", []) reward_extra_info = {key: data.non_tensor_batch[key] for key in reward_extra_keys} return {"reward_tensor": data.batch["rm_scores"], "reward_extra_info": reward_extra_info} else: return data.batch["rm_scores"] ================================================ FILE: verl/workers/reward_manager/batch.py ================================================ # Copyright 2025 Individual Contributor: Mert Unsal # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from typing import Any import torch from verl import DataProto from verl.workers.reward_manager import register from verl.workers.reward_manager.abstract import AbstractRewardManager, RawRewardFn @register("batch") class BatchRewardManager(AbstractRewardManager): """ A batch reward manager that computes rewards for a batch of data. Args: tokenizer (Tokenizer): The tokenizer to use for decoding the responses. num_examine (int): The number of responses to examine. compute_score (callable): The function to compute the rewards. reward_fn_key (str): The key to use for the reward function. reward_kwargs (dict): The keyword arguments to pass to the reward function. """ def __init__( self, tokenizer, num_examine, compute_score: RawRewardFn, reward_fn_key="data_source", **reward_kwargs ): self.tokenizer = tokenizer self.num_examine = num_examine self.compute_score = compute_score self.reward_fn_key = reward_fn_key self.reward_kwargs = reward_kwargs def verify(self, data): prompt_ids = data.batch["prompts"] response_ids = data.batch["responses"] attention_mask = data.batch["attention_mask"] prompt_len = prompt_ids.shape[-1] valid_response_lengths = attention_mask[:, prompt_len:].sum(dim=-1) responses_str = [] for i in range(len(data)): valid_len = valid_response_lengths[i] valid_response_ids = response_ids[i][:valid_len] response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) responses_str.append(response_str) ground_truths = [item.non_tensor_batch["reward_model"].get("ground_truth", None) for item in data] data_sources = data.non_tensor_batch[self.reward_fn_key] rollout_reward_scores = data.non_tensor_batch.get("reward_scores", [{} for _ in range(len(data))]) extras = data.non_tensor_batch.get("extra_info", [{} for _ in range(len(data))]) for i in range(len(data)): extras[i]["rollout_reward_scores"] = rollout_reward_scores[i] scores = self.compute_score( data_sources=data_sources, solution_strs=responses_str, ground_truths=ground_truths, extra_infos=extras, **self.reward_kwargs, ) return scores def __call__(self, data: DataProto, return_dict: bool = False) -> torch.Tensor | dict[str, Any]: # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn reward_from_rm_scores = self._extract_reward_from_rm_scores(data, return_dict) if reward_from_rm_scores is not None: return reward_from_rm_scores reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) reward_extra_info = defaultdict(list) prompt_ids = data.batch["prompts"] prompt_len = prompt_ids.shape[-1] attention_mask = data.batch["attention_mask"] valid_response_lengths = attention_mask[:, prompt_len:].sum(dim=-1) data_sources = data.non_tensor_batch[self.reward_fn_key] scores = self.verify(data) rewards = [] already_printed: dict[str, Any] = {} for i in range(len(data)): length = valid_response_lengths[i].item() score = scores[i] if isinstance(score, dict): reward = score["score"] for key, value in score.items(): reward_extra_info[key].append(value) else: reward = score rewards.append(reward) reward_tensor[i, length - 1] = reward data_source = data_sources[i] if already_printed.get(data_source, 0) < self.num_examine: response_str = self.tokenizer.decode(data.batch["responses"][i][:length], skip_special_tokens=True) prompt_str = self.tokenizer.decode(data.batch["prompts"][i], skip_special_tokens=True) ground_truth = data[i].non_tensor_batch["reward_model"].get("ground_truth", None) print("[prompt]", prompt_str) print("[response]", response_str) print("[ground_truth]", ground_truth) print("[score]", scores[i]) already_printed[data_source] = already_printed.get(data_source, 0) + 1 data.batch["acc"] = torch.tensor(rewards, dtype=torch.float32, device=prompt_ids.device) if return_dict: return {"reward_tensor": reward_tensor, "reward_extra_info": reward_extra_info} else: return reward_tensor ================================================ FILE: verl/workers/reward_manager/dapo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict import torch from verl import DataProto from verl.utils.reward_score import default_compute_score from verl.workers.reward_manager import register from verl.workers.reward_manager.abstract import AbstractRewardManager @register("dapo") class DAPORewardManager(AbstractRewardManager): """The reward manager.""" def __init__( self, tokenizer, num_examine, compute_score=None, reward_fn_key="data_source", max_resp_len=None, overlong_buffer_cfg=None, ) -> None: self.tokenizer = tokenizer self.num_examine = num_examine # the number of batches of decoded responses to print to the console self.compute_score = compute_score or default_compute_score self.reward_fn_key = reward_fn_key self.overlong_buffer_cfg = overlong_buffer_cfg self.max_resp_len = max_resp_len if self.overlong_buffer_cfg is not None: assert self.max_resp_len is not None, ( f"max_resp_len must be provided if {overlong_buffer_cfg=}, but got None" ) assert self.max_resp_len >= self.overlong_buffer_cfg.len, ( "max_resp_len must be larger than overlong_buffer.len" ) assert not self.overlong_buffer_cfg.enable or self.overlong_buffer_cfg.len > 0, ( "overlong_buffer.len must be positive when overlong penalty is enabled," f"but got {self.overlong_buffer_cfg.len}." "To disable the overlong penalty, set overlong_buffer.enable = False" ) def __call__(self, data: DataProto, return_dict: bool = False): """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn reward_from_rm_scores = self._extract_reward_from_rm_scores(data, return_dict) if reward_from_rm_scores is not None: return reward_from_rm_scores reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) reward_extra_info = defaultdict(list) already_print_data_sources = {} for i in range(len(data)): data_item = data[i] # DataProtoItem prompt_ids = data_item.batch["prompts"] prompt_length = prompt_ids.shape[-1] valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum() valid_prompt_ids = prompt_ids[-valid_prompt_length:] response_ids = data_item.batch["responses"] valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode prompt_str = self.tokenizer.decode(valid_prompt_ids, skip_special_tokens=True) response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) eos_token = self.tokenizer.eos_token if response_str.endswith(eos_token): response_str = response_str[: -len(eos_token)] ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] data_source = data_item.non_tensor_batch[self.reward_fn_key] extra_info = data_item.non_tensor_batch.get("extra_info", {}) rollout_reward_scores = data_item.non_tensor_batch.get("reward_scores", {}) extra_info["rollout_reward_scores"] = rollout_reward_scores result = self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, ) score: float if isinstance(result, dict): score = result["score"] # Store the information including original reward for key, value in result.items(): reward_extra_info[key].append(value) else: score = result reward_extra_info["acc"].append(score) reward = score if self.overlong_buffer_cfg.enable: overlong_buffer_len = self.overlong_buffer_cfg.len expected_len = self.max_resp_len - overlong_buffer_len exceed_len = valid_response_length - expected_len overlong_penalty_factor = self.overlong_buffer_cfg.penalty_factor overlong_reward = min(-exceed_len / overlong_buffer_len * overlong_penalty_factor, 0) reward += overlong_reward if self.overlong_buffer_cfg.log: reward_extra_info["overlong_reward"].append(overlong_reward) reward_extra_info["overlong"].append(overlong_reward < 0) reward_tensor[i, valid_response_length - 1] = reward if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 print("[prompt]", prompt_str) print("[response]", response_str) print("[ground_truth]", ground_truth) if isinstance(result, dict): for key, value in result.items(): print(f"[{key}]", value) else: print("[score]", score) if return_dict: return { "reward_tensor": reward_tensor, "reward_extra_info": reward_extra_info, } else: return reward_tensor ================================================ FILE: verl/workers/reward_manager/naive.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from typing import Any import torch from verl import DataProto from verl.utils.reward_score import default_compute_score from verl.workers.reward_manager import register from verl.workers.reward_manager.abstract import AbstractRewardManager @register("naive") class NaiveRewardManager(AbstractRewardManager): """The reward manager.""" def __init__(self, tokenizer, num_examine, compute_score=None, reward_fn_key="data_source") -> None: """ Initialize the NaiveRewardManager instance. Args: tokenizer: The tokenizer used to decode token IDs into text. num_examine: The number of batches of decoded responses to print to the console for debugging purpose. compute_score: A function to compute the reward score. If None, `default_compute_score` will be used. reward_fn_key: The key used to access the data source in the non-tensor batch data. Defaults to "data_source". """ self.tokenizer = tokenizer # Store the tokenizer for decoding token IDs self.num_examine = num_examine # the number of batches of decoded responses to print to the console self.compute_score = compute_score or default_compute_score self.reward_fn_key = reward_fn_key # Store the key for accessing the data source def __call__(self, data: DataProto, return_dict: bool = False) -> torch.Tensor | dict[str, Any]: """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn reward_from_rm_scores = self._extract_reward_from_rm_scores(data, return_dict) if reward_from_rm_scores is not None: return reward_from_rm_scores reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) reward_extra_info = defaultdict(list) already_print_data_sources = {} for i in range(len(data)): data_item = data[i] # DataProtoItem prompt_ids = data_item.batch["prompts"] prompt_length = prompt_ids.shape[-1] valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum() valid_prompt_ids = prompt_ids[-valid_prompt_length:] response_ids = data_item.batch["responses"] valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode prompt_str = self.tokenizer.decode(valid_prompt_ids, skip_special_tokens=True) response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True) ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"] data_source = data_item.non_tensor_batch[self.reward_fn_key] extra_info = data_item.non_tensor_batch.get("extra_info", {}) num_turns = data_item.non_tensor_batch.get("__num_turns__", None) rollout_reward_scores = data_item.non_tensor_batch.get("reward_scores", {}) extra_info["num_turns"] = num_turns extra_info["rollout_reward_scores"] = rollout_reward_scores score = self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, ) if isinstance(score, dict): reward = score["score"] # Store the information including original reward for key, value in score.items(): reward_extra_info[key].append(value) else: reward = score reward_tensor[i, valid_response_length - 1] = reward if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 print("[prompt]", prompt_str) print("[response]", response_str) print("[ground_truth]", ground_truth) if isinstance(score, dict): for key, value in score.items(): print(f"[{key}]", value) else: print("[score]", score) if return_dict: return { "reward_tensor": reward_tensor, "reward_extra_info": reward_extra_info, } else: return reward_tensor ================================================ FILE: verl/workers/reward_manager/prime.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio from concurrent.futures import ProcessPoolExecutor from functools import partial from typing import Any, Callable, Optional import psutil import torch from transformers import PreTrainedTokenizer from verl import DataProto from verl.utils.ray_utils import get_event_loop from verl.utils.reward_score import default_compute_score from verl.workers.reward_manager import register from verl.workers.reward_manager.abstract import AbstractRewardManager async def single_compute_score(evaluation_func, completion, reference, task, task_extra_info, executor, timeout=300.0): loop = get_event_loop() try: # Ensure process_completion is called properly future = loop.run_in_executor(executor, partial(evaluation_func, task, completion, reference, task_extra_info)) return await asyncio.wait_for(future, timeout=timeout) except asyncio.TimeoutError: print(f"[Timeout] Task timeout: {completion}") return None # Default value for timed-out rows except Exception as e: print(f"[Error] Task failed: {e}, completion: {completion[:80]}") return None # Default value for failed rows async def parallel_compute_score_async( evaluation_func, completions, references, tasks, extra_info=None, num_processes=64 ): if extra_info is None: extra_info = [None] * len(tasks) scores = [] with ProcessPoolExecutor(max_workers=num_processes) as executor: # to prevent very occasional starvation caused by some anomalous programs ( like infinite loop ), the # exceptions in async programs will instantly halt the evaluation, and all summoned processes will be killed. try: # Create tasks for all rows tasks_async = [ single_compute_score(evaluation_func, c, r, t, ei, executor, timeout=300.0) for c, r, t, ei in zip(completions, references, tasks, extra_info, strict=True) ] results = await asyncio.gather(*tasks_async, return_exceptions=False) except Exception as e: print(f"[Exception] async gather failed: {e}") raise finally: terminated_count = 0 for pid, proc in executor._processes.items(): try: p = psutil.Process(pid) p.terminate() try: p.wait(timeout=5) except psutil.TimeoutExpired: p.kill() terminated_count += 1 except Exception: pass print(f"[Shutdown] {terminated_count} subprocess(es) terminated.") # Process results for result, completion, reference, task in zip(results, completions, references, tasks, strict=True): if isinstance(result, Exception) or result is None: # Handle failed or timed-out tasks scores.append(0.0) elif isinstance(result, int | float | bool): scores.append(float(result)) else: scores.append(float(result[0])) return scores def run_reward_scoring(evaluation_func, completions, references, tasks, extra_info=None, num_processes=64): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) try: return loop.run_until_complete( parallel_compute_score_async(evaluation_func, completions, references, tasks, extra_info, num_processes) ) finally: loop.close() @register("prime") class PrimeRewardManager(AbstractRewardManager): """ The Reward Manager used in https://github.com/PRIME-RL/PRIME """ def __init__( self, tokenizer: PreTrainedTokenizer, num_examine: int, compute_score: Optional[Callable] = None, reward_fn_key: str = "data_source", ) -> None: self.tokenizer = tokenizer self.num_examine = num_examine # the number of batches of decoded responses to print to the console self.compute_score = compute_score or default_compute_score self.reward_fn_key = reward_fn_key def verify(self, data): """ verify the batch and save as ``acc`` tensor """ # batched scoring prompt_ids = data.batch["prompts"] response_ids = data.batch["responses"] sequences_str = self.tokenizer.batch_decode(response_ids, skip_special_tokens=True) ground_truth = [data_item.non_tensor_batch["reward_model"]["ground_truth"] for data_item in data] data_sources = data.non_tensor_batch[self.reward_fn_key] extra_info = data.non_tensor_batch.get("extra_info", None) assert len(sequences_str) == len(ground_truth) == len(data_sources) try: scores = run_reward_scoring( self.compute_score, completions=sequences_str, references=ground_truth, tasks=data_sources, extra_info=extra_info, num_processes=64, ) except asyncio.TimeoutError: print("[Timeout] Global reward scoring timed out. Setting all as 0.") scores = [0.0 for _ in range(len(sequences_str))] except Exception as e: print(f"[Error] Unexpected error during scoring. Setting all as 0. {e}") scores = [0.0 for _ in range(len(sequences_str))] data.batch["acc"] = torch.tensor(scores, dtype=torch.float32, device=prompt_ids.device) return scores def __call__(self, data: DataProto, return_dict: bool = False) -> torch.Tensor | dict[str, Any]: """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn reward_from_rm_scores = self._extract_reward_from_rm_scores(data, return_dict) if reward_from_rm_scores is not None: return reward_from_rm_scores reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32) already_print_data_sources = {} # batched scoring prompt_ids = data.batch["prompts"] prompt_length = prompt_ids.shape[-1] response_ids = data.batch["responses"] valid_response_length = data.batch["attention_mask"][:, prompt_length:].sum(dim=-1) sequences_str = self.tokenizer.batch_decode(response_ids, skip_special_tokens=True) data_sources = data.non_tensor_batch["data_source"] scores = self.verify(data) for i in range(len(data)): data_source = data_sources[i] reward_tensor[i, valid_response_length[i].item() - 1] = scores[i] if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 print(sequences_str) if return_dict: return {"reward_tensor": reward_tensor} else: return reward_tensor ================================================ FILE: verl/workers/reward_manager/registry.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable from verl.workers.reward_manager.abstract import AbstractRewardManager __all__ = ["register", "get_reward_manager_cls"] REWARD_MANAGER_REGISTRY: dict[str, type[AbstractRewardManager]] = {} def register(name: str) -> Callable[[type[AbstractRewardManager]], type[AbstractRewardManager]]: """Decorator to register a reward manager class with a given name. Args: name: `(str)` The name of the reward manager. """ def decorator(cls: type[AbstractRewardManager]) -> type[AbstractRewardManager]: if name in REWARD_MANAGER_REGISTRY and REWARD_MANAGER_REGISTRY[name] != cls: raise ValueError( f"Reward manager {name} has already been registered: {REWARD_MANAGER_REGISTRY[name]} vs {cls}" ) REWARD_MANAGER_REGISTRY[name] = cls return cls return decorator def get_reward_manager_cls(name: str) -> type[AbstractRewardManager]: """Get the reward manager class with a given name. Args: name: `(str)` The name of the reward manager. Returns: `(type)`: The reward manager class. """ if name not in REWARD_MANAGER_REGISTRY: raise ValueError(f"Unknown reward manager: {name}") return REWARD_MANAGER_REGISTRY[name] ================================================ FILE: verl/workers/rollout/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BaseRollout, get_rollout_class from .hf_rollout import HFRollout from .naive import NaiveRollout from .replica import RolloutReplica __all__ = ["BaseRollout", "NaiveRollout", "HFRollout", "get_rollout_class", "RolloutReplica"] ================================================ FILE: verl/workers/rollout/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib from abc import ABC, abstractmethod from typing import Generator import torch from torch.distributed.device_mesh import DeviceMesh from verl import DataProto from verl.utils.config import omega_conf_to_dataclass from verl.workers.config import HFModelConfig, RolloutConfig __all__ = ["BaseRollout"] class BaseRollout(ABC): """Base class for rollout.""" def __init__( self, config: RolloutConfig, model_config: HFModelConfig, device_mesh: DeviceMesh, *args, **kwargs, ): self.config = omega_conf_to_dataclass(config) self.model_config: HFModelConfig = omega_conf_to_dataclass(model_config, dataclass_type=HFModelConfig) self.device_mesh = device_mesh @abstractmethod async def resume(self, tags: list[str]): """Resume rollout weights or kv cache in GPU memory. Args: tags: weights or kv_cache. """ pass @abstractmethod async def update_weights( self, weights: Generator[tuple[str, torch.Tensor], None, None], **kwargs, ): """Update the weights of the rollout model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ pass @abstractmethod async def release(self): """Release weights and kv cache in GPU memory.""" pass def generate_sequences(self, prompts: DataProto) -> DataProto: """Batch generate sequences in sync mode. Args: prompts: The input prompts. Returns: The output sequences. """ raise NotImplementedError _ROLLOUT_REGISTRY = { ("vllm", "async"): "verl.workers.rollout.vllm_rollout.ServerAdapter", ("sglang", "async"): "verl.workers.rollout.sglang_rollout.sglang_rollout.ServerAdapter", ("trtllm", "async"): "verl.workers.rollout.trtllm_rollout.trtllm_rollout.ServerAdapter", } def get_rollout_class(rollout_name: str, mode: str = "async") -> type[BaseRollout]: """Get the rollout class by name. Args: rollout_name: The name of the rollout. mode: The mode of the rollout, async: server mode. Returns: The rollout class. """ assert (rollout_name, mode) in _ROLLOUT_REGISTRY, f"Rollout {rollout_name} with mode {mode} not found" fqdn = _ROLLOUT_REGISTRY[(rollout_name, mode)] module_name, class_name = fqdn.rsplit(".", 1) rollout_module = importlib.import_module(module_name) return getattr(rollout_module, class_name) ================================================ FILE: verl/workers/rollout/hf_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Rollout with huggingface models. TODO: refactor this class. Currently, it will hang when using FSDP HybridShard. We should actually create a single GPU model. Then, get full state_dict and bind the state_dict to the single GPU model. Then, use the single GPU model to perform generation. """ import contextlib import torch import torch.distributed from tensordict import TensorDict from torch import nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from transformers import GenerationConfig from verl import DataProto from verl.utils.device import get_device_name, get_torch_device from verl.utils.torch_functional import get_response_mask from .base import BaseRollout __all__ = ["HFRollout"] class HFRollout(BaseRollout): def __init__(self, module: nn.Module, config): super().__init__() self.config = config self.module = module def generate_sequences(self, prompts: DataProto) -> DataProto: batch_size = prompts.batch.batch_size[0] num_chunks = max(batch_size // self.config.get("micro_batch_size", batch_size), 1) batch_prompts = prompts.chunk(chunks=num_chunks) output = [self._generate_minibatch(p) for p in batch_prompts] output = DataProto.concat(output) return output @torch.no_grad() def _generate_minibatch(self, prompts: DataProto) -> DataProto: # make sampling args can be overridden by inputs do_sample = prompts.meta_info.get("do_sample", self.config.do_sample) is_validate = prompts.meta_info.get("validate", False) temperature = prompts.meta_info.get("temperature", self.config.temperature) response_length = prompts.meta_info.get("response_length", self.config.response_length) top_p = prompts.meta_info.get("top_p", self.config.get("top_p", 1.0)) top_k = max(0, prompts.meta_info.get("top_k", self.config.get("top_k", 0))) # to be compatible with vllm if not do_sample: # do_sample==False -> greedy decoding kwargs = { "do_sample": False, "num_beams": 1, } elif is_validate: # do validate and do sample -> use val_kwargs kwargs = { "do_sample": True, "num_beams": 1, "top_k": max(0, self.config.val_kwargs.top_k), # to be compatible with vllm "top_p": self.config.val_kwargs.top_p, "temperature": self.config.val_kwargs.temperature, "num_return_sequences": 1, # if validate, already repeat in ray_trainer } else: # do_sample -> use rollout config kwargs = { "do_sample": True, "num_beams": 1, "top_p": top_p, "top_k": top_k, "temperature": temperature, # already repeat in ray_trainer # https://github.com/volcengine/verl/blob/2fdfbdcba6f2e076f64bc47922d8fe6cf7dc7da5/verl/trainer/ppo/ray_trainer.py#L1117 "num_return_sequences": 1, } # make config according to generate mode generation_config = GenerationConfig(**kwargs) idx = prompts.batch["input_ids"] # (bs, prompt_length) prompt_length = idx.size(1) attention_mask = prompts.batch["attention_mask"] # left-padded attention_mask position_ids = prompts.batch["position_ids"] # used to construct attention_mask eos_token_id = prompts.meta_info["eos_token_id"] pad_token_id = prompts.meta_info["pad_token_id"] self.module.eval() param_ctx = contextlib.nullcontext() if isinstance(self.module, FSDP): # recurse need to set to False according to https://github.com/pytorch/pytorch/issues/100069 param_ctx = FSDP.summon_full_params(self.module, writeback=False, recurse=False) with param_ctx, torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16): output = self.module.generate( input_ids=idx, attention_mask=attention_mask, position_ids=position_ids, do_sample=do_sample, max_new_tokens=response_length, eos_token_id=eos_token_id, pad_token_id=pad_token_id, generation_config=generation_config, output_scores=False, # this is potentially very large return_dict_in_generate=True, use_cache=True, ) # TODO: filter out the seq with no answers like ds-chat seq = output.sequences generated_batch_size = seq.size(0) # bs * num_return_sequences # huggingface generate will stop generating when all the batch reaches [EOS]. # We have to pad to response_length sequence_length = prompt_length + self.config.response_length delta_length = sequence_length - seq.shape[1] if delta_length > 0: delta_tokens = torch.ones(size=(generated_batch_size, delta_length), device=seq.device, dtype=seq.dtype) delta_tokens = pad_token_id * delta_tokens seq = torch.cat((seq, delta_tokens), dim=1) assert seq.shape[1] == sequence_length # make necessary reputations if num_return_sequences > 1 num_return_sequences = kwargs.get("num_return_sequences", 1) if num_return_sequences > 1: position_ids = position_ids.repeat_interleave(num_return_sequences, dim=0) attention_mask = attention_mask.repeat_interleave(num_return_sequences, dim=0) prompt = seq[:, :prompt_length] # (generated_batch_size, prompt_length) response = seq[:, prompt_length:] # (generated_batch_size, response_length) response_length = response.size(1) delta_position_id = torch.arange(1, response_length + 1, device=position_ids.device) delta_position_id = delta_position_id.unsqueeze(0).repeat(generated_batch_size, 1) response_position_ids = position_ids[:, -1:] + delta_position_id position_ids = torch.cat([position_ids, response_position_ids], dim=-1) response_attention_mask = get_response_mask( response_id=response, eos_token=eos_token_id, dtype=attention_mask.dtype ) attention_mask = torch.cat((attention_mask, response_attention_mask), dim=-1) batch = TensorDict( { "prompts": prompt, "responses": response, "input_ids": seq, "attention_mask": attention_mask, "position_ids": position_ids, }, batch_size=generated_batch_size, ) # empty cache before compute old_log_prob get_torch_device().empty_cache() self.module.train() return DataProto(batch=batch) ================================================ FILE: verl/workers/rollout/naive/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .naive_rollout import NaiveRollout __all__ = ["NaiveRollout"] ================================================ FILE: verl/workers/rollout/naive/naive_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ In single GPU rollout, the sequences are generated directly by sampling from the model. The output will contain 1. output_ids 2. attention_masks (left padding) 3. eos_masks 4. log_probs """ import torch import torch.nn.functional as F from tensordict import TensorDict from torch import nn from verl import DataProto from verl.utils.torch_functional import logprobs_from_logits from ..base import BaseRollout __all__ = ["NaiveRollout"] class NaiveRollout(BaseRollout): def __init__(self, module: nn.Module, config): """A naive rollout. It requires the module to be compatible with huggingface APIs. That is: The module should define __call__ to receive input_ids, attention_mask and position_ids. It outputs a structure that contains logits field. Args: module: module here follows huggingface APIs config: DictConfig """ super().__init__() self.config = config self.module = module @torch.no_grad() def generate_sequences(self, prompts: DataProto) -> DataProto: """Generate sequences""" idx = prompts.batch["input_ids"] # (bs, prompt_length) attention_mask = prompts.batch["attention_mask"] # left-padded attention_mask position_ids = prompts.batch["position_ids"] # used to construct attention_mask eos_token_id = prompts.meta_info["eos_token_id"] batch_size = idx.size(0) prompt_length = idx.size(1) self.module.eval() prev_attention_mask = torch.ones(size=(batch_size, 1), dtype=attention_mask.dtype, device=attention_mask.device) logits_lst = [] for _ in range(self.config.response_length): # if the sequence context is growing too long we must crop it at block_size # idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] idx_cond = idx # forward the model to get the logits for the index in the sequence # we use huggingface APIs here output = self.module(input_ids=idx_cond, attention_mask=attention_mask, position_ids=position_ids) logits = output.logits # pluck the logits at the final step and scale by desired temperature logits = logits[:, -1, :] / self.config.temperature # (bs, vocab_size) # optionally crop the logits to only the top k options if self.config.top_k is not None: v, _ = torch.topk(logits, min(self.config.top_k, logits.size(-1))) logits[logits < v[:, [-1]]] = -float("Inf") # apply softmax to convert logits to (normalized) probabilities probs = F.softmax(logits, dim=-1) # sample from the distribution if self.config.do_sample: idx_next = torch.multinomial(probs, num_samples=1) else: idx_next = torch.argmax(probs, dim=-1, keepdim=True) attention_mask = torch.cat((attention_mask, prev_attention_mask), dim=-1) for token_id in eos_token_id: prev_attention_mask = torch.logical_and(idx_next != token_id, prev_attention_mask.bool()) prev_attention_mask.to(attention_mask.dtype) position_ids = torch.cat((position_ids, position_ids[:, -1:] + 1), dim=-1) # append sampled index to the running sequence and continue idx = torch.cat((idx, idx_next), dim=1) logits_lst.append(logits) logits = torch.stack(logits_lst, dim=1) # (bs, response_length, vocab_size) prompts = idx[:, :prompt_length] # (bs, prompt_length) response = idx[:, prompt_length:] # (bs, response_length) log_probs = logprobs_from_logits(logits=logits, labels=response) batch = TensorDict( { "input_ids": prompts, "responses": response, "sequences": idx, "old_log_probs": log_probs, "attention_mask": attention_mask, "position_ids": position_ids, }, batch_size=batch_size, ) self.module.train() return DataProto(batch=batch) ================================================ FILE: verl/workers/rollout/replica.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os from abc import ABC, abstractmethod from enum import Enum from typing import Any, Callable, Optional import ray from omegaconf import DictConfig from pydantic import BaseModel from ray.actor import ActorHandle from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup, ResourcePoolManager from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import is_torch_npu_available from verl.workers.config import HFModelConfig, RolloutConfig logger = logging.getLogger(__file__) # Max number of concurrent calls to the methods of Rollout, # excluding calls to generate method. CONTROL_METHOD_CONCURRENCY = 16 class TokenOutput(BaseModel): token_ids: list[int] """response token ids""" log_probs: Optional[list[float]] = None """logprobs of response token ids""" routed_experts: Optional[Any] = None """routed experts of response token ids""" stop_reason: Optional[str] = None """stop reason: 'completed', 'aborted', or None for unknown""" num_preempted: Optional[int] = None """number of preempted times for metric calculation""" extra_fields: dict[str, Any] = {} """Extra fields for dynamic addition.""" class RolloutMode(Enum): # Rollout engine and training engine(fsdp/megatron) fused in same process # Rollout and trainer share GPUs, switch context with weight synchronization. # Usage scenarios: on-policy training. HYBRID = "hybrid" # Rollout engine colocated with hybrid engine in same ray placement group but in separate process. # Rollout and hybrid processes share GPUs, switch context without weight synchronization. # Usage scenarios: GRM (LLM as a judge). COLOCATED = "colocated" # Standalone rollout server with separate GPU resource, disaggregated architecture. # Usage scenarios: off-policy training. STANDALONE = "standalone" class RolloutReplica(ABC): """Rollout replica is an individual server instance, which may be deployed on single or multiple nodes. It is equivalent to launch server in each node with command line: SGLang: ``` python -m sglang.launch_server --node-rank 0 --nnode 2 ... python -m sglang.launch_server --node-rank 1 --nnode 2 ... ``` vLLM: ``` vllm serve --data-parallel-size 16 --data-parallel-size-local 8 --data-parallel-start-rank 0 ... vllm serve --data-parallel-size 16 --data-parallel-size-local 8 --data-parallel-start-rank 8 ... ``` Args: replica_rank: int, rank of this rollout replica. config: RolloutConfig, full config. model_config: DictConfig, model config. gpus_per_node: int, number of gpus per node. """ def __init__( self, replica_rank: int, config: RolloutConfig, model_config: DictConfig, gpus_per_node: int = 8, is_reward_model: bool = False, ) -> None: self.replica_rank = replica_rank self.config: RolloutConfig = omega_conf_to_dataclass(config) self.model_config: HFModelConfig = model_config self.world_size = ( self.config.tensor_model_parallel_size * self.config.data_parallel_size * self.config.pipeline_model_parallel_size ) self.gpus_per_node = gpus_per_node self.gpus_per_replica_node = min(gpus_per_node, self.world_size) assert self.world_size % self.gpus_per_replica_node == 0, ( f"world_size {self.world_size} must be divisible by gpus_per_node {self.gpus_per_replica_node}" ) self.nnodes = self.world_size // self.gpus_per_replica_node self.is_reward_model = is_reward_model self.rollout_mode: RolloutMode = None self.workers: list[ActorHandle] = [] self.resource_pool: RayResourcePool = None self.bundle_indices: list[int] = [] self.servers: list[ActorHandle] = [] self._server_address: str = None self._server_handle: ActorHandle = None async def init_hybrid(self, worker_group: RayWorkerGroup): """Init hybrid rollout server, rollout engine and training engine(fsdp/megatron) fused in same process. Args: worker_group: RayWorkerGroup, fused workers where training engine(fsdp/megatron) have been initialized. """ self.rollout_mode = RolloutMode.HYBRID self.workers = worker_group.workers[ self.world_size * self.replica_rank : self.world_size * (self.replica_rank + 1) ] await self.launch_servers() async def init_hybrid_colocated(self, worker_group: RayWorkerGroup, resource_pool: RayResourcePool): """Init hybrid rollout server, rollout engine and training engine(fsdp/megatron) fused in same process. Args: worker_group: RayWorkerGroup, fused workers where training engine(fsdp/megatron) have been initialized. resource_pool: RayResourcePool, ray placement group where hybrid engine processes have been launched. bundle_indices: list[int], bundle indices for this rollout replica. """ self.rollout_mode = RolloutMode.HYBRID self.workers = worker_group.workers[ self.world_size * self.replica_rank : self.world_size * (self.replica_rank + 1) ] self.resource_pool = resource_pool self.bundle_indices = [self.replica_rank * self.world_size + idx for idx in range(self.world_size)] await self.launch_servers() # TODO(sgm): this should be the default solution, but need to make the RolloutMode more clear. async def init_colocated(self, resource_pool: RayResourcePool): """Init colocated rollout server, rollout engine and hybrid engine colocated in same ray placement group but in separate processes. Args: resource_pool: RayResourcePool, ray placement group where hybrid engine processes have been launched. """ self.rollout_mode = RolloutMode.COLOCATED self.resource_pool = resource_pool use_gpu = self.rollout_worker_use_gpu() worker_group = RayWorkerGroup( resource_pool=self.resource_pool, ray_cls_with_init=self.get_ray_class_with_init_args(), bin_pack=False, name_prefix=f"rollout_colocate_{self.replica_rank}" if not self.is_reward_model else f"rollout_reward_colocate_{self.replica_rank}", use_gpu=use_gpu, device_name="cuda" if not is_torch_npu_available(check_device=False) else "npu", ) self.workers = worker_group.workers await self.launch_servers() async def init_standalone(self): """Init standalone rollout server, create new resource pool for this rollout.""" # create resource pool for this rollout self.rollout_mode = RolloutMode.STANDALONE resource_pool_name = ( f"rollout_pool_{self.replica_rank}" if not self.is_reward_model else f"rollout_pool_reward_{self.replica_rank}" ) resource_pool_spec = { resource_pool_name: [self.gpus_per_replica_node] * self.nnodes, } resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=None) resource_pool_manager.create_resource_pool() self.resource_pool = resource_pool_manager.resource_pool_dict[resource_pool_name] # create worker group for this rollout use_gpu = self.rollout_worker_use_gpu() worker_group = RayWorkerGroup( resource_pool=self.resource_pool, ray_cls_with_init=self.get_ray_class_with_init_args(), bin_pack=False, name_prefix=f"rollout_standalone_{self.replica_rank}" if not self.is_reward_model else f"rollout_reward_standalone_{self.replica_rank}", use_gpu=use_gpu, device_name="cuda" if not is_torch_npu_available(check_device=False) else "npu", ) self.workers = worker_group.workers await self.launch_servers() def get_ray_class_with_init_args(self) -> RayClassWithInitArgs: """Get rollout worker actor class for colocated and standalone mode.""" from verl.checkpoint_engine.base import CheckpointEngineWorker rollout_worker_actor_cls = ray.remote(CheckpointEngineWorker) return RayClassWithInitArgs( cls=rollout_worker_actor_cls, rollout_config=self.config, model_config=self.model_config, replica_rank=self.replica_rank, ) @abstractmethod async def launch_servers(self): """Launch http server in each node.""" raise NotImplementedError @property def server_address(self) -> str: """Get rollout server address for OpenAI chat completion.""" return self._server_address @property def server_handle(self) -> ActorHandle: """Get rollout server handle for Token-in-token-out generation.""" return self._server_handle @property def max_concurrency(self) -> int: # 1000 is Ray's default max_concurrency for async execution. # Add some margin to account for control method call. return max(1000, self.config.max_num_seqs + CONTROL_METHOD_CONCURRENCY) def rollout_worker_use_gpu(self) -> bool: return True async def wake_up(self): """Wake up each rollout server.""" await asyncio.gather(*[server.wake_up.remote() for server in self.servers]) async def sleep(self): """Sleep each rollout server.""" await asyncio.gather(*[server.sleep.remote() for server in self.servers]) async def abort_all_requests(self): """Partial rollout: abort and save all unfinished requests in each rollout server.""" await asyncio.gather(*[server.abort_all_requests.remote() for server in self.servers]) async def resume_generation(self): """Resume generation on all servers after abort_all_requests.""" await asyncio.gather(*[server.resume_generation.remote() for server in self.servers]) async def clear_kv_cache(self): """reset kv cache in each rollout server.""" await asyncio.gather(*[server.clear_kv_cache.remote() for server in self.servers]) async def start_profile(self, **kwargs): """Start profiling on the replica.""" await asyncio.gather(*[server.start_profile.remote(**kwargs) for server in self.servers]) async def stop_profile(self): """Stop profiling on the replica.""" await asyncio.gather(*[server.stop_profile.remote() for server in self.servers]) class RolloutReplicaRegistry: """Factory for managing rollout replica implementations.""" _registry: dict[str, Callable[[], type[RolloutReplica]]] = {} @classmethod def register(cls, name: str, loader: Callable[[], type[RolloutReplica]]) -> None: """Register a new rollout replica type.""" cls._registry[name] = loader @classmethod def get(cls, name: str) -> type[RolloutReplica]: """Get a rollout replica class by name.""" if name not in cls._registry: raise ValueError(f"Unknown rollout mode: {name}. Available: {list(cls._registry.keys())}") return cls._registry[name]() # Loader functions for built-in types def _load_vllm(): from verl.workers.rollout.vllm_rollout.vllm_async_server import vLLMReplica return vLLMReplica def _load_sglang(): os.environ["SGLANG_USE_CPU_ENGINE"] = "1" try: import vllm # noqa: F401 except ImportError: import sys import types from unittest.mock import Mock mock_vllm = types.ModuleType("vllm") mock_custom_ops = types.ModuleType("vllm._custom_ops") mock_custom_ops.scaled_fp8_quant = Mock() mock_vllm._custom_ops = mock_custom_ops mock_model_executor = types.ModuleType("vllm.model_executor") mock_layers = types.ModuleType("vllm.model_executor.layers") mock_activation = types.ModuleType("vllm.model_executor.layers.activation") class GeluAndMul: # noqa: N801 pass class SiluAndMul: # noqa: N801 pass mock_activation.GeluAndMul = GeluAndMul mock_activation.SiluAndMul = SiluAndMul mock_layers.activation = mock_activation mock_model_executor.layers = mock_layers mock_vllm.model_executor = mock_model_executor sys.modules["vllm"] = mock_vllm sys.modules["vllm._custom_ops"] = mock_custom_ops sys.modules["vllm.model_executor"] = mock_model_executor sys.modules["vllm.model_executor.layers"] = mock_layers sys.modules["vllm.model_executor.layers.activation"] = mock_activation from verl.workers.rollout.sglang_rollout.async_sglang_server import SGLangReplica del os.environ["SGLANG_USE_CPU_ENGINE"] return SGLangReplica def _load_trtllm(): from verl.workers.rollout.trtllm_rollout.trtllm_async_server import TRTLLMReplica return TRTLLMReplica # Register built-in types RolloutReplicaRegistry.register("vllm", _load_vllm) RolloutReplicaRegistry.register("sglang", _load_sglang) RolloutReplicaRegistry.register("trtllm", _load_trtllm) # Original function for backward compatibility def get_rollout_replica_class(rollout: str) -> type[RolloutReplica]: return RolloutReplicaRegistry.get(rollout) ================================================ FILE: verl/workers/rollout/schemas.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import difflib import logging import os from enum import Enum from typing import Any, Optional import torch from pydantic import BaseModel, ConfigDict, model_validator from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast, ProcessorMixin from verl.tools.schemas import OpenAIFunctionToolCall, OpenAIFunctionToolSchema, ToolResponse from verl.utils.model import compute_position_id_with_mask logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) BASE_CHAT_HISTORY = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "I am a user."}, ] class FinishReasonTypeEnum(str, Enum): """The enum for finish reason type.""" LENGTH = "length" STOP = "stop" TOOL_CALL = "tool_calls" @classmethod def from_str(cls, value: str) -> "FinishReasonTypeEnum": if value == "stop": return cls.STOP elif value == "length": return cls.LENGTH elif value == "tool_calls": return cls.TOOL_CALL else: raise ValueError(f"Unsupported finish reason type: {value}") class Message(BaseModel): role: str content: str | dict[str, Any] | list[dict[str, Any]] | ToolResponse tool_calls: Optional[list[OpenAIFunctionToolCall]] = None class AsyncRolloutRequestStateEnum(str, Enum): """The enum for async rollout request state.""" PENDING = "pending" RUNNING = "running" COMPLETED = "completed" FAILED = "failed" TOOL_CALLING = "tool_calling" INTERACTING = "interacting" class TokenizationSanityCheckModeEnum(str, Enum): """The enum for tokenization sanity check mode.""" DISABLE = "disable" STRICT = "strict" IGNORE_STRIPPABLE = "ignore_strippable" class AsyncRolloutRequest(BaseModel): """The data model for async rollout.""" model_config = ConfigDict(arbitrary_types_allowed=True) batch_data_id: int = 0 rollout_offset: int = 0 request_id: str state: AsyncRolloutRequestStateEnum messages: list[Message] multi_modal_keys: Optional[list[str]] = None multi_modal_data: Optional[dict[str, Any]] = None multi_modal_inputs: Optional[dict[str, torch.Tensor]] = None tool_schemas: Optional[list[OpenAIFunctionToolSchema]] = None tools_kwargs: dict[str, Any] = {} interaction_kwargs: dict[str, Any] = {} input_ids: Optional[torch.Tensor] = None prompt_ids: Optional[torch.Tensor] = None response_ids: Optional[torch.Tensor] = None attention_mask: Optional[torch.Tensor] = None prompt_attention_mask: Optional[torch.Tensor] = None response_attention_mask: Optional[torch.Tensor] = None position_ids: Optional[torch.Tensor] = None prompt_position_ids: Optional[torch.Tensor] = None response_position_ids: Optional[torch.Tensor] = None loss_mask: Optional[torch.Tensor] = None prompt_loss_mask: Optional[torch.Tensor] = None response_loss_mask: Optional[torch.Tensor] = None reward_scores: dict[str, float] max_prompt_len: int max_response_len: int = 8192 max_model_len: int = 32768 metrics: dict[str, list[Any]] = {} output_token_ids: torch.Tensor | None = None rollout_log_probs: torch.Tensor | None = None use_inference_chat_template: bool tokenization_sanity_check_mode: TokenizationSanityCheckModeEnum generation_prompt_ids: Optional[torch.Tensor] = None base_conv_wo_gen_prompt_end_pos: int base_conv_with_gen_prompt_end_pos: int @model_validator(mode="before") @classmethod def initialize_request(cls, values): if not (messages := values.get("messages")): raise ValueError("messages is required for AsyncRolloutRequest initialization") if not (max_prompt_len := values.get("max_prompt_len")): raise ValueError("max_prompt_len is required for AsyncRolloutRequest initialization") if not (processing_class := values.pop("processing_class", None)): raise ValueError("processing_class is required for AsyncRolloutRequest initialization") values["messages"] = [Message.model_validate(msg) for msg in messages] # If there is no multi_modal_keys, we assume the multi-modal data is image and video. if not values.get("multi_modal_keys"): values["multi_modal_keys"] = ["image", "video"] if not values.get("multi_modal_data"): values["multi_modal_data"] = {key: [] for key in values["multi_modal_keys"]} else: # check if all multi_modal_keys are in multi_modal_data for key in values["multi_modal_keys"]: if key not in values["multi_modal_data"]: values["multi_modal_data"][key] = [] if not values.get("multi_modal_inputs"): values["multi_modal_inputs"] = {} tools = ( [tool.model_dump() for tool in tool_schemas] if (tool_schemas := values.get("tool_schemas", [])) else None ) multi_modal_data = values["multi_modal_data"] tokens_without_prompt = cls._handle_apply_chat_template( processing_class, messages, multi_modal_data=multi_modal_data, tools=tools, add_generation_prompt=False, tokenize=True, ) if ( values.get("input_ids") is None or values.get("attention_mask") is None or values.get("position_ids") is None ): tokenization_dict_with_prompt = cls._handle_apply_chat_template( processing_class, messages, multi_modal_data=multi_modal_data, tools=tools, add_generation_prompt=True, tokenize=True, return_dict=True, ) values["input_ids"], values["attention_mask"] = ( tokenization_dict_with_prompt["input_ids"], tokenization_dict_with_prompt["attention_mask"], ) if values["input_ids"].shape[-1] > max_prompt_len: # Only log the warning to avoid truncating in the middle of generation prompt. Consider raising an # error for this case in the future. # Ensure batch_data_id exists with default value if not provided if "batch_data_id" not in values: values["batch_data_id"] = cls.model_fields["batch_data_id"].default logger.warning( f"Prompt {values['batch_data_id']} has length {values['input_ids'].shape[-1]} " f"which is greater than max_prompt_len {max_prompt_len} after applied chat template with tools." ) # Process multi_modal_inputs multi_modal_inputs = tokenization_dict_with_prompt.copy() multi_modal_inputs.pop("input_ids", None) multi_modal_inputs.pop("attention_mask", None) values["multi_modal_inputs"] = multi_modal_inputs values["position_ids"] = values["prompt_position_ids"] = cls._get_position_ids( processing_class, values["input_ids"], values["attention_mask"], multi_modal_inputs ) values["prompt_ids"], values["prompt_attention_mask"] = values["input_ids"], values["attention_mask"] values["loss_mask"] = values["prompt_loss_mask"] = torch.zeros_like(values["input_ids"], dtype=torch.bool) values["generation_prompt_ids"] = values["input_ids"][..., tokens_without_prompt.shape[-1] :] values["base_conv_wo_gen_prompt_end_pos"] = cls._handle_apply_chat_template( processing_class, BASE_CHAT_HISTORY, multi_modal_data=multi_modal_data, tools=tools, add_generation_prompt=False, tokenize=True, ).shape[-1] values["base_conv_with_gen_prompt_end_pos"] = cls._handle_apply_chat_template( processing_class, BASE_CHAT_HISTORY, multi_modal_data=multi_modal_data, tools=tools, add_generation_prompt=True, tokenize=True, ).shape[-1] return values @staticmethod def _handle_apply_chat_template( processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, messages: list[Message], multi_modal_data: dict[str, Any], tools: Optional[list[OpenAIFunctionToolSchema]] = None, add_generation_prompt: bool = False, tokenize: bool = False, return_dict: bool = False, ): raw_prompt = processing_class.apply_chat_template( messages, tools=tools, add_generation_prompt=add_generation_prompt, tokenize=False ) if not tokenize: return raw_prompt if isinstance(processing_class, PreTrainedTokenizer) or isinstance(processing_class, PreTrainedTokenizerFast): if any(len(values) > 0 for values in multi_modal_data.values()): logger.warning( "There is multi_modal_data but you are not using a processor. Multi-modal data will be ignored." ) model_inputs = processing_class(text=[raw_prompt], return_tensors="pt") elif isinstance(processing_class, ProcessorMixin): # When we update multi_model_keys, we also need to update this logic images = images if len(images := multi_modal_data.get("image", [])) > 0 else None videos = videos if len(videos := multi_modal_data.get("video", [])) > 0 else None model_inputs = processing_class(text=[raw_prompt], images=images, videos=videos, return_tensors="pt") else: raise ValueError(f"Unsupported processing class type: {type(processing_class)}") model_inputs = dict(model_inputs) if return_dict: return model_inputs else: return model_inputs["input_ids"] @staticmethod def _get_position_ids( processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, input_ids: torch.Tensor, attention_mask: torch.Tensor, multi_modal_inputs: Optional[dict[str, torch.Tensor]] = None, ) -> torch.Tensor: # special case for qwen2vl is_qwen2vl = ( hasattr(processing_class, "image_processor") and "Qwen2VLImageProcessor" in processing_class.image_processor.__class__.__name__ ) if is_qwen2vl: from verl.models.transformers.qwen2_vl import get_rope_index image_grid_thw = video_grid_thw = second_per_grid_ts = None if multi_modal_inputs: image_grid_thw = multi_modal_inputs.get("image_grid_thw") video_grid_thw = multi_modal_inputs.get("video_grid_thw") second_per_grid_ts = multi_modal_inputs.get("second_per_grid_ts") assert input_ids.dim() == 2 and input_ids.shape[0] == 1, ( f"input_ids should be 2D with batch size 1, but got shape {input_ids.shape}" ) assert attention_mask.dim() == 2 and attention_mask.shape[0] == 1, ( f"attention_mask should be 2D with batch size 1, but got shape {attention_mask.shape}" ) new_position_ids = get_rope_index( processing_class, input_ids=input_ids.squeeze(0), image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, second_per_grid_ts=second_per_grid_ts, attention_mask=attention_mask.squeeze(0), ) return new_position_ids # (3, seq_len) else: return compute_position_id_with_mask(attention_mask) # (1, seq_len) def _update_input_ids( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, new_input_ids: torch.Tensor, attention_mask: bool, loss_mask: bool, new_multi_modal_inputs: Optional[dict[str, torch.Tensor]] = None, ) -> None: """ Update the input_ids, attention_mask, position_ids, and loss_mask of the request in additive manner. """ self.input_ids = torch.cat([self.input_ids, new_input_ids], dim=-1) attention_mask = torch.ones_like(new_input_ids) * int(attention_mask) self.attention_mask = torch.cat([self.attention_mask, attention_mask], dim=-1) loss_mask = torch.ones_like(new_input_ids) * int(loss_mask) self.loss_mask = torch.cat([self.loss_mask, loss_mask], dim=-1) if new_multi_modal_inputs: self._update_multi_modal_inputs(new_multi_modal_inputs) new_position_ids = self._get_position_ids( processing_class, new_input_ids, attention_mask, new_multi_modal_inputs ) last_pos = self.position_ids[..., -1:] new_position_ids = new_position_ids + (last_pos + 1) self.position_ids = torch.cat([self.position_ids, new_position_ids], dim=-1) assert ( self.input_ids.shape[-1] == self.attention_mask.shape[-1] == self.position_ids.shape[-1] == self.loss_mask.shape[-1] ), f"""Request {self.request_id} has different length of {self.input_ids.shape[-1]=}, {self.attention_mask.shape[-1]=}, {self.position_ids.shape[-1]=}, {self.loss_mask.shape[-1]=}""" def _update_multi_modal_inputs(self, new_multi_modal_inputs: dict[str, torch.Tensor]) -> None: """ Update the multi_modal_inputs of the request in additive manner. """ for key in new_multi_modal_inputs: input_tensor = new_multi_modal_inputs[key] self.multi_modal_inputs[key] = ( torch.cat([self.multi_modal_inputs[key], input_tensor], dim=0) if key in self.multi_modal_inputs else input_tensor ) def get_generation_prompt_ids( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin ) -> list[int]: """ Get the generation prompt ids for rollout engine. Because rollout engine(SGLang) requires the ids to be a list, we need to convert the tensor to a list. """ generation_prompt_ids = ( None if self.input_ids[..., -self.generation_prompt_ids.shape[-1] :].eq(self.generation_prompt_ids).all() else self.generation_prompt_ids ) if generation_prompt_ids is not None: self._update_input_ids(processing_class, generation_prompt_ids, attention_mask=True, loss_mask=False) if self.use_inference_chat_template: messages = [msg.model_dump() for msg in self.messages] tools = [tool.model_dump() for tool in self.tool_schemas] if self.tool_schemas else None generation_prompt_ids = self._handle_apply_chat_template( processing_class, messages, multi_modal_data=self.multi_modal_data, tools=tools, add_generation_prompt=True, tokenize=True, ) return generation_prompt_ids.squeeze(0).tolist() else: return self.input_ids.squeeze(0).tolist() def add_user_message( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, content: str, ) -> None: self.messages.append(Message(role="user", content=content)) messages = [*BASE_CHAT_HISTORY, self.messages[-1]] tools = [tool.model_dump() for tool in self.tool_schemas] if self.tool_schemas else None # We don't need to pass multi_modal_data here because we don't have any multi-modal data from Engine # Inference, it is pure text. content_ids = self._handle_apply_chat_template( processing_class, messages, multi_modal_data={}, tools=tools, add_generation_prompt=False, tokenize=True )[..., self.base_conv_wo_gen_prompt_end_pos :] self._update_input_ids(processing_class, content_ids, attention_mask=True, loss_mask=False) def add_assistant_message( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, content: str, content_ids: Optional[torch.Tensor] = None, tool_calls: Optional[list[OpenAIFunctionToolCall]] = None, ) -> None: self.messages.append(Message(role="assistant", content=content, tool_calls=tool_calls)) if content_ids is None: messages = [*BASE_CHAT_HISTORY, self.messages[-1]] tools = [tool.model_dump() for tool in self.tool_schemas] if self.tool_schemas else None # We don't need to pass multi_modal_data here because we don't have any multi-modal data from Engine # Inference, it is pure text. content_ids = self._handle_apply_chat_template( processing_class, messages, multi_modal_data={}, tools=tools, add_generation_prompt=False, tokenize=True )[..., self.base_conv_with_gen_prompt_end_pos :] self._update_input_ids(processing_class, content_ids, attention_mask=True, loss_mask=True) def add_tool_response_messages( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, contents: list[ToolResponse], ) -> None: if not contents or all(content.is_empty() for content in contents): return # We also handle the case when tool returns image # We require the processing of the image and video to be done at tool.execute() level delta_multi_modal_data = {key: [] for key in self.multi_modal_keys} for content in contents: if content.is_text_only(): self.messages.append(Message(role="tool", content=content.text)) else: content_list = [] # When we update multi_model_keys, we also need to update this logic if content.image: content_list.extend([{"type": "image"} for _ in content.image]) delta_multi_modal_data["image"].extend(content.image) if content.video: content_list.extend([{"type": "video"} for _ in content.video]) delta_multi_modal_data["video"].extend(content.video) if content.text: content_list.append({"type": "text", "text": content.text}) self.messages.append(Message(role="tool", content=content_list)) messages = [*BASE_CHAT_HISTORY, *self.messages[-len(contents) :]] tools = [tool.model_dump() for tool in self.tool_schemas] if self.tool_schemas else None for key in self.multi_modal_keys: if len(delta_multi_modal_data[key]) > 0: self.multi_modal_data[key].extend(delta_multi_modal_data[key]) # We just passed the new multi-modal data to the chat template to update the input_ids. content_info = self._handle_apply_chat_template( processing_class, messages, multi_modal_data=delta_multi_modal_data, tools=tools, add_generation_prompt=False, tokenize=True, return_dict=True, ) content_ids = content_info["input_ids"][..., self.base_conv_wo_gen_prompt_end_pos :] # process multi_modal_inputs multi_modal_inputs = content_info.copy() multi_modal_inputs.pop("input_ids", None) multi_modal_inputs.pop("attention_mask", None) # chat templates include generation prompt tokens (e.g., "assistant\n") # So when tool response is added, we need to explicitly remove these tokens. self._remove_generation_prompt_ids_if_present() self._update_input_ids( processing_class, content_ids, attention_mask=True, loss_mask=False, new_multi_modal_inputs=multi_modal_inputs, ) def update_metrics(self, metrics: Any, tool_id: str) -> None: """ metrics: should be a dict of tools_name -> Any """ if self.metrics.get(tool_id) is None: self.metrics[tool_id] = [] self.metrics[tool_id].append(metrics) def _get_prompt_diffs( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, full_prompt_ids: torch.Tensor, current_prompt_ids: torch.Tensor, diff_surrounding_chars: int = 10, ) -> list[dict[str, Any]]: """Get differences between full prompt and current prompt with surrounding context. This function helps debug tokenization mismatches by showing the differences between full prompt and current prompt with surrounding context. Instead of just showing the exact diff, it includes additional tokens before and after to help locate the issue in the chat template. For example, if the actual diff is a newline change from "\n\n" to "\n", with diff_surrounding_chars the output might look like: full_prompt_chunk: "<|im_start|>assistant\n\nI think..." current_prompt_chunk: "<|im_start|>assistant\nI think..." This context makes it much easier to identify where in the chat template the mismatch occurs. Args: processing_class: The processing class to use for decoding the token IDs full_prompt_ids: Token IDs from applying chat template to all messages at once current_prompt_ids: Token IDs from incremental chat template application diff_surrounding_chars: Number of surrounding characters to include for context (default: 10) Returns: List of dicts containing the differing chunks with context and their indices """ full_prompt_ids = full_prompt_ids.squeeze(0) current_prompt_ids = current_prompt_ids.squeeze(0) full_prompt = processing_class.decode(full_prompt_ids, skip_special_tokens=False) current_prompt = processing_class.decode(current_prompt_ids, skip_special_tokens=False) s = difflib.SequenceMatcher(None, full_prompt, current_prompt, autojunk=False) diffs = [] for tag, i1, i2, j1, j2 in s.get_opcodes(): if tag == "equal": continue # Get the surrounding context for better readability start_i = max(0, i1 - diff_surrounding_chars) end_i = min(len(full_prompt), i2 + diff_surrounding_chars) start_j = max(0, j1 - diff_surrounding_chars) end_j = min(len(current_prompt), j2 + diff_surrounding_chars) diffs.append( { "full_prompt_chunk": full_prompt[start_i:end_i], "current_prompt_chunk": current_prompt[start_j:end_j], "indices": (start_i, end_i, start_j, end_j), } ) return diffs def _remove_generation_prompt_ids_if_present(self) -> None: """ Remove generation prompt IDs from input tensors if they are present at the end. """ if self.input_ids[..., -self.generation_prompt_ids.shape[-1] :].eq(self.generation_prompt_ids).all(): self.input_ids = self.input_ids[..., : -self.generation_prompt_ids.shape[-1]] self.attention_mask = self.attention_mask[..., : -self.generation_prompt_ids.shape[-1]] self.position_ids = self.position_ids[..., : -self.generation_prompt_ids.shape[-1]] self.loss_mask = self.loss_mask[..., : -self.generation_prompt_ids.shape[-1]] def finalize( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin, reward_scores: dict[str, list[float]], finish_reason_type: FinishReasonTypeEnum = FinishReasonTypeEnum.STOP, ) -> None: self.state = AsyncRolloutRequestStateEnum.COMPLETED self.reward_scores = reward_scores # In case we failed to generate the assistant message and the generation prompt ids were already added to # input_ids, remove them from the end of input_ids self._remove_generation_prompt_ids_if_present() self.response_ids = self.input_ids[..., self.prompt_ids.shape[-1] :] if self.tokenization_sanity_check_mode != TokenizationSanityCheckModeEnum.DISABLE: # When there is a diff, we log the diffs with diff_surrounding_chars context diff_surrounding_chars = 10 messages = [msg.model_dump() for msg in self.messages] tools = [tool.model_dump() for tool in self.tool_schemas] if self.tool_schemas else None full_prompt_info = self._handle_apply_chat_template( processing_class, messages, multi_modal_data=self.multi_modal_data, tools=tools, add_generation_prompt=False, tokenize=True, return_dict=True, ) full_prompt_ids = full_prompt_info["input_ids"] # We must use dict(full_prompt_info) to convert BatchFeature values to a new dict # because np.array() only keeps the keys for BatchFeature. full_prompt_multi_modal_inputs = full_prompt_info.copy() full_prompt_multi_modal_inputs.pop("input_ids", None) full_prompt_multi_modal_inputs.pop("attention_mask", None) for multi_modal_inputs_key in self.multi_modal_inputs: if multi_modal_inputs_key in full_prompt_multi_modal_inputs: if ( not self.multi_modal_inputs[multi_modal_inputs_key] .eq(full_prompt_multi_modal_inputs[multi_modal_inputs_key]) .all() ): logger.warning( f"Multi-modal data {multi_modal_inputs_key} is not consistent. " f"This may lead to unexpected behavior during training. " f"Please review your multi_modal_inputs logic." ) else: logger.warning( f"Multi-modal inputs key {multi_modal_inputs_key} is not found in the multi_modal_inputs. " f"This may lead to unexpected behavior during training." f"Please review your multi_modal_inputs logic." ) if diffs := self._get_prompt_diffs( processing_class, full_prompt_ids, self.input_ids, diff_surrounding_chars=diff_surrounding_chars ): log_warning = False if self.tokenization_sanity_check_mode == TokenizationSanityCheckModeEnum.STRICT: log_warning = True elif self.tokenization_sanity_check_mode == TokenizationSanityCheckModeEnum.IGNORE_STRIPPABLE: non_strippable_diffs_exist = any( d["full_prompt_chunk"].strip() or d["current_prompt_chunk"].strip() for d in diffs ) if non_strippable_diffs_exist: log_warning = True if log_warning: mode_str = f" ({self.tokenization_sanity_check_mode.value})" logger.warning( f"Inconsistent training and inference tokenization detected{mode_str}. This may lead to " f"unexpected behavior during training. Please review your chat template to determine if this " f"is intentional. For more information, refer to the multiturn README.md." ) logger.warning( f"Showing {diff_surrounding_chars} characters before and after the diffs for context and " f"better readability." ) diff_details_list = [] for d in diffs: i1, i2, j1, j2 = d["indices"] diff_details_list.append( f"idx {i1}:{i2} -> {j1}:{j2} | full_prompt_chunk: {repr(d['full_prompt_chunk'])} | " f"current_prompt_chunk: {repr(d['current_prompt_chunk'])}" ) diff_details = "\n".join(diff_details_list) logger.warning(f"Found differences:\n{diff_details}") if finish_reason_type == FinishReasonTypeEnum.STOP: pass elif finish_reason_type == FinishReasonTypeEnum.LENGTH: pass else: raise ValueError(f"Unsupported finalize finish reason type: {finish_reason_type}") self.truncate_output_ids(processing_class) assert ( self.input_ids.shape[-1] == self.attention_mask.shape[-1] == self.position_ids.shape[-1] == self.loss_mask.shape[-1] ), f"""Request {self.request_id} has different length of {self.input_ids.shape[-1]=}, {self.attention_mask.shape[-1]=}, {self.position_ids.shape[-1]=}, {self.loss_mask.shape[-1]=}""" def truncate_output_ids( self, processing_class: PreTrainedTokenizer | PreTrainedTokenizerFast | ProcessorMixin ) -> None: self.input_ids = self.input_ids[..., : self.max_model_len] self.attention_mask = self.attention_mask[..., : self.max_model_len] self.position_ids = self.position_ids[..., : self.max_model_len] self.loss_mask = self.loss_mask[..., : self.max_model_len] self.response_ids = self.input_ids[..., self.prompt_ids.shape[-1] :][..., : self.max_response_len] self.response_attention_mask = self.attention_mask[..., self.prompt_attention_mask.shape[-1] :][ ..., : self.max_response_len ] self.response_position_ids = self.position_ids[..., self.prompt_position_ids.shape[-1] :][ ..., : self.max_response_len ] self.response_loss_mask = self.loss_mask[..., self.prompt_loss_mask.shape[-1] :][..., : self.max_response_len] ================================================ FILE: verl/workers/rollout/sglang_rollout/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and ================================================ FILE: verl/workers/rollout/sglang_rollout/async_sglang_server.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import dataclasses import json import logging import os from typing import Any, Optional import ray import sglang import sglang.srt.entrypoints.engine import torch from packaging import version from ray.actor import ActorHandle from sglang.srt.entrypoints.http_server import ( ServerArgs, _GlobalState, _launch_subprocesses, app, set_global_state, ) from sglang.srt.managers.io_struct import ( ContinueGenerationReqInput, GenerateReqInput, PauseGenerationReqInput, ReleaseMemoryOccupationReqInput, ResumeMemoryOccupationReqInput, ) from sglang.srt.managers.tokenizer_manager import ServerStatus from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import get_visible_devices_keyword from verl.utils.net_utils import get_free_port, is_valid_ipv6_address from verl.utils.profiler import DistProfiler, build_sglang_profiler_args from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.replica import RolloutMode, RolloutReplica, TokenOutput from verl.workers.rollout.sglang_rollout.sglang_rollout import _set_envs_and_config from verl.workers.rollout.utils import get_max_position_embeddings, run_uvicorn logger = logging.getLogger(__file__) logger.setLevel(logging.INFO) visible_devices_keyword = get_visible_devices_keyword() class SGLangHttpServer: """SGLang http server in single node, this is equivalent to launch server with command line: ``` python -m sglang.launch_server --node-rank 0 --nnode 1 ... ``` Args: config (DictConfig): full config. rollout_mode (RolloutMode): rollout mode. replica_rank (int): replica rank, a replica may contain multiple nodes. node_rank (int): node rank. nnodes (int): number of nodes. cuda_visible_devices (str): cuda visible devices. """ def __init__( self, config: RolloutConfig, model_config: HFModelConfig, rollout_mode: RolloutMode, workers: list[ActorHandle], replica_rank: int, node_rank: int, nnodes: int, cuda_visible_devices: str, base_gpu_id: int, ): print(f"SGLang http server: {rollout_mode=}, {replica_rank=}, {node_rank=}, {nnodes=}, {cuda_visible_devices=}") os.environ[visible_devices_keyword] = cuda_visible_devices self.config: RolloutConfig = omega_conf_to_dataclass(config) self.model_config: HFModelConfig = omega_conf_to_dataclass(model_config, dataclass_type=HFModelConfig) max_position_embeddings = get_max_position_embeddings(self.model_config.hf_config) if self.config.max_model_len is None: self.config.max_model_len = max_position_embeddings else: if self.config.max_model_len > max_position_embeddings: raise ValueError( f"max_model_len ({self.config.max_model_len}) should be less than or equal to " f"max_position_embeddings ({max_position_embeddings})" ) self.rollout_mode = rollout_mode self.workers = workers self.replica_rank = replica_rank self.node_rank = node_rank self.nnodes = nnodes self.base_gpu_id = base_gpu_id # model weights version, set by ServerAdapter when update weights. self.global_steps = None if self.rollout_mode != RolloutMode.HYBRID and self.config.load_format == "dummy": logger.warning(f"rollout mode is {self.rollout_mode}, load_format is dummy, set to auto") self.config.load_format = "auto" # used for http server self._server_address = ray.util.get_node_ip_address().strip("[]") self._server_port = None # used for controlling sglang server profiler profiler_config = self.config.profiler tool_config = None if profiler_config is not None: if profiler_config.tool in ["torch", "npu"]: tool_config = omega_conf_to_dataclass((profiler_config.tool_config or {}).get(profiler_config.tool)) else: logger.warning(f"agent loop only support torch and npu profiler, got {profiler_config.tool}") profiler_config = None self.profiler_controller = DistProfiler(self.replica_rank, config=profiler_config, tool_config=tool_config) # For multi-node, we need dist_init_addr so nodes can coordinate NCCL init. # For single-node, let SGLang handle port selection internally via nccl_port, # which also avoids port conflicts. self._master_address = None self._master_port = None self._master_sock = None if self.nnodes > 1 and self.node_rank == 0: self._master_address = self._server_address self._master_port, self._master_sock = get_free_port(self._server_address, with_alive_sock=True) logger.info( f"SGLangHttpServer, replica_rank: {self.replica_rank}, " f"master address: {self._master_address}, port: {self._master_port}" ) def get_master_address(self): """Get master address and port for init NCCL process group.""" return self._master_address, self._master_port def get_server_address(self): """Get http server address and port.""" assert self._server_port is not None, "http server is not launched, port is None" return self._server_address, self._server_port async def launch_server(self, master_address: str = None, master_port: int = None): if self.nnodes > 1: if self.node_rank != 0: assert master_address and master_port, "non-master node should provide master address and port" self._master_address = master_address self._master_port = master_port else: self._master_sock.close() engine_kwargs = self.config.get("engine_kwargs", {}).get("sglang", {}) or {} attention_backend = engine_kwargs.pop("attention_backend", None) quantization = self.config.get("quantization", None) if quantization is not None: if quantization == "fp8": assert version.parse(sglang.__version__) >= version.parse("0.5.5"), ( "sglang>=0.5.5 is required for FP8 quantization" ) FP8_BLOCK_QUANT_KWARGS = { "activation_scheme": "dynamic", "fmt": "e4m3", "quant_method": "fp8", "weight_block_size": [128, 128], } fp8_block_quant_kwargs = dict(FP8_BLOCK_QUANT_KWARGS) else: raise ValueError(f"Currently only support fp8 quantization, got: {quantization}") infer_tp = self.config.tensor_model_parallel_size * self.config.data_parallel_size args = { "model_path": self.model_config.local_path, "dtype": self.config.dtype, "mem_fraction_static": self.config.gpu_memory_utilization, "disable_cuda_graph": self.config.enforce_eager, "enable_memory_saver": True, "base_gpu_id": self.base_gpu_id, "gpu_id_step": 1, "tp_size": infer_tp, "dp_size": self.config.data_parallel_size, "ep_size": self.config.expert_parallel_size, "node_rank": self.node_rank, "load_format": self.config.load_format, "nnodes": self.nnodes, "trust_remote_code": self.model_config.trust_remote_code, "max_running_requests": self.config.get("max_num_seqs", None), "log_level": "error", "mm_attention_backend": "fa3", "attention_backend": attention_backend if attention_backend is not None else "fa3", "skip_tokenizer_init": self.config.skip_tokenizer_init, "skip_server_warmup": True, "quantization": quantization, "json_model_override_args": json.dumps({"quantization_config": fp8_block_quant_kwargs}) if quantization == "fp8" else json.dumps({}), **engine_kwargs, } # Only set dist_init_addr for multi-node; for single-node, let SGLang # handle port selection internally via nccl_port to avoid conflicts. if self.nnodes > 1: dist_init_addr = ( f"[{self._master_address}]:{self._master_port}" if is_valid_ipv6_address(self._master_address) else f"{self._master_address}:{self._master_port}" ) args["dist_init_addr"] = dist_init_addr if self.config.prometheus.enable: if self.config.prometheus.served_model_name: # Extract model name from path if it's a full path served_model_name = self.config.prometheus.served_model_name if "/" in served_model_name: # If it's a full path, extract the last part as model name served_model_name = served_model_name.split("/")[-1] args["served_model_name"] = served_model_name # start sglang metrics args["enable_metrics"] = True # enable_weights_cpu_backup is supported in sglang>=0.5.3 if "enable_weights_cpu_backup" in [f.name for f in dataclasses.fields(ServerArgs)]: enable_weights_cpu_backup = True if self.rollout_mode == RolloutMode.COLOCATED else False args["enable_weights_cpu_backup"] = enable_weights_cpu_backup if self.config.enable_rollout_routing_replay: args.update({"enable_return_routed_experts": True}) # mtp if self.config.mtp.enable and self.config.mtp.enable_rollout: # Enable weights CPU backup for sglang >= 0.5.6 if sglang.__version__ < "0.5.6": raise ValueError(f"sglang version {sglang.__version__} is not supported for MTP rollout") args["speculative_algorithm"] = self.config.mtp.speculative_algorithm args["speculative_num_steps"] = self.config.mtp.speculative_num_steps args["speculative_eagle_topk"] = self.config.mtp.speculative_eagle_topk args["speculative_num_draft_tokens"] = self.config.mtp.speculative_num_draft_tokens args["enable_weights_cpu_backup"] = True args["enable_draft_weights_cpu_backup"] = True # NOTE: We can't directly call SGLang's launch_server since it's not an async function. # https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/entrypoints/http_server.py sglang.srt.entrypoints.engine._set_envs_and_config = _set_envs_and_config os.environ["SGLANG_BLOCK_NONZERO_RANK_CHILDREN"] = "0" server_args = ServerArgs(**args) if version.parse(sglang.__version__) >= version.parse("0.5.7"): self.tokenizer_manager, self.template_manager, self.scheduler_info, *_ = _launch_subprocesses( server_args=server_args, init_tokenizer_manager_func=sglang.srt.entrypoints.engine.init_tokenizer_manager, run_scheduler_process_func=sglang.srt.entrypoints.engine.run_scheduler_process, run_detokenizer_process_func=sglang.srt.entrypoints.engine.run_detokenizer_process, ) else: self.tokenizer_manager, self.template_manager, self.scheduler_info, *_ = _launch_subprocesses( server_args=server_args ) # In multi-node cases, non-zero rank nodes should not launch http server. if self.node_rank > 0: return set_global_state( _GlobalState( tokenizer_manager=self.tokenizer_manager, template_manager=self.template_manager, scheduler_info=self.scheduler_info, ) ) app.is_single_tokenizer_mode = True # Set warmup_thread_{kw}args to avoid AttributeError in lifespan function app.server_args = server_args app.warmup_thread_kwargs = {"server_args": server_args} app.warmup_thread_args = (server_args, None, None) # Manually add Prometheus middleware before starting server # This ensures /metrics endpoint is available immediately if server_args.enable_metrics: from sglang.srt.utils.common import add_prometheus_middleware add_prometheus_middleware(app) self._server_port, self._server_task = await run_uvicorn(app, server_args, self._server_address) self.tokenizer_manager.server_status = ServerStatus.Up async def wake_up(self): if self.node_rank != 0: return if self.rollout_mode == RolloutMode.HYBRID: # In hybrid mode, rollout is wake up in `update_weights` raise ValueError(f"wake_up not support rollout_mode {self.rollout_mode}") elif self.rollout_mode == RolloutMode.COLOCATED: # Directly call engine to wake up without sync weights. obj = ResumeMemoryOccupationReqInput(tags=["kv_cache", "weights"]) await self.tokenizer_manager.resume_memory_occupation(obj, None) await self.tokenizer_manager.flush_cache() elif self.rollout_mode == RolloutMode.STANDALONE: # In standalone mode, resume kv_cache if free_cache_engine is enabled obj = ResumeMemoryOccupationReqInput(tags=["kv_cache"]) await self.tokenizer_manager.resume_memory_occupation(obj, None) await self.tokenizer_manager.flush_cache() async def sleep(self): if self.node_rank != 0 or not self.config.free_cache_engine: return if self.rollout_mode == RolloutMode.HYBRID: obj = ReleaseMemoryOccupationReqInput(tags=["kv_cache", "weights"]) await self.tokenizer_manager.release_memory_occupation(obj, None) elif self.rollout_mode == RolloutMode.COLOCATED: obj = ReleaseMemoryOccupationReqInput(tags=["kv_cache", "weights"]) await self.tokenizer_manager.release_memory_occupation(obj, None) elif self.rollout_mode == RolloutMode.STANDALONE: # In standalone mode, resume kv_cache if free_cache_engine is enabled obj = ReleaseMemoryOccupationReqInput(tags=["kv_cache"]) await self.tokenizer_manager.release_memory_occupation(obj, None) async def clear_kv_cache(self): if self.node_rank == 0: await self.tokenizer_manager.flush_cache() async def generate( self, prompt_ids: torch.Tensor, sampling_params: dict[str, Any], request_id: str, image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, ) -> TokenOutput: """Generate sequence with token-in-token-out.""" # TODO(@wuxibin): switch to `/generate` http endpoint once multi-modal support ready. max_possible_tokens = self.config.max_model_len - len(prompt_ids) if max_possible_tokens < 0: raise ValueError( f"Prompt length ({len(prompt_ids)}) exceeds the model's maximum context length " f"({self.config.max_model_len})." ) if "max_new_tokens" in sampling_params: max_new_tokens = sampling_params.pop("max_new_tokens") elif "max_tokens" in sampling_params: # support vllm-style 'max_tokens' param max_new_tokens = sampling_params.pop("max_tokens") else: # Cap max_tokens by response_length to ensure tensor alignment, # and by remaining budget to prevent OOM in multi-turn rollouts. max_new_tokens = min( self.config.response_length, self.config.prompt_length + self.config.response_length - len(prompt_ids) ) # Clamp max_new_tokens to the valid range [0, max_possible_tokens] max_new_tokens = max(0, min(max_new_tokens, max_possible_tokens)) assert max_new_tokens <= max_possible_tokens, ( f"max_new_tokens {max_new_tokens} exceeds available context space {max_possible_tokens}" ) sampling_params["max_new_tokens"] = max_new_tokens return_logprob = sampling_params.pop("logprobs", False) request = { "rid": request_id, "input_ids": prompt_ids, "sampling_params": sampling_params, "return_logprob": return_logprob, "image_data": image_data, # TODO: support video input for sglang # video_data=video_data, } if self.config.enable_rollout_routing_replay: request.update({"return_routed_experts": True}) generate_request = GenerateReqInput(**request) output = await self.tokenizer_manager.generate_request(generate_request, None).__anext__() finish_reason = output["meta_info"]["finish_reason"] finish_reason = finish_reason["type"] if finish_reason else None if return_logprob: output_token_logprobs = output["meta_info"]["output_token_logprobs"] log_probs, token_ids = zip( *[(log_prob, token_ids) for log_prob, token_ids, _ in output_token_logprobs], strict=True ) else: token_ids = output["output_ids"] log_probs = None routed_experts = None if self.config.enable_rollout_routing_replay: if self.config.skip_tokenizer_init: routed_experts = output.get("meta_info", {}).get("routed_experts", None) else: from sglang.srt.layers.moe.routed_experts_capturer import extract_routed_experts_from_meta_info hf_config = self.model_config.hf_config if not hasattr(hf_config, "num_hidden_layers") or not hasattr(hf_config, "num_experts_per_tok"): raise AttributeError( "enable_rollout_routing_replay is set, but hf_config is missing " "'num_hidden_layers' or 'num_experts_per_tok'. This feature requires an MoE model " "configuration that defines these attributes." ) routed_experts = extract_routed_experts_from_meta_info(output).reshape( -1, hf_config.num_hidden_layers, hf_config.num_experts_per_tok ) return TokenOutput( token_ids=token_ids, log_probs=log_probs, routed_experts=routed_experts, stop_reason=finish_reason, extra_fields={"global_steps": self.global_steps}, ) async def set_global_steps(self, global_steps: int): """Set the global steps of the model weights.""" self.global_steps = global_steps async def abort_all_requests(self): await self.tokenizer_manager.pause_generation(PauseGenerationReqInput(mode="abort")) async def resume_generation(self): await self.tokenizer_manager.continue_generation(ContinueGenerationReqInput()) async def start_profile(self, **kwargs): if ( self.profiler_controller.check_enable() and self.profiler_controller.check_this_rank() and self.profiler_controller.is_discrete_mode() ): profile_args = build_sglang_profiler_args( self.profiler_controller.config, self.profiler_controller.tool_config, self.replica_rank ) await self.tokenizer_manager.start_profile(**profile_args) async def stop_profile(self): if ( self.profiler_controller.check_enable() and self.profiler_controller.check_this_rank() and self.profiler_controller.is_discrete_mode() ): await self.tokenizer_manager.stop_profile() class SGLangReplica(RolloutReplica): def __init__( self, replica_rank: int, config: RolloutConfig, model_config: HFModelConfig, gpus_per_node: int = 8, is_reward_model: bool = False, ): super().__init__(replica_rank, config, model_config, gpus_per_node, is_reward_model) self.server_class = ray.remote(SGLangHttpServer) async def launch_servers(self): """Launch http server in each node.""" assert len(self.workers) == self.world_size, ( f"worker number {len(self.workers)} not equal to world size {self.world_size}" ) # get (node_id, CUDA_VISIBLE_DEVICES) of all workers worker_infos = await asyncio.gather( *[ worker.__ray_call__.remote( lambda self: (ray.get_runtime_context().get_node_id(), os.environ[visible_devices_keyword]) ) for worker in self.workers ] ) worker_cuda_visible_devices = [worker_info[1] for worker_info in worker_infos] worker_node_ids = [worker_info[0] for worker_info in worker_infos] base_gpu_id = 0 infer_tp = self.config.tensor_model_parallel_size * self.config.data_parallel_size replica_world_size = infer_tp * self.config.pipeline_model_parallel_size if os.environ.get(f"RAY_EXPERIMENTAL_NOSET_{visible_devices_keyword}", None): logger.warning(f"RAY_EXPERIMENTAL_NOSET_{visible_devices_keyword} is set True!") base_gpu_id = (0 + self.replica_rank * replica_world_size) % self.gpus_per_node # create server actor in each node with node affinity and cuda visible devices for node_rank in range(self.nnodes): workers = self.workers[ node_rank * self.gpus_per_replica_node : (node_rank + 1) * self.gpus_per_replica_node ] node_cuda_visible_devices_set = worker_cuda_visible_devices[ node_rank * self.gpus_per_replica_node : (node_rank + 1) * self.gpus_per_replica_node ] node_cuda_visible_devices = ",".join( map( str, sorted( set( int(device) for worker_devices_set in node_cuda_visible_devices_set for device in worker_devices_set.split(",") if device.strip() ) ), ) ) node_id = worker_node_ids[node_rank * self.gpus_per_replica_node] name = ( f"sglang_server_{self.replica_rank}_{node_rank}" if not self.is_reward_model else f"sglang_server_reward_{self.replica_rank}_{node_rank}" ) server = self.server_class.options( scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy( node_id=node_id, soft=False, ), runtime_env={"env_vars": {f"RAY_EXPERIMENTAL_NOSET_{visible_devices_keyword}": "1"}}, name=name, max_concurrency=self.max_concurrency, ).remote( config=self.config, model_config=self.model_config, rollout_mode=self.rollout_mode, workers=workers, replica_rank=self.replica_rank, node_rank=node_rank, nnodes=self.nnodes, cuda_visible_devices=node_cuda_visible_devices, base_gpu_id=base_gpu_id, ) self.servers.append(server) # launch http server in each node master_address, master_port = None, None if self.nnodes > 1: master_address, master_port = await self.servers[0].get_master_address.remote() await asyncio.gather( *[ server.launch_server.remote(master_address=master_address, master_port=master_port) for server in self.servers ] ) # get http server address from first server server_address, server_port = await self.servers[0].get_server_address.remote() self._server_handle = self.servers[0] self._server_address = ( f"[{server_address}]:{server_port}" if is_valid_ipv6_address(server_address) else f"{server_address}:{server_port}" ) ================================================ FILE: verl/workers/rollout/sglang_rollout/http_server_engine.py ================================================ # Copyright 2025 z.ai # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # This file is adapted from multiple sources: # 1. THUDM/slime project # Original source: https://github.com/THUDM/slime/blob/main/slime/backends/sglang_utils/http_server_engine.py # Copyright 2025 z.ai # Licensed under the Apache License, Version 2.0 # 2. SGLang project # Original source: https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/entrypoints/http_server_engine.py # Copyright 2023-2024 SGLang Team # Licensed under the Apache License, Version 2.0 # # Modifications made by z.ai and ModelBest Inc. include but are not limited to: # - Enhanced error handling and retry logic # - Added async support with connection pooling # - Extended functionality for distributed weight updates # - Improved logging and monitoring capabilities # - Additional configuration options and optimizations """HTTP Server Engine Adapter for SGLang. This module provides HTTP-based adapters for SGLang engines, allowing communication with SGLang servers through HTTP requests instead of direct engine calls. Classes: HttpServerAdapter: Synchronous HTTP adapter for SGLang engines AsyncHttpServerAdapter: Asynchronous HTTP adapter for SGLang engines Functions: launch_server_process: Launch and initialize an SGLang HTTP server process """ import asyncio import logging import multiprocessing import os import time from contextlib import asynccontextmanager from typing import Any, Callable, Optional import aiohttp import requests from sglang.srt.entrypoints.EngineBase import EngineBase from sglang.srt.entrypoints.http_server import launch_server from sglang.srt.managers.io_struct import ( UpdateWeightsFromTensorReqInput, ) from sglang.srt.server_args import ServerArgs from sglang.srt.utils import kill_process_tree # Configure logger logger = logging.getLogger(__name__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) # Default configuration constants DEFAULT_TIMEOUT = 60.0 DEFAULT_MAX_ATTEMPTS = 3 DEFAULT_RETRY_DELAY = 2.0 DEFAULT_MAX_CONNECTIONS = 2000 DEFAULT_MAX_WAIT_TIME = 300.0 def _read_response(response: requests.Response): if response.status_code == 204 or not response.content: return {} try: return response.json() except ValueError: return { "content_type": response.headers.get("Content-Type", ""), "text": response.text, } async def _read_async_response(resp: aiohttp.ClientResponse) -> dict[str, Any]: if resp.status == 204 or (resp.content_length == 0): return {} try: return await resp.json(content_type=None) except Exception: try: text = await resp.text() except Exception: return {} return { "content_type": (resp.headers.get("Content-Type") or ""), "text": text, } def launch_server_process( server_args: ServerArgs, timeout: float = DEFAULT_TIMEOUT, max_wait_time=DEFAULT_MAX_WAIT_TIME, first_rank_in_node=False, ) -> multiprocessing.Process: """Launch an SGLang HTTP server process and wait for it to be ready. This function starts a new process running an SGLang HTTP server, then waits for the server to become ready by polling its health endpoints. It ensures the server is fully operational before returning. Args: server_args (ServerArgs): Server configuration arguments including host, port, and other settings timeout (float, optional): Timeout for individual HTTP requests during health checks. Defaults to DEFAULT_TIMEOUT. Returns: multiprocessing.Process: The launched multiprocessing.Process instance Raises: RuntimeError: If the server process terminates unexpectedly during startup or cache flush TimeoutError: If server fails to become ready within reasonable time (300 seconds) requests.RequestException: If health check requests fail repeatedly Note: This function will return immediately for non-master nodes (node_rank != 0), but the process will still be started and returned. This is for consistency; except for the process obtained by node_rank = 0, other processes have no actual effect. """ p = multiprocessing.Process(target=launch_server, args=(server_args,)) if server_args.node_rank != 0 or not first_rank_in_node: logger.info(f"Server process started with PID {p.pid} for node rank {server_args.node_rank}", flush=True) return p p.start() base_url = server_args.url() headers = { "Content-Type": "application/json; charset=utf-8", "Authorization": f"Bearer {server_args.api_key}", } # Health check with overall timeout start_time = time.time() with requests.Session() as session: while time.time() - start_time < max_wait_time: if not p.is_alive(): raise RuntimeError("Server process terminated unexpectedly during startup") try: if server_args.is_embedding: response = session.get(f"{base_url}/health", headers=headers, timeout=timeout) else: response = session.get(f"{base_url}/health_generate", headers=headers, timeout=timeout) if response.status_code == 200: break except requests.RequestException as e: logger.debug(f"Health check failed: {e}") time.sleep(2) else: p.terminate() logger.error(f"Server in {base_url} failed to become healthy within timeout period") raise TimeoutError("Server failed to become healthy within timeout period") # Ensure cache is ready while time.time() - start_time < max_wait_time: if not p.is_alive(): raise RuntimeError("Server process terminated unexpectedly during cache flush") try: response = session.get(f"{base_url}/flush_cache", headers=headers, timeout=timeout) if response.status_code == 200: break except requests.RequestException as e: logger.debug(f"Cache flush check failed: {e}") time.sleep(2) else: p.terminate() raise TimeoutError("Server cache flush failed within timeout period") return p class HttpServerAdapter(EngineBase): """HTTP-based adapter for SGLang engines. This adapter allows interaction with SGLang engines through HTTP requests instead of direct engine calls. It launches an HTTP server process and provides methods to communicate with it via REST API calls. You can use this class to launch a server from a HttpServerAdapter instance. We recommend using this class only when you need to use http server. Otherwise, you can use Engine directly. Attributes: router_ip (Optional[str]): IP address of the router for worker registration router_port (Optional[int]): Port of the router for worker registration server_args (ServerArgs): Server configuration arguments node_rank (int): Rank of this node in distributed setup process (multiprocessing.Process): The launched server process timeout (float): HTTP request timeout in seconds max_attempts (int): Maximum number of attempts for requests retry_delay (float): Base delay between retries in seconds """ def __init__( self, router_ip: Optional[str] = None, router_port: Optional[int] = None, timeout: float = DEFAULT_TIMEOUT, max_attempts: int = DEFAULT_MAX_ATTEMPTS, retry_delay: float = DEFAULT_RETRY_DELAY, first_rank_in_node: bool = False, max_start_wait_time: float = DEFAULT_MAX_WAIT_TIME, launch_server: bool = True, **kwargs: Any, ) -> None: """Initialize the HTTP server engine adapter. Args: router_ip (Optional[str], optional): IP address of router for worker registration. Defaults to None. router_port (Optional[int], optional): Port of router for worker registration. Defaults to None. timeout (float, optional): HTTP request timeout in seconds. Defaults to DEFAULT_TIMEOUT. max_attempts (int, optional): Maximum number of retry attempts for failed requests. Defaults to DEFAULT_MAX_ATTEMPTS. retry_delay (float, optional): Base delay between retries in seconds. Defaults to DEFAULT_RETRY_DELAY. launch_server (bool, optional): Whether to launch the server process. Defaults to True. **kwargs (Any): Additional arguments passed to ServerArgs Note: TODO: @ChangyiYang Enable SGLang router for this http server engine If both router_ip and router_port are provided and this is the master node (node_rank == 0), the adapter will automatically register with the router. """ self.router_ip: Optional[str] = router_ip self.router_port: Optional[int] = router_port self.timeout: float = timeout self.max_attempts: int = max_attempts self.retry_delay: float = retry_delay self.server_args: ServerArgs = ServerArgs(**kwargs) self.node_rank: int = self.server_args.node_rank self.max_start_wait_time: float = max_start_wait_time logger.info( f"Launch HttpServerAdapter at: {self.server_args.host}:{self.server_args.port} with {first_rank_in_node}" ) if launch_server: self.process: multiprocessing.Process = launch_server_process( self.server_args, self.timeout, self.max_start_wait_time, first_rank_in_node ) if self.node_rank == 0 and self.router_ip and self.router_port: self._register_with_router() def _register_with_router(self) -> None: """Register worker with router with error handling. This method attempts to register the current worker with a router service. If registration fails, it logs an error but does not raise an exception, allowing the server to continue operating without router integration. Raises: Does not raise exceptions - all errors are logged and handled gracefully. """ try: url = f"http://{self.router_ip}:{self.router_port}/add_worker" params = {"url": f"http://{self.server_args.host}:{self.server_args.port}"} response = requests.post(url, params=params, timeout=self.timeout) response.raise_for_status() logger.info("Successfully registered with router") except Exception as e: logger.error(f"Failed to register with router: {e}") # Don't raise here - server can still work without router def _make_request( self, endpoint: str, payload: Optional[dict[str, Any]] = None, method: str = "POST", timeout: float = DEFAULT_TIMEOUT, only_master: bool = True, ) -> dict[str, Any]: """Make a HTTP request with retry logic and consistent error handling. Args: endpoint (str): The API endpoint to call (without leading slash) payload (Optional[Dict[str, Any]], optional): The JSON payload to send. Defaults to empty dict if None. method (str, optional): HTTP method to use. Defaults to "POST". Returns: Dict[str, Any]: The JSON response from the server Raises: requests.HTTPError: If the HTTP request fails with a client/server error RuntimeError: If all retry attempts are exhausted Note: - For non-master nodes (node_rank != 0), returns empty dict immediately - Uses exponential backoff for retries - Logs warnings for timeout and connection errors, errors for HTTP errors """ if only_master and self.node_rank != 0: return {} url = f"http://{self.server_args.host}:{self.server_args.port}/{endpoint}" for attempt in range(self.max_attempts): try: if method.upper() == "GET": response = requests.get(url, timeout=self.timeout) else: response = requests.post(url, json=payload or {}, timeout=self.timeout) response.raise_for_status() return _read_response(response) except requests.exceptions.Timeout: logger.warning(f"Request to {endpoint} timed out (attempt {attempt + 1})") except requests.exceptions.ConnectionError: logger.warning(f"Connection error for {endpoint} (attempt {attempt + 1})") except requests.exceptions.HTTPError as e: logger.error(f"HTTP error for {endpoint}: {e}") raise except Exception as e: logger.error(f"Unexpected error for {endpoint}: {e}") if attempt == self.max_attempts - 1: raise if attempt < self.max_attempts - 1: time.sleep(self.retry_delay * (2**attempt)) raise RuntimeError(f"Failed to complete request to {endpoint} after {self.max_attempts} attempts") def update_weights_from_tensor(self, req: UpdateWeightsFromTensorReqInput) -> dict[str, Any]: """Update model weights from tensor data. The HTTP server will only post meta data, and the real weights will be copied directly from GPUs. Args: serialized_named_tensors (List[str]): List of serialized tensor data load_format (Optional[str], optional): Format specification for loading weights. Defaults to None. flush_cache (bool, optional): Whether to flush cache after updating weights. Defaults to False. Returns: Dict[str, Any]: Server response containing update status Note: The model should be on GPUs rather than CPU for this functionality to work properly. If you encounter issues, ensure your model is loaded on GPU devices rather than CPU. """ import base64 named_tensors = req.serialized_named_tensors load_format = req.load_format flush_cache = req.flush_cache if named_tensors: serialized_named_tensors = [ base64.b64encode(named_tensor).decode("utf-8") for named_tensor in named_tensors ] else: serialized_named_tensors = [] return self._make_request( "update_weights_from_tensor", { "serialized_named_tensors": serialized_named_tensors, "load_format": load_format, "flush_cache": flush_cache, }, ) def shutdown(self) -> None: """Shutdown the HTTP server and clean up resources. This method performs the following cleanup operations: 1. Unregisters the worker from the router (if configured) 2. Terminates the server process tree All operations are performed with error handling to ensure graceful shutdown even if individual steps fail. Note: This method should be called when the adapter is no longer needed to ensure proper cleanup of resources and processes. """ # Unregister from router if self.router_ip and self.router_port: try: url = f"http://{self.router_ip}:{self.router_port}/remove_worker" params = {"url": f"http://{self.server_args.host}:{self.server_args.port}"} requests.post(url, params=params, timeout=5.0) # Short timeout for shutdown logger.info("Successfully unregistered from router") except Exception as e: logger.warning(f"Failed to unregister from router: {e}") # Kill server process if hasattr(self, "process") and self.process is not None: try: kill_process_tree(self.process.pid) logger.info("Server process terminated") except Exception as e: logger.error(f"Failed to terminate server process: {e}") def generate( self, prompt: Optional[str] = None, sampling_params: Optional[dict[str, Any]] = None, input_ids: Optional[list[int]] = None, image_data: Optional[Any] = None, return_logprob: bool = False, logprob_start_len: Optional[int] = None, top_logprobs_num: Optional[int] = None, token_ids_logprob: Optional[list[int]] = None, lora_path: Optional[str] = None, custom_logit_processor: Optional[Callable] = None, ) -> dict[str, Any]: """Generate text using the SGLang server. Args: prompt (Optional[str], optional): Text prompt for generation. Defaults to None. sampling_params (Optional[Dict[str, Any]], optional): Parameters controlling text generation sampling. Defaults to None. input_ids (Optional[List[int]], optional): Alternative to prompt, direct token IDs input. Defaults to None. image_data (Optional[Any], optional): Image data for multimodal generation. Defaults to None. return_logprob (bool, optional): Whether to return log probabilities. Defaults to False. logprob_start_len (Optional[int], optional): Starting length for log probability calculation. Defaults to None. top_logprobs_num (Optional[int], optional): Number of top log probabilities to return. Defaults to None. token_ids_logprob (Optional[List[int]], optional): Specific token IDs for log probability calculation. Defaults to None. lora_path (Optional[str], optional): Path to LoRA adapter weights. Defaults to None. custom_logit_processor (Optional[Callable], optional): Custom logit processing function. Defaults to None. Returns: Dict[str, Any]: Generated text and associated metadata from the server Note: Either prompt or input_ids should be provided, but not both. The response format depends on the server configuration and parameters. """ payload = { "text": prompt, "sampling_params": sampling_params, "input_ids": input_ids, "image_data": image_data, "return_logprob": return_logprob, "logprob_start_len": logprob_start_len, "top_logprobs_num": top_logprobs_num, "token_ids_logprob": token_ids_logprob, "lora_path": lora_path, "custom_logit_processor": custom_logit_processor, } # Filter out None values payload = {k: v for k, v in payload.items() if v is not None} return self._make_request("generate", payload, only_master=False) def reward_score( self, prompt: Optional[str] = None, input_ids: Optional[list[int]] = None, image_data: Optional[Any] = None, lora_path: Optional[str] = None, ) -> dict[str, Any]: assert self.server_args.is_embedding, "Score is only supported for embedding models" payload = { "text": prompt, "input_ids": input_ids, "image_data": image_data, "lora_path": lora_path, } # Filter out None values payload = {k: v for k, v in payload.items() if v is not None} return self._make_request("classify", payload, only_master=False) def flush_cache(self) -> dict[str, Any]: """Flush the cache of the server. This method repeatedly attempts to flush the server cache until successful. The flush operation will not return status 200 when there are pending requests. Returns: Dict[str, Any]: Server response indicating cache flush status. For non-master nodes, returns empty dict. Note: Uses retry logic with limited attempts (max_attempts * 2) to avoid infinite loops. Each retry includes a delay to allow pending requests to complete. """ if self.node_rank != 0: return {} # Use retry logic with limited attempts to avoid infinite loops for attempt in range(self.max_attempts * 2): # Allow more retries for cache flush try: response = requests.get( f"http://{self.server_args.host}:{self.server_args.port}/flush_cache", timeout=self.timeout ) if response.status_code == 200: return _read_response(response) except Exception as e: logger.warning(f"Error flushing cache (attempt {attempt + 1}): {e}") time.sleep(self.retry_delay) logger.error("Failed to flush cache after maximum attempts") return {} def release_memory_occupation(self, tags: Optional[list[str]] = None) -> dict[str, Any]: """Release GPU memory occupation temporarily. Args: tags (Optional[List[str]], optional): List of tags to specify which memory to release. If None, releases all memory. Defaults to None. ["weights", "kv_cache"] Returns: Dict[str, Any]: Server response indicating memory release status """ return self._make_request("release_memory_occupation", {"tags": tags}) def resume_memory_occupation(self, tags: Optional[list[str]] = None) -> dict[str, Any]: """Resume GPU memory occupation. Args: tags (Optional[List[str]], optional): List of tags to specify which memory to resume. If None, resumes all memory. Defaults to None. ["weights", "kv_cache"] Returns: Dict[str, Any]: Server response indicating memory resume status """ return self._make_request("resume_memory_occupation", {"tags": tags}) def abort_request(self, rid: str = "", abort_all: bool = False) -> dict[str, Any]: """Abort a request. Args: rid (str): The ID of the request to abort abort_all (bool, optional): Whether to abort all requests. Defaults to False. Returns: Dict[str, Any]: Server response indicating abort status """ return self._make_request("abort_request", {"rid": rid, "abort_all": abort_all}) class AsyncHttpServerAdapter(HttpServerAdapter): """Asynchronous HTTP-based adapter for SGLang engines. This class inherits from HttpServerAdapter and adds async capabilities for non-blocking HTTP requests to the SGLang server. It provides the same functionality as the synchronous version but with async/await support. The async adapter is useful when you need to make multiple concurrent requests or integrate with async frameworks. It uses aiohttp for efficient async HTTP communication and maintains connection pooling for better performance. Attributes: max_connections (int): Maximum number of connections in the connection pool """ def __init__( self, router_ip: Optional[str] = None, router_port: Optional[int] = None, timeout: float = DEFAULT_TIMEOUT, max_attempts: int = DEFAULT_MAX_ATTEMPTS, retry_delay: float = DEFAULT_RETRY_DELAY, max_connections: int = DEFAULT_MAX_CONNECTIONS, first_rank_in_node: bool = False, launch_server: bool = True, **kwargs: Any, ) -> None: """Initialize the async HTTP server engine adapter. Args: router_ip (Optional[str], optional): IP address of router for worker registration. Defaults to None. router_port (Optional[int], optional): Port of router for worker registration. Defaults to None. timeout (float, optional): HTTP request timeout in seconds. Defaults to DEFAULT_TIMEOUT. max_attempts (int, optional): Maximum number of retry attempts for failed requests. Defaults to DEFAULT_MAX_ATTEMPTS. retry_delay (float, optional): Base delay between retries in seconds. Defaults to DEFAULT_RETRY_DELAY. max_connections (int, optional): Maximum number of connections in the connection pool. Defaults to DEFAULT_MAX_CONNECTIONS. launch_server (bool, optional): Whether to launch the server process. Defaults to True. **kwargs (Any): Additional arguments passed to ServerArgs """ super().__init__( router_ip, router_port, timeout, max_attempts, retry_delay, first_rank_in_node, launch_server=launch_server, **kwargs, ) self.max_connections: int = max_connections @asynccontextmanager async def _get_session(self) -> aiohttp.ClientSession: """Context manager for safe session access with proper connection pooling. Yields: aiohttp.ClientSession: Session instance for making HTTP requests Note: This method creates a new session for each request to avoid resource competition while still maintaining proper connection pooling through the shared connector. """ # Create a new session for each request to avoid resource competition connector = aiohttp.TCPConnector( limit=self.max_connections, limit_per_host=self.max_connections // 4, ttl_dns_cache=300, use_dns_cache=True, ) timeout = aiohttp.ClientTimeout(total=self.timeout) session = aiohttp.ClientSession(connector=connector, timeout=timeout) try: yield session finally: # Always close the session to free up resources if not session.closed: await session.close() async def _make_async_request( self, endpoint: str, payload: Optional[dict[str, Any]] = None, method: str = "POST", timeout: float = DEFAULT_TIMEOUT, only_master: bool = True, ) -> dict[str, Any]: """Make an async HTTP request with retry logic and consistent error handling. Args: endpoint (str): The API endpoint to call (without leading slash) payload (Optional[Dict[str, Any]], optional): The JSON payload to send. Defaults to empty dict if None. method (str, optional): HTTP method to use. Defaults to "POST". Returns: Dict[str, Any]: The JSON response from the server Raises: aiohttp.ClientResponseError: If the HTTP request fails with a client/server error RuntimeError: If all retry attempts are exhausted Note: - For non-master nodes (node_rank != 0), returns empty dict immediately - Uses exponential backoff for retries - Logs warnings for timeout and connection errors, errors for HTTP errors """ if only_master and self.node_rank != 0: return {} url = f"http://{self.server_args.host}:{self.server_args.port}/{endpoint}" for attempt in range(self.max_attempts): try: async with self._get_session() as session: if method.upper() == "GET": async with session.get(url, timeout=timeout) as response: response.raise_for_status() return await _read_async_response(response) else: async with session.post(url, json=payload or {}, timeout=timeout) as response: response.raise_for_status() return await _read_async_response(response) except asyncio.TimeoutError: logger.warning(f"Async request to {endpoint} timed out (attempt {attempt + 1})") except aiohttp.ClientConnectorError: logger.warning(f"Connection error for {endpoint} (attempt {attempt + 1})") except aiohttp.ClientResponseError as e: logger.error(f"HTTP error for {endpoint}: {e}") raise except Exception as e: logger.error(f"Unexpected error for {endpoint}: {e}") if attempt == self.max_attempts - 1: raise if attempt < self.max_attempts - 1: await asyncio.sleep(self.retry_delay * (2**attempt)) raise RuntimeError(f"Failed to complete async request to {endpoint} after {self.max_attempts} attempts") async def release_memory_occupation(self, tags: Optional[list[str]] = None) -> dict[str, Any]: """Release GPU memory occupation temporarily (async version). Args: tags (Optional[List[str]], optional): List of tags to specify which memory to release. If None, releases all memory. Defaults to None. ["weights", "kv_cache"] Returns: Dict[str, Any]: Server response indicating memory release status """ return await self._make_async_request("release_memory_occupation", {"tags": tags}) async def resume_memory_occupation(self, tags: Optional[list[str]] = None) -> dict[str, Any]: """Resume GPU memory occupation (async version). Similar to AsyncEngine, this method handles first-time weight reloading by calling release_memory_occupation if needed. Args: tags (Optional[List[str]], optional): List of tags to specify which memory to resume. If None, resumes all memory. Defaults to None. ["weights", "kv_cache"] Returns: Dict[str, Any]: Server response indicating memory resume status """ return await self._make_async_request("resume_memory_occupation", {"tags": tags}) async def update_weights_from_tensor( self, req: UpdateWeightsFromTensorReqInput, ) -> dict[str, Any]: """Update model weights from tensor data asynchronously. Args: serialized_named_tensors (List[str]): List of serialized tensor data load_format (Optional[str], optional): Format specification for loading weights. Defaults to None. flush_cache (bool, optional): Whether to flush cache after updating weights. Defaults to True. Returns: Dict[str, Any]: Server response containing update status """ import base64 named_tensors = req.serialized_named_tensors load_format = req.load_format flush_cache = req.flush_cache serialized_named_tensors = [base64.b64encode(named_tensor).decode("utf-8") for named_tensor in named_tensors] return await self._make_async_request( "update_weights_from_tensor", { "serialized_named_tensors": serialized_named_tensors, "load_format": load_format, "flush_cache": flush_cache, }, ) async def flush_cache(self) -> dict[str, Any]: """Flush the cache of the server asynchronously. Similar to the sync version, this method retries until the cache is successfully flushed. It uses async sleep between retries. Returns: Dict[str, Any]: Server response indicating cache flush status. For non-master nodes, returns empty dict. Note: Uses retry logic with limited attempts (max_attempts * 4) to avoid infinite loops. Each retry includes an async delay to allow pending requests to complete. """ if self.node_rank != 0: return {} # Use retry logic with limited attempts to avoid infinite loops for attempt in range(self.max_attempts * 4): # Allow more retries for cache flush try: async with self._get_session() as session: url = f"http://{self.server_args.host}:{self.server_args.port}/flush_cache" async with session.get(url) as response: if response.status == 200: return await _read_async_response(response) except Exception as e: logger.warning(f"Error flushing cache (attempt {attempt + 1}): {e}") await asyncio.sleep(self.retry_delay) logger.error("Failed to flush cache after maximum attempts") return {} async def generate( self, prompt: Optional[str] = None, sampling_params: Optional[dict[str, Any]] = None, input_ids: Optional[list[int]] = None, image_data: Optional[Any] = None, return_logprob: bool = False, logprob_start_len: Optional[int] = None, top_logprobs_num: Optional[int] = None, token_ids_logprob: Optional[list[int]] = None, lora_path: Optional[str] = None, custom_logit_processor: Optional[Callable] = None, ) -> dict[str, Any]: """Generate text using the SGLang server asynchronously.""" logger.info("generate() started") payload = { "text": prompt, "sampling_params": sampling_params, "input_ids": input_ids, "image_data": image_data, "return_logprob": return_logprob, "logprob_start_len": logprob_start_len, "top_logprobs_num": top_logprobs_num, "token_ids_logprob": token_ids_logprob, "lora_path": lora_path, "custom_logit_processor": custom_logit_processor, } # Filter out None values payload = {k: v for k, v in payload.items() if v is not None} # Send request response = await self._make_async_request("generate", payload, timeout=self.timeout, only_master=False) return response async def async_generate( self, prompt: Optional[str] = None, sampling_params: Optional[dict[str, Any]] = None, input_ids: Optional[list[int]] = None, image_data: Optional[Any] = None, return_logprob: bool = False, logprob_start_len: Optional[int] = None, top_logprobs_num: Optional[int] = None, token_ids_logprob: Optional[list[int]] = None, lora_path: Optional[str] = None, custom_logit_processor: Optional[Callable] = None, ) -> dict[str, Any]: """Async generate method that mirrors AsyncEngine.async_generate interface. This method provides compatibility with AsyncEngine's async_generate method by forwarding the call to the generate method. It ensures API consistency between direct engine usage and HTTP-based engine usage. Args: prompt (Optional[str], optional): Text prompt for generation. Defaults to None. sampling_params (Optional[Dict[str, Any]], optional): Parameters controlling text generation sampling. Defaults to None. input_ids (Optional[List[int]], optional): Alternative to prompt, direct token IDs input. Defaults to None. image_data (Optional[Any], optional): Image data for multimodal generation. Defaults to None. return_logprob (bool, optional): Whether to return log probabilities. Defaults to False. logprob_start_len (Optional[int], optional): Starting length for log probability calculation. Defaults to None. top_logprobs_num (Optional[int], optional): Number of top log probabilities to return. Defaults to None. token_ids_logprob (Optional[List[int]], optional): Specific token IDs for log probability calculation. Defaults to None. lora_path (Optional[str], optional): Path to LoRA adapter weights. Defaults to None. custom_logit_processor (Optional[Callable], optional): Custom logit processing function. Defaults to None. Returns: Dict[str, Any]: Generated text and associated metadata from the server Note: This method is provided for API compatibility with AsyncEngine. It forwards all calls to the generate method. """ return await self.generate( prompt=prompt, sampling_params=sampling_params, input_ids=input_ids, image_data=image_data, return_logprob=return_logprob, logprob_start_len=logprob_start_len, top_logprobs_num=top_logprobs_num, token_ids_logprob=token_ids_logprob, lora_path=lora_path, custom_logit_processor=custom_logit_processor, ) async def reward_score( self, prompt: Optional[str] = None, input_ids: Optional[list[int]] = None, image_data: Optional[Any] = None, lora_path: Optional[str] = None, ) -> dict[str, Any]: logger.info("reward_score() started") payload = { "text": prompt, "input_ids": input_ids, "image_data": image_data, "lora_path": lora_path, } # Filter out None values payload = {k: v for k, v in payload.items() if v is not None} # Send request response = await self._make_async_request("classify", payload, timeout=self.timeout, only_master=False) return response async def async_reward_score( self, prompt: Optional[str] = None, input_ids: Optional[list[int]] = None, image_data: Optional[Any] = None, lora_path: Optional[str] = None, ) -> dict[str, Any]: return await self.reward_score( prompt=prompt, input_ids=input_ids, image_data=image_data, lora_path=lora_path, ) async def abort_request(self, rid: str = "", abort_all: bool = False) -> dict[str, Any]: """Abort a request asynchronously. Args: rid (str): The ID of the request to abort abort_all (bool, optional): Whether to abort all requests. Defaults to False. Returns: Dict[str, Any]: Server response indicating abort status """ return await self._make_async_request("abort_request", {"rid": rid, "abort_all": abort_all}) ================================================ FILE: verl/workers/rollout/sglang_rollout/sglang_rollout.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import logging import multiprocessing as mp import os from typing import Generator import ray import sglang.srt.entrypoints.engine import torch from sglang.srt.server_args import ServerArgs from sglang.srt.utils import ( assert_pkg_version, is_cuda, set_prometheus_multiproc_dir, set_ulimit, ) from sglang.srt.weight_sync.utils import update_weights as sgl_update_weights from torch.distributed.device_mesh import DeviceMesh, init_device_mesh from verl.utils.net_utils import is_valid_ipv6_address from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.base import BaseRollout from verl.workers.rollout.sglang_rollout.http_server_engine import AsyncHttpServerAdapter from verl.workers.rollout.sglang_rollout.utils import get_named_tensor_buckets logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) # patch to avoid issue https://github.com/sgl-project/sglang/issues/6723 def _set_envs_and_config(server_args: ServerArgs): # Set global environments os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" os.environ["NCCL_CUMEM_ENABLE"] = "0" os.environ["NCCL_NVLS_ENABLE"] = str(int(server_args.enable_nccl_nvls)) os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "4" os.environ["CUDA_MODULE_LOADING"] = "AUTO" # Enable faulthandler in subprocesses os.environ["PYTHONFAULTHANDLER"] = "1" # Set prometheus env vars if server_args.enable_metrics: set_prometheus_multiproc_dir() # Set ulimit set_ulimit() # Check flashinfer version if server_args.attention_backend == "flashinfer": assert_pkg_version( "flashinfer_python", "0.2.5", "Please uninstall the old version and reinstall the latest version by following the instructions at https://docs.flashinfer.ai/installation.html.", ) if is_cuda(): assert_pkg_version( "sgl-kernel", "0.1.1", "Please reinstall the latest version with `pip install sgl-kernel --force-reinstall`", ) # Set mp start method mp.set_start_method("spawn", force=True) sglang.srt.entrypoints.engine._set_envs_and_config = _set_envs_and_config # because chatCompletion is an async method, it makes the whole ray actor be an async actor # which can not call loop.run_until_complete. So we need to make the engine to be an async class class ServerAdapter(BaseRollout): """SGLang server adapter used in native http server mode, serve as http client to request SGLang server to resume/release/update weights and kv_cache. - hybrid mode: reside in each hybrid worker to sync weights between training engine and SGLang server. - standalone/colocated mode: just a dummy placeholder to occupy the GPU to prevent ray scheduling new GPU actor. """ def __init__( self, config: RolloutConfig, model_config: HFModelConfig, device_mesh: DeviceMesh, replica_rank: int = -1, ): if config.get("quantization", None) == "fp8": import sglang from packaging import version assert version.parse(sglang.__version__) >= version.parse("0.5.5"), ( "sglang>=0.5.5 is required for FP8 quantization" ) FP8_BLOCK_QUANT_KWARGS = { "activation_scheme": "dynamic", "fmt": "e4m3", "quant_method": "fp8", "weight_block_size": [128, 128], } fp8_block_quant_kwargs = dict(FP8_BLOCK_QUANT_KWARGS) model_config.hf_config.quantization_config = fp8_block_quant_kwargs super().__init__(config, model_config, device_mesh) self._engine: AsyncHttpServerAdapter = None rank = int(os.environ["RANK"]) local_world_size = int(os.environ["RAY_LOCAL_WORLD_SIZE"]) rollout_world_size = self.config.tensor_model_parallel_size * self.config.data_parallel_size if replica_rank == -1: self.replica_rank = rank // rollout_world_size else: self.replica_rank = replica_rank self.rollout_rank = rank % rollout_world_size self.node_rank = self.rollout_rank // local_world_size self.local_rank = self.rollout_rank % local_world_size async def _init_server_adapter(self): if self._engine is not None: return # device_mesh is needed to gather cuda ipc handle to update weights if self.device_mesh is None: assert torch.distributed.is_initialized(), "torch distributed must be initialized" infer_tp = self.config.tensor_model_parallel_size * self.config.data_parallel_size infer_pp = self.config.pipeline_model_parallel_size infer_world_size = infer_tp * infer_pp dp = torch.distributed.get_world_size() // infer_world_size self.device_mesh = init_device_mesh( "cpu", mesh_shape=(dp, infer_tp, infer_pp), mesh_dim_names=["dp", "infer_tp", "infer_pp"] ) # Only init http server adapter in tp rank 0 if self.device_mesh["infer_tp"].get_local_rank() != 0: return # Lazy init http server adapter because http server is launched after hybrid engine. self.server_actor = ray.get_actor(f"sglang_server_{self.replica_rank}_{self.node_rank}") server_address, server_port = await self.server_actor.get_server_address.remote() logger.debug( f"replica_rank={self.replica_rank} node_rank={self.node_rank}, " f"server address: {server_address}, port: {server_port}" ) host = f"[{server_address}]" if is_valid_ipv6_address(server_address) else server_address self._engine = AsyncHttpServerAdapter( model_path=self.model_config.local_path, host=host, port=server_port, launch_server=False, trust_remote_code=self.model_config.trust_remote_code, ) async def resume(self, tags: list[str]): """Resume rollout weights or kv cache in GPU memory. Args: tag: weights or kv_cache. """ await self._init_server_adapter() if self.device_mesh["infer_tp"].get_local_rank() == 0 and self.config.free_cache_engine: await self._engine.resume_memory_occupation(tags=tags) async def release(self): """Release weights and kv cache in GPU memory.""" await self._init_server_adapter() if self.device_mesh["infer_tp"].get_local_rank() == 0 and self.config.free_cache_engine: await self._engine.release_memory_occupation(tags=["kv_cache", "weights"]) async def update_weights( self, weights: Generator[tuple[str, torch.Tensor], None, None], global_steps: int = None, **kwargs ): """ Update model weights using tensor buckets, similar to THUDM/slime's implementation. Notes: - For the best performance of `rebuild_cuda_tensor`, it is recommended to: 1. Enable `RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES`. 2. Manually set `CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7` when using Tensor Parallelism (TP >= 8). - See reference implementations in SLIME: - Main logic: https://github.com/THUDM/slime/blob/fb7605cc5fb09af0f9369d37f7192f12bddee577/slime/ray/ppo_actor.py#L452 - runtime envs: https://github.com/THUDM/slime/blob/fb7605cc5fb09af0f9369d37f7192f12bddee577/slime/ray/ppo_actor.py#L39 """ await self._init_server_adapter() update_weights_bucket_bytes = int(self.config.checkpoint_engine.update_weights_bucket_megabytes) << 20 if self.config.get("quantization", None) == "fp8": from verl.utils.sglang.sglang_fp8_utils import SGLangFP8QuantizerHelper logger.info("Convert bf16 weights to fp8 format before loading") fp8_quantizer_helper = SGLangFP8QuantizerHelper(self.model_config.hf_config.quantization_config) weights = fp8_quantizer_helper.quant_weights_by_name( weights, dtype=self.model_config.hf_config.dtype, ) else: weights = weights async for params_batch in get_named_tensor_buckets(weights, update_weights_bucket_bytes): await sgl_update_weights( engine=self._engine, params_batch=params_batch, device_mesh_key="infer_tp", device_mesh=self.device_mesh, ) if self.device_mesh["infer_tp"].get_local_rank() == 0: await self._engine.flush_cache() if global_steps is not None: await self.server_actor.set_global_steps.remote(global_steps) ================================================ FILE: verl/workers/rollout/sglang_rollout/utils.py ================================================ # Copyright 2023-2024 SGLang Team # Copyright 2025 ModelBest Inc. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pickle from typing import Any, Iterator, Optional import numpy as np import torch import torch.distributed as dist from verl.utils.device import get_device_name from verl.workers.rollout.utils import ensure_async_iterator def broadcast_pyobj( data: list[Any], rank: int, dist_group: Optional[torch.distributed.ProcessGroup] = None, src: int = 0, force_cpu_device: bool = False, ): """from https://github.com/sgl-project/sglang/blob/844e2f227ab0cce6ef818a719170ce37b9eb1e1b/python/sglang/srt/utils.py#L905 Broadcast inputs from src rank to all other ranks with torch.dist backend. The `rank` here refer to the source rank on global process group (regardless of dist_group argument). """ device = torch.device(get_device_name() if not force_cpu_device else "cpu") if rank == src: if len(data) == 0: tensor_size = torch.tensor([0], dtype=torch.long, device=device) dist.broadcast(tensor_size, src=src, group=dist_group) else: serialized_data = pickle.dumps(data) size = len(serialized_data) tensor_data = torch.ByteTensor(np.frombuffer(serialized_data, dtype=np.uint8)).to(device) tensor_size = torch.tensor([size], dtype=torch.long, device=device) dist.broadcast(tensor_size, src=src, group=dist_group) dist.broadcast(tensor_data, src=src, group=dist_group) return data else: tensor_size = torch.tensor([0], dtype=torch.long, device=device) dist.broadcast(tensor_size, src=src, group=dist_group) size = tensor_size.item() if size == 0: return [] tensor_data = torch.empty(size, dtype=torch.uint8, device=device) dist.broadcast(tensor_data, src=src, group=dist_group) serialized_data = bytes(tensor_data.cpu().numpy()) data = pickle.loads(serialized_data) return data async def get_named_tensor_buckets( iterable: Iterator[tuple[str, torch.Tensor]], bucket_bytes: int ) -> Iterator[list[tuple[str, torch.Tensor]]]: """ Group tensors into buckets based on a specified size in megabytes. Args: iterable: An iterator of tuples containing tensor names and tensors. bucket_bytes: The maximum size of each bucket in bytes. Yields: Lists of tuples, where each tuple contains a tensor name and its corresponding tensor. Example: >>> tensors = [('tensor1', torch.randn(1000, 1000)), ('tensor2', torch.randn(2000, 2000))] >>> for bucket in get_named_tensor_buckets(tensors, bucket_size_mb=10): ... print(bucket) [('tensor1', tensor(...)), ('tensor2', tensor(...))] """ if bucket_bytes <= 0: raise ValueError(f"bucket_bytes must be greater than 0, got {bucket_bytes}") current_bucket = [] current_size = 0 async for name, tensor in ensure_async_iterator(iterable): tensor_size = tensor.element_size() * tensor.numel() if current_size + tensor_size > bucket_bytes: if current_bucket: yield current_bucket current_bucket = [(name, tensor.clone())] current_size = tensor_size else: current_bucket.append((name, tensor.clone())) current_size += tensor_size if current_bucket: yield current_bucket ================================================ FILE: verl/workers/rollout/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The base tokenizer class, required for any hybrid engine based rollout or inference with vLLM. """ from abc import ABC, abstractmethod import numpy as np import torch __all__ = ["HybridEngineBaseTokenizer"] class HybridEngineBaseTokenizer(ABC): """the tokenizer property and function name should align with HF's to meet vllm requirement""" @property @abstractmethod def vocab_size(self): """ `int`: Size of the base vocabulary (without the added tokens). """ pass @property @abstractmethod def pad_token_id(self): """ `Optional[int]`: Id of the padding token in the vocabulary. Returns `None` if the token has not been set. """ pass @property @abstractmethod def eos_token_id(self): """ `Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been set. """ pass @property @abstractmethod def all_special_ids(self) -> list[int]: """ `List[int]`: List the ids of the special tokens(`''`, `''`, etc.) mapped to class attributes. """ pass @property @abstractmethod def all_special_tokens(self) -> list[str]: """ `List[str]`: A list of the unique special tokens (`''`, `''`, ..., etc.). Convert tokens of `tokenizers.AddedToken` type to string. """ pass @abstractmethod def encode(self, text): """ Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Args: text (`str`, `List[str]` or `List[int]`): The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers. text_pair (`str`, `List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers. """ pass @abstractmethod def decode( self, token_ids: int | list[int] | np.ndarray | torch.Tensor, skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, **kwargs, ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces`. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ pass @abstractmethod def convert_ids_to_tokens(self, ids: int | list[int], skip_special_tokens: bool = False) -> str | list[str]: """ Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and added tokens. Args: ids (`int` or `List[int]`): The token id (or token ids) to convert to tokens. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. Returns: `str` or `List[str]`: The decoded token(s). """ pass @abstractmethod def get_added_vocab(self) -> dict[str, int]: """ Returns the added tokens in the vocabulary as a dictionary of token to index. Results might be different from the fast call because for now we always add the tokens even if they are already in the vocabulary. This is something we should change. Returns: `Dict[str, int]`: The added tokens. """ pass @abstractmethod def convert_tokens_to_string(self, tokens: list[str]) -> str: """ Converts a sequence of tokens in a single string. The most simple way to do it is `" ".join(tokens)` but we often want to remove sub-word tokenization artifacts at the same time. Args: tokens (`List[str]`): The token to join in a string. Returns: `str`: The joined tokens. """ pass @property def is_fast(self): return False ================================================ FILE: verl/workers/rollout/trtllm_rollout/trtllm_async_rollout.md ================================================ # Running VeRL with TensorRT-LLM Rollout We provide initial support for [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) as an asynchronous rollout engine in VERL's reinforcement learning pipeline. It covers key features such as distributed inference with Ray-based orchestration, dynamic weight updates via IPC (Inter-Process Communication), and efficient GPU memory management for GRPO training. TRT-LLM rollout uses hybrid engine colocate mode, where training and inference workers are colocated on the same GPUs. Memory is managed via `resume()`/`release()` APIs to enable GPU sharing between training and inference workloads. While the current design factors in multi-node use cases, more extensive multi-node testing and functionality will be delivered in the near future. Current focus is on FSDP and Megatron backend support for Qwen model variants. --- ## 1. Quick Start ```bash # GRPO with FSDP training engine and TP1 >> bash examples/grpo_trainer/run_qwen2-7b_math_trtllm.sh 1 ``` Note that using the TRT-LLM rollout requires setting the following environment variables before launching the Ray cluster, as included in the above script. ```bash # Clean all SLURM/MPI/PMIx env to avoid pmix mismatch error. for v in $(env | awk -F= '/^(PMI|PMIX|MPI|OMPI|SLURM)_/{print $1}'); do unset "$v" done ``` ## 2. Architecture Design ### 2.1 High-Level Component Diagram ```mermaid %%{init: {'theme':'base', 'themeVariables': { 'fontSize':'18px', 'edgeLabelBackground':'#eeeeee'}}}%% flowchart TB space1[" "] style space1 fill:none,stroke:none subgraph VERL["VERL Training Pipeline"] subgraph Workers["Training Workers"] Actor["Actor Worker"] Critic["Critic Worker"] RefModel["Ref Model Worker"] end Actor -->|Weight Updates
IPC| Rollout["TensorRT-LLM Rollout"] subgraph RayCluster["Rollout Workers
(Ray Cluster)"] space2[" "] style space2 fill:none,stroke:none subgraph AsyncRollout["ServerAdapter
(per DP rank)"] DPLeader["• DP Leader coordination"] IPCMgmt["• IPC handle management"] HTTPAdapter["• HTTP adapter for server communication"] end AsyncRollout -->|HTTP/REST API| HTTPServer subgraph HTTPServer["TRTLLMHttpServer
(Ray Actor per Replica)"] OpenAI["• OpenAI Server wrapper"] EngMgmt["• AsyncLLM engine management"] MemMgmt["• Memory management (resume/release)"] end HTTPServer --> AsyncLLM subgraph AsyncLLM["TensorRT-LLM
AsyncLLM Engine"] GPUWorkers["• GPU workers (Tensor Parallel)"] KVCache["• KV Cache management"] CUDAGraph["• CUDA Graph optimization"] end end end space1 ~~~ VERL style VERL fill:#e1f5ff style RayCluster fill:#fff4e6 style AsyncRollout fill:#f3e5f5 style HTTPServer fill:#e8f5e9 style AsyncLLM fill:#fce4ec ``` ### 2.2 Agent Loop Architecture TRT-LLM rollout follows the same Agent Loop architecture described in the [VERL documentation](https://verl.readthedocs.io/en/latest/advance/agent_loop.html). With TensorRT-LLM rollout, the AsyncLLM engine runs in the same process as the TRTLLMHttpServer (Ray actor). The engine spawns Ray workers as ModelRunner through Ray's native orchestration with placement groups. AsyncLLM engine communicates with Ray workers through TensorRT-LLM's internal communication layer. When the server receives a request, it directly calls the AsyncLLM engine to generate response_ids. The Ray workers are separate processes from FSDP/Megatron-LM workers but are co-located on the same GPUs in hybrid engine mode. The diagram below illustrates TRT-LLM's implementation in hybrid engine mode (Ray Workers and FSDP workers share GPUs): ```mermaid flowchart TB generate[generate] generate --> Server Server[TRTLLMHttpServer
AsyncLLM Engine] Server --> Workers subgraph Workers["TRT-LLM group (TP4)"] direction LR subgraph W0[ ] RW0[Ray Worker-0] F0[FSDP-0] end subgraph W1[ ] RW1[Ray Worker-1] F1[FSDP-1] end subgraph W2[ ] RW2[Ray Worker-2] F2[FSDP-2] end subgraph W3[ ] RW3[Ray Worker-3] F3[FSDP-3] end end style Server fill:#ffb6c1 style RW0 fill:#ffffe0 style RW1 fill:#ffffe0 style RW2 fill:#ffffe0 style RW3 fill:#ffffe0 style F0 fill:#ffb6c1 style F1 fill:#ffb6c1 style F2 fill:#ffb6c1 style F3 fill:#ffb6c1 style W0 fill:#d3d3d3 style W1 fill:#d3d3d3 style W2 fill:#d3d3d3 style W3 fill:#d3d3d3 style Workers fill:#f5f5f5 ``` ### 2.3 Ray Placement Group Architecture 1. **Placement APIs & GPU Assignment**: TRT-LLM rollout leverages TRT-LLM's Ray-based APIs (`placement_groups`, `placement_bundle_indices`, `per_worker_gpu_share`) to control GPU placement. Each replica (corresponding to one `TRTLLMHttpServer`) is assigned GPU bundles from placement groups based on its replica rank and TP size. 2. **Server Placement**: `TRTLLMHttpServer` is pinned to the same node as its first bundle using `NodeAffinitySchedulingStrategy`, ensuring efficient communication between the HTTP server and its Ray workers. 3. **GPU Sharing**: In hybrid engine mode, training and inference workers share GPUs. Memory is managed via `resume()`/`release()` APIs. The resource pool uses `max_colocate_count=3` internally to support colocation of ActorRollout, RewardModel, and Critic workers. 4. **Multi-Node Design**: The placement group slicing algorithm supports spanning multiple placement groups for multi-node deployments. **Note**: Formal multi-node testing and functionality will be delivered in subsequent MRs. The following diagram shows an example of TP=4 and DP=2. Replica 0 takes bundles 0-3 and Replica 1 takes bundles 4-7 from the same placement group, with each replica managing TP workers across its assigned bundles: ```mermaid flowchart TB subgraph RayCluster["Ray Cluster Resource Pool"] subgraph PG0["Placement Group 0 (Node 0)"] B0_0["Bundle 0: GPU 0"] B0_1["Bundle 1: GPU 1"] B0_2["Bundle 2: GPU 2"] B0_3["Bundle 3: GPU 3"] B0_4["Bundle 4: GPU 4"] B0_5["Bundle 5: GPU 5"] B0_6["Bundle 6: GPU 6"] B0_7["Bundle 7: GPU 7"] end subgraph PG1["Placement Group 1 (Node 1)"] B1_0["Bundle 0: GPU 0"] B1_1["Bundle 1: GPU 1"] B1_2["Bundle 2: GPU 2"] B1_3["Bundle 3: GPU 3"] B1_4["Bundle 4: GPU 4"] B1_5["Bundle 5: GPU 5"] B1_6["Bundle 6: GPU 6"] B1_7["Bundle 7: GPU 7"] end PG0 --> Assignment PG1 --> Assignment Assignment["Assigned to TRTLLMReplica"] Assignment --> Replica0 Assignment --> Replica1 Replica0["Replica 0
(bundles 0-3 from PG0)
TP=4, DP=2"] Replica1["Replica 1
(bundles 4-7 from PG0)
TP=4, DP=2"] end style PG0 fill:#e3f2fd style PG1 fill:#e3f2fd style Replica0 fill:#c8e6c9 style Replica1 fill:#c8e6c9 ``` --- ## 3. Core Components ### 3.1 `TRTLLMHttpServer` **Purpose**: Ray actor that wraps TensorRT-LLM's AsyncLLM engine and exposes an OpenAI-compatible HTTP API. **Key Responsibilities**: - Initialize and manage AsyncLLM engine with placement group constraints - Wrap AsyncLLM with OpenAIServer to expose HTTP endpoints - Handle HTTP server lifecycle (launch, shutdown) - Process generation requests with sampling parameters - Coordinate memory management (wake_up/sleep) for GPU sharing with training workers ### 3.2 `TRTLLMReplica` **Purpose**: Manages the mapping between replicas and Ray placement groups, orchestrating server deployment. **Key Responsibilities**: - Calculate placement group and bundle index assignments per replica - Pin TRTLLMHttpServer to specific nodes using NodeAffinitySchedulingStrategy - Launch and coordinate HTTP servers across distributed nodes - Validate placement group configurations ### 3.3 `ServerAdapter` **Purpose**: Rollout worker that handles weight updates, memory management, and generation via HTTP adapter. Each DP rank has one leader (the first TP rank within that DP group), and that leader coordinates weight updates to the corresponding TRTLLMHttpServer replica. **Key Responsibilities**: - Act as DP leader for weight synchronization across exclude_dp mesh - Convert PyTorch tensors to IPC handles for zero-copy weight updates - Stream weight updates in chunks to avoid memory exhaustion - Coordinate resume/release operations for memory management - Initialize HTTP adapter for server communication ### 3.4 `AsyncTRTLLMHttpAdapter` **Purpose**: HTTP client for communicating with TRTLLMHttpServer. **Key Features**: - Async request handling with retry logic - Connection pooling for high throughput - Exponential backoff on failures - Timeout management --- ## 4. Data Flow Diagrams ### 4.1 Generation Request Flow ```mermaid sequenceDiagram participant Client as Client/Actor participant Rollout as ServerAdapter participant Adapter as AsyncHttpAdapter participant Server as TRTLLMHttpServer participant AsyncLLM as AsyncLLM Engine Client->>Rollout: generate(prompts) rect rgb(240, 248, 255) Note over Rollout: Init adapter if needed end Rollout->>Adapter: POST /v1/completions
{prompt_ids, sampling_params} rect rgb(255, 250, 240) Note over Adapter: Retry loop with backoff end Adapter->>Server: HTTP POST rect rgb(245, 255, 245) Note over Server: Parse request
Validate params end Server->>AsyncLLM: generate_async() rect rgb(255, 245, 245) Note over AsyncLLM: Schedule to execution queue Note over AsyncLLM: Run inference (TP workers)
- Forward pass
- Sample tokens
- Update KV cache end AsyncLLM-->>Server: Output (token_ids, log_probs) Server-->>Adapter: JSON response Adapter-->>Rollout: TokenOutput Rollout-->>Client: Results ``` ================================================ FILE: verl/workers/rollout/trtllm_rollout/trtllm_async_server.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import os from typing import Any, Optional import ray import torch from omegaconf import DictConfig from ray.actor import ActorHandle from ray.util import placement_group_table from ray.util.placement_group import PlacementGroup from verl.single_controller.ray import SubRayResourcePool from verl.utils.config import omega_conf_to_dataclass from verl.utils.net_utils import is_valid_ipv6_address from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.replica import RolloutMode, RolloutReplica, TokenOutput from verl.workers.rollout.trtllm_rollout.trtllm_rollout import ServerAdapter from verl.workers.rollout.utils import get_max_position_embeddings, qwen2_5_vl_dedup_image_tokens, run_uvicorn logger = logging.getLogger(__file__) logger.setLevel(logging.INFO) @ray.remote class TRTLLMHttpServer: """TensorRT LLM HTTP server in single node. Args: config (DictConfig): full config. model_config (HFModelConfig): model config. is_reward_model (bool): whether this is a reward model. rollout_mode (RolloutMode): rollout mode. workers (list[ActorHandle]): list of rollout workers. replica_rank (int): replica rank, a replica may contain multiple nodes. max_colocate_count (int): max colocate count. pgs (list[PlacementGroup]): placement groups. bundle_indices (list[list[int]]): bundle indices. """ def __init__( self, config: RolloutConfig, model_config: HFModelConfig, is_reward_model: bool, rollout_mode: RolloutMode, workers: list[ActorHandle], replica_rank: int, max_colocate_count: int, pgs: list[PlacementGroup] = None, bundle_indices: list[list[int]] = None, ): os.environ["TRT_LLM_DISABLE_LOAD_WEIGHTS_IN_PARALLEL"] = "1" assert torch.cuda.is_available(), "TRTLLM http server should run on GPU node" self.config: RolloutConfig = omega_conf_to_dataclass(config) self.model_config: HFModelConfig = omega_conf_to_dataclass(model_config, dataclass_type=HFModelConfig) self.is_reward_model = is_reward_model max_position_embeddings = get_max_position_embeddings(self.model_config.hf_config) if self.config.max_model_len is None: self.config.max_model_len = max_position_embeddings else: if self.config.max_model_len > max_position_embeddings: raise ValueError( f"max_model_len ({self.config.max_model_len}) should be less than or equal to " f"max_position_embeddings ({max_position_embeddings})" ) self.rollout_mode = rollout_mode self.workers = workers self.replica_rank = replica_rank self.max_colocate_count = max_colocate_count self.pgs = pgs self.bundle_indices = bundle_indices # model weights version, set by ServerAdapter when update weights. self.global_steps = None if self.rollout_mode != RolloutMode.HYBRID and self.config.load_format == "dummy": logger.warning(f"rollout mode is {self.rollout_mode}, load_format is dummy, set to auto") self.config.load_format = "auto" self.is_vlm_model = ( self.model_config.hf_config is not None and hasattr(self.model_config.hf_config, "vision_config") ) or hasattr(self.model_config, "vision_config") # used for http server self._server_address = ray.util.get_node_ip_address().strip("[]") self._server_port = None logger.info(f"TRTLLMHttpServer, replica_rank: {self.replica_rank}") self.sampling_args = { "detokenize": False, "end_id": -1, "pad_id": self.model_config.hf_config.pad_token_id, "stop_token_ids": [self.model_config.hf_config.eos_token_id], "include_stop_str_in_output": True, } def get_server_address(self): """Get http server address and port.""" assert self._server_port is not None, "http server is not launched, port is None" return self._server_address, self._server_port async def launch_server(self): from tensorrt_llm import AsyncLLM from tensorrt_llm.llmapi import CapacitySchedulerPolicy, CudaGraphConfig, KvCacheConfig, SchedulerConfig from tensorrt_llm.serve import OpenAIServer assert self.config.pipeline_model_parallel_size == 1, "pipeline_model_parallel_size > 1 is not supported yet" engine_kwargs = self.config.get("engine_kwargs", {}).get("trtllm", {}) or {} kv_cache_config = KvCacheConfig( enable_block_reuse=self.config.enable_prefix_caching, free_gpu_memory_fraction=self.config.gpu_memory_utilization, ) per_worker_gpu_share = 1.0 / self.max_colocate_count quantization = self.config.quantization if quantization is not None: if quantization == "fp8": FP8_BLOCK_QUANT_KWARGS = { "activation_scheme": "dynamic", "fmt": "e4m3", "quant_method": "fp8", "weight_block_size": [128, 128], } engine_kwargs["model_kwargs"] = {"quantization_config": FP8_BLOCK_QUANT_KWARGS} if self.config.load_format != "dummy": raise ValueError("FP8 quantization is only supported for dummy load format") else: raise ValueError(f"Currently only support fp8 quantization, got: {quantization}") llm_kwargs = { "model": self.model_config.local_path, "backend": "pytorch", "dtype": self.config.dtype, "enable_chunked_prefill": self.config.enable_chunked_prefill, "skip_tokenizer_init": self.config.skip_tokenizer_init, "orchestrator_type": "ray", "kv_cache_config": kv_cache_config, "max_seq_len": self.config.max_model_len, "max_batch_size": self.config.max_num_seqs, "max_num_tokens": self.config.max_num_batched_tokens, "tensor_parallel_size": self.config.tensor_model_parallel_size, "pipeline_parallel_size": self.config.pipeline_model_parallel_size, "moe_expert_parallel_size": self.config.expert_parallel_size, "moe_tensor_parallel_size": self.config.moe_tensor_parallel_size, "load_format": self.config.load_format, "trust_remote_code": self.model_config.trust_remote_code, "placement_groups": self.pgs, "placement_bundle_indices": self.bundle_indices, "per_worker_gpu_share": per_worker_gpu_share, "enable_sleep": self.config.enable_sleep_mode, "allreduce_strategy": "NCCL", "sampler_type": "TRTLLMSampler", **engine_kwargs, } self_defined_extension = { "ray_worker_extension_cls": "verl.workers.rollout.trtllm_rollout.trtllm_worker_extension.WorkerExtension", } if self.is_vlm_model: llm_kwargs.update(self_defined_extension) else: llm_kwargs.update( { "ray_worker_extension_cls": "tensorrt_llm.llmapi.rlhf_utils.WorkerExtension", } ) if self.is_reward_model: llm_kwargs.update( { "cuda_graph_config": None, "disable_overlap_scheduler": True, } ) else: llm_kwargs.update( { "cuda_graph_config": CudaGraphConfig( enable_padding=True, batch_sizes=self.config.cudagraph_capture_sizes, max_batch_size=0 if self.config.cudagraph_capture_sizes else self.config.max_num_seqs, ), "scheduler_config": SchedulerConfig( capacity_scheduler_policy=CapacitySchedulerPolicy.MAX_UTILIZATION, ), } ) self.llm = await AsyncLLM(**llm_kwargs) import inspect init_params = inspect.signature(OpenAIServer.__init__).parameters if "generator" in init_params: trtllm_server = OpenAIServer( generator=self.llm, model=self.model_config.local_path, tool_parser=None, server_role=None, metadata_server_cfg=None, ) else: trtllm_server = OpenAIServer( llm=self.llm, model=self.model_config.local_path, tool_parser=None, server_role=None, metadata_server_cfg=None, ) app = trtllm_server.app self._server_port, self._server_task = await run_uvicorn(app, None, self._server_address) async def generate( self, prompt_ids: str | list[int], sampling_params: dict[str, Any], request_id: str, image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, ) -> TokenOutput: from tensorrt_llm.llmapi import SamplingParams max_tokens = min(self.config.response_length, self.config.max_model_len - len(prompt_ids)) sampling_params["max_tokens"] = max_tokens sampling_params["logprobs"] = 1 if sampling_params.pop("logprobs", False) else None if sampling_params["top_k"] == -1: sampling_params["top_k"] = 0 sampling_params.update(self.sampling_args) trt_llm_sampling_params = SamplingParams(**sampling_params) if self.is_vlm_model and (image_data or video_data): deduped_ids = qwen2_5_vl_dedup_image_tokens(prompt_ids, self.model_config.processor) org_prompt = self.llm.tokenizer.decode(deduped_ids) input_dict = { "prompt": org_prompt, "multi_modal_data": {}, "mm_processor_kwargs": {}, } if image_data: input_dict["multi_modal_data"]["image"] = image_data if video_data: input_dict["multi_modal_data"]["video"] = video_data outputs = await self.llm.generate_async( inputs=input_dict, sampling_params=trt_llm_sampling_params, ) else: outputs = await self.llm.generate_async( inputs=prompt_ids, sampling_params=trt_llm_sampling_params, ) token_ids = outputs.outputs[0].token_ids log_probs = None if outputs.outputs[0].logprobs is not None: # When logprobs=1, TRT-LLM returns only the sampled token's logprob at each position log_probs = [list(d.values())[0].logprob for d in outputs.outputs[0].logprobs] return TokenOutput(token_ids=token_ids, log_probs=log_probs, extra_fields={"global_steps": self.global_steps}) async def set_global_steps(self, global_steps: int): """Set the global steps of the model weights.""" self.global_steps = global_steps async def abort_all_requests(self): raise NotImplementedError async def resume_generation(self): raise NotImplementedError async def wake_up(self): if self.rollout_mode == RolloutMode.HYBRID: # In hybrid mode, rollout is wake up in `update_weights` raise ValueError(f"wake_up not support rollout_mode {self.rollout_mode}") if self.rollout_mode == RolloutMode.COLOCATED: await self.llm.resume(tags=ServerAdapter.get_full_tags()) elif self.rollout_mode == RolloutMode.STANDALONE: logger.info("skip wake_up in standalone mode") async def sleep(self): if not self.config.free_cache_engine: return if self.rollout_mode == RolloutMode.HYBRID: await self.llm.release(tags=ServerAdapter.get_full_tags()) elif self.rollout_mode == RolloutMode.COLOCATED: await self.llm.release(tags=ServerAdapter.get_full_tags()) elif self.rollout_mode == RolloutMode.STANDALONE: logger.info("skip sleep in standalone mode") async def report_device_ids(self) -> list[str]: """Report GPU device UUIDs from TRT-LLM workers.""" return await self.llm.collective_rpc( "report_device_id", unique_reply_rank=0, ) class TRTLLMReplica(RolloutReplica): def __init__( self, replica_rank: int, config: RolloutConfig, model_config: DictConfig, gpus_per_node: int = 8, is_reward_model: bool = False, ) -> None: super().__init__(replica_rank, config, model_config, gpus_per_node, is_reward_model) self.node_ip = ray.util.get_node_ip_address().strip("[]") def rollout_worker_use_gpu(self) -> bool: return False def get_pgs_and_bundle_indices(self) -> tuple[list[PlacementGroup], list[list[int]]]: """Get placement groups and bundle indices for the replica.""" start_pg_index = 0 local_bundle_index = 0 # For SubRayResourcePool, the replica is assigned sub pool specific for this replica. if isinstance(self.resource_pool, SubRayResourcePool): assert self.resource_pool.subgroup_world_size == self.world_size, ( "Subgroup world size must be equal to world size" ) local_bundle_index = self.resource_pool.start_bundle_index # For RayResourcePool, the replica is assigned to entire resource pool. # We need to find start pg index and local bundle index based on replica rank. else: local_bundle_index = self.world_size * self.replica_rank while local_bundle_index >= self.resource_pool.pgs[start_pg_index].bundle_count: start_pg_index += 1 local_bundle_index -= self.resource_pool.pgs[start_pg_index].bundle_count assert ( start_pg_index < len(self.resource_pool.pgs) and local_bundle_index < self.resource_pool.pgs[start_pg_index].bundle_count ), "Start pg index or local bundle index out of range" # Global Bundle View for Replica x 2 & TP=4: # ┌───────────────────┬───────────────────┐ # │ Placement Group 0 │ Placement Group 1 │ # ├────┬────┬────┬────┼────┬────┬────┬────┤ # │ 0 │ 1 │ 2 │ 3 │ 0 │ 1 │ 2 │ 3 │ # └────┴────┴────┴────┴────┴────┴────┴────┘ # └───────────────┘ └───────────────┘ # Replica 0 Replica 1 # (4 GPUs) (4 GPUs) left_bundle_count = self.world_size pgs = [] bundle_indices = [] for pg in self.resource_pool.pgs[start_pg_index:]: if left_bundle_count == 0: break left_bundle_count_in_pg = min(left_bundle_count, pg.bundle_count - local_bundle_index) pg_bundle_indices = [local_bundle_index + idx for idx in range(left_bundle_count_in_pg)] pgs.append(pg) bundle_indices.append(pg_bundle_indices) left_bundle_count -= left_bundle_count_in_pg local_bundle_index = 0 assert left_bundle_count == 0, "all bundle indices should be assigned" return pgs, bundle_indices async def launch_servers(self): assert self.nnodes == 1, "TRTLLMReplica doesn't support multiple nodes for single replica yet." assert self.resource_pool.pgs is not None, "placement groups are not initialized" pgs, bundle_indices = self.get_pgs_and_bundle_indices() # Check server process should be launched on the same node as first bundle of first pg. first_pg_data = placement_group_table(pgs[0]) node_id = first_pg_data["bundles_to_node_id"][bundle_indices[0][0]] print(f"TRTLLMReplica: {self.replica_rank}") print(f"pg node_id: {node_id}") print(f"pgs: {pgs}") print(f"bundle_indices: {bundle_indices}") # TRTLLMReplica is a 1:1 map from replica to TRTLLMHttpServer. name = ( f"trtllm_server_{self.replica_rank}" if not self.is_reward_model else f"trtllm_server_reward_{self.replica_rank}" ) server = TRTLLMHttpServer.options( scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy( node_id=node_id, soft=False, ), runtime_env={"env_vars": {"RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES": "1", "NCCL_CUMEM_ENABLE": "0"}}, name=name, max_concurrency=self.max_concurrency, ).remote( config=self.config, model_config=self.model_config, is_reward_model=self.is_reward_model, rollout_mode=self.rollout_mode, workers=self.workers, replica_rank=self.replica_rank, max_colocate_count=self.resource_pool.max_colocate_count, pgs=pgs, bundle_indices=bundle_indices, ) self.servers.append(server) # launch http server in each node await asyncio.gather(*[server.launch_server.remote() for server in self.servers]) # get http server address from first server server_address, server_port = await self.servers[0].get_server_address.remote() self._server_handle = self.servers[0] self._server_address = ( f"[{server_address}]:{server_port}" if is_valid_ipv6_address(server_address) else f"{server_address}:{server_port}" ) ================================================ FILE: verl/workers/rollout/trtllm_rollout/trtllm_rollout.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import asyncio import base64 import contextlib import gc import logging import os import pickle import threading from contextlib import asynccontextmanager from typing import Any, Generator, Optional import aiohttp import pynvml import ray import torch import torch.distributed as dist from torch.distributed.device_mesh import DeviceMesh, init_device_mesh from torch.multiprocessing.reductions import reduce_tensor from verl.utils.device import get_torch_device from verl.utils.net_utils import is_valid_ipv6_address from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.base import BaseRollout logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) # Default configuration constants DEFAULT_TIMEOUT = 60.0 DEFAULT_MAX_ATTEMPTS = 3 DEFAULT_RETRY_DELAY = 2.0 DEFAULT_MAX_CONNECTIONS = 2000 DEFAULT_MAX_WAIT_TIME = 300.0 @contextlib.contextmanager def nvml_context(): """Context manager for NVML initialization and shutdown. Raises: RuntimeError: If NVML initialization fails """ try: pynvml.nvmlInit() yield except pynvml.NVMLError as e: raise RuntimeError(f"Failed to initialize NVML: {e}") from e finally: try: pynvml.nvmlShutdown() except pynvml.NVMLError: pass _NVML_INITIALIZED = False _NVML_LOCK = threading.Lock() def get_device_uuid(id: str | int) -> str: """Get the UUID of a CUDA device using NVML.""" id = int(id) # pynvml expects int; ray.get_gpu_ids() may return str global _NVML_INITIALIZED with _NVML_LOCK: if not _NVML_INITIALIZED: try: pynvml.nvmlInit() _NVML_INITIALIZED = True except pynvml.NVMLError as e: raise RuntimeError(f"Failed to initialize NVML: {e}") from e # Get the device handle and UUID try: handle = pynvml.nvmlDeviceGetHandleByIndex(id) uuid = pynvml.nvmlDeviceGetUUID(handle) # Ensure the UUID is returned as a string, not bytes if isinstance(uuid, bytes): return uuid.decode("utf-8") elif isinstance(uuid, str): return uuid else: raise RuntimeError(f"Unexpected UUID type: {type(uuid)} for device {id} (global index: {id})") except pynvml.NVMLError as e: raise RuntimeError(f"Failed to get device UUID for device {id} (global index: {id}): {e}") from e async def _read_async_response(resp: aiohttp.ClientResponse) -> dict[str, Any]: if resp.status == 204 or (resp.content_length == 0): return {} try: return await resp.json(content_type=None) except Exception: try: text = await resp.text() except Exception: return {} return { "content_type": (resp.headers.get("Content-Type") or ""), "text": text, } class AsyncTRTLLMHttpAdapter: def __init__( self, host: str, port: int, timeout: float = DEFAULT_TIMEOUT, max_attempts: int = DEFAULT_MAX_ATTEMPTS, retry_delay: float = DEFAULT_RETRY_DELAY, max_connections: int = DEFAULT_MAX_CONNECTIONS, ): self.host = host self.port = port self.timeout = timeout self.max_attempts = max_attempts self.retry_delay = retry_delay self.max_connections = max_connections @asynccontextmanager async def _get_session(self) -> aiohttp.ClientSession: """Context manager for safe session access with proper connection pooling. Yields: aiohttp.ClientSession: Session instance for making HTTP requests Note: This method creates a new session for each request to avoid resource competition while still maintaining proper connection pooling through the shared connector. """ # Create a new session for each request to avoid resource competition connector = aiohttp.TCPConnector( limit=self.max_connections, limit_per_host=self.max_connections // 4, ttl_dns_cache=300, use_dns_cache=True, ) timeout = aiohttp.ClientTimeout(total=self.timeout) session = aiohttp.ClientSession(connector=connector, timeout=timeout) try: yield session finally: # Always close the session to free up resources if not session.closed: await session.close() async def _make_async_request( self, endpoint: str, payload: Optional[dict[str, Any]] = None, timeout: float = DEFAULT_TIMEOUT, method: str = "POST", return_status: bool = False, ) -> dict[str, Any] | int: """Make an async HTTP request with retry logic and consistent error handling. Args: endpoint (str): The API endpoint to call (without leading slash) payload (Optional[Dict[str, Any]], optional): The JSON payload to send. Defaults to empty dict if None. method (str, optional): HTTP method to use. Defaults to "POST". Returns: Dict[str, Any]: The JSON response from the server Raises: aiohttp.ClientResponseError: If the HTTP request fails with a client/server error RuntimeError: If all retry attempts are exhausted Note: - Uses exponential backoff for retries - Logs warnings for timeout and connection errors, errors for HTTP errors """ url = f"http://{self.host}:{self.port}/{endpoint}" for attempt in range(self.max_attempts): try: async with self._get_session() as session: if method.upper() == "GET": async with session.get(url, timeout=timeout) as response: response.raise_for_status() return response.status if return_status else await _read_async_response(response) else: async with session.post(url, json=payload or {}, timeout=timeout) as response: response.raise_for_status() return response.status if return_status else await _read_async_response(response) except asyncio.TimeoutError: logger.warning(f"Async request to {endpoint} timed out (attempt {attempt + 1})") except aiohttp.ClientConnectorError: logger.warning(f"Connection error for {endpoint} (attempt {attempt + 1})") except aiohttp.ClientResponseError as e: logger.error(f"HTTP error for {endpoint}: {e}") raise except Exception as e: logger.error(f"Unexpected error for {endpoint}: {e}") if attempt == self.max_attempts - 1: raise if attempt < self.max_attempts - 1: await asyncio.sleep(self.retry_delay * (2**attempt)) raise RuntimeError(f"Failed to complete async request to {endpoint} after {self.max_attempts} attempts") async def resume_memory_occupation(self, tags: list[str]): """Resume GPU memory occupation (async version). Similar to AsyncEngine, this method handles first-time weight reloading by calling release_memory_occupation if needed. Args: tags (Optional[List[str]], optional): List of tags to specify which memory to resume. If None, resumes all memory. Defaults to None. ["weights", "kv_cache"] Returns: Dict[str, Any]: Server response indicating memory resume status """ return await self._make_async_request("resume_memory", {"tags": tags}) async def release_memory_occupation(self, tags: list[str]): """Release GPU memory occupation temporarily (async version). Args: tags (Optional[List[str]], optional): List of tags to specify which memory to release. If None, releases all memory. Defaults to None. ["weights", "kv_cache"] Returns: Dict[str, Any]: Server response indicating memory release status """ return await self._make_async_request("release_memory", {"tags": tags}) async def update_weights(self, weights: dict[str, str]): """Update model weights from tensor data asynchronously. Args: weights: A dictionary that maps the device uuid of the weight handles. Returns: Dict[str, Any]: Server response containing update status """ return await self._make_async_request("update_weights", {"weights": weights}) class ServerAdapter(BaseRollout): _WEIGHTS_TAGS = [ "sampler", "drafter", "guided_decoder", "spec_resource_manager", "model_extra", "executor_extra", "model", "draft_model", ] @staticmethod def get_full_tags() -> list[str]: return ServerAdapter._WEIGHTS_TAGS + ["kv_cache"] def __init__( self, config: RolloutConfig, model_config: HFModelConfig, device_mesh: DeviceMesh, replica_rank: int = -1 ): if config.get("quantization", None) == "fp8": FP8_BLOCK_QUANT_KWARGS = { "activation_scheme": "dynamic", "fmt": "e4m3", "quant_method": "fp8", "weight_block_size": [128, 128], } fp8_block_quant_kwargs = dict(FP8_BLOCK_QUANT_KWARGS) model_config.hf_config.quantization_config = fp8_block_quant_kwargs super().__init__(config, model_config, device_mesh) self._adapter = None self.hybrid_device_mesh = None self.gpu_id = None self.is_leader_rank = None self.replica_rank = None self.is_dp_rank = None self._supports_partial_loading = None # hybrid mode if self.device_mesh is not None: assert device_mesh.mesh_dim_names.index("dp") == 0, "DP dim should always be the first dimension" # Clone a new device mesh for CPU backend only (used for internal ranks communication) device_mesh_kwargs = dict( mesh_shape=device_mesh.mesh.shape, mesh_dim_names=device_mesh.mesh_dim_names, ) self.hybrid_device_mesh = init_device_mesh("cpu", **device_mesh_kwargs) self.hybrid_device_mesh[self.hybrid_device_mesh.mesh_dim_names[1:]]._flatten(mesh_dim_name="exclude_dp") self.is_leader_rank = self.hybrid_device_mesh["exclude_dp"].get_local_rank() == 0 logger.info(f"is_dp_leader: {self.is_leader_rank}") logger.info(f"exclude_dp_rank = {self.hybrid_device_mesh['exclude_dp'].get_local_rank()}") logger.info(f"exclude_dp_size = {self.hybrid_device_mesh['exclude_dp'].size()}") self.gpu_id = ray.get_gpu_ids()[0] self.replica_rank = self.hybrid_device_mesh["dp"].get_local_rank() assert len(ray.get_gpu_ids()) == 1, "ServerAdapter should run on a single GPU node" else: rank = int(os.environ["RANK"]) self.replica_rank = replica_rank self.is_leader_rank = rank == 0 # Below is required for all modes. assert self.replica_rank >= 0, "replica_rank is not set" assert self.is_leader_rank is not None, "is_leader_rank is not set" self.node_ip = ray.util.get_node_ip_address().strip("[]") async def get_supports_partial_loading(self) -> bool: """Query and cache whether the model supports partial weight loading.""" if self._supports_partial_loading is not None: return self._supports_partial_loading await self._init_server_adapter() try: self._supports_partial_loading = await self.server_actor.supports_partial_loading.remote() except Exception as e: logger.warning(f"Failed to query partial loading support: {e}, defaulting to False") self._supports_partial_loading = False logger.info(f"Model supports partial loading: {self._supports_partial_loading}") return self._supports_partial_loading async def _init_server_adapter(self): if self._adapter is not None: return # Lazy init http server adapter because http server is launched after hybrid engine. self.server_actor = ray.get_actor(f"trtllm_server_{self.replica_rank}") server_address, server_port = await self.server_actor.get_server_address.remote() assert server_address == self.node_ip, f"server address: {server_address} != node_ip: {self.node_ip}" logger.debug(f"replica_rank={self.replica_rank}, server address: {server_address}, port: {server_port}") host = f"[{server_address}]" if is_valid_ipv6_address(server_address) else server_address self._adapter = AsyncTRTLLMHttpAdapter( host=host, port=server_port, timeout=self.config.server.timeout, max_attempts=self.config.server.max_attempts, retry_delay=self.config.server.retry_delay, max_connections=self.config.server.max_connections, ) async def resume(self, tags: list[str]): """Resume rollout weights or kv cache in GPU memory. Args: tag: weights or kv_cache. """ # Synchronize all ranks before resuming KV cache to ensure non-leader ranks # have completed actor offloading to CPU, preventing OOM issue. if "kv_cache" in tags and self.config.free_cache_engine: await asyncio.to_thread(dist.barrier, group=self.hybrid_device_mesh["exclude_dp"].get_group()) if self.is_leader_rank and self.config.free_cache_engine: if "weights" in tags: tags = self._WEIGHTS_TAGS elif "kv_cache" in tags: tags = ["kv_cache"] else: raise ValueError(f"Invalid tag: {tags}") await self._init_server_adapter() await self._adapter.resume_memory_occupation(tags=tags) async def release(self): """Release weights and kv cache in GPU memory.""" if self.is_leader_rank and self.config.free_cache_engine: await self._init_server_adapter() tags = self._WEIGHTS_TAGS + ["kv_cache"] await self._adapter.release_memory_occupation(tags=tags) async def update_weights_from_ipc_handles(self, device_handles): assert self.hybrid_device_mesh is not None, "hybrid_device_mesh is not set" """Update weights from IPC handles.""" if self.is_leader_rank: gathered_handles = [None for _ in range(self.hybrid_device_mesh["exclude_dp"].size())] else: gathered_handles = None await asyncio.to_thread( dist.gather_object, obj=device_handles, object_gather_list=gathered_handles, group_dst=0, group=self.hybrid_device_mesh["exclude_dp"].get_group(), ) if self.is_leader_rank: all_handles = {k: v for d in gathered_handles for k, v in d.items()} await self._adapter.update_weights(all_handles) await asyncio.to_thread(dist.barrier, group=self.hybrid_device_mesh["exclude_dp"].get_group()) async def update_weights( self, weights: Generator[tuple[str, torch.Tensor], None, None], global_steps: int = None, **kwargs ): assert self.hybrid_device_mesh is not None, "hybrid_device_mesh is not set" """Update the weights of the rollout model. Args: weights: A generator that yields the name of the weight tensor and the tensor itself. """ if self.is_leader_rank: await self._init_server_adapter() total_available_bytes = int(self.config.checkpoint_engine.update_weights_bucket_megabytes) * 1024 * 1024 if self.config.get("quantization", None) == "fp8": from verl.utils.trtllm.trtllm_fp8_utils import TRTLLMFP8QuantizerHelper fp8_quantizer_helper = TRTLLMFP8QuantizerHelper(self.model_config.hf_config.quantization_config) weights = fp8_quantizer_helper.quant_weights_by_name( weights, dtype=self.model_config.hf_config.dtype, ) try: device_uuid = get_device_uuid(int(self.gpu_id)) except Exception as e: logger.error(f"Failed to get device UUID in update_weights(): {e}") device_uuid = None raise e cur_available_bytes = total_available_bytes cur_handles = [] async def flush(): nonlocal cur_available_bytes, cur_handles if not cur_handles: return serialized_device_handles = {device_uuid: base64.b64encode(pickle.dumps(cur_handles)).decode("utf-8")} await self.update_weights_from_ipc_handles(serialized_device_handles) cur_available_bytes = total_available_bytes cur_handles = [] # Query if model supports partial loading supports_partial_loading = await self.get_supports_partial_loading() for name, param in weights: if supports_partial_loading: size_in_bytes = param.element_size() * param.numel() if size_in_bytes > cur_available_bytes: await flush() assert cur_available_bytes >= size_in_bytes, ( f"cur_available_bytes: {cur_available_bytes:,} size_in_bytes: {size_in_bytes:,} name: {name}" ) cur_available_bytes -= size_in_bytes handle = reduce_tensor(param.detach()) cur_handles.append((name, handle)) await flush() if self.is_leader_rank: # Finalize update weights await self._adapter.update_weights(None) if global_steps is not None: await self.server_actor.set_global_steps.remote(global_steps) await asyncio.to_thread(dist.barrier, group=self.hybrid_device_mesh["exclude_dp"].get_group()) del weights gc.collect() get_torch_device().empty_cache() def _get_attribute(self, name: str): return getattr(self, name) ================================================ FILE: verl/workers/rollout/trtllm_rollout/trtllm_worker_extension.py ================================================ # Copyright 2026 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import inspect from typing import Optional from tensorrt_llm import serialization from tensorrt_llm._ray_utils import control_action_decorator from tensorrt_llm._torch.modules.fused_moe.moe_load_balancer import MoeLoadBalancer from tensorrt_llm._torch.utils import get_device_uuid from tensorrt_llm.llmapi.rlhf_utils import WorkerExtension as TrtllmWorkerExtension from tensorrt_llm.logger import logger class WorkerExtension(TrtllmWorkerExtension): def __init__(self): pass @control_action_decorator def supports_partial_loading(self) -> bool: """Check if the model supports partial weight loading.""" try: model = self.engine.model_engine.model load_weights_args = inspect.getfullargspec(model.load_weights).args return "allow_partial_loading" in load_weights_args except Exception as e: logger.warning(f"Failed to check partial loading support: {e}") return False @control_action_decorator def update_weights(self, ipc_handles: Optional[dict] = None): try: if not hasattr(self.engine.model_engine.model, "first_pre_reload_weights"): for module in self.engine.model_engine.model.modules(): if hasattr(module, "pre_reload_weights") and not getattr(module, "_weights_removed", False): module.pre_reload_weights() self.engine.model_engine.model.first_pre_reload_weights = True if ipc_handles is not None: logger.info("Update weights from IPC handles") device_uuid = get_device_uuid(self.device_id) if device_uuid not in ipc_handles: raise ValueError(f"Device UUID {device_uuid} not found in ipc_handles") weights = {} serialized_handles = ipc_handles[device_uuid] if isinstance(serialized_handles, str): # Data is base64-encoded pickled bytes - deserialize it # using restricted unpickler from tensorrt_llm.serialization logger.info("Deserializing base64-encoded weight handles") decoded_data = base64.b64decode(serialized_handles) # Allow basic builtins and torch tensor reconstruction classes approved_imports = { "builtins": [ "list", "tuple", "str", "int", "float", "bool", "bytes", "dict", "NoneType", "type", ], "torch": [ "Tensor", "FloatTensor", "DoubleTensor", "HalfTensor", "BFloat16Tensor", "IntTensor", "LongTensor", "ShortTensor", "CharTensor", "ByteTensor", "BoolTensor", "Size", "dtype", "device", "float32", "float16", "int32", "int64", "int16", "int8", "uint8", "bool", ], "torch.multiprocessing.reductions": [ "rebuild_cuda_tensor", "rebuild_tensor", ], "torch._utils": [ "_rebuild_tensor_v2", ], "torch.storage": [ "_load_from_bytes", "_TypedStorage", "UntypedStorage", "TypedStorage", ], } all_handles = serialization.loads( decoded_data, approved_imports=approved_imports, ) # Verify the result is a list as expected if not isinstance(all_handles, list): raise ValueError(f"Deserialized data must be a list, got {type(all_handles).__name__} instead") else: # Data is already in the correct format (backward compatibility) all_handles = serialized_handles for param_name, tensor_handle in all_handles: func, args = tensor_handle list_args = list(args) list_args[6] = self.device_id tensor = func(*list_args) weights[param_name] = tensor logger.info(f"weights key size: {len(weights.keys())}") # Check if model supports partial loading and use appropriate strategy model = self.engine.model_engine.model load_weights_args = inspect.getfullargspec(model.load_weights).args supports_partial_loading = "allow_partial_loading" in load_weights_args if supports_partial_loading: self.engine.model_engine.model_loader.reload(model, weights, allow_partial_loading=True) else: self.engine.model_engine.model_loader.reload(model, weights, allow_partial_loading=False) else: logger.info("Finalize update weights") for module in self.engine.model_engine.model.modules(): if hasattr(module, "process_weights_after_loading") and not getattr( module, "_weights_removed", False ): module.process_weights_after_loading() if hasattr(module, "post_load_weights") and not getattr(module, "_weights_removed", False): module.post_load_weights() moe_load_balancer = getattr(self.engine.model_engine, "moe_load_balancer", None) if isinstance(moe_load_balancer, MoeLoadBalancer): moe_load_balancer.register_weight_slots_after_to_cuda() logger.info("moe_load_balancer finalizing model...") moe_load_balancer.finalize_model() logger.info("moe_load_balancer finalize model done") self.engine.reset_prefix_cache() delattr(self.engine.model_engine.model, "first_pre_reload_weights") except Exception as e: logger.error("Encountered an error in update_weights") raise e ================================================ FILE: verl/workers/rollout/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import logging import numpy as np import uvicorn from fastapi import FastAPI logger = logging.getLogger(__file__) def get_max_position_embeddings(hf_config) -> int: max_len = getattr(hf_config, "max_position_embeddings", None) if max_len is None: text_config = getattr(hf_config, "text_config", None) if text_config is not None: max_len = getattr(text_config, "max_position_embeddings", None) if max_len is None: raise ValueError("max_position_embeddings not found in HFModelConfig!") return int(max_len) class _UvicornServerAutoPort(uvicorn.Server): """Uvicorn Server that reports the system-assigned port when port=0.""" def __init__(self, config: uvicorn.Config) -> None: super().__init__(config) self.actual_port: int | None = None self._startup_done: asyncio.Event = asyncio.Event() async def startup(self, sockets=None) -> None: try: await super().startup(sockets=sockets) if self.servers and self.config.port == 0: sock = self.servers[0].sockets[0] self.actual_port = sock.getsockname()[1] else: self.actual_port = self.config.port finally: self._startup_done.set() async def get_port(self) -> int | None: await self._startup_done.wait() return self.actual_port async def run_uvicorn(app: FastAPI, server_args, server_address) -> tuple[int, asyncio.Task]: app.server_args = server_args config = uvicorn.Config(app, host=server_address, port=0, log_level="warning") server = _UvicornServerAutoPort(config) server_task = asyncio.create_task(server.serve()) server_port = await server.get_port() if server_port is None: # server.startup() failed. await the task to re-raise exception from server.serve() await server_task # Fails on unexpected situation. raise RuntimeError("Unexpected: HTTP server started without reporting listened port") logger.info(f"HTTP server started on port {server_port}") return server_port, server_task async def ensure_async_iterator(iterable): """Convert an iterable to an async iterator.""" if hasattr(iterable, "__aiter__"): async for item in iterable: yield item else: for item in iterable: yield item def qwen2_5_vl_dedup_image_tokens(prompt_ids: list[int], processor): """Deduplicate consecutive image tokens in prompt_ids for Qwen2.5-VL, since vLLM will replicate the <|image_pad|> and <|video_pad|> token by image_data. For example, ``` <|vision_start|><|image_pad|><|image_pad|>...<|image_pad|><|vision_end|> => <|vision_start|><|image_pad|><|vision_end|> ``` """ if processor is not None and "Qwen2VLImageProcessor" in processor.image_processor.__class__.__name__: prompt_ids = np.array(prompt_ids) mask = np.ones(len(prompt_ids), dtype=bool) is_value = (prompt_ids == processor.image_token_id) | (prompt_ids == processor.video_token_id) mask[1:] &= ~(is_value[1:] & is_value[:-1]) return prompt_ids[mask].tolist() else: return prompt_ids ================================================ FILE: verl/workers/rollout/vllm_rollout/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from importlib.metadata import PackageNotFoundError, version from .vllm_rollout import ServerAdapter # noqa: F401 def get_version(pkg): try: return version(pkg) except PackageNotFoundError: return None vllm_package_name = "vllm" vllm_package_version = get_version(vllm_package_name) if vllm_package_version is None: raise PackageNotFoundError( "To use vllm rollout, please ensure the 'vllm' package is properly installed. See " "https://verl.readthedocs.io/en/latest/start/install.html for more details" ) if "ROCM_PATH" in os.environ: import re match = re.match(r"(\d+\.\d+\.?\d*)", vllm_package_version) if match: vllm_package_version = match.group(1) else: raise ValueError(f"Warning: Could not parse version format: {vllm_package_version}") ================================================ FILE: verl/workers/rollout/vllm_rollout/bucketed_weight_transfer.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Bucketed weight transfer via ZMQ + IPC (or shared memory fallback). Not recommended depending on vllm for this file. """ import gc import logging import os from multiprocessing import shared_memory from typing import Callable, TypedDict import torch import zmq from torch.multiprocessing.reductions import reduce_tensor from verl.utils.device import get_device_id, get_device_name, get_torch_device logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "INFO")) class TensorMetadata(TypedDict): name: str shape: torch.Size dtype: torch.dtype offset: int # copy from https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/rlhf_utils.py def rebuild_ipc(handle: tuple[Callable, tuple], device_id: int | None = None) -> torch.Tensor: func, args = handle list_args = list(args) if device_id is not None: # the key is to change device id to the current device id # in case two processes have different CUDA_VISIBLE_DEVICES list_args[6] = device_id buffer = func(*list_args) return buffer def create_shared_memory(size: int, name: str): """Create shared memory for weight transfer. If already exists, attach to it.""" try: shm = shared_memory.SharedMemory(name=name, create=True, size=size) except FileExistsError: shm = shared_memory.SharedMemory(name=name) assert shm.size >= size, f"Stale shm segment '{name}': expected {size} bytes, got {shm.size}" return shm def rebuild_shared_memory(name: str, size: int, dtype=torch.uint8): """Rebuild tensor from shared memory.""" shm = shared_memory.SharedMemory(name=name) tensor = torch.frombuffer(shm.buf[:size], dtype=dtype) return tensor, shm class BucketedWeightSender: """ Send model weights via bucketed IPC transfer over ZMQ. Packs weight tensors into a fixed-size communication buffer and sends them in buckets to the receiver. Supports CUDA IPC and shared memory fallback. Args: zmq_handle: ZMQ IPC socket path (e.g., "ipc:///tmp/rl-colocate-zmq-.sock") bucket_size_mb: Communication buffer size in MB use_shm: Use shared memory instead of CUDA IPC (for NPU compatibility) """ def __init__( self, zmq_handle: str, bucket_size_mb: int = 512, use_shm: bool = False, ): self.zmq_handle = zmq_handle self.bucket_size_mb = bucket_size_mb self.bucket_size = int(bucket_size_mb) << 20 self.use_shm = use_shm self.zmq_context = zmq.Context.instance() self.socket = None self.buffer = None self.shm = None async def async_send_weights(self, weights): """ Send weights to the receiver. Accepts a sync generator or async iterator. Args: weights: Generator or async iterator yielding (name, tensor) pairs """ from verl.workers.rollout.utils import ensure_async_iterator try: self._init_socket() self._init_buffer() # send bucket weights offset = 0 bucket_meta: dict[str, TensorMetadata] = {} # dtype = PrecisionType.to_dtype(self.config.dtype) async for name, weight in ensure_async_iterator(weights): # model parameters are in fp32 full precision # (vermouth1992) we should not force cast weight here because some parameters # (such as moe gate) have to keep fp32 precision. If a weight is bf16 in the rollout side, # the rollout should automatically cast on demand. However, this would incur a higher weight # transfer volume. # weight = weight.to(dtype, non_blocking=True) # fill the tensor bucket if offset + weight.nbytes > self.bucket_size: get_torch_device().synchronize() self.socket.send_pyobj({"bucket_meta": bucket_meta, "is_last": False}) self.socket.recv() bucket_meta = {} offset = 0 # TODO: slice embedding layer weight into chunks assert offset + weight.nbytes <= self.bucket_size, ( f"Weight {name}({weight.shape}, {weight.dtype}) is too large to fit in the bucket." f"Please increase rollout.update_weights_bucket_megabytes({self.bucket_size_mb} MB)." ) bucket_meta[name] = { "name": name, "shape": weight.shape, "dtype": weight.dtype, "offset": offset, } self.buffer[offset : offset + weight.nbytes].copy_(weight.view(-1).view(torch.uint8), non_blocking=True) offset += weight.nbytes # send the last bucket get_torch_device().synchronize() self.socket.send_pyobj({"bucket_meta": bucket_meta, "is_last": True}) self.socket.recv() finally: self._cleanup() def _init_socket(self): """Initialize ZMQ REQ socket and bind.""" self.socket = self.zmq_context.socket(zmq.REQ) self.socket.bind(self.zmq_handle) def _init_buffer(self): """build communication buffer""" buffer, shm = None, None if not self.use_shm: buffer = torch.empty(self.bucket_size, dtype=torch.uint8, device=f"{get_device_name()}:{get_device_id()}") handle = reduce_tensor(buffer) self.socket.send_pyobj(handle) else: import uuid # Create unique name for shared memory shm_name = f"verl_weights_{uuid.uuid4().hex}" shm = create_shared_memory(self.bucket_size, shm_name) buffer = torch.frombuffer(shm.buf, dtype=torch.uint8) comm_metadata = {"name": shm_name, "size": self.bucket_size} self.socket.send_pyobj(comm_metadata) self.socket.recv() self.buffer = buffer self.shm = shm def _cleanup(self): """clean up""" if self.socket is not None: self.socket.close() self.socket = None del self.buffer self.buffer = None if self.shm is not None: self.shm.close() self.shm.unlink() del self.shm self.shm = None gc.collect() get_torch_device().ipc_collect() get_torch_device().empty_cache() class BucketedWeightReceiver: """ Receive model weights via bucketed IPC transfer over ZMQ. Receives weight tensors from BucketedWeightSender and passes each bucket to a callback for processing (e.g., loading into the model). Args: zmq_handle: ZMQ IPC socket path (must match sender) device: Target device for received tensors use_shm: Use shared memory instead of CUDA IPC """ def __init__( self, zmq_handle: str, device: torch.device, use_shm: bool = False, ): self.zmq_handle = zmq_handle self.device = device self.use_shm = use_shm self.zmq_context = zmq.Context.instance() self.socket = None self.buffer = None self.shm = None def receive_weights(self, on_bucket_received: callable): """ Receive weights from sender and process each bucket via callback. Args: on_bucket_received: Callback function(weights: list[(name, tensor)]) called per bucket. """ try: self._init_socket() self._init_buffer() # receive bucket and update weights while True: metadata = self.socket.recv_pyobj() weights, tensor = [], None for name, meta in metadata["bucket_meta"].items(): shape, dtype, offset = meta["shape"], meta["dtype"], meta["offset"] size = dtype.itemsize * shape.numel() # NOTE: we need to clone the tensor to release CUDA IPC memory # but for shared memory, it's not necessary and if we do clone, # it will cause extra memory copy overhead and slow down the process. tensor = self.buffer[offset : offset + size].view(dtype=dtype).view(shape) if not self.use_shm: tensor = tensor.clone() else: tensor = tensor.to(self.device) weights.append((name, tensor)) get_torch_device().synchronize() self.socket.send(b"") on_bucket_received(weights) del weights, tensor if metadata["is_last"]: break finally: self._cleanup() def _init_socket(self): """Initialize ZMQ REP socket and connect.""" self.socket = self.zmq_context.socket(zmq.REP) self.socket.connect(self.zmq_handle) def _init_buffer(self): """Receive and rebuild communication buffer from sender.""" comm_metadata = self.socket.recv_pyobj() buffer, shm = None, None if not self.use_shm: handle = comm_metadata buffer = rebuild_ipc(handle, self.device.index) assert buffer.dtype == torch.uint8 else: shm_name = comm_metadata["name"] shm_size = comm_metadata["size"] buffer, shm = rebuild_shared_memory(shm_name, shm_size, dtype=torch.uint8) self.socket.send(b"") self.buffer = buffer self.shm = shm def _cleanup(self): """clean up""" if self.socket is not None: self.socket.close() self.socket = None # Synchronize before releasing the buffer to ensure all async ops # referencing it (e.g. clone, .to()) have completed. get_torch_device().synchronize() del self.buffer self.buffer = None if self.shm is not None: self.shm.close() del self.shm self.shm = None gc.collect() get_torch_device().ipc_collect() get_torch_device().empty_cache() ================================================ FILE: verl/workers/rollout/vllm_rollout/utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ctypes import json import logging import os import platform import signal import threading from types import MethodType from typing import Any, Literal, get_args import torch from verl.utils.device import is_npu_available from verl.utils.vllm import TensorLoRARequest, VLLMHijack from verl.utils.vllm.patch import patch_vllm_moe_model_weight_loader from verl.utils.vllm.vllm_fp8_utils import apply_vllm_fp8_patches, is_fp8_model, load_quanted_weights logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) # magic numbers that ensure we are using the same LoRA adapter during the rollout and training process VLLM_LORA_INT_ID = 123 VLLM_LORA_NAME = "123" VLLM_LORA_PATH = "simon_lora_path" VLLM_ASCEND_REQUIRED_ENV_VARS = {"VLLM_ALL2ALL_BACKEND": "flashinfer_all2allv", "VLLM_ASCEND_ENABLE_NZ": "0"} def set_death_signal(): """Kill the current process when the parent process exits.""" if platform.system() != "Linux": return libc = ctypes.CDLL("libc.so.6") libc.prctl(1, signal.SIGKILL) if os.getppid() == 1: os.kill(os.getpid(), signal.SIGKILL) def get_device_uuid(device_id: int) -> str: from vllm.platforms import current_platform # Convert torch.npu.current_device to its corresponding ASCEND_RT_VISIBLE_DEVICES. if is_npu_available: if os.getenv("ASCEND_RT_VISIBLE_DEVICES") is not None: npu_visible_devices = os.environ["ASCEND_RT_VISIBLE_DEVICES"].split(",") assert device_id < len(npu_visible_devices), f"device_id {device_id} must less than {npu_visible_devices}" return "NPU-" + npu_visible_devices[device_id] else: return f"NPU-{device_id}" else: return current_platform.get_device_uuid(device_id) def get_vllm_max_lora_rank(lora_rank: int): """ For vLLM, automatically adjusts the `max_lora_rank` to the nearest allowed value. The allowed values are retrieved from vLLM's MaxLoRARanks type definition. """ assert lora_rank > 0, f"lora_rank must be greater than 0, get {lora_rank}" try: from vllm.config.lora import MaxLoRARanks except Exception: # FIXME: migrate vllm version https://github.com/vllm-project/vllm/blob/main/vllm/config/lora.py#L25 MaxLoRARanks = Literal[1, 8, 16, 32, 64, 128, 256, 320, 512] vllm_max_lora_ranks = sorted(get_args(MaxLoRARanks)) if lora_rank > vllm_max_lora_ranks[-1]: raise ValueError(f"lora_rank must be less than or equal to {vllm_max_lora_ranks[-1]}, but got {lora_rank}") for rank in vllm_max_lora_ranks: if lora_rank <= rank: return rank # https://github.com/vllm-project/vllm/issues/13175 def monkey_patch_compute_logits(model, vocab_size: int): original_compute_logits = model.compute_logits def compute_logits( self, *args, **kwargs, ) -> torch.Tensor: logits = original_compute_logits(*args, **kwargs) logits[..., vocab_size:] = float("-inf") return logits model.compute_logits = MethodType(compute_logits, model) class vLLMColocateWorkerExtension: """ The class for vLLM's worker to inherit from, in the colocate setting. By defining an extension class, the code can work no matter what is the underlying worker class. This way, the code can be compatible with both vLLM V0 and V1. NOTE: we define this class in a separate module, and the main module should pass the full qualified name as `worker_extension_cls` argument. Feature support: 1. LoRA 2. Online FP8 quantization """ def __new__(cls, **kwargs): set_death_signal() # 1. patch for Lora VLLMHijack.hijack() # 2. patch online fp8 quant if os.environ.get("VERL_VLLM_FP8_QUANT_ENABLED", "0") == "1": apply_vllm_fp8_patches() # 3. patch QAT (compressed-tensors NVFP4) for dynamic weight loading vllm_config = kwargs.get("vllm_config") quant_config = getattr(vllm_config, "quant_config", None) if vllm_config else None _is_qat_model = getattr(quant_config, "quant_format", None) == "nvfp4-pack-quantized" if _is_qat_model: from verl.utils.qat import apply_qat_patches apply_qat_patches() logger.info("Applied QAT patches in vLLM worker subprocess") # TODO: For ascend NPU, when the corresponding vllm-ascend version is upgraded to v0.13.0, # please remove the VLLM_ASCEND_REQUIRED_ENV_VARS variable replacement action. # This is only a fix for vllm version < v0.13.0. if is_npu_available: for k in VLLM_ASCEND_REQUIRED_ENV_VARS: if k not in os.environ: os.environ[k] = VLLM_ASCEND_REQUIRED_ENV_VARS[k] instance = super().__new__(cls) instance._is_qat_model = _is_qat_model return instance def monkey_patch_model(self, vocab_size: int): # patch compute_logits to avoid sampling OOV token monkey_patch_compute_logits(self.model_runner.model, vocab_size) # patch weight loader to support MoE model patch_vllm_moe_model_weight_loader(self.model_runner.model) def update_weights_from_ipc(self, peft_config: dict = None, base_sync_done=False, use_shm: bool = False): """Update the weights of the rollout model.""" from vllm.platforms import current_platform from verl.workers.rollout.vllm_rollout.bucketed_weight_transfer import BucketedWeightReceiver if current_platform.device_type == "npu" and self.device is None: self.device = torch.device(f"npu:{self.local_rank}") # In async mode, make sure the old lora is removed before adding the new one if peft_config and base_sync_done: self.remove_lora(VLLM_LORA_INT_ID) use_standard_weight_load = not (peft_config and base_sync_done) and not is_fp8_model( self.model_runner.vllm_config ) if self._is_qat_model: # QAT: Prepare for weight loading BEFORE receiving any buckets from verl.utils.qat import prepare_qat_for_load_weights prepare_qat_for_load_weights(self.model_runner.model, device=self.device) logger.info("QAT: prepare_qat_for_load_weights completed") elif use_standard_weight_load: # Re-apply here because async IPC weight sync can happen long after init and lose MoE weight_loader attrs. patch_vllm_moe_model_weight_loader(self.model_runner.model) assert self.device is not None receiver = BucketedWeightReceiver( zmq_handle=self._get_zmq_handle(), device=self.device, use_shm=use_shm, ) receiver.receive_weights( on_bucket_received=lambda weights: self._update_weights( weights, peft_config=peft_config, base_sync_done=base_sync_done ) ) if self._is_qat_model: # QAT: call process_weights_after_loading AFTER all buckets are received from verl.utils.qat import manual_process_weights_after_loading manual_process_weights_after_loading(self.model_runner.model) logger.info("QAT: process_weights_after_loading completed") elif use_standard_weight_load: # Some post-load transforms are non-idempotent; run once after all buckets. from vllm.model_executor.model_loader.utils import process_weights_after_loading model = self.model_runner.model model_config = self.model_runner.vllm_config.model_config process_weights_after_loading(model, model_config, self.device) def _update_weights(self, weights: list[tuple[str, torch.Tensor]], peft_config: dict, base_sync_done: bool): if peft_config and base_sync_done: weights = dict(weights) lora_request = TensorLoRARequest( lora_name=VLLM_LORA_NAME, lora_int_id=VLLM_LORA_INT_ID, lora_path=VLLM_LORA_PATH, peft_config=peft_config, lora_tensors=weights, ) self.add_lora(lora_request) logger.info(f"vLLM load weights, loaded_params: {len(weights)}") else: # Add the FP8 related logic here as sharding manager has been deprecated. # Check if FP8 quantization is enabled and apply appropriate weight loading if is_fp8_model(self.model_runner.vllm_config): logger.info(f"FP8 model detected (async): {self.model_runner.vllm_config.quant_config}") # Convert bf16 weights to fp8 format before loading loaded_params = load_quanted_weights(weights, self.model_runner) logger.info(f"FP8 weights loaded (async), loaded_params: {len(loaded_params)}") else: logger.info("Loading standard weights (non-FP8, async)") self.model_runner.model.load_weights(weights) def _get_zmq_handle(self) -> str: """Get ZMQ handle for communication.""" if not hasattr(self, "device_uuid") or not self.device_uuid: self.device_uuid = get_device_uuid(self.device.index) return f"ipc:///tmp/rl-colocate-zmq-{self.device_uuid}.sock" class SuppressSignalInThread: def __enter__(self): self.original_signal = signal.signal def no_op_signal(sig, action): if threading.current_thread() is not threading.main_thread(): print(f"Ignored signal {sig} in thread {threading.current_thread().name}") return return self.original_signal(sig, action) signal.signal = no_op_signal return self def __exit__(self, exc_type, exc_val, exc_tb): signal.signal = self.original_signal def build_cli_args_from_config(config: dict[str, Any]) -> list[str]: """ Convert a config dictionary to CLI arguments for vLLM server. Handles different value types appropriately: - None: skipped - bool True: adds '--key' - bool False: skipped - list: expands to '--key item1 item2 ...' - empty list: skipped (vLLM uses nargs="+" which requires at least one value) - dict: JSON serialized - other: string converted Args: config: Dictionary of configuration key-value pairs Returns: List of CLI argument strings """ cli_args = [] for k, v in config.items(): if v is None: continue if isinstance(v, bool): if v: cli_args.append(f"--{k}") elif isinstance(v, list): if not v: # Skip empty lists - vLLM uses nargs="+" which requires at least one value continue # Lists need to be expanded as multiple separate arguments # e.g., --cuda-graph-sizes 1 2 4 8 becomes ['--cuda-graph-sizes', '1', '2', '4', '8'] cli_args.append(f"--{k}") cli_args.extend([str(item) for item in v]) else: cli_args.append(f"--{k}") # Use json.dumps for dict to ensure valid JSON format cli_args.append(json.dumps(v) if isinstance(v, dict) else str(v)) return cli_args ================================================ FILE: verl/workers/rollout/vllm_rollout/vllm_async_server.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import asyncio import inspect import json import logging import os from pprint import pprint from typing import Any, Callable, Optional import ray import vllm.entrypoints.cli.serve from packaging import version from ray.actor import ActorHandle from vllm import SamplingParams from vllm.engine.arg_utils import AsyncEngineArgs from vllm.entrypoints.cli.serve import run_headless from vllm.entrypoints.openai.api_server import build_app, init_app_state from vllm.inputs import TokensPrompt from vllm.lora.request import LoRARequest from vllm.outputs import RequestOutput from vllm.usage.usage_lib import UsageContext from vllm.v1.engine.async_llm import AsyncLLM from verl.utils.config import omega_conf_to_dataclass from verl.utils.device import get_resource_name, get_visible_devices_keyword, is_npu_available, is_torch_npu_available from verl.utils.net_utils import get_free_port, is_valid_ipv6_address from verl.utils.profiler import DistProfiler, build_vllm_profiler_args from verl.utils.tokenizer import normalize_token_ids from verl.utils.vllm.vllm_fp8_utils import apply_vllm_fp8_patches from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.replica import RolloutMode, RolloutReplica, TokenOutput from verl.workers.rollout.utils import get_max_position_embeddings, qwen2_5_vl_dedup_image_tokens, run_uvicorn from verl.workers.rollout.vllm_rollout.utils import ( VLLM_LORA_INT_ID, VLLM_LORA_NAME, VLLM_LORA_PATH, SuppressSignalInThread, build_cli_args_from_config, get_vllm_max_lora_rank, ) _VLLM_VERSION = version.parse(vllm.__version__) if _VLLM_VERSION > version.parse("0.11.0"): from vllm.utils.argparse_utils import FlexibleArgumentParser if _VLLM_VERSION == version.parse("0.12.0"): from vllm.entrypoints.harmony_utils import get_encoding elif _VLLM_VERSION >= version.parse("0.13.0"): from vllm.entrypoints.openai.parser.harmony_utils import get_encoding else: get_encoding = None if get_encoding is not None and os.getenv("VERL_USE_GPT_OSS", "0") == "1": get_encoding() else: from vllm.utils import FlexibleArgumentParser logger = logging.getLogger(__file__) logger.setLevel(logging.INFO) class vLLMHttpServer: """vLLM http server in single node, this is equivalent to launch server with command line: ``` vllm serve --tensor-parallel-size=8 ... ``` """ def __init__( self, config: RolloutConfig, model_config: HFModelConfig, rollout_mode: RolloutMode, workers: list[ActorHandle], replica_rank: int, node_rank: int, gpus_per_node: int, nnodes: int, cuda_visible_devices: str, ): """ Args: config (RolloutConfig): full config. model_config (HFModelConfig): model config. rollout_mode (RolloutMode): rollout mode. replica_rank (int): replica rank, a replica may contain multiple nodes. node_rank (int): node rank. gpus_per_node (int): number of gpus per node. nnodes (int): number of nodes. cuda_visible_devices (str): cuda visible devices. """ os.environ[get_visible_devices_keyword()] = cuda_visible_devices self.config: RolloutConfig = omega_conf_to_dataclass(config) self.model_config: HFModelConfig = omega_conf_to_dataclass(model_config, dataclass_type=HFModelConfig) max_position_embeddings = get_max_position_embeddings(self.model_config.hf_config) if self.config.max_model_len is None: self.config.max_model_len = max_position_embeddings else: if self.config.max_model_len > max_position_embeddings: raise ValueError( f"max_model_len ({self.config.max_model_len}) should be less than or equal to " f"max_position_embeddings ({max_position_embeddings})" ) self.rollout_mode = rollout_mode self.workers = workers self.replica_rank = replica_rank self.node_rank = node_rank self.gpus_per_node = gpus_per_node self.nnodes = nnodes # model weights version, set by ServerAdapter when update weights. self.global_steps = None if self.rollout_mode != RolloutMode.HYBRID and self.config.load_format == "dummy": logger.warning(f"rollout mode is {self.rollout_mode}, load_format is dummy, set to auto") self.config.load_format = "auto" # used for http server self._server_address = ray.util.get_node_ip_address().strip("[]") self._server_port = None # used for controlling vllm server profiler profiler_config = self.config.profiler tool_config = None if profiler_config is not None: if profiler_config.tool in ["torch", "npu"]: tool_config = omega_conf_to_dataclass((profiler_config.tool_config or {}).get(profiler_config.tool)) else: logger.warning(f"agent loop only support torch and npu profiler, got {profiler_config.tool}") profiler_config = None self.profiler_controller = DistProfiler(self.replica_rank, config=profiler_config, tool_config=tool_config) # used for data parallel: --data-parallel-address, --data-parallel-rpc-port if self.node_rank == 0: self._master_address = self._server_address # used for torch.distributed.init_process_group self._master_port, self._master_sock = get_free_port(self._server_address, with_alive_sock=True) # used for data parallel: --data-parallel-address, --data-parallel-rpc-port self._dp_rpc_port, self._dp_rpc_sock = get_free_port(self._server_address, with_alive_sock=True) self._dp_master_port, self._dp_master_sock = get_free_port(self._server_address, with_alive_sock=True) else: self._master_address = None self._master_port = None self._dp_rpc_port = None self._dp_master_port = None logger.info( f"vLLMHttpServer, replica_rank: {self.replica_rank}, node_rank: {self.node_rank}, " f"{get_visible_devices_keyword()}: {cuda_visible_devices}, " f"master_address: {self._master_address}, master_port: {self._master_port}, " f"data_parallel_rpc_port: {self._dp_rpc_port}, data_parallel_master_port: {self._dp_master_port}" ) def get_master_address(self): """Get master address and port for data parallel. Returns: tuple: (master_address, master_port, dp_rpc_port) """ return self._master_address, self._master_port, self._dp_rpc_port def get_server_address(self): """Get http server address and port.""" assert self._server_port is not None, "http server is not launched, port is None" return self._server_address, self._server_port @property def lora_as_adapter(self) -> bool: return ( self.model_config.lora_rank > 0 or self.model_config.lora.get("rank", 0) > 0 ) and not self.model_config.lora.get("merge", False) async def collective_rpc( self, method: str | Callable, timeout: float | None = None, args: tuple = (), kwargs: dict[str, Any] | None = None, ): await self.engine.collective_rpc( method=method, timeout=timeout, args=args, kwargs=kwargs, ) async def launch_server(self, master_address: str = None, master_port: int = None, dp_rpc_port: int = None): if self.node_rank != 0: assert master_address and master_port and dp_rpc_port, ( "non-master node should provide master_address, master_port and dp_rpc_port" ) self._master_address = master_address self._master_port = master_port self._dp_rpc_port = dp_rpc_port # 1. setup vllm serve cli args engine_kwargs = self.config.get("engine_kwargs", {}).get("vllm", {}) or {} engine_kwargs = {key: val for key, val in engine_kwargs.items() if val is not None} if self.config.get("limit_images", None): # support for multi-image data engine_kwargs["limit_mm_per_prompt"] = {"image": self.config.get("limit_images")} if self.config.cudagraph_capture_sizes: engine_kwargs["cuda_graph_sizes"] = self.config.cudagraph_capture_sizes # Override default generation config from hugging face model config, # user can still override them by passing kwargs in each request. override_generation_config = dict( temperature=self.config.temperature, top_k=self.config.top_k, top_p=self.config.top_p, repetition_penalty=1.0, max_new_tokens=self.config.response_length, ) logger.info(f"override_generation_config: {override_generation_config}") logger.info(f"enable_sleep_mode: {self.config.enable_sleep_mode}") if not self.config.enable_sleep_mode: from verl.utils.device import set_expandable_segments set_expandable_segments(True) quantization = self.config.quantization hf_overrides = {} if is_torch_npu_available(check_device=False): from verl.utils.vllm.npu_vllm_patch import check_vllm_ascend_before_server_launch check_vllm_ascend_before_server_launch() # Handle QAT (Quantization-Aware Training) configuration qat_config_dict = getattr(self.config, "qat", {}) or {} if qat_config_dict.get("enable", False): # QAT uses compressed-tensors quantization, apply patches for dynamic weight loading from verl.utils.qat import QATConfig, apply_qat_patches, load_quantization_config apply_qat_patches() # Load quantization config from JSON file qat_config = QATConfig(**qat_config_dict) quantization_config_dict = load_quantization_config(qat_config) hf_overrides["quantization_config"] = quantization_config_dict quantization = "compressed-tensors" logger.info("QAT quantization config injected to vLLM async server") elif quantization is not None: # Handle other quantization methods (fp8, torchao) _SUPPORTED_QUANTIZATION = ["fp8", "torchao"] if quantization not in _SUPPORTED_QUANTIZATION: raise ValueError(f"Currently only support {_SUPPORTED_QUANTIZATION} quantization, got: {quantization}") if quantization == "fp8": # Ignore MoE router layers for FP8 quantization all_mlp_gate_layers = [] for layer in range(self.model_config.hf_config.num_hidden_layers): all_mlp_gate_layers.append(f"model.layers.{layer}.mlp.gate") FP8_BLOCK_QUANT_KWARGS = { "activation_scheme": "dynamic", "fmt": "e4m3", "quant_method": "fp8", "weight_block_size": [128, 128], "ignored_layers": all_mlp_gate_layers, } hf_overrides["quantization_config"] = dict(FP8_BLOCK_QUANT_KWARGS) # Apply vllm fp8 patches # Will remove the patch after vllm support on-the-fly quant for rollout natively. apply_vllm_fp8_patches() # for subprocesses patching os.environ["VERL_VLLM_FP8_QUANT_ENABLED"] = "1" if quantization is not None and self.config.quantization_config_file is not None: hf_overrides["quantization_config_file"] = self.config.quantization_config_file compilation_config = engine_kwargs.pop("compilation_config", None) or {} if isinstance(compilation_config, str): compilation_config = json.loads(compilation_config) compilation_config.setdefault("cudagraph_mode", "FULL_AND_PIECEWISE") # FULL cuda graph is not yet supported with DCP, downgrade to PIECEWISE dcp_size = engine_kwargs.get("decode_context_parallel_size", 1) or 1 if dcp_size > 1 and compilation_config["cudagraph_mode"] == "FULL_AND_PIECEWISE": logger.warning( "FULL cuda graph is not supported with DCP (decode_context_parallel_size=%d), " "downgrading cudagraph_mode to PIECEWISE.", dcp_size, ) compilation_config["cudagraph_mode"] = "PIECEWISE" compilation_config = json.dumps(compilation_config) args = { "dtype": self.config.dtype, "load_format": self.config.load_format, "skip_tokenizer_init": False, "distributed_executor_backend": "mp", "worker_extension_cls": "verl.workers.rollout.vllm_rollout.utils.vLLMColocateWorkerExtension", "trust_remote_code": self.model_config.trust_remote_code, "max_model_len": self.config.max_model_len, "max_num_seqs": self.config.max_num_seqs, "enable_chunked_prefill": self.config.enable_chunked_prefill, "max_num_batched_tokens": self.config.max_num_batched_tokens, "enable_prefix_caching": self.config.enable_prefix_caching, "enable_sleep_mode": self.config.enable_sleep_mode, "logprobs_mode": self.config.logprobs_mode, "enforce_eager": self.config.enforce_eager, "gpu_memory_utilization": self.config.gpu_memory_utilization, "disable_log_stats": self.config.disable_log_stats, "tensor_parallel_size": self.config.tensor_model_parallel_size, "seed": self.replica_rank + self.config.get("seed", 0), "override_generation_config": json.dumps(override_generation_config), "quantization": quantization, "hf_overrides": hf_overrides, "scheduling_policy": self.config.scheduling_policy, "compilation_config": compilation_config, **engine_kwargs, } # update profiler args profiler_args = build_vllm_profiler_args( self.profiler_controller.config, self.profiler_controller.tool_config, self.replica_rank ) if _VLLM_VERSION >= version.parse("0.13.0"): # vLLM >= 0.13.0 supports profiler config via CLI args; env vars still work but will be deprecated args.update(profiler_args) if self.config.prometheus.enable: if self.config.prometheus.served_model_name: # Extract model name from path if it's a full path served_model_name = self.config.prometheus.served_model_name if "/" in served_model_name: # If it's a full path, extract the last part as model name served_model_name = served_model_name.split("/")[-1] args["served_model_name"] = served_model_name # mtp if self.config.mtp.enable and self.config.mtp.enable_rollout: speculative_config = { "method": self.config.mtp.method, "num_speculative_tokens": self.config.mtp.num_speculative_tokens, } args["speculative_config"] = speculative_config if self.config.data_parallel_size > 1: assert self.gpus_per_node % self.config.tensor_model_parallel_size == 0, ( "gpus_per_node should be divisible by tensor_model_parallel_size" ) data_parallel_size_local = self.gpus_per_node // self.config.tensor_model_parallel_size assert len(self.workers) == data_parallel_size_local * self.config.tensor_model_parallel_size, ( f"num workers ({len(self.workers)}) should be equal to " f"dp_size_local ({data_parallel_size_local}) * tp_size ({self.config.tensor_model_parallel_size})" ) dp_args = { "data_parallel_size": self.config.data_parallel_size, "data_parallel_size_local": data_parallel_size_local, "data_parallel_start_rank": self.node_rank * data_parallel_size_local, "data_parallel_address": self._master_address, "data_parallel_rpc_port": self._dp_rpc_port, } args.update(dp_args) args.update({"enable_expert_parallel": self.config.expert_parallel_size > 1}) # used for torch.distributed.init_process_group if self.nnodes > 1: args.update( { "master_addr": self._master_address, "master_port": self._master_port, "node_rank": self.node_rank, "nnodes": self.nnodes, "data_parallel_address": self._master_address, "data_parallel_rpc_port": self._dp_rpc_port, } ) # update lora-related args lora_rank = self.model_config.lora.get("rank", 0) if lora_rank <= 0: lora_rank = ( self.model_config.lora_rank ) # FIXME: fallback to lora_rank for now, we should unify lora settings. if self.model_config.lora.get("merge", False): lora_rank = 0 if lora_rank > 0: lora_args = { "enable_lora": True, "max_loras": 1, "max_lora_rank": get_vllm_max_lora_rank(lora_rank), } if self.model_config.lora.get("fully_sharded_loras", False): lora_args["fully_sharded_loras"] = True args.update(lora_args) if self.config.enable_rollout_routing_replay: args.update({"enable_return_routed_experts": True}) server_args = ["serve", self.model_config.local_path] + build_cli_args_from_config(args) if self.replica_rank == 0: pprint(server_args) CMD_MODULES = [vllm.entrypoints.cli.serve] parser = FlexibleArgumentParser(description="vLLM CLI") subparsers = parser.add_subparsers(required=False, dest="subparser") cmds = {} for cmd_module in CMD_MODULES: new_cmds = cmd_module.cmd_init() for cmd in new_cmds: cmd.subparser_init(subparsers).set_defaults(dispatch_function=cmd.cmd) cmds[cmd.name] = cmd server_args = parser.parse_args(args=server_args) server_args.model = server_args.model_tag if server_args.subparser in cmds: cmds[server_args.subparser].validate(server_args) # 3. launch server if self.node_rank == 0: self._master_sock.close() self._dp_rpc_sock.close() self._dp_master_sock.close() await self.run_server(server_args) else: # TODO: avoid connect before master_sock close await asyncio.sleep(3) await self.run_headless(server_args) async def run_server(self, args: argparse.Namespace): engine_args = AsyncEngineArgs.from_cli_args(args) usage_context = UsageContext.OPENAI_API_SERVER vllm_config = engine_args.create_engine_config(usage_context=usage_context) vllm_config.parallel_config.data_parallel_master_port = self._dp_master_port fn_args = set(dict(inspect.signature(AsyncLLM.from_vllm_config).parameters).keys()) kwargs = {} if "enable_log_requests" in fn_args: kwargs["enable_log_requests"] = engine_args.enable_log_requests if "disable_log_stats" in fn_args: kwargs["disable_log_stats"] = engine_args.disable_log_stats engine_client = AsyncLLM.from_vllm_config(vllm_config=vllm_config, usage_context=usage_context, **kwargs) # Don't keep the dummy data in memory await engine_client.reset_mm_cache() await engine_client.collective_rpc( method="monkey_patch_model", kwargs={"vocab_size": len(self.model_config.tokenizer)} ) build_app_sig = inspect.signature(build_app) supported_tasks: tuple[Any, ...] = () if "supported_tasks" in build_app_sig.parameters: supported_tasks = await engine_client.get_supported_tasks() app = build_app(args, supported_tasks) else: app = build_app(args) init_app_sig = inspect.signature(init_app_state) if "vllm_config" in init_app_sig.parameters: await init_app_state(engine_client, vllm_config, app.state, args) elif "supported_tasks" in init_app_sig.parameters: await init_app_state(engine_client, app.state, args, supported_tasks) else: await init_app_state(engine_client, app.state, args) if self.replica_rank == 0 and self.node_rank == 0: logger.info(f"Initializing a V1 LLM engine with config: {vllm_config}") self.engine = engine_client self._server_port, self._server_task = await run_uvicorn(app, args, self._server_address) async def run_headless(self, args: argparse.Namespace): """Run headless server in a separate thread.""" args.api_server_count = 0 def run_headless_wrapper(): with SuppressSignalInThread(): run_headless(args) def on_run_headless_done(future: asyncio.Future): try: exc = future.exception() if exc: logger.exception(f"run_headless failed with exception: {exc}") else: logger.warning("run_headless completed successfully, but it's not expected.") except Exception as e: logger.exception(f"get result from run_headless failed: {e}") finally: os._exit(1) self.task = asyncio.create_task(asyncio.to_thread(run_headless_wrapper)) self.task.add_done_callback(on_run_headless_done) async def generate( self, prompt_ids: list[int], sampling_params: dict[str, Any], request_id: str, image_data: Optional[list[Any]] = None, video_data: Optional[list[Any]] = None, priority: int = 0, ) -> TokenOutput: """Generate sequence with token-in-token-out.""" prompt_ids = normalize_token_ids(prompt_ids) # Calculate the maximum possible new tokens based on available context space # This serves as a safety upper bound max_possible_tokens = self.config.max_model_len - len(prompt_ids) if max_possible_tokens < 0: raise ValueError( f"Prompt length ({len(prompt_ids)}) exceeds the model's maximum context length " f"({self.config.max_model_len})." ) # Determine max_tokens from sampling_params or use configured response_length as default if "max_tokens" in sampling_params: max_tokens = sampling_params.pop("max_tokens") elif "max_new_tokens" in sampling_params: # support sglang-style 'max_new_tokens' param max_tokens = sampling_params.pop("max_new_tokens") else: # Default to a calculation that considers configured lengths # Cap max_tokens by response_length to ensure tensor alignment, # and by remaining budget to prevent OOM in multi-turn rollouts. max_tokens = min( self.config.response_length, self.config.prompt_length + self.config.response_length - len(prompt_ids) ) # Clamp max_tokens to the valid range [0, max_possible_tokens] max_tokens = max(0, min(max_tokens, max_possible_tokens)) assert max_tokens <= max_possible_tokens, ( f"max_tokens {max_tokens} exceeds available context space {max_possible_tokens}" ) sampling_params["logprobs"] = 0 if sampling_params.pop("logprobs", False) else None sampling_params.setdefault("repetition_penalty", self.config.get("repetition_penalty", 1.0)) sampling_params = SamplingParams(max_tokens=max_tokens, **sampling_params) prompt_ids = qwen2_5_vl_dedup_image_tokens(prompt_ids, self.model_config.processor) multi_modal_data = {} if image_data is not None: multi_modal_data["image"] = image_data if video_data is not None: multi_modal_data["video"] = video_data prompt = TokensPrompt(prompt_token_ids=prompt_ids, multi_modal_data=multi_modal_data) # Add lora request lora_request = None if self.lora_as_adapter: # Make sure we also check that the lora is already loaded in the engine lora_loaded = VLLM_LORA_INT_ID in await self.engine.list_loras() if lora_loaded: lora_request = LoRARequest( lora_name=VLLM_LORA_NAME, lora_int_id=VLLM_LORA_INT_ID, lora_path=VLLM_LORA_PATH ) generator = self.engine.generate( prompt=prompt, sampling_params=sampling_params, request_id=request_id, lora_request=lora_request, priority=priority, ) # Get final response final_res: Optional[RequestOutput] = None async for output in generator: final_res = output assert final_res is not None token_ids = final_res.outputs[0].token_ids log_probs = None if sampling_params.logprobs is not None: log_probs = [logprobs[token_ids[i]].logprob for i, logprobs in enumerate(final_res.outputs[0].logprobs)] routed_experts = None if self.config.enable_rollout_routing_replay: routed_experts = final_res.outputs[0].routed_experts # Determine stop reason from finish_reason finish_reason = final_res.outputs[0].finish_reason if finish_reason == "abort": stop_reason = "aborted" elif finish_reason in ("stop", "length"): stop_reason = "completed" else: stop_reason = finish_reason # for more stop reason in the future num_preempted = None if hasattr(final_res.outputs[0], "num_preempted"): num_preempted = final_res.outputs[0].num_preempted return TokenOutput( token_ids=token_ids, log_probs=log_probs, routed_experts=routed_experts, stop_reason=stop_reason, num_preempted=num_preempted, extra_fields={"global_steps": self.global_steps}, ) async def wake_up(self): if self.node_rank != 0: return if self.rollout_mode == RolloutMode.HYBRID: # In hybrid mode, rollout is wake up in `update_weights` raise ValueError(f"wake_up not support rollout_mode {self.rollout_mode}") elif self.rollout_mode == RolloutMode.COLOCATED: # Directly call engine to wake up without sync weights. await self.engine.wake_up(tags=["kv_cache", "weights"]) await self.engine.reset_prefix_cache() elif self.rollout_mode == RolloutMode.STANDALONE: logger.info("skip wake_up in standalone mode") async def sleep(self): if self.node_rank != 0 or not self.config.free_cache_engine: return if self.rollout_mode == RolloutMode.HYBRID: # Don't use engine.sleep(level=2) here # lora only update adapter weights, so set sleep level to 1 if self.lora_as_adapter or is_npu_available: sleep_level = 1 else: sleep_level = 2 await self.engine.collective_rpc("sleep", kwargs={"level": sleep_level}) # clear encoder cache: https://github.com/vllm-project/vllm/pull/33452 # await self.engine.reset_encoder_cache() elif self.rollout_mode == RolloutMode.COLOCATED: await self.engine.sleep(level=1) elif self.rollout_mode == RolloutMode.STANDALONE: logger.info("skip sleep in standalone mode") async def start_profile(self, **kwargs): if ( self.profiler_controller.check_enable() and self.profiler_controller.check_this_rank() and self.profiler_controller.is_discrete_mode() ): await self.engine.start_profile(**kwargs) async def stop_profile(self): if ( self.profiler_controller.check_enable() and self.profiler_controller.check_this_rank() and self.profiler_controller.is_discrete_mode() ): await self.engine.stop_profile() async def clear_kv_cache(self): if self.node_rank == 0: await self.engine.reset_prefix_cache() async def set_global_steps(self, global_steps: int): """Set the global steps of the model weights.""" self.global_steps = global_steps async def wait_for_requests_to_drain(self): await self.engine.wait_for_requests_to_drain() async def abort_all_requests(self, reset_prefix_cache: bool = True) -> dict[str, Any]: """Abort all ongoing generation requests. On vLLM >= 0.12.0, uses AsyncLLM.pause_generation() to abort in-flight requests, drain, and clear caches. The engine remains paused after this call — use resume_generation() to accept new requests (e.g. before validation). On vLLM < 0.12.0, manually aborts each request and resets prefix cache. Returns: dict[str, Any]: Dictionary containing: - aborted_count: Number of requests aborted - request_ids: List of aborted request IDs """ try: if _VLLM_VERSION >= version.parse("0.12.0"): # Snapshot request IDs before pausing for reporting request_ids = list(self.engine.output_processor.request_states.keys()) # pause_generation with wait_for_inflight_requests=False will: # 1. Set engine to paused state (blocks new generate calls) # 2. Abort all in-flight requests # 3. Wait for requests to drain # 4. Clear prefix and mm caches if clear_cache=True await self.engine.pause_generation( wait_for_inflight_requests=False, clear_cache=reset_prefix_cache, ) else: # Take an atomic snapshot to avoid race conditions with the vLLM engine thread request_states_snapshot = list(self.engine.output_processor.request_states.items()) request_ids = [req_id for req_id, _ in request_states_snapshot] if not request_ids: return {"aborted_count": 0, "request_ids": []} # For each request, create an abort output and put it to its queue # This allows the generator to receive the aborted result from vllm.v1.engine import FinishReason for _, req_state in request_states_snapshot: request_output = req_state.make_request_output( [], pooling_output=None, finish_reason=FinishReason.ABORT, stop_reason=None ) req_state.queue.put(request_output) # Abort requests in the output processor and engine core self.engine.output_processor.abort_requests(request_ids) await self.engine.engine_core.abort_requests_async(request_ids) # Try to reset prefix cache to ensure clean state if reset_prefix_cache: await self.clear_kv_cache() logger.info("Prefix cache reset after abort") logger.info(f"Aborted {len(request_ids)} requests: {request_ids}") return {"aborted_count": len(request_ids), "request_ids": request_ids} except Exception as e: logger.error(f"Error aborting requests: {e}") return {"aborted_count": 0, "request_ids": [], "error": str(e)} async def resume_generation(self): """Resume generation after abort_all_requests (pause_generation). Only effective on vLLM >= 0.12.0 where pause_generation is used. No-op on older versions. """ if self.node_rank != 0: return if _VLLM_VERSION >= version.parse("0.12.0"): await self.engine.resume_generation() async def abort_request(self, request_id: str, reset_prefix_cache: bool = True) -> dict[str, Any]: """Abort a specific generation request. Args: request_id: The ID of the request to abort. Returns: dict[str, Any]: Dictionary containing abort result. """ try: request_states = self.engine.output_processor.request_states req_state = request_states.get(request_id) if req_state is None: return {"aborted": False, "error": f"Request {request_id} not found"} # Create abort output and put it to the queue from vllm.v1.engine import FinishReason request_output = req_state.make_request_output( [], pooling_output=None, finish_reason=FinishReason.ABORT, stop_reason=None ) req_state.queue.put(request_output) # Abort in output processor and engine core self.engine.output_processor.abort_requests([request_id]) await self.engine.engine_core.abort_requests_async([request_id]) # Try to reset prefix cache to ensure clean state if reset_prefix_cache: await self.clear_kv_cache() logger.info(f"Prefix cache reset after abort request {request_id}") logger.info(f"Aborted request: {request_id}") return {"aborted": True, "request_id": request_id} except Exception as e: logger.error(f"Error aborting request {request_id}: {e}") return {"aborted": False, "request_id": request_id, "error": str(e)} class vLLMReplica(RolloutReplica): def __init__( self, replica_rank: int, config: RolloutConfig, model_config: HFModelConfig, gpus_per_node: int = 8, is_reward_model: bool = False, ): super().__init__(replica_rank, config, model_config, gpus_per_node, is_reward_model) self.server_class = ray.remote(vLLMHttpServer) async def launch_servers(self): """Launch http server in each node.""" assert len(self.workers) == self.world_size, ( f"worker number {len(self.workers)} not equal to world size {self.world_size}" ) # NOTE: We always use MP Executor backend whether it's single-node or multi-node. # For multi-node without DP (e.g TP=16), need vllm>=0.11.1, https://github.com/vllm-project/vllm/pull/23691 if self.config.data_parallel_size == 1 and self.nnodes > 1: assert _VLLM_VERSION >= version.parse("0.11.1"), ( "For multi-node MP Executor, either (1) set data_parallel_size > 1 or (2) upgrade vLLM to >= 0.11.1" ) # get (node_id, CUDA_VISIBLE_DEVICES) of all workers worker_infos = await asyncio.gather( *[ worker.__ray_call__.remote( lambda self: ( ray.get_runtime_context().get_node_id(), ray.get_runtime_context().get_accelerator_ids()[get_resource_name()][0], ) ) for worker in self.workers ] ) worker_cuda_visible_devices = [worker_info[1] for worker_info in worker_infos] worker_node_ids = [worker_info[0] for worker_info in worker_infos] # create server actor in each node with node affinity and cuda visible devices nnodes, gpus_per_replica_node = self.nnodes, self.gpus_per_replica_node for node_rank in range(nnodes): workers = self.workers[node_rank * gpus_per_replica_node : (node_rank + 1) * gpus_per_replica_node] node_cuda_visible_devices = ",".join( worker_cuda_visible_devices[node_rank * gpus_per_replica_node : (node_rank + 1) * gpus_per_replica_node] ) node_id = worker_node_ids[node_rank * gpus_per_replica_node] name = ( f"vllm_server_{self.replica_rank}_{node_rank}" if not self.is_reward_model else f"vllm_server_reward_{self.replica_rank}_{node_rank}" ) server = self.server_class.options( scheduling_strategy=ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy( node_id=node_id, soft=False, ), runtime_env={ "env_vars": { "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES": "1", "RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES": "1", # To prevent hanging or crash during synchronization of weights between actor and rollout # in disaggregated mode. See: # https://docs.vllm.ai/en/latest/usage/troubleshooting.html?h=nccl_cumem_enable#known-issues # https://github.com/vllm-project/vllm/blob/c6b0a7d3ba03ca414be1174e9bd86a97191b7090/vllm/worker/worker_base.py#L445 "NCCL_CUMEM_ENABLE": "0", } }, name=name, max_concurrency=self.max_concurrency, ).remote( config=self.config, model_config=self.model_config, rollout_mode=self.rollout_mode, workers=workers, replica_rank=self.replica_rank, node_rank=node_rank, gpus_per_node=gpus_per_replica_node, nnodes=nnodes, cuda_visible_devices=node_cuda_visible_devices, ) self.servers.append(server) # launch http server in each node master_address, master_port, dp_rpc_port = await self.servers[0].get_master_address.remote() await asyncio.gather( *[ server.launch_server.remote( master_address=master_address, master_port=master_port, dp_rpc_port=dp_rpc_port ) for server in self.servers ] ) # get http server address from first server server_address, server_port = await self.servers[0].get_server_address.remote() self._server_handle = self.servers[0] self._server_address = ( f"[{server_address}]:{server_port}" if is_valid_ipv6_address(server_address) else f"{server_address}:{server_port}" ) async def sleep(self): """Sleep each rollout server.""" # Drain DP engines for safe sleep. await self.servers[0].wait_for_requests_to_drain.remote() await asyncio.gather(*[server.sleep.remote() for server in self.servers]) async def abort_all_requests(self) -> dict[str, Any]: """Abort all ongoing generation requests across all servers. Returns: dict[str, Any]: Combined abort results from all servers. """ results = await asyncio.gather(*[server.abort_all_requests.remote() for server in self.servers]) total_aborted = sum(r.get("aborted_count", 0) for r in results) all_request_ids = [] for r in results: all_request_ids.extend(r.get("request_ids", [])) return { "aborted_count": total_aborted, "request_ids": all_request_ids, "server_results": results, } async def resume_generation(self): """Resume generation on all servers after abort_all_requests.""" await asyncio.gather(*[server.resume_generation.remote() for server in self.servers]) async def abort_request(self, request_id: str) -> dict[str, Any]: """Abort a specific request. Tries all servers since we don't know which one has it. Args: request_id: The ID of the request to abort. Returns: dict[str, Any]: Abort result. """ # TODO(petersh6): we should only abort on the server that has the request. results = await asyncio.gather(*[server.abort_request.remote(request_id) for server in self.servers]) for r in results: if r.get("aborted", False): return r return {"aborted": False, "request_id": request_id, "error": "Request not found on any server"} ================================================ FILE: verl/workers/rollout/vllm_rollout/vllm_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The vllm_rollout that can be applied in different backend When working with FSDP: - Use DTensor weight loader (recommended) or HF weight loader - Utilize state_dict from the FSDP to synchronize the weights among tp ranks in vLLM When working with Megatron: - Use Megatron weight loader - During training, only the current pp stage holds the parameters - Before inference, broadcast the parameters of the current pp rank to all other pp ranks (all pp ranks holds all the parameters) - Bind the parameters to the inference engine - Do inference in tp. pp is treated as additional dp - After inference, all the parameters that doesn't belong to this pp rank is freed. """ import logging import os import time from typing import Any, Generator, Optional import ray import torch from packaging import version as vs from torch.distributed.device_mesh import DeviceMesh from verl import DataProto from verl.third_party.vllm import VLLM_SLEEP_LEVEL, get_version from verl.utils.device import get_device_id, is_support_ipc from verl.workers.config import HFModelConfig, RolloutConfig from verl.workers.rollout.base import BaseRollout from verl.workers.rollout.vllm_rollout.bucketed_weight_transfer import BucketedWeightSender from verl.workers.rollout.vllm_rollout.utils import get_device_uuid logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "INFO")) def _check_vllm_version_for_sleep_level(): # https://github.com/vllm-project/vllm/issues/25171 minver = "0.11.0" current_version = get_version("vllm") if not current_version: logger.warning("Could not determine vLLM version, assuming an older version for sleep_level configuration.") return False return vs.parse(current_version) >= vs.parse(minver) class ServerAdapter(BaseRollout): """ vLLM server adapter used in native async mode, serve as a client to request vLLM server to resume/release/update weights and kv_cache. """ def __init__( self, config: RolloutConfig, model_config: HFModelConfig, device_mesh: DeviceMesh, replica_rank: int = -1, ): super().__init__(config, model_config, device_mesh) self.server_handle: ray.actor.ActorHandle = None rank = int(os.environ["RANK"]) local_world_size = int(os.environ["RAY_LOCAL_WORLD_SIZE"]) rollout_world_size = ( self.config.tensor_model_parallel_size * self.config.data_parallel_size * self.config.pipeline_model_parallel_size ) if replica_rank == -1: self.replica_rank = rank // rollout_world_size else: self.replica_rank = replica_rank self.rollout_rank = rank % rollout_world_size self.node_rank = self.rollout_rank // local_world_size if config.layered_summon or (config.expert_parallel_size > 1 and not _check_vllm_version_for_sleep_level()): logger.warning("Setting the sleep level to 1 may cause a memory overflow.") self.sleep_level = 1 else: self.sleep_level = VLLM_SLEEP_LEVEL self.device_uuid = get_device_uuid(get_device_id()) self.zmq_handle = f"ipc:///tmp/rl-colocate-zmq-{self.device_uuid}.sock" self.use_shm = not is_support_ipc() if self.use_shm: logger.warning( "IPC is not supported on your devices. Falling back to shared memory for weight transfer, " "which may cause performance degradation. If you are using Ascend NPUs, please ensure that " "your software and CANN toolkit versions meet the requirements for IPC support. (Ascend HDK version " ">= 25.3.rc1 and CANN toolkit version >= 8.3.RC1)" ) async def _execute_method( self, method: str, non_block: bool = False, timeout: Optional[float] = None, args: tuple = (), kwargs: Optional[dict] = None, ) -> Any: """Execute method on inference engine via ray. Args: method: The method name to execute on the server. non_block: If True, execute the method asynchronously and return immediately. timeout: Timeout for the collective_rpc call. args: Positional arguments for the method. kwargs: Keyword arguments for the method. Returns: The result of the method execution, or None if non_block=True. """ if self.rollout_rank != 0: return None # Lazy init http server adapter because http server is launched after hybrid engine. if self.server_handle is None: self.server_handle = ray.get_actor(f"vllm_server_{self.replica_rank}_{self.node_rank}") future = self.server_handle.collective_rpc.remote(method, timeout=timeout, args=args, kwargs=kwargs) return future if non_block else await future async def resume(self, tags: list[str]): """Resume rollout weights or kv cache in GPU memory. Args: tags: weights or kv_cache. """ if self.config.free_cache_engine: await self._execute_method("wake_up", kwargs={"tags": tags}) async def release(self): """Release weights and kv cache in GPU memory.""" if self.config.free_cache_engine: await self._execute_method("sleep", kwargs={"level": self.sleep_level}) @torch.no_grad() async def update_weights( self, weights: Generator[tuple[str, torch.Tensor], None, None], global_steps: int = None, **kwargs ): """Update model weights via CUDA IPC (fallback to shared memory if IPC not supported) to inference workers.""" start_time = time.time() future = await self._execute_method( "update_weights_from_ipc", non_block=True, kwargs={**kwargs, "use_shm": self.use_shm}, ) bucket_size_mb = self.config.checkpoint_engine.update_weights_bucket_megabytes sender = BucketedWeightSender( zmq_handle=self.zmq_handle, bucket_size_mb=bucket_size_mb, use_shm=self.use_shm, ) await sender.async_send_weights(weights) if future is not None: await future # reset prefix cache after updating weights if self.rollout_rank == 0: await self.server_handle.clear_kv_cache.remote() if global_steps is not None: await self.server_handle.set_global_steps.remote(global_steps) if self.replica_rank == 0 and self.rollout_rank == 0: logger.info(f"update_weights done, time cost: {time.time() - start_time:.2f}s") def generate_sequences(self, prompts: DataProto) -> DataProto: """Batch generate sequences in sync mode. Note: ServerAdapter uses async server mode and does not support synchronous generation. Since SPMD mode was retired (PR #4411), the generation workflow should use the async server interface instead. Raises: NotImplementedError: Always raised as sync generation is not supported. """ raise NotImplementedError( "ServerAdapter does not support synchronous generate_sequences(). " "The vLLM SPMD mode was retired in PR #4411. For batch generation, " "please use the async server interface via vLLMReplica and AsyncLLMServerManager, " "or use HFRollout for synchronous generation. " "See https://github.com/volcengine/verl/issues/4682 for more details." ) ================================================ FILE: verl/workers/sharding_manager/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/workers/sharding_manager/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Sharding manager to implement HybridEngine """ from verl import DataProto class BaseShardingManager: def __init__(self): self.timing = {} def __enter__(self): pass def __exit__(self, exc_type, exc_value, traceback): pass def preprocess_data(self, data: DataProto) -> DataProto: return data def postprocess_data(self, data: DataProto) -> DataProto: return data ================================================ FILE: verl/workers/sharding_manager/fsdp_ulysses.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contains a resharding manager that binds weights from FSDP zero3 to XPerfGPT """ from torch.distributed.device_mesh import DeviceMesh from verl import DataProto from verl.protocol import all_gather_data_proto from verl.utils.ulysses import get_ulysses_sequence_parallel_group, set_ulysses_sequence_parallel_group from .base import BaseShardingManager class FSDPUlyssesShardingManager(BaseShardingManager): """ Sharding manager to support data resharding when using FSDP + Ulysses """ def __init__(self, device_mesh: DeviceMesh): super().__init__() self.device_mesh = device_mesh self.seed_offset = 12345 def __enter__(self): if self.device_mesh is not None: # We have a global SP group # so we have to change to use model-specific sp group self.prev_sp_group = get_ulysses_sequence_parallel_group() set_ulysses_sequence_parallel_group(self.device_mesh["sp"].get_group()) # TODO: check how to set seed for each model def __exit__(self, exc_type, exc_value, traceback): # restore random states if self.device_mesh is not None: # revert to previous sp group set_ulysses_sequence_parallel_group(self.prev_sp_group) # TODO: check how to set seed for each model def preprocess_data(self, data: DataProto) -> DataProto: """ AllGather data from sp region This is because the data is first sharded along the FSDP dimension as we utilize the DP_COMPUTE In Ulysses, we need to make sure the same data is used across a SP group """ if self.device_mesh is not None: group = self.device_mesh["sp"].get_group() all_gather_data_proto(data=data, process_group=group) return data def postprocess_data(self, data: DataProto) -> DataProto: """ Split the data to follow FSDP partition """ if self.device_mesh is not None: sp_size = self.device_mesh["sp"].size() sp_rank = self.device_mesh["sp"].get_local_rank() data = data.chunk(chunks=sp_size)[sp_rank] return data ================================================ FILE: verl/workers/utils/__init__.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/workers/utils/losses.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn.functional as F from tensordict import TensorDict from verl.trainer.ppo.core_algos import agg_loss, compute_value_loss, get_policy_loss_fn, kl_penalty from verl.utils import tensordict_utils as tu from verl.utils.dataset.dataset_utils import DatasetPadMode from verl.utils.metric import AggregationType, Metric from verl.utils.torch_functional import masked_mean, masked_sum from verl.workers.config import ActorConfig, CriticConfig from verl.workers.utils.padding import no_padding_2_padding def sft_loss(config: ActorConfig, model_output, data: TensorDict, dp_group=None): pad_mode = tu.get_non_tensor_data(data=data, key="pad_mode", default=DatasetPadMode.NO_PADDING) dp_size = data["dp_size"] batch_num_tokens = data["batch_num_tokens"] log_prob = model_output["log_probs"] if pad_mode == DatasetPadMode.NO_PADDING: # log_prob and loss mask are nested tensors of shape [bsz, j1] # for each sample, loss mask shape is [1, prompt_length + response_length] loss_mask = data["loss_mask"] log_prob_flatten = log_prob.values() loss_mask_flatten = loss_mask.values() # left-shift the loss mask by one token to align with log_prob loss_mask_flatten = torch.roll(loss_mask_flatten, shifts=-1, dims=0) # NOTE: loss is averaged over all tokens in the batch across all data parallel groups, # For FSDP backend, the loss is directly used for backward; while for Megatron backend, # the loss should be scaled by `num_microbatches` for pp schedule. loss = -masked_sum(log_prob_flatten, loss_mask_flatten) / batch_num_tokens * dp_size else: response_mask = data["response_mask"].to(bool) loss = -masked_sum(log_prob, response_mask) / batch_num_tokens * dp_size return loss, {} def _slice_response_from_unpad_output(tensor: torch.Tensor, data: TensorDict) -> torch.Tensor: """Slice response from unpad model output. Args: tensor: model output tensor of shape [bsz, 1] data: TensorDict with "prompt_ids", "response_ids", "attention_mask" Returns: tensor: sliced response tensor of shape [bsz, max_response_len] """ values = tensor.values() if tensor.is_nested else tensor prompt_ids = data["prompts"] response_ids = data["responses"] attention_mask = data["attention_mask"] if prompt_ids.is_nested: prompt_lens = prompt_ids.offsets().diff() response_lens = response_ids.offsets().diff() max_response_len = response_ids.offsets().max().item() else: assert not attention_mask.is_nested prompt_lens = attention_mask[:, : prompt_ids.shape[1]].sum(dim=1) response_lens = attention_mask[:, prompt_ids.shape[1] :].sum(dim=1) max_response_len = response_ids.shape[1] sequence_lens = prompt_lens + response_lens sequence_offsets = sequence_lens.cumsum(dim=0) assert sequence_offsets[-1].item() == values.shape[0] response_list = [] for resp_len, seq_offset in zip(response_lens, sequence_offsets, strict=True): pad_size = max_response_len - resp_len # left-shift model output by one token for log_probs/values response_list.append(F.pad(values[seq_offset - resp_len - 1 : seq_offset - 1], (0, pad_size))) output = torch.stack(response_list, dim=0) return output def ppo_loss(config: ActorConfig, model_output, data: TensorDict, dp_group=None): """Computes ppo loss from model output (log_prob, entropy, values, etc. ) and old_log_probs from data.""" log_prob = no_padding_2_padding(model_output["log_probs"], data) entropy = model_output.get("entropy", None) if entropy is not None: entropy = no_padding_2_padding(entropy, data) # global batch info for loss aggregation config.global_batch_info["dp_size"] = data["dp_size"] config.global_batch_info["batch_num_tokens"] = data["batch_num_tokens"] config.global_batch_info["global_batch_size"] = data["global_batch_size"] config.global_batch_info["loss_scale_factor"] = config.loss_scale_factor # assumes that if any of the global batch info is set, the policy_loss_fn will # normalize using dp_size/global_bsz/global_token; in this case, metric aggregation should be SUM # to reflect the mean loss over the global batch if ( data["dp_size"] > 1 or data["batch_num_tokens"] is not None or data["global_batch_size"] is not None or config.loss_scale_factor is not None ): metric_aggregation = AggregationType.SUM else: metric_aggregation = AggregationType.MEAN metrics = {} response_mask = data["response_mask"].to(bool) # compute policy loss old_log_prob = data["old_log_probs"] advantages = data["advantages"] rollout_is_weights = data.get("rollout_is_weights", None) loss_agg_mode = config.loss_agg_mode loss_mode = config.policy_loss.get("loss_mode", "vanilla") policy_loss_fn = get_policy_loss_fn(loss_mode) pg_loss, pg_metrics = policy_loss_fn( old_log_prob=old_log_prob, log_prob=log_prob, advantages=advantages, response_mask=response_mask, loss_agg_mode=loss_agg_mode, config=config, rollout_is_weights=rollout_is_weights, ) # AggregationType.MEAN for pg metrics: assumes policy_loss_fn normalizes by local_bsz/local_tokens # Ex: in compute_policy_loss_vanilla, pg_metrics are pg_clipfrac, ppo_kl, pg_clipfrac_lower pg_metrics = Metric.from_dict(pg_metrics, aggregation=AggregationType.MEAN) metrics.update(pg_metrics) metrics["actor/pg_loss"] = Metric(value=pg_loss, aggregation=metric_aggregation) policy_loss = pg_loss # add entropy loss if entropy is not None: entropy_loss = agg_loss( loss_mat=entropy, loss_mask=response_mask, loss_agg_mode=loss_agg_mode, **config.global_batch_info ) entropy_coeff = config.entropy_coeff policy_loss -= entropy_coeff * entropy_loss metrics["actor/entropy_loss"] = Metric(value=entropy_loss, aggregation=metric_aggregation) # add kl loss if config.use_kl_loss: ref_log_prob = data["ref_log_prob"] # compute kl loss kld = kl_penalty(logprob=log_prob, ref_logprob=ref_log_prob, kl_penalty=config.kl_loss_type) kl_loss = agg_loss( loss_mat=kld, loss_mask=response_mask, loss_agg_mode=config.loss_agg_mode, **config.global_batch_info ) policy_loss += kl_loss * config.kl_loss_coef metrics["kl_loss"] = Metric(value=kl_loss, aggregation=metric_aggregation) metrics["kl_coef"] = config.kl_loss_coef return policy_loss, metrics def value_loss(config: CriticConfig, model_output, data: TensorDict, dp_group=None): """value loss Args: config: CriticConfig model_output: model output from the model data: the input to the model dp_group: data paralle group Returns: value loss """ vpreds = _slice_response_from_unpad_output(model_output["values"], data) # (bsz, response_length) values = data["values"] returns = data["returns"] response_mask = data["response_mask"].to(bool) vf_loss, vf_clipfrac = compute_value_loss( vpreds=vpreds, values=values, returns=returns, response_mask=response_mask, cliprange_value=config.cliprange_value, loss_agg_mode=config.loss_agg_mode, ) metrics = {} metrics.update( { "critic/vf_loss": vf_loss.detach().item(), "critic/vf_clipfrac": vf_clipfrac.detach().item(), "critic/vpred_mean": masked_mean(vpreds, response_mask).detach().item(), } ) return vf_loss, metrics ================================================ FILE: verl/workers/utils/padding.py ================================================ # Copyright 2025 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn.functional as F from tensordict import TensorDict from verl.utils import tensordict_utils as tu from verl.utils.attention_utils import index_first_axis, unpad_input def left_right_2_no_padding(data: TensorDict) -> TensorDict: """ Convert TensorDict from left-right padding to no-padding format. Args: data: TensorDict with "input_ids", "attention_mask", "response_mask", "position_ids" Returns: data: TensorDict with - Tensor includes NestedTensors like "input_ids", "loss_mask", "position_ids" - NonTensorData includes "max_seq_len", "max_response_len", "indices" Note: 1. the return input_ids/position_ids/loss_mask are nested tensor. 2. we will remove "attention_mask", "response" in the return data, but "response_mask" is kept. """ assert "input_ids" in data, "input_ids is required in left-right padding data" assert "attention_mask" in data, "attention_mask is required in left-right padding data" assert "response_mask" in data, "response_mask is required in left-right padding data" assert "position_ids" in data, "position_ids is required in left-right padding data" input_ids = data.pop("input_ids") attention_mask = data["attention_mask"] response_mask = data["response_mask"] position_ids = data["position_ids"] # (bs, seq_len) or # (bs, 4, seq_len) max_seq_len, max_response_len = input_ids.shape[1], response_mask.shape[1] tu.assign_non_tensor_data(data, "max_seq_len", max_seq_len) tu.assign_non_tensor_data(data, "max_response_len", max_response_len) input_ids_rmpad, indices, cu_seqlens, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask) tu.assign_non_tensor_data(data, "indices", indices) input_ids_nested = torch.nested.nested_tensor_from_jagged(input_ids_rmpad.squeeze(-1), offsets=cu_seqlens) position_ids_list = [] for i in range(attention_mask.shape[0]): curr_mask = attention_mask[i].bool() curr_pos_ids = position_ids[i] if curr_pos_ids.dim() == 1: # (seq_len,) valid_ids = curr_pos_ids[curr_mask] else: # (4, seq_len) valid_ids = curr_pos_ids[:, curr_mask] position_ids_list.append(valid_ids) position_ids_nested = torch.nested.as_nested_tensor(position_ids_list, layout=torch.jagged) data["input_ids"] = input_ids_nested data["position_ids"] = position_ids_nested data["loss_mask"] = data["response_mask"] routed_experts = data.get("routed_experts", None) if routed_experts is not None and not routed_experts.is_nested: if routed_experts.max() <= 255: routed_experts = routed_experts.to(torch.uint8) routed_experts_rmpad = index_first_axis(routed_experts.unsqueeze(-1).flatten(0, 1), indices) routed_experts_nested = torch.nested.nested_tensor_from_jagged( routed_experts_rmpad.squeeze(-1), offsets=cu_seqlens ) data["routed_experts"] = routed_experts_nested return data def no_padding_2_padding(tensor: torch.Tensor, data: TensorDict) -> torch.Tensor: """Slice response from unpad model output. Args: tensor: a nested tensor or a 1D tensor in shape (total_nnz,), total_nnz is the total number of tokens across all sequences in the batch data: TensorDict with "prompts", "responses", "attention_mask" Returns: tensor: sliced response tensor of shape [bsz, max_response_len] """ values = tensor.values() if tensor.is_nested else tensor prompt_ids = data["prompts"] response_ids = data["responses"] attention_mask = data["attention_mask"] max_response_len = tu.get_non_tensor_data(data=data, key="max_response_len", default=-1) if prompt_ids.is_nested: prompt_lens = prompt_ids.offsets().diff() response_lens = response_ids.offsets().diff() if max_response_len < 0: max_response_len = response_lens.max().item() else: assert not attention_mask.is_nested prompt_lens = attention_mask[:, : prompt_ids.shape[1]].sum(dim=1) response_lens = attention_mask[:, prompt_ids.shape[1] :].sum(dim=1) max_response_len = response_ids.shape[1] sequence_lens = prompt_lens + response_lens sequence_offsets = sequence_lens.cumsum(dim=0) assert sequence_offsets[-1].item() == values.shape[0] response_list = [] for resp_len, seq_offset in zip(response_lens, sequence_offsets, strict=True): pad_size = max_response_len - resp_len # left-shift model output by one token for log_probs/values response_list.append(F.pad(values[seq_offset - resp_len - 1 : seq_offset - 1], (0, pad_size))) output = torch.stack(response_list, dim=0) return output